...an overview of the principles of social media, and an outlined straight-to-the-point process of how to develop a strategy in order to effectively market a businesses using social media Executive Director at Deuel Area Development, Inc ...an overview of the principles of social media, and an outlined straight-to-the-point process of how to develop a strategy in order to effectively market a businesses using social media TITLE OF THE INVENTION Diagnosis and Prognosis of Infectious Diseases Clinical Phenotypes and Other Physiologic States Using Host Gene Expression Biomarkers in Blood STATEMENT REGARDING FEDERALLY FUNDED PROJECT The United States Government owns rights in the present
invention pursuant to funding from the Defense Threat Reduction Agency (DTRA; Interagency Cost Reimbursement Order (IACRO #02-4118), MIPR numbers 01-2817, 02-2292, 02-2219, and 02-2887), the Office of the U.S. Air Force Surgeon General (HQ USAF SGR; MIPR Numbers NMIPR035203650, NMIPRONMIPRO35203881, NMIPRONMIPRO35203881), the U.S. Army Medical Research Acquisition Activity (Contract # DAMD17-03-2-0089), the Defense Advance Research Projects Agency (DARPA; MIPR Number M189/02), and the Office of
Naval Research (NRL Work Unit 6456). CROSS-REFERENCE TO RELATED APPLICATIONS The present application claims priority to U.S. 60/626,500, filed on November 5, 2004. TECHNICAL FIELD The present invention relates to a specific set of gene expression markers from whole blood and/or peripheral blood leukocytes (PBL) that are indicative of a host response to exposure, response, and recovery from infectious pathogens. The present invention further relates to methods for identifying the specific set of
gene expression markers, methods of monitoring disease progression and treatment of infectious pathogen infections, methods of predicting the onset of the symptoms and/or manifestation of an infectious pathogen infection, and methods of diagnosing an infectious pathogen infection and classifying the pathogen involved. The present invention also relates to the following: (1) methods for validating the differential gene expression markers in a cohort (such as a Basic Military Trainee (BMT)
population). Such a method can be used to validate and/or expand upon a subset of biomarkers identified by alternative techniques for a specific disorder, (2) methods for designing and implementing a process of determining pre-symptomatic gene expression changes in an exposed population, (3) methods for statistical (e.g. Bayesian) inference to combine other (e.g. metadata) information into a overall diagnosis or assessment, and (4) alternative measurement techniques other than Genechip
microarrays, though not necessarily excluding Genechip microarray, that could be used to measure changes in a small, differentiating subset of genes (i.e., a subset of genes identified by the microarray-based method of the present invention) in a minimal volume of blood (lancet to produce drops of blood instead of intravenous blood draw to produce milliliters of blood) in a period of hours instead of days. Moreover, the present invention relates to an overall business model, components of which
include: (1) assessment of the morbidity potential of individuals who were exposed to an infectious pathogen or agent of chembio-terrorism using pre-symptomatic gene expression markers, (2) pre-assessment of the morbidity potential for select individuals (e.g. aircrews prior to the start of a 24 hour mission) or for general public use for pro-active intervention against infectious disease prior to the onset of major symptoms, and (3) assessment of human behavioral activities (i.e. Exercising,
eating, fasting, smoking, etc) that affect physiology and blood gene expression, thus enabling discovery of biomarkers related to these behaviors that may be used to established past activities of an individual at a certain probability of confidence. The present invention further relates to: (1) methods for extrapolating the methods developed herein (e.g., PAXgene processing and metadata) for use in other disease diagnostics (e.g., blood-related; autoimmune diseases, leukemia); (2) methods for
assembly of metadata in a format that allows it to be assimilated into inferential models of disease assessment; and (3) methods for establishing a comprehensive human gene expression baseline database, against which perturbations, such a pathogen exposure, infection, and other disease states would be compared. 1 2568722_1 (GHMatters) P71850.AU WO 2007/011412 PCT/US2005/040196 BACKGROUND ART Recent years have witnessed an explosive growth in the number of applications involving the use of DNA
microarrays to monitor the expression of genes in various forms of tissues and cultured cells (1-5). Such "expression profiling" requires a measurable change in the relative abundance of transcribed messenger RNA (mRNA) in host cells in response to some type of perturbation. The measurement is usually performed 5 indirectly by reverse transcription (RT) of the labile mRNA into more stable complementary DNA (cDNA) which is in turn labeled with a fluorophore (true for most work, but the Affymetrix
process involves re-conversion of cDNA back to RNA, which is in turn labeled and hybridized) and allowed to hybridize with the microarrays containing a plurality of DNA "probe" molecules that bind the target cDNA of interest. Typically, colored fluorophores are used to label the "control" and "experimental" pools of cDNA, allowing the relative transcript abundances to be deduced from the ratio of fluorescence intensities. Alternatively, a single color measurement can be enabled by scaling of the
10 intensities between different microarrays, as in the case with Affymetrix high-density microarrays (vide infra) because the variation from among Affymetrix arrays are minimal compared to most spotted array platforms. Defining sets of genes that are modulated in response to the external perturbation is non-trivial and is complicated by "noise" due to biologic variability, microarray production batch, handling factors, and variability emerging during sample processing (6). 15 Types of
microarrav probe molecules Significantly, the DNA probes themselves can be of highly variable lengths. Probes comprised of cDNA molecules (which are RT/PCR products of transcriptional isolates known as "Expressed Sequence Tags"; ESTs) can have varying lengths (usually hundreds of base pairs) and are often adsorbed (non-covalently) and then cross-linked (chemically or using ultraviolet radiation) to positively-charged poly-lysine or aminosilane coated microscope slides. In contrast, probes
comprised of defined "long" (70-mer) or "short" (25-mer) oligonucleotides are of fixed length and are 20 almost invariably attached by a covalent bond via one terminus of the DNA molecule. Higher degrees of transcript detection sensitivity can usually be achieved with 70-mer probes compared to shorter ones (e.g. 20-25mers). However, specificity is reduced because 70-mer target/probe hybridizations are generally insensitive to small numbers (e.g., 2-3) of single base mismatches, whereas shorter
probes are sensitive to single mismatches and thus provide greater specificity. In contrast, little can be said about transcript-specific cDNA binding to complementary cDNA probes prepared from EST libraries, because the length of the probes (hundreds of base pairs) can result in binding of multiple smaller transcription-specific 25 cDNA molecules. The separation of these contributions would be impossible from a single fluorescent intensity signal as measured by a microarray scanner. At least a
few research groups have developed microarrays that are capable of distinguishing varying levels of "sequence resolution". Within the human genome, only a small percentage of the total sequences called "exons" actually encode for functional polypeptides and these segments are interspersed with non-coding segments called "introns". Shoemaker et al (7) developed "exon arrays" comprised of long (50-60 bases) 30 targeting predicted exon regions, and "tiling arrays" which used sets of similar length
overlapping oligonucleotides to completely blanket a genomic region of interest for human chromosome 22. This allows for determination of most RNA transcripts from this chromosome, including transcripts that are not traditionally considered as genes. Additionally, these microarrays should also be able to locate mutations in the chromosomal DNA itself. Further, this allows determination of which exons are represented in the formation of specific splice variants of transcripts coding for
functional proteins. 35 For the present invention, the authors have used Affymetrix HG-U133A and HG-U133B Human Genome Expression Chips (Part No. 900444; for detailed information refer to the product literature available from the manufacturer) as well as the HG-U133 plus 2.0 chip (Part No. 900467) which contains probes from HG-U133A, HG-U133B, and an additional 10,000 probeset on one cartridge. A GeneChip@ probe array contains "cells", each having a large number of copies of a unique 25-mer
probe and arranged in probe pairs consisting of a perfect match (PM) and a mis-match (MM) wherein the middle (number 13) position is varied. Normally, RNA is extracted from samples and reverse transcribed into cDNA 40 then into double stranded cDNA with a T7 promoter region added. Then in vitro transcription is carried out to linearly amplify the RNA and incorporate biotinylated nucleotides to make biotin labeled cRNA. The labeled cRNA target is hybridized onto the microarray, usually over
night, then follow by washing and detection via strepavidin conjugated fluorescent dyes the next day. Following hybridization of the labeled transcriptional targets to the microarray (for detailed information refer to the product literature available from Affymetrix entitled 'Eukaryotic Sample and Array Processing'), the Affymetrix GCOS software (manual available from Affymetrix) (8) is used to reduce the raw scanned image (.DAT) file to a 45 simplified file format (.CEL file) with intensities
assigned to each of the corresponding probe positions. 2 WO 2007/011412 PCT/US2005/040196 A grAPhidal'6estI6rof th' Orob pliaoodi the expression analysis algorithm is found in the Affymetrix GCOS manual on pages 505-523 (8). On the U133A and B GeneChips@, each (- 39,000) known and putative genefrom the Unigene database U133 build of the human genome (for detailed information refer to the product literature available from the manufacturer) are represented by 10 probe pairs spaced across some
length of the gene, with some bias towards the 3' end (maps and analysis available through the NetAffx website available through the 5 Affymetrix website). The GCOS software executes algorithms to assign an overall intensity that is used to infer abundance of a transcript and calculate fold changes of expression between two or more experiments. It also provides a metric to indicate whether a gene is "present" (detectably expressed) or absent. Following these calculations, the individual probe
intensities are not explicitly referenced but they remain part of the permanent data in the CEL file for each experiment. Thus, there are considerable differences in the interpretability of "gene expression" measurements, depending on the types and numbers 10 of microarray probes used and the algorithms used to analyze the spatial patterns of intensity from the probes. Transcriptional markers Of equal significance, relative to the "sequence resolution" of the measurement of transcript abundance
in metazoan systems is the variation in the composition of "genes" and transcriptional gene products. Initial drafts of the human genome (9, 10) indicate that the human genome 15 is comprised of approximately 30,000 genes, mostly identified by computational methods having significant limitations (11). Yet, orders of magnitude greater numbers of different proteins can be produced from these genes through the recombination of the internal coding sequences (exons) that are interspersed with
non-coding sequences (introns). Hence, probes comprised of cDNA clones derived from a transcriptional library are biased towards detection of the complete gene product sequences that are obtained under a specific set of times and conditions, and cannot represent the multiform nature of mammalian gene expression in more general conditions where alternative splice variants will change the transcriptional sequence 20 composition. Prior art in gene expression profiling in the immune response to
pathogens Cell culture models Several groups have also measured the gene expression profiles of individual immune cell types following exposure to microbes or 25 microbial components in vitro. Groups at Whitehead Institute (12) and Stanford (13) have used Affymetrix and spotted cDNA microarray types, respectively, to observe relatively stereotyped responses of cultured human peripheral blood mononuclear cells (PBMCs; i.e. circulating macrophage precursor cells, T lymphocytes, B lymphocytes),
eosinophils, and basophils when exposed to a variety of killed bacteria and bacterial cell wall components. The similarity of the responses is reflective of evolutionarily conserved pro-inflammatory responses within the innate immune system and do not suggest that pathogen-specific responses would be obviously detectable. Chaussable et al (14) describe a study with in vitro generated 30 macrophages and dendritic cells, which provides insights into the innate immune response to diverse pathogens
but is impractical for surveillance, as these cells types can only be isolated by laboratory procedures that will change their natural gene expression. Peripheral Blood Leukocytes (PBLs) Drawn from the Infected Host Craig Cummings, David Relman and Patrick Brown (Stanford University) hypothesized that the unique mixtures of virulence factors 35 expressed by specific pathogens will give rise to a correspondingly unique transcriptional response in the host (15). They reasoned that an attractive
host tissue source would be peripheral blood leukocytes (PBLs) because any pathogen gaining access to the body will elicit a multiplicity of immune response mechanisms, each characterized by combinations of specific gene modulations. They also pointed out that this technique might allow early diagnosis of even uncultivable or uncharacterized pathogens, that variations in host expression profiles could allow inference of time since exposure, and that a single technique could be used to diagnose a
large number of different diseases. 40 Relman et al have used variations of the "Lymphochip" (16, 17) (which is comprised of probes for approximately 3,000-3,500 "lymphold" genes comprised of cDNA clones prepared from transcriptional libraries of human lymphold tissues) to analyze expression changes in cultured PBMCs (13), and in PBLs (PBL contributions-all white blood cells and the differential is typically 41-77% neutrophils, 20-51% lymphocytes, 1.7-9% monocytes and less than one percent of
basophils and eosinophils), from RNA isolated from PAXgene Blood RNA tubes from 75 healthy human donors (18). The latter study (18) illustrated that relative gene expression levels in PBLs are related to variations in specific blood cell types, gender, 45 age, and time of day. Reiman et al have also observed changes in PBMC expression in non-human primates (NHPs) following experimental inoculation with Variola major, the virus responsible for human smallpox. In addition, Relman et al compared
Ebola infection of NHP. However, the 3 WO 2007/011412 PCT/US2005/040196 inventors nereir nare-unaware oranymastosure etrlelafftse changes to NHP inoculations using other pathogens or to baseline gene expression in humans. Because of the type of microarray (cDNA EST clones) it is not possible to ascribe particular transcriptional sequences that are responsible for assigning fold changes to particular genes. The present inventors are unaware of any written descriptions existing in the public
domain that describe these data. 5 In short, all of Relman's papers use cDNA arrays and PBMCs (which require on site isolation centrifuge and technicians). If they used paxgene, they processed it within 24 hours. This is not practical for surveillance. Whereas in the present invention, the inventors demonstrate that the paxgene tubes can give decent gene expression profiles even when handled in conditions amendable to surveillance. Reiman did not know and/or test this, hence they did everything
within 24 hours to be safe in the notation that the RNA has not degraded. Also, for cDNA arrays, Reiman required reference RNA with gene expression profiles similar to tissue of interest to compare 2 colors for all chips, which makes it impractical to study 10 large population expressing different genes than what is contained within their reference RNA. Whereas the Affy chip is single color so no reference common RNA is needed allowing us to compare large numbers of chips overtime, especially
when we spike in normalization control RNA. Differential gene and protein expression following exposure to biological warfare agents At least one US Patent 6,316,197 Bi (19) makes claim to methods for determining characteristic gene expression changes from an 15 infected host to diagnose exposure to biological warfare (or bioterrorism) agents. The inventors of that application described a series of steps that begin with the use of differential display PCR (DD-PCR) to discover genes that are
expressed differently in cultured cells following incubation with biological toxins (e.g. Staphyloccocus enterotoxin B; SEB, and Botulinum toxin) or microbes (e.g. Bacillus anthracis). Briefly, DD-PCR involves the use of reverse transcriptase to convert host RNA transcripts to cDNAs, which are in turn amplified with PCR and separated by gel electrophoresis. Specific sequences are determined for each of the corresponding electrophoretic bands to identify the differentially expressed genes. The
inventors 20 of US Patent 6,316,197 described methods for measuring (including the use of reverse transcriptase PCR and DNA microarray hybridization) correlating the observed changes with methods for measurement in animals exposed to the same agents, and found gene expression changes that corresponded to those observed in culture. Overall, this work makes use of a commonly used method of discovering genes that are involved in differential biological responses and implicates several
transcriptional markers that correlate with the exposure to several types of toxic insult. However, there is no ethical way to perform the same experiments using humans, and consequently, no manner of obtaining clinically relevant data 25 for a human population. Nor is there an attempt in this work to compare the perturbations to a baseline human expression profile. Also, none of the methods disclosed by Relman et al are amendable to a surveillance setting Differential gene expression
measurement in an integrated biodefense system The concept of a microarray used for broad-spectrum pathogen identification has considerable and obvious appeal to both medical 30 practice and national defense. This was best illustrated in the recommendations of the Defense Sciences Board (DSB) Summer 2000 Panel, which made recommendations to the DATSD (ATL) that the U.S. Defense Department develop a "Zebra Chip"; that is, a hypothetical microarray of unspecified technology that could include gene
expression markers, that would be in widely distributed use (DoD TriCare System) as a routine clinical diagnostic for both common and uncommon (e.g. bioterrorism) infectious agents. In addition to having probes for common infectious agents, the Zebra Chip would also contain a large number of probes for unusual ("zebra") pathogens. If such a device were in widespread use at the time of 35 a biological terrorism event or a natural epidemic (e.g. SARS), the cost savings, both financial and in human
suffering, could be enormous, due to the earliest possible detection of the agent when only minor (fiu-like) symptoms were manifest. Furthermore, there is a need to unambiguously define "baseline" expression profiles, against which the "perturbed" state profiles are compared, as they may be variable in time and between individuals. Because it may not always be possible to identify the specific cause of an infection through pathogen genomic markers (e.g. using PCR or 40 microarrays), there
remains a critical need to determine alternative "biomarkers' from the host that would elucidate the character of the disease etiology and guide the clinician in the proper management of the infection. Heretofore, none of the published prior art methods are amendable to large long-term field studies/surveillance. All of the published methods are simply for a quick one-time gene expression study. Therefore, and in view of the foregoing, there remains a critical need of methods for determining
characteristics gene expression changes that arise from an infected host to diagnose disease states, help guide treatment regimens, and 45 assist in making treatment/operational decisions, Further, there exists a critical need for rapid, near real-time methods useful for field implementation that may be used individually or in combination with additional detection and diagnostic methods and apparatuses. 4 DISCLOSURE OF THE INVENTION A first aspect provides a method for identifying gene
expression markers for distinguishing between healthy, febrile, or convalescence in subjects that have been exposed to one or more infectious pathogens comprising a) acquiring a gene expression profile for a subject that has been exposed to said one or more infectious pathogens by i) isolating RNA from a biological sample from the subject that has been exposed to said one or more infectious pathogens; ii) removing DNA contaminants from said RNA; iii) spiking into said RNA a normalization
control; iv) synthesizing cDNA from the RNA; v) in vitro transcribing cRNA from said cDNA and labeling said cRNA; vi) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and vii) acquiring a gene expression profile from said gene chip; b) acquiring a gene expression profile for a subject that has recovered from exposure to said one or more infectious pathogens by i) isolating RNA from a biological sample from the subject that has recovered from exposure to said one
or more infectious pathogens; ii) removing DNA contaminants from said RNA; iii) spiking into said RNA a normalization control; iv) synthesizing cDNA from the RNA; v) in vitro transcribing cRNA from said cDNA and labeling said cRNA; vi) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and vii) acquiring a gene expression profile from said gene chip; c) acquiring a gene expression profile for a healthy subject that has not been exposed to said one or more
infectious pathogens by i) isolating RNA from a biological sample from the subject that has not been exposed to said one or more infectious pathogens; ii) removing DNA contaminants from said RNA; iii) spiking into said RNA a normalization control; iv) synthesizing cDNA from the RNA; v) in vitro transcribing cRNA from said cDNA and labeling said cRNA; vi) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and vii) acquiring a gene expression profile from said gene
chip; d) comparing the gene expression profiles for the subjects from (a), (b), and (c) by a pairwise comparison; e) determining a nested to minimal set(s) of genes that classify the subject as healthy, febrile, or convalescent by class prediction algorithm based on said pairwise comparison; and t) assigning a classification of healthy, febrile, or convalescent based on the gene expression profile of the nested to minimal set(s) of genes determined in (e). A second aspect provides a method of
classifying a subject in need thereof as healthy, febrile, or convalescence, comprising a) isolating RNA from a biological sample from said subject; b) removing DNA contaminants from said RNA; c) spiking into said RNA a normalization control; d) synthesizing cDNA from the RNA; e) in vitro transcribing cRNA from said cDNA and labeling said cRNA; f) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; g) acquiring a gene expression profile from said gene chip and
analyzing the gene expression profile represented by the RNA; and h) determining the gene expression profile in said subject of the minimal set of genes that classify the subject as healthy, febrile, or convalescent determined by the method of the first aspect; 5 2508722_1 (GHMattsm) i) classifying the subject in need thereof as being healthy, febrile, or convalescent by comparing the gene expression profile obtained in (h) to that of the classification assignment of healthy, febrile, or
convalescent based on gene expression profile of the minimal set of genes as determined by the method of the first aspect. BRIEF DESCRIPTION OF THE FIGURES A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following Figures in conjunction with the detailed description below. Figure 1 shows a diagram relating the two conditions used to handle blood collected in PAX tube.
Condition E describes the isolation of total RNA from PAX tube collected blood after the minimum incubation time of 2 hours at room temperature, whereas condition 0 allows for an extended incubation time of 9 hours at room temperature followed by freezing at -20'C for 6 days before RNA isolation. Figure 2 shows DNA contamination and removal. (A) DNA contamination of total RNA isolated from PAX tube even after on-column DNase treatment. Gel electrophoresis of real-time-PCR reactions for detection
of gapdh DNA. Lane 1: molecular weight (MW) markers; lanes 2-7: gapdh 290 bp product amplified from total RNA isolated from PAX tube with on-column DNase treatment; lane 8: no template negative control. (B) In-solution DNase treatment removed contaminating DNA to a level undetectable by PCR. Gel electrophoresis of real-time-PCR reactions detecting gapdh DNA in various samples. Lane 1: MW markers; lanes 2 & 4: in-solution DNase treated RNA isolated from PAX tube; lanes 3 & 5: treated as
in lanes 2 & 4, but without DNase; lane 6: cDNA positive control; lane 7: on-column DNase treated sample as positive control; lane 8: no template 6 2568722_1 (GHMatter) WO 2007/011412 PCT/US2005/040196 negauve contror:" U) |-NATlegrItywavnfaintrid aite-fT-Mution DNase treatment as determined by real-time RT-PCR, Lane 1: MW markers; lanes 2-5: cDNA from RNA samples used in lanes 2-5 of panel (B); lane 6: no reverse transcriptase negative control of sample corresponding to lane 4 in panel (B);
lane 7: no template negative control. Figure 3 shows total RNA were of similar quality pre- and post- DNase treatment and between conditions. Bioanalyzer traces of 5 fluorescence versus migration time of various total RNA samples. (A) Total RNA isolated from blood in PAX tube before DNase treatment. Black traces are from samples of condition E; gray traces are from samples of condition 0. First peak at -23sec is the marker control. Second peak at -41sec is 18S ribosomal RNA. Third peak at -47sec
is the 28S ribosomal RNA. Large humps after -50sec indicated DNA contamination. (B) Total RNA after DNase treatment. Descriptions are as in (A). (C) Comparison of pre- and post- DNase treatment traces. Black traces, one for each condition, are pre-DNase, whereas gray traces, also one for each condition, are post-DNase. 10 Figure 4 shows characteristic profiles of double stranded cDNA, cRNA, and fragmented cRNA. Bioanalyzer traces of fluorescence versus migration time of various samples.
Thick-dark-gray trace is a sample from condition E. Thin-black trace is a sample from condition 0. Thick-light gray trace is a no sample negative control trace. (A) Purified double stranded DNA. (B) Purified cRNA. (C) Fragmented cRNA. Figure 5 shows individual line charts relating the quality control metrics of various samples for HG-U133A and HG-U133B chips. Order of chips on the x-axis is based on the time of generation of the CEL file. UCL stands for upper control limit; LCL stands for lower
control limit. The limits 15 are set at ±3 standard deviations. Figure 6 shows gene-expression levels from the two conditions are highly correlated compared to related samples. Clustering dendrograms for HG-U133A (left panel) and HG-U133B (right panel) chips. The sample names with letters 'E' and '0' correspond to samples processed at the same time as described in Figure 1; also, sample names with the same letters designate technical replicates. Further descriptions for all samples are shown
below the sample names. Each character encodes a sample descriptive ontology. For the Condition variable, 'E' 20 designates samples processed similar to condition E, while '0' designates samples processed similar to condition 0. For Operator, '0' designates one individual operator, while '1' designates another operator. For Type of RNA, 'T' designates total RNA; 'H designates IP RP HPLC purified mRNA; and 'p' designates polyA RNA. For Donor ID, each number represents a different volunteer.
Figure 7 shows optimization of class prediction for non-febriles vs. febriles (A & B), healthy vs. convalescents (C & D), and febriles with adenovirus versus febriles without adenovirus infection (E & F). A, C, & E shows increments of the univariate significance alpha level (x-axes of A, C, 25 & E), resulting percent correct classification (left y-axes) for various algorithms (color traces), and the number of genes in the classifier (right y-axes, black trace with filled
circles); arrows indicate largest alpha level that resulted in the highest percent correct classification. In B, D, & F, at the optimal alpha level for each of the three classifications, classifier genes were further filtered by fold change level (x-axes of B, D, & F), with resulting percent correct classification (left y-axes) for various algorithms (color traces), and the number of genes in the classifier (right y-axes, black trace with filled circles); arrows indicate fold change
level that resulted in the highest percent correct classification. 30 Figure 8 shows cRNA profiles derived from Jurkat, Jurkat+Globin (JG), and paxgene RNA in different technical conditions. Fig. 8A Elecropherograms for cRNA derived from JG RNA treated with biotinylated globin oligos (JGA), with PNA (JGP), no treatment (JGC) and Jurkat RNA with no treatment (JC). Fig. 8B- Gel view of cRNA derived from four RNA and showed the size of globin molecules (arrow indicated -0.8 kb) in JGP and JGC. Fig.
8C- Electropherograms for cRNA derived from paxgene RNA treated with biotinylated globin oligos (BA), with PNA oligos (BP) and no treatment (BC). Fig. 8D- Gel view of cRNA derived from BA, BP and BC RNA indicated the size of globin (arrow). 35 Figure 9 shows Venn Diagrams demonstrating present call concordance among globin reduced Jukat +Globin RNA samples relative to Jurkat RNA and relationship among paxgene RNA in three different technical conditions. Fig. 9A- Identification of a control gene
set (JCAP) commonly present in JA, JP and JC. Fig. 9B- There were additional 1394 genes present in JGA and JCAP relative to genes present in JGP and JCAP. Fig. 9C- Paxgene RNA followed by biotinylated globin oligos treatment resulting in additional 4159 (2607+1552) genes relative to no treatment of globin reduction (BC). At least 62.5% (2607/4159) were likely to be called present due to globin removal. 40 Figure 10 shows Signal variation for each technical condition. Fig. I0A- Coefficient of
variance (CV) vs. scaling signal intensities graph using all probe set data derived from Jurkat (J) and Jurkat+Globin (JG) RNA samples treated with biotinylated globin oilgos (JA, JGA), with PNA (JP and JGP) and no treatment of globin reduction (JC, JGC) were shown. Fig. 1OB- CV vs. scaling signal intensities graph using all probe set data derived from paxgene RNA treated with biotinylated globin oligos (BA), with PNA (BP) and no treatment (BC). All of data were smoothed by Loess fitting with 2
degree freedom. 45 Figure 11 shows multidimensional scaling cluster analyses performed on gene expression obtained from Jurkat RNA (J) and Jurkat RNA spiked in globin (JG) and paxgene RNA. All of probe sets with log raw signal intensity were used. Fig. 11A- Greater correlation within each triplicate 7 WO 2007/011412 PCT/US2005/040196 resulted in 5 tidt cluster foredhtfifolidafes. ThetfiplIcaftd'dntters derived from Jurkat RNA with each technical condition were more closely located relative to
any JG RNA. However, removal of globin (JGA, JGP) brought the triplicate clusters closer to Jurkat RNA relative to JGC. Fig. 11B Triplicate for each paxgene RNA with different technical conditions was clustered more closely. Three technical variations resulted in three separate triplicate clusters. 5 Figure 12 shows hierarchal cluster analyses performed on gene expression profiles for Jurkat and JG RNA and paxgene RNA samples. All probe sets on GeneChip Human Genome U133 plus 2.0 (approximately
56,000) with scaling signal intensities were shown on overview of gene expression profiles. The differentially gene expression profiles were obtained from Univariate test in Random Variance Model with false discovery ratio of 0.001. Fig. 12A- Overview of gene expression profiles among 18 samples representing Jurkat and JG RNA with three technical conditions. Globin removal from JG RNA by biotinylated globin oligos resulted in higher signal correlation to Jurkat RNA, thus, JGA triplicate and
Jurkat RNA 10 were clustered into the same group. Fig. 12B- Cluster analyses conducted by using differentially expressed gene profile among these 18 samples. The analyses resulted in 8614 differentially expressed genes and genes were divided into 1, 11, 111, and IV based on JGA expression pattern. Fig. 12C- Cluster analyses performed on overall gene expression profiles derived from paxgene RNA. Globin removal from paxgene RNA by biotinylated globin oligos (BA) and PNA oligos (BP) exhibited more
similar expression pattern relative to no globin reduction (BC). Fig. 12D- Class comparison analyses among 9 paxgene RNA samples resulted in 1988 differentially expressed genes. 15 Figure 13 shows quality RNA derived from the PAX system of samples from the BMT population. (a) Overlay of electropherograms from BMTs with various phenotypes and handling conditions. The 18S and 28S ribosomal peaks are indicated. (b) Box plots of quality metrics calculated from the electropherograms. (c) Correlation
between gapdh 3'/5'values on the A arrays versus degradation factor (r = 0.3, P = 0.008, ANOVA). (d) Lack of RNA degradation over days elapsed from blood collection to processing. Samples marked by '+' x', or 'z' had an additional thawed-froze cycle before final thawed for RNA isolation. (e) Correlation between the Mean Corpuscular Hemoglobin (MCH) and number of probesets called 20 Present in the B arrays, (r = -0.272; P = 0.008, ANOVA). Line shown is from equation: Number Present = 8108 - 117
MCH Figure 14 shows gene expression profiles of the BMTs. To remove undetected transcripts, those with >80% absent calls across samples were filtered resulting in 15,721 from 44,928 probesets. To remove uninformative transcripts, probesets in which less than 20% had a 1.5 fold or greater change from the probeset's median value were removed, resulting in 7682 probesets. To focus on transcripts with differences in expression among the four infection status phenotypes, those probesets with P>
0.01 by ANOVA were excluded, resulting in 4414 probesets. The heat-map 25 shows the transcript abundance (green to red intensities) detected by these 4414 probesets (rows) in each blood sample (column). The rows were hierarchically clustered with 1-correlation distance and average linkage, while the columns were sorted into the infection status phenotypes. Top blue, brown, yellow, and light blue bars denote samples from healthy, febrile without and with adenovirus, and convalescent patients,
respectively. Bottom scale denotes standardized values for the green to red intensities in the heat-map. Side gray, orange, and purple bars denote clusters of transcripts that differ among the phenotypes. 30 Figure 15 shows optimization of class prediction for non-febrile vs. febrile (a), healthy vs. convalescent (b), and febrile without adenovirus versus febrile with adenovirus infection (c) phenotypes. Shown in the lower left corners of the three panels are the estimated optimal P-value
cut-off levels for each of the three classifications, Classifier transcripts were further filtered by fold change level (x-axes), with resulting percent correct classification (left y-axes) for various algorithms (color traces), and the number of probesets in the classifier (right y-axes, beaded black trace); arrows indicate fold change level that resulted in a highest percent correct classification. 35 Figure 16 shows identities and expression of genes in classifiers found from class prediction
analysis. In each panel, top bar indicates the classification phenotypes of the samples (columns). Panel a has a second bar that further indicates healthy, convalescent, febrile without and with adenovirus samples as blue, light blue, brown, and yellow, respectively, The middle set of color bars in each panel mark samples that were misclassified (black) by various algorithms. The heat-maps indicate relative expression levels of genes (green to red intensities) identified by gene symbols on the
right; for cDNA clones without gene symbols, probeset identifiers are displayed instead. Dendrograms are from clustering of 40 standardized transcript levels (rows) using 1-correlation distance and average linkage. Bottom scale denotes standardized values for the green to red intensities in the heat-map. The transcript sets in panels a, b, and c gave results marked by arrows in Figure 3a, b, and c, respectively. MODES FOR CARRYING OUT THE INVENTION Unless specifically defined, all technical and
scientific terms used herein have the same meaning as commonly understood by a skilled 45 artisan in enzymology, biochemistry, cellular biology, molecular biology, and the medical sciences. 8 WO 2007/011412 PCT/US2005/040196 "All iithods anfieilssir"iilar di- 6i 6Nierffti"those described herein can be used in the practice or testing of the present invention, with suitable methods and materials being described herein. In case of conflict, the present specification, including definitions, will
control. Further, the materials, methods, and examples are illustrative only and are not intended to be limiting, unless otherwise specified. The present invention provides a method for identifying human gene transcripts in blood, and their expression patterns, to identify a 5 causative agent of respiratory infection, and provide a measure of recovery during the period of time following infection. The methods developed here can be extended to the discovery of gene expression profiles that will
be indicative of exposure and predictive for the actual development of disease. These abilities have not previously been demonstrated in a human population. Gene expression: the following description details the importance of the present invention and its utility in gene expression analysis: 1. Identification of uncultivatable organisms: Mycoplasma pneumoniae, Bordetella pertussis and Chlamydia pneumoniae, which commonly 10 cause respiratory disease in all age groups. These organisms require
special transport media for sample collection of respiratory secretions. Even with optimal transport, it is tremendously difficult to cultivate these common organisms; therefore, healthcare workers are often unable to make a diagnosis and have little opportunity to direct antimicrobial therapy to potentially shorten the duration or to prevent transmission of disease with these organisms. Bordetella petussis is the causative organism for whooping cough in children and carries a high morbidity.
Adults infected with this organism often develop prolonged, dry cough and remain undiagnosed during the period of infectivity and possible transmission. It is likely that 15 adults represent a typically undiagnosed reservoir of disease for this organism that can have significant impact on the health of children. 2. Analysis of organisms for which no sample can be taken, for example TB from children. Young children tend to have disseminated tuberculosis infection and will not tend to have a
productive cough; this means that it is very difficult to collect sputum to look for the organism. Having an assay in blood that detects an immunologic signature for tuberculosis infection and disease in children would be a significant medical breakthrough. Worldwide, tuberculosis is a significant cause of morbidity and mortality in children, especially in impoverished regions of the world. 20 Early detection of infection can significantly limit disease. Therefore, this area is of particular
interest in the present invention. 3. Analysis of and identification of multiple organisms in a single blood sample. 4. Differentiation of a pathogen from colonization (discussed further below). 5. Determination of pre-symptomatic-exposed individuals. 6. Expansion to non-infectious/toxin exposure. 25 7. Identification of normal baseline for comparison for all studies. Based on the foregoing and the embodiments specifically described herein, the present invention provides an opportunity to direct
treatment options. In other words, by determining the gene expression patterns (both baseline healthy and ill) the artisan would be enabled to determine the diagnosis and the corresponding treatment, i.e. whether an individual has a bacterial infection-give antibiotics or viral infection-no antibiotics, In this manner the medical professional may reduce inappropriate antibiotic use and decrease resistance. 30 Further, the present invention may be employed to measure response to treatment- i.e.,
is there evidence that the host is resolving the infection? At times, individuals will be hospitalized and treated for respiratory infection, they appear to get better, but then develop fever again-the causes of fever can be: new infection-intravenous line is now infected or patient has developed urinary tract infection due to indwelling Foley catheter-typically multiple tests have to be sent-blood, urine, sputum to determine whether there is a new site of infection. Also, diseases like
pancreatitis or cholecystitis that develops in very ill patients while hospitalized can be non-infectious causes of fever that develops after admission. 35 Gene expression as described herein provides a means to take a single sample, blood, and differentiate infectious from non-infectious cause of fever and to identify whether a new pathogen at a new anatomic site is responsible for the new fever-e.g., if an individual was admitted with S. pneumoniae pneumonia and had gene expression pattern
consistent with this, but then developed a new fever in the hospital and had a changing gene expression pattern consistent with a S. aureus (skin pathogen) infection, then the new gene expression pattern would direct the practitioner to look at IV sites and other skin sites, such as decubitus ulcers, for a new source of infection. If the gene expression pattern did not appear to be consistent 40 with a response to an infectious agent, then the practitioner should consider diagnoses such as
pancreatitis or cholecystitis. The development of fever during hospitalization is not uncommon and often is a vexing problem for the health care practitioner, especially in severely ill patients in the Intensive Care Unit. Therefore, techniques as described herein would be well received in the medical profession. The present invention was accomplished following successful adaptation of a commercial technology (Affymetrix Human Genome U133 chip set) that has not been demonstrated prior to this to
be effective for whole blood expression profiling due to interferences from high-abundance 45 globin RNA (20). The demonstration of the enablement of the present invention has been assisted, in part, by the employment of enhanced sample preparation methods (e.g., PAXgeneTM). Further, by employing rigorous screening and control functions the present invention offers a significant 9 WO 2007/011412 PCT/US2005/040196 advantagein tft tlife datddbtalhed'tlhrdby afe'fraefibm"thbonfounding environmental
influences that pervade other gene monitoring studies. Moreover, the gene products used to distinguish between varying febrile respiratory disease states can be targeted for a variety of other assay types that do not require whole genome transcriptional monitoring or the attendant processing steps. Herein, the present inventors demonstrate that high density DNA microarray technology can be adapted for insertion into an accelerated 5 system for discovery of blood transcriptional markers of
infectious disease and other factors important of health, occupational, and military significance. When considering host gene expression profiling, the capacity to conduct thousands of assays simultaneously poses challenges regarding data analysis, storage, and management. While data storage and management issues are largely technical concerns for information technology specialists, no clear consensus on analysis techniques has emerged for making use of host gene expression profiles. The major
role for 10 bioinformatics is the identification of patterns associated responses to pathogens which may not only provide a means of detection, but also elucidation of genetic networks underlying initiation and progression of disease. The most commonly exploited tool for analysis of gene expression profiles is hierarchical clustering (21, 22) where the fundamental assumption is that similar trends, computed through a measure of distance, in the relative magnitudes of gene modulation imply
similarity of function. A critical need for the interpretation of large data files is the visualization of information, which can be readily accomplished by 15 dendrograms that can be derived from cluster analysis. Interpretation of expression profiling data has been used to gain profound insights into gene function. Clustering of genes expressed in yeast coupled with statistical algorithms yielded a model of regulatory transcriptional sub-network (23). A significant demonstration of the utility
of clustering has been offered by Hughes et al. (24), where a compendium of expression profiles of 300 diverse yeast mutations was used to identify novel open reading frames that encoded proteins of several cell functions. In regard to pathogen detection, different pathological conditions reflected by particular expression profiles could also be clustered (clustering by arrays rather than by 20 genes), but variation among a broad set of genes or dimensions may reduce the ability to discern
pathogen exposure states. Efforts in functional genomics related to cancer research have yielded major successes in the pursuit of gene expression signatures. Expression-based criteria or class predictors have been defined based on neighborhood analysis (25), Bayesian regression models (26), and artificial neural networks (27-29). These predictors were successfully used to classify novel samples in a manner consistent with clinical assessments. In fact, classifications based on gene expression
alone or class discovery has also been demonstrated, suggesting that gene expression profiling has 25 the capacity to identify subtypes that have not been previously defined (25). While promising, one should note that cancer line gene expression analyses are one-dimensional; in contrast, a host expression profile evoked by pathogen exposure would be expected to be temporal and "dose-dependent". Comprehensive sets of gene expression profiles that explore temporal and dose ranges for pathogen
exposure must be produced to map the continuum of gene expression changes. The present invention has been developed, in part, based on the rigorous assessment of the RNA quality from PAX tubes from a relatively 30 large sample of humans with various disease phenotypes, to determine the following: nested sets of genes that could optimally classify the four phenotypes of (a) healthy, (b) recovered, (c) febrile with adenovirus infection, and (d) febrile without adenovirus infection; lists of
differential genes among the four phenotypes; and the pathways in blood cells involved in respiratory disease due to adenovirus infection versus non-adenovirus infection. These results demonstrate possibilities and issues involved in measurement of gene expression from whole blood at the population level; show the potential of using host gene-expression responses in blood cells to distinguish pathogen classes; elucidate functional pathways involved in 35 adenoviral respiratory disease; and
provide a data set to develop statistical models to answer other biological questions of interest. The present invention was accomplished as a result of the availability of the BMT population of the U.S. Air Force to the present inventors. The BMT population offered advantages for surveillance studies. The major advantage is that the BMT population is racially and ethnically diverse and is representative of the racial/ethnic diversity observed in the United States. The BMT population undergoes
environmental factors similar to those of other populations to include: smoking, exercise, stress, schooling (education), activities of daily living; while the activities of daily living may 40 appear to be more regimented than their civilian counterparts, they largely reflect typical schedules (early breakfast, exercise, education for 6 hours, regular lunch and dinner, cleaning of dorms or TV in evening). These characteristics are advantageous for many research questions. One difference between
the BMT and the civilian population is that there is a predominance of males in the BMT population (90% male, 10% female) and the age range is typically from 18-25 years. In order to address this, the present inventors are extending this study to a civilian population that includes individuals of all ages greater than 18, male and female, who present to medical clinics and hospital wards with symptoms of upper respiratory tract 45 infection. The ability to ascribe differential gene expression
profiles in a relatively homogeneous population is directly applicable to military 10 WO 2007/011412 PCT/US2005/040196 applications antd'is bnablirig forth deVopment"of hithodi ecessary for the discovery of a subset of markers that will be predictive for a larger population. Sample Preparation 5 There has been considerable speculation within the research community that blood would provide the best range of gene expression biomarkers involved with the immune response to a broad range of viral and
bacterial infections. A variety of blood cell isolation kits and reagents might be useful for collecting blood cells and isolating RNA for gene expression analysis, including CPT vacutainer tubes (Beckman Dickenson) which collect blood and after a spin can segregate the PBMCs; the Paxgene blood RNA system, which has an RNA stabilizer reagent inside the vacutainer tube for blood collection; and the Tempus blood collection tube from Applied Bioscience which also has a stabilizer, but is relatively
new 10 on the market. Relman (18) has used PAXgene to successfully measure gene expression changes in blood using cDNA and long oligonucleotide (70 mer) microarrays. However, the stability of RNA in PAX tubes over handling conditions practical for multicenter surveillance was not assessed. Relman (18) processed all the PAX tubes within 24 hours of collection, which is not practical for large multicenter surveillance. Also, in principle, a higher degree of sequence resolution would be obtainable
using shorter (25-mer) oligonucleotide arrays have high-density probe tiling (e.g. 15 Affymetrix GeneChip) that blanket entire genomic regions of interest. However, prior observations have been that PAXgene produced an insufficient number of "percent present" calls (i.e. the percentage of total genes determined to be measurably expressed as determined by the Affymetrix GCOS gene expression software) on Affymetrix GeneChip expression microarrays. Presumably, the unsatisfactory level of "percent
present" calls was caused by the interference of high abundance globin RNA on binding of lower abundance transcriptional markers. Thus, there have been no prior reports of the combined use of PAXgene blood RNA kits and the Affymetrix GeneChip@ platform prior to that described herein. 20 From a logistical perspective, the use of PAXgene technology would be highly preferred for discovery of expression markers during opportunistic encounters of infectious agents with a mobile human population, This
is because of the proposition of the unique abilities of the PAXgene reagents to rapidly terminate gene expression in cells and stabilize RNA at the time of blood draw, minimizing the confounding effects of variable RNA degradation and gene expression perturbations caused by varying storage and processing times and conditions in a military clinical setting, rather than controlled laboratory environment using controlled exposures and sampling times. Traditionally, studies of blood cells utilize
25 gradient-density based methods to collect live mononuclear cells for analysis such as cell sorting, genotyping, and expression profiling. However, the RNA population may have changed or become degraded due to the processing of live cells, as transcript levels can fluctuate early after blood collection (30-32). Additionally, these methods do not isolate neutrophils, which typically pass through the gradient-density and are not collected for analysis. These methods are labor intensive and do
not translate well to mobile populations. In contrast, the PAX tube contains a proprietary solution that reduces RNA degradation and gene induction as 2.5 ml of blood is flowed into the tube (30-32). However, the blood cells are killed and 30 cannot be sorted, nor can DNA be isolated using procedures described in the PAX kit handbook (33). Since the goal of the present inventors is to measure RNA transcript levels for diagnosis or epidemiologic surveillance, we decided that the RNA stabilization
capability of the PAX tube complemented our interests, especially for situations where one cannot process the blood samples soon after collection. It is to be understood that alternative sample preparation methods may be used in the methods of the present application, so long as these alternative sample preparation methods do not compromise the integrity of the RNA material contained within the sample. 35 In view of the foregoing, the present inventors have developed a modified protocol for
gene-expression analysis of RNA isolated from human blood collected and processed with the PAXgene Blood RNA System that works with the Affymetrix GeneChip@ platform. The protocol was used to compare profiles of blood samples collected in PAX tubes that were handled in two ways that may provide practicality to surveillance and clinical studies (conditions E and 0). These methods entailed collecting blood samples in a PAX tube and then either, (a) incubating the sample for a minimum of 2 hours at
room temperature (condition E) and then isolating RNA from the PAX tube-collected blood samples, or (b) incubating the 40 sample at room temperature for nine hours followed by storage at -20*C for 6 days (condition 0) and then isolating RNA from the PAX tube-collected blood samples. The present inventors found differences between the two handling methods (although either of these conditions may be employed in the context of the present invention). Samples of condition E had higher DNA
contamination, lower total RNA yield, and higher double-stranded cDNA yield than samples of condition 0. ANOVA indicated that the two conditions contributed to differences in gene expression levels, but the magnitude 45 was minimal, being 0.09% of the total variation. These results should facilitate incorporation of expression profiling protocols and handling methods into clinical and surveillance level procedures. 11 WO 2007/011412 PCT/US2005/040196 "Gefidmd-wide &p issnfuldies 6f hifUn%
blibd"amples in the context of clinical diagnosis and epidemiologic surveillance face numerous challenges--one of the foremost being the capability to produce reliable detection of transcript levels. Many factors contribute to the variability of target detection, including: the method of blood collection, sample handling, RNA stabilization, RNA isolation, and other downstream processes. 5 The Affymetrix* GeneChip* platform can measure a significant subset of the transcriptome. In design, it
incorporates a DNA oligonucleotide microarray, manufactured via photolithography to detect labeled cRNA targets amplified from RNA populations. However, some labs have observed a lower percentage of genes detected using RNA from whole blood compared to RNA from mononuclear cells regardless of the blood collection or processing method. This phenomenon may be due to the dilution of leukocyte RNA by RNA from reticulocytes, the activation of leukocytes during the isolation procedure, and/or the
degradation of RNA isolated from the PAX tubes. 10 The RNA, isolated from blood in PAX tubes that is stored at room temperature, at -20*C, at - 80*C, or after freeze-thaw cycles has been shown to be stable as determined by ribosomal RNA bands on agarose gel, fluorescence profiles on the bioanalyzer (Agilent Technologies), or RT PCR for a few genes (31, 34-45). However, the integrity of the RNA at the transcriptome level as measured by Affymetrix microarrays has not been determined. In the
context of multi-centered epidemiological studies, one needs to stabilize the transcriptome at the point of sample collection and during sample storage and transportation. Therefore, we compared the gene-expression profiles of parallel blood samples drawn into PAX tubes 15 handled in two ways (Condition 0 and E described above) (Fig. 1). In the first way (Fig. 1. Condition E, as in fresh), RNA was extracted after the minimum incubation time of 2 hours from phlebotomy; while in the second way
(Fig. 1. Condition 0, as in frozen), the blood sat for 9 hours at room temperature followed by storage at -20"C for 6 days, followed by RNA extraction, If there were no differences between these two methods as related to gene expression, then this would allow for a reasonable time frame before the samples have to be processed or frozen for transportation or later processing. Otherwise, one needs to consider the magnitude of the differences and weigh its contribution to transcriptome variability
versus the 20 flexibility, practicality, and feasibility of sample handling, storage, and processing. In the present specification, the present inventors relate a quality assured and controlled protocol that is capable of producing reliable gene-expression profiles, using the GeneChip* system and RNA isolated from whole blood using the PAXgeneTM Blood RNA System. We used this protocol to compare quality control (QC) metrics and gene-expression profiles of PAX tube collected blood that was
handled by the methods diagramed in Figure 1. These results direct protocols for clinical studies and progress us towards the goal of using the transcriptome in diagnosis 25 and surveillance. Our results implied several recommendations as to sample handling for multi-centered studies. Since there were differences between the conditions but they both showed good within-group reliability, one should preferably pick one method to reduce variability, In which case, condition 0 seemed advantageous
over E, as it provided time before one had to process or freeze the samples and allowed for transportation while frozen. If one needed the flexibility of the range of handling methods between the conditions, then this would still be possible, as long as during subsequent 30 analysis, one increased statistical stringency. Therefore, in a preferred embodiment of the present invention blood samples are obtained and prepared for microarray analysis by the following general protocol: (a) Blood
collection -Preferably using PAX vacutainer tubes which has RNA stabilization reagent; 35 -Alternatively, the skilled artisan may use capillary tubes to obtain a few drops of blood then place in RNAstat to stabilize RNA; -Another alternative is the use of Tempus tubes from Applied Biosystems, which also have RNA stabilizing reagent; -Also within the scope of the present invention, the skilled artisan may use single cells from drops of blood and pass the sample through microfluidic channels to
different stations that measure different things about the cell including the transcriptome. In so doing, this technique may provide sufficient rapid measurements that one does not need to stabilize RNA; 40 (b) Target RNA isolation -Preferably using PAX tubes, the PAX kit system is used to isolate target RNA with modifications to the manufacturer's instructions (described herein elsewhere); -Other kits that are commercial available and may be used in the present invention include those available
from Qiagen (e.g., Qiamp), or from Zymogen, or from Gentra to isolate RNA from whole blood not in stabilizing solution; 45 -Also suitable for use are robotics system available for purifying RNA from blood in a high-throughput manner; (c) Labeling and/or amplification of target RNA 12 WO 2007/011412 PCT/US2005/040196 -Preferably, for anplifictiffif'tbthefa?@t RNA, the purified RNA is reversed transcribed to cDNA then to double stranded cDNA with a T7 promoter for subsequent in vitro transcription
to amplify and label the resulting cRNA target; -Alternatively, if enough RNA is isolated from blood, then one could label the RNA directly with fluorescent dye or other molecules of high light output for high sensitivity of detection, thus providing a time savings; 5 -Other RNA amplification and strategies may also be employed, including, but not limited to, the Ovation RNA amplification technology (Affymetrix) using one-cycle and two-cycle to reduce initial amount of RNA needed and also to
reduce processing time; (d) Hybridization onto microarray -Preferably, using the Affymetrix hybridization oven for 15 to 17 hours at 45*C of hybridization of labeled target onto the 10 Genechip microarray. Conditions, including incubation time and temperature, may be further modified, so long as sensitivity and accuracy are maintained. -Other platforms (described elsewhere) may be suitable for use in the present invention in which one may be able to reduce the hybridization time; (e) Detection
of bound target RNA 15 -Preferably, using strepavidin phycoerythrin to bind the biotin on the target RNA, followed by further signal amplification with biotinylated anti-strepavidin antibody and another staining with strepavidin phycoerythrin to increase sensitivity; -Alternatively, one can replace this step with a molecule that can emit more light without much quenching. Examples of such molecules include: quantum dots, alexi dyes, or biotinylated viruses. Thereby, detection and/or
hybridization times may be shortened; 20 (f) data integration and analysis. Although the PaxGene-based methods worked well in the present invention, the present invention contemplates and includes additional optimized processes. One adjustment to the existing protocol is to omit the increase in proteinase K during RNA isolation. To this end, some reports have stated that sufficient pellet formation is possible by simply increasing centrifugation time. Therefore, it is also possible to increase
the 25 centrifugation time concomitant with the omission of the proteinase K increase. Alternatively, the protein K digestion step may be shortened by using a more concentrated proteinase K and a shorter incubation time. Also, the eluent volume during mRNA elution was 100 pl, but a 200 pi total eluent might give better yield. The in-solution DNase treatment was used to ascertain removal of DNA. However, the amount of DNA left after on-column DNase treatment might not interfere with subsequent
steps. Further, to improve preparation time on the PaxGene technology itself, vacuum-filtering methods may be employed to collect the cells 30 rather than spinning the tubes to pellet the cells. Another permissible modification would be to use filtering methods to collect the supernatant after proteinase K digestion rather than spinning down the debris for a defined time (e.g., 30 min). Robotic systems could also be employed to considerably shorten liquid handling time, For alternatives to
existing protocols, other related sample collection methods and transcriptome measurement technologies may be used, These include: 35 1) The Tempus T M Blood Collection Tube from Applied Biosystems; 2) The CPTTM Cell Preparation Tube from Becton Dickenson, which can collect live cells and isolate peripheral blood monocytes after a spin down; 3) Nanoarrays of oligomer probes on nano wires and transcriptome measurements from single cells flowing through microfluidics channels; 40 4) Microcapillary
tubes to collect a few drops of blood perhaps followed by lysing of the red blood cells and storage in RNALater for RNA stabilization. Then, when needed, the RNA can be extracted from blood cells using other kits such as the Qiamp kit from Qiagen or the blood RNA isolation kit from Zymogen. Additional alternative and/or supplemental preparation methods are also contemplated, which may shorten duration time and reduce initial input RNA amount, for Example: 13 WO 2007/011412 PCT/US2005/040196 1) 1
he new method published by Attymetrix that can label total or polyA RNA directly without amplification (46) (Cole K, et al. "Direct labeling of RNA with multiple biotins allows sensitive expression profiling of acute leukemia class predictor genes." Nucleic Acids Res. 2004 Jun 17;32(11):e86.); 2) Direct chemical labeling of the RNA, for example by the method of Label IT@ pArray T M Biotin Labeling Kit by Mirus; 5 3) The Ovation kit available from NuGEN Technologies, Inc., which can generate a
large quantity of RNA using only 15 ng of RNA in 4 hr. This technology might even allow direct substitution of the PAX system, as only a few drops of blood would be needed; 4) The Dynabeads@ mRNA DIRECTTM Kit from Dynalbiotech, which uses magnetic beads to extract mRNA in 15 min in a single tube. Can be performed using whole blood. 5) The MessageAmp T m 11 aRNA Amplification Kit available from Ambion. 10 Other methods that are also contemplated to increase sensitivity of the sample preparation
processes include: 1) Adding unlabeled globin RNA or DNA to the hybridization step to block background, thereby perhaps increasing detection calls; 2) Removal of the globin mRNA via magnetic beads isolation; and 3) Adding more cRNA onto the chips and/or background reduction as in item #2. As stated above, the present invention was accomplished following successful adaptation of a commercial technology (Affymetrix Human 15 Genome U133 chip set) that has not been demonstrated prior to this to be
effective for whole blood expression profiling due to interferences from high-abundance globin RNA (20). Therefore, globin reduction for whole blood RNA is an Important step for improving gene expression profile from whole blood sample, since 70% total RNA in whole blood samples are globin mRNA, which would result in decreased percent present calls, decreased call concordance and increased signal variation. In Example 4, the present inventors evaluated biotinylated globin oligos (Ambion) and PNA
oligos (Affymetrix), which prove to be the two 20 most effective methods to reduce globin mRNA from whole blood RNA. However, heretofore there was no systematic comparison on gene expression profiles derived from these two methods. The present inventors' studies using Jurkat RNA and globin spiked in Jurkat RNA (JG) in parallel with paxgene RNA provides a detailed insight of comparison between these two methods for cRNA profiles, present calls, call concordance, signal variation, multidimensional
scaling and hierarchal cluster analysis in gene expression profiles. Although neither of two globin reduction methods gave the same gene expression profile (gxp) as Jurkat RNA, the globinclear method 25 using Biotinylated globin oligos gave closer gxp than PNA method. The data set forth in Example 4 indicate that the globinclear RNA resulted in significantly higher number of present calls (%), higher call concordance %, lower false negative discovery, and closer gene expression profile to no
globin control relative to the single step PNA reduction method in Jurkat and JG RNA, However, it also resulted in higher signal variation, lower triplicate correlation coefficient and no difference in correlation to no globin control relative to the PNA method, possibly due to the multi-step procedure that involves a 2 hour processing time. It is notable that highly pure RNA free from RNase contamination is required for the globinclear 30 method, necessitating in solution Dnase digested paxgene
RNA to be subjected to cleaning and concentration using the Rneasy Minelute column (Qiagen). In contrast, the single step PNA process is easy to perform simply by adding the oligo mixture to the downstream application tube. But we noticed that higher ratios of 3'/5' GAPDH and 3'/5' Actin appeared in paxgene RNA samples and smaller cRNA size in PNA treated paxgene RNA. Reduction in cRNA size may lead to a higher ratio of the two control probe sets and likely is the cause of the higher CV seen
with paxgene RNA. PNA oligos specifically hybridized to the 3' end of globin mRNA to prevent reverse transcription, while biotinylated capture globin specific 35 oligos hybridized to globin mRNA followed by removal of globin mRNA via strepavidin magnetic beads. Thus, because the globin clear method physically separates globin mRNA from the sample, it allowed non 3' bias techniques downstream, such as direct labeling of globinclear RNA for target preparation. Globinclear method produces a good
quality RNA with the ratio of 260/280 beyond 2.0. However, from paxgene RNA not from J and JG RNA, the cRNA yield reduces to half of the amount of no treatment or PNA treated sample and at least 5 pg paxgene RNA is required to get enough cRNA for hybridization. Whereas, I pg paxgene RNA treated with PNA oligo is able to amplify enough cRNA (approximately 20 pg) for 40 hybridization In sum, the present inventors have compared pros and cons for the globinclear and PNA methods. Based on this
comparison, the present inventors have found that the both of these methods may be used to reduce the amount of globin in whole blood RNA. Choice of methods depends on the individual project setup and goals. However, in either scenario by employing one of these methods a significantly higher number of present calls (%), higher call concordance %, lower false negative discovery, and closer gene expression profile to no globin control can be obtained. 45 14 WO 2007/011412 PCT/US2005/040196 "Basdd
dh the f6eyirig thri present lieiitbr§hMe developed a method for identifying gene expression markers for distinguishing between healthy, febrile, or convalescence in subjects that have been exposed to one or more of various infectious pathogens. In general, a preferred method of the present invention is as follows: a) sample collection; 5 b) Isolation of RNA from said sample; c) Removal of DNA contaminants from said sample; d) Optional concentration and clean-up of RNA; e) Spike-in controls for
normalization; f) Optional globin mRNA reduction/elimination; 10 g) Synthesis of cDNA; h) IVT (in vitro transcription) labeling and cRNA synthesis; i) cRNA quantification and quality control; j) Gene chip hybridization, wash, stain, and scan; k) Optional second gene chip hybridization, wash, stain, and scan; 15 1) Data acquisition and management; and m) Statistical analysis. Within the context of the present invention, including this preferred embodiment, the sample is preferably whole blood.
