Note: Within nine months of the publication of the mention of the grant of the European patent in the European PatentBulletin, any person may give notice to the European Patent Office of opposition to that patent, in accordance with theImplementing Regulations. Notice of opposition shall not be deemed to have been filed until the opposition fee has beenpaid. (Art. 99(1) European Patent Convention).

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(12) EUROPEAN PATENT SPECIFICATION

(45) Date of publication and mention of the grant of the patent: 24.04.2013 Bulletin 2013/17

(21) Application number: 07871360.9

(22) Date of filing: 03.11.2007

(51) Int Cl.:G06F 19/00 (2011.01)

(86) International application number: PCT/US2007/083555

(87) International publication number: WO 2008/100352 (21.08.2008 Gazette 2008/34)

(54) DIAGNOSIS OF METASTATIC MELANOMA AND MONITORING INDICATORS OF IMMUNOSUPPRESSION THROUGH BLOOD LEUKOCYTE MICROARRAY ANALYSIS

DIAGNOSE VON METASTASIERENDEM MELANOM UND ÜBERWACHUNG VON IMMUNSUPPRESSIONSINDIKATOREN MITTTELS LEUKOZYTEN-MIKROARRAY

DIAGNOSTIC DE MELANOME METASTATIQUE ET SURVEILLANCE D’INDICATEURS D’IMMUNOSUPPRESSION PAR ANALYSE DE MICRORESEAUX DE LEUCOCYTES SANGUINS

(84) Designated Contracting States: AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IS IT LI LT LU LV MC MT NL PL PT RO SE SI SK TR

(30) Priority: 03.11.2006 US 856406 P

(43) Date of publication of application: 22.07.2009 Bulletin 2009/30

(60) Divisional application: 12152482.1 / 2 506 17212196231.0 / 2 579 17412196232.8 / 2 570 951

(73) Proprietor: Baylor Research InstituteDallas, TX 75204 (US)

(72) Inventors: • PALUCKA, Anna Karolina

Dallas, TX 75204 (US)• BANCHEREAU, Jacques F.

Dallas, TX 75230 (US)• CHAUSSABEL, Damien

Richardson, TX 75082 (US)

(74) Representative: Sonn & Partner PatentanwälteRiemergasse 141010 Wien (AT)

(56) References cited: US-A1- 2005 222 029

• PAVEY SANDRA ET AL: "Microarray expression profiling in melanoma reveals a BRAF mutation signature", ONCOGENE, vol. 23, no. 23, 20 May 2004 (2004-05-20), pages 4060-4067, XP002644753, ISSN: 0950-9232

• ZHOU YOUWEN ET AL: "Osteopontin expression correlates with melanoma invasion", JOURNAL OF INVESTIGATIVE DERMATOLOGY, vol. 124, no. 5, May 2005 (2005-05), pages 1044-1052, XP002644754, ISSN: 0022-202X

• BITTNER M ET AL: "MOLECULAR CLASSIFICATION OF CUTANEOUS MALIGNANT MELANOMA BY GENE EXPRESSION PROFILING", NATURE, NATURE PUBLISHING GROUP, LONDON, GB, vol. 406, no. 6795, 3 August 2000 (2000-08-03), pages 536-540, XP000990000, ISSN: 0028-0836, DOI: DOI:10.1038/35020115

• CARR ET AL.: ’Gene-expression profiling in human cutaneous melanoma’ ONCOGENE vol. 22, 2003, pages 3073 - 3080, XP008109734

• DAVIES ET AL.: ’Mutations of the BRAF gene in human cancer’ NATURE vol. 417, June 2002, pages 949 - 954, XP001188240

• SHU ET AL.: ’RNCP1, an RNA-binding protein and a target of the p53 family, is required for maintaining the stability of the basal and stress-induced p21 transcript’ GENES AND DEVELOPMENT vol. 20, 2006, pages 2961 - 2972, XP008109736

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• ZHANG ET AL.: ’Identification of Direct Serum-response Factor Gene Targets during Me2So-induced P19 Cardiac Cell Differentiation’ JOURNAL OF BIOLOGICAL CHEMISTRY vol. 280, May 2005, pages 19115 - 19126, XP008109738

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Description

TECHNICAL FIELD OF THE INVENTION

[0001] The present invention relates in general to the field of diagnostic for monitoring indicators of metastatic melanomaand/or immunosuppression, and more particularly, to a system, method and apparatus for the diagnosis, prognosis andtracking of metastatic melanoma and monitoring indicators of immunosuppression associated with transplant recipients(e.g., liver).