However, within the context of the present invention, any RNA source may be utilized whether from whole blood or extracted from some other source. In a preferred 20 embodiment, and as described above and in the Examples, when the sample is whole blood the collection device is a PAXgene blood RNA tube. Within the context of the present invention, including this preferred embodiment, the RNA may be isolated by any known RNA isolation technique. As stated above, the RNA isolation technique may be
facilitated by use of a commercially available kit, including the PAX kit system or Qiamp. Preferably, RNA isolation may be performed without on-comun Dnase treatment. In addition, in an embodiment of the present invention, RNA isolation may be performed with a Qiashredder column (Qiagen Corp.), which helps to increase the yield of RNA obtained from samples 25 obtained from sick subjects. Within the context of the present invention, including this preferred embodiment, the DNA may be removed by
any known technique. In a preferred embodiment, the DNA is removed from the sample by in-solution Dnase treatment. The Dnase treatment may be performed with or without use of an inactivation reagent. In the case of use of an inactivation reagent, it is preferred that the inactivation reagent be added after a defined period after onset of Dnase treatment. In this case, the defined period is preferably set by the level of DNA remaining in the sample. In case where 30 the DNase inactivation reagent
is not used is because subsequent use of column to clean (hence DNase and metal ions are removed) and concentrate RNA for globinclear method. Within the context of the present invention, including this preferred embodiment, the RNA may be concentrated and cleaned-up where necessary. For subsequent techniques in the preferred protocol of the present invention it is preferred that there be a total of at least 8 og of RNA initially before going into column to clean and concentrate. As such, one or
more of several techniques may be used to concentrate and clean-up the 35 RNA. For example, a Minelute column may be used and the RNA eluted in BR5. Also it is possible to used ethanol precipitation techniques with resuspension in water although this is not compatible with globinclear downstream as this method does not clean the RNA enough (e.g., approximately 10 dl). Further, to determine whether additional concentration and/or clean-up is necessary the RNA and/or quality thereof may be
assessed on a bioanalyzer or a nanodrop. Within the context of the present invention, including this preferred embodiment, it is preferred for the subsequent steps (i.e., steps (e) 40 (m)) that the starting amount of total RNA be at least 5 og, although I og starting amount can work with PNA and no globin reduction methods. Within the context of the present invention, including this preferred embodiment, it is important that prior to cDNA synthesis that a spike-in control be added to the
reaction cocktail containing the subject RNA. This step is critical for normalization between diseases and patients and poses an improvement over existing techniques. The spike-in control for use in the present invention is preferably a polyA control or an ERCC universal control (http://www.cstl.nist.gov/biotech/workshops/ERCC2003/). 45 As stated above, 70% of mRNA in whole blood samples are globin mRNA, which would result in decreased percent present calls, decreased call concordance and
increased signal variation. As such, in a particularly preferred embodiment, the globin RNA content is either 15 WO 2007/011412 PCT/US2005/040196 reduced or elifhinated. To this end, the term "reldUCdt" ifdtemplated as meaning that there is a reduction in the total amount of globin RNA in the sample of at least 50%, preferably at least 60%, more preferably at least 70%, even more preferably at least 80%, still even more preferably at least 90%, and most preferably at least 95% as compared to the
sample prior to the reduction treatment. Within the context of the present invention, the globin RNA reduction may be performed using biotinylated globin capture oligos (Ambion globinclear kit) or PNA (Affymetrix GeneChip globin 5 reduction kit) according to modified manufacturers' procedures (see the Examples of the present invention). When the globin RNA reduction method is that of using biotinylated globin capture oligos, it is preferred that biotinylated globin capture oligos are added to
the total RNA and, subsequently, the globin mRNA were removed by contacting the RNA mixture with streptavidin beads (e.g., Strepavidin magnetic beads). Globinclear RNA was further purified using magnetic RNA bead. Alternatively, it is possible to replace the magnetic bead based total RNA isolation step with Qiagen column chromatography. In either event, the subject RNA is preferably eluted with water or BR5 10 (preferably diluted such that following speedvac concentration the total sait content
is 1x BR5 or if water is used for elution, then speedvac to small volume and then increase to appropriate volume using BR5). Accordingly, when the globin RNA reduction method is that of using biotinylated globin capture oligos is employed it is a highly preferred embodiment that the RNA be concentrated and cleaned-up before and/or after said method. It is important to note that the Elution buffer that comes with the Globin clear kit does not work with downstream speed vac concentration and
affymetrix target prep. Ambion test their Elution buffer with their Message Amp target prep method, whereas the present invention preferably uses Affymetrix 15 target prep. When the PNA method is used as the RNA reduction method, this step is performed simultaneously with cDNA synthesis. In this method, PNA is spiked in with the cDNA synthesis cocktail. Peptide nucleic acid (PNA) oligonucleotides specifically bind to the 3' end of globin mRNA to inhibit reverse transcription during cDNA
synthesis. However, when employing this method, care must be taken to preserve the stability of PNA and one has to take measures to prevent PNA aggregation and precipitation. It may also be advisable to run Jurkat globin as a control for efficient globin 20 removal. When the method above is practiced in the absence of a globin RNA reduction protocol low sensitivity and high variance are observed. When the PNA method is followed the sensitivity is boosted, low variance is observed, but this
method only works for 3'biased reverse transcription assays. When the biotinylated globin capture oligo method is followed the best sensitivity is obtained, low variance is observed, and the RNA may be used for nay reverse transcription assay including non-3 biased assays. With the biotinylated globin capture oligo method very high quality RNA is 25 required, whereas the PNA method is useful even without high quality RNA, It is important to note that if ERCC controls are uses, then the data can
be normalized across highly different gene expression profiles. Within the context of the present invention, including this preferred embodiment, it is preferred that the purified target RNA be amplified via reverse transcription to cDNA utilizing a T7 polyT primer (or a random primer for non 3-biased assay alternative for exon arrays) then to double stranded cDNA with a T7 promoter for subsequent in vitro transcription. Following production of double stranded cDNA, the double stranded cDNA 30
should be cleaned-up and concentrated as appropriate. Within the context of the present invention, including this preferred embodiment, commercially available in vitro transcription kits are preferably used to amplify and label the resulting cRNA. Examples of such kits are readily available through Enzo Biochem or Affymetrix. These methods may be performed as instructed by the manufacturer with a subsequent cRNA clean-up as appropriate. Within the context of the present invention, including this
preferred embodiment, the cRNA is quantiated and the quality of the sample 35 assessed to determine the cRNA yield and purity of the sample, respectively. To determine whether additional concentration and/or whether further clean-up is necessary the RNA and/or quality thereof may be assessed on a bioanalyzer, nanodrop, and/or UV spectrophotometer (cuvette or plate reader). If necessary, if an increased cRNA yield is necessary, Ambions Message Amp kit may be used in accordance with the
manufacturers' instructions. Among the quality controls within this embodiment are the ratio of 260/280, the yield of cRNA, etc. Within the context of the present invention, including this preferred embodiment, gene chip (first, second, or subsequent chips) 40 hybridization, washing, staining, and scanning may be conducted as directed by standard Affymetrix protocols. For example, hybridization may be conducted by contacting approximately 10 Og of biotin incorporated cRNA to the genechip in the
Affymetrix hybridization oven for 15 to 17 hours at 45"C of hybridization of labeled target onto the Genechip microarray. Conditions, including incubation time and temperature, may be further modified, so long as sensitivity and accuracy are maintained. In addition, the washing and staining conditions may also be modified so long as the sensitivity and accuracy of the technique are maintained. The nature, identity, and composition of the genechip for use in the present invention are 45 not
limited; however in a preferred embodiment the genechip is selected from Affymetrix U133A, U133B, and U133 plus 2.0. In a preferred embodiment, it is preferred that either U133 plus 2.0 or both U133A and U133B are used as the genechip. 16 WO 2007/011412 PCT/US2005/040196 "As Escd'sded if6elo d~taa"s uisitibna hd" hanThlh'may be performed by any means known by the skilled artisan. For example, data acquisition and handling may be performed by hand and passing through various programs, including
the manufacturer developed software accompanying the genechip reader. A more complete discussion of data management and statisticallfunctional analysis is provided in the description below and the Examples 5 that follow. However, briefly, data management is conducted by using Affymetrix GCOS gene expression software data are exported to Excel. MAS5.0 signal and present calls are exported and saved as tab-delimited text files, as are scaled and unscaled Signal values, to test normalization
assumptions and strategies. The text files (and file names) are subsequently reformatted for import into Arraytools in house R-script. QC analysis software, datamatrix, and JMP IN (SAS Institute) programs are used for analysis of variance and further data exploitation. Where appropriate, the 10 data for U133A and U133B are joined in Arraytools. For analysis software the following can be mentioned: - Statistical analysis software: SAS and JMP; - Class Prediction analysis software: BRB-Arraytools;
- Clustering analysis software: BRB-Arraytools and dChip; and 15 - Functional analysis software: EASE, DAVID, Pathway Assist, and lobion Stratagene. To identify gene expression profiles resulting from pathogen exposure and to enable the general technology described herein, the following program was undertaken with an adenovirus model system. 20 GXP program details Description of program: Lackland Air Force Base (LAFB) in San Antonio, Texas is the location of Basic Military Training for all
recruits to the United States Air Force. Approximately 40,000 basic military trainees (BMTs) undergo a 6-week training course prior to assignment of duty. These BMTs are organized into flights of 50-60 individuals that eat, sleep, and train in close quarters. Each flight is paired with a brother or sister flight with which 25 there is increased contact due to co-localization for scheduled activities, and multiple flights are grouped into squadrons which reside in the same dormitory building,
subdivided into dorms for individual flights. Compared with their civilian peers, young healthy adults serving in the U.S. Military are at a significantly elevated risk of respiratory infections. Crowding and numerous stressors facilitate the transmission of respiratory pathogens. During the 6-week basic training course, approximately 20% of BMTs will develop fever and respiratory symptoms. Adenoviruses are the most common respiratory pathogens seen in the BMT population today. Before an
adenoviral vaccine was 30 available, adenovirus was consistently isolated in 30-70% of BMTs with acute respiratory disease. The outbreaks often incapacitate commands, halting the flow of new trainees through basic training. In 1971, the adenoviral vaccine directed against serotypes 4 and 7 became routinely available to new military trainees. This vaccine had a dramatic impact on trainee illness, reducing total respiratory disease by 50-60%, and reducing adenovirus-specific disease rates by
95-99%. The use of the adenoviral vaccine continued uninterrupted for 25 years until the manufacturer of the vaccine halted production. After discontinuation of the vaccine, 1814 of the 3413 (53%) throat cultures from symptomatic military trainees yielded 35 adenovirus during the period from October 1996 to June 1998. At that time, adenovirus types 4, 7, 3, and 21 accounted for 57%, 25%, 9%, and 7% of the isolates, respectively, and currently a predominance of adenovirus type 4 is recognized.
Since the discontinuation of the adenoviral vaccine, approximately 20% of BMTs develop symptoms of fever and respiratory illness and 60% of these cases are due to adenovirus. Other pathogens such as influenza A, Mycoplasma pneumoniae, Chlamydia pneumoniae, Bordetella pertussis, and Streptococcus pyogenes continue to cause a significant minority of respiratory disease in this population. Mixed infections are known to occur but the frequency and types of pathogens involved 40 in mixed infections
are largely uncharacterized. Resolution of mixed pathogens is the topic of a related patent application by the present group of inventors (U.S. Provisional Patent Application No. 60/590,931, filed on July 2, 2004). In the present invention, the present inventors do not attempt to characterize multiple pathogens but rely on the predominance of a single pathogen (human Adenovirus type 4; Ad4) to create a category of infection and compare cases of that to other categories comprised of non-Ad4 FRI
and convalescent Ad4 FRI. With the current state of the art, differentiating the serotypes and strains of adenovirus and influenza is a time-consuming and labor 45 intensive undertaking. Cultures of adenovirus may take a week to grow and subsequent typing of the adenovirus isolate must then be performed using hemagglutination-inhibition and neutralization assays which are cumbersome and subject to significant reciprocal cross-reactions, making 17 WO 2007/011412 PCT/US2005/040196 serotype
idsntifilatidn 1ake-6slIhg'as 2-3 weels'SByfth&frfiethat the virus is identified, the BMT has often has already transmitted the infection to multiple others. There is great need for more rapid diagnostic assays and a need to detail the epidemiology of these respiratory outbreaks so that public health measures can be directed appropriately. More importantly, especially with regard to the present invention, there are no known methods to determine reliable physiological markers 5 that relate
the exposure of an individual to an infectious pathogen to the actual infection. Thus, while a sample such as a throat swab or nasal wash might produce nucleic acid markers for the presence of a respiratory pathogen, there are no techniques available to determine whether the individual will become ill or has just recovered from infection caused by that pathogen(s). In addition, an organism may be recovered from a sampling of the respiratory tract. Generally, it may be unclear whether this
organism is simply colonizing the respiratory tract or is the cause of disease; assaying for the presence of an immunologic signature to this organism is expected to assist in the differentiation of colonization from disease. Furthermore, 10 within the group of individuals who present with febrile respiratory illness, there are no methods for determining the severity of infection, or the degree and type of interaction with the host immune system. The present invention describes methods for
performing these latter assessments in a statistically valid manner. Entry criteria and sample collection 15 In order to determine whether gene expression profiling could differentiate individuals infected and ill with adenovirus versus other infectious pathogens, the present inventors undertook an Institutional Review Board (iRB) approved study (vide infra). BMTs arriving at LAFB underwent informed consent to participate in this study. Approximately 15 ml of blood, filling 4 to 5 PAX tubes,
were drawn from each volunteer. On day 1-3 of training, blood samples were drawn from healthy BMTs into PAX tubes by standard protocol (described herein elsewhere), but no nasal wash was collected for this group. A complete blood cell count (CBC) was also obtained. These individuals were determined to be healthy by 20 screening with a standardized questionnaire, which eliminated any initial BMT with acute medical illness within 4 weeks of arriving at basic training. In Phase I of the study, BMTs
who presented at a later stage in training with a temperature greater than 100.4 0 F and respiratory symptoms were consented for a nasal wash, throat swab and blood draw for PAX tubes and CBC. These individuals were categorized into either the febrile with- or without- adenovirus infection groups. At times, a rapid antigen capture assay for adenovirus was used to screen for individuals who were adenovirus negative; this was done to improve enrollment of individuals in this group. All results of
rapid assay were confirmed with culture. 25 In Phase Ill of the study, approximately three weeks after sample collection from febrile volunteers with adenovirus, additional blood (PAX tube and CBC) and nasal wash were collected from these individuals when they recovered, forming the convalescent group. All PAX tubes were maintained at room temperature for 2 hrs and then were frozen at -20*C and shipped on dry-ice to the Navy Research Laboratory (NRL) in Washington, DC within 7 days for
processing. Nasal washes were performed by standard protocol using 5 ml of normal saline to lavage the nasopharynx followed by collection of the eluent in a sterile container. Nasal wash eluent was stored at 4*C for 1-24 hrs 30 before being aliquoted and stored at -20*C and shipped to NRL within 7 days for processing. The nasal wash and throat swab was sent for standard viral culture of adenovirus, influenza, parainfluenza 1, 2, and 3 and RSV. The nasal wash and throat swab were also tested by a
multiplex PCR for adenovirus type 4 to further confirm culture results for this pathogen. Although the foregoing describes the protocol undertaken in the present study, it is understood that the present invention further contemplates alternative storage and shipment conditions so long as the integrity of the sample is not compromised. 35 All BMTs underwent a standardized questionnaire at initial presentation, during presentation with illness, and at follow-up. Questions posed to BMTs include:
vaccination history, allergies, last meal, last exercise, last injury, medication taken, smoking history, observed subjective symptoms, and last menstruation (if appropriate). Among the observed subjective symptoms asked and monitored are: sore throat, sinus congestion, cough (productive or non-productive), fever, chills, nausea, vomiting, diarrhea, malaise, body aches, runny nose, headache, pain w/deep breath, and rash. All data was stored in electronic format using personal identification
numbers and date of sample collection. 40 During the period of sample collection, two outbreaks of Streptococcus pyogenes occurred. Throat swab and blood samples were collected as above on acutely ill BMTs and on those who recovered from illness and were still in basic training. Diagnosis of Streptococcus pyogenes was confirmed by bacterial culture and subsequently by PCR. For the experiment supporting the present invention all male BMTs who were determined to be healthy (no acute medical
illness in 4 weeks prior to initiation of basic training) were eligible for study. In Phase ll, any male BMT with T>100.4 and respiratory symptoms were eligible for 45 consent. In the experiments described in the examples below, the patient population enrolled consisted of male BMTs between the ages of 17-25. Seventy percent were white, 12% Hispanic, 12% black and 6% Asian. Thirty BMTs who were determined to be healthy were enrolled, 30 who had 18 WO 2007/011412 PCT/US2005/040196 ever and
respifstory sympt6rrfi ald ditermindd'fi5li9e Ed'dovirus by rapid assay (confirmed by viral culture and PCR) were enrolled, 19 with fever, respiratory symptoms and non-adenoviral infection were enrolled. The 30 BMTs with fever, respiratory symptoms and adenovirus had another nasal wash and blood draw performed during convalescence from their illness. Metadata for the experiments supporting the present invention were obtained by providing the healthy incoming BMTs with a standardized 5
questionnaire. These individuals were excluded from inclusion if they had fever, sinus congestion, nausealvomiting, burning with urination, cough, sore throat, diarrhea or chills in the 4 weeks prior to basic training. In order to determine conditions that might affect baseline gene expression, these individuals were screened for: race/ethnicity, vaccination status, time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and
smoking history. For Phase li, when BMTs were presenting with fever and respiratory symptoms, a standardized questionnaire was administered. In order 10 to determine conditions that might affect baseline gene expression, these individuals were screened for: race/ethnicity, vaccination status, time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history. The duration and type of respiratory symptoms to include sore
throat, sinus congestion, cough, fever, chills, nausea, vomiting, diarrhea, fatigue, body aches, runny nose, headache, chest pain and rash were recorded on standardized forms. A physical examination was recorded on standardized form to detail signs of illness in the BMT. Type and duration of medications taken were recorded. 15 For Phase IlI when the BMT with adenoviral illness had recovered (14-28 days after presenting ill) another standardized questionnaire was administered, including questions
on time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history. The total duration of each symptom from the Phase i questionnaire was noted and the total period of recovery from each symptom was determined. A detailed history of medication use between the time of Phase il and Phase Ill was taken. The ability to collect samples in a longitudinal study enables one to study gene expression throughout the course of an
infectious illness. 20 In a study as outlined hereinabove and further supported by the examples of the present application, the present inventors particularly followed BMTs who were ill with adenovirus through the time of their recovery from disease. The detailed database on type and duration of symptoms thus enabled the present inventors to determine whether these factors impact the gene expression signature for adenovirus and Streptococcus pyogenes. Further, the detailed database also enabled
the present inventors to discriminate early versus late disease and the severity of disease (for example, expected duration of illness/symptoms). 25 The detailed and standardized collection of information such as recent meal, recent exercise, perceived stress level, recent injuries, current medications, and smoking history enable control of confounding variables, strengthening the conclusion that identified gene expression patterns are specific immunologic signatures of particular pathogens.
This collected information also can be used to determine whether such conditions significantly impact gene expression patterns in a population. A statistical assessment of whether these factors are necessary or confounding for correct classification will determine whether it will be necessary to monitor for them in future experiments and applications. 30 In the future, gene expression patterns (immunologic signatures) for particular pathogens at different stages of disease may be used to predict
morbidity and mortality. This may assist the healthcare professional in determining the appropriate level of care (type of medications to use, level of care required-admit to hospital or provide care in the outpatient setting). There currently are algorithms for determining whether individuals with respiratory infection (particularly pneumonia) should be admitted to the hospital (and to what level of care) and these algorithms rely on such factors as degree of fever, heart rate, respiratory
rate, blood gases and blood chemistries (47, 48) (49). A detailed understanding of the state of 35 immunologic activation of the ill individual through gene expression may further assist with determining severity of illness. Moreover, understanding gene expression patterns, based on the inventive techniques herein, in individuals who are recovered from a particular infectious illness would enable forensic analysis of past outbreaks. Subsequently, this information may be used to determine whether
certain pathogens are naturally endemic in specific geographic areas or whether new infections have been imported to regions (e.g., how many have been previously infected with West Nile Virus?). 40 Further, for an individual, the present invention enables determination of whether these individuals have been infected with a particular infectious pathogen in the past and from this information determines the likelihood of immunity/protection against future infection with the same or related
organism. Such information would be valuable as it could guide whether vaccination or prophylaxis is necessary for particular deploying/deployed troops or hospital workers. 45 Assessment of use of PAX tubes in "real world" scenario 19 WO 2007/011412 PCT/US2005/040196 Haviiig etablisfiid' sl5iscive, 1nifutiaI"st'dy using PAX tubes, this gave the present inventors the opportunity to assess the quality of the modified protocol for gene-expression analysis of RNA using PAX tubes and the Affymetrix
Genechip platform in a real world test bed of ongoing epidemics of upper respiratory disease. Many factors contribute to the variability of target detection, with the quality of RNA being one of the most important. The quality of RNA 5 from PAX tubes collected blood could be influenced by the disease status of the donors, sample handling, and other downstream processes. Previously, the present inventors showed that under two conditions representative of practical sample handling, the PAX system
was capable of preserving blood RNA to produce good quality metrics and relatively stable transcriptome measurements (50). Recently, new RNA quality metrics have been proposed based on associations between experimental treatment of cells or purified RNA to induce RNA degradation and metrics derived from electropherograms of the RNA on the bioanalyzer (51). One new metric is the degradation factor (%Dgr/18S), which is the ratio of the average 10 intensity of bands from degraded RNA, that is peaks
of lesser molecular weight than the 18S ribosomal peak, to the 18S band intensity multiplied by 100. It is a continuous variable that is used to derive a categorical variable named 'Alert'. Alert has five values: BLACK--indicating that the ribosomal peaks were not detected; NULL-no RNA degradation and corresponds to degradation factor values 58; YELLOW-for RNA degradation can be detected and values from >8 to16; 15 ORANGE-for severe degradation and values from >16 to 24; RED--for highest
alert, strong degradation, for values from >24. Another new metric is the apoptosis factor (28S11 8S), which is the ratio of the height of the 28S to 18S peak and is indicative of the percentage of cells undergoing apoptosis (51). The present inventors compared the RNA QC methods of electropherograms from the Agilent 2100 bioanalyzer, the degradation factor, Alert, and the apoptosis factor to determine which is the best indicator of sample processing quality for RNA used in microarray 20 gene
expression analysis. Thus, for PAX system isolated RNA from the present inventors previous study (50) and current BMTs cohort, the distributions of RNA quality metrics were reported, which would be useful for comparisons and planning of protocols by other labs; determined the up-stream quality metrics that are most indicative of the quality of microarray target detection outcomes; and determined the effects of inter-individual hemoglobin variability on the sensitivity of target detection. 25 The
present inventors demonstrate that the Alert metric was a robust indicator of microarray results and will be useful for high throughput RNA quality control, especially as one practically cannot look at all the electropherograms directly during an ongoing study and must be able to rely on an indicator to flag a sample for further evaluations. The magnitude of the apoptosis factor suggested that a high percentage of blood cells underwent apoptotic cell death. This could be due to the PAX RNA
stabilizing reagent inducing cell death via apoptosis upon contact with blood cells, or simply due to differences between whole blood 30 and cultured cells from which the apoptosis factor was derived, If interested in studying apoptosis related pathways, one would have to investigate this property further with the PAX system technology, In this manner it may be possible to correlate the apoptosis factor with gene-expression profiles to implicate apoptotic pathways. The stability of the RNA from
PAX tube blood that was handled a variety of ways suggest that for future studies one can be more confident in the stability of RNA throughout the range of these handling conditions. 35 The present inventors were next able to explore appropriate methods of scaling of gene expression arrays when applied to detection of clinical phenotypes. While global scaling approaches have been advocated for other study designs and uses involving gene expression arrays, we concluded that the use of the 100
housekeeping genes provided the least biased approach, although 5 approaches were considered: 1) double scaled global normalization 2) no normalization at all 40 3) 100 hk gene scaling 4) 100 hk gene median normalization 5) empirical set of normalization gene After QC/QA of the PAX tube RNA and the microarray scaling, we undertook class prediction and class comparison modeling (a summary 45 appears in Tables 7, 10, and 11). The class prediction using gene-expression, suggestively, performed
better than using CBC or electropherograms alone. This could be that gene-expression does in fact contain more information about the sample or that it simply has more variables thus providing 20 WO 2007/011412 PCT/US2005/040196 more opportunities fb find s-dOid dlasifier by Shsidi e7" More specifically, the p-value for the significance test of classification rate suggests that gene expression is better for classification than the CBC or electropherogram and that it is not likely a function of
number of variables acquired because the CBC actually has 10 times as many as gene expression and performed poorly. 5 Study to increase number of pathogens recovered (the Hospital study) In order to study another patient population (broader age range, male and female, civilian) and to increase the number of pathogens recovered, another protocol was undertaken which focused on patients presenting to medical clinics and hospital wards at the Wilford Hall Medical Center at Lackland AFB (sometimes
referred to herein as "the Hospital study"). For the Hospital study, patient selection (Inclusion criteria) was conducted as follows. Adults (male and female) greater than the age of 18 10 were included. All were presenting to the hospital or hospital clinics with temperature > 100.4 0 F and respiratory symptoms. Nasal wash and throat swab were collected most commonly by a study nurse or by medical personnel who had been instructed by the study nurse. A portion of the nasal wash was used to
screen for influenza A or B by rapid antigen capture assay (52) and this result was confirmed by culture and PCR. All nasal wash specimens were additionally cultured for Parainfluenza 1, 2, 3, RSV and adenovirus. Accordingly, in an embodiment of the present invention, the gene expression analysis may be combined with one or more pre-screening methods. For example, the pre-screening method may include 15 abovementioned influenza A or B rapid antigen capture assay, a culture assay, a PCR-based
assay, a method described in US 60/590,931, filed on July 2, 2004. A CBC will be obtained for all enrollees with differential. In addition, each enrollee will be given a standardized questionnaire including questions relating to race/ethnicity, vaccination status, time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history. The duration and type of respiratory symptoms to include sore throat, sinus congestion,
cough, fever, 20 chills, nausea, vomiting, diarrhea, fatigue, body aches, runny nose, headache, chest pain and rash are recorded on standardized forms. Physical examination findings are recorded on standardized forms. This is a cross-sectional study that includes adults of all ages with differing severity of disease (some will be in the outpatient clinic setting and others admitted to the hospital). The ability to collect blood samples over more than one influenza season will enable the present
inventors to determine the gene expression pattern to influenza A and B and may allow us to determine whether there is a specific gene expression pattern for 25 different strains of influenza A (HI N1 vs. H3N2). For this study, the present inventors will monitor whether individuals received the injectable form of the influenza vaccine and the timing of vaccine relative to illness. The present inventors will discern whether the gene expression pattern differs between individuals with
"breakthrough" influenza-illness occurring greater than 2 weeks after time of influenza vaccine compared to the gene expression pattern seen in unvaccinated individuals with illness. The present inventors will perform the same comparison for those individuals who receive FluMist (MedImmune Vaccines) 30 intranasal vaccination with a live, attenuated strain of influenza. Understanding gene expression patterns after vaccination may predict likelihood of protection from disease and likelihood of
breakthrough illness; the efficacy of the influenza vaccine is considered to be 70-80% Because the Lackland BMT population will be receiving FluMist as a strategy of prophylaxis during the 2004-2005 flu season, the present inventors will assess gene expression profiles in individuals who receive FluMist and develop flu-like symptoms and those without in the 7 days following vaccination; it is well know that individuals receiving FluMist may develop cough, sore throat and muscle aches in 2-7 days
post-vaccination 35 as they shed the attenuated virus (CID 2004:38 (1 March), 760-762 full reference below), but the gene expression pattern post vaccination has not been determined. This study will allow us to determine whether there is a gene expression pattern that enables us to differentiate which individual is symptomatic after FluMist vaccination, but developing a protective immune response and which individual has actually developed cough, sore throat, muscle aches due to acquisition of
circulating wild type influenza in the population. This is a critical distinction to make in a closed population, such as the BMTs or college students in dormitories, because it is this age group that is most appropriate to receive the FluMist vaccine and yet the most 40 likely to have transmission of wild type influenza in closed quarters. Presymptomatic Study Individuals typically become infected with an infectious pathogen and remain asymptomatic during the incubation period prior to onset of
disease. During this incubation period, the host begins to mount an immune response to the infecting pathogen. Typically the initial response is the 45 innate immune response mounted by natural killer cells and neutrophils. Later in infection, the specific host immune response comprised of T lymphocyte, B lymphocyte and antibody responses becomes effective, In some infections, such as with the bioagent, Francisella tularensis, as few 21 WO 2007/011412 PCT/US2005/040196 as 10 organisnitari
uTtimail7"OdUse"yMptor fditedsewhiie this small number of organisms can be difficult to detect directly, the host immune response typically constitutes an amplified response of literally millions of immune cells and this immunologic signature can likely be detected prior to the onset of clinical symptoms. There are clinical scenarios in which it would be advantageous to the health care provider, public health officers and commanders/public 5 officials to determine not only who is infected with a
particular pathogen, but who has also been exposed to this same pathogen either by direct exposure or through transmission from an infected index case. For example, if the infectious agent of smallpox was released and an index case was detected, it is anticipated that each index case would significantly expose close contacts (face-to-face contact within 3 feet) via respiratory droplets and nuclei. Typically, for each index case of smallpox as many as 10 other susceptible individuals may develop
the disease. In view of the limited amount of smallpox vaccine and potential adverse reactions to the vaccine, predicting who amongst the exposed would develop disease could direct 10 resources and limit adverse side effects of the vaccine. Gene expression studies can detect developing, specific immunologic signatures for pathogens and assist in determining who in a population has been significantly exposed and infected (carrying organism) and who amongst the exposed-infected will ultimately
develop disease. Therefore, the methods of the present invention are particularly useful for the identification of gene expression signatures and the results obtained thereby may be used directly to guide and/or tailor therapeutic regimens. To this end, the following study design permits the study of cues and expression profiles at various stages of pathogen exposure and 15 onset. Since the majority of BMTs arriving to basic training from their respective home communities will be susceptible to
infection with adenovirus, the present inventors are able to screen BMTs presenting with fever and respiratory symptoms to Lackland AFB clinics with a rapid assay for adenovirus. Once a BMT is identified as being infected with adenovirus, the BMTs with whom he/she has had face-to-face contact can be followed for infection and subsequent development of disease. Significantly exposed BMTs can have blood drawn for gene expression during the exposed/asymptomatic period and again after development of
disease and during recovery. Gene expression patterns obtained from these time 20 points are then analyzed to determine the gene expression pattern that best predicts development of disease. In anticipation of the abovementioned study, BMTs who are ill with fever and respiratory symptoms during basic training are receiving a standardized questionnaire to determine other BMTs with whom they have had face-to-face contact within the last week; a database is being generated which labels the infected
BMT as the current "index case" and all BMTs with who he/she has had recent contact as "exposed". Data on the exposed and their relationship to the index case are maintained; for example, the exposed may have been the Training Instructor or Dorm Chief 25 or Element Leader of the index case. If an exposed case next presents to a clinic with fever and respiratory illness, then that case is linked to the initial index case as well as to other BMTs to which he/she may now have exposed. The
epidemiology is followed to determine whether there are situations in which the infectious respiratory disease is most likely transmitted; i.e., do Dorm Chief or Element Leaders most commonly transmit to individuals within their dorms or elements? This will direct the EOS clinical team on who constitutes the best case definition for "significant exposure" and, thus, which BMTs would be best to draw for gene expression studies in the "exposed" group. This group will be followed for subsequent 30
development of disease and blood will be drawn if these individuals present with fever and respiratory symptoms. Next the present inventors describe the present invention in terms of GXP Protocols and Data handling Description of transcriptome/mRNA measurement techniques: There are several techniques to quantitatively measure mRNA at various level of throughput. Some of them are Northern blot, RT-PCR, Nuclease protection assay, Quantigene, SAGE, differential display, in situ hybridization,
nanoarrays and microarrays. Some of these are not readily 35 adapted for high throughput or can measure at the transcriptome level. For our purposes of surveillance and biomarker discovery, microarray based techniques are most amendable for these purposes. Once biomarkers are discovered, techniques that have short processing time, but less parallel processing capability may be more useful for diagnostic purposes, such as RT-PCR or Quantigene. Techniques to measure mRNA generally involves sample
preparation, mRNA amplification and labeling if needed, followed by hybridization, then washing, staining, and/or detection of signals. There are variations to all these major steps. Sample preparation may be extensive such as for the Affymetrix genechip platform or minimal 40 such as the Quantigene system from Genospectra. Ideally, for our purpose, we want to measure the most number of transcripts in the shortest time and the highest sensitivity and specificity. Although we have used the
Genechip technology to discover biomarkers and pathways, there are many possible improvements on the current Affymetrix technology or other technologies that one can think of or already available to assess in the field (several of which are discussed herein and form a part of the present invention). Improvements over standard microarrav techniques: 45 For the platform that the present inventors have tested, the Affymetrix genechip platforms, recent improvements include reducing the amount of
initial RNA needed, shortened time of processing, or robotics to facilitate high throughput and reduce operator variability. Several options 22 WO 2007/011412 PCT/US2005/040196 are availaoie orrthe-markertovicorponte into 'helamleoCessing step of the Genechip platform. One is the new IVT kit from Affymetrix that can use 1 og starting amount of total RNA versus 5 Eg previously. Another is the double cycle IVT from Affymetrix that can start with 10 ng total RNA, however, the processing time and
complexity of the assayed is increased. The Ovation kit can also amplify and label RNA starting with as low as 5 ng, and they claim the time is in 4 hours. However, it has not been extensively tested with the Genechip microarray. A recent publication also 5 attempted to label the mRNA directly without amplification to shorten processing time, but the sensitivity was reduced. There are many areas of improvements at various steps in the processing that the present inventors contemplate in the
present invention. One is to combine and develop various steps in the surveillance process. For sample collection, instead of Paxgene, one could use microcapillary tubes to collect blood, then stabilize with RNAstat, then isolate RNA via several available kits for RNA isolation from small volumes of blood, such as the Dynabeads@ mRNA DIRECTTM Kit that can isolate mRNA using only I tube in 15 min, then use the Ovation kit to amplify and label, followed by 10 hybridization onto Genechip and wash
and stain the next day. In addition, the hybridization time may be reduced from it current time of 16 hrs on the Genechip to a time ranging from 8-14 hours, preferably 10-12 hours, or even shorter times. To further reduce the hybridization time, the present invention contemplates applying a strong electric/magnetic field to the chip during hybridization. Also to reduce hybridization time, the hybridizing temperature may be increased and then ramp down to 45"C, the current temperature for
hybridization. To improve sensitivity, the skilled artisan may employ alternative signal emitters. Currently, the signal emitter is the strepavidin 15 phycoerythrin followed by further amplification with biotinylated anti-strepavidin. However, the present invention contemplates the use of the branch DNA from Genospectra to amplify signal, quantum dots followed by multiple scans as the quantum dots do not quench, alexi dyes, or biotin labeled viruses which greatly increase signals because of
reduced quenching, higher quantum yields and up to 120 biotin molecule per virus, or RLS particles. Even further, the present invention contemplates the use of probes that are synthesized onto a conductive material, thereby it is possible to detect via electrical signals upon duplex formation, and then one can detect signals right away, In even a further embodiment, another mRNA 20 measurement technology may be employed altogether, especially a nanoarray developed to measure mRNA from single
cells. Data acquisition: In the present invention data acquisition is performed using scanner (genechip) and computer. Data handling and analysis: Data acquisition and handling may be performed by any means known by the skilled artisan. For example, data acquisition and handling 25 may be performed by hand and passing through various programs. The present inventors are in the process of developing software to perform all necessary data analysis automatically and provide results. Algorithms for
metadata and microarray parsing, grouping, etc.: - Pseudocode: Genes are ranked by likelihood to discriminate - Binary vs. multi-characteristic classifiers. Binary classifiers form binary trees to classify clinical phenotypes into groups. Each node 30 of the binary tree is determined by the minimal percent misclassification. The result is that at the tip of each tree should be each group of phenotypes; although some phenotypes may not always be able to be segregated because of lack of
classifiers discovered. A multi-characteristic classifier immediately sorts out the phenotypes instead of dividing through a tree. Both methods are currently methods of research. The present inventors' results so far suggest that for a mixture of phenotypes with large and small optimal classifiers, the binary method may make more sense. For instance for distinguishing the healthy and sick, one can obtained a 35 relatively large number of genes in the classifier, whereas for distinguishing sick
with adenovirus and sick without adenovirus, only a relatively small number of genes in the classifier may be found. The present inventors' example analysis of the gxp class prediction is basically a binary analysis with comparisons between nonfebriles vs. febriles, then healthy vs. convalescents, then febriles with adenovirus vs. without. This is basically a manual version of binary class prediction. A multi-characteristic classifier would classify healthy, convalescent, febriles with, and
febriles without adenovirus all at once, without going through binary nodes. The current 40 ArrayTools software can only implement binary tree classification with equal univariate alpha parameters for all tree nodes resulting in large classifiers for the first node, and smaller ones for subsequent nodes for our gxp data. One possible future method is to allow for different univariate alphas at each node to equalize the size of the classifiers for each node. Binary tree methods are also very
computationally intensive, especially for finding p-values of misclassification rate. One needs to perform further in silicon experiments to find the best algorithm for class prediction especially where the dynamic range of differences among classes vary 45 greatly, as in our case. For binary classification, one can also consider different information from outside non-gene-expression assays to include at each node in deciding which branch the case shall be classified. Based on our current gxp
results described 23 WO 2007/011412 PCT/US2005/040196 'hefein, the-dsta duIidbe classifidTiiffo tie'f6ur groups with less than 50 genes at each binary node at a certain percent accuracy at a certain probability of certainty. Full Analysis of gene expression data: For analysis of the GXP results from the N = 30 study, first, normalization of complete cell count data, electropherogram data, and gene-expression data was carried out after considering various methods. Then, data quality 5 was
assessed via individual control charts to determine measurement process stability, outliers, and comparisons to standards suggested by Affymetrix or from other laboratories. This quality control results in a set of reliable samples for analysis. Then RNA quality from pax tubes is assessed via overlaying graph of electropherograms and RNA quality metrics. And the relationship between RNA quality variability and microarray variability is determined. Once quality and reliability is established,
then filtering parameters are set to reduce number of variables. Then, class prediction analysis using supervised methods was performed and 10 optimized to determine sets of genes that could classify clinical phenotypes at a certain percent accuracy with a certain reliability using permutation tests. Potential confounders for clinical phenotypes are also assessed to assure that the classifier genes are most likely due to clinical phenotypes rather than confounders. Then, class comparisons
analysis is carried out to determine genes that show differences between clinical phenotypes. Finally, functional analysis is carried out to determine pathways involved in disease phenotypes. Many more analysis can to performed, such as gene ontology comparisons, promoter analysis, genome distribution, 15 variation of immune responses in the population, modeling of differential gene expression while controlling for cell count heterogeneity, and comparisons with public microarray databases, and
cross platform analysis, discover functions of genes with unknown functions. Diagnostic Capability: This is assessed by determining sensitivity, specificity, positive predictive values, negative predictive values of the assay. Some of the sensitivity and specificity of the class prediction for the gxp study has been calculated as described 20 herein. Overall, the goal is to optimize the ROC curve of class prediction results, which is analogous to minimizing the misclassification rate. Negative
and positive predicted values can be calculated once the prevalence of a disease is known. Improving assaying time, sensitivity, reliability, and automation of the assay and analysis would further facilitate diagnostic capability. To this end, once ethical issues are resolved, the human implanted chips to connect a patient to medical histories would aid in automated analysis and prediction of disease outcomes. The utility of gene-expression data for many diseases also greatly 25 enhances
diagnostic capability. Linkage to genomic variations would also provide much medical prognosis of patient. Also advancement of gene-expression technologies to nano scaled microarrays should greatly enhance diagnostic potential. For the gxp study exemplified herein, the diagnostic classifiers will be validated with a larger prediction set; however, even with the data set supporting the examples of the present invention, this can be assessed. For the minimal classifiers of healthy versus fever,
the prediction set was 100% accurate regardless of processing differences from the training set, But processing differences in 30 measuring gene expression has a greater effect on classes with less different phenotypes, such as among the sick alone. Further analysis study into the effect of the number of genes in classifiers on class prediction results of the prediction will be assessed, Future prospective studies will more assuredly assess the diagnostic capability of the classifiers we have
found and began to validate in the gxp study. 35 GXP for Prognostic Ability - Experimental protocol o Baseline patient and track through disease onset In order to determine the prognostic capability of gene expression for prediction of disease timing, severity and response to treatment, one must have a cohort that can be followed from healthy status through infectious exposure to disease/symptom 40 onset. The Lackland BMT population is unique in that this population has ongoing, significant
endemic rates of upper respiratory disease with frequent epidemic rates. This enables studies to determine gene expression markers in pre symptomatic individuals. An index case with a specific febrile respiratory disease will be identified and those BMTs significantly exposed will be assayed for gene expression to determine the immunologic signature that predicts later development of disease. BMTs with disease will be followed to assess severity of disease and relationship to gene expression. 45
o Challenge with biologically hostile environment 24 WO 2007/011412 PCT/US2005/040196 M i s wnoare naiuraiiyexposemand infected with a biological agent, such as adenovirus, will be assayed for gene expression. This group may or may not subsequently develop disease and the comparison of gene expression profiles will be made between the groups. - Opportunity to track genes as function of time and disorder 5 - Prognosis relating to a) propensity to become ill, b) timeline to onset of disorder, c)
efficacy of treatment regimen, d) recovery, etc. Ability to validate diagnostic and prognostic methods and classifiers - rationale and methodology To validate diagnostic and prognostic methods and classifiers. First the present inventors performed an experiment to discover 10 classifiers for certain diseases and/or phenotypes. Then, the percent correct classification is optimized by varying various methods and parameters. These classifiers are validated at this stage via leave a subset of
samples out cross validation methods. Also, the reliability of the optimal percent correct classification using the discovered classifiers is assessed via the permutation test. Once the optimal classifier and algorithm is found and validated with the training set, then additional samples are collected and measure to form the prediction set. The optimal classifier and algorithm is used to classify cases in the prediction set to further validate the classifiers because 15 the prediction set is
completely independent of the training set which was used to discover the classifier genes and to validate them statistically. Additionally, the classifiers are further validated using different assaying methodologies, such as RT-PCR, to further confirm that the classifier gene set is biologically significant and not simply assaying mythology specific. Then the classifiers are tested further in a larger sample of the population for which the assay is intended to be used. - The present method
permits detection of independent gene signatures for virtually any microorganisms. Notable examples include: 20 o Influenza: Influenza A and B immunologic markers will be determined to both naturally-occurring disease as well as vaccine induced immunity (both intramuscular and intranasal vaccination). o Streptococcus Pyogenes: Ongoing studies are assessing the gene expression biomarkers for S. pyogenes in the BMT and clinic population. o Ad4: Currently we have identified gene expression
biomarkers distinguishing febrile adenovirus positive patients from 25 adenovirus negative patients. o Additional microbial infections include those caused by Adenovirus species, N. meningitides, Influenza A and B, Bordetella pertussis, Parainfluenza 1,lllll, S. pneumoniae, Rhinovirus, C. pneumoniae, RSV, S. pyogenes, West Nile Virus, B. anthracis, Coronavirus, Variola major, Ebola virus, Lassa virus, F. tularensis, Y. pestis - Combinations of disorders 30 o Additionally, gene-expression of the
host indicates functional bioactivity of a subset of agents among a set of agents challenging the body. Thus, results from host gene expression should synergized with results from other assays that measure only pathogen genomes, such as PCR, RPM, or chembioagent antigens, such as immunoassays. Because of current highly parallel usage of these assays, often one gets multiple results, such as indication of multiple infection in the presence of asymtopmatic infection, where it is not clear which
agent is the causative agent. Gene-expression profiles 35 may provide information to sort this out. Also, for multiple etiologic agents inducing similar diseases, the results from gene-expression profiles may be analyzed for common nodal pathways with high connectivity, which then can be targeted as treatments intervention via therapeutics such as drugs. This would also suggest usage of therapeutics that is known to target a pathway for a particular disease to other diseases that activate the
same pathway. The present invention also offers the practitioner and clinician an ability to monitor and/or validate expression profiles identified by other 40 assays. For example, the Griffiths et al (71) report biomarkers for malaria determined by monitoring host gene expression in whole blood from patients suffering from acute malaria or other febrile illnesses. Cobb et al (72) report the effect of traumatic injury upon the gene expression profile of blood leukocytes. While Rubins et al (73)
report the gene expression profile determined for primates suffering from smallpox. The methods of the present invention can be used to assess the accuracy and reliability of the biomarkers identified in these, and similar, and to determine whether these biomarkers can be utilized to trace disease progression. 45 Exploiting prior acquired knowledge (Bayesian priors) 25 WO 2007/011412 PCT/US2005/040196 "redognitidN'6t'gig ttife"(hoW~tsposethR In this method, the present invention may be combined
with other diagnosis methods (i.e., RPM, standard blood test, immunoassay, etc.) to enhance accuracy of diagnosis. Diagnosing the health status of an individual and prognosing their course of disease usually require several assays ranging from assessment of signs and symptoms to laboratory diagnostic tests. Each assaying 5 provides a pretest probability of positive and negative predictive values for the next assay. Bayesian statistical theory takes into account this pre-test probability (whether
subjectively determined or via an assay) to determine the predictive values of the subsequent test, which should provide more accurate information to help the clinician in discerning course of action. An example of this is the present inventors' analysis of class prediction based on the Complete Blood cell count (CBC) and then the electropherogram data, and then the gene expression data. Although these different assays are not what the clinician normally use for class prediction of disease, the
statistical 10 analysis illustrates that the gene-expression profiles provided the highest amount of accuracy for prediction of infection status. If binary class prediction algorithms are considered, than for each node in the binary tree, one might consider diagnostic and prognostic probabilities from other established assays in addition to the gene-expression biomarker assays which likely will provide the most information for better diagnosis and prognosis. 15 Questions and hypotheses that may
be explored with the database approach developed by the present invention In addition to determining the gene expression profiles in response to pathogen exposure, there are many more questions and hypotheses that could be explored with the database developed by the present inventors. Some of these questions are listed below: 1) Can one find classifiers for clinical subtypes, such as those who are febrile and negative for adenovirus by culture, put positive by PCR? There are some discordances
between infection status as determined by assay type, such as culturing, PCR, or pathogen 20 microarray. Can one use gene-expression data to classify these discordances? 2) What are the concordance, sensitivity, and specificity relationships between these culture, PCR, and gene-expression classification? 3) Is there a circadian rhythm relationship between time of PAX tube collection and certain genes in the expression profiles? Gene expression profiles that correlate with time of day should
relate to circadian rhythm functions 4) Do lot numbers affect anything? 25 5) How do different statistical models to determine transcripts abundance compare to current results? There are multiple models for determining the quantity of transcripts based on amount of light emitted from each cell for each probe. Some of these are Mas4 algorithm, MAS 5 algorithm, and multi-chip models: RMA, dChip, Plier, and mix models. The GXP results herein suggest that one cannot use the multi-chip models because
those models usually assumes relatively small changes in gene expression profiles between experimental groups, which is definitely not the case in surveillance studies of multiple disease states. 30 6) How will different normalization algorithms compare to current results? There are many normalization methods: median scaling, trimmean scaling, quantile, splines, and others. Generally, we cannot use any normalization method that assumes that the distribution of the gene expression profiles is
generally the same for groups such as healthy vs. sick. Thus the present inventors have found from the current study, that spiking in polyA RNA would be most logical for normalization for quantitative comparisons among samples. 35 7) How will we reduce the dimension of the data? (Principle Component Analysis, Singular Value Decomposition, robust Singular Value Decomposition?) This analysis will give an idea of how many independent components explain the majority of variation in the gene
expression data. 8) What is the variation structure of the data and which of the metadata variables contribute most to the variation? Which contribute least? 40 9) Which of the component of the variation structure of the data classify certain metadata variables most accurately? 10) What is the latest in gene expression analysis from the literature? Can we use any of these new methods and/or software? 11) Are there subgroups in the adenovirus negative sick population? The adenovirus negative sick
population can be due to multiple agents. Can evidence for this be found in the data set obtained by the present inventive methods? 12) What is the difference between poly A and total RNA samples? 45 14) What are the functions of the genes found to be involved in classifying the different phenotypes? 26 WO 2007/011412 PCT/US2005/040196 15)For the nbrrhaiyalip iieciatWWhdiaThariation of gene-expression for genes that are biologically equal in expression in the cohort? What genes show more
variation among individuals than background variation? 16) Is there more than normal variation in immune related genes in the cohort? How many types of immune responses are there to virus infection? Is there a Th2 versus Th2 response? 5 17) Do genes that show high variation in expression correlate with variations in DNA sequences? 18) Is there a clustering of gene locations on the chromosomes for genes that differ among phenotypes? 19) Is there a high occurrence of certain promoter sequences for
the genes that changed? 20) Further investigation of the pathways adenovirus infection and fever? What does this imply about the biological mechanism of adenovirus infection and fever in humans? 10 21) Can we confirm differences in these genes with RT-PCR? What is the percentage of concordance? 22) How do the genes that we found relevant in our study compare with published in vitro study of adenovirus infection? Other virus infection? Other phenotypes such as Smoking exposure? 23) Use genes that
are cell type specific to decipher whether our gene list is associated with certain cell type differences 24) Can we do cross platform and/or lab analysis? 15 25) How do the different published methods for low level analysis, unsupervised and supervised clustering, and others compare with our data as oppose to cancer data? 26) Can we come up with better models? 27) Can one come up with a statistical model determine differential gene expression at the per cell level for groups with differing CBC?
28) What are the genes correlating with other quantitative traits recorded? Such as time of last meal, exercise, etc. These genes may be 20 able to be used for determining the activity of a person at some previous time at a certain probability level. 29) Once pathways involved in fever are determined, one maybe able to find genes involved with less variability across the population than others. This may imply that these genes should be targets of drug development with effects that would be more
efficacious for the population, Whereas pathways with genes that show high variation across the population imply these genes may not be good targets for drugs intended for the general population. 25 Application to normal gene expression measurement The present invention will certainly find application in the measurement of "baseline" (i.e. normal) gene expression signature measurement. This would have great value in defining the establishment of baseline gene expression profiles across defined
demographic populations. Such baseline measurements would have high value in discovery of fundamental differences between sexes, races, and the 30 development and ageing processes. The value of such population gene expression profiling is illustrated in the phenomena such as Gulf War Illness following putative exposures to chemical weapons and environmental toxins wherein a variety of immune disorders were reported (53, 54) without the identification of a specific etiology. In response to Gulf
War Illness, the Department of Defense initiated a broad baseline study known as the Millennium Cohort that has collected general health questionnaires from hundreds of thousands of active duty military personnel in hopes of establishing "baseline" indices of normal health. In contrast, baseline gene expression for 105 to 106 specific 25-mer transcriptional sequences would 35 provide orders of magnitude greater information regarding the possible genomic and physiological etiologies of phenotypic
or asymptomatic illnesses caused by external perturbations. Application to diagnosis other blood disorders and disease The present invention may also be used for diagnoses of: oncology diseases including: CML (bcr/abO) (30), circulating tumor cell 40 detection, colorectal cancer recurrence, neurology (MS), hemostatus and thrombosis, inflammatory disease (48 inflammatory genes for Rheumatoid Arthritis from Source Precision Medicine), diabetes, respiratory disease, and cytotoxicity and toxicology.
(55). Generally, the present invention may find utility in any diseases or physiological states that have mRNA biomarkers from blood can use similar methods described herein. Pre-symptomatic prognosis and assessment of disease risk 45 Although it has been speculated that gene expression profiles could be diagnostic for asymptomatic disease diagnosis and prognosis, the practical reduction of that concept to practice has proven quite elusive. At least one prior study has shown that peripheral
blood leukocytes obtained 27 WO 2007/011412 PCT/US2005/040196 using FXgene'kits has"yleldd"eviddnce df tiTs utility'6f'obtaining cDNA microarray baseline (i.e. healthy) expression signatures (Whitney et al 2003) (18). Other studies and prior art have shown time exposure of a known dosage of an infectious agent can lead to detectable signatures. However, it has been exceptionally difficult, if not impossible to obtain experimental cohorts that allow simultaneous measurement of gene expression
profiles in a homogeneous, isolated and experimentally accessible human population that contains statistically significant numbers of the 5 following categories: (1) healthy baseline individuals in the identical physical environment as those who will be infected with a pathogen, (2) individuals who do not have an acquired immunity against a pathogen but encounter a low level of pathogen exposure to that pathogen, or have a high innate immunity, and exhibit distinguishable "successful" immune
responses against the pathogen and do not become symptomatic for illness, (3) individuals who become ill following actual pathogen exposure and manifest symptoms without becoming febrile, (4) individuals who are exposed to the pathogen and develop illness with symptoms satisfying criteria for "febrile respiratory ill" (FRI) but who do not become so ill as to require 10 hospitalization, (5) same as 4 except that severe illness develops and the individual meets medical criteria for
hospitalization, and (6) individuals in various stages of recovery from categories 3-5. While individuals are incubating an infectious agent and before the onset of symptoms, the innate immune system begins to mount a rudimentary response followed by a more effective specific immune response. During these phases, immune cells manufacture various cytokines and chemokines to mount an effective response. These biomarkers of the immune response provide an immunologic signature that may precede 15
clinical symptoms. Thus, there is a critical need to develop methods for discovery of unique gene expression patterns for various time points within the above mentioned classes, and the present invention successfully demonstrates those methods. Preferred uses of pre-symptomatic assavs based on gene expression profiles 20 Assays for pre-symptomatic diagnosis and prognosis of infectious disease would find utility in a variety of applications where the information is of sufficient quality to
provide decision-quality information. For example, individuals who are at risk to themselves, to others, or to the completion of an important task as a result of probable or imminent illness can be temporarily replaced until the impending illness is managed. Examples would include pilots (commercial or military) prior to long-range flights, surgeons, etc. Another use would be in the mitigation of an act of bioterrorism or industrial accident where hundreds, thousands, or even millions of 25
individuals would be exposed to varying degrees of a toxic or infectious agent. Data obtained following the 2001 anthrax attacks in Washington, DC and New York, NY indicated that for every I person who obtained a sufficient exposure to anthrax cause illness and death, there were another 1,500 "worried well" persons who were candidates for prophylactic administration of antibiotics. This number could have been orders of magnitude higher if the agent had been infectious (e.g. smallpox virus)
instead of anthrax. If the remedial action, such as the administration of a high dosage of vaccine, antibiotic, or drug carries an associated risk (e.g. highly adverse reaction in 1 out of every 250 persons) then the remedial action could be of greater 30 threat to public health than the initial attack or accident without the appropriate assessment of risk within an exposed population. Alternatively, the vaccine, antibiotic, or drug may be in short supply and a triaging of exposed individuals
would be highly desirable to make maximal use of available quantities. Thus, a set of pre-symptomatic indicators could be of critical importance in the appropriate application of countermeasures in the above mentioned situations. 35 Alternative methods and platforms for detection of transcriptional markers In the above-mentioned applications, it will be necessary to measure specific sets of transcriptional markers in a more rapid and cost effective manner than that using a DNA microarray. Thus,
the high density DNA microarray is a high-content discovery tool that teaches the distillation of the most meaningful transcriptional markers, Although, recent advances, such as shortening time of sample and target preparation with small initial amounts of RNA may allow the high density DNA microarray to be a direct diagnostic platform instead of simply being a biomarker 40 discovery platform. Other platforms for highly parallel measurements of gene expression include SAGE and MPSS (56), but
these methods are technically challenging. MPSS can provide the exact number of an RNA molecule per cell, even the ones at very low levels. Thus, MPSS might be used to confirm results from microarrays. Definition of subsequences within "genes' 45 The first step in the reduction to an alternative platform involves a statistical reduction of the number of specific transcriptional markers that are required to still make a high percentage of classifications with an acceptable probability of error.
Unlike discoveries of "gene expression" 28 WO 2007/011412 PCT/US2005/040196 using microsrrays'prep'erstrusmg'0lJNA nlc'ules (s8Veral hundred base pairs of double stranded DNA) or even long oligonucleotides (e.g. single stranded 70-mers), the Affymetrix gene expression microarrays probe all known genes with a combination of at least ten 25-mer probe pairs across the wherein one of the pair members is a perfect sequence match to the predicted gene sequence and the other is a mismatch, comprised of
the same sequence as the its partner except for the middle (number 13 position) nucleotide. Complementary binding between a 25-mer probe and its 5 target transcriptional marker is severely attenuated by even a single mismatch (unlike long oligonucleotide and cDNA probes). Hence, it is critical to recognize that only small oligonucleotide probes provide probe-wise interrogation of the highly heterogeneous transcriptome, the content of which varies with not only gene activation and deactivation
but also with alternative exon splice variation, depending on exact physiological conditions. Although the GCOS software makes "present" or "absent" calls for a known or predicted full length gene sequence based on an algorithm which considers the probe pair intensity profiles across the three prime end of the gene sequences, the result can be de-convoluted into individual 10 probe pair intensities. The intensity values that are available for each probe set within each known gene sequence are
relatively high confidence sequence identifications that are independent of whether that 25-mer transcriptional sequence has been spliced into different resultant mRNAs. A cDNA probe for a full length gene product would be entirely incapable of making such a discrimination, and the 70-mer probe array should show intermediate level of sequence determination, but would require higher hybridization stringency. Moreover, the error rate in a transcriptional sequence determined from the long
oligonucleotide 70-mer would be intermediate to high inaccuracies. 15 Reduction of subsequence content In a manner similar to that described in the present invention for reducing the number of full sequence genes required to make classifications, the number of subsequences within the full length gene sequences may also be selected for use in classification, irrespective of whether the Affymetrix GCOS software identified the full length "gene" as being "present" or "absent". In this manner, the
classification problem will 20 be reduced to a set of defined 25-mer subsequences having experimentally-verified abundance variations instead of full-length gene sequences which will be comprised of subsequences might or might not actually be present or change in abundance. Alternative assay design The Affymetrix GeneChipo platform provides an excellent format for the discovery genome-wide expression changes in research, and 25 possibly for clinical diagnostics in situations that allows one or
more days for a result (e.g. tumor prognosis). However, many applications, including infectious diagnostics, will be more critically time-dependent. Ideally, these assays will be performed in several hours. In several very preferable embodiments, the information gleaned from whole genome GeneChip@ experiments will be used produce a greatly reduced set of markers that can be measured rapidly in an alternative format that is optimized for both speed and simplicity. In one very preferable
embodiment, a reduced set of gene expression markers is analyzed by reverse transcription PCR (RT/PCR) without requiring isolation of 30 total RNA. An example of this can be found with the Ambion (Austin, TX) "Cells-to-Signal T M " Kit, which allows RT/PCR amplification directly from cell lysates following a 5 minute incubation with the reagent, bypassing the need for mRNA isolation. Such a technique might be applied to whole blood lysates or to lysates of specific cell types that are separated
from whole blood by any of a number of methods, including centrifugation, fluorescence activated cell sorting (FACS), or by other flow cytometry techniques, such as with the use of the Agilent Bioanalyzer 2100 or the like. The cDNA products from the preparations described above can be analyzed directly in small numbers using real-time PCR techniques 35 (e.g. TaqMan, or Fluorescence Energy Transfer (FRET) techniques, molecular beacons, etc.) or in larger numbers using DNA microarrays having a
much smaller probe content than the whole genome Affymetrix GeneChips in a system that is optimized for speed and simplicity (57). The microarrays used for this purpose could be selected from a large number of options described in a previous overview (58). In a highly preferred embodiment, the volume of blood required to perform an assay of the type described above would be greatly reduced relative to that required for the experiments described in the present invention. 40 There are two small
aliquot techniques available on the market currently. Both can amplify from nanograms amount of RNA to microgram amounts. One is from Affymetrix which supports its two-cycle amplification protocol. This protocol basically doubles the in vitro transcription step to obtain more cRNA products. Of course, this would also increase the workload and the time considerably. A new protocol for amplifying nanograms of RNA in a relative short time is available from OvationTM. Although this technique has not
been extensively tested on the Affymetrix system, it holds much promise and is contemplated by the present invention. By these techniques only a few drops of blood is needed to isolate nanograms of RNA. 45 Additional methods may be developed to collect drops of blood and RNA stabilization. One such possibility is to use RNAstat to stabilize the blood and for transportation and storage, followed by RNA isolation when needed. 29 WO 2007/011412 PCT/US2005/040196 "AltdrfatVely, thd infbfh2ntio 6btsi
ftefh Wftri ilbenome GeneChip® experiments could be used produce assays that probe for the polypeptides that are coded for by the transcriptional markers detected by the GeneChip@ whole genome assay. These polypeptides could be detected in blood or from cell lysates using microarrays comprised of antibodies (59) instead of DNA probes or by mass spectrometry methods that measure relative protein abundances. 5 As part of an overall business model However, it is a central hypothesis of the Epidemic
Outbreak Surveillance (EOS) program and the present invention that the only economical method to realistically widely deploy a parallel pathogen surveillance assay in a clinical environment is to do so in parallel with assays that have validity in their own right for routine clinical diagnosis of common pathogens. That is, unlike a reimbursable diagnostic assay for a common 10 pathogen, an un-reimbursable assay for bloweapons surveillance will only burden a clinical operation and will not be
widely adopted. Because it may not always be possible to identify the specific cause of an infection through pathogen genomic markers (e.g. using PCR or microarrays), there remains a critical need to determine alternative "biomarkers' from the host that would elucidate the character of the disease etiology and guide the clinician in the proper management of the infection. Gene expression monitoring is thought of as a potentially revolutionary technology that could provide hundreds if not
thousands of such "biomarkers". However, in order for gene expression-based bio-defense assays to move beyond scientific 15 curiosity and into the realm of clinical diagnostics, a significant work must be carried out to demonstrate that the principle is applicable to routine clinical diagnostics. Hence, there is a critical need to develop databases of baseline (normal) human gene expression levels and to understand the nature of perturbations caused by various levels and stages of pathogen
infection. The above written description of the invention provides a manner and process of making and using it such that any person skilled in this art 20 is enabled to make and use the same, this enablement being provided in particular for the subject matter of the appended claims. As used above, the phrases "selected from the group consisting of," "chosen from," and the like include mixtures of the specified materials. Where a numerical limit or range is stated herein, the endpoints are
included. Also, all values and subranges within a numerical limit or range are specifically included as if explicitly written out. The above description is presented to enable a person skilled in the art to make and use the invention, and is provided in the context of a 25 particular application and its requirements. Various modifications to the preferred embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other
embodiments and applications without departing from the spirit and scope of the invention. Thus, this invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. Having generally described this invention, a further understanding can be obtained by reference to certain specific examples, which are 30 provided herein for purposes of illustration only, and are not intended to be limiting
unless otherwise specified. EXAMPLES Overview 35 Informed consented Basic Military Trainees (BMTs) generously donated blood and/or nasal washes. Blood collection and RNA isolation was performed using the Paxgene Blood RNA System (PreAnalytiX), which consists of an evacuated tube (PAX tube) for blood collection and a processing kit (PAX kit) for isolation of total RNA from whole blood (35). The isolated RNA was amplified, labeled, and interrogated on HG-U133A (A) and HG-U133B (B) Genechips from
Affymetrix. The Affymetrix GeneChip platform measures a significant subset of the transcriptome. In design, it incorporates a DNA oligonucleotide microarray, manufactured via photolithography to detect labeled cRNA targets amplified from RNA 40 populations. Nasal washes were aliquot and sent for determination of adenovirus infection via culture and real-time PCR. Example 1: Sample collection Lackland Air Force Base (LAFB) in San Antonio, Texas is the location of Basic Military Training for all
recruits to the United States Air Force. More than 50,000 Basic Military Trainees (BMTs) undergo a 6 week training course prior to assignment of duty. These BMTs are organized 45 into flights of 50-60 individuals that eat, sleep and train in close quarters. Each flight is paired with a brother or sister flight with which there is 30 WO 2007/011412 PCT/US2005/040196 increased Cont|et dUe'to 0i'daliztlof for s dtlleddt ffties and multiple flights are grouped into squadrons which reside in the same
dormitory building, subdivided into dorms for individual flights. BMTs arriving to LAFB underwent informed consent to participate in this study. On day 1-3 of training, approximately 15 milliliters of blood were drawn from each BMT into a total of 5 Paxgene tubes, per standard protocol, to establish baseline gene expression profiles. BMTs who 5 presented during training with a temperature of 100,5 or greater and respiratory symptoms were consented for a nasal wash and Paxgene blood draw. All
Paxgene tubes were maintained at room temperature for 2 hours and then were frozen at -20C and shipped on dry ice to the Naval Research Laboratory (NRL) within 7 days for processing. Nasal washes were performed by standard protocol using 5 cc of normal saline to lavage the nasopharynx with collection of the eluent in a sterile container. Nasal wash eluent was stored at 41C for 1-24 hours before being aliquoted and stored at -200C and shipped to NRL within 7 days for processing. 10 All BMTs
underwent a standardized questionnaire at initial presentation, during presentation with illness, and at follow-up. Questions posed to BMTs include: vaccination history, allergies, last meal, last exercise, last injury, medication taken, smoking history, observed subjective symptoms, and last menstruation (if appropriate). Among the observed subjective symptoms asked and monitored are: sore throat, sinus congestion, cough (productive or non-productive), fever, chills, nausea, vomiting, diarrhea,
malaise, body aches, runny nose, headache, pain w/deep breath, and rash. All data was stored in electronic format using personal identification numbers. 15 The present inventors sought to determine the gene expression patterns that developed in Basic Military Trainees (BMT) populations as they were naturally exposed to respiratory pathogens and subsequently developed disease during their 6 week training period. Up to 50% of BMTs experience upper respiratory tract infection (URI) during training
and 40% of these will have fever and URI symptoms. Approximately 60-80% of febrile respiratory disease is due to adenovirus type 4. Other pathogens that cause a significant minority of disease include Streptococcus pyogenes, Chlamydia pneumoniae, Mycoplasma pneumoniae, and Bordetella pertussis. 20 BMTs maintain set schedules throughout the 6 week training program and are kept in close proximity; the BMT population offers a unique opportunity to evaluate gene expression profiles resulting from
pathogen exposure and/or infection in the absence of confounding external/environmental factors, In the first 18 months of the EOS program, a Lackland and Air Force Surgeon General Institutional Review Board (IRB)-approved protocol was implemented. This protocol continues to be supported by the Lackland 37th Training Wing Commander and the Base Commander. The present 25 inventors implemented an experimental model for comparing whole blood expression profiles from four categories of BMTs: 1.
Healthy (baseline), 2. Febrile Respiratory Illness (FRI) adenovirus 4 infected (Ad4+), 3. FRI without adenovirus (Ad4-), and 4. post-FRI Ad4+ (individuals recovered from adenoviral infection, i.e. #2 above). 30 Individuals were identified as healthy if they were in week 0 of basic training and had no respiratory symptoms in the prior 4 weeks. Individuals with FRI were identified by primary providers and study nurses as the BMTs presented to health clinics and dispensaries. All BMTs were
consented and underwent blood draw to determine gene expression profiles, All ill BMTs were administered a standardized questionnaire to determine the type of presenting symptoms and the onset and duration of symptoms. Physical examination and complete blood counts were 35 recorded. BMTs who were determined to have an adenoviral illness by rapid immunoassay/PCRculture underwent a subsequent blood draw and nasal wash 14-21 days after their initial FRI presentation; the majority of these
individuals had no further symptoms of infection at the time of the follow-up blood draw, PCR for adenovirus and culture for all respiratory viruses was performed on nasal washes. One hundred BMTs were entered on the study, including 30 healthy BMTs. Whole blood gene expression profiling for 33,000 known genes and open reading frames (ORFs) was performed on PAXgene blood RNA samples using Affymetrix U133A/B chip sets. Data from 76 BMTs is available with the following breakdown: 40 healthy
(n=38), febrile without adenovirus infection (n=14), febrile with adenovirus infection as determined by culture (n=24), and those who recovered from adenovirus associated febrile illness (n=26). Initial search for genes that show expression level differences of >= 1.5 fold-change of the lower 90% confidence interval between groups showed that: 913 genes differ between healthy and febriles at 0.1% median false discovery rate (FDR); 203 genes differ between healthy and recovered at 2.0% FDR.
Ongoing recruitment with the addition of a screening rapid assay for adenovirus has enabled increased enrollment of FRI Ad4- BMTs and will enable statistical analysis between the FRI Ad4+ and Ad4- groups. 45 Example 2: Sample Preparation 31 WO 2007/011412 PCT/US2005/040196 iMarenals ana iremoug PAX tube blood collection. Blood was collected into the PAX tubes from volunteers according to the manufacturer's directions (60), For the experiment described in Figure 1, twelve PAX tubes were collected
from one person. Then, the tubes were split into two groups of six for the two conditions. Subsequently, RNA from pairs of tubes had to be pooled to obtain enough RNA for further processing. This resulted in three replicates in 5 each condition. Total RNA isolation. After sample collection, the PAX tubes were incubated at room temperature for 2 or 9 hours, followed by immediate total RNA isolation or freezing at -20*C for 6 days before further processing. For total RNA isolation, we followed the
PAX kit handbook (33), but with modifications to aid tight pellet formation after proteinase K treatment. Loose pellets were problematic. To form tight pellets, we increased the proteinase K added from 40 pi to 80 pl (>600 mAU/ml) per sample and the 55*C incubation time from 10 min to 30 min. After spinning the samples, 10 if a tight pellet still did not form, then we remixed the samples, incubated at 55"C for another 5 min, and followed by centrifugation. The optional on column DNase
digestion mentioned in the PAX kit handbook was not carried out. Thus, OD measurements at this point would not give accurate quantification due to DNA contamination; however, the 260/280 ratio may indicate other contaminants. Approximately 4 pl of the 80 pl eluted RNA was needed to obtain an absorbance greater than 0.1. All aliquots were diluted in 10 mM Tris-Cl pH 7.5 for OD readings. In-solution DNase digestion. Subsequently, in-solution DNase treatment was carried out using the DNA-freeTM kit
(Ambion). Briefly, for 15 each sample eluted in 80 pl BR5 buffer, we added 7 pI 1OX DNase I buffer and 1 pl DNase, followed by mixing and incubation at 370C for 20 min. Afterwards, 7 pl of DNase inactivation reagent was added, incubated at room temperature for 2 min, and spun down to pellet tlhe beads that were in the inactivation reagent. The treated RNA in the supernatant was pipetted off without disruption of the pellet. An aliquot of each RNA sample was run on the bioanalyzer for
quantification and QC measurements. Poly-A RNA isolation. After DNase treatment, duplicate samples were pooled, and mRNA was isolated using the OligotexTM mRNA kit 20 (Qiagen). The mRNA was eluted in 100 pi total of OEB buffer. Sample concentration. Next, the samples were concentrated via ethanol precipitation. For each 100 pl sample, we added 1 pi glycogen (5 mg/mi) (Ambion), 15 p 5M ammonium acetate, and 200 pl 100% ethanol chilled at -20'C. The reaction was incubated at -20"C overnight. The
next day, the samples were spun down at 13,791g at4"C for 30 min. The pellet was washed twice with 80% ethanol chilled at-20*C; air-dried; and resuspended in 12 pl of nuclease free water (Ambion). 25 Generation of cRNA. All subsequent steps were carried out as described in the GeneChip* expression analysis manual (6). Ten microliters of each sample were used in the first strand cDNA synthesis reaction. Ten microliters of purified double-stranded cDNA were used for synthesis of biotin-labeled
cRNA. Fragmentation, hybridization, and detection were performed as described in the manual (6). Measurements on the bloanalyzer. One microliter, from pre- and post-DNase total RNA, purified double stranded cDNA, purified cRNA diluted 1:10, and fragmented cRNA, was run on the bioanalyzer using the protocols described in the RNA 6000 Nano Assay (Agilent Technologies) 30 (61). The usage of the bioanalyzer was analogous to gel electrophoresis, except that the gel matrix and samples were flowed
through microfluidic channels of a cartridge, thus facilitating small sample usage and automated quantification. Real-time PCR for gapdh gene. Each real-time PCR reaction for gapdh DNA included: 12.5 pl 2X SYBR green PCR master mix (Applied Biosystem), 0.5 pl 5'GTGAAGGTCGGAGTCAACGG forward primer (10 pM), 0,5 pl of 5'GCCAGTGGACTCCACGACGTA reverse primer (10 pM), 10.5 pl of water, and 1 pi of template from total RNA or cDNA samples. The reactions were carried out in the iCycler (Biorad) with
cycling 35 settings of 95*C 3 min; 95*C 30 s, 58*C 30 s, and 720C 30 s for 40 cycles; followed by melting curve analysis and/or a 4*C hold. The completed reactions were also analyzed by gel electrophoresis. Reverse transcription. For RNA quality assessment during protocol development, synthesis of cDNA was carried out using the SuperScriptTM First-Strand synthesis system for RT-PCR kit (Invitrogen Life Technologies). Statistical analysis. Statview (SAS Institute) software was used to perform the
nonparametric Mann-Whitney U test to determine 40 statistically significant differences between 260/280 OD ratios, concentrations via 260 nm absorbance, concentrations via integration of fluorescence profiles, relative amounts of contaminating DNA via threshold cycle, RNA quality via ribosomal 28S/18S peak ratios, double stranded cDNA yields, purified cRNA yields, and 260/280 ratios of purified cRNA. A P-value of less than or equal to 0.05 was considered statistically significant. Affymetrix
Microarray Suite 5.0 (MAS 5.0) (62) was used for generation of QC metrics including: noise(RawQ), an indicator of variation in pixel intensities; average background; scale factor, an indicator of variation of intensities between chips; percent present calls, an indicator of the 45 number of genes detected; and gapdh 3'/5' signals and actin 3/5' signals, indicators of RNA degradation. Dataplot (63) was used to assess 32 WO 2007/011412 PCT/US2005/040196 autocorrelationftbf ' cw ed t~riRnIidividual
line charts and to set quality control limits at ±3 standard deviations from the mean. MAS 5.0 CEL files, which contained intensity values of each probe, and gene expression present calls were imported into dChip (64, 65) for further analysis. In dChip, HG-U133A and HG-U133B chips were analyzed separately. dChip uses intensity values of probes on multiple arrays 5 to calculate an expression index, which is a measure of transcript abundance. The expression index is analogous to the signal
statistic output by MAS 5.0, dChip was used for hierarchical clustering and fold-change determinations, and the expression indices were exported to JMP IN (SAS Institute) for analysis of variance. Results 10 Adaptation of RNA from PAX tube for use with the GeneChipo system. RNA from a PAX tube was isolated using the protocol provided with the PAX kit. As determined by spectrometry, the yield was 4.8 pg; the 260/280 ratio was 2.01; and the concentration was 0,06 pg/pl. This was not sufficient for
use with the GeneChipe protocol which prescribed an initial total RNA amount of 5 pg at 0.5 pg/ pl (6). Thus, RNA isolated from two PAX tubes were pooled, followed by ethanol precipitation and resuspension in 15 pl of BR5 buffer. This resulted in a yield of 10.4 pg, a 260/280 ratio of 2,07, and a concentration of 0.7 pg/pl, which met the amounts recommended in the GeneChipO protocol. 15 The optional on-column DNase digestion step was performed as described in the PAX kit. However, for quality
assurance, the presence of DNA in the purified RNA was assessed via real-time PCR for the gapdh gene. PCR could detect the presence of gapdh DNA (Fig. 2A), suggesting that the on-column DNase digestion was not efficient enough to remove DNA to a level undetectable by PCR. Thus, the RNA was treated with DNase in solution. Afterwards, gapdh DNA was not detected by real-time PCR (Fig. 2B), suggesting that most DNA had been digested. However, the RNA integrity may be compromised during in-solution
DNase treatment; thus, reverse transcription followed by real-time PCR for gapdh was 20 performed on the in-solution DNase treated samples. The gapdh DNA was detected following reverse transcribed-PCR (Fig. 2C), suggesting that the RNA was still of good quality. The use of Oligotex purified mRNA was based on a preliminary experiment comparing the number of genes detected when using total RNA versus mRNA isolated from blood in PAX tubes, The resulting present calls, signifying the number of genes
detected, were 33% for total RNA and 41% for mRNA on the HG-U133A chips, Comparisons were also made between mRNA isolated via Oligotex and mRNA isolated via ion-pair 25 reversed-phase high performance liquid chromatography (IP RP HPLC) (66). The resulting present calls were 17% and 19% for IP RP HPLC and 35% and 40% for Oligotex mRNA, Since Oligotex isolated mRNA showed the highest percent present calls, the step was incorporated into the protocol. The protocol used for gene-expression profiles
of human blood samples using the PAXgene Blood RNA System and the GeneChip* platform includes at least 2 PAX tubes per donor, total RNA isolation without on-column DNase digestion but with in-solution DNase digestion, mRNA 30 isolation, precipitation for concentration, followed by standard protocols from the GeneChip® manual. Comparison of QC measures for conditions E and 0. We compared the quality control measures of PAX tube-collected blood samples whose RNA were isolated after the minimum
incubation time of 2 hours at room temperature (Fig. 1, condition E) and after incubation at room temperature for nine hours followed by storage at -20C for 6 days (Fig, 1, condition 0). To compare the purity and yield of total RNA from the two conditions, we performed spectrometric analysis on the RNA samples. There 35 was no difference in the 260/280 ratio between the two treatments (Table 1, row 1), suggesting that RNA purity was equivalent for the samples. The yield before DNase treatment
was 1.0 pg higher for condition E than 0 (Table 1, row 2). However, this measure may be confounded by differential DNA contamination in the samples. Thus, after in-solution DNase treatment, we quantitated the RNA using the bloanalyzer (Fig. 3B). Surprisingly, the yield was 0.9 pg higher in condition 0 than E (Table 1, row 3). This implied that there was more DNA contamination in E compared to 0. Therefore, we measured the relative amount of DNA contamination in the two treatments via real-time
PCR for gapdh, The threshold crossing cycle 40 was lower in E compared to 0 (Table 1, row 4), indicating that there was more DNA in E. These observations indicated that more DNA contamination occurred in E than 0 but that the yield of RNA was higher in 0 than E. TABLE 1 - Comparisons between condition E versus 0 of quality metrics relating purity, yield, and stability of total RNA isolated from PAX tube. Each mean ± SEM value displayed in each cell was calculated from n = 6. 33 WO 2007/011412
PCT/US2005/040196 Row # Description Treatment of Method Condition E (mean Condition 0 Mann RNA samples 1SEM) (mean ± SEM) WhitneyU test P-value 1 Purity via 260/280 OD No DNase Spectrometry 2.07 ± 0.04 2.07 ± 0.05 0.631 ratio 2 Concentration via 260 No DNase Spectrometry 7.3 ± 0.2 pg 6.3 ± 0.2 pg 0.007* Absorbance 3 Concentration via In-solution Bioanalyzer 3.8 ± 0.2 pg 4.7 ± 0,2 pg 0.025* integration of DNase fluorescence profiles 4 Relative amounts via No DNase Realtime PCR for 14.7 ± 0.8 24.3
± 0.6 0.004* threshold cycle gapdh DNA 5 RNA quality via 28S/1 8S In-solution Bioanalyzer 1.7 ± 0.1 1.6 ± 0.1 0.200 peak ratio DNase RNA from various samples produced different profiles on the bioanalyzer, and we would like to use such profiles for QC. Therefore, we overlaid RNA profiles from our samples to assess inter-sample variability and RNA quality (Fig. 3). Before DNase treatment, fluorescence profiles from condition E were, on average, higher than samples from 0 (Fig. 3A). After
in-solution DNase treatment, the fluorescence profiles decreased 5 overall and reversed with respect to the conditions (Fig. 3B). Interestingly, comparisons of pre- and post- DNase treatment profiles suggested that DNA tended to show up between the two ribosomal peaks and as a hump at later times (Fig. 3A & C). These observations corroborated the yield and DNA contamination results determined by spectrometry and real-time PCR. The ratios of the 28S to the 16S ribosomal RNA peaks averaged
around 1.6 (Table 1, row 5) based on the bioanalyzer automatic peak detection and calculation software. However, manual adjustment indicated that the 28S/1 6S ratio averaged around 2. There was no difference in the 28S/16S ratio between condition E and 0 (Table 1, row 5). The shapes of the 10 fluorescence profiles were similar in both treatments (Fig. 38). These results suggested that the RNA populations from both conditions were of similar good quality. Since the RNA were of similar quality for
the two conditions, we continued through the procedures to make fragmented labeled cRNA. We used the bioanalyzer to monitor double stranded cDNA synthesis (Fig. 4A), purified cRNA (Fig. 4B), and fragmented cRNA (Fig. 4C). The characteristic profiles in Figure 4 were indicative of successful reactions. The yield of double stranded cDNA was 0.09 pg higher in condition E than 15 0 (Table 2, row 1), while the yield of purified cRNA was around 30 pg with no detectable differences between the two
conditions (Table 2, row 2). The 260/280 ratios were similar between the two groups (Table 2, row 3). TABLE 2 - Comparisons between condition E versus 0 of quality metrics relating yields and purity of double stranded cDNA and cRNA derived from mRNA isolated from PAX tube. Each mean ± SEM value displayed in each cell was calculated from n = 3. Row # Description Method Condition E (mean Condition 0 (mean ± Mann-Whitney U test ± SEM) SEM) P-value 1 Double stranded cDNA Bloanalyzer 0.56 ± 0.03 pg
0.47 ± 0.03 pg 0.050* yield 34 WO 2007/011412 PCT/US2005/040196 2 Purified cRNA yield Spectrometry 34 * 4 pg 30 & 3 pg 0.513 3 260/280 of purified cRNA Spectrometry 2.3 0.03 2.4 0.06 0.275 Since the QC metrics suggested that sample preparation was successful, we hybridized the samples to human HG-U133A chips followed by hybridization onto the HG-U133B chips using the same hybridization cocktails, which had been stored at -80"C. Hybridization, washing, detection, and scanning were done as
described in the GeneChipo manual (6). 5 Afterwards, we assessed the QC metrics along with other samples processed in our facility (Fig. 5). To determine if the metrics were fluctuating randomly over time, each QC metric shown in Figure 5 was graphed on lag- and autocorrelation plots (not shown) (67). There was no obvious pattern in the plots, suggesting that the metrics were randomly drawn from a fixed distribution, thus enabling the setting of control limits at ±3 standard deviations from the
center mean. All measures were within the control limits. Average Background centered around 70, which was within the typical range of 20 to 100 (68). Importantly, the percent present centered at 39% for HG-U133A chips and 25% for HG-U133B chips. Finally, the 10 3' to 5' signal ratio for both gapdh and actin centered at -1.2, indicating that the RNA was of good quality and cRNA synthesis was efficient. Comparisons of these QC metrics for the samples from conditions E and 0 indicated no
significant differences, These QC results suggested strong confidence in the reliability of our process. Analysis of gene-expression profiles. To determine the contributions of handling conditions, microarray chips, and differing genes to the variation in measures of transcript abundance, we performed a three-way analysis of variance on dChip-derived gene expression indices from 15 HG-U133A chips. Quantile-normal plot of expression indices from 6 chips indicated that the expression indices were
not normally distributed. Thus, 100 genes were randomly sampled from the 22,577 genes, and their expression indices were transformed by adding '1' to every value to remove zeros followed by a Box-Cox transformation to bring the distribution closer to normality. Subsequently, the transformed data was fitted into the following model: Yijk = M + Ci+ Pj + Gk + Elik 20 Where Y stands for the transformed expression indices, M for the grand mean, C for the two conditions (i = 1, 2), G for the 100
sampled genes (k = 1, 2, 3, . . . 100), and E for the residual error. P has three levels (j = 1, 2, 3) and encompasses variations due to the order of the blood draw, order of processing, and/or between chips. For example, level j = 1 of P contains expression indices from one chip of each condition, and these two chips detected targets from PAX tube samples that were drawn first (draw order numbered 1, 3 for condition E and 2, 4 for condition 0, Figure 1) and processed together. After model
fitting, the residual versus predicted plot showed no correlation, and the residuals were normally 25 distributed (Shapiro-Wilk W test, P = 0.24). The coefficient of determination (R 2 ) was 0,993. These results suggested that the model adequately explained most of the variation in the data. The analysis of variance results are shown in Table 3. TABLE 3 - 3-Way ANOVA results Source Degree of Sum of Squares % of total Mean F ratio P-value freedom variation Square Condition (C) 1 50,843 0.090
50,843 60.2 <0.0001 Chip (P) 2 94,662 0.167 47,331 56.1 <0.0001 Gene (G) 99 56,189,455 99.004 567,570 672.4 0.0000 Residual (E) 497 419,519 0.739 844 30 The 'Sum of Squares' column indicates the magnitude of the variations explained by the factors listed under the 'Source' column, while the '% of total variation' column converted the sum of squares into percentages. The F ratio (mean square of a factor / mean square of the residual) is used to test whether the variation explained by a
factor is statistically greater than the variation of the residuals; a P-value of less than 0.05 indicated 35 WO 2007/011412 PCT/US2005/040196 statistical significaribef Tht resljts indfosteitft;-alhre'fadtors: C, P, and G, significantly explained portions of the total variation. However, the gene (G) factor explained most of the variation (99%), while the handling conditions contributed minimally (0.09%) to differences in gene expression levels. These results were generalizable to all genes on
the chips since the 100 genes analyzed were randomly selected. To determine the correlations of gene levels among the samples of the two conditions relative to other PAX-tube-derived samples 5 processed in our lab, cluster analysis was performed. Samples were clustered via hierarchical clustering with average linkage, no gene filtering, and no standardization of genes or samples. The distances among samples were 1 - r, where r is Pearson's linear correlation coefficient. This distance measure
quantified dissimilarities between entire expression profiles. The resulting dendrograms with descriptive ontologies of samples are shown in Figure 6. The samples from conditions E and 0 clustered together away from samples that differed by other factors such operator and individual donors, and they segregated into E and 0 conditions for genes on the HG-U133B chips. This result further support the analysis of variance in that 10 the differing conditions did not induced large changes in gene
profiles. To quantitate differences between the two conditions in terms of fold-changes, we compared fold changes of all genes between the conditions. From the set of non-filtered genes (-22,600 genes for HG-U133 chips, with 7,600 genes for HG-U133A and 5600 genes for HG-U133B called present by MAS 5.0), we filtered for genes that showed greater than 1.3 fold changes between the conditions using the lower bound of the 90% confidence interval of fold-change estimates. This resulted in 5 genes for
HG-U133A chips and 22 genes for HG-U133B chips (Table 4), When 15 the lower bound was set to 1.5, only 1 gene remained for HG-U133A chips and none for HG-U133B chips. These results indicated that the differences between the two conditions were due to genes whose expression indices differ by no more than 1.5 fold of the 90% lower bound. TABLE 4 - List of genes that showed greater than 1.3 fold change using the lower bound of the 90% confidence interval between condition E and 0 Fold Lower bound
of Upper bound of probe set gene E mean' 0 mean 2 change fold-change fold-change U133A chips 200032_s_at ribosomal protein L9 731.73 1272.5 1.74 1.31 2.18 204661_at CDW52 antigen (CAMPATH-1 antigen) 834.26 1394.3 1.67 1,34 2.02 206207-at Charot-Leyden crystal protein 657.73 1085.4 1.65 1.36 1.96 210510_sat neuropilin 1 224.6 492.39 2,19 1.9 2.54 glutamate decarboxylase 2 (pancreatic islets and brain, 211264-at 65kD) 30.97 49.3 1.59 1.3 2 U133B chips 222787_s-at hypothetical protein FLJ11273
168.39 106.06 -1.59 -1.41 -1.79 222791_at hypothetical protein FLJ 11220 226.09 142.84 -1.58 -1.39 -1,84 222793_at RNA helicase 754.62 490 -1.54 -1.36 -1.73 222833 at hypothetical protein FLJ20481 317.62 221.84 -1.43 -1,32 -1.56 36 WO 2007/011412 PCT/US2005/040196 223243_s.at chromosome 1 open reading frame 22 206.55 135.11 -1.53 -1.33 -1.78 224737_x_at Consensus includes gb:BG541830 /FEA=EST 65.17 36.26 -1.8 -1.47 -2.23 phosphoprotein associated with glycosphingolipid 225626_at enriched 307.44
205.34 -1.5 -1.36 -1.66 226119_at similar to hypothetical protein FLJ10883 299.72 185.48 -1.62 -1.39 -1.89 226148_at Consensus includes gb:AU144305 /FEA=EST 274.02 183,58 -1.49 -1.35 -1.66 226465_s_at SON DNA binding protein 243.4 154.52 -1.58 -1.4 -1.77 226641_at Consensus includes gb:AU157224 /FEA=EST 715.14 457.8 -1.56 -1.34 -1.86 226979_at mitogen-activated protein kinase kinase kinase 2 408.84 261.97 -1.56 -1.35 -1.82 227405_s-at frizzled homolog 8 (Drosophila) 636 373.97 -1.7 -1.41 -2.01
227772_at Consensus includes gb:AV700849 /FEA=EST 211.74 138.2 -1.53 -1.32 -1.8 228248_at Consensus includes gb:W49629 /FEA=EST 549.67 356.03 -1.54 -1.31 -1.83 228328_at Consensus includes gb:A1982758 /FEA=EST 158.3 102.72 -1.54 -1,32 -1.82 232744_x_at Consensus includes gb:BG485129 /FEA=EST 27.38 16.57 -1.65 -1.41 -1.96 237403_at Consensus includes gb:A1097490 /FEA=EST 979.37 603.12 -1.62 -1.37 -1.95 240784_at Consensus includes gb:BE549627 /FEA=EST 624.51 390.38 -1.6 -1.38 -1.85 241202_at
Consensus includes gb:AA779283 /FEA=EST 676.47 416.03 -1.63 -1.31 -2.01 241260_at Consensus includes gb:N39326 /FEA=EST 13.67 22.95 1.68 1.39 2.04 243589_at Consensus includes gb:Ai823453 /FEA=EST 264.88 160,86 -1.65 -1.41 -1.91 'The mean of expression indices of condition E (n = 3) 2 The mean of expression indices of condition 0 (n = 3) In comparing the two conditions, there were more genes that showed changes on the HG-U133B chips than on the HG-U133A chips, 5 even though more genes were
detected on the HG-U133A chips. Also, the genes that changed on the HG-U133B chips mostly went down in condition 0. Our results implied several recommendations as to sample handling for multi-centered studies. Since there were differences between the conditions but they both showed good within-group reliability, one should preferably pick one method to reduce variability. In which case, condition 0 seemed advantageous over E, as it provided time before one had to process or freeze the samples
and allowed for transportation while frozen, If 10 one needed the flexibility of the range of handling methods between the conditions, then this would still be possible, as long as during subsequent analysis, one increased statistical stringency, such as only passing genes greater than 1.5 fold change of the 90% lower bound, 37 WO 2007/011412 PCT/US2005/040196 Example 3. GXP Pfotfiard"Qmid3O"'Eerim6nft' Materials and Methods Culture of adenovirus from nasal washes. All samples are cultured for
Adenovirus, Parainfluenza 1,2, and 3, Influenza A and B and RSV. Standard cell types, including Rhesus Monkey Kidney-PMK or Cynomologous Monkey Kidney-CYN are most commonly used in addition to 5 A549 cells. Standard'culture and shell vial with direct fluorescent antibody are used. All respiratory cultures are held for 10-14 days until called negative. Fluorogenic real-time PCR for adenovirus serotype 4 from nasal washes. DNA was extracted from 100 p of nasal washes using the MasterPureTm DNA
purification kit (Epicentre Technologies, Madison, WI) and resuspended in 10 p) nuclease free water (Ambion Inc., Austin, TX). Two different fluorogenic real-time PCR were used to detect adenovirus serotype 4 hexon and fiber genes. For hexon gene specific PCR, each 10 reaction was 15 pi total volume containing 20 mM Tris-HCI (pH 8.4), 50 mM KCI, 4 mM MgCI2, 200 pM dNTPs (Invitrogen Life Technologies, Carlsbad, CA), 200 nM primers, 100 pM TaqMan probe (Integrated DNA technologies, Inc.
Coralville, IA), 0.6 U of Platinum Taq DNA polymerase (Invitrogen Life Technologies, Carlsbad, CA), and 0.6 pI purified DNA from nasal washes. The sequences of adenovirus 4 specific hexon primers are: 5'-GTTGCTAACTACGATCCAGATATTG-3' (forward; SEQ ID NO:1) and 5'-CCTGGTAAGTGTCTGTCAATCC-3' (reverse; SEQ ID NO:2). The sequence of adenovirus 4 hexon specific probe is 5'-FAM-CAGTATGTGGAATCAGGCGGTGGACAGC-TAMRA-3' (SEQ ID NO:3), where FAM is the 15 fluorescent reporter, and TAMRA is the fluorescence
quencher. The reaction conditions were: 940C 3 min denaturation, then 35 two-step cycles of ramping to 950C and 600C 20 s. For fiber gene specific PCR, each reaction was also 15 p total volumes containing 1.5 pl FastStart DNA Master SYBR Green I (Roche Applied Science, Indianapolis, IN), 3 mM MgCl2, 200 nM primers, and 0.6 pi purified DNA from nasal washes. The sequences of adenovirus 4 specific fiber primers are: 5'-TCCCTACGATGCAGACAACG-3' (forward; SEQ ID NO:4) and 5'-AGTGCCATCTATGCTATCTCC-3'
(reverse; SEQ ID NO:5). The reaction conditions were 940C 10 min denaturation, then 40 two-step cycles of ramping to 950C and 600C 20 s. Both 20 reactions were carried out in the RAPID LightCyclerTM (Idaho Technology Inc., Salt Lake City, Utah). Total RNA isolation from blood. Frozen PAX tubes were thawed at room temperature for 2 hrs followed by total RNA isolation as described in the PAX kit handbook (60), but modified to aid in tight pellet formation by increasing proteinase K from 40 pl to
80 pi (>600 mAU/ml) per sample, extending the 55*C incubation time from 10 min to 30 min, and the centrifugation time to 30 min or more. The optional on-column DNase digestion was not carried out. Purified total RNA was stored at -80"C. 25 Target preparation. For more complete removal of DNA from purified RNA samples, RNA isolated from multiple PAX tubes of blood from the same donor at a specific collection date were pulled, followed by in-solution DNase treatment using the DNA-freeTM kit
(Ambion). However, to facilitate removal of the DNase inactivating beads, the completed reaction was spun through a spin column (Qiagen, Cat#79523), rather than attempting to pipette off the supernatant without disturbing the bead pellet. Subsequently, one micro liter from each post-DNase total RNA sample was run on the bloanalyzer using the RNA 6000 Nano Assay (Agilent Technologies) for assessment of RNA quality and quantification of RNA 30 amount. Next, for most samples, 5 pg of RNA were
concentrated via ethanol precipitation. For each 100 p of RNA sample, we added 1 p glycogen (5 mg/ml) (Ambion), 15 p 5M ammonium acetate, and 200 pl 100% ethanol chilled at -20"C. The reaction was incubated at -20*C overnight. The next day, the samples were spun down at 13,791g at4*C for 30 min. The pellet was washed twice with 80% ethanol chilled at-20"C; air-dried; and resuspended in 10 or 12 p1 of nuclease free water (Ambion). All subsequent steps were as described in the GeneChipO Expression
Analysis Technical Manual (6). 35 Database integration. The database can be divided into two major categories: 1) metadata, all information relating to the sample processing that is not gene-expression measurements; and 2) gene-expression data. The metadata consists of several subcategories: clinical, laboratory handling, and quality metrics of microarray results. Clinical data captures information about the patients as transcribed from the questionnaire, complete blood count (CBC), and about
handling of the collected PAX tube blood samples. 40 Laboratory data contains information about the processing of blood samples. For steps from blood in PAX tubes to total RNA extraction, fields such as date of processing, reagent lots, and operator are captured. Subsequent bloanalyzer measurements of DNased treated RNA samples resulted in fluorescent intensities versus time data, which graphically, form the electropherograms and were treated as metadata as well. The electropherograms were
analyzed by the Biosizing (Agilent Technologies) software to output 28S-to-18S intensity ratios and RNA yields, and by the Degradometer 1.1 (51) software to consolidate, scale, and calculate quality metrics such as degradation factors and apoptosis factors. For steps 45 from after bloanalyzer analysis to hybridization, variables such as yields of cRNA and processing batches were recorded. 38 WO 2007/011412 PCT/US2005/040196 ' Qalityinstridof'iid6afyresults"daWsiffirmation associated with the
scanned chip. This included fields such as lot numbers of chips and date of scanned images stored in DAT files. Also included were fields from the Report files generated by the GeneChip Operating Software 1.1 (GCOS 1.1) (Affymetrix), which summarized the quality of target detection for a chip. Microsoft Access and Excel worksheets were used to enter manually clinical and laboratory handling data. Outputs from Degradometer 5 1.1 were in Excel worksheets, An in-house script called ReportToMatrix
(script provided hereinbelow) was used to reformat and consolidate Report files into a data matrix in Excel. Metadata from GCOS 1.1 were exported into Access. ReportToMatrix Script: Sub Macrol() 10 filenum = 0 WorkingDir = Workbooks(1).Path 15 MyFile = Dir(WorkingDir & "\*.RPT") Do While MyFile <> Worksheets("Processing").Range("A1 :Z1 0000").ClearContents 20 With ActiveSheet.QueryTables.Add(Connection:= _ "TEXT;" & WorkingDir & "\ & MyFile, Destination:=Range("A1")) .Name
= Left(MyFile, InStr(1, MyFile, ".") -1) 25 FieldNames = True .RowNumbers = False .FillAdjacentFormulas = False .PreserveFormatting = True .RefreshOnFileOpen = False 30 .RefreshStyle = xilnsertDeleteCells .SavePassword = False .SaveData = True .AdjustColumnWidth = True .RefreshPeriod = 0 35 .TextFilePromptOnRefresh = False .TextFilePlatform = xlWindows .TextFileStartRow = .TextFileParseType = xlDelimited .TextFileTextQualifier = xlTextQualifierDoubleQuote 40 .TextFileConsecutiveDelimiter = True
39 WO 2007/011412 PCT/US2005/040196 'ITextemicloeimiter aew .TextFileSemicolonDelimiter = False .TextFileCommaDelimiter = False .TextFileSpaceDelimiter = False 5 .TextFileOtherDelimiter = ": " .TextFileColumnDataTypes = Array(1, 1, 1, 1) .Refresh BackgroundQuery:=False End With 10 if FileNum = 0 Then MatrixHeaders End If For Each Cell In Range("Processing!A1:A100") 15 Select Case UCase(Replace(Cell.Value, "AFFX-", "", 1,1)) Case "REPORT TYPE" FilIMatrix filenum, Cell, "B" Case "DATE" FillMatrix
filenum, Cell, "C:D", "Concat+:" 20 Case "FILENAME" FillMatrix filenum, Cell, "B" Case "PROBE ARRAY TYPE" FillMatrix filenum, Cell, "B" Case "ALGORITHM" 25 FillMatrix filenum, Cell, "B" Case "PROBE PAIR THR" FillMatrix filenum, Cell, "B" Case "CONTROLS" FillMatrix filenum, Cell, "B" 30 Case "CONTROLS:" FillMatrix filenum, Cell, "C" Case "ALPHA 1" FillMatrix filenum, Cell, "B" Case "ALPHA2" 35 FillMatrix filenum, Cell, "B" Case "TAU" FillMatrix filenum, Cell, "B" Case "NOISE (RAWQ)" FillMatrix
filenum, Cell, "B" 40 WO 2007/011412 PCT/US2005/040196 Case"%5AMPAtf0RWPf,"%d'AMPACTOR(SF)" FillMatrix filenum, Cell, "B" Case "TGT VALUE" FillMatrix filenum, Cell, "B" 5 Case "NORM FACTOR (NF)", "NORM FACTOR(NF)" FillMatrix filenum, Cell, "B" Case "BACKGROUND" FillMatrix filenum, Cell, "B:C", "Row+1,Concat,1" 10 FillMatrix filenum, Cell, "D:E", "Row+1,Concat,1" FillMatrix filenum, Cell, "F:G", "Row+1,Concat," FillMatrix filenum, Cell, "H:l", "Row+1,Concat,1" Case "NOISE" 15 FillMatrix filenum,
Cell, "B:C", "Row+1,Concat,1" FillMatrix filenum, Cell, "D:E", "Row+1,Concat,1" FillMatrix filenum, Cell, "F:G", "Row+1,Concat," FillMatrix filenum, Cell, "H-I", "Row+1,Concat,1" 20 Case "CORNER+" FillMatrix filenum, Cell, "B:C", "Row+1,Concat,1" FillMatrix filenum, Cell, "D:E", "Row+1,Concat," Case "CORNER-" 25 FillMatrix filenum, Cell, "B:C", "Row+1,Concat," FillMatrix filenum, Cell, "D:E", "Row+1,Concat,1" Case "CENTRAL-" FillMatrix filenum, Cell, "B:C", "Row+1,Concat,1" 30 FillMatrix
filenum, Cell, "D:E", "Row+1,Concat,1" Case "TOTAL PROBE SETS" FillMatrix filenum, Cell, "B" Case "NUMBER PRESENT" FillMatrix filenum, Cell, "B", "(#)" 35 FillMatrix filenum, Cell, "C", (%) Case "NUMBER ABSENT" FillMatrix filenum, Cell, "B", "(#)" FillMatrix filenum, Cell, "C", (%)" Case "NUMBER MARGINAL" 41 WO 2007/011412 PCT/US2005/040196 Fild ihifSIgI" () FillMatrix filenum, Cell, "C", Case "AVERAGE SIGNAL (P)", "AVERAGE SIGNAL(P)" FillMatrix filenum, Cell, "B" 5 Case "AVERAGE SIGNAL (A)",
"AVERAGE SIGNAL(A)" FillMatrix filenum, Cell, "B" Case "AVERAGE SIGNAL (M)", "AVERAGE SIGNAL(M)" FillMatrix filenum, Cell, "B" Case "AVERAGE SIGNAL (ALL)", "AVERAGE SIGNAL(ALL)" 10 FillMatrix filenum, Cell, "B" Case "HUMISGF3A/M97935" FillMatrix filenum, Cell, "B", "ColumnHeaderProbe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" 15 FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F",
"ColumnHeader,Probe Set" FillMatrix filenum, Cell, 'G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "", "ColumnHeader,Probe Set" 20 Case "HUMRGE/M10098" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, 'D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" 25 FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set"
FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeaderProbe Set" Case "HUMGAPDH/M33197" 30 FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrixfilenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" 35 FillMatrix filenum, Cell,
"G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "HSAC07/X00351" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" 42 WO 2007/011412 PCT/US2005/040196 FillMatr ftlanUfi;CefI,""" "tolurtmiHader,Probe Set" FillMatrix filenum, Cell, "0", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" 5 FillMatrix filenum,
Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "M27830" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" 10 FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, " D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe
Set" 15 FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "BIOB" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" 20 FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "0", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" 25 FillMatrix filenum, Cell,
"H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "BIOC" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" 30 FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 35
FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "BIOD" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" 43 WO 2007/011412 PCT/US2005/040196 FIMetrixtd CslI? "E noumotieaer,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 5 FillMatrix
filenum, Cell, "I", "ColumnHeader,Probe Set" Case "BIODN"'Old Format Only FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "0", "ColumnHeader,Probe Set" 10 FilIMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I",
"ColumnHeader,Probe Set" 15 Case "CRE" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" 20 FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case
"CREX" 'Old Format Only 25 FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeaderProbe Set" 30 FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeaderProbe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "DAP" FillMatrix
filenum, Cell, "B", "ColumnHeader,Probe Set" 35 FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" 44 WO 2007/011412 PCT/US2005/040196 Filtalrlktffl6ier CelIl"';"F bluriHeaderi'robe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "DAPX" 'Old Format Only
FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" 5 FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" 10 FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "LYSX" 'Old Format Only FillMatrix
filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeaderProbe Set" 15 FillMatrix filenum, Cell, "0", "ColumnHeader,Probe Set" FiliMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 20 FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "LYS" FillMatrix filenum, Cell, "B",
"ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" 25 FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" 30 Case "PHEX" 'Old Format Only FillMatrix filenum, Cell, "B",
"ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" 35 FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "PHE" 45 WO 2007/011412 PCT/US2005/040196 FilMstrMl&NI1Hi Cell"B";'
OlurnMnHeaaer,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" 5 FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "THRX"'Old Format Only 10 FillMatrix filenum, Cell, "B", "ColumnHeader,Probe
Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" 15 FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "THR" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" 20 FillMatrix filenum, Cell, "C",
"ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" 25 FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "TRPNX"'Old Format Only FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe
Set" 30 FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 35 FillMatrix filenum, Cell, "", "ColumnHeader,Probe Set" Case "TRP" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D",
"ColumnHeader,Probe Set" 46 WO 2007/011412 PCT/US2005/040196 Fi AoClf"M ltni~eader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 5 FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-EC-BIOB" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" 10 FillMatrix filenum, Cell, "D",
"ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 15 FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-EC-BIOC" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe SeV FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" 20
FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" 25 Case "R2-EC-BIOD" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnfHeader,Probe Set" FillMatrix filenum, Cell, "0", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E",
"ColumnHeader,Probe Set" 30 FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-PI -CRE" 35 FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set"
FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" 47 WO 2007/011412 PCT/US2005/040196 r r C 1olamweader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-BS-DAP" 5 FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum,
Cell, "F", "ColumnHeader,Probe Set" 10 FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-BS-LYS" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" 15 FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F",
"ColumnHeader,Probe Set" FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" 20 FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-BS-PHE" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" 25 FillMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set"