BACKGROUND OF THE INVENTION

[0002] Pharmacological immunosuppression has been instrumental in transplantation, transforming a last resort ex-perimental procedure into a routinely successful one. Cyclosporin and tacrolimus/FK506 currently constitute the mainstayfor transplant recipients. Activated T cells have been considered the main cellular targets for immunosuppressive treat-ments, but recent reports have also noted a marked effect of these drugs on antigen presenting cells, probably contributingfurther to the establishment of a generalized state of immune unresponsiveness (Lee et al., 2005b; Woltman et al.,2003). While severe immunosuppression generated by pharmacological treatment is necessary for the maintenance ofgraft survival and function, it also exposes the recipient to life-threatening infections and malignancies. Skin cancer isa well-established complication in organ transplant recipients (Gerlini et al., 2005). A recent epidemiological study showedthat nearly 20% of 1115 renal transplant patients developed skin malignancies in Europe (Bordea et al., 2004), withmuch higher incidences being observed in less temperate climates, e.g., as high as 28% in Australia (Carroll et al.,2003). In addition to occurring more often, skin malignancies tend to take a more aggressive clinical course in transplantedpatients, with a higher propensity to metastasize distantly and lead to a fatal outcome (Barrett et al., 1993).[0003] Tumors maintain their survival by compromising the immune system. Different mechanisms have been identifiedincluding secretion of immunosuppressive factors such as cytokines (e.g., IL-10, TGF-beta), hormones (e.g., Prostag-landin E2), and others (e.g., MIA: Melanoma inhibitory activity, Tenascin C)(Jachimczak et al., 2005; Puente Navazo etal., 2001). Furthermore, tumors might promote development of suppressor T cells (Liyanage et al., 2002; Viguier et al.,2004), possibly via modulating dendritic cells (Gabrilovich, 2004; Lee et al., 2005a; Monti et al., 2004).[0004] Thus, immunosuppression, whether from tumors or pharmacological treatments, has been linked to cancerprogression. Therefore, a common molecular signature could be identified by profiling genome-wide transcriptionalactivity in peripheral blood mononuclear cell ("PBMC") samples obtained from immunosuppressed patients with eithermetastatic melanoma or liver allografts. The analysis of Leukocyte transcriptional profiles generated in the context ofthe present study supports this notion and identifies a blood signature of immunosuppression.[0005] In Pavey S et al. (Oncogene 23(2004): 4060-4067) a microarray is described which can be used to predict thepresence of BRAF mutations in a panel of 61 melanoma cell lines.[0006] Zhou Y et al. (J Invest Dermatol 124(2005): 1044-1052) describe the overexpression of over 190 genes inmetastatic melanomas. Thereby they noted that one of the most abundantly expressed genes in metastatic melanomamodules is osteopontin.[0007] In Bittner M et al. (Nature 406(2000): 536-540) gene expression profiling was used to clarify cutaneous malignantmelanomas.

SUMMARY OF THE INVENTION

[0008] Genomic research is facing significant challenges with the analysis of transcriptional data that are notoriouslynoisy, difficult to interpret and do not compare well across laboratories and platforms. The present inventors havedeveloped an analytical strategy emphasizing the selection of biologically relevant genes at an early stage of the analysis,which are consolidated into analytical modules that overcome the inconsistencies among microarray platforms. Thetranscriptional modules developed may be used for the analysis of large gene expression datasets. The results derivedfrom this analysis are easily interpretable and particularly robust, as demonstrated by the high degree of reproducibilityobserved across commercial microarray platforms.[0009] Applications for this analytical process are illustrated through the mining of a large set of PBMC transcriptionalprofiles. Twenty-eight transcriptional modules regrouping 4,742 genes were identified. Using the present invention is itpossible to demonstrate that diseases are uniquely characterized by combinations of transcriptional changes in, e.g.,blood leukocytes, measured at the modular level. Indeed, module-level changes in blood leukocytes transcriptionallevels constitute the molecular fingerprint of a disease or sample.[0010] This invention has a broad range of applications. It can be used to characterize modular transcriptional com-ponents of any biological system (e.g., peripheral blood mononuclear cells (PBMCs), blood cells, fecal cells, peritonealcells, solid organ biopsies, resected tumors, primary cells, cells lines, cell clones, etc.). Modular PBMC transcriptional