FillMatrix filenum, Cell, "G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" 30 FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" Case "R2-BS-THR" FillMatrix filenum, Cell, "B", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "C", "ColumnHeader,Probe Set" FilIMatrix filenum, Cell, "D", "ColumnHeader,Probe Set" 35 FillMatrix filenum, Cell, "E", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "F", "ColumnHeader,Probe Set" FillMatrix filenum, Cell,
"G", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "H", "ColumnHeader,Probe Set" FillMatrix filenum, Cell, "I", "ColumnHeader,Probe Set" 48 WO 2007/011412 PCT/US2005/040196 uase ise 'do nothing End Select 5 Next If filenum = 0 Then filenum = 3 Else 10 filenum = filenum + 1 End If MyFile = Dir Loop 15 End Sub Private Sub FillMatrix(ByVal LineNum As Long, ByVal Cell As Object, ByVal ColRange As String, Optional ByVal OutType As Variant) 20 Dim DataElement As String 'Process Header Information
If IsMissing(OutType) Then 25 OutType = vbNullString End If If Right(Cell.Value, 1) = "X Then DataElement = Mid(Cell.Value, 1, Len(Cell.Value) - 1) 30 Else DataElement = Cell.Value End If DataElement = Replace(DataElement, (", "(", 1,1) 35 DataElement = Replace(DataElement, "AFFX-", "", 1, 1) Select Case DataElement Case "BIODN" DataElement = "BIOD" 49 WO 2007/011412 PCT/US2005/040196 "Cage," I RPN'" DataElement = "TRP" Case Else 'Do Nothing 5 End Select If Len(CoIRange) = I And Left(OutType,
13) <> "ColumnHeader," Then 'Simple ID/Value Combination ColHdr = DataElement & OutType 'Replace(Cell.Value, "AFFX-", "", 1, 1) & OutType Else 10 If OutType = "Concat+:" Then ColHdr = DataElement'Replace(Cell.Value, "AFFX-", "", 1, 1) End If If OutType = "Row+1,Concat,1" Then 15 ColHdr = DataElement & "(" & Range("Processing!" & Left(ColRange, 1) & Cell.Row + 1).Value & ")" End If If Left(OutType, 13) = "Column Header," Then searchCH = Cell.Row - 1 20 Do While
Range(ColumnLetter(Cell.Column) & searchCH).Value <> Mid(OutType, 14) And searchCH <> 0 searchCH = searchCH - 1 Loop If searchCH <> 0 Then ColHdr = DataElement &" "& Range(ColRange & searchCH).Value 25 Else ColHdr = DataElement End If End If End If 30 If LineNum = 0 Then For Each chk In Range("DataMatrix!Al:IUI") If Len(chk.Value) = 0 Then chkValue = ColHdr 35 colletter = ColumnLetter(chk.Column) Exit For End If Next 50 WO 2007/011412 PCT/US2005/040196 uneiNum
="4, Else For Each chk in Range("DataMatrix!A1:lUl) If chk.Value = ColHdr Then 5 colletter = ColumnLetter(chk.Column) Exit For End if Next 10 if Len(colletter) = 0 Then For Each chk in Range("DataMatrix!A1:lU1") If Len(chk.Value) = 0 Then chk.Value = ColHdr colletter = ColumnLetter(chk.Column) 15 Exit For End if Next End if End if 20 if Len(ColRange) = I Then Range("DataMatrx!" & colletter & LneNum).Value = Range("Processing!" & ColRange & Cell.Row).Value Else if OutType =
"Concat+:" Then 25 Range("DataMatrix!" & colletter & LineNum).Value = Range("Processing!" & Left(ColRange, 1) & Cell.Row).Value & ":" & Range("Processing!" & Right(ColRange, 1) & Cell.Row).Value End if If OutType = "Row+1,Concat," Then 30 Range("DataMatrix!" & colletter & LineNum),Value = Range("Processing!" & Right(ColRange, 1) & Cell.Row + 1).Value End if End if End Sub 35 Private Function ColumnLetter(ByVal vingNum As Long) As String If vingNum
> 26 Then 51 WO 2007/011412 PCT/US2005/040196 , I= U Do While vIngNum > 26 C1 =C1 +1 vingNum = vIngNum -26 5 Loop Ca = Chr(64 +C1) Cb = Chr(64 + vIngNum) Else 10 Ca= vbNuIlString Cb = Chr(64 +vIngNum) End If ColumnLetter = Ca & Cb 15 End Function Finally, the JMP IN (SAS Institute) software was used to join these various data tables together using identifiers, usually the volunteer's ID number and date of blood collection. The metadata table has more than a thousand columns. In regard
to the gene-expression data, the scanned images of chips were captured and stored in Microarray Suite 5.0 (MAS 5.0) 20 (Affymetrix) and later transported to GCOS 1.1, Signal values, which quantify the abundance of genes from intensities of probes, and detection calls, which qualify the detection of genes into present (P), marginal (M), or absent (A), were calculated in GCOS1.1 which uses the MAS5.0 algorithm. For both HG-U1 33A and B chips, the scaling factor and normalization value were set to
1, resulting in no scaling or normalization after generating Signal values. This allows for testing of various scaling and normalization procedures. Signals and detection calls were exported to Excel and saved as tab-delimited text files with A chips in one folder and B chips in another. 25 Statistical analysis. Statistical quality control and relations among metadata variables were analyzed in JMP IN and StatView (SAS). ANOVA, t-tests, and class prediction of clinical phenotypes using CBC or
electropherogram data were performed in BRB-Arraytools 3.2.0 Beta (Arraytools) developed by Dr. Richard Simon and Amy Peng Lam (available through the web-site for the Biometric Research Branch, Division of Cancer Research and Diagnosis, National Cancer Institute, U.S. National Institutes of Health). Arraytools is written for analysis of gene-expression data, but here we have imported certain quantitative metadata fields, such as CBC, to be treated as 'genes' by Arraytools to take advantage of
its 30 class prediction algorithm. Relations between metadata variables and gene-expression profiles were analyzed in Arraytools. To facilitate import of text files with Signals and detection calls, in-house scripts were written in R to move files of interest into a different folder and renaming and reformatting the files to be compatible with ArrayTools: (Script provided herein below) 35 Script for reformatting the files to be compatible with ArrayTools: # objects in R scaled each chip via
trimmean: # "from": vector of DAT file names # "sample_D": dataframe of renamed file names for Arraytools keyed to DAT file names # "t": older, one error version of 'sampleID' 40 # "training": Arraytools file names for the training set samples # "rename: function to rename the DAT files in a folder to Arraytools acceptable names 52 WO 2007/011412 PCT/US2005/040196 Tuncuon promjoj {for (I in 1:length(from)) {file.rename (paste(from[i), ".txt", sep = ""),paste(to[i], ".txt", sep = } 5
#"sampleID-only": from "sampleID", but with Arraytools name column only, no DAT files names #"target": set value to scale to #"training_files": similar to "training", but no column name #"to": vector of Arraytools compatible file names, corresponding to "from" DAT names 10 # rewrite: function to reformat GCOS CHP files exported to excel # saved as tab delimited file text file to be compatible with Arraytools function(to) {for (i in I:length(to)) {tempfile <- read.table(paste(toli], ".txt",
sep = ""), sep = "\t", header = TRUE); 15 names(tempfile) <- c("Probe Set Name", "Signal", "Detection"); write.table (tempfile, file = paste(to[i], ".txt", sep = ""), sep = "\t", quote = FALSE, row.names FALSE); } 20 #select-training-set: given a list of training set file names #move these files in the original to folder to a separate folder for Arraytools function (trainingjiles) { for (i in 1:length(trainingjiles)) #file.create(paste("C:\\Dzung on Affy3\\files for R conversion\\test
training set\\", trainingiles[],".txt", sep 25 file.copy (paste("C:\\Dzung on Affy3\\files for R conversion\\reformated B chips text files no scaling or normalization\\", trainingfilesfi], "_B.txt" sep = paste("C:\\Dzung on Affy3\\files for R conversion\\test training set\\", training-filesi],"_.B.txt", sep = } 30 Selected metadata fields were imported into the Experiment descriptors worksheet of Arraytools. After data import, Arraytools were used to determine differential gene expression and
ontology, class prediction, and quantitative trait correlations, with, between, and/or among clinical phenotypes. CBC data were obtained from two machines. The first partitioned the white blood cells (WBC) into only three groups: lymphocytes, 35 monocytes, and granulocytes, while the second partitioned the WBC into five groups: lymphocytes, monocytes, neutrophils, eosinophils, and basophils. Therefore, to make CBC comparable between the two machines, the following in-silico transformations were
performed. Since granulcytes consist of neutrophils, eosinophils, and basophils, samples with five groups were converted to three by summing up the neutrophils, eosinophils, and basophils counts. Also, blood samples from 25 volunteers not in this study were run on both machines. Their CBC showed linear correlations between the two machines (data not shown). Therefore, linear regression equations were calculated for CBC variables between the two 40 machines. These equations were used to normalize
the CBC of the current BMT cohort. The Degradometer 1.1 software scales the electropherograms using the spiked in marker peak (51). 53 WO 2007/011412 PCT/US2005/040196 "Scdlfig Wds psifofiedfo Sie-efpfss6 nce for each blood sample, the same hybridization cocktail went onto the A chip and then the B chip, concatenation of the data from the two chips together in-silico to form a virtual array would be logical and bypasses issues with analyzing the two chip types separately; also, the 100 control
probe sets common between the A and B chips should detect genes to result in similar Signal distributions. Several methods were considered to concatenate the A and B chips profiles. 5 First, if each A and B chips were separately globally scaled to a target value of 500, then the resulting Scale Factors (SF) was significantly higher for the B chips than for A (data not shown) (f-test, p < 0.0001), suggesting that generally, Signals from B chips were actually lower than from A. Confirmatory of
this bias was that Signals of the 100 control genes were higher in B chips than in A after globally scaling each chip. The lower overall Signals in B are probably due to the B chip containing probesets that detect mostly low expressing genes (69). These observations suggested that the above step of globally scaling each chip was not appropriate to perform prior to concatenating data from the two array types. 10 Thus, another method was assessed, which was to scale all A and B chips using only
the 100 control genes to a target value of 500. This resulted in stable SF over time (data not shown) and that there was no significant differences in SF among the four phenotypes of healthy, sick with adenovirus infection and convalescents, and sick without adenovirus infection (data not shown) (ANOVA, p = 0.1047 A chips, p = 0.1782 B chips). The 100 control genes were selected based on stability in expression from a large study of various tissue types (69); therefore, this scaling method would
allow for the concatenation of corresponding A and B chips and also should remove assay variations independent of gene concentration. This 15 scaling procedure was carried out using an in-house R script (Script provided herein below): Script for scaling: function scaled (sample_)Donly) {for (i in 1:length(samplejIDoonly)) 20 {tempfileA <- read.table(paste("C:\\Dzung on Affy3\\hk then global scaling\\reformated A chips text files no scaling or normalization\\", samplejD-only[i), ".txt", sep
=), sep = "\t", header = TRUE, check.names = FALSE); tempfileB <- read.table(paste("C:\\Dzung on Affy3\\hk then global scaling\\reformated B chips text files no scaling or normalization\\, samplejD-only[i], "_B.txt", sep ""), sep = "\t', header = TRUE, checknames = FALSE); target <- 500; 25 hk_scalefactorA <- target / mean(tempfileA$Signal[69:168], trim = 0.02); tempfileA$Signal <- (tempfileA$Signal) * hkscalejfactorA; hk_scale_factorB <- target / mean(tempfileB$Signall69:168),
trim = 0.02); 30 tempfileB$Signal <- (tempfileB$Signal) * hk-scalejfactorB; #hkscalefactors <- paste (samplejD-only[fP\t, hkscalefactorA,"\t", hk.scale-factorB); #write.table (hk-scalejactors, file = "C\Dzung on Affy3\\hk then global scaling\\hk_scalefactors.txt", append = TRUE, quote FALSE, rownames = FALSE); 35 #virtuaLchip-signals <- c(tempfileA$Signal, tempfileB$signal); #globalscale_factor <- target I mean(virtual-chip-signals, trim = 0.02); #tempfileA$Signal <-
(tempfileA$Signal) * global-scalejfactor; #tempfileB$Signal <- (tempfileB$Signal) * globaLscale-factor; 40 #global-scale-factor-list <- c(global-scalefactor-list, global-scalejfactor); 54 WO 2007/011412 PCT/US2005/040196 write.table (tempfileA, file = paste("C:\\Dzung on Affy3\\hk then global scaling\\HKscaled A chipsW, sampleID-only(i], ".txt", sep = quote = FALSE, rownames = FALSE, sep = "\t); write.table (tempfileB, file = paste("C:\\Dzung on Affy3\\hk then global scaling\\HKscaled B
chips\\", sampleiDonly[i], "B.txt", sep 5 ""), quote = FALSE, rownames = FALSE, sep } #above is for generating scale factors for A and B chips if only the 100 house keepking genes were used to scaled 10 After scaling using the 100 control genes, the expression profiles from corresponding A and B chips were concatenated to form virtual arrays. Furthermore, the present inventors considered globally scaling these virtual arrays to further remove assay variations. However, the SF from this procedure
showed differences among the four phenotypes: highest SF in the healthy group, then convalescents, followed by the febrile group (data not shown) (ANOVA, p <0.0001). Therefore, this step was not used for the whole data set, although it might still be useful in increasing the sensitivity of detection of genes with differential expression between groups with equivalent SF, such as between sick with- versus without 15 adenovirus infection. These results also suggested that relatively large
subsets of transcripts differ among healthy, convalescents, and febrile, while relatively small subsets of transcripts differ between sick with- and without- adenovirus. These analysis steps were also carried out using an in-house R script (Script provided herein below): Script to scale 'virtual' chips: 20 # to normalize A and B chips via trimmean of 100 house keeping genes, then scale concatenated A and B chips # (virtual chip) to 'target' value using the trimmean of the virtual chip signals #
input an object containing names of files for A and B chips (sampleIDonly) function(to) {for (i in 1:length(sample-lD-only)) 25 {# read in files tempfileA <- read.table(paste("C:\\Dzung on Affy3\\files for R conversion\\hk then global scaling\\reformated A chips text files no scaling or normalization\\, sample-lD-only[i], '.txt", sep = ""), sep = "\t", header = TRUE, check.names = FALSE); tempfileB <- readtable(paste("C:\\Dzung on Affy3\\files for R conversion\\hk then global
scaling\\reformated B chips text files no 30 scaling or normalizationW", sampleID_only[i], "_B.txt", sep ""), sep = "\t", header = TRUE, checknames = FALSE); target <- 500; #set target values #scale chip A and B signal via trimmean of 100 house keeping genes 35 hkscale_factorA <- target / mean(tempfileA$Signa[69:168], trim = 0.02); tempfileA$Signal <- (tempfileA$Signal) * hkscaleifactorA; hk_scale_factorB <- target / mean(tempfileB$Signal[69:168], trim = 0.02); tempfileB$Signal <-
(tempfileB$Signal) * hk-scalefactorB; 40 #scale virtual chip signals 55 WO 2007/011412 PCT/US2005/040196 -viruaienip-signalsstempme-ignaTtempmes$signal); global-scalefactor <- target / mean(virtualchip-signals, trim = 0.02); tempfileA$Signal <- (tempfileA$Signal) * global-scalefactor; tempfileB$Signal <- (tempfileB$Signal) * globalscalefactor; 5 #output scaled files to different folder write.table (tempfileA, file = paste("C:\\Dzung on Affy3\\files for R conversion\\hk then global
scaling\\scaled A chips\\, sampleID-onlyi], ".txt", sep = ""), quote = FALSE, row.names = FALSE, sep = "\t"); write.table (tempfileB, file = paste("C:\\Dzung on Affy3\\files for R conversion\\hk then global scaling\\scaled B chips\\, 10 sample-IDonly[i), "_Btxt", sep = ""), quote = FALSE, rownames = FALSE, sep = "\t"); } Results 15 Quality and variations of RNA derived from PAX system from the BMTs population. Many factors contribute to the variability of target detection, with the quality of
RNA being one of the most important. The quality of RNA from PAX tubes collected blood could be influenced by the disease status of the donors, sample handling, and other downstream processes. Previously, we showed that under two conditions representative of practical sample handling, the PAX system was capable of preserving blood RNA to produce good quality metrics and relatively stable transcriptome measurements (50). Recently, new RNA quality metrics have been proposed based on associations
between experimental treatment of cells or 20 purified RNA to induce RNA degradation and metrics derived from electropherograms of the RNA on the bloanalyzer (51). One new metric is the degradation factor (%Dgr/1 8S), which is the ratio of the average intensity of bands from degraded RNA, that is peaks of lesser molecular weight than the 18S ribosomal peak, to the 18S band intensity multiplied by 100. It is a continuous variable that is used to derive a categorical variable named 'Alert'. Alert
has five values: BLACK-indicating that the ribosomal peaks were not detected; 25 NULL-no RNA degradation and corresponds to degradation factor values 58; YELLOW--for RNA degradation can be detected and values from >8 to16; ORANGE-for severe degradation and values from >16 to 24; RED--for highest alert, strong degradation, for values from >24. The degradation factor is a more sensitive indicator of RNA degradation than the traditional 28S to 18S band intensities ratio. Another 30 new
metric is the apoptosis factor (28S/18S), which is the ratio of the height of the 28S to 18S peak and is indicative of the percentage of cells undergoing apoptosis (51), Apoptosis factors from I to 3 inversely correlate with 80% to 0% of cultured cells positive for annexin V. Thus, for PAX system isolated RNA from our previous study (50) and current BMTs cohort, we report the distributions of RNA quality metrics, which would be useful for comparisons and planning of protocols by other labs;
determined the up-stream quality metrics that are most indicative of the quality of microarray target detection outcomes; and determined the effects of inter-individual hemoglobin variability on the sensitivity of target detection. 35 Electropherograms from Thach et al (50) were reanalyzed for the two PAX tube handling conditions, wherein condition E as in fresh, the RNA was extracted after the minimum incubation time of 2 hours from phlebotomy, and condition 0 as in frozen, the blood sat for 9
hours at room temperature followed by storage at -20"C for 6 days, followed by RNA extraction, The degradation factor was 5.34 ± 0,53 (mean t SE, n = 6) for E and 6.53 ± 0,40 for 0 with no difference between the two handling methods (Wilcoxon, p = 0.13); the magnitude indicated that no degradation was detected (data not shown). Linear correlation between the degradation factor and gapdh and actin 3'/5' is tissue dependent (51), and was not 40 detected here (data not shown). The apoptosis factor
was 1.39 ± 0.06 for E and 1.29 ± 0.09 for 0, also with no differences between conditions (Wilcoxon, p = 0.38) (data not shown). These results confirmed the lack of major differences between the handling conditions, The reanalysis above were from samples that only have technical variation, whereas the current BMTs cohort captures inter-individual and disease states variations and has more samples; therefore, electropherograms from the BMTs were assessed. The degradation factor for the BMTs 56 WO
2007/011412 PCT/US2005/040196 conort was 8.47 ±0.47 (mean ±SE, n = 120) aiidth6 ai6ptosis factor was 1.17 ±0.02. The distribution of the Alerts was: 77 NULL, 36 YELLOW, 3 ORANGE, and 4 RED. A closer look at the electropherograms of ORANGE and RED samples suggested that these samples, mostly from the same run, had high degradation factors due to increased noise in the bioanalyzer rather than true RNA degradation. In contrast to the reanalysis of Condition E and 0 5 samples above, linear
correlations were detected between the degradation factor and gapdh and actin 3'/5', probably because of greater variation and larger number of samples. However, the magnitudes of the correlations were modest (A chips gapdh r = 0.526, actin r = 0.303; B chips gapdh r = 0,325, actin r = 0.284). There was no significant correlation between 28S to 18S band intensity ratio versus degradation factor, gapdh 3/5, or actin 3/5'. Also, only about 50% of the 28S to 18S band intensity ratio values derived
from the bioanalyzer software fell between the 1.8 and 2.1 range, while the rest fell outside of this standard range, 10 Finally, the distribution of yields of total RNA as determined by the bioanalyzer ranges from 1 to 15 pg per PAX tube. These results suggest that of the metrics relating to RNA quality obtained at the bioanalyzer step: RNA yield, 28S to 18S band intensity ratio, degradation factor, and Alert, the variable Alert would be most useful in assessment of individual RNA samples for
continuation of processing, as the other metrics had large variation outside of the traditional range, although microarrays with acceptable quality metrics were still obtained from those RNA samples. In condition 0, the frozen time was 6 day; whereas in the current BMT study, samples were frozen at -20"C for up to 20 days, and a few 15 samples had been frozen and thawed a couple of times. Therefore, to determine if frozen time and freeze-thaw affected RNA quality derived from PAX system, linear
correlations were performed between the time the samples were frozen before RNA extraction and RNA quality metrics. There was no significant correlation detected between frozen time versus degradation factor, apoptosis factor, total RNA yield per PAX tube, 28S to 18S band intensity ratio, gapdh and actin 3'/5'. These results suggest that RNA derived from PAX system is stable over these conditions, Many factors affect number of present calls, an indication of sensitivity of detection of targets.
One obvious factor is average background. 20 As average background increases, then number of present calls decrease. This was observed in the current data set, but the effect was minor (A chips, r = -0.397, p = 0.00003; B chips, r = -0.211, p = 0.032). A less obvious factor affecting sensitivity is the percent of globin transcripts of the mRNA population. When increasing amounts of globin mRNA transcripts were spiked into total RNA from cell line, the percent present calls decreases linearly
(20). To determine if this effect is present and to quantitate its magnitude in the current data set, linear correlation was performed between Number Present and Mean Cell Hemoglobin (MCH), a measurement of picograms of hemoglobin per red blood cell that is likely to be 25 directly related to globin mRNA amounts. A significant although minor effect was detected (r = 0.229, p = 0.020), but only for the B chips only. The equation of the regression line suggested that for every picogram increase in
hemoglobin, there is a loss in present detection calls of 100 genes, or about 2% of the average number of present call genes detected on the B chips. These results suggested that the quality of RNA from PAX tubes collected blood of the BMT population with various disease phenotypes and handling conditions are of good and reproducible quality for gene-expression analysis, although variation in hemoglobin amounts contributed a 30 minor effect to the sensitivity of detection of target by the
Genechip microarray. The Alert metric seemed to be a robust indicator for continuation to the target preparation steps, with values of NULL and YELLOW indicating acceptable microarray results, Quality of microarray measurements of PAX system derived RNA from the BMTs population. The numbers of arrays processed and their allocations were determined. A total of 145 A and B chip sets were processed from hybridization cocktail samples from PAX system derived RNA. 35 Of these, 128 were from the BMTs,
and the remaining 17 were from civilians. Of the 17, 6 were from the same donor and were samples used in the condition 0 versus E study (50); 6 were from another donor to compare using total versus poly A RNA; 2 were technical replicates from a third donor; and 3 were technical replicates from a female donor. The 128 chips sets from the BMTs were run in 10 batches (variable name 'RNA to hyb cocktail Batch #'). Batch 1 had 8 blood samples and polyA RNA was used as in Thach et al. (50). Batch 2
had 12 chip sets with 8 blood samples that were processed as in Batch 1, but the RNA 40 was over fragmented; four of these samples had more than 5 pg of cRNA left over, so these were hybridized to the arrays resulting in the 12 chip sets for Batch 2. Batch 3 also had 12 chip sets with 8 blood samples that were processed using total RNA; 4 of the eight blood samples yielded enough total RNA to have duplicates using polyA RNA instead. The remaining batches totaling 96 chip sets were processed as
the 8 total RNA blood samples from Batch 3. One of the 96 chip sets was from a convalescent BMT whose nasal wash still had positive adenoviral culture; therefore, this singular case was excluded from most analysis. The resulting 95 chip sets were used as the training set in class prediction analysis. The other 45 50 chip sets, regardless of processing differences were placed into the test set. The 95 chips sets and the 8 from Batch 3 summed to 103 chip sets that were processed similarly, and
these 103 chip sets were used for most other analysis such as class comparisons. Each batch had about equal 57 WO 2007/011412 PCT/US2005/040196 representation Ot the tour pie-OtyP6 healthydnrildtfiabnovirus and convalescents, and febrile without adenovirus. Therefore, comparisons among these four groups should detect biological differences as these four groups have similar variations due to processing. These results above are summarized in Table 5 below: 5 TABLE 5 batch Convalescents healthy
febrile w/ febrile w/o total number adenvirus adenovirus 10 3 3 3 1 10 3 2 2 2 2 8 4 2 2 2 2 8 5 2 2 2 2 8 6 7 4 7 2 20 7 5 8 4 1 18 8 3 4 4 4 15 9 3 4 4 5 16 total 27 29 28 19 103 The correlation of signals and concentrations and the sensitivity of the boB, bloC, bioD, and cre cRNA spike-ins were evaluated. The spike-ins showed strong linear relationship with known concentration across all chips (data not shown) and that the percent present calls of bloB, whose concentration is at the level of
assay sensitivity, was 100% of the time suggesting good sensitivity for all the chips. After scaling via 100 10 control genes, the spike-ins still showed strong linear relationship with known concentration, suggesting that the scaling procedure did not introduce significant artifacts (data not shown). Individual control charts versus the date the microarray was scanned were plotted to look for stability of quality metrics, to determine outliers and excluded arrays when error in processing was
known, and to compare our results with values from other labs and values proposed by Affymetrix. The in silico parameter settings were uniform throughout as expected. For the A chips, there was an upward drift in background and 15 noise due to drifting in the scanner as these metrics returned to normal after recalibration of the scanner. Most of the B chips were processed before drifting and after recalibration so this factor did not affect them. The percent present was 32 ± 10 (average ± 3SD)
for A chips and 21 ± 6 for B chips. Batch 2 had been over fragmented resulting in high gapdh and actin 3/5' and was excluded from analysis where appropriate. All other chips showed gapdh and actin 3/5' values well less than three, the limit proposed by Affymetrix (68). All quality metrics, including background and noise were stable for the 103 chip sets from identical protocol. 20 These QC results suggested the reliability of our process and facilitated the inclusion and exclusion of microarrays
to form subsets suitable for a particular statistical analysis to answer certain questions. Class prediction of infection status. To determine if sets of genes could classify the four phenotypes, healthy, febrile with adenovirus and convalescents, and febrile without adenovirus, class prediction on the training set was performed. For supervised class prediction, the class labels 25 were results from the gold standard assay of culture for adenovirus from samples of the febrile and convalescent
groups. Unsupervised clustering of samples suggested that the predominant variation among gene expression profiles were febrile versus non-febrile patients (not shown). 58 WO 2007/011412 PCT/US2005/040196 - neretoreto aetermine-sets ot genes-hat'codldebbst classify febrile versus non-febrile patients, febrile with adenovirus versus without, and healthy versus convalescents, class prediction was performed and optimized for these three comparisons (Figure 7). Four parameters were varied to obtain
optimal percent correct classification. One is the algorithm for classification, which consisted of six methods tested: compound covariate predictor, diagonal linear discriminant analysis, 1-nearest neighbor, 3-nearest neighbors, nearest centroid, and support vector machines. 5 For all these six methods, the 'univariate significant p-value cut off' or the 'univariate misclassification rate' was varied, Also the effect of using the randomized variance model for univariate tests was assessed.
Finally, in combination with the optimal univariate p-value or classification rate and present or absent of randomized variance model, the fold ratio of geometric means between two classes was optimized. The optimized percent correctly classified and the optimal conditions for the three comparisons results are shown in Table 6 below: 10 TABLE6 Classes to predict Optimal parameters values optimum univariate percent misciass fold Data used Group I Group 2 correct algorithm rate change alpha SVM,
NN, or 0,05, 0.4, gene-expression non-febriles febriles 99 3NN 0.5 1.2, 2-3 0.01 convalescents healthy 87 DLDA 1.9 0.001 febrile w/ febrile w/o adenovirus adenovirus 91 SVM 1.5-1.7 0.00001 CBC non-febriles febriles 91 SVM 0.2 1.1-1.2 convalescents healthy 77 DLDA 0.3 none febrile w/ febrile w/o adenovirus adenovirus 77 3NN 1.1 0.1 Electropherogram non-febriles febriles 81 SVM 0.4 1.02 convalescents healthy 67 SVM 1,02 0.3 febrile w/ febrile w/o adenovirus adenovirus 81 SVM 1.02 0.4 Also shown in
the table are optimized percent correct and conditions when using CBC or electropherograms data. The results showed that under optimal conditions for each data types, gene-expression data provided information that best classified the four groups, with 99% correct between febrile versus non-febrile, 87% between healthy and convalescents, and 91% between sick with adenovirus versus without, The optimal 15 number of genes for equal optimal classifications among the four groups tended to be nested
sets, with the smallest set that gave the same optimal class prediction accuracy containing genes with the most differential expression. This was likely so because some genes are correlated with each other and thus provided equivalent amounts of information for classification. Tables 7, 10, and 11 provide the p-values as a measure of reliability of prediction and lists the minimal set of genes used to classify the following classes: febrile versus non-febrile patients - 99% Feverstatus, p <
5E-4, number of genes in classifier = 47 (Table 7); healthy versus convalescents -87% accurate between healthy and convalescents, p = 0.001, number of 59 WO 2007/011412 PCT/US2005/040196 'geris in"classifir 'I );" f etdenovirus versus without - 91% Febriles with vs. without adenovirus infection, p <5E-4, number of genes,in classifier = 11 (Table 11). TABLE 7 - Minimal Set Of Genes Used To Classify Febrile Versus Non-Febrile Patients (Sorted by T-value) Geom mean of Geom mean of t- Parametric
% CV G Chip Gene intensities in intensities in Probe set Description value p-value support c#s :H cas2 symbol class 1: H class 2: S 000001 4 5.9 227458t crammed cell death 1 ligand 1 PDCD1LGI p2[220 i<0.000001 10 [220.4 -1320.5 ]202446 s at phospholipid scramblase 1 LCRI p <0.00000 93.1 611 21f 41' 03 81' -100 .[91 5 C spholipid scramblase 1 PLSCRI - ifChip Ec fragment of lgG, high affinity ]a, -7p7<0.000001 100 911 69 2145_atA t o FCGRIA Chip carcinoembryonic antigen-related cell 6
-18.07 p <0.000001 100 73.3 389.5 209498 at adhesion molecule 1 (biliary CEACAMI glycoprotein) serene (or cysteine) proteinase inhibitor, clade G (C1 inhibitor), member 1, 7 -16.48 p<0.000001 100 56.3 557.4 200986 at Chip (angedemat e bit r SERPINGI 'A' cysteine) proteinase inhibitor, dlade G (C1 inhibitor), member 1, (angioedema, hereditary) 8 -1.6p00000 10 69.2 i 37 4 . 3 206025sat Chip tumor necrosis factor, alpha-induced TNFAIP6 'A r iprotein_6 _ _ _ _ 9 152 p<0.000001
238439 at rin repeat domain 22 2ANKRD2 0 15 p0.0000011100 148.1 9476 227609at epithelial stromal interaction I (breast) EPSTl1 -15:6:3 EL 3at Chip hypothetical protein FLJ39885 FLJ39885 < p < 0000001 ] 34.1 235 f222154_sat Ch pDNA polymerase-transactivated protein DNAPTP6 pH< 00 I] t interferon-induced protein 35 14________ Li-14''.466a I 2',5 -oligoadenylate tea 1 60.8 368.9 lI205552sat 4 y -oioaden a OAS 60 WO 2007/011412 PCT/US2005/040196 ________ ____ __________________ _______El
synthetase 1, 40146kDa [____ I lppolycythemia rubra vera 111/ P RV 1] polycythemia rubra vera 1 K1L121 <o~oooi 10 i~1_______,b84 2966_at Chip fserine/threonine kinase 3 (STE20 16 -14.15 p<0.000001 100 3.8 32.8 204068 at ' homolog, yeast) I serine/threonine STK3 kinase 3 (STE20 homolog, yeast) H~~~~ ~ [1.2F 000E . ] 4.4 Chp uanylte binding protein 1, interferon- BP 8.952770at Chip guanylate binding protein 1, interferon 18 -13.65 p<0.000001 100 6.1 57.7 20227 tnducible, 67kDalN guanylate
binding GBP protein 1, interferon-inducible, 67kDa 8 ga C uanylate binding protein 1, interferon 20 3.41p < .00001 00 1.8 0.1 0750 at Chi caspase 5, apoptosis-related cysteine 8 -13.65 p <0.000001 100 86.9 5.7 2070at protease /// caspase 5, apoptosis-related CASP5 cysteine protease tinteferon-induced protein with <0.000001 1 a tetratricopeptide repeats 4 II 2 -3 1 001 260.3 206637 at protei-coupled receptor 105 GP5 H314. 2243 at] -I hypothetical protein BC012330 MGC20410 8 .00 00 83 5 t
ical protein LOC129607 LOC129607 HE] E 00 43.8 3 hp it ir~ddcc poen 4NF4 I Chip'A interferon-induced protein wih44 ~ T H[ 1003 2, If 20.3Chi protein-ope reetr5 GP5 .8 ] 2 Chip complement components 1 pep ide E.9F .: _ XA ssoiatd f r- HSXIAPAF1 <E0.000001 598 703.8 2 137at viperin cig5 fl. E ] 0 0 0 1 2034at Chip uridine phosphorylase 1// uridine UPP1 Chip II,'sDg 32 -11.68 p<0.000001 100 16.2 178 'A promyelocytic leukemia PML 61 WO 2007/011412 PCT/US2005/040196 lysosomal-associated
membrane protein 33 -11.67 p<0.000001 100 18.9 113 205569at 3// lysosomal-associated membrane LAMP3 protein 3 H00000 10 I2.20.2164a Chip ]28kD interferon responsive protein /1 IR2 E~}[1.67 __________o ___ E_______ -- I F]____ ________ 'A' j28kO interferon responsive protein RG8 .Ch Iinterferon, alpha-inducible protein (clone 351127 p 0 000001 10 1777 1 .3 20 sat IFI-1 5K) /// interferon, alpha-inducible G1 P2 protein (clone IFI-15K) 6j <10 .000 F{ 0.8 l[04439_at ip~ chromosome 1 open
reading frame 29/1N t01or29 _ ] _6 __O 00000 0 443 9L 1'A chromosome 1 open reading frame 29 98 299 ~ 4059 at Chip Li[1.76[ <0000 ]f _ ]_ JI25.4 05 7___ __ A ' interferon-induced protein 44 J[F144 p 0 0391 <229390 at Full length insert cDNA clone ZA84A12 '1'esterase (Wolman disease) 1 LP {~ <.0000 10 9.6617[Chip9~t jf4-ol46kadnl synthetase 1, ]A 4046k~aOAS1 myxovirus (influenza virus) resistance 1, interferon-inducible protein p78 (mouse) 41 -9.98 p 0000001 100 241.4 1315 202086_at ///
myxovirus (influenza virus) resistance MX1 1, interferon-inducible protein p78 (mouse) 61 33 1787 229391_s at Full length insert cDNA clone ZA84A12 504.4 20153_at C sialoadh e F A nerrninduced protein withterriopid 45-.3 p <0.000001 100 22. 34.S013t Ci ertioetd eet / nefr n F 'A' inueprtiwihttarcppie repeats 1 H6 ~ <00000 if 0 f 3 10. 53.a -5-oligoadenylate synthetase 2, 1OS [46E-8.1 ___li __ _ ________] _____]0 69/71kDa _______ Hi Chip 0:00]F ]E] interferon, alpha-inducible protein 2711/
f~: 8.48fP<000001If00 I~ 12 ]at 'A' interferon, alpha-inducible protein 27 From the genes listed above, a table of 'Observed v. Expected' table of GO classes and parent classes, in list of 47 genes shown above can be prepared to help elucidate the molecular function (Table 8) and/or biological processes (Table 9) in which the identified genes take part. Only GO classes and parent classes with at least 5 observations in the selected subset and with an 'Observed vs. Expected' ratio of at least
2 are shown. 5 62 WO 2007/011412 PCT/US2005/040196 I ABLE F- Moledular Fuahttfbh", GO id Il' GO classification Observed in Expected in Observedl selected subset selected subset Expected P050525 GTP binding 5 0.83 599 j purine nucleotide binding 59 10006 nceotide binding 8 .62 - 2.21 TABLE 9 - Biological Process GO classification Observed in Expected in Observed! selected subset selected subset Expected 015 response to virus 15 55 fne response 7.69 0 response to biotic stimulus _2j0 7,14 ffll]2~2
2dfs 81pos 77.12 00504 onsm to pest\ pathogen or parasite 2245 i51 105896 rsoetostimulus 22.58 response to external stimulus 9 2.493.61 0006 response to stress 258 3.49 5 TABLE 10 - Minimal Set Of Genes Used To Classify Healthy Versus Convalescent Patients (Sorted by T-value) Geom Geom mean of mean of t-value ae % CV support intensities intensities Probe set Chip # Description sybo in class 1: in class 2: FNE HND 4.61 8e05 100 12.8 278 t213642at ]Chip'A' ribosomal protein L27 RPL27 214.27 8.8e05
F100 24.3 7 2 13 94 1 Cathi'A ]rbosomal protein S7 ____ disabled homolog 2, mitogen 3 4.04 0.000185 87 39.6 20,3 201280_s_at Chip'A' responsive phosphoprotein DAB2 (Drosophila) laminin, alpha 2 (merosin, congenital muscular dystrophy)II 4 4.13 0.000139 100 19.3 8.9 205116_at Chip'A' cAnnital m ropy// LAMA2 laminin, alpha 2 (merosin, congenital muscular dystrophy) 5~{4.13 J[0. 5.100 182 76 Chip'A mumunoglobulin heavy constant LfIGHM 63 WO 2007/011412 PCT/US2005/040196 61225 'iAjimmunoglobulin
heavy constant FG 71 4,7 ~ 1 6116 203701 Chp'A'epithelial V-ike antigen 1 N V~ .5 e0 6 7 pA A "EA _______ 1 .. ~eu5 iu 'I _______epithelial V-like antigen 1I j.5F72 at] i fkringle containing transmembraneKRM1 8[4.7 f~e05 -____ ___ ____ __ _ ____ __ ~protein 1 I _ 98 103.5 .5 {22725at B'KRMN TABLE 11 - Minimal Set Of Genes Used To Classify Febrile With Adenovirus Versus Febrile Without Adenovirus Patients (Sorted by T-value) Geom Geom mean of mean of t-value Parametric p- % CV support intensities
intensities Probe set Chip # Description Gene value in class 1: in class 2: symbol S AD SNE interleukin 1 receptor 1 -5.15 7e-06 53 47.5 118.4 205227at Chip 'A accessory protein // interleukin IL1]RAP 1 receptor accessory protein F 18 0][.3 h06.s]At 1 zinc finger, CCHC domain ZCC _-06 containing 2 interferon-induced protein 44 3 5.39 3e-06 100 356.9 129.5 214453s at Chip 'A' interferon-induced protein IF144 44 3e-06 0 54. 12.3 23342..at hip'B zinc finger, CCHC domain ZCC : _______ ______ FO__ .
33425 ___ containing 2If_ ___ putative secreted protein 5 5.4 3e-06 100 26 11.8 218548_xat Chip'A ZSIG11 I / putative secreted ZSIGII protein ZSIG11 nucleolar protein 1006 307 1. 23096.at] Cip'B NI 5O5 NOP5/N0P58 lectin, galactoside-binding, 7 5.73 le-06 100 136.1 42 200923at Chip 'A' e 3 n a tein n LGALS3BP lectin, galactoside-binding, soluble, 3 binding protein membrane-spanning 4 5.9 p < 0.000001 100 354.3 180.2 223343_at Chip'B' domains, subfamily A, member MS4A7 7 lymphocyte antigen 6
complex, 9 6.24 p < 0.000001 100 1128.4 226 202145_at Chip 'A' locus E ///lymphocyte antigen LY6E 6 complex, locus E 1. 0tChip'A' butyrophilin, subfamily 3 B N3A3 Member A3 interferon, alpha 64 WO 2007/011412 PCT/US2005/040196 protein 27//N interferon, alpha inducible protein 27 Categorical and continuous metadata variables co-varying with the four phenotypes above were assessed. The only categorical variables that correlated with the four phenotypes involved the lots of the PAX system used.
These covariates were unlikely to affect gene expression outcomes because the manufacturers have QC their products for consistency. 'Perceived Stress' showed increasing qualitative trend with sickness, 5 but this was expected. This increase our confidence that our class prediction set of genes is due to infection health status rather than other confounding variables. Tables 18, 22, and 26 provide a larger list of genes that still give high percent correct classification, in order of: febrile
versus non-febrile patients, febrile with adenovirus versus without adenovirus patients, and healthy versus convalescent patients, respectively. In Tables 18, 22, and 10 26, the composition of classifiers is listed for genes significant at the 0,001 level and is sorted by t-value. The t-value ranged from -22.99 to 14.6, excluding -2.62 to +2.62. Tables 16, 20, and 24 provide a detailed summary for the performance of classifiers during cross-validation used for Tables 18, 22, and 26. Tables 17,
21, and 25 provide further details as to the performance of classifiers during cross-validation with respect to Performance of the 15 Compound Covariate Predictor Classifier, Performance of the 1-Nearest Neighbor Classifier, Performance of the 3-Nearest Neighbors Classifier, Performance of the Nearest Centroid Classifier, Performance of the Support Vector Machine Classifier, and Performance of the Linear Diagonal Discriminant Analysis Classifier. Specifically, Tables 17, 21, and 25 reports the
parameters used for each classification method and each class. For compilation of the data in Tables 17, 21, and 25, the following formulae were employed: Let, for some class A, 20 n11 = number of class A samples predicted as A n1 2 = number of class A samples predicted as non-A n21 = number of non-A samples predicted as A n22 = number of non-A samples predicted as non-A Then the following parameters can characterize performance of classifiers: 25 Sensitivity = n11/(nlI1+n12) Specificity =
n22/(n21 +n22) Positive Predictive Value (PPV) = n11/(n11+n21) Negative Predictive Value (NPV) = n22/(n12+n22) 30 Tables 19, 23, and 27 provides a table of 'Observed v. Expected' table of GO classes and parent classes, and lists the frequency of genes reported in Tables 18, 22, and 26 to help elucidate the cellular component, molecular function and/or biological processes in which the identified genes take part. Only GO classes and parent classes with at least 5 observations in the selected
subset and with an 'Observed vs. Expected' ratio of at least 2 are shown, 35 Class comparisons. To determine lists of genes that are differentially expressed among the four phenotypes, class comparisons were performed, Tables 28, 30, and 32 show the list of genes found to be different between febrile versus non-febrile patients, febrile with adenovirus versus without, and healthy versus convalescents, respectively. Tables 29, 31, and 33 provide a table of 'Observed v. Expected' table of GO
classes and parent classes, and lists the frequency of genes reported in Tables 28, 30, and 32 to help elucidate the cellular component, molecular function and/or biological processes in which the identified genes take part. The composition of classifiers is listed for genes significant at the 0.001 level and is 40 sorted by t-value. The t-value ranged from -22.99 to 14.6, excluding -2,62 to +2.62. Only GO classes and parent classes with at least 5 observations in the selected subset and with an
'Observed vs. Expected' ratio of at least 2 are shown, For Table 28 Description of the problem: 65 WO 2007/011412 PCT/US2005/040196 -omoer oT classes: z Number of genes: 44928 Number of genes that passed filtering criteria: 15720 Type of univariate test used: Two-sample T-test (with random variance model) 5 Column of the Experiment Descriptors sheet that defines class variable : Fever status Multivariate Permutations test was computed based on 1000 random permutations Nominal significance level
of each univariate test: 0.001 Confidence level of false discovery rate assessment: 90 % 10 Maximum allowed number of false-positive genes: 10 Maximum allowed proportion of false-positive genes: 0.1 Summary of Results: Number of genes significant at 0.001 level of the univariate test: 5768 15 Probability of getting at least 5768 genes significant by chance (at the 0.001 level) if there are no real differences between the classes: 0 Genes which discriminate among classes: Table 28 - Sorted by
p-value of the univariate test. The first 5768 genes are significant at the nominal 0.001 level of the univariate test 20 With probability of 90 % the first 5142 genes contain no more than 10 false discoveries. With probability of 90% the first 6430 genes contain no more than 10% of false discoveries. Further extension of the list was halted because the list would contain more than 100 false discoveries For Table 30 25 Description of the problem: Number of classes: 2 Number of genes: 44928
Number of genes that passed filtering criteria: 15720 Type of univariate test used: Two-sample T-test (with random variance model) 30 Column of the Experiment Descriptors sheet that defines class variable: HND vs. FNE only Multivariate Permutations test was computed based on 1000 random permutations Nominal significance level of each univariate test: 0,001 Confidence level of false discovery rate assessment: 90 % 35 Maximum allowed number of false-positive genes: 10 Maximum allowed proportion of
false-positive genes: 0.1 Summary of Results: Number of genes significant at 0.001 level of the univariate test: 2943 40 Probability of getting at least 2943 genes significant by chance (at the 0.001 level) if there are no real differences between the classes: 0 Genes which discriminate among classes: Table 30 - Sorted by p-value of the univariate test. The first 2943 genes are significant at the nominal 0.001 level of the univariate test 45 With probability of 90 % the first 2151 genes contain
no more than 10 false discoveries. 66 WO 2007/011412 PCT/US2005/040196 "With"probbilitybft9Wtheitfst 4bz-gens contain no more than 10% of false discoveries. Further extension of the list was halted because the list would contain more than 100 false discoveries For Table 32 5 Description of the problem: Number of classes: 2 Number of genes: 44928 Number of genes that passed filtering criteria: 15720 Type of univariate test used: Two-sample T-test (with random variance model) 10 Column of the
Experiment Descriptors sheet that defines class variable: SAD vs. SNE only Multivariate Permutations test was computed based on 1000 random permutations Nominal significance level of each univariate test: 0,001 Confidence level of false discovery rate assessment: 90 % 15 Maximum allowed number of false-positive genes: 10 Maximum allowed proportion of false-positive genes: 0.1 Summary of Results: Number of genes significant at 0.001 level of the univariate test: 445 20 Probability of getting at
least 445 genes significant by chance (at the 0.001 level) if there are no real differences between the classes: 0.001 Genes which discriminate among classes: Table 32 - Sorted by p-value of the univariate test, 25 The first 445 genes are significant at the nominal 0.001 level of the univariate test With probability of 90 % the first 229 genes contain no more than 10 false discoveries, With probability of 90% the first 758 genes contain no more than 10% of false discoveries, However, because of
differences in CBC (Table 12 below), these differences in RNA could be due to cell type heterogeneity and/or 30 differential expression at the per cell level. Although large expression differences are likely to be due to differential expression at the per cell level because the differences in CBC variables cannot likely to account for these large differences. Statistical models would have to be developed to sort out these two effects. Serendipitously, there were no differences in CBC for
comparisons between febrile with adenovirus versus without (Table 12 below). 35 TABLE 12 Complete Blood non- conval- febrile w febriles wlo Count (CBC) febriles febriles p-value healthy esents p-value adenovirus adenovirus p-value WBC 7.32 11.26 <0.0001 6.83 7.84 0.1787 10.37 12.57 0.0929 LymCount 1.84 1.14 0,0000 1.93 1.75 0.0697 1.12 1,15 0.7528 MidCount 0.57 0.97 <0.0001 0.60 0.53 0.1664 0.83 1.18 0.0615 Monocytes 4.93 8.94 0.0000 4.33 5.57 0.0561 8.39 9.80 . 0.2329 LymPerc 26.38 11.64
0.0000 28.83 23.75 0.0163 11.85 11.32 0.7368 MidPerc 7.99 8.97 0.5036 9.03 6.87 0,0103 8.77 9.27 0.2547 GranPerc 66.31 80.85 0.0000 62.95 69.91 0.0028 80.27 81.75 0.5433 RBC 4.73 4.61 0.0696 4.84 4.60 0,0034 4.63 4.59 0.5952 HGB 14.29 14.00 0.1831 14.79 13.75 0.0004 13.96 14.06 0.9309 67 WO 2007/011412 PCT/US2005/040196 HCT 1416 : 43.00 40.41 0.0011 40.81 41.16 0.9827 MCV 88.32 88.84 0.4581 88.68 87.94 0.5825 86.21 89.76 0.1786 MCH 30.30 30.39 0.6288 30.64 29.94 0.2036 30.21 30.65 0.3510 MCHC
34.18 34.13 0,5067 34.38 33.97 0.0391 34.18 34.05 0.8193 RDW 14.07 13.69 0,0107 13.96 14.19 0.4554 13.74 13.61 0.5152 PLT 267.34 254.22 0.3079 258.21 277.15 0.3093 250.16 260.21 0,9568 MPV 9.56 9.53 0.8920 9.41 9.73 0.3330 9.73 9.25 0,0907 Differences in CBC between non-febriles versus febriles, healthy versus convalescents, but not between febriles with versus without adenovirus. P-value columns are from Wilcoxon testing for differences in CBC variables between the groups. Highlights indicate
significant differences. Therefore, one could surmise that the differentially expressed genes were at the per cell level, suggesting that the biomolecular pathways 5 involving these genes are involved in differences between adenovirus infection and non-adenovirus infection. To determine these pathways, the gene list was integrated with the KEGG pathway and the Genetic Association databases using EASE (70) to elucidate the functions of these genes in known pathways. The results for the KEGG
pathway database search are as follows: 9 hsaOO071 Fatty acid metabolism 10 2180 ACSLI; acyl-CoA synthetase long-chain family member I (EC:6.2.1.3] (SP:LCF1-HUMAN] 51703 ACSL5; acyl-CoA synthetase long-chain family member 5 [EC:6.2.1.3] [SP:LCF5_HUMAN] * hsaOO190 Oxidative phosphorylation 1355 COX15; COX15 homolog, cytochrome c oxidase assembly protein (yeast) 15 522 ATP5J; ATP synthase, H+ transporting, mitochondrial FO complex, subunit F6 [EC:3.6.3.14] [SP:ATPRHUMAN] e hsa00193 ATP synthesis
522 ATP5J; ATP synthase, H+ transporting, mitochondrial FO complex, subunit F6 (EC:3.6.3.14] (SP:ATPRHUMAN] 20 * hsa00230 Purine metabolism 3614 IMPDH1; IMP (inosine monophosphate) dehydrogenase I [EC:1.1.1.205] [SP:IMD1_HUMAN] 6241 RRM2; ribonucleotide reductase M2 polypeptide [EC:1.17.4.1] [SP:RIR2_HUMAN] 953 ENTPD1; ectonucleoside triphosphate diphosphohydrolase I [EC:3.6.1.5] [SP:ENP1_HUMAN] 25 * hsa00240 Pyrimidine metabolism 6241 RRM2; ribonucleotide reductase M2 polypeptide [EC:1.17.4.1]
[SP:RIR2_HUMAN] 7298 TYMS; thymidylate synthetase [EC:2.1.1.45] [SP:TYSY_HUMAN] 953 ENTPD1; ectonucleoside triphosphate diphosphohydrolase 1 [EC:3.6.1.5] {SP:ENP1_HUMAN] 30 * hsa00252 Alanine and aspartate metabolism 1615 DARS; aspartyl-tRNA synthetase [EC:6.1.1.12] [SP:SYD..HUMANJ * hsa00361 gamma-Hexachlorocyclohexane degradation 93650 ACPT; acid phosphatase, testicular [EC:3.1.3.2] 35 * hsaOO510 N-Glycans biosynthesis 6185 RPN2; ribophorin li [EC:2.4.1.119] [SP:RIB2_HUMAN] * hsa00532
Chondroitin I Heparan sulfate biosynthesis 68 WO 2007/011412 PCT/US2005/040196 obu i U5 Tz; CsmU1nyrate6crfr0ti 4')"Sulfotransferase 12 * hsa00561 Glycerolipid metabolism 2710 GK; glycerol kinase [EC:2.7.1.30] [SP:GLPKHUMAN] 5 e hsa00670 One carbon pool by folate 10588 MTHFS; 5,10-methenyltetrahydrofolate synthetase (5-formyltetrahydrofolate cyclo-ligase) [EC:6.3.3.2] [SP:FTHCHUMAN] 7298 TYMS; thymidylate synthetase (EC:2.1.1.451 [SP:TYSYHUMAN] 10 * hsa00740 Riboflavin metabolism 93650 ACPT;
acid phosphatase, testicular [EC:3.1.3.2] * hsa00920 Sulfur metabolism 55501 CHST12; carbohydrate (chondroitin 4) sulfotransferase 12 15 * hsaOO970 Aminoacyl-tRNA biosynthesis 1615 DARS; aspartyl-tRNA synthetase [EC:6.1.1,121 [SP:SYDHUMAN] * hsa03022 Basal transcription factors 20 2965 GTF2H1; general transcription factor IIH, polypeptide 1, 62kDa [SP:TFH1_HUMAN] * hsa03O5O Proteasome 10213 PSMD14; proteasome (prosome, macropain) 26S subunit, non-ATPase, 14 25 * hsa04010 MAPK signaling pathway
6416 MAP2K4; mitogen-activated protein kinase kinase 4 [EC:2.7.1].- [SP:MPK4.HUMAN] 7850 IL1 R2; interleukin 1 receptor, type i [SP:lL1SHUMAN] * hsa04O6O Cytokine-cytokine receptor interaction 30 1436 CSFI R; colony stimulating factor I receptor, formerly McDonough feline sarcoma viral (v-fms) oncogene homolog [EC:2.7.1.1121 [SP:KFMS.HUMAN] 1524 CX3CR1; chemokine (C-X3-C motif) receptor I [SP:C3X1.HUMAN] 3556 IL1 RAP; interleukin 1 receptor accessory protein 7850 IL1 R2; interleukin I receptor,
type i1 [SP:IL1S.HUMAN] 35 e hsa04110 Cell cycle 1028 CDKN1C; cyclin-dependent kinase inhibitor 1C (p57, Kip2) [SP:CDNCHUMAN 4171 MCM2; MCM2 minichromosome maintenance deficient 2, mitotin (S. cerevisiae) 4175 MCM6; MCM6 minichromosome maintenance deficient 6 (MIS5 homolog, S. pombe) (S. cerevisiae) [SP:MCM6 HUMAN] 40 5111 PCNA; proliferating cell nuclear antigen [SP:PCNAHUMAN] * hsa04120 Ubiquitin mediated proteolysis 54926 UBE2R2; ubiquitin-conjugating enzyme E2R 2 45 * hsa04210 Apoptosis 3556
ILI RAP; interleukin 1 receptor accessory protein 69 WO 2007/011412 PCT/US2005/040196 ObtJ /KKAK1A; protein Kinase; cAMi -aepenoent; regulatory, type 1, alpha (tissue specific extinguisher 1) [SP:KAPOHUMAN] e hsa04310 Wnt signaling pathway 6934 TCF7L2; transcription factor 7-like 2 (T-cell specific, HMG-box) 5 * hsa04350 TGF-beta signaling pathway 3398 ID2; inhibitor of DNA binding 2, dominant negative helix-loop-helix protein [SP:ID2_HUMAN] * hsa04610 Complement and coagulation cascades 10 712
C1QA; complement component 1, q subcomponent, alpha polypeptide [SP:CIQAHUMANI 966 CD59; CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) [SP:CD59_HUMAN] e hsa04611 712 C1QA; complement component 1, q subcomponent, alpha polypeptide [SP:C1QAHUMAN] 15 966 CD59; CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) [SP:CD59_HUMAN] a hsa04620 Toll-like receptor signaling pathway 6416 MAP2K4;
mitogen-activated protein kinase kinase 4 [EC:2.7.1.-] [SP:MPK4_HUMAN] 6772 STAT1; signal transducer and activator of transcription 1, 91 kDa [SP:STA1_HUMAN] 20 * hsa04630 Jak-STAT signaling pathway 6772 STAT1; signal transducer and activator of transcription 1, 91 kDa [SP:STA1_HUMAN] 868 CBLB; Cas-Br-M (murine) ecotropic retroviral transforming sequence b 25 e hsa05110 Cholera - Infection 377 ARF3; ADP-ribosylation factor 3 [SP:ARF3_HUMAN] A batch search of the Genetic Association database was
performed for the following genes: CX3CR1, TRIM14, ARF3, BRD7, PILRB, ENTPD1, CSF1R, RABGAP1, ICAM2, KLHL2, PUM1, MTHFS, LY6E, MRPL47, NPM1, C12orf8, TNFAIP3, CHESI, SIP1, MYOZ2, ATP5J, IF144, 30 SEC14L1, G1P2, GTF2H1, FBXO2, USP18, ACPT, SP100, AIP, ABHD5, SC02, PWWP1, RAN, GRN, MX1, SLC1A4, GZMB, SNRPA1, IMPDH1, TARDBP, ZCCHC2, IER5, CBLB, STATIC, WBSCR20A, MEA, TNRC6, MAK, TCF7L2, TINF2, HNRPH1, HNRPH2, GK, SART3, HIFX, PTP4A2, PSMD14, EIF3S4, BTN3A3, LETMI, TIMM23, HIVEP2, USP22, MTIL, CIQA,
IL1RAP, MS4A7, NICAL, KBTBD7, Clorf29, PNUTL2, RPN2, ILF3, PCNA, HMGB1, BAGI, MCM2, TYMS, MT1X, CPD, COX15, MCM6, SN, C6orf133, BACE2, SYT6, OASI, FACL2, OAS2, C6orf209, NUP98, PRKAR1A, OAS3, CHST12, FACL5, SLPI, CD59, IFITI, IF127, SORLI, RNPC4, IFIT4, HMGN4, CECRI, CDCA7, MTSS1, C6orf37, CDKN1C, 35 RBPSUH, IL1 R2, YWHAQ, RRM2, DARS, UBE2R2, SFRS7, FCGR2A, OASL, ID2, PLCL2, LGALS3BP, KPNA2, and MAP2K4. Of these genes, the following hits were returned: CX3CR1 1) Disease Class = Infection; Broad
Phenotype (Disease) = HIV/SIV infection; 2) Disease Class = Unknown; Broad Phenotype (Disease) = Human Renal Transplantation; 40 SCO2 1) Disease Class = Cardiovascular; Broad Phenotype (Disease) = hypertrophic cardiomyopathy and cytochrome c oxidase deficiency; FCGR2A 1) Disease Class = Infection; Broad Phenotype (Disease) = Severe Malaria; 45 2) Disease Class = Infection; Broad Phenotype (Disease) = fulminant meningococcal septic shock in children; 3) Disease Class = Immune; Broad Phenotype
(Disease) = atopic disease; 70 WO 2007/011412 PCT/US2005/040196 4)' Diss6Cisss ='h lrfiuli; B't-o Phenotype (Disease) = rheumatoid arthritis; 5) Disease Class = Immune; Broad Phenotype (Disease) = systemic lupus erythematosus. Example 4: Effects of two globin mRNA reduction methods on gene expression Profiles from whole blood 5 Materials and Methods Sample collection. With approval of the Lackland AFB IRB and after informed consent, approximately 25 ml of blood, filling 10 PAX tubes, were drawn
from each healthy volunteer. Blood was drawn into PAX tubes by standard protocol {Preanalytix #23*}. All PAX tubes were maintained at room temperature for 2 hrs, then frozen at -20"C, stored at -80"C for 5 days, and shipped on dry-ice to the Navy Research Laboratory in Washington, DC for processing. 10 Sample processing. Blood collection and RNA isolation was performed using the PAX System, which consists of an evacuated tube (PAX tube) for blood collection and a processing kit (PAX kit) for
isolation of total RNA from whole blood {*Jurgensen #32; Jurgensen #33). The isolated RNA underwent globin reduction procedures and was amplified, labeled, and interrogated on the HG-U133 plus 2,0 Genechipo microarrays (Affymetrix). Total RNA isolation from blood. Frozen PAX tubes were thawed at room temperature for 2 hrs followed by total RNA isolation as 15 described in the PAX kit handbook {*Preanalytix #24), but modified to aid in tight pellet formation by increasing proteinase K from 40 pl
to 80 pl (>600 mAU/ml) per sample, extending the 55*C incubation time from 10 min to 30 min, and passing through a QlAshredder spin column (Qiagen). The optional on-column DNase digestion was not carried out. Purified total RNA was stored at -80*C. Total RNA cleanup and concentration. For more complete removal of DNA from purified RNA, duplicate RNA samples were pooled, followed by in-solution DNase treatment using the DNA-freeTM kit (Ambion), but without addition of DNase inactivation
reagent. After DNase 20 treatment, RNA were subjected to RNAeasy MinElute Cleanup (Qiagene cat#74204) and concentrated according to the manufacturer's procedure. Subsequently, one microliter from each sample was run on the bioanalyzer 2100 (Agilent) for assessment of RNA quality while the nanodrop (NanoDrop) was used for quantification. Usage of the bioanalyzer was analogous to capillary gel electrophoresis. This resulted in electropherograms displaying florescent intensity versus time, which
correlates with the amount of RNA versus the size of RNA, respectively. Globin reduction and target preparation. To remove globin mRNA, biotinylated globin capture oligos (Ambion Globinclear kit) and PNA 25 (Affymetrix GeneChip Globin Reduction kit) were used according to modified manufacturers' procedures. In brief, for the Globinclear's procedure, biotinylated globin capture oligos were added to 5 pg total RNA and globin mRNA were removed by strepavidin magnetic beads. Then the remaining
globin-reduced total RNA was purified using magnetic beads and eluted in 30 pL of water. One microliter of RNA was used for bioanalyzer measurement and the remaining RNA was concentrated to 8 pL using Speed Vac concentration at room temperature. For the PNA globin reduction procedure, 5 pg of total RNA in 9 pL BR5 from the RNAeasy MinElute Cleanup step was used for the downstream procedure. The column that 30 came with the Globin Reduction kit was not used. All subsequent steps were as described
in the GeneChip Expression Analysis Technical Manual version 701021 Rev. 3. Database integration. Laboratory data contained information about the processing of samples from blood in PAX tubes to cRNA target preparation, as well as bloanalyzer and nanodrop measurements. Electropherograms were analyzed by the Biosizing software (Agilent) to output 28S/18S intensity ratios and RIN QC metrics while the nanodrop output RNA quantity and 260/280 ratios. Report files summarizing the quality of 35 target
detection for an array were generated by GeneChip® Operating Software 1.1 (Affymetrix). JMP (SAS) was used to join these various data tables together into a metadata table. For gene-expression data, Signal values were calculated using the Microarray Suite 5.0 algorithm with and without scaling to test the effects on various downstream analytical methods. Statistical analysis. Statistical quality control and relations among metadata variables and gene expression profiles were analyzed in JMP.