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data generated through this approach can be used for molecular diagnostic, prognostic, assessment of disease severity,response to drug treatment, drug toxicity, etc. Other data processed using this approach can be employed for instancein mechanistic studies, or screening of drug compounds. In fact, the data analysis strategy and mining algorithm can beimplemented in generic gene expression data analysis software and may even be used to discover, develop and testnew, disease- or condition-specific modules. The present invention may also be used in conjunction with pharmacoge-nomics, molecular diagnostic, bioinformatics and the like, wherein in-depth expression data may be used to improve theresults (e.g., by improving or sub-selecting from within the sample population) that mat be obtained during clinical trails.[0011] More particularly, the present invention includes methods for diagnosing a disease or condition by obtainingthe transcriptome of a patient; analyzing the transcriptome based on one or more transcriptional modules that areindicative of a disease or condition; and determining the patient’s disease or condition based on the presence, absenceor level of expression of genes within the transcriptome in the one or more transcriptional modules. The transcriptionalmodules may be obtained by: iteratively selecting gene expression values for one or more transcriptional modules by:selecting for the module the genes from each cluster that match in every disease or condition; removing the selectedgenes from the analysis; and repeating the process of gene expression value selection for genes that cluster in asub-fraction of the diseases or conditions; and iteratively repeating the generation of modules for each clusters until allgene clusters are exhausted.[0012] Examples of clusters selected for use with the present invention include, but are not limited to, expression valueclusters, keyword clusters, metabolic clusters, disease clusters, infection clusters, transplantation clusters, signalingclusters, transcriptional clusters, replication clusters, cell-cycle clusters, siRNA clusters, miRNA clusters, mitochondrialclusters, T cell clusters, B cell clusters, cytokine clusters, lymphokine clusters, heat shock clusters and combinationsthereof. Examples of diseases or conditions for analysis using the present invention include, e.g., autoimmune disease,a viral infection a bacterial infection, cancer and transplant rejection. More particularly, diseases for analysis may beselected from one or more of the following conditions: systemic juvenile idiopathic arthritis, systemic lupus erythematosus,type I diabetes, liver transplant recipients, melanoma patients, and patients bacterial infections such as Escherichia coli,Staphylococcus aureus, viral infections such as influenza A, and combinations thereof. Specific array may even be madethat detect specific diseases or conditions associated with a bioterror agent.[0013] Cells that may be analyzed using the present invention, include, e.g., peripheral blood mononuclear cells(PBMCs), blood cells, fetal cells, peritoneal cells, solid organ biopsies, resected tumors, primary cells, cells lines, cellclones and combinations thereof. The cells may be single cells, a collection of cells, tissue, cell culture, cells in bodilyfluid, e.g., blood. Cells may be obtained from a tissue biopsy, one or more sorted cell populations, cell culture, cell clones,transformed cells, biopies or a single cell. The types of cells may be, e.g., brain, liver, heart, kidney, lung, spleen, retina,bone, neural, lymph node, endocrine gland, reproductive organ, blood, nerve, vascular tissue, and olfactory epitheliumcells. After cells are isolated, these mRNA from these cells is obtained and individual gene expression level analysis isperformed using, e.g., a probe array, PCR, quantitative PCR, bead-based assays and combinations thereof. The indi-vidual gene expression level analysis may even be performed using hybridization of nucleic acids on a solid supportusing cDNA made from mRNA collected from the cells as a template for reverse transcriptase.[0014] Pharmacological immunosuppression promotes graft survival in transplant recipients. Endogenous immuno-suppression promotes tumor survival in cancer-bearing patients. Leukocytes from patients with metastatic melanomadisplay an endogenous immunosuppression signature common with liver transplant recipients under pharmacologicalimmunosuppression. Blood microarray analyses were carried out in 25 healthy volunteers, 35 patients with metastaticmelanoma, and 39 liver transplant recipients. Disease signatures were identified, and confirmed in an independentdataset, in comparison to healthy controls. Analysis of a set of 69 transcripts over-expressed preferentially in melanomaand transplant groups in comparison to six other diseases revealed remarkable functional convergence, including severalrepressors of interleukin-2 transcription, powerful inhibitors of NF-kappaB and MAPK pathways as well as antiproliferativemolecules. Thus, patients with metastatic melanoma display an endogenous transcriptional signature of immunosup-pression. This signature may now be used to identify patients at the high risk of metastatic melanoma progression.[0015] The present invention includes a system and a method to analyze samples for the prognosis and diagnosis ofmetastatic melanoma and/or monitoring indicators of immunosuppression associated with transplant recipients (e.g.,liver) using multiple variable gene expression analysis. The gene expression differences that remain can be attributedwith a high degree of confidence to the unmatched variation. The gene expression differences thus identified can beused, for example, to diagnose disease, identify physiological state, design drugs and monitor therapies.[0016] The sample may be screened by quantitating the mRNA, protein or both mRNA and protein level of the ex-pression vector. When the screening is for mRNA levels, it may be quantitated by a method selected from polymerasechain reaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hybridization,probe hybridization, and gene expression array. The screening method may also include detection of polymorphismsin the biomarker. Alternatively, the screening step may be accomplished using at least one technique selected from thegroup consisting of polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphism anal-ysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting,