ANOVAs, multidimensional scaling, and functional analysis of gene-expression data were performed in Arraytools 3.2.0. Beta developed by 40 Richard Simon and Amy Lam (http://linus.nci.nih.qov/BRB-ArravTools.html). Heat-maps and dendrograms were graphed using dChip {Li, 2001 #41; Li, 2001 #42}. Scaled expression data showed no differences in Scale Factors among treatment groups. Results Quality of RNA, globin reduction, and target preparation. The following RNA samples were used to study the
effects of two globin 45 reduction methods on gene expression profiles: 71 WO 2007/011412 PCT/US2005/040196 1) JOrkat RNA-isolated frdm JuLWfclir1 (J) 2) Jurkat RNA with globin mRNA spiked-in (JG) 3) Paxgene RNA from whole blood (B) 5 The globin reduction protocols tested were: 1) Ambion's Globinclear method using biotinylated globin capture oligos (A) 2) Affymetrix' method using PNA oligos (P) 3) No globin reduction treatment as technical control (C). 10 The same lot of J and JG RNA were used
throughout. RNA treated with Ambion globinclear had -90% recovery for J and JG RNA, The yields of cRNA for the Ambion group were the lowest among the three technical conditions for each RNA species; however, RNA purity judged by the ratio of 260/280 for Ambion globinclear group was the highest (Table 13). 15 TABLE 13 - Comparison of pre-hybridization variables and post-hybridization chip results in RNA species with different treatment RNA Jurkat RNA Jurkat RNA + Paxgene Globin Treatment Ambion
PNA Control Ambion PNA Control Ambion PNA Control Starting 4 4 4 4 4 4 5 5 5 material (9g) Yields after 3.56±0.41 4 4 3.43±0.24 4 4 3.71±0.32 5 5 treatment Adjusted 71.13±5.412 96,4±30.66 113.47 58.33±2.91 107.93±29.99 124.27 25.87±3.91 30.61 41.18 cRNA yield ±40.77 ±30.96 ±17.05 ±7.76 260/280 for 2.01±0026 1.98±0.035 1.92 2.03±0.02 1,95±0.05 1.85 2.13±0.02 2.08 2.06 cRNA ±0.047 ±0.02 ±0.02 ±0.01 Results Present 46.8±1.18 45.5±0.62 44.8 41.53±0.83 37.4±0.7 32.37 39.33±1.38 38.53 32.77 Calls (%)
±1,65 ±1.56 ±2.39 ±1.39 Scale 4.50±1.38 3.98±0.62 4.42 5.13±1.06 5.10±0.50 5.41 7,78±1.82 7.40 10.6 Factors ±0.52 ±0.89 ±1.17 ±80.71 Background 64.21±12.46 68.47±11.30 60.91 56.06±3.18 70.90±5.86 86.6 57.59±3.19 61.27 54.27 ±3.71 ±4,22 ±5.58 ±5.17 Noise 3.36±0.71 3.58±0.70 3.40 2.92±0.28 4.02±0.75 5.34 3.23±0.34 3.34 3.07 ±0.29 ±0.10 ±0.45 ±0.40 3'/5' GAPDH 1.06±0.04 1.05±0.03 1.09 1.06±0.07 1.09±0.10 1.14 1.70±0.11 3.59 2.25 ±0.07 ±0.02 ±1.86 ±0.11 3/5'Actin 1.33±0.15 1.23±0.06 1.31 1,25±0.01
1.17±0.05 1.05 2.55±0.30 5.94 3.16 ±0.03 ±0.03 ±3.74 ±0.26 72 WO 2007/011412 PCT/US2005/040196 Profiles of cRNA for J and JG RNA compared using the bioanalyzer (Fig. 8A, B) indicated that JG RNA treated with Ambion (JGA) and JG RNA treated with PNA (JGP) had a significantly reduced globin peak (arrow in Fig. 8A) and globin band (Fig.8B) relative to JGC. The electropherogram and gel profiles for JGA and JGP were very similar to Jurkat RNA without treatment (JC). There was no difference in cRNA
profiles 5 derived from JC, or Jurkat RNA treated with Ambion globinclear (JA) or with the PNA globin reduction procedure (JP) (data not shown). There was no biological variation among paxgene RNA, since paxgene RNA used for each technical condition was derived from the pooled paxgene tubes collected from the same individual in one bleeding, Paxgene RNA with a ratio 260/280 between 1.9-2.1 was used as starting RNA and -75% recovery for paxgene RNA (Table 13) Decreasing globin peaks and band were
also seen in cRNA profiles derived from paxgene RNA samples treated with Ambion globinclear 10 (BA) and PNA globin reduction (BP) compared to BC (no treatment) (arrow in Figs. 8C and D). However, the cRNA size from BA was larger than BP. Overall, our result demonstrated that both Ambion globinclear and the PNA globin reduction protocols decreased globin mRNA contaminants effectively. Quality of microarray measurements for each technical condition For microarray data quality assessment, poly A
control graphs for each microarray were plotted using scaling signal intensity and non-scaling data. Linearity was achieved among the four control probe sets for all 15 samples (data not shown). All of the constants and major variables, such as scale factors (SF), background, and noise (see Table 13) obtained from RPT report were assessed using the ANOVA and Wilcoxon tests. There was no statistically significant difference in SF and noise among JA, JC, JP, JGA, JGP and JGC, neither in BA, BP and
BC, Thus, scaling signal intensities for all probe sets were used in the gene expression profile comparison, For Jurkat RNA, background was highest in JGC and was significantly different from the others, possibly due to the spiked globin mRNA. There was no difference in background among all paxgene RNA. Ratios of 3'/5' GAPDH for all microarrays were all below 5 and indicated 20 that there was no RNA degradation, A slightly higher ratio of 3/5' Actin and GAPDH was noted in paxgene RNA with PNA
treatment, possibly due to the reduction of cRNA size (BP in Fig. 8C). Since no significant difference in other variables was detected, we conducted further statistical analysis and comparison of gene expression profiles. Globin removal increases number of present calls (%) and call concordance in gene expression Removal of globin by both methods significantly increased the number of present calls (%) in JGA, JGP, BA, BP compared to their corresponding controls, JGC and BC 25 (ANOVA, Wilcoxon
test); however, there was no difference among three technical conditions in Jurkat RNA using the ANOVA and Wilcoxon tests, Further analysis of these methods with the student t-test revealed statistically significant higher present calls in JGA than JGP (student t-test, p<0.05), but there was no significant difference in paxgene RNA between BA and BP (Table 13). The present call concordance among Jurkat RNA for the three technical conditions was compared and a gene subset containing 19731
genes, called JCAP, which was not affected by technical conditions (JCAP in Fig. 9A) was identified to serve as a control gene set for JG RNA. The present calls for JGA and JGP were then compared to 30 JCAP, resulting in 18176 (=16349+1827) genes present in both JCAP and JGA and 16782 (=16349+433) genes present in both JCAP and JGP (Fig. 9B), while there were only 14069 genes present in both JCAP and JGC (data not shown). Our data indicated that JGA exhibited 1394 additional concordant calls
relative to JGP and 4107 additional concordant calls relative to JGC, For the paxgene RNA, BA/BP had 2104 additional concordant calls present relative to BA/BC and 2406 additional concordant calls present relative to BC/BP (Fig, 9C). In addition to assessing present call concordance, the overall call concordance excluding margin calls between Jurkat and JG RNA was 35 tabulated and the percentages of false positive and negative among technical conditions were compared (Table 14). Our data
demonstrated that JGA and JGP increased concordant present calls by 8% and 5%, respectively, relative to JGC had 7% and 4% increased false negative calls compared to JGA and JGP, respectively. False positive present calls occurred in 1% and 0,22% of JGA and JGP processed samples, respectively, compared to JGC. Calculated sensitivities for JGA, JGP and JGC compared to the "gold standard" of Jurkat RNA were 86%, 79.5% and 68.2%, respectively. Specificity was retained with all processing methods
with specific values for JGA, JGP and JGC being 94.3%, 96.2% and 96.2%, 40 respectively. The data suggests that the Ambion globinclear method had significantly higher sensitivity percent present calls without significant loss of specificity relative to JGC (Table 15). Table 14 - Comparison of Pearson correlation coefficient Treatment Description Pearson correlation coefficient Triplates in each sample Mean±stdev 73 WO 2007/011412 PCT/US2005/040196 Jurkat-Ariibion 0.985±0.009 Jurkat-PNA
0.993±0.003 Jurkat-no treatment 0.993±0.001 Jurkat+Globin-Ambion 0.992±0.005 Jurkat+Globin-PNA 0.996±0.001 Jurkat+Globin-No treatment 0.993±0.004 Paxgene-Ambion 0.997±0.001 Paxgene-PNA 0.987±0.009 Paxgene-No treatment 0.996±0.001 Between Techniques Jurkat RNA Ambion vs. No treatment 0.986±0.005 PNA vs. No treatment 0.992±0.004 Ambion vs. PNA 0.987±0.006 Jurkat+Globin-RNA Jurkat RNA 0.966±0.011 Ambion vs. No treatment 0.983±0.003 PNA vs. No treatment 0.985±0.002 Paxgene blood RNA Jurkat RNA
0.978±0.006 Ambion vs. No treatment 0.967±0.006 PNA vs. No treatment 0.979±0.003 Between RNA species Jurkat vs. Jurkat+Globin JA/JGA 0,962±0.007 JP/JGA 0.963±0,006 JC/JGA 0.963±0.003 JA/JGP 0.960±0.006 JP/JGP 0.967±0.005 JC/JGP 0.967±0.002 JA/JGC 0.942±0.015 JP/JGC 0.946±0.014 JC/JGC 0.952±0.010 Table 15 - Cross tabulation for call concordance 74 WO 2007/011412 PCT/US2005/040196 JGP JGC Calls(%) P A P A P A Jurkat RNA P 21100±367 3455±594 19350±338 5055±761 16733±679 7583±1003 Jurkat RNA A
1359±261 27296±568 938±165 27795±714 822±124 27926±740 P = PNA globin reduction A = Ambion globinclear Variance caused by two globin reduction methods Signal variation among triplicates was assessed by comparing the coefficient of 5 variance (CV) (Fig. 10). Since there was no statistical difference in scaling factors for each technical condition, scaling signal intensities for all probe sets were used to plot CV graphs and Loess fitting with 2 degree freedom was introduced to fit the curves.
Higher CV introduced by technical conditions was seen either in JA or JP compared to JC (dash lines in Fig. 10A), However, globin removal by biotinylated globin oligos and PNA significantly reduced the variation for each corresponded technical condition in JG RNA (solid lines in Fig. 10A). JA had the highest CV among all, especially in gene sets with signal intensities greater than 104. This high CV could be due to the multistep globinclear procedure, In contrast, in 10 paxgene RNA, CV among
globinclear triplicates was as low as no treatment. RNA species and purity may affect technical variation caused by globinclear. In paxgene RNA, CV for PNA triplicates was the highest among all technical conditions (Fig, 10B) possibly due to reduction of cRNA size from PNA oligo treatment (Fig. 8C). In addition to CV(%) comparison, Pearson correlation coefficient(again-it was difficult for me to determine whether any of these observations was significant) was also calculated and compared in each
triplicate between technical conditions within the same RNA species and 15 between RNA species (Table 15). Higher signal correlation was seen within triplicates compared to that seen between technical conditions or between RNA species. In JG RNA, globin removal by biotinylated globin oligos (Ambion) had lower signal correlation with no treatment JGC (0.966), but JGP has higher correlation (0.983) with JGC. This indicated that globinclear JG RNA has more difference in gene expression profile
relative to JGC than JGP. In paxgene RNA, PNA treatment has lower signal correlation (0.967) with no treatment (BC), but JGA higher correlation (0.978) with BC. This suggested that more difference in gene expression were seen in BP and BC than BA and BC. Removal of globin mRNA from paxgene RNA 20 or JG RNA resulted in higher signal correlation in the same RNA species or between Jurkat and Jurkat+Globin RNA (between RNA species in Table 15). Multidimensional scaling cluster analysis of gene
expression profiles To further evaluate correlation between groups of samples for each technical condition, multidimensional scaling (MDS) cluster analysis was conducted. Since non-scaling data and scaling data exhibited similar clustering pattern, we only showed MDS plots using all probe sets with non-scaling signal intensities (Fig. 11). Our data indicated that each triplicate 25 was tightly clustered and triplicate clusters for Jurkat RNA with different technical conditions were close to one
another. Triplicate clusters for JG RNA with different technical conditions were more separated from each other than those from Jurkat RNA with the JGA triplicate cluster located closest to the Jurkat RNA cluster (Fig. I 1A). Paxgene RNA also formed three separate triplicate clusters corresponding to each technical condition (Fig. 11B). Hierarchal cluster analysis of gene expression profiles The overall expression profiles for Jurkat and JG RNA samples with different technical conditions were
analyzed using center correlation and average linkage parameters (Fig. 12A). Consistent with the MDS plot, removal of 30 globin mRNA from JG RNA samples by biotinylated globin oligos revealed similar gene expression profiles to the Jurkat RNA group and were clustered in the same group with Jurkat RNA samples (Fig. 12A). These 18 chips were grouped into six classes as JA, JP, JC, JGA, JGP and JGC and gene expression profiles were compared among these classes using the univariate test in the
Random Variance model. The class comparison resulted in 8614 differentially expressed genes, which were further clustered using dChip software analysis. We divided these differentially expressed genes into 4 groups as indicated on the right side of the dendrogram (Fig. 12B). Group I 35 represented most of down-regulated genes in JGA and all Jurkat RNA samples and it included globin genes and genes affected by globin mRNA cross hybridization. Group 1l represented upregulated genes in Jurkat RNA
samples, but down-regulated in all of JG samples. This could include some false negative genes shown in Table 15. False negative genes could result from a negative impact caused by globin RNA noise resulting in low signal intensities Group Ill represented genes that could be revealed after globin RNA reduction with biotinylated globin oligos protocol, but remained down-regulated with PNA protocol and no treatment (Ill in Fig. 12B). Group IV represented unique up-regulated genes resulting from
biotinylated 40 globin oligos protocol. This group could include some false positive genes in Table 14. Using the same approach, gene expression profiles and differentially expressed gene profiles among BA, BP, and BC, with total of 9 paxgene blood RNA samples were analyzed and clustered using center correlation and average linkage. Our results revealed that removal of globin 75 WO 2007/011412 PCT/US2005/040196 mRNA usingboti'hylate lotW"ligz'and PNA'biosIeRlled more similar gene expression
profile and were clustered within the same group possibly due to globin reduction (Fig. 12C). Moreover, there were 1988 differentially expressed genes among paxgene blood RNA samples using the univariate test for Random Variance model (Fig. 12D). The cluster analysis result indicated that differentially expressed gene profiles for BA and BC were more similar than BP. This is consistent with higher correlation between BA and BC (Table 14). 5 Example 5: Surveillance of transcriotomes in basic
military trainees with normal, febrile respiratory illness, and convalescent phenotypes Materials and Methods Entry criteria and sample collection. LAFB is the location of Basic Military Training for all recruits to the United States Air Force. The BMTs are organized into flights of 50-60 individuals that eat, sleep, and train in close quarters. As many as 40-50 BMTs/week present with FRI and 10 50-70% are due to adenovirus. With approval of LAFB IRB and after informed consent, approximately 15
ml of blood, filling 4 to 5 PAX tubes, were drawn from each volunteer, On day 1-3 of training, blood was drawn from healthy BMTs into PAX tubes by standard protocol (Preanalytix #23), but no nasal wash was collected for this group. During training, BMTs who presented with a temperature of 38.1"C or greater and FRI provided a nasal wash and blood draw, These individuals were categorized into either the FRI without adenovirus or with adenovirus group. Approximately three weeks after sample
collection from the FRI volunteers with adenovirus, additional blood and nasal wash were collected to constitute samples for the 15 convalescent group. All PAX tubes were maintained at room temperature for 2 hrs, then frozen at -20"C and shipped on dry-ice to the Navy Research Laboratory in Washington, DC for processing. Nasal washes were performed using a standard protocol, with 5 ml of normal saline lavage of the nasopharynx, followed by collection of the eluent in a sterile container. Nasal
wash eluent was stored at 40C for 1-24 hrs before being aliquotted and sent for adenoviral culture. All BMTs underwent standardized questionnaires before each sample collection. Healthy individuals were screened for acute medical illness within 4 weeks of arriving at basic training. BMTs were screened for race/ethnicity, allergies, recent injuries, and 20 smoking history to assess confounding variables for gene expression. The duration and type of respiratory symptoms to include sore throat,
sinus congestion, cough, fever, chills, nausea, vomiting, diarrhea, fatigue, body aches, runny nose, headache, chest pain and rash were recorded. A physical examination was recorded. Sample processing. Blood collection and RNA isolation was performed using the PAX System, which consists of an evacuated tube (PAX tube) for blood collection and a processing kit (PAX kit) for isolation of total RNA from whole blood {Jurgensen #32; Jurgensen #33). The 25 isolated RNA was amplified, labeled, and
interrogated on the HG-U133A and HG-U133B Genechipe microarrays (Affymetrix), noted here as A and B arrays, respectively. Total RNA isolation from blood. Frozen PAX tubes were thawed at room temperature for 2 hrs followed by total RNA isolation as described in the PAX kit handbook {Preanalytix #24}, but modified to aid in tight pellet formation by increasing proteinase K from 40 pl to 80 pl (>600 mAU/ml) per sample, extending the 550C incubation time from 10 min to 30 min, and the
centrifugation time to 30 min or more. The optional on 30 column DNase digestion was not carried out. Purified total RNA was stored at -80*C. Target preparation. For more complete removal of DNA from purified RNA, duplicate RNA samples were pooled, followed by in-solution DNase treatment using the DNA-freeTM kit (Ambion). However, to facilitate removal of the DNase inactivating beads, the completed reaction was spun through a spin column (Qiagen, Cat#79523), rather than attempting to pipette off
the supernatant without disturbing the bead pellet. Subsequently, one microliter from each sample was run on the bloanalyzer (Agilent) for assessment of RNA quality and quantity. The usage of the 35 bioanalyzer was analogous to capillary gel electrophoresis. This resulted in electropherograms displaying florescent intensity versus time (Fig. 1 3a), which correlates with the amount of RNA versus the size of RNA, respectively. Next, 5 pg of RNA were concentrated via ethanol precipitation as
previously described {Thach, 2003 #18). All subsequent steps were as described in the GeneChip Expression Analysis Technical Manual version 701021 Rev.3. Database integration. The database consisted of clinical data such as information transcribed from standardized questionnaires, the 40 complete blood count (CBC), and the handling of blood samples. Laboratory data contained information about the processing of samples, from blood in PAX tubes to RNA extraction, as well as subsequent bloanalyzer
measurements. Electropherograms were analyzed by the Biosizing (Agilent) software to output 28S/1 8S intensity ratios and RNA yields, and by the Degradometer 1.1 {Auer, 2003 #26) software to consolidate, scale, and calculate degradation and apoptosis factors. Report files summarizing the quality of target detection for an array were generated by GeneChipe Operating Software 1.1 (Affymetrix). JMP (SAS) was used to join these various data tables together into a metadata table with more than a
thousand 45 columns, For gene-expression data, Signal values were calculated using the Microarray Suite 5.0 algorithm with no scaling or normalization. This allows for subsequent testing of various scaling and normalization methods. 76 WO 2007/011412 PCT/US2005/040196 'Vatistical ahlysi st.- ficdlutifty-tFotahd relations among metadata variables were analyzed in JMP. ANOVAs and class prediction of phenotypes using gene-expression data were performed in Arraytools 3.2.0 Beta developed by Richard
Simon and Amy Lam (http://linus.nci.nih.gov/BRB-ArrayTools.html). Heat-maps and dendrograms were graphed using dChip {Li, 2001 #41; Li, 2001 #42). Analysis of gene functions was aided by Arraytools and EASE {Hosack, 2003 #30}. Data analysis was performed primarily by D.T. 5 Scaling was carried out for gene-expression data. For each blood sample, the same hybridization cocktail went onto the A and then the B array, allowing concatenation of the data from the two arrays to form a virtual array.
This bypassed issues with analyzing the two data sets separately. The 100 control probesets common between the A and B arrays were selected based on stability in expression from a large study of various tissue types {Affymetrix, 2002 #27}. Thus, all array data were scaled to a target value of 500 using the trimmed mean of the 100 control probesets. This resulted in stable Scale Factors (SF) overtime and no differences in SF among the infection status phenotypes (ANOVA, P= 0.1047 10 A arrays, P =
0.1782 B arrays). This scaling method allowed for the concatenation of corresponding A and B arrays and should also remove variations that are not gene-specific. Results Clinical Phenotypes. Thirty healthy, 19 with FRI and negative by culture for adenovirus, 30 with FRI and positive by culture for 15 adenovirus, and 30 convalescing from adenovirus-positive FRI were enrolled in this study. Enrollees in these four infection status phenotypes were matched for age + 3 years and race/ethnicity. Only
male BMTs were enrolled. After selection of samples meeting standards for gene expression analysis, 17 FRI without adenovirus had been ill for 5 + 3 days (median + SD), whereas 26 FRI with adenovirus had been ill for 8 + 4 days (P = 0.006, Wilcoxon). The incidence of symptoms over all the groups was: sore throat (95.3%), cough (93%), sinus congestion (90.7%), headache (88%), chills (84%), rhinorrhea (81%), body aches (65%), malaise (63%), nausea (54%), diarrhea (14%), pleuritic chest pain (14%),
vomiting (14%), 20 and rash (0%), with no significant differences between the FRI groups. There was also no significant difference in allergies, recent injuries, and smoking history among the infection status phenotypes. Quality and variations of RNA derived from PAX system from the BMT population. In order to identify clinically relevant gene expression profile differences for phenotypes in a population, it is essential that the RNA sample applied to the microarray is representative of the
amount of transcripts in vivo. The PAX system was used to minimize handling of blood cells post collection and to immediately stabilize RNA and 25 halt transcription. We previously have shown two methods using this PAX system that provide stable RNA for microarray analysis {Thach, 2003 #18}. To assess RNA quality on each of the 95 microarrays analyzed in this study, recently published metrics derived from electropherograms of the RNA were used {Auer, 2003 #26}. Assessment of the degradation
factor, which is the ratio of the average intensity of bands of lesser molecular weight than the 18S ribosomal peak to the 18S band intensity multiplied by 100, demonstrated minimal degradation of RNA (Fig. 13). This 30 degradation factor for the samples correlated with gapdh 3'15' on the A arrays (Fig. 13c; r = 0.3, P= 0.008, ANOVA) and actin 3'/5' on the B arrays (r = 0.2; P < 0.05, ANOVA), the internal measurements for assessment of RNA quality on the microarray. There was no significant
correlation between 28S/18S versus degradation factor, gapdh 3'/5', and actin 3'/5', suggesting that the degradation factor is a superior method for assessing RNA quality for microarray analysis. No significant difference in degradation factor was seen among the phenotype groups. Assessment of the apoptosis factor, which is the ratio of the height of the 28S to 18S peak {Auer, 2003 #26}, suggested that a high 35 percentage of blood cells underwent apoptotic cell death. The distribution of the
degradation factor, apoptosis factor, 28S/188S, and yields of total RNA are shown in Figure 13b. No significant difference in apoptosis factor was seen among the phenotype groups. There was no significant correlation between duration of freezing and degradation factor (Fig 13d); nor was there correlation with apoptosis factor, RNA yield, 28S/1 8S, or gapdh and actin 3/5'. We determined if blood cell type heterogeneity affected the sensitivity of transcript detection. Assessment of complete blood
count (CBC) 40 variables that affect the number of present calls on the microarray demonstrated a linear correlation between number of probesets called Present and Mean Corpuscular Hemoglobin (MCH). A significant effect was detected (r = 0.272; P = 0.008, ANOVA) for the B arrays only (Fig 13e). The equation of the regression line suggested that for every picogram increase in hemoglobin, there is a loss in present detection calls of 100 probesets or 2% of the average number of present called
probesets on the B arrays. There was no difference in MCH among the infection status phenotypes. Quality of microarray measurements of PAX system-derived RNA from the BMT population, Individual control charts versus the 45 date of microarray scanning were plotted to look for stability of quality metrics over time, determine outliers, and compare with values proposed by the array manufacturer. The percent Present of transcripts was 32 ± 10 (average ± 3SD) for A arrays and 21 ± 6 for B arrays. The
gapdh and actin 77 WO 2007/011412 PCT/US2005/040196 3'/5' valde Weredles thnEtliVethe upparflifni pibp&eby Affymetrix {Affymetrix, 2004 #29). Noise was 3.6 ± 1.3 forA arrays and 2.9 ± 0.8 forB arrays. Average Background was 100 ± 48 for A arrays and 78 ± 33 for B arrays. After exclusions of array sets that were known to have been processed differently or erroneously, a total of 95 A and B array sets with stable quality metrics remained. These 95 sets were processed in batches with nearly
equal representation of the four infection status phenotypes. Therefore, comparisons among these four groups should detect biological 5 differences as these groups have similar variations due to processing. Gene expression profiles. The gene expression profiles were displayed on a heat-map with hierarchical clustering of transcripts to characterize and visualize patterns in the profiles of our cohort (Fig. 14). Initial examination revealed a large number of transcripts with high expression
levels (Fig. 14, orange bar) and a smaller number of transcripts with low expression levels (Fig. 14, purple bar) in the febrile group compared to the non-febrile healthy and convalescent patients. There were also transcripts that showed differences between healthy and 10 convalescent patients (Fig. 14, gray bar), while there was no obvious group of transcripts that showed differences between febrile without adenovirus versus febrile with adenovirus from this visual inspection. Within each
group, inter-individual variation was observed, suggesting diverse immune responses in this population. Class prediction of infection status phenotype. The pattern recognition above suggested that there were transcripts with differences in expression levels among healthy, febrile, and recovered patients. Therefore, class prediction was performed, to find sets of transcripts that best 15 classify the four infection status phenotypes. Probesets with >80% absent calls across samples were
filtered resulting in 15,721 probesets for further analysis. For supervised class prediction, the class labels for the febrile group were determine from respiratory viral culture results identifying presence or absence of adenovirus. Figure 14 suggested that the fever status of individuals was the predominant source of variation in gene expression profiles among samples and this was confirmed by unsupervised clustering of samples. Thus, supervised class prediction analysis was used to find sets
of 20 transcripts that classified non-febrile versus febrile patients first (node 1), then of the non-febrile patients, further classified to healthy or convalescent (node 2), and among the febrile patients, further classified to without or with adenovirus infection (node 3), The segregation of the samples via this nodal scheme was confirmed via binary tree class prediction analysis. Unlike data from cancer studies {Golub, 2004 #34; Valk, 2004 #9}, there are no reported transcript selection
methods or class prediction algorithms that are optimal for classification of infectious diseases. Therefore, we determined the transcript selection method and classification 25 algorithm that would result in the highest percent correct classification during leave-one-out cross-validation. To estimate the optimal transcript selection parameters for classification in each node, the cut-off level of the univariate P-value was varied, selecting for probesets that showed statistically significant
differences between the two groups at a P-value that was equaled to or smaller than a set cut-off level. As the P-value cut-offs became more stringent, the number of probesets selected decreased. For each P-value cut-off level, the selected probesets were subsequently used to classify the samples using various algorithms along with cross-validation analysis. For classification of node 1, 2 and 3, an optimal P-value 30 cut-off level of 10-2, 10-, 10-5 (Fig. 15a-c, lower-left corner) was chosen,
respectively. Once an optimal P-value cut-off level was estimated and held constant, the additional criterion of fold-change cut-off threshold was varied (Fig. 15a-c, x-axes) for each node. Figure 15 shows the percent-correct traces for the six algorithms tested tracking closely as fold-change cut-off level increases, but can differ by as much as 10-20% between methods. The black arrows in Figure 15 indicate an optimal percent-correct classification at the specific P-value and fold change
cut-off. For non-febrile vs. febrile, a percent correct call of 99% was achieved using the support 35 vector machines algorithm at a P-value cut-off level of 10-2 and a fold-change threshold of >5 which selected for 47 probesets to be in the classifier (Fig. 15a). For classification of healthy versus convalescent patients, an optimal percent correct of 87% using the diagonal linear discriminant analysis algorithm at a P-value cut-off level of 10-3 and a fold-change threshold of >1.9 which
selected for 8 probesets to be in the classifier was obtained (Fig. 15b). For classification of febrile patients without- versus with adenovirus infection, an optimal percent-correct of 91% using the support vector machine algorithm at a P-value cut-off level of 10-5 and a fold-change threshold of >1.7 which selected for 11 probesets to be in the 40 classifier was obtained (Fig. 15c). The samples that were misclassified by various algorithms and the associated gene expression profiles for the
selected transcript set are shown in Figure 16. For node 1, no individuals were misclassified in the febrile with adenovirus group and misclassified samples tended to belong to the febrile without adenovirus or the convalescent group. For node 2, the misclassified samples seemed to be equally distributed between healthy and convalescent, while for node 3, the misclassified samples tended to be in the febrile without adenovirus group. One observes that some samples 45 were misclassified
regardless of algorithm. 78 WO 2007/011412 PCT/US2005/040196 The dstirnatd otitMbl pere'nt-etbit~assffeetion of non-febrile versus febrile, healthy versus convalescents, and febrile without versus with adenovirus infection patients were 99%, 87%, and 91%, respectively, To determine the reliability of these percentages, the permutation test was performed with 2000 permutations. This resulted in P-values of <0,0005, 0.001, and <0.0005, respectively. Functions of genes in the classifier sets.
The identifiers of the discovered transcript sets for the class prediction results are shown in 5 Figure 16. The 47 probesets used to classify fever status (Figure 16a and Table 7) represent 40 transcripts, These included many that are induced by interferon, including: IF127, IF144, /F/35, IFRG28, IFITI, IFIT4, OAS1, OAS2, GBP1, CASP5, MX1, and GIP2. Furthermore, OAS1 and OAS2 catalyze 2', 5' oligomers of adenosine to activate RNaseL and inhibit cellular protein synthesis, while MXI is a member
of the GTPase family. OAS1, OAS2, and MXI have been shown to have antiviral functions, and interestingly, have also been found to be activated shortly after infection of nonhuman primates with high titers of smallpox {Rubins, 2004 #35}. Transcripts involved in the complement cascade, CIQG which is downstream of 10 antibodylantigen complexes and SERPINGI which inhibits activation of the first component of complement were associated with fever. The TNF alpha and IL-I induced gene, TNFA IP6, which
is a secretory protein involved in extracellular matrix stability and cell migration, and STK3 and CASP5, which are involved in the MAPK signaling pathway and are downstream of the TNF and ILL receptors were identified as class predictors. FCGRIA, which functions in the adaptive immune response and binds IgG, was part of the classifier. Other transcripts with associated known functions less clearly related to FRI or with unknown functions were also identified. Some gene ontology descriptions
and, in parenthesis, their ratios 15 of observed to expected number of occurrences were as follows (see Tables 8-9): GTP binding (6), guanyl nucleotide binding (6), response to virus (32), immune response (8), defense response (7), response to pest/pathogen/parasite (6), and response to stress (3). The 8 probeset classifier (Table 10) for distinguishing healthy versus convalescent patients mapped to 7 transcripts, including RP127 and RPS7 associated with ribosomal structure; IGHM, the
immunoglobulin heavy constant mu transcript; LAMA2, which is involved with cell adhesion, migration, and tissue remodeling; and transcripts related to other functions such as DAB2, KREMENI, and EVA 1. 20 The 10 transcript classifier (Table 11) for distinguishing febrile without adenovirus versus with adenovirus infection included the interleukin-1 receptor accessory protein, IL1RAP; two interferon induced genes, 1F127 and IF144, which were also in the classifier for fever status; and LGALS3BP,
which is involved in cell-cell and cell-matrix interactions and has been found elevated In individuals infected with the human immunodeficiency virus. Other transcripts with known functions less clearly related to adenoviral FRI or with unknown functions included ZCCHC2, ZSIG1I, NOP5/NOP58, MS4A7, LY6E, and BTN3A3. 25 Discussion After having rigorously assessed the RNA quality of samples processed with PAX tubes in a relatively large sample of humans with differing infection status phenotypes,
we characterized and compared the transcriptomes from whole blood samples of healthy, FRI without and with adenovirus infection, and convalescent individuals, evaluated class prediction methodologies, discovered nested sets of transcripts that could 30 optimally classify the infection status phenotypes and have begun to implicate pathways and gene functions involved in FRI. We applied a previously reported quality control metric called the degradation factor {Auer, 2003 #26} to our RNA samples
and determined that this factor correlates with quality control metrics (gapdh 3/5' and actin 3/5') present on the microarray. This degradation factor can easily be applied to microarray studies on large populations by assessing electropherogram data that is available from a bioanalyzer prior to processing microarrays and an indicator can be set to flag poor quality samples. We find that quality metrics typically used, such as the 28S/1 8S 35 ratio have high variability outside the traditional
standard range of 1.8 to 2.1 and poorly correlate with the quality control metrics present on the microarray. When assessing signal to noise quality metrics, we discovered that MCH significantly affects number of present calls on the B array only, likely due to detection of low expression transcripts on the B array compared to the A array {Affymetrix, 2002 #27}. At the time of probe design, the probes on the A chip were associated with more annotation than those on the B chip, The MCH is a
measure of picograms of hemoglobin per red 40 blood cell and likely is directly related to amounts of globin mRNA in whole blood samples; prior studies have demonstrated that spiking of increasing amounts of globin mRNA transcripts into total RNA from a cell line decreases the percent present calls linearly {Affymetrix, 2003 #28). This factor would need to be controlled in future microarray studies or globin mRNA would need to be reduced. In the present study, there was no difference of MCH
among the infection status phenotypes. During supervised analysis, we varied the fold-change cut-off threshold in addition to the P-value cut-off to optimize percent correct 45 classification. These combined criteria select for transcripts that not only are statistically different between two groups, but also vary above a specific fold-change threshold, reducing transcripts that may represent noise. The accuracy of classification seemed to be resistant to transcript selection 79 parameters and
algorithms when the gene-expression profiles showed large consistent differences, such as between non-febrile versus febrile patients; stricter P-value and fold change cut-off levels were needed to select informative transcripts that classify the healthy and convalescent or the febrile patients to an accuracy of 87% and 91%, respectively. Misclassified samples tended to belong to groups more likely to be heterogeneous, suggesting that the misclassification may be due to the lack of specificity
of the class labels. In future studies of larger size, the convalescent group might be further sub-classified based on duration of recovery and the febrile without adenovirus group sub-classified based on specific pathogen identified. The majority of transcripts in the classifiers shown in Figure 16 remained in the classifier 100% of the time during leave-one-out cross-validation (100% CV support). Thus, these transcripts in the classifiers are consistently different between individuals of two
clinical phenotypes at the time when they present for study, as exemplified in Figure 16a. Individuals in the FRI with adenovirus group tend to present later in illness than those without, potentially accounting for gene expression differences in the two groups. The correlation of changes in expression of these genes with infection status may also suggest that these genes are involved in the human host fever and immune responses to adenovirus infection in vivo. These transcripts consistently
showed the largest fold changes between groups, suggesting that the changes in expression were at the pathway level and were unlikely to be accounted for by differences in cell concentration alone. Furthermore, there were no significant differences in cell-type concentration between the febrile without- versus with adenovirus groups. This correlation of transcripts to fever and immune responses was derived from in vivo natural infections of humans, suggesting the important role of these genes in
the host response at the population level. Nested sets of transcripts resulted in similar percent-correct classifications, likely due to the fact that the expression of each transcript is not independent but correlated with other transcripts in related pathways. The discovery of transcripts with functions unrelated to immune response or with unknown functions implies that these should be further studied in infection phenotype model systems to elucidate mechanistic functions. Our demonstration
that one can predict the class of a patient with FRI due to adenovirus infection from background cases of FRI due to other etiologies support the possibility of using gene-expression in biosurveillance and pathogenesis. To our knowledge, this is the first in vivo demonstration of classification of infectious diseases via transcriptional signatures of the host. We intend to extend these findings to other respiratory pathogens, both viral and bacterial and to women, to further determine the
capability of applying this technology to biodefense and infectious disease surveillance. Numerous modifications and variations on the present invention are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the accompanying claims, the invention may be practiced otherwise than as specifically described herein. It is to be understood that, if any prior art publication is referred to herein, such reference does not constitute an admission that
the publication forms a part of the common general knowledge in the art, in Australia or any other country. In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" is used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in
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101, 15190-15195. 83 2508722_1 (GHMatters) P71850.AU Supplemental info: List of Tables provided in electronic form and brief description Table 16 - Performance of classifiers during cross-validation for Class Prediction for fever status (i.e., febrile versus non-febrile patients) Table 17 - Performance of classifiers during cross-validation, table of parameters for Table 16 Table 18 - Composition of classifier, list of genes significant at the 0.01 level (sorted by t-value) for Class Prediction
for fever status Table 19- 'Observed v. Expected' table of GO classes and parent classes, in list of significant genes shown in Table 18 Table 20 - Performance of classifiers during cross-validation for Class Prediction for febrile with adenovirus versus without adenovirus patients Table 21 - Performance of classifiers during cross-validation, table of parameters for Table 20 Table 22 - Composition of classifier, list of genes significant at the 0.01 level (sorted by t-value) for Class
Prediction for rile with adenovirus versus without adenovirus patients Table 23 - 'Observed v. Expected' table of GO classes and parent classes, in list of significant genes shown in Table 22 Table 24 - Performance of classifiers during cross-validation for Class Prediction for healthy versus convalescent patients Table 25 - Performance of classifiers during cross-validation, table of parameters for Table 24 Table 26 - Composition of classifier, list of genes significant at the 0.01 level
(sorted by t-value) for Class Prediction for healthy versus convalescent patients Table 27 - 'Observed v. Expected' table of GO classes and parent classes, in list of significant genes shown in Table 26 Table 28 - List of genes that discriminate for fever status (i.e., febrile versus non-febrile patients) Table 29- 'Observed v. Expected' table of GO classes and parent classes, in list of significant genes shown in Table 28 Table 30 - List of genes that discriminate for adenovirus versus without
adenovirus patients Table 31 - 'Observed v. Expected' table of GO classes and parent classes, in list of significant genes shown in Table 30 Table 32 - List of genes that discriminate for healthy versus convalescent patients Table 33 - 'Observed v. Expected' table of GO classes and parent classes, in list of significant genes shown in Table 32 84 2508722_1 (GHMaters) P7185O.AU Table 16 - Performance of classifiers during cross-validation: Array id Class A B C D E F G label 1 913344_07_2503 H
6979 YES YES YES YES YES YES 2 191589_04_12_03 H 7019 YES YES YES NO YES YES 3 288558_04_12_03 H 6969 YES YES YES YES YES YES 4 396378_07_25_03 H 6990 YES YES YES YES YES YES 5 822340_04_25_03 H 7068 YES YES YES YES YES YES 6 148906_09_04_03 H 6898 YES YES YES YES YES YES 7 028392_02_20_03 H 6951 YES YES YES YES YES YES 8 818141_03_22_03 H 7082 YES YES YES YES YES YES 9 638040_02_20_03 H 6931 YES YES YES YES YES YES 10 620587_04_21_03 H 7021 YES YES YES YES YES YES 11 162524_05_08_03 H 6922 YES
YES YES YES YES YES 12 866242_03_29_03 H 7156 NO NO YES YES NO NO 13 035239_0220_03 H 6868 YES YES YES YES YES YES 14 005192_04_26_03 H 7060 YES NO YES YES YES YES 15 8648400220_03 H 6949 YES YES YES YES YES YES 16 178636_07_12_03 H 7019 YES YES YES YES YES YES 17 241993_05_08_03 H 6944 YES YES YES YES YES YES 18 518251031703 H 7081 YES YES YES YES YES YES 19 777617_03_29_03 H 7040 YES YES YES YES YES YES 20 609124_04_01_03 H 6998 YES YES YES YES YES YES 21 851762_03_29_03 H 7153 YES YES YES YES
YES YES 22 3427620315_03 H 7068 YES YES YES YES YES YES 23 972194_05_08_03 H 6946 YES YES YES YES YES YES 24 047313_06_07_03 H 7016 YES YES YES YES YES YES 25 054914_05_08_03 H 7012 YES YES YES YES YES YES 26 007808_07_24_03 H 7086 YES YES YES YES YES YES 27 699520_07_24_03 H 6978 YES YES YES YES YES YES 28 486801_03_22_03 H 7172 YES YES YES YES YES YES 29 800380_07_24_03 H 6901 YES YES YES YES YES YES 30 589657_07_12_03 H 7028 YES YES YES YES YES YES 31 267240_07_25_03 H 7028 YES YES YES YES
YES YES 32 721312_07_25_03 H 7001 YES YES YES YES YES YES 33 576224072403 H 6945 YES YES YES YES YES YES 34 806203_05_10_03 H 7021 YES YES YES YES YES YES 35 706120_07_24_03 H 6942 YES YES YES YES YES YES 36 770482_05_24_03 H 7135 YES YES YES YES YES YES 37 019089_07_25_03 H 7019 YES YES YES YES YES YES 38 081293_07_24_03 H 6944 YES YES YES YES YES YES 39 403356_07_24_03 H 6986 YES YES YES YES YES YES 40 103898_06_07_03 H 7149 YES YES YES YES YES YES 41 569752_09.04_03 H 6939 YES YES YES YES YES
YES 42 708734_07_12_03 H 7083 YES YES YES YES YES YES 85 2568722_ 1 (GHMettorm) P71850 AU 43 392264_07_24_03 H 7049 YES YES YES YES YES YES 44 536912_07_12_03 H 7020 YES YES YES YES YES YES 45 875574_07_25_03 H 6998 YES YES YES YES YES YES 46 534050_07_25_03 H 6989 YES YES YES YES YES YES 47 901069_09_17_03 H 6962 YES YES YES YES YES YES 48 318859_0904_03 H 6943 YES YES YES YES YES YES 49 763605_06_07_03 H 6941 YES YES YES YES YES YES 50 988168_06_07_03 H 7032 YES YES YES YES YES YES 51
307208_07_24_03 H 6954 YES YES YES YES YES YES 52 097617_07_24_03 H 6938 YES YES YES YES YES YES 53 2885580324_03 S 7098 YES YES YES YES YES YES 54 822340_04_03_03 S 7001 YES YES YES YES YES YES 55 191589_03_27_03 S 7018 YES YES YES YES YES YES 56 203014_082903 S 6987 YES YES YES YES YES YES 57 818141_03_05_03 S 7057 YES YES YES YES YES YES 58 310740_04_12_03 S 6858 YES YES YES YES YES YES 59 620587_0404_03 S 7056 YES YES YES YES YES YES 60 127596_03_11_03 S 6985 YES YES YES YES YES YES 61
866242_03_05_03 S 7010 YES YES YES YES YES YES 62 572234_060403 S 7020 YES YES YES YES YES YES 63 005192_032603 S 6995 YES YES YES YES YES YES 64 148161_06_25_03 S 7001 YES YES YES YES YES YES 65 178636_06_26_03 S 6966 YES YES YES YES YES YES 66 518251_02_27_03 S 7057 YES YES YES YES YES YES 67 436639_03_03_03 S 7040 YES YES YES YES YES YES 68 777617_0304_03 S 6903 YES YES YES YES YES YES 69 851762_03_07_03 S 7001 YES YES YES YES YES YES 70 342762_02_24_03 S 7011 YES YES YES YES YES YES 71
086477_04_16_03 S 6955 YES YES YES YES YES YES 72 047313_05_22_03 S 6950 YES YES YES YES YES YES 73 867060_0416_03 S 7048 NO NO NO NO NO NO 74 486801_03_07_03 S 6983 YES YES YES YES YES YES 75 589657_06_24_03 S 7145 YES YES YES YES YES YES 76 806203_04_1603 S 7001 YES YES YES YES YES YES 77 721312_07_09_03 S 7007 YES YES YES YES YES YES 78 770482_05_02_03 S 7001 YES YES YES YES YES YES 79 103898_05_2103 S 6935 YES YES YES YES YES YES 80 050853_08_28_03 S 6885 YES YES YES YES YES YES 81
927492_04_03_03 S 6933 YES YES YES YES YES YES 82 011470_091003 S 7009 YES YES YES YES YES YES 83 708734_06_24_03 S 6955 YES YES YES YES YES YES 84 664013_09_15_03 S 6892 YES YES YES YES YES YES 85 536912_06_24_03 S 6976 YES YES YES YES YES YES 86 063961_091003 S 6960 YES YES YES YES YES YES 87 901069082803 S 7077 YES YES YES YES YES YES 88 114071_08_22_03 S 6913 YES YES YES YES YES YES 86 2508722_1 (GHMattm) P71850 AU 89 763605_0519_03 S 7087 YES YES YES YES YES YES 90 539852_09_05_03 S 7022
YES YES YES YES YES YES 91 988168_0521_03 S 6941 YES YES YES YES YES YES 92 379661_09_02_03 S 6967 YES YES YES YES YES YES 93 827495_09_0803 S 6980 YES YES YES YES YES YES 94 097881_0826_03 S 7120 YES YES YES YES YES YES 95 596752_08_27_03 S 7034 YES YES YES YES YES YES Percent correctly 98 97 99 98 98 98 classified: A = number of genes in classifier B = Compound Covariate Predictor Correct? C = Diagonal Linear Discriminant Analysis Correct? D = 1-Nearest Neighbor Correct? E = 3-Nearest
Neighbors Correct? F = Nearest Centroid Correct? G = Support Vector Machines Correct? Table 17 - Performance of classifiers during cross-validation Performance of the Compound Covariate Predictor Classifier: Class Sensitivity Specificity PPV NPV H 0.981 0.977 0.981 0.977 S 0.977 0.981 0.977 0.981 Performance of the 1-Nearest Neighbor Classifier: Class Sensitivity Specificity PPV NPV H 1 0.977 0.981 1 S 0.977 1 1 0.981 Performance of the 3-Nearest Neighbors Classifier: Class Sensitivity
Specificity PPV NPV H 0.981 0.977 0.981 0.977 S 0.977 0.981 0.977 0.981 Performance of the Nearest Centroid Classifier: Class Sensitivity Specificity PPV NPV H 0.981 0.977 0.981 0.977 S 0.977 0.981 0.977 0.981 Performance of the Support Vector Machine Classifier: Class Sensitivity Specificity PPV NPV H 0.981 0.977 0.981 0.977 S 0.977 0.981 0.977 0.981 87 2588722_1 (GHMatters) P71650.AU Performance of the Linear Diagonal Discriminant Analysis Classifier: Class Sensitivity Specificity PPV NPV H
0.962 0.977 0.98 0.955 S 0.977 0.962 0.955 0.98 Table 18 203276_at, 227458_at, 210592_s_at, 202446_s-at, 226459_at, 216950_s_at, 218280_x_at, 202430_s-at, 214511_x-at, 223502_s-at, 214329_x_at, 223501_at, 203455_s-at, 214290_s-at, 226117_at, 202912_at, 219014_at, 211368_s_at, 223220_s_at, 208966_x_at, 209498.at, 201601 .x_at, 213988_s-at, 221492_s-at, 205896_at, 209369at, 202708_s_at, 201061_s_at, 202688_at, 206332_s-at, 226603_at, 202687_s-at, 211883_x_at, 210166_at, 223834_at, 203964_at,
200673_at, 200986_at, 200985_sat, 211275_sat, 212268_at, 209970_x_at, 205098_at, 20601 1_at, 227266_s_at, 213293_s-at, 208012_x_at, 214022_s-at, 209762_x-at, 212657_s-at, 226353_at, 207574_s-at, 202307_s-at, 208959_s_at, 208436_s-at, 230585_at, 206025_s-at, 238439_at, 204224_s-at, 225251_at, 211367_s-at, 227609_at, 39402_at, 202193_at, 230036_at, 201924_at, 211366_x_at, 212335_at, 217995_at, 204232_at, 201318_s-at, 210101_xat, 205067_at, 202087_s_at, 224414_s_at, 239196_at, 117_at, 214150_x-at,
204526_s_at, 201060.x_at, 222154_s_at, 238025at, 235568_at, 206765_at, 219938_s_at, 202748_at, 217933_s-at, 242907_at, 217167_x at, 213361_at, 201554_x-at, 231769_at, 209304_x at, 209091_s-at, 217823-s-at, 209417_s_at, 237563_s_at, 225622_at, 212807_s-at, 217883_at, 200983_x_at, 205552_s_at, 206026_s_at, 235508_at, 233375_at, 200615_s-at, 208659_at, 204780_s_at, 228152_s-at, 205241_at, 201649_at, 219669_at, 218809.at, 204068_at, 224707_at, 223993_s_at, 200096_s-al, 201921at, 201193_at,
218282_at, 202269_x-at, 218383at, 201296_s-at, 209310_s-at, 200663at, 233540_s-at, 204747_at, 224917_at, 235670_at, 225940_at, 218999_at, 210582_s_at, 241916_at, 202270_at, 231577_s-at, 201761_at, 222859_s-at, 201470_at, 229521_at, 204781_s-at, 222670_s_at, 227014_at, 224009_x-at, 230370.x_at, 219806_s_at, 217769_s_at, 55692_at, 201760_s_at, 207500_at, 200734_s-at, 221345_at, 203567_s_at, 218943_s-at, 229450_at, 218728_s-at, 228306_at, 224983at, 206637_at, 228439at, 223952_x-at, 201999_s-at,
242625_at, 200096_s_at, 203127_s-at, 200701at, 211764_s-at, 205842_s_at, 227697_at, 223880_x_at, 204502_at, 225783_at, 208405_s_at, 209069_s-at, 206584at, 208653_s_at, 210449_x.at, 208639_x_at, 212185_x_at, 225076_s_at, 211889_x_at, 203143_s-at, 204249_s-at, 211067_s-at, 221816_s_at, 203922_s-at, 207551_s-at, 203595_s_at, 226757_at, 212203_x at, 208392_x-at, 235514_at, 221528_s-at, 217835_x-at, 206978_at, 218323_at, 225636_at, 209040_s-at, 218986_s-at, 205781_at, 208965_s-at, 233632_s-at,
207181_s-at, 208724_s-at, 218559_s-at, 225415_at, 224374_s-at, 226354_at, 202907_s_at, 229937_x-at, 221827_at, 202530_at, 209004_s-at, 215884_s-at, 228869_at, 217738 at. 225931_s_at, 209451 at, 219209_at, 219622_at, 221485_at, 213294_at, 244050_at, 226968_at, 226702_at, 243271_at, 217898_at, 226416_at, 210784_x_at, 235971.at, 210140_at, 229968_at, 204279at, 217826_s-at, 214590_s-at, 214453_s-at, 229625_at, 211561_- x..at, 207104_x-at, 238581_at, 201422_at, 218618_s-at, 222986_s-at, 231513_at,
217502_at, 202100_at, 210224_at, 217733_s-at, 218404_at, 216252_x_at, 211075_s_at, 209124_at, 225344_at, 225353_s-at, 209575_at, 221653_x-at, 218130_at, 203397_s-at, 221641_s-at, 224756_s-at, 203535_at, 210190_at, 224701_at, 223376_s_at, 228617_at, 206710_s-at, 211999_at, 226748_at, 219863_at, 205660_at, 224656_s-at, 204860_s-at, 213797_at, 209276_s_at, 201172_x at, 200782at, 206513_at, 223218_s_at, 210648_x at, 225095_at, 219394_at, 230741_al, 223980_s-at, 203234_at, 201762_s-at, 217986_s-at,
205992_s-at, 201798_s-at, 224604_at, 204972_at, 204415_at, 203897at, 206576_s_at, 206565_x-at, 217762_s-at, 201641at, 203471_s at, 201647_s-at, 213734_at, 203610_s-at, 238858_at, 202531_at, 202917_s-at, 204554_at, 205220_at, 211012_s-at, 200668_s_at, 226155_at, 233982_x_at, 219684_at, 205569_at, 218465_at, 202864_s-at, 213716_s_at, 225032_at, 222410_s_at, 205936_s_at, 208974_x-at, 229285_at, 212658at, 220330_s_at, 209933_s_at, 202506_at, 200079_s_at, 202863at, 202464_s_at, 219202_at,
204211_x_at, 213418_at, 235276_at, 200863_s-at, 228726_at, 208901_s-at, 200996_at, 226406_at, 205698_s-at, 202874_s_at, 204804_at, 227925_at, 208975_s-at, 212014_x-at, 226276_at, 211509_s-at, 201537_s_at, 207157_s-at, 211864_s-at, 218334_at, 222845_x-at, 238725_at, 217388_s_at, 215719_x-at, 203233_at, 222555_s_at, 200629_at, 208527_x-at, 209911 _x.at, 205483_s-at, 218400_at, 203616_at, 200620_at, 207777_s_at, 229138_at, 208857_s_at, 207387_s-at, 232353_s-at, 228531at, 208865_at, 223767_at,
220419_s-at, 205191_at, 212334_at, 228607_at, 208374_s_at, 224376_s_at, 222435_s_at, 225814_at, 216565.xat, 200798_x-at, 204689_at, 205715at, 223204_at, 200667at, 243934_at, 227354_at, 200984_s-at, 217752_s-at, 215043_s-at, 216841_s-at, 235740at, 241869_at, 212862_al, 219403_s_al, 204439_at, 210225_x_at, 222793_at, 225059_at, 235529.x_at, 224602_at, 202833_s_at, 208864_s-at, 201786_s-at, 242020_s_at, 230405_at, 222662_at, 235306_at, 204994_at, 219607_s-at, 209969_s-at, 209806_at, 227066_at,
204929_s-at, 229194_at, 200650_s_at, 225929_s-at, 202200_s_at, 201180_s_at, 212112_s-at, 202625_at, 203923.s-at, 229560at, 203278_s_at, 204490_s-at, 223599.at, 212463_at, 237006_at, 207275_s-at, 208933_s_at, 203574_at, 204861_s_at, 219690_at, 214059_at, 211806_s_at, 210119_at, 205480_s_at, 225245_x-at, 203371_s-at, 210797_s-at, 211997_x_at, 222512_at, 217475_s-at, 229390_at, 224800_at, 222981_s-at, 201531_at, 235518_at, 206662_at, 206618_at, 205003_at, 236156_at, 203561_at, 201995_at,
200974_.at, 204834_at, 200881_s-at, 207072at, 203773_x-at, 225850_at, 202869at, 228437at, 235286_at, 210789_x_at, 217764_s_at, 201200_at, 218773_s_at, 209868_s-at, 233591_at, 216243_s_at, 217118_s-at, 205681_at, 227856_at, 212380_at, 219691_at, 221680_s-at, 219799_s-at, 211729_x at, 200961at, 200649at, 211336_x_at, 218543_s.at, 225626_at, 230314_at, 227889at, 214681_at, 35254_at, 205269_at, 223145_s-at, 220603_s_at, 221954_at, 223892_s-at, 219062_s-at, 200661_at, 213017_at, 223280_x_at,
209835_x_at, 231956_at, 225878.at, 205099_s-at, 204800_s-at, 213572_s-at, 204858_s_at, 225919_s_at, 207697_x_at, 202277_at, 201336_at, 239979_at, 225604_s-at, 207565_s_at, 233587_s-at, 31845_at, 209944_at, 219757_s-at, 218026_at, 212737_at, 209546_s-at, 215966_x_at, 33304_at, 223591at, 239825at, 205016_at, 202536_at, 235175_at, 208881_xat, 225710_at, 218085_at, 224967_at, 210176_at, 233085_s_at, 208654_s_at, 203420_at, 204118_at, 222980_at, 219161_s-at, 209640_at, 210427.x at, 227983at,
205170_at, 208749_x-at, 224356_x_at, 225414at, 202592_at, 207668_x-at, 215933_s-at, 200887_s-at, 201222_s-at, 201098_at, 217739_s-at, 211711_s-at, 200067_x-at, 228758_at, 200730_s at, 243196_s-at, 201400_at, 201358_s-at, 211135_x-at, 225056_at, 212659_s_at, 212460_at, 225787_at, 207691_x_at, 223396_at, 201858_s_at, 201926_s_at, 212536_at, 203140at, 208736_at, 232629at, 223886_s-at, 212864_at, 213507_s_at, 228234_at, 211133_x_at, 200669_s_at, 218153_at, 224225_s-at, 204326_x-at, 212632at,
235456_at, 200009_at, 225941_at, 208438_s-at, 226169at, 202901_x_at, 216316_x-at, 202086.at, 201132_at, 203596_s-at, 215977_x_at, 219055_at, 210561_s-at, 221484_at, 211967at, 229391_s-at, 213574_s-at, 208935_s_at, 242234_at, 223454_at, 208771_s-at, 215838_at, 205568_at, 225661_at, 203748.xat, 201963_at, 219290_x_at, 219519_s_at, 229770_at, 208988_at, 214629_x-at, 211395_x-at, 210772at, 224806_at, 202753_at, 209089_at, 202672_s-at, 208908_s_at, 239582_at, 207431_s_at, 203044_at, 213102_at,
201298_s-at, 202122_s-at, 216202_s-at, 203416_at, 200748_s-at, 209593_s-at, 226099_at, 215000_s-at, 218668_s_at, 218854_at, 217794at, 201146_at, 218232_at, 213137_s_at, 230795_at, 222503_s_at, 217492_s_at, 206133_at, 229174_at, 209761_s-at, 218627_at, 219183_s-at, 229743at, 202441_at, 201840_at, 201473_at, 201642_at, 215783_s-at, 204924_at, 226121_at, 226849_at, 202498_s-at, 228532_at, 200880_at, 212501_at, 217962_at, 200677_at, 200079_s_at, 223032_x_at, 213857_s_at, 238461_at, 88 2568722_1
fGHMutter) P71850 AU 202076_at, 200904_at, 202266_at, 204006_s-at, 208579_x-at, 210705_s-at, 226128_at, 209448,_at, 203275_at, 201312_s_at, 203765_at, 232024_at, 225475at, 234974at, 227466at, 200067_x-at, 234987_at, 235964_x-at, 203761_at, 213503_x_at, 212063at, 212602_at, 222148_s_at, 207094_at, 217977_at, 210992_- x at, 205237_at, 223592_s-at, 210149_s-at, 204166at, 225675_at, 202837_at, 225440at, 225269_s_at, 202779_s_at, 228273_at, 206420_at, 225557_at, 202343.x_at, 201899_s_at, 239888_at,
204122_at, 201780_s_at, 214430_at, 204745_x-at, 224341_x_at, 218520_at, 223144_s_at, 222127_s_at, 212527_at, 200733_s_at, 209499_x_at, 205026_at, 209906_at, 209250_at, 220404_at, 214472_at, 210218_s-at, 215127_s-at, 203153_at, 204646_at, 217825_s-at, 202659_at, 204053.x-at, 226386_at, 200646_s_at, 228037at, 229543_at, 204417at, 208087_s-at, 226726at, 202484_s-at, 212934_at, 203882_at, 218102at, 204563_at, 227792_at, 225447_at, 217763_s_at, 208680_at, 208398_s-at, 225593at, 203258at, 223416_at,
207291_at, 206488_s_at, 222495_at, 227638_at, 236285at, 221766_s_at, 200762at, 211456_x_at, 212305_s_at, 211742_s-at, 227015_at, 225602_at, 230550_at, 228648_at, 200989_at, 200738_s_at, 215223_s_at, 239669_at, 204881_s-at, 218880_at, 200971_s-at, 203749_s-at, 241388_at, 209238_at, 226126_at, 210657_sat, 218202_x-at, 222757_s_at, 211678_s-at, 204489_s-at, 207467_x at, 35820at, 223167_s_at, 231578_at, 219358_s-al, 235791_x-at, 218092_s_at, 225265_at, 223434_at, 208581_x.at, 210968_s_at, 224818_at,
203820_s-at, 202856_s-at, 205159_at, 224973_at, 222067_x-at, 222442.s-at, 219243at, 213624_at, 208671_at, 226825_s_at, 201590_x-at, 232876_at, 22241 1_s_at, 222881_at, 207677_s_at, 200678_x_at, 217755_at, 219099_at, 226633_at, 208836_at, 202422_s-at, 203504_s_at, 203823at, 202902_s-at, 203971at, 235385.at, 235812_at, 217868_s_at, 229548_at, 203132_at, 227013_at, 242521at, 204258_at, 201859_at, 207794.at, 213051_at, 201301_s_at, 212467_at, 235681_at, 209093_s_at, 208918_s-at, 207704_s-at,
212820at, 212681 at, 238446_at, 224437-s-at, 218101_s-at, 203511_s-at, 201669_s-at, 202638_s-at, 209418_s_at, 222858_s-at, 213803_at, 222217_s-at, 203505_at, 238428_at, 222872_.x - at, 202199_s-at, 201536_at, 205027_s-at, 224918_x-at, 204479_at, 213573_at, 209090_s-at, 220104at, 209930_s-at, 211025_x-at, 200889_s-at, 209476at, 212859_x-at, 222693_at, 226756_at, 208742_s_at, 221865_at, 200744_s-at, 201738_at, 218519_at, 38241_at, 217078_s-at, 235056at, 201238_s_at, 234950_s-at, 206697_s-at,
209684_at, 218472_s-at, 214257_s_at, 227021_at, 205585_at, 219439_at, 217989_at, 214706_at, 200009_at, 202437_s-at, 225468_at, 224840_at, 208066_s_at, 200737at, 244642at, 205147_x-at, 202910_s-at, 222833_at, 238973_s-at, 209864_at, 202121_s-at, 211961_s-at, 207266_x_at, 209949_at, 205863_at, 222642_s_at, 225897_at, 228167_at, 201925_s_at, 200742_s-at, 208594_x-at, 238327_at, 225973_at, 204821_at, 208761_s-at, 217356_s-at, 229865_at, 235427_at, 32069at, 229460_at, 222537_s-at, 206553_at,
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233316_at, 236762_at, 202930_s-at, 212854_x-at, 219191_s-at, 205045_at, 239084_at, 212446_s_at, 202615.at, 212678_at, 237444_at, 218699_at, 225084_at, 237668_at, 207131_x at, 208870_x at, 24.3, 238801_at, 237351.at, 201893_x at, 60.2, 210296_s_at, 41.6, 45.2, 31.1, 240269_at, 204174_at, 225672_at, 211189.x-at, 241501_at, 239259_at, 235113_at, 228582_x_at, 209960_at, 39318_at, 228555_at, 24.5, 212819_at, 204480_s at, 204140_at, 230793at, 10.3, 200092_s-at, 30.5, 229841_at, 235422_at, 212760_at,
201711_x_at, 218346_s-at, 243606_at, 225816_at, 204903.x_at, 202315_s_at, 218152_at, 219627_at, 223332_x_at, 229705_at, 218872_at, 209494_s_at, 34225_at, 215667_x_at, 233132_at, 66.9, 227917_at, 33.1, 221507_at, 39729.at, 235693_at, 203375_s_at, 225065_xat, 205297_s-at, 200025_s_at, 227708.at, 209724_s_at, 235493_at, 233690_at, 200949,_x_at, 208661 _sat, 20551 0_s_at, 200936_at, 20471 0_s_at, 212312_at, 225176at, 202268_s-at, 227093at, 218236_s_at, 225048_at, 205760_s_at, 228974_at, 243816_at,
238494_at, 202057at, 239735_at, 226290_at, 238653_at, 238929_at, 228106_at, 201665_x_at, 223649_s_at, 82.2, 226115_at, 202983_at, 209199_s-at, 209323_at, 203249at, 223023_at, 201769_at, 226016_at, 219667_s_at, 223039_at, 236248_x_at, 225219_at, 54970_at, 201344_at, 52.2, 203313_s_at, 227891_s_at, 226635_at, 226797_at, 73.4, 221820_s-at, 227968_at, 203322at, 229431_at, 226215_s_at, 200091_s-at, 210279.at, 227580_s-at, 202431_s_at, 212321_at, 209271_at, 226116_at, 214041_x-at, 202165at,
213400_s-at, 220696_at, 202981_- x at, 227754_at, 229741_at, 45.8, 217671_at, 233341_s-at, 229969.at, 226449_at, 213042_s-at, 200982_s.at, 235220_at, 210418_s-at, 230229_at, 212787_at, 212231at, 206342_x_at, 223740_at, 35626_at, 225486_at, 240094_at, 238520_at, 35.5, 232583_at, 236814_at, 238875_at, 227747at, 211783_s_at, 218639_s_at, 204985_s-at, 27, 232338_at, 229342_at, 30.9, 215772_x-at, 212221_x_at, 213300_at, 227784_s-at, 37.3, 201677_at, 211727_s-at, 244457_at, 227646_at, 202429_s_at,
206181at, 236235_at, 238431_at, 212537_xat, 229204_at, 212622_at, 225216_at, 229686_at, 209459_s_at, 225330at, 229.6, 202379_s-at, 227239_at, 201074_at, 225623_at, 235692_at, 9.4, 23, 235005_at, 214582_at, 202219_at, 224076_s_at, 223193_x_at, 227203_at, 203462_x_at, 164.6, 218921_at, 226053_at, 214394_xat, 221712_s_at, 41, 219497_s_at, 214085_x_at, 242797_x_at, 226372_at, 237673_at, 205419_at, 202704_at, 232312_at, 203174_s-at, 213340_s-at, 222526_at, 217627at, 221931_s_at, 209161_at,
201055_s_at, 214766_s_at, 202587_s_at, 230917_at, 203063_at, 223358_s_at, 228650_at, 227455_at, 34031_i.at, 203628_at, 226981_at, 230142_s_at, 205882_x_at, 216250_s-at, 277.6, 53, 238303at, 225738_at, 221824_s_at, 221648_s-at, 204676_at, 212472_at, 217914_at, 238761_at, 224582_s-at, 217796_s-at, 206542_s-at, 19.4, 218380_at, 207163_s_at, 99.7, 235274_at, 224904_at, 230252_at, 250.8, 214790_at, 229803_s-at, 226005_at, 229541_at, 209281_s-at, 137, 235231at, 202979_s-at, 212167_s-at, 204642_at,
235457_at, 213893.x_at, 88.8, 208445_s_at, 226447_at, 236583_at, 207734at, 37384_at, 222380_s-at, 218031 _s_at, 39.1, 219110_at, 201106_at, 226352at, 221475_s-at, 226660_at, 213546_at, 200686_s-at, 49, 217506_at, 21331 1s-at, 200063_s_at, 212692_s-at, 242476_at, 14.3, 201433_s_at, 235683_at, 204120_s_at, 202317_s_at, 209569_x_at, 210719_s_at, 225368_at, 201565_s_at, 208113_x_at. 235308_at, 218456_at, 201338_x.at, 225553_at, 221452_s-at, 208968_s-at, 208517_x-at, 17.6, 212757_s-at, 236796at,
209605_at, 242691_at, 223150_s-at, 213263_s-at, 242755_at, 201094_at, 215696_s_at, 236836_at, 200675_at, 87.4, 211675_s_at, 39248_at, 232431 _at, 239811 _at, 224902_at, 223008_s_at, 95 2568722_1 (GHMatters) P71850.AU 212697_at, 233210_at, 49.8, 53987_at, 34.9, 228005at, 240165_at, 212320_at, 225387_at, 228528_at, 239278_at, 212393_at, 208519_x_at, 239757_at, 47069_at, 225338_at, 68.5, 204103_at, 225889at, 222787_s_at, 205961_s-at, 202742_s-at, 76.5, 203956_at, 225002_s_at, 232681_at,
215735_s_at, 227639at, 236379_at, 224647at, 203.6, 271.2, 226934_at, 97.1, 224624at, 73.6, 225051_at, 218306_s-at, 220711_at, 215823_x_at, 208688_x_at, 208660_at, 200089_s-at, 231840_x-at, 217729_s_at, 207081.s-at, 66.8, 218610_s-at, 217147_s-at, 226496_at, 211893_x-at, 226102_at, 204661_at, 224653_at, 218638_s-at, 24.5, 40446_at, 204857at, 230332_at, 204501_at, 240344.xat, 221757_at, 218723_s_at, 216033_s-at, 204867_at, 201029_s-at, 208807_s-at, 226085at, 204890_s-at, 202387_at, 221483_s-at,
227641at, 215684_s-at, 225318_at, 213852at, 72.5, 95.5, 226528at, 229695_at, 226625_at, 226348_at, 235879_at, 220888_s_at, 200017_at, 202088_at, 201909at, 213864_s_at, 212178_s-at, 228788_at, 207522_s-at, 212219_at, 200715_.xat, 220942_x_at, 203182_s_at, 201225_s_at, 225155_at, 154.8, 149.8, 228752_at, 221834_at, 212672_at, 225703_at, 218764_at, 231550_at, 226518_at, 213608_sat, 217822_at, 135.9, 222136.x-at, 228191_at, 217106_x-at, 215599_at, 242299_at, 244872_at, 212055_at, 214659_x-at,
225768_at, 238558at, 68.7, 42, 209333_at, 208804_s-at, 28.2, 214615_at, 206515_at, 201498_at, 224252_s-at, 235023at, 204793_at, 225630_at, 202970_at, 212276_at, 200018_at, 218466_at, 231972_at, 242439_s_at, 235802_at, 10.1, 225350_s-at, 208619at, 226832_at, 239646at, 200030_s-at, 231252_at, 212593_s_at, 215716_s_at, 203485_at, 201456_s_at, 219471_at, 204892_x.at, 229353_s-at, 208931_s-at, 207556_s-at, 202230_s-at, 202645_s-at, 200972at, 228258_at, 213158_at, 201916_s-at, 217838_s-at,
232909_s-at, 242134_at, 227386_s-at, 227369at, 200063_s_at, 212538_at, 216176_at, 300, 213549_at, 227696_at, 240499_at, 231989_s-at, 200631_s_at, 224945at, 202478_at, 220099_s_at, 221267_s_at, 17.4, 229891_x_at, 211955_at, 202174_s_at, 232210_at, 225159_s-at, 206545_at, 43, 89.1, 223013_at, 226867_at, 205376_at, 235423_at, 203787at, 212388_at, 204131_s-at, 221965_at, 230444_at, 209390_at, 211475_s-at, 211685_s-at, 212693_at, 230961_at, 232001_at, 236935at, 100, 206267_s-at, 212270_x_at, 79.7,
73.4, 243641_at, 200089_s_at, 226336_at, 211272_s-at, 201030_x_at, 229629_at, 52.6, 223162_s-at, 203774_at, 67.6, 220684at, 203135_at, 200741_s_at, 230296_at, 230178_s-at, 228046_at, 221488_s_at, 226254_s_at, 212139_at, 217394.at, 217379_at, 232412_at, 203569_s_at, 41047_at, 37462_i-at, 208833_s_at, 243086.at, 228466_at, 207651_at, 222494at, 225918_at, 201803_at, 233300at, 242191at, 221768_at, 214167_s-at, 225619_at, 219734_at, 222266_at, 219870_at, 219034_at, 237441at, 200058_s-at, 214686_at,
224735_at, 212462_at, 56197_at, 224784_at, 222999_s-at, 216885_s-at, 228762at, 227525at, 201919_at, 225232_at, 227134.at, 201178_at, 200694_s-at, 218017_s-at, 243561 at, 212259_s-at, 201934 at, 226136 at, 200017_at, 219762_s_at, 226247_at, 219073_s-at, 225740_x-at, 201054 at, 236754_at, 239131_at, 207996_s-at, 201032_at, 213166_x_at, 225197_at, 212704at, 234875_at, 206042_x-at, 212229_s_at, 212607at, 204328_at, 225073_at, 218414_s_at, 226879_at, 208694_at, 241321_at, 234873.x_at, 224516_s_at,
226820_at, 225244_at, 212638_s_at, 212114_at, 228932_at, 225547_at, 225050_.at, 238342at, 229757_at, 44790_s.at, 200834_s-at, 50277_at, 222673_x_at, 210222_s_at, 211822_s_at, 202968_s-at, 212313_at, 212827_at, 36553.at, 213727_x_at, 227228_s_at, 218120_s_at, 212599_at, 213619 at, 225081_s_at, 226454_at, 211929.at, 205383_s-at, 231864at, 226482_s-at, 242463_x-at, 202741_at, 205005_s-at, 238549_at, 224724at, 218927_s-at, 227665_at, 209184_s-at, 209381_x_at, 242146_at, 222906at, 210746_s_at,
210724_at, 222490_at, 205434_s-at, 217906_at, 217964_at, 203566_s_at, 231810_at, 231093_at, 223287_s_at, 200058_s-at, 224838_at, 219040_at, 208723_at, 227356_at, 206761_at, 222544_s_at, 226142_at, 238005_s-at, 211902_x-at, 227663_at, 201853_s-at, 218019_s-at, 212473_s_at, 236218_at, 201600_at, 45572_s_at, 208657_s-at, 201892_s_at, 201369_s-at, 226318_at, 203380_x-at, 200031_s-at, 202656_s-at, 213047_xat, 239355_at, 221775_x-at, 242725_at, 208796_s_at, 227173_s-at, 210774_s-at, 208858_s-at,
214617_at, 201479_at, 218586_at, 209813_x-at, 216221_s_at, 212842_x -at, 213405_at, 226959.at, 243819_at, 200029_at, 215806_x-at, 227547_at, 200000_s-at, 201648at, 214177_s-at, 202136_at, 218735_s-at, 230245_s_at, 213947_s-at, 227934_at, 203804_s-at, 213835_x at, 227867_at, 213945_s-at, 225780_at, 204635_at, 212080_at, 230032_at, 203297_s-at, 205861_at, 228170_at, 244375_at, 226752_at, 208304_at, 217832_at, 223681_s-at, 214771_x.at, 216920_s_at, 208758_at, 202887_s_at, 221711_s_at, 225876 at,
218175_at, 236293_at, 229949_at, 227979at, 213356_x_at, 223068_at, 205006_s_at, 217802_s_at, 210425_.x..at, 228677_s-at, 225793_at, 226816_s-at, 201170_s_at, 229513_at, 212880_at, 213093_at, 212074_at, 214551_s-at, 228710_at, 200018_at, 218247_s_at, 200990_at, 236198_at, 218499at, 211971_s-at, 218495_at, 218648_at, 39582_at, 212967_x-at, 228760_at, 221756_at, 231968_at, 217922_at, 215785_s_at, 216191_s_at, 209240_at, 228131_at, 204198_s-at, 229235at, 208717_at, 212070at, 218950_at, 227761_at,
225191_at, 208825_x_at, 208442_s-at, 209337_at, 200031_s_at, 219378at, 202754_at, 205353_s-at, 225569_at, 202365_at, 213377.x-at, 241871_at, 208611 s-at, 238880_at, 202059_s-at, 36545_s-at, 225562_at, 210288_at, 212773_s-at, 226529_at, 203386_at, 213295_at, 227119_at, 225435at, 234107_s_at, 208714_at, 223836_at, 229265_at, 212197_x_at, 200819_s-at, 202747_s-at, 218494_s-at, 202853_s-at, 206828_at, 201656at, 228109_at, 212413_at, 239122_at, 220960_x_at, 212352_s_at, 209395_at, 228007_at,
213587_s_at, 229141_at, 214937_x_at, 201005_at, 219812_at, 224935at, 224910_at, 202657_s-at, 223259_at, 218025_s-at, 200809_x-at, 234512_x_at, 231406_at, 217719_at, 235199at, 242968_at, 212360_at, 226157_at, 200000_s-at, 202649_x-at, 226202at, 223283-s-at, 224597_at, 211976_at, 221081_s_at, 208768_x_at, 226480_at, 233955_x-at, 223092_at, 219541_at, 200823_x-at, 208752_x-at, 203156_at, 239237_at, 212144at, 204020at, 204102_s-at, 202380_s-at, 213090_s-at, 220485_s-at, 203427_at, 201049_s-at,
205291_at, 240413_at, 230350at, 225199at, 49329_at, 235985_at, 208635_x_at, 214789_x_at, 227208_at, 216945_x_at, 205254_x at, 210645_s_at, 219155_at, 217143_s_at, 225139at, 203685_at, 206983_at, 219452_at, 201581_at, 227552_at, 225256_at, 231940_at, 238523_at, 210038_at, 217833_at, 202969_at, 205495_s-at, 212329at, 211972_x_at, 200029_at, 215262_at, 227319_at, 228831_s-at, 226876at, 214271 _x_at, 211941_s_at, 210858_x_at, 204655_at, 202724_s_at, 220999_s_at, 209185_s_at, 235123_at, 209798_at,
215874_at, 206099_at, 200088.x_at, 222824_at, 211938_at, 209682_at, 219821_s-at, 211824_x-at, 204744_s-at, 217466_x-at, 236280_at, 205758_at, 226382_at, 226039_at, 217527_s-at, 227112_at, 221798_x at, 225629_s_at, 208662_s_at, 200763_s-at, 208904_s-at, 211144_x_at, 228145_s-at, 200937_s_at, 224836at, 212071_s-at, 239808_at, 37145_at, 205049_s-at, 210825_s-at, 203286_at, 235085_at, 241365_at, 212812_at, 212085_at, 218115_at, 224718_at, 200016_x at, 204197_s_at, 202206_at, 203385_at, 225405_at,
232889_at, 243764at, 243780at, 213666_at, 214339_s-at, 209153_s_at, 232231_at, 238783at, 208914_at, 203402_at, 226143_at, 212931_at, 228826_at, 226148_at, 238480_at, 225845_at, 209422at, 222630_at, 207416_s_at, 205798_at, 4351 1_s-at, 203020_at, 213915_at, 212914at, 213084_x-at, 212823_s-at, 205790_at, 22471 1_at, 209815_at, 209379_s_at, 228959at, 243154_at, 229145_at, 200674_s_at, 204346_s-at, 212846_at, 213588.x_at, 212406_s_at, 225716_at, 225007_at, 207979_s-at, 208834_x.at, 225706_at,
211734_s_at, 201522_x-at, 225327at, 204153_s-at, 214049_x-at, 200088_x-at, 212482_at, 208798_x_at, 220646_s-at, 205255_x_at, 210607_at, 225123_at, 222557_at, 209841_s-at, 223477_s-at, 213564_ xat, 236782at, 213414_s-at, 201429_s-at, 212689_s_at, 201528_at, 226548_at, 215235_at, 226842_at, 224603_at, 228841_at, 200858_s-at, 214450_at, 233302at, 213360_s.at, 213080_x-at, 46665_at, 201154_x_at, 202746.at, 203580_s-at, 208319_s-at, 203723_at, 213906_at, 209396_s_at, 231776_at, 212790_x_at,
224581_s-at, 209604_s-at, 200016_x_at, 222759_at, 212933_x_at, 212608_s_at, 224837_at, 49111 at, 213359_at, 200662_s-at, 215963.x at, 202208_s_at, 206207_at, 212706_at, 211623_s-at, 221790_s_at, 219315_s_at, 1405_i-at, 224964_s-at, 215157_x_at, 214032at, 224698_at, 219045_at, 225698_at, 200953_s_at, 204960_at, 209670_at, 208645_s_at, 201064_s-at, 218237_s-at, 221234_s-at, 227755at, 217740_x-at, 200869_at, 204951_at, 224930_x_at, 210972_x-at, 231124_x-at, 225704_at, 213958_at, 213534_s-at,
217122_s_at, 37652_at, 225522_at, 235046_at, 212826_s-at, 226131_s_at, 201033_x-at, 203107_x_at, 230653_at, 224591_at, 213347_x_at, 213539_at, 214280_x-at, 211005_at, 213969_.x..at, 229563_s-at, 234970_at, 200023_s-at, 224821at, 212145_at, 208856_x-at, 206059_at, 226B10_at, 202624_s-at, 213193_x-at, 200074_sat, 200074_s-at, 96 2568722_1 (GHMatters) P71850 AU 213039_at, 206666_at, 201368_at, 213801_x_at, 218149_s-at, 224719_s-at, 205821at, 210555_s_at, 204633_s-at, 206150_at, 202481_at,
201998_at, 200036_s-at, 221726_at, 211720_x_at, 201258at, 200036_s-at, 200024_at, 210646_x-at, 226771at, 222820at, 220755_s-at, 200909_sat, 225310_at, 212066_s-at, 202029-x-at, 212690_at, 210201_x_at, 228298_at, 208692_at, 203034_s_at, 212433_x-at, 211927_x_at, 212232_at, 211710_x_at, 212414_s-at, 202207_at, 210031_at, 206337_at, 212508_at, 202524_s-at, 215967_s_at, 200024at, 200651_at, 200716_x_at, 218421_at, 213890_x at, 226682_at, 213164_at, 208073_x-at, 214321_at, 200689_x-at, 200810_s_at,
41220_at, 205831_at, 200023_s-at, 200081_sat, 212205_at, 205590at, 214439_x_at, 200081_s-at, 216342_x_at, 214351_x at, 227353_at, 211345_x-at, 211339_s_at, 241435_at, 212017_at, 217969_at, 203408_s-at, 211942_x-at, 230489_at, 200933.x_at, 201739_at, 205456_at, 221011_s_at, 226430_at, 212400_at, 222895_s-at, 226218_at, 221558_s-at, 212039_x_at, 211796_s-at, 219528_s-at, 204019_s_at, 217846_at, 221476_s-at, 221601_s_at, 204891_s-at, 212734_x-at, 200725_x-at, 201254_x-at, 204777_s-at, 230078_at,
57082_at, 210915.x_at, 236295_sat, 209134_s_at, 227261_at, 208929_x-at, 211073_x_at, 200644_at, 212191_x at, 200094_s_at, 224579_at, 212605_s_at, 224833_at, 221602_s_at, 200094_s_at, 203413_at, 201217_x-at, 226905_at, 211666.x_at, 226272_at, 200965_s_at, 217807_s_at Table 19- 'Observed v. Expected' table of GO classes and parent classes, in list of 7019 genes shown in Table 18 Cellular Component GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected
42101 5 2.43 2.06 T-cell receptor complex 1772 5 2.43 2.06 immunological synapse 145 5 2.43 2.06 exocyst Molecular Function GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected 30911 7 3.38 2.07 TPR domain binding 17040 6 2.89 2.07 ceramidase activity 16721 7 3.38 2.07 'oxidoreductase activity\, acting on superoxide radicals as acceptor" 16454 5 2.41 2.07 C-palmitoyltransferase activity 16314 8 3.86 2.07 'phosphatidylinositol-3\,4\,5-trisphosphate
3-phosphatase activity' 15645 9 4.34 2.07 fatty-acid ligase activity 15266 5 2.41 2.07 protein channel activity 4785 5 2.41 2.07 'copper\, zinc superoxide dismutase activity" 4784 7 3.38 2.07 superoxide dismutase activity 4758 5 2.41 2.07 serine C-palmitoyltransferase activity 4618 5 2.41 2.07 phosphoglycerate kinase activity 4467 9 4.34 2.07 long-chain-fatty-acid-CoA ligase activity 4459 5 2.41 2.07 L-lactate dehydrogenase activity 4457 5 2.41 2.07 lactate dehydrogenase activity 4370 6 2.89
2.07 glycerol kinase activity 4213 5 2.41 2.07 cathepsin B activity 4185 9 4.34 2.07 serine carboxypeptidase activity 4145 5 2.41 2.07 diamine N-acetyltransferase activity 3951 5 2.41 2.07 NAD+ kinase activity Biological Process GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected 50672 6 2.91 2.06 negative regulation of lymphocyte proliferation 97 2508722_1 (GHMatterm) P7185O.AU 46486 13 6.31 2.06 glycerolipid metabolism 45429 7 3.4 2.06 positive
regulation of nitric oxide biosynthesis 45428 7 3.4 2.06 regulation of nitric oxide biosynthesis 45410 5 2.43 2.06 positive regulation of intereukin-6 biosynthesis 45408 5 2.43 2.06 regulation of interleukin-6 biosynthesis 43193 5 2.43 2.06 positive regulation of gene-specific transcription 42226 5 2.43 2.06 intedeukin-6 biosynthesis 19751 11 5.34 2.06 polyol metabolism 19377 6 2.91 2.06 glycolipid catabolism 18348 5 2.43 2.06 protein amino acid geranylgeranylation 18344 5 2.43 2.06 protein
geranylgeranylation 9598 5 2.43 2.06 detection of pathogenic bacteria 9596 9 4.37 2.06 'detection of pest\, pathogen or parasite" 7009 8 3.88 2.06 plasma membrane organization and biogenesis 6984 5 2.43 2.06 ER-nuclear signaling pathway 6662 13 6.31 2.06 glycerol ether metabolism 6641 13 6.31 2.06 triacylglycerol metabolism 6639 13 6.31 2.06 acylglycerol metabolism 6638 13 6.31 2.06 neutral lipid metabolism 6072 7 3.4 2.06 glycerol-3-phosphate metabolism 6071 11 5.34 2.06 glycerol metabolism
Table 20 - Performance of classifiers during cross-validation: Array id Class A B C D E F G label 1 191589_04_12_03 F_NE 1987 YES YES NO NO YES YES 2 288558_04_12_03 FNE 2127 NO NO YES NO NO NO 3 822340_04_25_03 F_NE 1764 YES YES YES YES YES YES 4 818141_03_22_03 F_NE 1717 YES YES YES YES YES YES 5 620587_04_21_03 F_NE 2028 NO NO YES NO NO NO 6 866242_0329_03 FNE 1630 YES YES YES YES YES YES 7 005192_04_26_03 F_NE 1762 YES YES YES YES YES YES 8 178636_0712_03 FNE 1836 YES YES YES YES YES YES 9
518251_0317_03 F_NE 1676 YES YES YES YES YES YES 10 777617_03_29_03 FNE 1880 YES YES YES YES YES YES 11 609124_04_01_03 F_NE 1908 YES YES NO YES YES NO 12 851762_03_29_03 F_NE 1603 YES YES YES YES YES YES 13 342762_0315_03 FNE 1739 YES YES YES YES YES YES 14 047313_06_07_03 FNE 1930 YES YES YES YES YES YES 15 486801_03_22_03 F_NE 1648 YES YES YES YES YES YES 16 589657_0712_03 F_NE 2000 YES YES NO YES YES YES 17 721312_07_25_03 F_NE 2020 NO NO YES YES NO YES 18 806203_0510_03 FNE 1873 YES YES YES
YES YES YES 19 770482_05_24_03 F_NE 1753 YES YES YES YES YES YES 20 103898_06_07_03 FNE 1640 YES YES YES YES YES YES 98 2568722_1 (GHMutters) P71850 AU 21 708734_07_1203 F_NE 1705 YES YES YES YES YES YES 22 536912_07_12_03 F_NE 1872 YES YES NO YES YES YES 23 901069_09_17_03 F_NE 2196 NO NO YES NO NO NO 24 763605 06_07 03 FNE 2343 NO NO NO NO NO NO 25 988168_06_07_03 F_NE 1863 YES YES YES YES YES YES 26 913344072503 H_ND 2051 NO NO YES YES NO NO 27 396378_07_25_03 HND 1870 YES YES NO YES YES NO
28 148906_09_04_03 HND 1741 YES YES YES YES YES YES 29 028392_02_20_03 HND 1749 YES YES YES YES YES YES 30 638040_0220_03 HND 1673 YES YES YES YES YES YES 31 162524_0508_03 HND 1792 YES YES YES YES YES YES 32 035239_02_20_03 HND 1634 YES YES YES YES YES YES 33 864840_02_20_03 HND 1726 YES YES YES YES YES YES 34 241993_05_0803 HND 1780 YES YES YES YES YES YES 35 972194_05_08_03 HND 1833 YES YES NO YES YES YES 36 054914050803 HND 2134 NO NO NO NO NO NO 37 007808_07_24_03 HND 2189 NO NO NO NO NO NO
38 699520_07_24_03 H_ND 1876 YES YES NO YES YES YES 39 800380 07 24 03 HND 1744 YES YES YES YES YES YES 40 267240_07_25_03 HND 2123 NO NO NO NO NO NO 41 576224_07_24_03 HND 1871 YES YES NO NO YES YES 42 706120_07_24_03 HND 1765 YES YES YES YES YES YES 43 019089_07_25_03 HND 2018 YES YES NO NO YES YES 44 081293_07_24_03 HND 1810 YES YES YES YES YES YES 45 403356_07_24_03 HND 1994 YES YES YES YES YES YES 46 569752_09_04_03 HND 1781 YES YES YES YES YES YES 47 392264_07_24_03 HND 2156 NO NO NO NO NO
NO 48 875574_07_2503 H_ND 2045 NO NO NO NO NO NO 49 534050_07_25_03 HND 2000 YES NO NO YES YES NO 50 318859_09_04_03 H_ND 1774 YES YES YES YES YES YES 51 307208_07_24_03 HND 1773 YES YES NO YES YES YES 52 097617_0724_03 H_ND 1792 YES YES YES NO YES YES Percent correctly 79 77 67 75 79 75 classified: A = number of genes in classifier B = Compound Covariate Predictor Correct? C = Diagonal Linear Discriminant Analysis Correct? D = 1-Nearest Neighbor Correct? E = 3-Nearest Neighbors Correct? F =
Nearest Centroid Correct? G = Support Vector Machines Correct? Table 21 - Performance of classifiers during cross-validation Performance of the Compound Covariate Predictor Classifier: 99 25e8722_1 (GHMatters) P7185O.AU Class Sensitivity Specificity PPV NPV F_NE 0.8 0.778 0.769 0.808 H_ND 0.778 0.8 0.808 0.769 Performance of the 1-Nearest Neighbor Classifier: Class Sensitivity Specificity PPV NPV FNE 0.8 0.556 0.625 0.75 HND 0.556 0.8 0.75 0.625 Performance of the 3-Nearest Neighbors Classifier:
Class Sensitivity Specificity PPV NPV F_NE 0.8 0.704 0.714 0.792 H_ND 0.704 0.8 0.792 0.714 Performance of the Nearest Centroid Classifier: Class Sensitivity Specificity PPV NPV F_NE 0.8 0.778 0.769 0.808 H_ND 0.778 0.8 0.808 0.769 Performance of the Support Vector Machine Classifier: Class Sensitivity Specificity PPV NPV F_NE 0.8 0.704 0.714 0.792 H_ND 0.704 0.8 0.792 0.714 Performance of the Linear Diagonal Discriminant Analysis Classifier: Class Sensitivity Specificity PPV NPV F.NE 0.8 0.741
0.741 0.8 H_ND 0.741 0.8 0.8 0.741 Table 22 211927_x_at, 201154_x_at, 211710_x_at, 200036_s_at, 200036_s_at, 208856_x_at, 211073_x_at, 34210_at, 212790_x-at, 221475_s_at, 213642_at, 211487_x-at, 200025_s-at, 211666_x at, 201049._s_at, 216520_s_at, 204661_at, 200002_at, 200025_s_at, 213941_x-at, 200002_at, 201217_x_at, 201338_x-at, 213687_s_at, 200032_s-at, 200099_s-at, 201.7, 200019_s-at, 200750_s_at, 213366_x at, 215963_x_at, 201094_at, 220547_s_at, 212039_x-at, 201257_x-at, 200062_s_at,
32837_at, 219657_s_at, 244520_at, 223944_at, 211702_s_at, 203110_at, 214483_s_at, 60794_f at, 202643_s_at, 207094_at, 213145at, 239804at, 200986at, 201484_at, 207876_s-at, 212515._s-at, 238295_at, 27.6, 206257_at, 205896at, 212811_.x-at, 204432_at, 235366_at, 45.7, 200766_at, 202665_s-at, 214816 x at, 226459_at, 200948_at, 224791at, 224846_at, 224103_at, 205851_at, 24041 1at, 242297_at, 230795_at, 229575_at, 68.6, 221664_s-at, 202309_at, 209539_at, 202449_s_at, 232981_s_at, 212383_at, 225637_at,
240385_at, 212058_at, 225954_sat, 205586_x.at, 220367_s-at, 204279_at, 212483_at, 212174_at, 234883_x_at, 214496_x_at, 202725_at, 204169_at, 222041_at, 226502_at, 224324_at, 203574_at, 205159_at, 231050_at, 235648_at, 212.5, 204287_at, 216269_s_at, 203749_s-at, 214080_x-at, 215985_at, 223220_s_at, 227452_at, 202430_s at, 200712_s-at, 230707_at, 233480_at, 242159_at, 209348_s_at, 211965at, 200997_at, 237003_at, 210554_s-at, 243941_at, 211543_s-at, 241983_at, 46323_at, 224864_at, 207760_s-at,
232469_x_at, 220419_s-at, 223299_at, 230421_at, 201460_at, 211249_at, 206398_s-at, 34225_at, 216092_s-at, 229747_x_at, 205080_at, 230208_at, 216993_s_at, 213607_x-at, 239979_at, 243707_at, 241107_at, 212506_at, 229578_at, 203310_at, 244449_at, 25.1, 213198_at, 213300_at, 208779_x_at, 218950_at, 222421at, 243099_at, 238757_at, 241797_at, 206850_at, 212218_s_at, 202221_s-at, 219977_at, 202687_s_at, 202328_s_at, 232891_at, 222310_at, 231448_at, 202123_s_at, 229684_s_at, 225090_at, 205027_s-at,
206245_s_at, 207755_at, 211304_x_at, 223135_s-at, 232168_x-at, 202299_s-at, 217838_s-at, 242117.at, 220036_s-at, 223417_at, 212763_at, 216828_at, 77.1, 211404_s_at, 228752_at, 204768_s_at, 215274_at, 235514_at, 202447_at, 226726_at, 243511 _at, 201647_s_at, 231954_at, 117_at, 204745_x_at, 219981_x_at, 537.2, 205079_s-at, 225614_at, 218035_s-at, 211753_s-at, 223635_s_at, 228599_at, 213154_s_at, 72.8, 226596_x-at, 226968_at, 241993_x_at, 226505_x_at, 226942_at, 213374_x-at, 202391_at, 213795_s-at,
201668_x_at, 218614_at, 231863_at, 225181_at, 204361_s_at, 213003_s_at, 219623_at, 225939_at, 208238_x-at, 220371_s-at, 211202_s-at, 226629_at, 222979_s_at, 230274_s-at, 243819_at, 228964_at, 215452_x_at, 239648_at, 242510_at, 92, 204599_s-at, 226121_at, 225183_at, 209863_s_at, 212247_at, 202156_s_at, 215707_s_at, 244251_at, 220113_x_at, 209498_at, 232680_at, 244358_at, 228188_at, 205170_at, 143.8, 63.9, 214511 x-at, 235456_at, 200890_s-at, 225321_s-at, 218725_at, 229773_at, 229987_at,
239792_at, 202886_sat, 223427_s-at, 225980_at, 200856_x_at, 201190_s-at, 213694_at, 201140_s-at, 228713_s_at, 236488_s_at, 212412_at, 104, 99.6, 221865_at, 225414_at, 215075_s_at, 241598_at, 205010_at, 206094_x-at, 237, 83.8, 27.9, 100 2568722_1 (GHMatter) P71850AU 225965_at, 226064_s-at, 202897_at, 44040_at, 226117_at, 232876_at, 216984_x_at, 216236_s-at, 240539_at, 242075_at, 242576_x.at, 241786_at, 204403_x-at, 225613_at, 225415_at, 218381_s_at, 227276_at, 223797_at, 226240_at, 219816_s_at,
244576_at, 203586_s_at, 201118_at, 202856_s_at, 223824_at, 235525_at, 211926_s-at, 208974_x-at, 203822_s-at, 214084_x-at, 219287_at, 231251at, 223763_at, 244803_at, 209525_at, 213113_s-at, 217226_s-at, 232407_at, 205444_at, 202025_x-at, 201554_x-at, 220504_at, 215813_s-at, 221681_s-at, 209418_s_at, 424.2, 216590_at, 225796at, 203157_s-at, 202824_s_at, 92.5, 64.8, 217695_x_at, 230322_at, 219471_at, 50374_at, 209018_s_at, 217566_s_at, 220236_at, 240371_at, 227518_at, 225764at, 241329_s-at,
218625_at, 202059_s-at, 215011_at, 222746_s-at, 203827_at, 224265_s_at, 236363_at, 220937_s_at, 218419_s-at, 190.5, 458.9, 212601_at, 203604_at, 228460at, 202804_at, 220071_xat, 204613_at, 234490_at, 233085_s-at, 205984_at, 200827_at, 27.6, 228813_at, 206472_s_at, 236816_at, 205351_at, 218915_at, 242447_at, 243147_x-at, 228497_at, 31, 204977_at, 40562at, 230060_at, 208839_s_at, 226603_at, 211922_s-at, 206247_at, 239260.at, 203276_at, 224572_s-at, 242972_at, 224817_at, 236685_at, 201552_at, 36.1,
218940_at, 203508_at, 235509_at, 204079_at, 230706_s_at, 212975_at, 226391_at, 224042_at, 211628_x-at, 201786_s-at, 218156_s-at, 241669_x-at, 202801_at, 204970_s-at, 224427_s-at, 210228_at, 207428_x_at, 204109_s_at, 229384.at, 225998_at, 239197_s-at, 223662_x-at, 37, 53720_at, 209044_x-at, 231956_at, 207361_at, 205367_at, 218458_at, 211327_x_at, 237444_at, 237747_at, 200889_s-at, 202497_x-at, 207467_x_at, 231927_at, 205374_at, 242573_at, 234665_x_at, 212241_at, 237783_at, 231271.x at,
202192_s_at, 49327_at, 224007_at, 209399_at, 205323_s-at, 209439_s-at, 207957_s-at, 232340_at, 210817_s-at, 38964_r-at, 238040_at, 244418_at, 215191_at, 208869_s_at, 224100_s-at, 240638_at, 201194at, 119.5, 201038_s-at, 209130_at, 209734at, 218520_at, 228363_at, 209004_s-at, 215148_s-at, 213681_at, 207187at, 236467at, 221485_at, 219394_at, 211277_x-at, 209287_s-at, 228089_x_at, 206388_at, 200775_s-at, 213836_s_at, 236002_at, 201432_at, 243730.at, 118.2, 39.5, 12.4, 225076_s_at, 241692_at,
210756_s_at, 217397_at, 220776_at, 219635_at, 213307at, 216748_at, 201276_at, 204804_at, 200721_s_at, 242108 at, 206708_at, 231476at, 208975_s-at, 48.8, 224907_s-at, 213511_-s-at, 209369_at, 202097_at, 229906_at, 211416_xat, 38447_at, 240319_at, 200071_at, 207358_x-at, 225289_at, 225883_at, 224088_at, 202791_s-at, 225776_at, 75.7, 239572at, 204407_at, 217133_x-at, 237284_at, 215479_at, 203389_at, 206268_at, 206263_at, 213076_at, 214053at, 112.3, 215558_at, 242008_at, 200764_s-at, 201943_s-at,
222707_s-at, 201003.x-at, 219941_at, 210176_at, 225271_at, 221641_s_at, 207966_s-at, 241715_x-at, 238819_at, 241967at, 225610_at, 215779_s_at, 225598_at, 230405_at, 238235_at, 244271 _at, 238701 _x_at, 206656_s-at, 239749at, 221708_s_at, 212547at, 207300_s-at, 218130_at, 209409at, 243158_at, 45.7, 16, 123.6, 39.5, 207630_s_at, 217967_s-at, 207969_x-at, 216005_at, 240299_at, 200733_s_at, 206833_s-at, 222300_at, 212786_at, 232196_at, 208274_at, 224425_x_at, 203344_s_at, 200996_at, 201075_s_at,
223650_s_at, 232517_s_at, 215269_at, 218812_s_at, 48659_at, 218776_s_at, 204166_at, 243465at, 242151_at, 215783_s_at, 219910 _at, 212560_at, 229543_at, 212971_at, 52.5, 208520_at, 203363_s_at, 233041_x_at, 32540_at, 240989_at, 205740_s_at, 218522_s_at, 210985_sat, 218137_s_at, 205199_at, 220987_s_at, 205986.at, 204739_at, 217872_at, 203134at, 240332_at, 38.9, 79.9, 23.8, 212549_at, 232044_at, 205726_at, 225654_at, 225564_at, 212261_at, 211825_s-at, 205682_x_at, 216757_at, 213180_s_at, 244642_at,
212237_at, 232685_at, 210889_s_at, 244296_at, 23, 37425_g-at, 203808at, 20001 1sat, 236927_at, 223921_s_at, 208052_x_at, 208864_s-at, 212335_at, 205660_at, 214200_s-at, 244495_x_at, 211779.x_at, 47608_at, 227344_at, 212056_at, 223141_at, 37860_at, 20.3, 218178_s_at, 200852_x-at, 226155at, 219923_at, 216110.x-at, 201795_at, 209073_s-at, 222148_s-at, 210058_at, 211988_at, 242461_at, 201977_s-at, 209426_s-at, 212590_at, 228758_at, 79.1, 210247_at, 227207_x-at, 242034_at, 202423_at, 240162_at,
201261_x at, 238836_at, 241359at, 48.4, 216036_x_at, 228871_at, 201210_at, 220537_at, 240933_at, 225371_at, 213622_at, 200622_x_at, 230314_at, 209808_x_at, 203317_at, 238461_at, 212657_s_at, 232221_x at, 204604_at, 208591_s_at, 244618_at, 207466_at, 205678_at, 218737_at, 33778_at, 219428_s_at, 214969.at, 218649_x-at, 218518_at, 229336_at, 226504_at, 235939_at, 205166_at, 212991_at, 205277at, 202583_s_at, 217899_at, 218401.s-at, 209743_s_at, 210639_s-at, 242147_at, 218554_s-at, 208917_x at,
224706_at, 202101_s-at, 237088_at, 211924_s-at, 216678at, 204027_s-at, 240435_at, 242145at, 80.8, 25.5, 50.6, 202925_s_at, 224708_at, 202272_s_at, 218829_s_at, 203513_at, 229137_at, 232700_at, 223405_at, 212184_s-at, 204408_at, 242279_at, 203585_at, 221352_at, 223751_x-at, 202216_x_at, 237553_at, 227325_at, 230207_s_at, 11.4, 138.6, 231.7, 222082_at, 235773_at, 240102_at, 204513_s_at, 220906_at, 200931_sat, 221803_s_at, 235775at, 202171_at, 209515_s_at, 202709_at, 207851.s-at, 222881_at, 71.5,
223360_at, 214965_at, 207474_at, 214107_x_at, 241368_at, 222446_s-at, 202464_s-at, 243045_at, 205756_s-at, 201251_at, 228.3, 59.2, 212317at, 221839_s-at, 201557_at, 208967_s-at, 202499_s-at, 205633_s-at, 226758_at, 90265_at, 216213_at, 210037_s_at, 209250_at, 240765_at, 201662_s_at, 212443_at, 31835_at, 202443_x_at, 216565_x_at, 225947_at, 46.5, 242068.at, 224084_at, 235696_at, 215245_x_at, 220566_at, 207059.at, 214590_s-at, 226054_at, 214688_at, 225175_s-at, 219677_at, 239156_at, 200706_s-at,
214047_s-at, 242913_at, 34.3, 189.2, 124, 203546_at, 214523_at, 203544_s_at, 40225_at, 219979_s-at, 237099_at, 225024_at, 208121_s_at, 216446_at, 207684_at, 211961_s_at, 223346_at, 201562_s-at, 233519_at, 241168_at, 47560_at, 218652_s-at, 233177_s_at, 234020_x_at, 225987_at, 213798_s-at, 216261_at, 233399_x-at, 222833_at, 207777_s-at, 218660_at, 240125_at, 214792_x_at, 224034_at, 202.6, 208863_s_at, 218966_at, 202329_at, 220574_at, 221669_s-at, 213510_x-at, 243748at, 200848at, 237062_at,
239082at, 212099_at, 217810.x-at, 205428_s_at, 160020_at, 40640_at, 209802_at, 229295_at, 224182_x_at, 201323_at, 219788_at, 220704_at, 206993_at, 232551_at, 231770_x_at, 227129_x_at, 211275_s_at, 203291_at, 213167_s_at, 200815_s-at, 47571at, 210879_s-at, 222187_x-at, 238816_at, 228309_at, 218322_s-at, 208899_x.at, 209806_at, 207464_at, 222088_s-at, 222835 at, 203378_at, 201745_at, 238903_at, 201886_at, 212882_at, 49878_at, 223682_s-at, 28, 47.8, 219080_s_at, 227693_at, 226712_at, 220873_at,
229168_at, 205357_s.at, 219105.x-at, 200049_at, 227609_at, 209003_at, 242688_at, 244313_at, 242974_at, 205338_s-at, 241244_at, 216071_xat, 215884_s_at, 223481_s_at, 217212_s_at, 221430_s_at, 232341_x-at, 213241_at, 232579_at, 60.1, 69.4, 42.7, 218866_s-at, 234521at, 237035_at, 396_fLat, 200979_at, 221306_at, 60528_at, 234671at, 201442_s-at, 224754_at, 220158_at, 211512_s-at, 208436_s-at, 233273_at, 223690.at, 202351_at, 227035_x-at, 243529_at, 225708_at, 73.8, 27.4, 27.3, 205207_at, 212501_at,
203668_at, 226266_at, 226954_at, 215696_s_at, 222591at, 227524_at, 213281_at, 204906_at, 227885_at, 241865_at, 202664_at, 202197_at, 217682_at, 227889._at, 234534_at, 243201_at, 225192_at, 224993_at, 200608_s_at, 120.9, 696.9, 234101at, 225639_at, 215639_at, 210255_at, 218302_at, 212202_s_at, 234727_at, 235473_at, 229832_x_at, 38241_at, 203164_at, 216950_s_at, 211004_s-at, 209099x -at, 240188_at, 243112_at, 424.3, 20.6, 316.7, 220366_at, 225582_at, 210242_x-at, 207196_s_at, 218973_at, 224960_at,
234326_at, 202716_at, 336_at, 212322_at, 226357_at, 209685_s-at, 208611 s-at, 210695_sat, 154.9, 80.8, 454.2, 218127_at, 230590_at, 226076_s-at, 221170_at, 217687_at, 243004_at, 201605_x-at, 39548_at, 207691_x-at, 201788_at, 202959_at, 208035_at, 55616_at, 218023_s-at, 56.1, 31.2, 26.1, 101.4, 210967_x at, 209028_s-at, 200677_at, 205600_x_at, 212276_at, 228271_at, 239311_at, 211190_x_at, 240878_at, 32032_at, 78047_s-at, 212331_at, 218904_s-at, 218499_at, 223000_s_at, 216217_at, 35974_at,
224414_s_at, 201949_x_at, 87.7, 62.2, 117.2, 234965_at, 223945_x.at, 208955_at, 222482_at, 211936_at, 227373_at, 200618_at, 216809_at, 209822_s_at, 232419_at, 202638_s-at, 222387_s-at, 202860_at, 232387_at, 200759_x-at, 220232_at, 52255_s-at, 203434_s_at, 210240_s-at, 209011 _at, 200911 _s_at, 206649_s-at, 218014_at, 231108_at, 201475_x_at, 202681_at, 210912_x at, 31837_at, 225059.at, 233665_x_at, 217235_x_at, 212796_s-at, 203921_at, 52.9, 54.7, 23.6, 47.8, 208994_s-at, 213572_s-at, 238969_at,
200047_s-at, 225616_at, 221643_s_at, 216266_s-at, 206219_s-at, 223303_at, 206765_at, 201167_x_at, 201280_s_at, 226440_at, 43934_at, 242858_at, 222429_at, 210200_at, 214706_at, 240689_at, 206058_at, 202662_s-at, 226457_at, 209436_at, 213120_at, 126.1, 132.3, 230503_at, 209124_at, 231836_at, 210886_x_at, 232405_at, 202149_at, 203560_at, 222152_at, 232963_at, 201296_s-at, 222239_s-at, 220189_s-at, 212666_at, 243676_at, 101 2568722_1 (GHMaftem) P71850 AU 201536_at, 239512_at, 219034_at, 213017_at,
218902_at, 206590_x-at, 82.1, 301.3, 74.9, 232746_at, 215769_at, 237068_at, 227807_at, 205495_s-at, 209286_at, 213918_s-at, 215766_at, 233734_s-at, 219577_s-at, 212020_s_at, 202692_s-at, 225170_at, 212543_at, 222662_at, 242863_at, 226484_at, 204789_at, 204438_at, 235536_at, 204398_s_at, 49, 60.1, 55.2, 45.6, 214299_at, 218471_s-at, 210939_s-at, 212550 at, 229587at, 36566_at, 206586_at, 211337_s-at, 210224_at, 212753_at, 221673_s-at, 244372_at, 224009_x_at, 229265_at, 212791_at, 51.8, 213294_at,
204529_s-at, 207391_s-at, 209002_s-at, 202380_s-at, 209448at, 224612_s-at, 212447_at, 202281_at, 204033_at, 209304_x-at, 233690_at, 240342_at, 213146_at, 214163_at, 227772_at, 226100_at, 201548_s_at, 200787_s_at, 226445_s_at, 233185_at, 204254_s_at, 209306_s-at, 38269_at, 203395_s-at, 207687_at, 218062_x-at, 201132_at, 216318_at, 43.2, 86.4, 28.9, 37.2, 222590_s-at, 205116_at, 202205_at, 236899_at, 244268_x_at, 213674_- x_at, 214549 x at, 226599_at, 232486_at, 207386_at, 206781_at, 209295_at,
217552_x_at, 202888_s_at, 221836_s-at, 240255_at, 223787_s-at, 210706_s_at, 219669_at, 230082_at, 238560_at, 239633_at, 239585_at, 101.6, 475, 111.4, 64.8, 36084_at, 225374_at, 222569_at, 55081at, 201929_s-at, 236930_at, 202374_s-at, 224564_s-at, 204495_s-at, 230592_at, 222986_s_at, 227649_s-at, 224390_s-at, 230832_at, 226393_at, 236321_at, 204099at, 233650_at, 204632_at, 228228_at, 218019_s-at, 244377_at, 235286_at, 215399_s-at, 221774_x-at, 218734_at, 205636_at, 209345_s_at, 209179_s_at,
225115_at, 217456_x_at, 212264_s_at, 234.7, 54, 76.9, 243326_at, 235256_s-at, 213567_at, 201835_s-at, 226833_at, 215366_at, 224806_at, 235559_at, 201585_s-at, 232336_at, 212481_s_at, 227244_s_at, 217751_at, 215836_s-at, 209061_at, 212862at, 200646_s_at, 238801_at, 244030_at, 227396_at, 232500_at, 200625_s-at, 240501_at, 242155_x_at, 206672_at, 221895at, 234210_x_at, 237668_at, 202058_s-at, 204859_s-at, 173.4, 143.8, 782.9, 43.3, 57.4, 37.4, 243690_at, 201349_at, 218961_s-at, 202488_s-at,
215621.s-at, 201924_at, 229647_at, 214752_x_at, 212393_at, 218124_at, 213036_x-at, 207095_at, 236461_at, 204232_at, 202822_at, 208200_at, 225032_at, 205269_at, 226333_at, 221856_s_at, 58994_at, 220979_s-at, 217427_sat, 229293_at, 211405_x-at, 205409_at, 238009_at, 219350_s_at, 210293_s-at, 216409_at, 228658_at, 202607_at, 229696_at, 202315_s-at, 221257_x_at, 214933_at, 202855_s_at, 242380_at, 235056_at, 206956_at, 235756_at, 211977_at, 212735_at, 221097_s_at, 214274_s_at, 213524_s-at, 212350_at,
228774_at, 205238_at, 37793-_rat, 225330_at, 222399_s-at, 239259_at, 241944.x-at, 236165_at, 208026_at, 207545_s_at, 31861at, 225210_s_at, 208240_s_at, 231116_at, 207809_s_at, 203689_s_at, 223609_at, 233731_at, 241898_at, 236719_at, 205518_s_at, 210796_x at, 209158_s_at, 233696_at, 211883_x-at, 209882_at, 207701_at, 212588_at, 235872_at, 238428_at, 203132_at, 202901_x_at, 214734_at, 244028_at, 226077_at, 220287_at, 220708_at, 208866_at, 219086_at, 212784_at, 202300at, 218997_at, 201074_at,
219164_s_at, 225502_at, 213605_s_at, 225981_at, 214663_at, 210153_s-at, 237749_at, 235865_at, 208540_x_at, 216243_s-at, 206816_s-at, 210981 _sat, 212672_at, 221957at, 212334_at, 205698_s_at, 222119_s-at, 233168_s-at, 209411 s_at, 201749_at, 208093_s-at, 227697_at, 216397_s_at, 210923_at, 224058_s-at, 239335_at, 227613_at, 201461_s-at, 203307_at, 212704_at, 212659_s_at, 242933_at, 218181_s-at, 236514_at, 243912_x_at, 91816_f at, 212566_at, 235175_at, 225993_at, 216505.x_at, 230143_at, 204007_at,
205400_at, 200083_at, 209970_x_at, 210971_s-at, 213716_s_at, 222650_s-at, 219622_at, 201703_s-at, 205488_at, 219544_at, 205132_at, 231443_at, 223218_s_at, 226377_at, 213851_at, 236001_at, 204908_s-at, 201099_at, 228437_at, 205292_s-at, 222021_x-at, 201087_at, 220140_s_at, 227769_at, 235001_at, 243055_at, 205327_s-at, 210011_s_at, 217033_x_at, 202530_at, 222071_s_at, 236846_at, 244207_at, 218807_at, 206332_s_at, 203892_at, 203315_at, 227527_at, 238638_at, 200814_at, 214198_s-at, 205931_s-at,
230196_x-at, 214398_s_at, 202220_at, 219799_s-at, 212043_at, 234013_at, 206576_s_at, 203278_s_at, 231812_x-at, 240169_at, 229921_at, 230118_at, 223067_at, 225685_at, 223759_s_at, 208996_s-at, 227069_at, 226691_at, 234403_at, 209578_s-at, 201975_at, 242862_x-at, 215557_at, 240565_at, 217614.at, 206936_x-at, 224190_x-at, 203741_s-at, 229548_at, 226542_at, 211366_x_at, 226426_at, 214574_x_at, 220138_at, 202200_s-at, 222634_s-at, 208149_x-at, 226226_at, 232814_x_at, 224099_at, 243612_at,
206431_x-at, 203831_at, 201941_at, 207486.x_at, 217788_s_at, 215435_at, 203055_s_at, 230091_at, 204038_s_at, 207890_s_at, 206930_at, 202239_at, 237568_at, 239561_at, 207435_s_at, 223900_s_at, 202951_at, 215760_s_at, 225899_x_at, 218655_s_at, 200983_x_at, 220079_s_at, 230999_at, 219569_s_at, 217572_at, 218566_s_at, 233315at, 55093_at, 218777_at, 212402_at, 202734_at, 211946_s-at, 201321_s-at, 231106_at, 210166_at, 204445_s-at, 219917_at, 230564_at, 202912_at, 35776_at, 236249_at, 224494_x_at,
208184_s_at, 209829_at, 241588_at, 207181_s_at, 228528_at, 223584_s_at, 214695_at, 206011_at, 235081_x_at, 210506_at, 203789_s_at, 227645_at, 218485_s_at, 226194_at, 207127_sat, 242403_at, 215082_at, 216782_at, 229871at, 210582_s_at, 208624_s_at, 209514_s_at, 224149_x at, 210716_s-at, 226732_at, 223658_at, 208382_s-at, 209303_at, 212476_at, 51774_s-at, 239102_s-at, 205566at, 213629_x_at, 206968_s_at, 208034_s_at, 204071_s_at, 208625_s_at, 221763_at, 224809_x-at, 203419_at, 211945_s_at,
203780_at, 36030_at, 202573_at, 204225_at, 224369_s-at, 221466_at, 230585_at, 233833_at, 211590.x_at, 239893_at, 207554_x_at, 220671_at, 238581_at, 226665_at, 228797_at, 233977at, 213686_at, 206437_at, 201997_s-at, 224570_s_at, 201612_at, 224984.at, 232171 _x_at, 205142_x_at, 216061_x_at, 230224_at, 225262_at, 209791_at, 224831at, 209543.s-at, 201827_at, 218959at, 218473_s-at, 222339_x_at, 52164_at, 223952_x-at, 223130_s_at, 203140_at, 241771_at, 213475_s_at, 217781_s_at, 204425_at, 228008_at,
203578_s_at, 232952_at, 44563_at, 220947_s-at, 214743_at, 227507_at, 213900_at, 216051_x at, 231838_at, 206704_at, 237295_at, 236856_x_at, 218107_at, 44783_s-at, 200799_at, 213909_at, 216502_at, 232629_at, 201964_at, 216773_at, 216049_at, 235923at, 47105_at, 212001_at, 224680_at, 220742_s-at, 218287_s_at, 211864_s-at, 207408_at, 202748_at, 210739_x_at, 202633_at, 220446_sat, 37872_at, 2028_s_at, 38290_at, 39891_at, 44790_s_at, 241831_at, 209310_s_at, 233890_at, 206818_s_at, 234974.at, 242907_at,
240854_x-at, 232725_s-at, 212602_at, 235298_at, 244129_at, 239582at, 211810_s-at, 229521_at, 204446_s_at, 227250at, 225393_at, 242284_at, 237563_s_at, 226160_at, 220320_at, 204533_at, 221767_x_at, 203254_s_at, 232351_at, 200709_at, 213919at, 223785_at, 214866_at, 223132_s-at, 202213_s_at, 214222_at, 230057_at, 221190_s_at, 243515_at, 210191_s_at, 204994_at, 223454_at, 203778.at, 221498_at, 201864_at, 228951at, 228800_x at, 203044_at, 228869_at, 223352_s-at, 205865_at, 222207_x at, 71933_at,
40829_at, 202481at, 215584_at, 231695_at, 212163_at, 215883_at, 212505_s_at, 205313_at, 211251_x_at, 241091_at, 214932_at, 215990_s-at, 225860_at, 229597sat, 209899_s_at, 236825_at, 224707_at, 220712_at, 225633_at, 220740_sat, 201271_s_at, 218010_x_at, 214766_s_at, 233333_x_at, 229228_at, 201063_at, 239277_at, 219065_s_at, 202498_s_at, 212895_s_at, 216903_s_at, 218912_at, 242857_at, 231625_at, 33323_r-at, 214923_at, 238601_at, 228648_at, 222301_at, 201350_at, 224845_s-at, 213501at, 216153.x_at,
221012_s_at, 214377_s_at, 231188_at, 213044_at, 203195_s_at, 202569_sat, 220251_at, 214356_s_at, 36888_at, 201320_at, 229422_at, 202117_at, 225231_at, 213056_at, 219070_s_at, 241973_x_at, 202375_at, 220684_at, 202193_at, 237652_at, 211364_at, 234750_at, 228670_at, 227066_at, 231953_at, 227937at, 224137_at, 217003_s-at, 204370_at, 220739_s-at, 213596_at, 239284_at, 209288_s-at, 212770_at, 203713_s-at, 34221.at, 210449_x_at, 209951_s-at, 202199_s_at, 201454_s_at, 216688at, 200603_at, 235542_at,
238988_at, 220404_at, 241891_at, 230648_at, 211738_x-at, 225824_at, 213513 x at, 201186_at, 204961_s-at, 222167at, 236571 at, 219515_at, 201100_s-at, 203445_s-at, 208918_s_at, 201272_at, 213376_at, 221216_s-at, 228120_at, 231796_at, 209214_s-at, 204022_at, 224991_at, 243046_at, 211582_x-at, 239949_at, 211250_s_at, 212429_s_at, 223160_s_at, 200984_s_at, 224543_at, 202189_x_at, 33768_at, 211367_s_at, 204024_at, 203906_at, 215383_x_at, 204567_s-at, 211509_s-at, 209332_s-at, 210102_at, 215038_s_at,
226579_at, 226474_at, 218682_s-at, 222243_s_at, 214268_s_at, 223562_at, 203935_at, 207071_s_at, 227376_at, 217475_s_at, 203907_s_at, 217787_s-at, 209760_at, 207574_s-at, 225499_at, 226307at, 234761_at, 218803_at, 220319_s_at, 121_at, 226673_at, 222047_s-at, 203241_at, 218047_at, 211113_s_at, 230925_at, 41387_r-at, 202385_s-at, 220964_s-at, 218426_s_at, 217189_s-at, 218104_at, 208966_x-at, 203837_at, 227015_at, 227925_at, 213408_s-at, 242621_at, 204297_at, 240310_at, 221978_at, 222294_s-at,
221156_x_at, 217102at, 215127_s-at, 237442_at, 102 2588722_I (GHMatr) P71850.AU 233510_s_at, 201963_at, 214430_at, 225437_s_at, 218443_s-at, 218797_s-at, 200881_s.at, 205270_s-at, 237338_at, 203087_s-at, 221230_s_at, 23161 1_at, 227651_at, 223637_s_at, 225672_at, 213775.x_at, 202211_at, 223776_x_at, 225878_at, 224076_s_at, 232555_at, 213656_s_at, 211115_.xat, 211962_s-at, 242763_at, 202720_at, 211159_s-at, 203818_s-at, 236297_at, 214972_at, 213448_at, 234661_at, 225359_at, 200958_s-at,
222861_x_at, 201545_s-at, 220603_s-at, 244329_at, 205539_at, 215489_xat, 206170_at, 204668_at, 202160_at, 213191at, 201908_at, 238973_s_at, 213280_at, 211960_s_at, 212301_at, 33322_i-at, 204516_at, 240170_at, 231205_at, 223469_at, 208638_at, 219444_at, 213671_s.at, 212377_s_at, 220244_at, 233303_at, 201680_x_at, 211368_s-at, 205786_s-at, 216323_x_at Table 23- 'Observed v. Expected' table of GO classes and parent classes, in list of 1936 genes shown in Table 22 Cellular Component GO id Observed
in Expected in Observed/ GO classification selected subset selected subset Expected 30176 6 2.52 2.38 integral to endoplasmic reticulum membrane 5923 5 2.12 2.36 tight junction Molecular Function GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected 5149 5 0.9 5.59 intereukin-1 receptor binding 15179 6 1.15 5.21 L-amino acid transporter activity 5242 6 1.15 5.21 inward rectifier potassium channel activity 8656 9 1.79 5.03 caspase activator activity
16505 9 1.92 4.69 apoptotic protease activator activity 16504 9 1.92 4.69 protease activator activity 15645 5 1.15 4.34 fatty-acid ligase activity 15149 5 1.15 4.34 hexose transporter activity 15145 5 1.15 4.34 monosaccharide transporter activity 5355 5 1.15 4.34 glucose transporter activity 4467 5 1.15 4.34 long-chain-fatty-acid-CoA ligase activity 43028 10 2.81 3.55 caspase regulator activity 15101 5 1.41 3.55 organic cation transporter activity 4712 5 1.53 3.26 protein threonine/tyrosine
kinase activity 4708 5 1.53 3.26 MAP kinase kinase activity 8514 7 2.3 3.04 organic anion transporter activity 8017 8 2.69 2.98 microtubule binding 5351 8 2.69 2.98 sugar porter activity 15370 6 2.05 2.93 solute\:sodium symporter activity 15294 7 2.43 2.88 solute\:cation symporter activity 15171 13 4.73 2.75 amino acid transporter activity 5070 14 5.11 2.74 SH3/SH2 adaptor protein activity 5069 14 5.11 2.74 transmembrane receptor protein tyrosine kinase docking protein activity 46943 17 6.39
2.66 carboxylic acid transporter activity 5342 17 6.39 2.66 organic acid transporter activity 15144 8 3.07 2.61 carbohydrate transporter activity 15085 5 1.92 2.61 calcium ion transporter activity 15631 9 3.58 2.51 tubulin binding 5066 15 6.14 2.44 transmembrane receptor protein tyrosine kinase signaling protein activity 103 2568722_ (GHMatrs) P71850AU 5275 13 5.37 2.42 amine transporter activity 15293 12 5.11 2.35 symporter activity 16877 5 2.17 2.3 "ligase activity\, forming carbon-sulfur
bonds" 5244 14 6.52 2.15 voltage-gated ion channel activity 30693 8 3.84 2.09 caspase activity 8094 7 3.45 2.03 DNA-dependent ATPase activity 3704 7 3.45 2.03 specific RNA polymerase 11 transcription factor activity 5249 8 3.96 2.02 voltage-gated potassium channel activity 5267 10 4.99 2.01 potassium channel activity Biological Process GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected 2009 6 1.05 5.74 morphogenesis of an epithelium 15711 7 2.61
2.68 organic anion transport 7596 16 6.27 2.55 blood coagulation 7588 6 2.35 2.55 excretion 50878 18 7.32 2.46 regulation of body fluids 50817 16 6.53 2.45 coagulation 7599 16 6.53 2.45 hemostasis 7398 7 2.87 2.44 ectoderm development 7519 5 2.09 2.39 myogenesis 8544 6 2.61 2.3 epidermis development 7126 8 3.53 2.27 meiosis 7131 5 2.22 2.25 meiotic recombination 8643 7 3.14 2.23 carbohydrate transport 8154 8 3.66 2.19 actin polymerization and/or depolymerization 6865 9 4.18 2.15 amino acid
transport 46942 13 6.14 2.12 carboxylic acid transport 15849 13 6.14 2.12 organic acid transport 9888 11 5.36 2.05 histogenesis 6898 8 3.92 2.04 receptor mediated endocytosis 6816 8 3.92 2.04 calcium ion transport 6400 9 4.44 2.03 tRNA modification 7128 5 2.48 2.01 meiotic prophase 1 7127 5 2.48 2.01 meiosis I 8015 11 5.49 2 circulation Table 24 - Performance of classifiers during cross-validation: Array id Class A B C D E F G label 1 288558_03_24_03 SAD 1352 YES YES YES YES YES YES 2
822340_04_03_03 SAD 1405 YES YES NO NO YES YES 3 191589_03_27_03 SAD 1333 YES YES YES YES YES YES 4 818141_03_05_03 SAD 1344 YES YES YES YES YES YES 104 2568722_1 (GHMatter) P71650 AU 5 620587_04_04_03 SAD 1375 YES YES YES YES YES NO 6 866242_03_05_03 SAD 1389 YES YES YES YES YES YES 7 005192_03_26_03 SAD 1343 YES YES YES YES YES YES 8 178636_06_26_03 SAD 1482 YES YES NO NO YES NO 9 518251_0227_03 SAD 1334 YES YES NO YES YES YES 10 436639_030303 SAD 1289 YES YES YES YES YES YES 11
777617_03_04_03 SAD 1512 YES YES YES YES YES YES 12 851762_03_07_03 SAD 1577 NO NO YES YES YES YES 13 342762_02_24_03 SAD 1370 YES YES YES YES YES YES 14 047313_0522_03 SAD 1410 YES YES YES YES YES YES 15 486801_03_07_03 SAD 1572 NO NO YES YES YES YES 16 589657_06_24_03 SAD 1262 YES YES YES YES YES YES 17 806203_04_16_03 SAD 1393 YES YES YES YES YES YES 18 770482_05_02_03 SAD 1390 YES YES YES YES YES YES 19 103898_05_21_03 SAD 1368 YES YES YES YES YES YES 20 927492_04_03_03 SAD 1467 YES YES YES
YES YES YES 21 708734_06_24_03 SAD 1379 YES YES YES YES YES YES 22 536912_06_24_03 SAD 1426 YES YES YES YES YES YES 23 901069_08_28_03 SAD 1325 YES YES YES YES YES YES 24 763605_05_19_03 SAD 1297 YES YES YES YES YES YES 25 988168_05_21_03 SAD 1483 YES NO YES YES YES NO 26 827495_09_08_03 SAD 1419 YES YES YES YES YES YES 27 203014_0829_03 SNE 1466 NO YES YES YES NO YES 28 310740_04_12_03 SNE 1272 YES YES NO NO NO YES 29 127596_0311_03 SNE 1328 YES YES YES YES YES YES 30 572234_06_04_03 SNE 1661
NO NO NO NO NO NO 31 148161_06_25_03 SNE 1427 YES YES YES YES YES YES 32 086477_04_16_03 SNE 1425 NO NO NO NO NO NO 33 867060_04_16_03 SNE 1303 YES YES YES YES YES YES 34 721312_07_09_03 SNE 1400 YES YES YES YES YES YES 35 050853_08_28_03 SNE 1098 YES YES YES YES YES YES 36 011470_09_10_03 SNE 1453 NO NO NO NO NO NO 37 664013_09_1503 SNE 1252 YES YES YES YES YES YES 38 063961_091003 SNE 1434 NO NO NO NO NO NO 39 11407108_2203 SNE 1266 YES YES YES YES YES YES 40 539852_09_05_03 SNE 1560 NO NO NO
NO NO NO 41 379661090203 SNE 1371 YES YES YES YES YES YES 42 097881_08_26_03 SNE 1535 YES YES YES YES YES YES 43 596752_08_27_03 SNE 1352 YES YES YES YES YES YES Percent correctly 81 81 79 81 84 81 classified: A = number of genes in classifier B = Compound Covariate Predictor Correct? C = Diagonal Linear Discriminant Analysis Correct? 105 2508722_1 (GHMotters) P71850 AU D = 1-Nearest Neighbor Correct? E = 3-Nearest Neighbors Correct? F = Nearest Centroid Correct? G = Support Vector Machines
Correct? Table 25 -Performance of classifiers during cross-validation Performance of the Compound Covariate Predictor Classifier: Class Sensitivity Specificity PPV NPV S_AD 0.923 0.647 0.8 0.846 S_NE 0.647 0.923 0.846 0.8 Performance of the 1-Nearest Neighbor Classifier: Class Sensitivity Specificity PPV NPV S_AD 0.885 0.647 0.793 0.786 S_NE 0.647 0.885 0.786 0.793 Performance of the 3-Nearest Neighbors Classifier: Class Sensitivity Specificity PPV NPV SAD 0.923 0.647 0.8 0.846 S_NE 0.647 0.923
0.846 0.8 Performance of the Nearest Centroid Classifier: Class Sensitivity Specificity PPV NPV S_AD 1 0.588 0.788 1 S_NE 0.588 1 1 0.788 Performance of the Support Vector Machine Classifier: Class Sensitivity Specificity PPV NPV S_AD 0.885 0.706 0.821 0.8 S_NE 0.706 0.885 0.8 0.821 Performance of the Linear Diagonal Discriminant Analysis Classifier: Class Sensitivity Specificity PPV NPV SAD 0.885 0.706 0.821 0.8 S_NE 0.706 0.885 0.8 0.821 Table 26 205227_at, 212760_at, 226382_at, 201941at,
228456_s-at, 225199_at, 224622_at, 212560_at, 217167_x at, 223796_al, 218191_s_at, 219157_at, 201943_ s-at, 242159_at, 234101_at, 237783_at, 224861_at, 225569at, 223609_- at, 218739at, 211612_s-at, 242766_at, 225330_at, 212641_at, 203021 _at, 237516_at, 203433_at, 204169_at, 226726_at, 225197_at, 207785_s-al, 201132_at, 223392_s-at, 200603_at, 227747at, 225639_at, 203126_at, 227524_at, 234965.at, 203561.at, 201599_at, 203194_s_at, 226741al, 242048_at, 223608_at, 227962_at, 233657_at, 453.1,
232229_at, 203266_s_at, 202457_s-at, 234883_.x..at, 243635_at, 237852_at, 235055_x_at, 226872_at, 217.3, 227129_x-at, 226850_at, 215977_x_at, 233168_s_at, 226971_at, 269.9, 218939_at, 241096_at, 232680_at, 223412_at, 226463_at, 232023_at, 222686_s-at, 240973_s-at, 232047_at, 110, 228959_at, 241444_at, 219816_s_at, 235001_at, 515.3, 205021_s-at, 220302_at, 211115_x-at, 209284_s-at, 226954.at, 225065_x-at, 240436_at, 228312at, 242253_at, 211974_x-at, 203232_s-at, 240887_at, 460.2, 244227 at,
222861 x-at, 229699_at, 211475_s_at, 41469_at, 234761_at, 208749_x-at, 235204at, 204334_at, 222496_s-at, 239506_sat, 227197_at, 203509_at, 226391_at, 201940_at, 207691_x_at, 216095_x_at, 224340_at, 201963_at, 200083_at, 202429_s_at, 528.6, 109, 224042_at, 236270_at, 232053_x-at, 240854_x_at, 141.2, 242.5, 226335_at, 226905_at, 224707_at, 232873_at, 810.7, 243095_at, 231981at, 219079_at, 228918_at, 238571_at, 75.6, 223084_s_at, 224809_x_at, 241669_x_at, 212515_s_at, 226454_at, 230231_at,
241948_at, 237231_at, 80, 210142_x_at, 202441_at, 239551_at, 223481_s_at, 235344_at, 625.2, 240793_at, 226050_at, 238739_at, 240391_at, 239094_at, 225899_x_at, 37028_at, 237415_at, 204470_at, 213511_s_at, 239687_at, 227396_at, 203765_at, 209829_at, 224862_at, 212463_at, 220603_s-at, 244449_at, 244401_at, 244186_at, 106 2568722_1 (GHMattm) P71650 AU 209898_x_at, 207922_s-at, 110, 225981_at, 225722_at, 555.4, 230949_at, 201 365_at, 228373_at, 241787_at, 224564_s-at, 202292_x-at, 210724_at,
232761_at, 212549at, 232324_x-at, 218539at, 35.5, 236522_at, 212063_at, 241881_at, 233510_s-at, 240727_sat, 225830_at, 208781_x_at, 233544_at, 229460_at, 237099.at, 229914_at, 230212_at, 225359_at, 224647_at, 229573_at, 228220_at, 219434_at, 178, 224084_at, 227376_at, 200958_s_at, 293.9, 243045_at, 241715_x_at, 213952_s-at, 157.2, 221430_s_at, 211998_at, 223342_at, 209931_s_at, 229247_at, 209207_s_at, 217739_s_at, 241805_at, 239242_at, 200605_s-at, 230769_at, 96.1, 228797_at, 244520_at,
208735_s_at, 229638_at, 220486_x_at, 228924_s-at, 218872_at, 232336_at, 241627_xat, 208734_x-at, 208.7, 209118_s-at, 225597_at, 225313_at, 241000_at, 234323at, 225672_at, 221477_s-at, 238863_x-at, 201635_s_at, 2014.4, 238476_at, 130.1, 219770_at, 217473_x_at, 224425.x_at, 233469_at, 225678_at, 58780_s-at, 231796_at, 231406_at, 227452_at, 229828_at, 210780_at, 243300_at, 388.6, 229964_at, 201972_at, 222852_at, 227920_at, 200067_x-at, 231251_at, 224763_at, 228813_at, 237322_at, 234434_at,
237295_at, 109.2, 202101_s-at, 209992_at, 216316_x_at, 241631_at, 230421_at, 220326_s-at, 229373_at, 217432_s-at, 223454at, 225416at, 235446_at, 232340_at, 74.1, 235222_x-at, 226357_at, 209369_at, 80.9, 242.3, 202764_at, 223511_at, 244329_at, 219281_at, 226362_at, 244875_at, 211938_at. 241430_at, 229635_at, 241432_at, 236298_at, 233451_at, 221218_s-at, 240031at, 236345_at, 213748_at, 233538_s-at, 204225_at, 230208_at, 213241at, 239156_at, 242362_at, 201874_at, 209105_at, 225437_s-at, 239284_at,
239536_at, 233919_s-at, 236927_at, 241947_at, 215966_x_at, 239883_s_at, 242666_at, 202333_s_at, 212217_at, 240950_s-at, 232059_at, 241262_at, 123.3, 228034_x_at, 242188_at, 226364_at, 203906_at, 240169_at, 243507_s-at, 225633_at, 225612_s_at, 243078_at, 122.9, 200961_at, 225262_at, 226461_at, 236574_at, 243314_at, 226927_at, 225069_at, 236129_at, 214428_x_at, 99.3, 225598_at, 232.7, 240175_at, 224760_at, 202300_at, 23401 1_at, 227616_at, 235473at, 211702_s-at, 233696_at, 240130_at, 230572.at,
224817_at, 58367_s-at, 216841_s-at, 202187_s-at, 210904_s-at, 242207_at, 210504_at, 222429at, 213501_at, 231051at, 54.4, 202211_at, 217930_s_at, 228648_at, 236354at, 226009_at, 207387_s-at, 50.9, 238035_at, 224137_at, 236297_at, 226518at, 238191_at, 201838_s_al, 230108_at, 239395_at, 248.3, 240540_at, 242765_at, 225914_s-at, 225754_at, 212473_s-at, 207536_s-at, 241231_at, 227185_at, 208093_s_at, 234671_-at, 243424_at, 227475_at, 112.4, 228492_at, 213405_at, 440.2, 243904_at, 238324_at,
227547_at, 220758_s_at, 243039_at, 26.1, 239322at, 231057_at, 225183_at, 202948_at, 242819_at, 228006_at, 224049_at, 227929_at, 228636_at, 240319_at, 242937_at, 202059_s_at, 217503_at, 248.2, 40569_at, 222339_x_at, 243912_x_at, 231683_at, 209157_at, 225987_at, 222421_at, 217379_at, 212987_at, 225343_at, 240835_at, 240919_at, 229770_at, 235110_at, 225339_at, 44783_s_at, 225573_at, 40.9, 232667_at, 226163_at, 62.2, 214969_at, 243055_at, 244738_at, 224088_at, 238929_at, 244637_at, 209758_s-at,
216236_s-at, 234393_at, 218660_at, 234936_s-at, 238816_at, 239631at, 221957_at, 229800_at, 206718_at, 210716_s-at, 240162_at, 226012_at, 202387_at, 54.5, 218583_s-at, 217682_at, 47.5, 276.5, 228.1, 201350_at, 202934_at, 233520_s-at, 230533_at, 218388_at, 218078_s_at, 232084_at, 223469at, 207624_s-at, 243453_at, 101.8, 202497_x-at, 205586_x_at, 224621_at, 217144_at, 229766_at, 238895_at, 207275_s-at, 230839_at, 241217_x at, 239975_at, 204646_at, 223110_.at, 228240_at, 237025_at, 201065_s-at,
218132_s_at, 216739_at, 200985_s at, 239260_at, 226297_at, 237338_at, 241700_at, 226064_s_at, 201974_s_at, 226959_at, 226957_x-at, 236388_at, 234611 at, 222746_s-at, 222982_xat, 235210_s_at, 224831_at, 239678_at, 196.1, 98.4, 219975_x_at, 241319_at, 15.8, 226761_at, 225776_at, 240188_at, 53.8, 236479_at, 225358_at, 200997_at, 235697_at, 64883_at, 210284_s-at, 226579_at, 232400_at, 243053.x-at, 224054_at, 226002_at, 224099_at, 24341 1_at, 242621_at, 243343_at, 243122_at, 231106_at, 200983.x-at,
224907_s_at, 242701 at, 240991 at, 200011 _s_at, 238757_at, 238145_at, 227138_at, 201604_s-at, 127, 219594_at, 241907_at, 59433_at, 206420_at, 230450_at, 234870_at, 226589_at, 81.5, 223816_at, 200729_s-at, 55.4, 230376_at, 233650_at, 224712_x-at, 200728at, 227566_at, 244394_at, 233541_at, 242092 at, 223159_s_at, 236283_x at, 218829_s-at, 236461.at, 229675_at, 1588, 202974_at, 227073_at, 231770_x at, 228226_s_at, 82.5, 224026_at, 231756_at, 233339_s-at, 213572_s-at, 220908_at, 225616_at,
39548_at, 225282_at, 201942_s_at, 205026at, 230850_at, 240495_at, 201887_at, 200919_at, 232419_at, 228958_at, 222166_at, 236825_at, 210771_at, 223740_at, 133.6, 213608_s-at, 217142_at, 221381_s_at, 204714_s_at, 226828_s_at, 209497_s-at, 229845_at, 206956_at, 213370_s-at, 230901_x_at, 200669_s-at, 244076_at, 224727_at, 205575_at, 240765_at, 87.1, 229421_s_at, 216453_at, 207793_s-at, 223234_at, 235808_at, 213510_x_at, 227624_at, 233197_at, 213264_at, 284.3, 240342_at, 210186_s_at, 212221_x_at,
239726_at, 217747_s_at, 234486_at, 215688_at, 229256_at, 219549_s-at, 209414_at, 205080_at, 243284_at, 204490_s_at, 234727_at, 201444_s-at, 239263_at, 242535_at, 200727_s-at, 230936_at, 238923 at, 236569.at, 238717_at, 210169_at, 233008_at, 223141 _at, 72.5, 237274_at, 219125_s_at, 232629_at, 219449_s_at, 212683_at, 223306_at, 234836_at, 202711_at, 244419_at, 223075_sat, 221367_at, 224390_s_at, 233757_x_at, 236300_at, 201581_at, 206850_at, 226786_at, 223785_at, 115.8, 232471_at, 249.5,
243485_at, 233381at, 217873_at, 229746_x_at, 200791_s_at, 221497_x at, 237046_x_at, 201052_s_at, 231159_at, 215127_s_at, 234403_at, 238127_at, 230500_at, 234848_at, 238447_at, 235051_at, 230288_at, 224915_x-at, 244700_at, 202057_at, 242053_at, 235483_at, 230228_at, 238296_at, 241588_at, 242533_at, 220671at, 111.7, 57.1, 234624_at, 236719_at, 216371_at, 220596_at, 236806_at, 244322_at, 37384_at, 228247_at, 215497_s_at, 233887_at, 244328_x-at, 241831at, 220045_at, 211763_s-at, 233196_at,
215899_at, 219892_at, 238888_at, 229293_at, 241222_at, 207554_x_at, 96.2, 234534_at, 232706_s_at, 225289_at, 236743.at, 238533_at, 208721_s-at, 239031_at, 223288_at, 223259_at, 242692_at, 223432_at, 231172_at, 231629_x-at, 219630_at, 236961_at, 226392_at, 229410_at, 224681_at, 223617_x at, 219645_at, 237652_at, 243941at, 236750_at, 241859at, 242046_at, 221060_s_at, 206656_s_at, 243326_at, 226266_at, 241329_s-at, 225855_at, 201686_x_at, 218.2, 214455at, 231166_at, 207765_s-at, 225993_at,
202299_s-at, 225024_at, 241596_at, 71.4, 212470_at, 226481 at, 646.1, 238124_at, 218763_at, 226016_at, 37145_at, 226184_at, 242405_at, 211699_x_at, 200887_s_at, 203556_at, 202114_at, 228788_at, 140.8, 222630_at, 239243_at, 242688at, 218147_s-at, 218057_x_at, 214659_x at, 206555_s-at, 221081_s-at, 212706_at, 202760_s-at, 201587_s-at, 211745-x-at, 266.8, 243908_at, 201037_at, 212783_at, 213279_at, 204670_x_at, 241891_at, 226763_at, 230918_at, 236922_at, 227.8, 221517_s-at, 237104_at, 218598_at,
222077_s-at, 202503_s-at, 227139_s_at, 212873_at, 229804_x-at, 202688_at, 24.3, 200959_at, 235767_x_at, 213294_at, 212657_s-at, 206082_at, 235041_at, 226085_at, 51.6, 218298_s-at, 229625_at, 204197_s_at, 212198_s_at, 214551_s_at, 202759_s-at, 203610_s-at, 208112_x-at, 235457_at, 75.5, 203773_x at, 215758.x_at, 201960_s_at, 227228_s_at, 20, 235005_at, 200680_x_at, 213002_at, 239409_at, 202880_s-at, 57.8, 240659_x_at, 208021_s_at, 209609_s-at, 218747s-at, 236524_at, 238303_at, 235346_at,
222838_at, 210949_s-at, 228426_at, 201729_s-at, 201163_s_at, 213366_x..at, 226801_s-at, 230748_at, 226318_at, 235927_at, 227150_at, 228157_at, 229686_at, 201360_at, 207513_sat, 232356at, 212835_at, 220646_s_at, 223244.s-at, 212967_x-at, 673.1, 202208_s_at, 218059_at, 52078_at, 208905_at, 243612_at, 201123_sat, 226334_s_at, 47.4, 203322_at, 200990_at, 207081_s.at, 2692.7, 226867_at, 201144_s-at, 203052_at, 200073_s-at, 203137_at, 217751_at, 211932_at, 226694_at, 221203_s-at, 210276_s_at, 64.2,
222691at, 200826_at, 205644_s_at, 218669_at, 238430_x-at, 207628_s-at, 209417_s-at, 214715_x at, 224779_s-at, 234762_x-at, 224797_at, 208685_x_at, 208758_at, 217870_sat, 229390_at, 219678_x_at, 202032_s-at, 60.9, 208722_s-at, 202984_s-at, 209259_s-at, 239179_at, 221653_x-at, 60471_at, 230314_at, 219001_s-at, 44.9, 219030_at, 201920_at, 223053.xat, 17.1, 205237_at, 220146_at, 224607_s_at, 221698_s-at, 222728_s_at, 55692_at, 218943_s_at, 214909_s-at, 226193_x-at, 215691_x_at, 225105_at, 202039at,
227002_at, 221637_s_at, 204745_x-at, 200818_at, 205495_s-at, 207812_s-at, 212076_at, 203839_s-at, 225534_at, 235983_at, 243561_at, 205436_s_at, 219433_at, 221505_at, 234341_x-at, 235306_at, 202270_at, 201471_s.at, 200978_at, 225026_at, 216559_x_at, 209341_s-at, 237426_at, 20951 1at, 211358_s_at, 36552_at, 207777_s_at, 213246_at, 200747_s-at, 221046_s-at, 202269_x-at, 202907_s-at, 217963_s-at, 218288_s_at, 203663_s_at, 209165_at, 200792_at, 240326_at, 224973_at, 238880_at, 212952_at, 218999_at,
201934_at, 231747_at, 213073_at, 223092_at, 107 2568722_1 (GHMaltors) P71850 AU 222715_s_at, 209330_s_at, 223097_at, 225344at, 203567_s-at, 235061_at, 236065_at, 213104_at, 214875_x-at, 200043at, 55081at, 206133_at, 243788_at, 216241_s-at, 112.3, 15.2, 219221_at, 229123_at, 228273_at, 213726.x_at, 211729_x_at, 200758_s_at, 200681_at, 231577_s_at, 207697_x_at, 618.5, 224665_at, 222398_s_at, 826.8, 204415at, 219392_xat, 228762_at, 201959_s_at, 225971_at, 212345_s_at, 217885_at, 224687_at,
200000_s_at, 202085_at, 226725_at, 229530_at, 56256_at, 227802_at, 225646_al, 223220_s_at, 175.8, 229350_x_at, 235359_at, 200686_s-at, 218085_at, 202534_x-at, 214329_x-at, 205992_s-at, 212613_at, 212687_at, 221923_s-at, 222233_s-at, 200943_at, 200743_s-at, 82, 240600_at, 209716_at, 228030_at, 241223_x-at, 211921_x-at, 37462_i-at, 212414_s_at, 201890_at, 231769_at, 236583_at, 222036_s-at, 201369_s-at, 93.4, 219147_s-at, 234970_at, 22341 1at, 204028_s_at, 222770.s-at, 209486_at, 212188_at,
237400_at, 1294_at, 217371_s_at, 209787_s-at, 200820at, 212929_s_at, 240282_at, 200742_s-at, 207730_x-at, 216187_x..at, 219467_at, 212036_s-at, 208087_s_at, 222477_s-at, 250.1, 235482_at, 219358_s.at, 212416_at, 202973_x_at, 212519_at, 212251_at, 235084_x-at, 225055.at, 225931_s_at, 228812_at, 204386_s-at, 209665_at, 224906_at, 157.7, 235023_at, 225353_s-at, 34689_at, 207508_at, 221918_at, 203572_s-at, 219534_x_at, 225663_at, 222428_s-at, 209846_s_at, 212341_at, 211714_x_at, 221786_at,
217933_s-at, 218039_at, 221829_s-at, 209762_.x at, 214196_s_at, 217729_s-at, 229872_s-at, 222756_s-at, 223679.at, 208691_at, 214594_x-at, 201469_s-at, 232312_at, 212959_s-at, 229510_at, 208926_at, 203345_s-at, 201542_at, 222217_s_at, 217957_at, 225929_s-at, 208893_s-at, 96.5, 214617 at, 216859_x-at, 200893_at, 389.7, 195.8, 233929_x-at, 213915_at, 219762_s-at, 200980_s-at, 37.5, 226743_at, 203594_at, 202567_at, 229138_at, 224885_s_at, 214170_x_at, 222244_s-at, 215343_at, 203505at, 242020_s-at,
211015_s-at, 204820_s-at, 215136_s_at, 207856_s_at, 204806_x_at, 81.8, 207614_s_at, 217502at, 244592_at, 223988_x_at, 241940_at, 235199_at, 220942_x_at, 201477_s_at, 201646_at, 229937_x_at, 206074_s_at, 210418_s-at, 224946_s_at, 242234_at, 217.8, 202113_s-at, 211762_s-at, 238549_at, 38069_at, 200960_x-at, 213183_s_at, 180.9, 205022_s-at, 219013_at, 39248_at, 201503_at, 202042_at, 202451_at, 231956_at, 201697_s-at, 224345_x-at, 212681_at, 231989_s-at, 200069_at, 200028_s-at, 227609at, 212647_at,
209054_s-at, 200986_at, 213399_x-at, 64.4, 202591_s_at, 240128_at, 223344_s_at, 212426_s_at, 221488_s_at, 213048_s_at, 225083_at, 216.9, 212320_at, 217768_at, 208693_s_at, 203596_s_at, 586.2, 139.1, 243993._at, 208887_at, 634.1, 209049_s_at, 204912_at, 212296_at, 230733_at, 230052_s-at, 224736_at, 212733_at, 215602_at, 244753_at, 232829_at, 213699_s-at, 210962_s_at, 235256_s_at, 211012_s_at, 219373_at, 225176_at, 211284_s-at, 238883_at, 225447_at, 229450_at, 228487_s-at, 349.6, 203258.at,
242059_at, 222282_at, 460.5, 234974_at, 200610_s-at, 200061_s-at, 211378_x-at, 213738_s_at, 212910_at, 222154_s-at, 202702.at, 212048_s-at, 236561_at, 217805_at, 229367_s-at, 208890_s-at, 226702_at, 207761_s-at, 230233_at, 201129.at, 223098_s-at, 219863_at, 225917_at, 200063_s_at, 212943_at, 201565_s_at, 231578_at, 215063.xat, 228055_at, 206553_at, 230405_at, 239979_at, 240452_at, 102.6, 217408_at, 236156_at, 201781_s at, 209457_at, 224569_s_at, 220954_s-at, 224983at, 200020_at, 202367_at,
202107_s-at, 242558_at, 219014_at, 225035_x_at, 218927_s-at, 210046_s-at, 203573_s-at, 238712_at, 74.3, 241.6, 210657_s-at, 204198_s-at, 237868_x-at, 200885_at, 169.8, 216035_x_at, 212859_x_at, 218639_s-at, 202325_s-at, 656.4, 229813_x-at, 201624_at, 207507_s-at, 200069_at, 216614_at, 243763_x-at, 212380_at, 200066_at, 221767 x at, 217877_s_at, 200678_xat, 206513_at, 219684_at, 227040_at, 213982_s_at, 223193_x-at, 214141_x_at, 218543_s_at, 16.3, 202041_s-at, 209045_at, 210146_x_at, 209682_at,
200063_s-at, 214938_x_at, 206055_s-at, 105.6, 208819at, 200750_s-at, 201216_at, 203104_at, 208886_at, 214059 at, 228159_at, 241742_at, 230036_at, 228531_at, 221875_x_at, 242201_at, 221766_sat, 348.7, 216237_s_at, 217317_s-at, 212137_at, 219696_at, 218986_s-at, 208683_at, 217526_at, 224705_s-at, 209610_s-at, 202644_s-at, 210797_s-at, 225321_s_at, 201164_s-at, 216041_x-at, 218400_at, 235879_at, 229434_at, 211967_at, 213619_at, 235276_at, 201088_at, 203037_s-at, 219209_at, 201306_s-at, 204805_s-at,
203148_s-at, 213309_at, 212185_x-at, 218232at, 202864_s_at, 209969_s-at, 230753_at, 218376_s-at, 44673.at, 219505_at, 222392_x-at, 216336_x-at, 213797_at, 210164_at, 213491_x_at, 38241_at, 205660.at, 202589_at, 228617_at, 244414_at, 201930_at, 202771_at, 202869_at, 212761.at, 201166_sat, 204972_at, 211456_x-at, 204439_at, 205552_s-at, 204326_x_at, 218611_at, 218061_at, 224428_s_at, 242625_at, 209786_at, 205898at, 209773_s-at, 202086at, 203153_at, 205241 at, 213313_at, 208581_x-at, 219211_at,
219691_at, 235985_at, 205483_s-at, 213348_at, 213620_s-at, 243271_at, 201202_at, 219519-s-at, 221776_s-at, 226093_at, 219062_s_at, 222592_s_at, 213773_x_at, 219777at, 218548_x_at, 214453_s_at, 223096_at, 233425_at, 200923_at, 223343_at, 202145_at, 204821 at, 202411 _at Table 27 - 'Observed v. Expected' table of GO classes and parent classes, in list of 1429 genes shown in Table 26 Cellular Component GO id Observed in Expected in Observed/ GO classification selected subset selected subset
Expected 30532 6 1.6 3.75 small nuclear ribonucleoprotein complex 5741 7 2.58 2.72 mitochondrial outer membrane 19867 10 4.45 2.25 outer membrane 16469 7 3.11 2.25 proton-transporting two-sector ATPase complex Molecular Function GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected 108 2568722_1 (GHMatters) P71850.AU 4370 5 0.54 9.34 glycerol kinase activity 8094 7 2.41 2.91 DNA-dependent ATPase activity 19838 7 2.5 2.8 growth factor binding 5507 5
1.96 2.55 copper ion binding 4860 5 1.96 2.55 protein kinase inhibitor activity 4896 10 4.01 2.49 hematopoetin/interferon-class (D200-domain) cytokine receptor activity 8236 14 5.71 2.45 serine-type peptidase activity 8139 5 2.05 2.44 nuclear localization sequence binding 4907 5 2.05 2.44 interleukin receptor activity 19965 5 2.14 2.34 interleukin binding 19210 5 2.14 2.34 kinase inhibitor activity 16886 8 3.48 2.3 'ligase activity\, forming phosphoric ester bonds' 46961 7 3.12 2.24
*hydrogen-transporting ATPase activity\, rotational mechanism' 16627 7 3.12 2.24 'oxidoreductase activity\, acting on the CH-CH group of donors' 4252 10 4.55 2.2 serine-type endopeptidase activity 19887 8 3.66 2.19 protein kinase regulator activity 16876 7 3.3 2.12 'ligase activity\, forming aminoacyl-tRNA and related compounds' 16875 7 3.3 2.12 'ligase activity\, forming carbon-oxygen bonds' 8452 7 3.3 2.12 RNA ligase activity 4812 7 3.3 2.12 tRNA ligase activity 46933 6 2.85 2.1
"hydrogen-transporting ATP synthase activity\, rotational mechanism" 19207 8 3.83 2.09 kinase regulator activity Biological Process GO id Observed in Expected in Observed/ GO classification selected subset selected subset Expected 6072 6 0.62 9.61 glycerol-3-phosphate metabolism 46486 8 1.16 6.9 glycerolipid metabolism 6662 8 1.16 6.9 glycerol ether metabolism 6641 8 1.16 6.9 triacylglycerol metabolism 6639 8 1.16 6.9 acylglycerol metabolism 6638 8 1.16 6.9 neutral lipid metabolism 19751 6 0.98
6.11 polyol metabolism 6071 6 0.98 6.11 glycerol metabolism 6958 6 1.96 3.06 "complement activation\, classical pathway' 7004 5 1.7 2.95 telomerase-dependent telomere maintenance 723 5 1.7 2.95 telomere maintenance 7259 7 2.41 2.91 JAK-STAT cascade 6956 6 2.14 2.8 complement activation 30203 6 2.32 2.59 glycosaminoglycan metabolism 6261 12 4.73 2.54 DNA-dependent DNA replication 43039 7 2.77 2.53 tRNA aminoacylation 43038 7 2.77 2.53 amino acid activation 6418 7 2.77 2.53 tRNA aminoacylation for
protein translation 6022 6 2.41 2.49 aminoglycan metabolism 109 2500722_1 (GHMatters)P71850.AU 6400 7 3.03 2.31 tRNA modification 86 7 3.03 2.31 G2/M transition of mitotic cell cycle 6310 9 4.1 2.19 DNA recombination 15986 7 3.21 2.18 ATP synthesis coupled proton transport 15985 7 3.21 2.18 "energy coupled proton transport\, down electrochemical gradient' 18193 5 2.32 2.16 peptidyl-amino acid modification 6260 20 9.28 2.16 DNA replication 79 5 2.32 2.16 regulation of cyclin dependent protein
kinase activity 7610 6 2.86 2.1 behavior 51052 6 2.94 2.04 regulation of DNA metabolism 45893 6 2.94 2.04 "positive regulation of transcription\, DNA-dependent' 6607 5 2.5 2 NLS-bearing substrate-nucleus import Table 28 227458_at, 203276_at, 210592_s_at, 202446_s-at, 226459_at, 216950_s_at, 214511_x_at, 223502_s_at, 218280_x_at, 202430_s-at, 214329_x_at, 203455_s_at, 223501_at, 214290_s_at, 226117_at, 211368_s_at, 219014_at, 202912_at, 223220_sat, 201601_x_at, 208966_x_at, 213988_s_at,
209369_at, 209498_at, 221492_s-at, 205896_at, 206332_s-at, 226603_at, 202687_s-at, 202688_at, 203964_at, 202708_s_at, 210166_at, 223834_at, 212268_at, 211275_s-at, 200985_s_at, 211883_x_at, 227266_s-at, 213293_s_at, 209970_x-at, 206011_-at, 200673_at, 201061_s_at, 205098_at, 208012_x-at, 214022_s-at, 206025_s-at, 238439_at, 211367_s-at, 207574_s-at, 204224_s-at, 208959_s_at, 227609_at, 208436_s-at, 230585_at, 202307_s_at, 209762 x at, 212657_s-at, 225251_at, 200986_at, 205067_at, 211366_x.at,
202193_at, 224414_s_at, 230036_at, 238025_at, 217933_s-at, 217995_at, 201924_at, 39402_at, 204232_at, 235568_at, 201318_s-at, 210101_x-at, 204526_s-at, 209417_s-at, 231769_at, 226353_at, 205241_at, 222154_s_at, 242907_at, 201554_x-at, 206026_s-at, 201060_x-at, 202748_at, 206765_at, 239196_at, 209304_x_at, 209091_s_at, 200983_x_at, 214150_x_at, 205552_s_at, 201649_at, 213361_at, 212335_at, 223993_s_at, 225622_at, 208659_at, 117_at, 204780_s-at, 228152_s-at, 219938_s_at, 204068_at, 201296_s_at,
202269_x_at, 218282_at, 218383_at, 235670_at, 212807_s-at, 218809_at, 217167_x.at, 233375_at, 218999_at, 217807_s-at, 202270_at, 211666_x-at, 209310_s-at, 231577_s-at, 204747_at, 224707at, 217883_at, 235508at, 224917_at, 225940_at, 201193_at, 200096_s-at, 201921_at, 224009_x-at, 200965_s_at, 200663_at, 210582_s-at, 237563_s-at, 222859_s-at, 204781_s-at, 201470_at, 201761_at, 226905at, 228439at, 203567_s-at, 218943_s_at, 226272_at, 229450_at, 201217_x_at, 221345_at, 241916_at, 200701_at,
219806_s-at, 202087_s_at, 229521at, 201760_s-at, 219669_at, 233540_s_at, 204502_at, 223952_xat, 206637at, 227014_at, 230370_x-at, 218728_s-at, 205842_s-at, 209069_s-at, 217823_s-at, 222670_s_at, 201999_s_at, 55692_at, 217769_s-at, 233632_s-at, 203127_s-at, 224983at, 227697_at, 203595_s_at, 225783at, 208392_xat, 203922_s_at, 211764_s.at, 207551_s-at, 225076_s-at, 242625_at, 225636_at, 207500_at, 210449_x-at, 223880_x-at, 221602_s_at, 206978_at, 226757_at, 218559_s-at, 200734_s_at, 206584_at,
209040_s-at, 228306_at, 235514_at, 202530at, 226354_at, 200094_s-at, 221816_s-at, 203143_s_at, 219209_at, 203413_at, 212203_x_at, 200615_s_at, 221528_s-at, 225931_s-at, 207181_s_at, 200096_s_at, 208653_s_at, 218986_s_at, 229937_x at, 217835_x at, 208965_s-at, 205781_at, 201422_at, 226702_at, 214590_s-at, 209451at, 204279 at, 231513_at, 213294_at, 218323_at, 202907_s-at, 217738_at, 221485_at, 229625_at, 224374_s_at, 226416_at, 217826_s-at, 214453_s_at, 243271_at, 228869at, 211561_ xat, 224579at,
215884_s_at, 211067_s-at, 221827_at, 238581at, 208639.x_at, 204249_s_at, 226968_at, 229968_at, 219622_at, 218130_at, 225415_at, 217502_at, 212185_x-at, 224701at, 209004_s-at, 210784_.x at, 200094_s_at, 200782_at, 200644_at, 207104_x_at, 221653_x_at, 211999_at, 221641_s_at, 219394_at, 225095_at, 203535_at, 201762_s-at, 208724_s-at, 202100_at, 212605_s-at, 204860_s_at, 211073_x-at, 223980_s-at, 211012_s_at, 210190at, 210648_x-at, 224833_at, 206513_at, 213797_at, 218404_at, 211075_s-at,
224656_s_at, 230741.at, 228617_at, 224756_s-at, 201798_s-at, 224604_at, 223376_s-at, 216252_.x_at, 208405_s-at, 210140_at, 222986_s-at, 203397_s-at, 208974_x-at, 217733_s-at, 225344_at, 218465_at, 202464_s-at, 202917_s-at, 217986_s-at, 201641_at, 201172_xat, 209134_s-at, 233982_x-at, 235971_at, 225032_at, 213418_at, 208975_s_at, 204415_at, 206710_s_at, 229285_at, 235276_at, 205936_s-at, 209276_s_at, 203234at, 211889_x-at, 209124_at, 202531.at, 219684_at, 204972_at, 213734_at, 217898at,
212191_x-at, 205698_s-at, 209933_s_at, 200079_s-at, 219863_at, 205569_at, 226155_at, 206565_x at, 238858_at, 210915.x_at, 200668_s_at, 222410_s_at, 203897_at, 206576_s_at, 227925_at, 226748_at, 203233at, 202506_at, 210224_at, 205660_at, 208901_s-at, 203610_s-at, 219202_at, 207157_s-at, 201537_s_at, 200629at, 213716_s-at, 204211_x-at, 204804_at, 203471_s-at, 223218_s-at, 202864_s_at, 225814_at, 202863_at, 207387_s-at, 220419_s_at, 204689_at, 212658_at, 204554_at, 208929_x-at, 226406_at,
209575_at, 207777_s-at, 211864_s_at, 225353_s-at, 205483_s-at, 227261_at, 220330_s_at, 201254_x-at, 228726_at, 211509_s_at, 217762_s_at, 226276_at, 217752_s-at, 235306at, 224602_at, 218400_at, 217388_s_at, 205191_at, 200996_at, 209969_s-at, 208933_s-at, 215043_s-at, 201786_s-at, 235529_x_at 202200_s-at, 204994_at, 232353_s_at, 202874_s-at, 205220_at, 203616_at, 201647_s-at, 218618_s-at, 200725_x_at, 228607_at, 238725_at, 200798x at, 212014_x_at, 200620_at, 205992_s-at, 204439_at, 228531_at,
230405_at, 57082_at, 200984_s-at, 224376_s-at, 222435_s_at, 225929_s-at, 241869at, 209911_x_at, 208865_at, 215719_x_at, 229390_at, 204861_s-at, 200863_s-at, 216841_s-at, 212334_at, 222845_x_at, 205715_at, 218334_at, 204891_s_at, 222555_s_at, 202625at, 222662_at, 207072_at, 210119_at, 210225_x-at, 207275_s_at, 204777_s-at, 212734_x-at, 219403_s-at, 236156_at, 204490_s-at, 229560_at, 230078_at, 211796_s-at, 209806_at, 221476_s-at, 203923_s-at, 222512_at, 222793_at, 211806_s_at, 223204_at,
203561_at, 229194_at, 236295_s-at, 235740_at, 237006_at, 202833_s-at, 227354_at, 212112.s-at, 200667_at, 203278_s_at, 244050_at, 211997_x.at, 205003_at, 217846at, 212463_at, 204929_s-at, 219607_s-at, 227066_at, 208864_s_at, 217118_s_at, 204834_at, 203574_at, 219528_s-at, 242020_s-at, 235518_at, 203371_s_at, 212039_x-at, 202869_at, 210797_s-at, 229138_at, 227856_at, 203773_x at, 201180_s_at, 219690_at, 209868_s_at, 210789_x_at, 225919_s_at, 220603_s_at, 217475_s_at, 200881_s_at, 216565_x_at,
204019_s_at, 224800_at, 221601_s-at, 204858_s-at, 243934_at, 225878at, 233587_s-at, 35254at, 213572_s-at, 31845_at, 211729_.x -at, 227889_at, 225850_at, 212380_at, 208374_s_at, 200887_s-at, 201336_at, 219062_s-at, 223599_at, 201531_at, 225059_at, 203420at, 33304_at, 210176_at, 206662_at, 214059_at, 212862_at, 208881_x_at, 228437_at, 204800_s_at, 243196_s_at, 226218_at, 218773_s-at, 206618_at, 200649_at, 239979_at, 218543_s-at, 231956_at, 219055_at, 208527_x-at, 210427_x-at, 209835_x-at,
221558_s-at, 208749.-xat, 225414_at, 215933_s_at, 223280_x_at, 209546_s_at, 219691_at, 221680_s-at, 205456_at, 205681_at, 235175_at, 223767_at, 200661_at, 208654_s_at, 20B857_s_at, 110 2588722_1 (GHMatters) P71850 AU 212737_at, 227983_at, 205170at, 233591_at, 205269_at, 224967_at, 200974at, 211336.x_at, 200961_at, 225056_at, 223145_s.at, 217764_s_at, 225710_at, 208988_at, 211135_x_at, 201132_at, 225787_at, 219290_x_at, 213507_s-at, 214681_at, 223591_at, 217969_at, 205480_s_at, 229391_s-at,
205016_at, 235286_at, 200650_s-at, 217739_s-at, 222980_at, 201098at, 2011739at, 219757_s-at, 224356_x-at, 210561_s_at, 207697_x-at, 224806at, 207565_s-at, 218153_at, 203140_at, 205568_at, 222895_s_at, 208935_s-at, 201400_at, 228234_at, 209944_at, 200067_x-at, 213574_s-at, 201995_at, 202086_at, 201858_s-at, 202901_x-at, 201200_at, 230795_at, 202277at, 204118_at, 209593_s_at, 203596_s-at, 208771_s-at, 201222_s-at, 232629_at, 200669_s-at, 203416_at, 208736_at, 21171 is_at, 216243_s-at, 201963_at,
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202787_s-at, 215730_at, 225350_s_at, 235408_x-at, 215600_x_at, 238711_s_at, 227639_at, 237896_at, 218273_s-at, 205884at, 242458_at, 230179_at, 222437_s-at, 203560_at, 229810_at, 204091at, 238235_at, 224058_s-at, 209833_at, 243240_at, 201772_at, 209305_s_at, 201338_x_at, 37793_r_at, 218866_s_at, 218306_s_at, 206209_s_at, 226140_s_at, 202566_s-at, 203313_s-at, 0.825, 211675_s_at, 200593_s_at, 36554_at, 31807_at, 20571 1x.at, 219774_at, 212276_at, 229293_at, 215645_at, 213348_at, 201225_s_at,
243717_at, 242147_at, 238075_at, 210588_x_at, 200686_s-at, 223048_at, 213332_at, 212208_at, 224824_at, 225002_s-at, 209333_at, 225528_at, 203845_at, 226387_at, 238972_at, 211536_x-at, 202093_s_at, 210999_s-at, 201498at, 215959_at, 209723at, 239600_at, 210731_s-at, 207445_s_at, 208942_s_at, 204658_at, 233808at, 212581_x-at, 203135_at, 223504_at, 224669_at, 214807_at, 235959_at, 203120_at, 203956_at, 207205_at, 214748_at, 226348_at, 243966_at, 230142_s_at, 219172_at, 206183_s_at, 204857_at,
201399_s_at, 244339_at, 201565_s-at, 223307_at, 221866_at, 235060_at, 233233_at, 239242_at, 213397.x-at, 223596_at, 228191_at, 212209_at, 200778_s-at, 234985_at, 223759_s-at, 214057_at, 222247.at, 227524at, 244036_at, 235714_at, 208968_s_at, 243802_at, 203436_at, 213608_s_at, 212373_at, 221541_at, 229908_s_at, 214983_at, 1.139, 208623_s-at, 215338_s-at, 235274_at, 230264_s-at, 202108_at, 211372_s-at, 244831_at, 202600_s_at, 236583_at, 200804_at, 224636_at, 226608_at, 203269_at, 0.795,
212626_x_at, 232349_x-at, 219434.at, 213011_s_at, 211009_s-at, 233236_at, 213311_s-at, 243846_x-at, 220326_s-at, 212188_at, 226681_at, 216105_x_at, 226845_s_at, 212296_at, 218842_at, 207598_x-at, 230901_x-at, 204638_at, 228478_at, 216739_at, 221268_s_at, 214047_s-at, 223040_at, 229804_x-at, 200043_at, 225307at, 240456_at, 232084_at, 205227_at, 228800_x-at, 226525_at, 226496_at, 231972_at, 204496_at, 236936_at, 202138_x_at, 218723_s_at, 226352_at, 240288_at, 205626_s-at, 233775_x_at, 218048_at,
210816_s_at, 238888_at, 240299_at, 237889_s_at, 224288_x-at, 223281_s_at, 34858_at, 234326_at, 210757_x-at, 203559_s-at, 204661_at, 213960_at, 74694_s-at, 208673_s_at, 202560_s_at, 233106_at, 202233_s.at, 225106_s-at, 242481_at, 226536_at, 212804_s-at, 224984_at, 208946_s-at, 211582_x_at, 222380_s-at, 227968_at, 223833_at, 242253_at, 224717_s-at, 204027_s-at, 218789_s-at, 208620_at, 202214_s-at, 207907_at, 224755at, 223222_at, 242075_at, 233520_s_at, 232221_x.at, 242820_at, 232431_at,
211139_s-at, 221778.at, 225338_at, 207724_s-at, 202157_s-at, 242176_at, 34187_at, 203261_at, 242933_at, 219015_s_at, 212714_at, 220776_at, 211758_x-at, 218963_s_at, 213893_x_at, 235162_at, 232312_at, 202959_at, 233222_at, 201165_s-at, 226102_at, 210988_s-at, 236796_at, 211784_s-at, 237647_at, 210679_x-at, 228323_at, 239135_at, 223193_x_at, 223494_at, 208137.x-at, 212625_at, 209579_s-at, 225091_at, 205401_at, 205419_ at, 226518_at, 236379_at, 223787_s_at, 216049_at, 241588_at, 209388_at,
226364_at, 239936_at, 243080_at, 239612_at, 22771 1at, 219429_at, 201138_sat, 209964_s-at, 224115_at, 212782_x_at, 202088_at, 240038_at, 232464_at, 241368_at, 230871at, 200021_at, 230178_s_at, 205882_x_at, 226447at, 220711_at, 202884_s_at, 203080_s_at, 219229_at, 207187_at, 230854_at, 227031_at, 227239_at, 219105.x_at, 214394_x_at, 239102_s-at, 227426_at, 201320_at, 35150_at, 239811 _at, 242688_at, 204169at, 206855_s-at, 222787_s-at, 218033_s-at, 228528_at, 218463_s_at, 202369_s-at, 215716_s_at,
223042_s-at, 210621_s_at, 203502_at, 212320_at, 211978_x-at, 215467_x-at, 230590at, 215671_at, 209605_at, 240131_at, 202914_s_at, 228189_at, 219132_at, 200056_s-at, 216988_s-at, 213003_s-at, 212493_s-at, 228212_at, 221508_at, 227943_at, 217750_s-at, 212593_s_at, 242131_at, 201345_s-at, 239575_at, 238761_at, 214419_s-at, 232695_at, 221495_s-at, 32836_at, 218655_s-at, 211931.s_at, 240165_at, 218070_s-at, 218803_at, 232654_s_at, 202156_s-at, 223000_s-at, 238672_at, 241805_at, 202012_s-at,
210283_x-at, 215218_s_at, 218320_s_at, 201029_s-at, 224377_s-at, 234958_at, 240847_at, 239723at, 228032_s-at, 203063.at, 208953at, 202053_s-at, 222987_s-at, 216409_at, 201352_at, 210818_s_at, 209286_at, 217897_at, 229307_at, 243201_at, 236989_at, 234762_x-at, 225884_s_at, 240342_at, 202406_s_at, 218946_at, 225779_at, 221717_at, 231059_x-at, 241429_at, 220941_s-at, 204255_s_at, 240608_at, 221755_at, 206542_s_at, 203459_s-at, 236825_at, 204985_s-at, 228708_at, 212055_at, 232376_at, 209061_at,
237354at, 241787_at, 236886_at, 200825_s_at, 210556_at, 201807_at, 217446_x-at, 200735_x-at, 212757_s-at, 58994_at, 225892_at, 203200_s-at, 210253_at, 220113_x_at, 243004_at, 117 2568722.1 (GHMa-tter) P71850 AU 228700_at, 224428_s-at, 203817_at, 233196_at, 240958_at, 240371_at, 239757_at, 214085x_at, 203708_at, 220477_s_at, 226194_at, 208688_x-at, 225918_at, 212499_s-at, 230949_at, 236192at, 222175_s-at, 238875at, 219428_s-at, 219502at, 37950_at, 210542.s-at, 235457_at, 225282_at, 208757_at,
232244_at, 216483_s_at, 213385_at, 202814_s-at, 230278_at, 213375_s-at, 0.862, 202149at, 227386_s-at, 235906_at, 244483_at, 241892_at, 212132_at, 34031i at, 201742_x at, 206056_x-at, 229321_s_at, 203855_at, 239471_at, 225275_at, 202024_at, 236381_s-at, 244076_at, 210436_at, 219296_at, 221648_s_at, 226273_at, 219724_s_at, 237444_at, 227997at, 240094at, 209452_s_at, 207347_at, 229074_at, 243086at, 222861_x_at, 231443_at, 52164_at, 228867_at, 226872_at, 201106_at, 240290_at, 200020_at, 235489 at,
239278 at, 225928_at, 201055_s-at, 207323_s-at, 222969_at, 243112_.at, 226068_at, 216514_at, 233210_at, 232283_at, 233550_s-at, 235451_-at, 227629at, 237488_at, 239031 _at, 206263_at, 209281_-s-at, 223697.x-at, 219028at, 203999.at, 204859_s-at, 228565_at, 213852_at, 219460_s-at, 209786_at, 210987_x-at, 203379_at, 225008_at, 242373_at, 233557_s_at, 204544_at, 200656_s-at, 207734_at, 202645_s_at, 200675_at, 242232_at, 217197_xat, 242316_at, 320_at, 218456_at, 209509_s_at, 226660_at, 241240_at,
201696_at, 212393_at, 242737_at, 228916_at, 244700_at, 240269_at, 212118_at, 236041_at, 243827_at, 227577_at, 236229_at, 215684_s-at, 213603_s-at, 238431 at, 223647_x-at, 210786_s-at, 230208_at, 207436_x-at, 230332_at, 229541 at, 233355at, 205474_at, 209653at, 224926_at, 214736_s-at, 233453.at, 219110_at, 202615_at, 238800_s-at, 204120_s_at, 225633_at, 207081_s-at, 239517at, 244335_at, 222424_s_at, 244329_at, 227608_at, 0.829, 233666at, 223352_s-at, 202704_at, 213146_at, 202119_s-at, 229618_at,
221673_s_at, 236921_at, 234574_at, 216153_x_at, 218799_at, 232957_x-at, 224612_s_at, 219846at, 212407at, 226439_s_at, 244030_at, 225772_s_at, 210840_sat, 214790_at, 202387_at, 235227_at, 226887_at, 223277at, 229447_x-at, 228292_at, 225813_at, 204367_at, 204998_s-at, 204842_x-at, 242678at, 201533_at, 225274_at, 235547_at, 236165_at, 244312_at, 235856_at, 224647_at, 205500at, 212219_at, 209492_x at, 232814_x_at, 215263_at, 210691_s-at, 204291_at, 220050_at, 209827_s-at, 219678_x-at, 216036_x-at,
208113_x-at, 218987_at, 230243at, 240620at, 222143_s-at, 236368_at, 217507_at, 221550at, 217914_at, 200608_s-at, 210739_x-at, 208660_at, 221749_at, 210734_x_at, 212932at, 227152_at, 203015_s_at, 243158_at, 232789_at, 218203_at, 222429_at, 209189at, 208426_x.at, 238324_at, 206357_at, 225486_at, 216051_x_at, 207513_s-at, 226357_at, 209787_s-at, 225359_at, 232742.at, 243487at, 239773_at, 235801_at, 226449_at, 212537_x_at, 223639_s_at, 208766_s-at, 230810_at, 227621 _at, 204206_at, 224166_at,
204219_s_at, 213042_s_at, 234403_at, 239614_x-at, 221681_s-at, 212052_s-at, 214749_s_at, 200621_at, 244401at, 205996_s-at, 224327_s_at, 235860_at, 217249_x_at, 215387_x_at, 231855_at, 202393_s-at, 201913_s-at, 225001_at, 228582_x_at, 210659_at, 240854_x_at, 218467.at, 235807_at, 244094_at, 208174_x-at, 219487at, 212697_at, 217294_s_at, 241907_at, 203403_s_at, 223134_at, 219520_s-at, 220964_s-at, 238281 _at, 204642_at, 204009_s-at, 208238_x_at, 209899_sat, 242356_at, 207034_s_at, 224865_at,
216954_x-at, 202379_s-at, 230882_at, 203658_at, 217724_at, 227510_x-at, 222627at, 222556_at, 218172_s_at, 218228_s-at, 235953_at, 209382_at, 209158_s-at, 236352_at, 243801_x-at, 213183_s-at, 225287_s-at, 218344_s-at, 235473.at, 219297_at, 203628_at, 205760_s_at, 202195_s_at, 211787_s_at, 202150_s_at, 203012_x-at, 218152.at, 203305_at, 230619_at, 221096_sat, 240344_x_at, 236243_at, 226146_at, 222853at, 212340_at, 218388_at, 238990_x_at, 225724_at, 218056_at, 208619_at, 202355_s_at, 201776_s_at,
225387_at, 212008_at, 208034_s-at, 238221_at, 232130_at, 202457_s-at, 205927_s-at, 0.84, 53987_at, 51176_at, 217729_s-at, 234168_at, 240859_at, 233333_x_at, 220199_s_at, 221752_at, 231012_at, 235462_at, 235346_at, 231990_at, 242099_at, 213180_s_at, 204628_s_at, 225953_at, 237163_x-at, 216606_x-at, 202981_x.at, 201757at, 225051_at, 203990_s-at, 226650_at, 228250at, 226020_s_at, 212333_at, 50965_at, 225294_s-at, 229906_at, 243258_at, 239506_s-at, 202706_s-at, 208674_x-at, 242645at, 205678_at,
210110_x_at, 219497_s_at, 216164_at, 239751_at, 243100_at, 239572_at, 211560_s-at, 203890_s-at, 242836_at, 203978_at, 243652_at, 243001_at, 204639_at, 202160_at, 203485_at, 208519_x_at, 232158_x_at, 217793_at, 217949_s-at, 226372at, 226280_at, 211452_- x..at, 214356_s-at, 244710_at, 241771_at, 216593_s_at, 214949_at, 229966_at, 221689_s-at, 200665_s-at, 232597_x at, 221931_s-at, 237424_at, 221951_at, 202979_s_at, 205052at, 207872_s_at, 236577_at, 200085_s_at, 223268_at, 201545_s-at, 231735_s-at,
210011_s-at, 225356_at, 212405_s-at, 220528_at, 226422_at, 214953_s_at, 222972_at, 231623_at, 214042_s-at, 206174_s-at, 237782_at, 216540_at, 201599_at, 240638_at, 228369_at, 212998_x-at, 203249at, 206860_s-at, 227189_at, 234977_at, 218351_at, 232272at, 203525-s-at, 225125_at, 212059_s-at, 207986_x-at, 225537_at, 224782at, 236814_at, 224582_s_at, 230518_at, 208360_s_at, 238693_at, 225148at, 231945_at, 225260_s_at, 1.262, 240326_at, 201121_s_at, 202268_s-at, 244419_at, 218982_s-at, 233303_at,
227669_at, 202568_s-at, 229903_x_at, 223236_at, 206178_at, 206493_at, 201862_s_at, 232922_s_at, 219330_at, 211023_at, 211424_x-at, 215696_s_at, 211727.s_at, 244762_at, 210279_at, 240979_at, 215883_at, 234239_at, 225579_at, 235051_at, 202596_at, 209275_s_at, 211933_s-at, 227197_at, 209161_at, 212043_at, 244609at, 242447at, 208403_x-at, 209459_s_at, 235683at, 236715_x_at, 205885_s-at, 208738.x_at, 1.267, 239715_at, 218401_s_at, 239395_at, 228022_at, 238638_at, 36936_at, 224936_at, 225731_at,
242966_x_at, 211576.s_at, 227135_at, 244846_at, 217393_x.at, 233838_at, 215566_x.at, 223835_x_at, 222826_at, 203284_s_at, 244075_at, 207357_s_at, 202788_at, 229686_at, 229987_at, 208631_s-at, 218921_at, 225838_at, 238272_at, 202808_at, 212293at, 226005_at, 236552_at, 228769at, 221575_at, 208741_at, 227164_at, 240727_s-at, 218746_at, 226635_at, 226509_at, 213263_s-at, 202431_s-at, 218178_s-at, 227917_at, 225565_at, 205172_x-at, 212565_at, 204314_s-at, 215176_x-at, 226630_at, 216207_x_at,
214113_s_at, 208517_x_at, 242299_at, 203843at, 226431_at, 209945_s-at, 212138_at, 226290_at, 206181_at, 203157_s-at, 213583.x_at, 229586_at, 241799_x_at, 204042_at, 214135_at, 240221_at, 237553_at, 212532_s_at, 240427_at, 242263_at, 240765_at, 203119_at, 217931_at, 217877_s_at, 201711_x_at, 200091_s_at, 239876_at, 236836_at, 228628_at, 220173_at, 226747_at, 211581_xat Table 29 - 'Observed v. Expected' table of GO classes and parent classes, in list of 6430 genes shown in Table 28 Cellular
Component GO id GO classification Observed in Expected in Observed/ selected subset selected subset Expected 42101 T-cell receptor complex 5 2.23 2.24 1772 immunological synapse 5 2.23 2.24 145 exocyst 5 2.23 2.24 5885 Arp2/3 protein complex 10 4.91 2.04 Molecular Function GO id GO classification Observed in Expected in Observed/ 118 2568722_1 (GHMatters) P71850.AU selected subset selected subset Expected 30911 TPR domain binding 7 3.11 2.25 17040 ceramidase activity 6 2.67 2.25 16721
"oxidoreductase activity\, 7 3.11 2.25 acting on superoxide radicals as acceptor" 16454 C-palmitoyltransferase activity 5 2.22 2.25 16314 'phosphatidylinositol-3\,4\, 8 3.56 2.25 5-trisphosphate 3-phosphatase activity" 15645 fatty-acid ligase activity 9 4 2.25 15266 protein channel activity 5 2.22 2.25 4785 'copper\, zinc superoxide 5 2.22 2.25 dismutase activity" 4784 superoxide dismutase activity 7 3.11 2.25 4758 serine C-palmitoyltransferase 5 2.22 2.25 activity 4467 long-chain-fatty-acid-CoA
9 4 2.25 ligase activity 4370 glycerol kinase activity 6 2.67 2.25 4213 cathepsin B activity 5 2.22 2.25 4185 serine carboxypeptidase 9 4 2.25 activity 4145 diamine N-acetyltransferase 5 2.22 2.25 activity 3951 NAD+ kinase activity 5 2.22 2.25 19865 immunoglobulin binding 9 4.45 2.02 42605 peptide antigen binding 8 4 2 19957 C-C chemokine binding 8 4 2 16493 C-C chemokine receptor activity 8 4 2 5540 hyaluronic acid binding 16 8.01 2 Biological Process GO id GO classification Observed in
Expected in Observed/ selected subset selected subset Expected 50672 negative regulation of 6 2.68 2.24 lymphocyte proliferation 46486 glycerolipid metabolism 13 5.81 2.24 45429 positive regulation of nitric 7 3.13 2.24 oxide biosynthesis 45428 regulation of nitric oxide 7 3.13 2.24 biosynthesis 43193 positive regulation of gene- 5 2.23 2.24 specific transcription 42434 indole derivative metabolism 5 2.23 2.24 42430 indole and derivative 5 2.23 2.24 119 2568722_1 (GHMatters) P71850.AU metabolism
19751 polyol metabolism 11 4.91 2.24 19377 glycolipid catabolism 6 2.68 2.24 18348 protein amino acid 5 2.23 2.24 geranylgeranylation 18344 protein geranylgeranylation 5 2.23 2.24 7009 plasma membrane organization 8 3.57 2.24 and biogenesis 6984 ER-nuclear signaling pathway 5 2.23 2.24 6662 glycerol ether metabolism 13 5.81 2.24 6641 triacylglycerol metabolism 13 5.81 2.24 6639 acylglycerol metabolism 13 5.81 2.24 6638 neutral lipid metabolism 13 5.81 2.24 6586 indolalkylamine metabolism 5 2.23
2.24 6072 glycerol-3-phosphate metabolism 7 3.13 2.24 6071 glycerol metabolism 11 4.91 2.24 45165 cell fate commitment 13 6.25 2.08 1709 cell fate determination 13 6.25 2.08 Table 30 216323_x_at, 231205_at, 205786_s-at, 212377_s-at, 231611_at, 242763at, 238973_s-at, 222861 x_at, 219444_at, 204516_at, 213280_at, 244329_at, 213671_s_at, 223469at, 211962_s-at, 220603_s-at, 202160_at, 220244_at, 233303_at, 201154_x_at, 234661at, 233510_s-at, 211159_s_at, 201545_s_at, 200958_s_at, 201680_x-at,
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218764_at, 213102at, 202046_s-at, 207643_s_at, 232578_at, 226032at, 242421_at, 206689_x_at, 239517_at, 212333_at, 210434_x_at, 210491_at, 214139_at, 223419_at, 240262_at, 208620_at, 204212_at, 205359_at, 200765_x-at, 233056_xat, 217715_x_at, 228980_at, 239575_at, 206788_s_at, 211 100_x_at, 235661_at, 214204_at, 244152_at, 205189_s_at, 224848_at, 201266_at, 208138_at, 225530_at, 213293_s-at, 226049_at, 213578_at, 213208_at, 236546at, 218491_s_at, 222732_at, 209037_s_at, 231821_x_at, 228685_at,
204116_at, 215333.x_at, 221561_at, 229419.at, 202527_s_at, 226584_s_at, 214241_at, 209416_s_at, 202050_s_at, 239151_at, 239316_at, 241561_at, 243507_s-at, 243750_x-at, 223061_at, 209117_at, 211135_x-at, 209668_x-at, 40020at, 211551_at, 200614at, 224878_at, 225263_at, 218850_s-at, 217892_s-at, 210112_at, 217959 s-at, 201379_s-at, 200855_at, 208389_s_at, 221192_x_at, 205349_at, 242139_s-at, 208360_s_at, 206925_at, 202270_at, 200714_x at, 229035_s-at, 212070 at, 226068at, 201555_at, 213557_at,
1.258, 225029_at, 203133at, 204391_x.at, 218965_s-at, 226829_at, 200021_at, 208781_xat, 201662_s-at, 226438.at, 234093_at, 226871_s-at, 202032_s-at, 220517at, 235479at, 213598_at, 218251at, 226386_at, 214121_x at, 224842_at, 206916_x_at, 215535_s_at, 244328_x_at, 211388_s_at, 225145_at, 208270_s_at, 223044_at, 203439_s-at, 54970_at, 227561_at, 223507_at, 231437_at, 221864_at, 219290_x_at, 218205_s_at, 244481_at, 220954_s_at, 209354_at, 227558_at, 218593_at, 212049_at, 239180_at, 212862_at,
236129_at, 217496_s-at, 235799_at, 204692_at, 219147_s-at, 204596_s-at, 232346_at, 230320_at, 213370_s_at, 221744_at, 229460_at, 204316at, 213214_x_at, 232862_at, 205677_s-at, 210271_at, 228793_at, 210629_x.at, 203526_s-at, 218255_s-at, 232213_at, 224859_at, 205310_at, 213573_at, 229101_at, 212934_at, 201444_s.at, 204639_at, 213051_at, 201823_s-at, 201218_at, 46947_at, 225244.at, 224591_at, 205526_s_at, 242272_at, 201715_s-at, 200905_x-at, 208713_at, 215909_x at, 217697_at, 200057_s-at,
202427_s-at, 207102_at, 213788_s-at, 205220_at, 217609_at, 243293_at, 205088_at, 214229_at, 202589_at, 237631_at, 204837_at, 221249_s_at, 209091_s_at, 208488_s-at, 216126_at, 244075.at, 230422_at, 209238at, 242990at, 233480_at, 231294_at, 239395_at, 241823_at, 221817_at, 210920_x_at, 221283_at, 224129_s_at, 225730_s_at, 233553_at, 214610_at, 244792_at, 242081_at, 203914_x_at, 205328_at, 203600_s_at, 230532_at, 226547at, 228846_at, 225373_at, 1.167, 222115_x_at, 236908_at, 218871_x_at,
207387_s_at, 213416_at, 208783_s_at, 208018_s_at, 215056_at, 201157_s-at, 65591_at, 208246_x_at, 221394_at, 243149_at, 236361_at, 239629_at, 213225_at, 232455_x_at, 218823_s_at, 243513_at, 237852_at, 219746_at, 202146_at, 203426_s-at, 202663at, 210809_s-at, 226757at, 211085_s-at, 236705_at, 217149_x_at, 240869_at, 230204_at, 227431_at, 225011_at, 215322_at, 213678_at, 200605_s-at, 223599_at, 223231_at, 243271 _at, 238999_at, 242121_at, 228754_at, 209221_s-at, 213675_at, 217157_xat, 226683_at,
202761_s-at, 218248_at, 206618_at, 201477_s-at, 218920_at, 213574_s-at, 210371_sat, 230737_s_at, 226861_at, 224429_x-at, 236000_s-at, 229400_at, 203528_at, 204409_s.at, 224583_at, 225077_at, 200895_s-at, 225950_at, 201494_at, 209941at, 209093_s-at, 212968_at, 224284 x at, 223034_s_at, 203279_at, 218215_s-at, 202679at, 232474_at, 237475_ x at, 235085at, 217662_x-at, 222407_s-at, 226259_at, 201546_at, 211995_x-at, 240673at, 200869_at, 206830_at, 228226_s_at, 200617_at, 244710_at, 243030_at,
214047_s-at, 205423_at, 240862_at, 219620_x_at, 233122_at, 51226_at, 214746_s_at, 227749at, 231272_at, 201364_s_at, 225205_at, 204174_at, 222077_s_at, 200860_s_at, 200857_s_at, 242310_at, 244517_x_at, 232793_at, 201534_s-at, 212176_at, 233876at, 219105_x-at, 240126_x_at, 215287_at, 214973_x-at, 229066_at, 232720_at, 242898_at, 226144_at, 242620_at, 212678_at, 211505_s-at, 223455at, 201940at, 224594_x-at, 202264_s-at, 235959_at, 243993_at, 206974at, 218880_at, 232679_at, 208212_s-at, 242638 at,
233591 at, 209743_s_at, 1.386, 223022-s-at, 224445-s-at, 216557_x-at, 218078_s-at, 229934_at, 231043_at, 221367_at, 38892_at, 227467_at, 219253_at, 212880at, 217979_at, 202175_at, 235331_x-at, 221925_s-at, 212647at, 225184_at, 221832_s-at, 241075_at, 241388_at, 215553.xat, 218088_s-at, 238788_at, 23431 1_s-at, 223884_at, 237889_s-at, 208124_s-at, 225224_at, 225210_s_at, 242253_at, 212485_at, 202614at, 201794_s-at, 228709_at, 222590_s.at, 204069_at, 206200sat, 59644_at, 219429_at, 223256_at,
217020_at, 218983_at, 228318_s_at, 215126_at, 1.183, 240046_at, 205108_s-at, 217487_x-at, 207455.at, 222852_at, 209395_at, 223371_s-at, 237782at, 205568_at, 224893_at, 230426_at, 218733_at, 236388_at, 242056_at, 39313_at, 212087_s-at, 212086_x_at, 203188_at, 205609_at, 240295_at, 1.172, 49077_at, 201695_s-at, 212658_at, 237663_at, 244831_at, 204828_at, 227189_at, 220957_at, 213079_al, 220577_at, 212137_at, 200953_s-at, 244655_at, 203275_at, 224180_x_at, 233911_s_at, 235958_at, 201840_at,
227335_at, 214735_at, 240608_at, 226641_at, 200781_s_at, 214949_at, 202561_at, 1.305, 201194_at, 212441_at, 224969_at, 208280_at, 60794 f at, 226577_at, 211:325_x-at, 212445_s-at, 212757_s-at, 216399_s_at, 237056_at, 234488_s-at, 202731at, 209517_s-at, 244061_at, 224346_at, 221007_s-at, 31799_at, 236978_at, 241876_at, 219350_s_at, 208694_at, 211123_at, 218132_s_at, 202420_s_at, 221247_s_at, 220000_at, 232218at, 209197_at, 202781_s-at, 200800_s-at, 205389_s_at, 209880_s_at, 223332_x_at,
204809_at, 224701_at, 208082_x_at, 226371_at, 213496_at, 213682_at, 225630_at, 203945_at, 203097_s_at, 243966_at, 218115_at, 218241_at, 244322_at, 218424_s-at, 223615_at, 208652_at, 212637_s-at, 227313_at, 204360_s-at, 125 2568722_1 (GHMattmo) P71860 AU 200731 _s_at, 210771 at, 226906_s-at, 218137_s-at, 201559_s-at, 212168_at, 230742_at, 211654.x_at, 214212_x_at, 212974_at, 213697_at, 242369_xat, 205991_s_at, 224026_at, 217954_s_at, 205996_s_at, 208039at, 202100_at, 217061_s_at, 209076_s_at,
208030_s_at, 215758_x_at, 225973_at, 223075_s_at, 211336_x-at, 205552_s-at, 225058_at, 222642_s-at, 218498_s-at, 238987_at, 218136_s.at, 201540_at, 238221_at, 215585_at, 201754_at, 229781_at, 216028_at, 218198_at, 218301at, 241940_at, 36129_at, 204602_at, 242943_at, 207168_s-at, 210657_s_at, 228490_at, 240205_x-at, 224412_s-at, 200791_s-at, 216292_at, 236479_at, 225187_at, 205070at, 201935_s-at, 202247_s-at, 242209_at, 216369at, 217993_s-at, 222622_at, 244021_at, 203748.x_at, 204039_at, 1.303,
215236_s_at, 221916_at, 222263_at, 204647_at, 1.277, 204490_s-at, 225416_at, 204367at, 217864_s_at, 58367_s-at, 202957_at, 221138_s-at, 207598.x-at, 216526.x_at, 223406_x_at, 204211_x_at, 230376_at, 202689_at, 201762_s-at, 226273_at, 202068_s.at, 233571_x_at, 242854.x_at, 234634_at, 230226_s_at, 222421_at, 201493_s_at, 229428_at, 225830_at, 204026_s-at, 231616_at, 207919at, 212748_at, 214284_s_at, 212783_at, 201592_at, 213826_s-at, 207535_s-at, 224598_at, 230266_at, 214657_s-at, 218279_s-at,
243449_at, 205807_s-at, 1.265, 236923_x-at, 212152_x-at, 226224_at, 217794_at, 217506_at, 222212_s_at, 213947_s_at, 212869_x-at, 214759at, 201092at, 204919_at, 227438_at, 224115at, 203986_at, 215037_s-at, 237125at, 216253_s-at, 202783_at, 237792_at, 204759_at, 215706_x-at, 225494_at, 216176_at, 235940_at, 202255_s-at, 202515_at, 218126at, 218949_s-at, 215765_at, 201814_at, 217737_x_at, 228769_at, 219305_x_at, 212718_at, 236703_at, 231776_at, 223401_at, 212015_x_at, 205403_at, 203085_s-at,
214677_x-at, 207724_s-at, 227338_at, 242861_at, 48808_at, 211800_s-at, 237231at, 244471_x_at, 221442_at, 241810_at, 230098_at, 204164_at, 205055_at, 234687_x_at, 202447_at, 225887_at, 209831_x-at, 226048_at, 204470_at, 31845_at, 215616_s-at, 202083_s-at, 223461_at, 1007_s-at, 201363_s_at, 242188_at, 207480_s-at, 235166_at, 209640_at, 229385_s_at, 243109_at, 223960_s_at, 1.306, 218521.s-at, 233818at, 234571at, 224100_s_at, 238673_at, 219472_at, 217650_x_at, 240159_at, 212787_at, 203379_at,
204972_at, 213929_at, 225372at, 238461at, 225956_at, 200097_s-at, 225884_s-at, 222950_at, 223192.at, 216041_x_at, 209459_s_at, 209947_at, 224858_at, 205471_s_at, 219863_at, 234594_at, 224624_at, 218242_s_at, 221736_at, 228591_at, 229684_s_at, 208965_s-at, 221551_x-at, 228061at, 212204_at, 201332_s-at, 204480_s-at, 203274_at, 218659_at, 215114at, 208951_at, 209015_s_at, 209034_at, 229515_at, 209883_at, 236826_at, 216778_s-at, 228810_at, 201529_s_at, 225197_at, 215221at, 213263_s-at, 211576_s_at,
208527_x-at, 218982_s-at, 226796_at, 222708_s-at, 208988_at, 240908_at, 211956_s-at, 226870_at, 208767_s-at, 239995_at, 203883_s_at, 220081_x-at, 236762_at, 200734_s_at, 242865_at, 238065_at, 218131_s-at, 201603_at, 214220_s-at, 233903_s-at, 204290.s-at, 226135_at, 215276_at, 212370_x_at, 214315_x_at Table 31 - 'Observed v. Expected' table of GO classes and parent classes, in list of 4562 genes shown in Table 30 Cellular Component GO id GO classification Observed in Expected in Observed/
selected subset selected subset Expected 5790 smooth endoplasmic reticulum 6 2.08 2.89 5770 late endosome 5 1.78 2.81 5952 cAMP-dependent protein kinase 6 2.67 2.25 complex 5581 collagen 8 3.86 2.07 Molecular Function GO id GO classification Observed in Expected in Observed/ selected subset selected subset Expected 16909 SAP kinase activity 6 1.75 3.43 8066 glutamate receptor activity 5 1.46 3.43 5522 profilin binding 6 1.75 3.43 3951 NAD+kinase activity 5 1.46 3.43 5149 interleukin-1 receptor
binding 6 2.04 2.94 5242 inward rectifier potassium 7 2.62 2.67 channel activity 8373 sialyltransferase activity 9 3.5 2.57 4703 G-protein coupled receptor 5 2.04 2.45 kinase activity 4675 transmembrane receptor protein 5 2.04 2.45 senne/threonine kinase activity 15179 L-amino acid transporter 7 2.91 2.4 activity 4707 MAP kinase activity 7 2.91 2.4 126 2588722_1 (GHMatters) P71850.AU 15645 fatty-acid ligase activity 6 2.62 2.29 15149 hexose transporter activity 6 2.62 2.29 15145 monosaccharide
transporter 6 2.62 2.29 activity 8603 cAMP-dependent protein kinase 8 3.5 2.29 regulator activity 5355 glucose transporter activity 6 2.62 2.29 4467 long-chain-fatty-acid-CoA 6 2.62 2.29 ligase activity 8656 caspase activator activity 9 4.08 2.21 30276 clathrin binding 5 2.33 2.14 3708 retinoic acid receptor activity 5 2.33 2.14 16505 apoptotic protease activator 9 4.37 2.06 activity 16504 protease activator activity 9 4.37 2.06 8227 amine receptor activity 6 2.91 2.06 4984 olfactory receptor
activity 9 4.37 2.06 4468 lysine N-acetyltransferase 6 2.91 2.06 activity 4402 histone acetyltransferase 6 2.91 2.06 activity 4712 protein threonine/tyrosine 7 3.5 2 kinase activity 4708 MAP kinase kinase activity 7 3.5 2 Biological Process GO id GO classification Observed in Expected in Observed/ selected subset selected subset Expected 45655 regulation of monocyte 5 1.76 2.84 differentiation 7202 phospholipase C activation 5 1.76 2.84 6911 phagocytosis\, engulfment" 5 1.76 2.84 2009
morphogenesis of an epithelium 6 2.34 2.56 51056 regulation of small GTPase 5 2.05 2.44 mediated signal transduction 45637 regulation of myeloid blood 5 2.05 2.44 cell differentiation 7171 transmembrane receptor protein 5 2.05 2.44 tyrosine kinase activation (dimerization) 6898 receptor mediated endocytosis 20 8.79 2.28 30099 myeloid blood cell 10 4.69 2.13 differentiation 9620 response to fungi 5 2.34 2.13 30224 monocyte differentiation 8 3.81 2.1 10033 response to organic substance 8 3.81 2.1
127 2588722.1 (GHMattera) P71850 AU 30111 regulation of Wnt receptor 6 2.93 2.05 signaling pathway Table 32 202411 _at, 204821_-at, 202145at, 218548_x_at, 222592_s-at, 200923at, 223343at, 213773_x_at, 205227.at, 224622.at, 233425_at, 219777_at, 201202_at, 225199_at, 214453_s_at, 226382_at, 235985_at, 213313_at, 218061_at, 223096_at, 218376_s_at, 221776_s_at, 219062_s_at, 38241_at, 219519_s_at, 228456_s-at, 223796_at, 212560_at, 203153_at, 213309_at, 201941at, 237783_at, 219696_at, 212761_at,
209786_at, 203433_at, 242159at, 244414at, 205552_s-at, 204805_s-at, 242766_at, 242625_at, 213348_at, 205898at, 202644_s-at, 202869_at, 218191_s_at, 230753_at, 234101_at, 204439_at, 201930_at, 205483_s_at, 202589_at, 203561_at, 218739_at, 200603_at, 205241_at, 223609_at, 214938_x_at, 235055_x-at, 225197.at, 209682_at, 232680_at, 243635_at, 21861 _at, 226726_at, 225569_at, 202086_at, 229434_at, 217167_x-at, 243271_at, 204972_at, 233168_s-at, 224428_s-at, 201088_at, 216041_x-at, 212760_at,
233657_at, 224705_s-at, 221875_x-at, 213797_at, 207785_s-at, 237516at, 225330_at, 211456_x_at, 232229_at, 211612_s-at, 209773_s-at, 202864_s-at, 210164_at, 228617_at, 223392_s_at, 223776_x-at, 21921 1_at, 202083_s-at, 237852_at, 227747at, 201166_s-at, 219505_at, 219691_at, 223608.at, 212137_at, 201943_s_at, 206055_s-at, 219157_at, 200069_at, 203194_s_at, 218639_s_at, 202771_at, 226971_at, 200020.at, 223412_at, 208581_x_at, 225639_at, 224569_s_at, 234965_at, 205660_at, 218939_at, 241096_at,
203513_at, 237868_x_at, 201132_at, 216035_x_at, 244227_at, 225065_x-at, 203148_s-at, 218986_s_at, 240887_at, 205021 s_at, 213491_ xat, 209969_s_at, 242048at, 241742_at, 200678_x at, 227129_x_at, 226850at, 219684_at, 1.378, 225035_x_at, 216614_at, 202325_s-at, 235879_al, 200750_s-at, 236270_at, 220954_s-al, 218232_at, 226954_at, 203021_at, 218400_at, 228159_at, 213620_s-at, 234761_at, 220302_at, 211284_s-at, 232047at, 204169_at, 210797_s_at, 202107_s_at, 211967_at, 219209_at, 209610_s_at,
200011_s-at, 200063_s-at, 219816_s_at, 202451_at, 200083_at, 203037_s-at, 215977_x_at, 201565_s_at, 201216_at, 214141_x-at, 224861_at, 217805_at, 232023_at, 226741_at, 211475_s_at, 234883_x_at, 227040_at, 208689_s_at, 203266_s-at, 224809_x_at, 223481_s_at, 222861_xat, 213619_at, 217202_s-at, 201781_s-at, 211974_x-at, 232375_at, 208617_s-at, 228312_at, 222496_s-at, 241715_x-at, 229699_at, 210657_s-at, 204326_x at, 205403_at, 203509_at, 214059_at, 207691_x_at, 227962_at, 234323_at, 232324_x-at,
200063_s-at, 208887_at, 239687_at, 211115_x at, 226093_at, 221766_s-at, 212426_s_at, 212296at, 218927_s-at, 222446_s-at, 237415at, 204747at, 236522at, 212641_at, 221767_x-at, 225535_s-at, 235204at, 201963at, 240973_s_at, 44673_at, 230036at, 240436 at, 201624_at, 235199_at, 200069_at, 242253_at, 231989_s-at, 212859 x-at, 206553at, 209284_s-at, 212910_at, 210146_x_at, 235276_at, 208819_at, 201164_s_at, 201306_s-at, 238558_at, 224340at, 212614_at, 209045_at, 225917at, 231981_at, 200885_at,
240793_at, 238712at, 212320_at, 232761_at, 217928_s-at, 200083_at, 208749_x_at, 224707at, 219373_at, 228959_at, 200066_at, 216095_x-at, 219014at, 203594_at, 231578_at, 236561_at, 244520_at, 243095at, 202501_at, 216336_x_at, 212341_at, 239979_at, 227524_at, 240128at, 222686_s-at, 226872_at, 202042at, 208886_at, 225083_at, 204470at, 241669_x_at, 239094_at, 217809_at, 203505_at, 236156_at, 225722_at, 238035_at, 210046_s-at, 238571at, 213738_s-at, 241631_at, 202041_s-at, 21351 1sat, 243993_at,
218543_s_at, 229367_s-at, 230949_at, 212380_at, 229247_at, 225321_s-at, 239551_at, 226702at, 238549_at, 212329_at, 232873_at, 222728_s-at, 242044_at, 206513_at, 224736_at, 241805_at, 244753_at, 201129_at, 200960_x_at, 235222_x-at, 202113_s-at, 224042.at, 235001_at, 244329_at, 37028_at, 213699_s_at, 211714_x-at, 228531at, 209457at, 228487_s-at, 224763_at, 226335_at, 213048_s-at, 244875_at, 221829_s-at, 41469at, 228924_s_at, 239179_at, 240854_x_at, 217768_at, 240452at, 219762_s-at, 230733_at,
204806_x-at, 217526_at, 229914_at, 208683_at, 243045_at, 201599_at, 221488_s-at, 229987_at, 212959_s_at, 212048_s-at, 221430_s-at, 219863_at, 217877_s_at, 217317_s-at, 212251_at, 227452_at, 209665_at, 222816_s-at, 203596_s-at, 241444_at, 207507_s_at, 209054_s_at, 235256_s_at, 224345_x_at, 206631_at, 233538_s_at, 208693_s-at, 227376_at, 212185_x-at, 208752_x.at, 240950_s-at, 235473_at, 210418_s-at, 227197_at, 235446_at, 219799_s_at, 213982_s-at, 209049_s_at, 237231_at, 225678_at, 202457_s-at,
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224665_at, 224564_s-at, 214428_x at, 231956_at, 232053_x_at, 209341_s-at, 222244_s_at, 242188_at, 241881_at, 204386_s_at, 209898_x_at, 208012_xat, 237295_at, 210276_s_at, 200986_at, 229872_s-at, 209330_s-at, 200605_s-at, 202764_at, 223344_s-at, 209762_x-at, 239156_at, 222428_s-at, 201338_x-at, 209829_at, 230421_at, 231251_at, 244401_at, 201365_at, 234974_at, 242053_at, 239259_at, 220720_x_at, 201471_s_at, 202032_s_at, 203345_s_at, 242362_at, 209157at, 232340_at, 224687at, 202085_at, 236065_at,
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212549_at, 221816_s-at, 241432_at, 212515_s-at, 202429_s-at, 224817_at, 239395_at, 241700_at, 225672_at, 40569_at, 218178_s-at, 208734_x-at, 234760_at, 244449_at, 240162_at, 212414_s-at, 225573_at, 221367_at, 44783_s-at, 209846_s-at, 226080at, 241627_x_at, 224645at, 226589_at, 230208at, 218467_at, 238127_at, 222282_at, 229813.x_at, 240130_at, 242819_at, 208781_x_at, 213104_at, 209716_at, 224917at, 214170_x_at, 219125_s_at, 239284_at, 228788_at, 224054_at, 230572_at, 215088_s-at, 210724_at,
217930_s_at, 217379_at, 235023_at, 200043_at, 242201_at, 218583_s_at, 204028_s_at, 219079_at, 237025_at, 225598_at, 231051_at, 58367_s-at, 206133_at, 204912_at, 228918_at, 202387_at, 225597_at, 244076_at, 228240_at, 234848_at, 225353_s_at, 208945_s_at, 208986-at, 243453_at, 221923_s-at, 243300_at, 200758_s-at, 234393 at, 243507_s-at, 223454_at, 223098_s-at, 225754_at, 225358_at, 128 2568722_1 (GHMatters) P71850 AU 242535_at, 218660_at, 226364_at, 241947_at, 229123_at, 239242_at, 241217_x.at,
217473.x_at, 222217_s-at, 224938_at, 222429_at, 212929_s_at, 213246at, 212476_at, 207624_s-at, 207812_s_at, 202101_s-at, 217739_s-at, 204646_at, 212416_at, 228648at, 224327_s_at, 215688_at, 240282_at, 52078_at, 243314_at, 242930_at, 237819_at, 202973_x_at, 231683_at, 206718_al, 226026_at, 228958_at, 241771_at, 223740_at, 201339_s-at, 212647_at, 229964_at, 225776_at, 231770_x-at, 217933_s-at, 205495_s-at, 206420at, 55692at, 200686_s-at, 240188_at, 210775_x_at, 200820_at, 240342_at, 243411 .at,
224681 _at, 231769_at Table 33 -'Observed v. Expected' table of GO classes and parent classes, in list of 758 genes shown in Table 32 Cellular Component GO id GO classification Observed in Expected in Observed/ selected subset selected subset Expected 16469 proton-transporting two-sector 6 1.69 3.55 ATPase complex 5941 unlocalized 7 3.23 2.16 5740 mitochondrial membrane 12 5.65 2.12 5743 mitochondrial inner membrane 8 3.81 2.1 Molecular Function GO id GO classification Observed in Expected in
Observed/ selected subset selected subset Expected 8094 DNA-dependent ATPase activity 5 1.28 3.92 46961 "hydrogen-transporting ATPase 6 1.65 3.63 activity\, rotational mechanism" 19887 protein kinase regulator 7 1.94 3.61 activity 16886 "ligase activity\, forming 7 1.94 3.61 phosphoric ester bonds' 19207 kinase regulator activity 7 2.03 3.45 16876 "ligase activity\, forming 6 1.8 3.34 aminoacyl-tRNA and related compounds' 16875 "ligase activity\, forming 6 1.8 3.34 carbon-oxygen bonds" 8452 RNA
ligase activity 6 1.8 3.34 4812 tRNA ligase activity 6 1.8 3.34 46933 'hydrogen-transporting ATP 5 1.51 3.31 synthase activity\, rotational mechanism" 4896 hematopoietinlinterferon-class 6 2.13 2.82 (D200-domain) cytokine receptor activity 8236 serine-type peptidase activity 8 2.98 2.69 8235 metalloexopeptidase activity 5 2.03 2.46 8083 growth factor activity 7 3.07 2.28 8238 exopeptidase activity 7 3.12 2.24 5125 cytokine activity 10 4.68 2.14 4252 serine-type endopeptidase 5 2.36 2.12 activity
129 258722_1 (GHMat.rs) P71850.AU Biological Process GO id GO classification Observed in Expected in Observed/ selected subset selected subset Expected 43039 tRNA aminoacylation 6 1.55 3.88 43038 amino acid activation 6 1.55 3.88 6418 tRNA aminoacylation for protein 6 1.55 3.88 translation 6400 tRNA modification 6 1.69 3.55 6261 DNA-dependent DNA replication 9 2.56 3.52 15986 ATP synthesis coupled proton 6 1.74, 3.45 transport 15985 'energy coupled proton 6 1.74 3.45 transport\, down
electrochemical gradient" 51052 regulation of DNA metabolism 5 1.55 3.24 45893 "positive regulation of 5 1.59 3.14 transcription\, DNA-dependent" 6754 ATP biosynthesis 6 1.98 3.03 6753 nucleoside phosphate metabolis 6 1.98 3.03 9451 RNA modification 6 2.17 2.76 6260 DNA replication 14 5.07 2.76 9615 response to virus 5 1.83 2.73 6119 oxidative phosphorylation 8 2.95 2.72 9206 purine ribonucleoside 6 2.22 2.7 triphosphate biosynthesis 9201 ribonucleoside triphosphate 6 2.22 2.7 biosynthesis 9145
purine nucleoside triphosphate 6 2.22 2.7 biosynthesis 9142 nucleoside triphosphate 6 2.22 2.7 biosynthesis 9152 purine ribonucleotide 7 2.7 2.59 biosynthesis 6399 tRNA metabolism 6 2.37 2.54 8203 cholesterol metabolism 5 1.98 2.53 6164 purine nucleotide biosynthesis 7 2.8 2.5 46034 ATP metabolism 6 2.41 2.49 82 G1/S transition of mitotic 5 2.03 2.47 cell cycle 9260 ribonucleotide biosynthesis 7 2.85 2.46 6752 group transfer coenzyme 6 2.46 2.44 metabolism 6869 lipid transport 5 2.12 2.35 45941
positive regulation of 5 2.17 2.3 transcription 45935 "positive regulation of 5 2.17 2.3 130 2568722. (GHMatrs) P71850AU nucleobase\, nucleoside\, nucleotide and nucleic acid metabolism" 9108 coenzyme biosynthesis 7 3.09 2.27 9205 purine ribonucleoside 6 2.66 2.26 triphosphate metabolism 9199 rbonucleoside triphosphate 6 2.66 2.26 metabolism 9144 purine nucleoside triphosphate 6 2.66 2.26 metabolism 16125 sterol metabolism 5 2.22 2.25 8202 steroid metabolism 9 4.01 2.25 7160 cell-matrix adhesion
5 2.22 2.25 9150 purine ribonucleotide 7 3.14 2.23 metabolism 67 DNA replication and chromosome 14 6.28 2.23 cycle 6163 purine nucleotide metabolism 7 3.24 2.16 46138 coenzyme and prosthetic group 8 3.72 2.15 biosynthesis 15992 proton transport 6 2.8 2.14 9141 nucleoside triphosphate 6 2.8 2.14 metabolism 6818 hydrogen transport 6 2.8 2.14 9259 ribonucleotide metabolism 7 3.38 2.07 131 2568722_ 1 (GHMatters) P71850AU 1. A method for identifying gene expression markers for distinguishing between healthy, febrile, or convalescence in subjects that have been exposed to one or more infectious pathogens comprising a) acquiring a gene expression profile for a subject that has been exposed to said one or more infectious pathogens by i) isolating RNA from a biological
sample from the subject that has been exposed to said one or more infectious pathogens; ii) removing DNA contaminants from said RNA; iii) spiking into said RNA a normalization control; iv) synthesizing cDNA from the RNA; v) in vitro transcribing cRNA from said cDNA and labeling said cRNA; vi) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and vii) acquiring a gene expression profile from said gene chip; b) acquiring a gene expression profile for a subject that
has recovered from exposure to said one or more infectious pathogens by i) isolating RNA from a biological sample from the subject that has recovered from exposure to said one or more infectious pathogens; ii) removing DNA contaminants from said RNA; iii) spiking into said RNA a normalization control; iv) synthesizing cDNA from the RNA; v) in vitro transcribing cRNA from said cDNA and labeling said cRNA; vi) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and
vii) acquiring a gene expression profile from said gene chip; c) acquiring a gene expression profile for a healthy subject that has not been exposed to said one or more infectious pathogens by i) isolating RNA from a biological sample from the healthy subject that has not been exposes to said one or more infectious pathogens; ii) removing DNA contaminants from said RNA; iii) spiking into said RNA a normalization control; iv) synthesizing cDNA from the RNA; v) in vitro transcribing cRNA from said
cDNA and labeling said cRNA; vi) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and vii) acquiring a gene expression profile from said gene chip; d) comparing the gene expression profiles for the subjects from (a), (b), and (c) by a pairwise comparison; e) determining a nested to minimal set(s) of genes that classify the subject as healthy, febrile, or convalescent by class prediction algorithm based on said pairwise comparison; and f) assigning a
classification of healthy, febrile, or convalescent based on the gene expression profile of the nested to minimal set(s) of genes determined in (e). 2. A method of classifying a subject in need thereof as healthy, febrile, or convalescence, comprising a) isolating RNA from a biological sample from said subject; b) removing DNA contaminants from said RNA; c) spiking into said RNA a normalization control; d) synthesizing cDNA from the RNA; e) in vitro transcribing cRNA
from said cDNA and labeling said cRNA; f) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning g) acquiring a gene expression profile from said gene chip and analyzing the gene expression profile represented by the RNA; and h) determining the gene expression profile in said subject of the minimal set of genes that classify the subject as healthy, febrile, or convalescent determined by the method of Claim 1; 132 2568722_1 (GHMattom) i) classifying the subject in need
thereof as being healthy, febrile, or convalescent by comparing the gene expression profile obtained in (h) to that of the classification assignment of healthy, febrile, or convalescent based on gene expression profile of the minimal set of genes as determined by the method of Claim 1. 3. The method of Claim 2, wherein said biological sample is whole blood. 4. The method of Claim 2, further comprising, between (b) and (c), - concentrating and
purifying said RNA. 5. The method of Claim 2, further comprising, between (c) and (d), - reducing and/or eliminating globin mRNA in said RNA. 6. The method of Claim 5, wherein said reducing and/or eliminating globin mRNA in said RNA comprises adding biotinylated globin capture oligos to said RNA to bind the globin mRNA and removing the resulting bound globin mRNA by strepavidin magnetic beads leaving globin-reduced RNA.
7. The method of Claim 6, further comprising further purifying the globin-reduced RNA by contacting said globin-reduced RNA with magnetic RNA beads. 8. The method of Claim 2, further comprising, coincident with (d), - reducing and/or eliminating globin mRNA in said RNA by adding PNA to said sample during said synthesizing cDNA. 9. The method of Claim 2, further comprising, between (f) and (g), repeating (f) with a second gene chip which is
distinct from said gene chip in (f), wherein in (g) following acquisition the gene expression profile from said first and second gene chips is merged. 10. The method of Claim 2, wherein the minimal set of genes to distinguish non-febrile from febrile patients comprises PDCD1 LG1, PLSCR1, FCGR1A, PLSCR1, FCGR1A, CEACAM1, SERPING1, TNFAIP6, ANKRD22, EPST11, FLJ39885, DNAPTP6, IF135, OAS1, PRV1, STK3, GBP1, GBP1, CASP5, IFIT4, GPR105, MGC20410, cig5, LOC129607, IF144,
GBP5, C10G, HSXIAPAF1, cig5, UPP1, PML, LAMP3, IFRG28, G1P2, Clorf29, IF144, LIPA, OAS1, MX1, SN, HSXIAPAF1, IFIT1, OAS2, and IF127. 11. The method of Claim 2, wherein the minimal set of genes to distinguish healthy versus convalescent patients comprises RPL27, RPS7, DAB2, LAMA2, IGHM, EVA1, and KREMEN1. 12. The method of Claim 2, wherein the minimal set of genes to distinguish febrile with adenovirus versus febrile without adenovirus
patients comprises ILl RAP, ZCCHC2, IF144, ZCCHC2, ZSIG11, NOP5/NOP58, LGALS3BP, MS4A7, LY6E, BTN3A3, and IF127. 13. The method of Claim 1 or Claim 2, substantially as hereinbefore described with refemce to the accompanying examples and/or figures. 133 2568722_1 (GHMatter) Free format text: THE NATURE OF THE AMENDMENT IS: AMEND THE INVENTION TITLE TO READ DIAGNOSIS AND PROGNOSIS OF INFECTIOUS DISEASE CLINICAL PHENOTYPES AND OTHER PHYSIOLOGIC STATES USING HOST GENE EXPRESSION BIOMARKERS IN BLOOD How do I find YouTube on Google?Sign in and go to myactivity.google.com to find videos that you've viewed on the YouTube app or website while signed in. It also includes videos you've watched in a YouTube player on other websites while signed in to Google Chrome.
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