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enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing. For use with the presentinvention the sample may be any of a number of immune cells, e.g., leukocytes or sub-components thereof.[0017] The present invention relates to a method of identifying a patient with melanoma by examining the phenotypea sample for combinations of six, seven, eight, ten, fifteen, twenty, twenty-five or more genes selected from modulesM1.1 and M1.2 of Table 12.[0018] In one aspect, the present invention includes a method of identifying gene expression response to pharmaco-logical immunosuppression in transplant recipients by examining the phenotype a sample for combinations of six, seven,eight, ten, fifteen, twenty, twenty-five or more genes selected from modules M1.1 and M1.2 of Table 13.[0019] The sample may be screened by quantitating the mRNA, protein or both mRNA and protein level of the ex-pression vector. When mRNA level is examined, it may be quantitated by a method selected from polymerase chainreaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hybridization, probehybridization, and gene expression array. The screening method may also include detection of polymorphisms in thebiomarker. Alternatively, the screening step may be accomplished using at least one technique selected from the groupconsisting of polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphism analysis,ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting, en-zyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing. For use with the presentinvention the sample may be any of a number of immune cells, e.g., leukocytes or sub-components thereof.[0020] The expression vector may be screened by quantitating the mRNA, protein or both mRNA and protein level ofthe expression vector. When the expression vector is mRNA level, it may be quantitated by a method selected frompolymerase chain reaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hy-bridization, probe hybridization, and gene expression array. The screening method may also include detection of poly-morphisms in the biomarker. Alternatively, the screening step may be accomplished using at least one technique selectedfrom the group consisting of polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphismanalysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting,enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing. For use with the presentinvention the sample may be any of a number of immune cells, e.g., leukocytes or sub-components thereof.[0021] For example, a method of identifying a subject with melanoma by determining a database that includes thelevel of expression of one or more metastatic melanoma expression vectors. Another embodiment of the present inventionincludes a computer implemented method for determining the genotype of a sample by obtaining a plurality of sampleprobe intensities. Diagnosing metastatic melanoma is based upon the sample probe intensities and calculating a linearcorrelation coefficient between the sample probe intensities and reference probe intensities.[0022] The present invention also includes a computer readable medium including computer-executable instructionsfor performing the method of determining the genotype of a sample. The method of determining the phenotype includesobtaining a plurality of sample probe intensities and diagnosing melanoma based upon the sample probe intensities fortwo or more metastatic melanoma expression vectors selected from those listed in modules M1.1 and M1.2 of Table 12into a dataset; and calculating a linear correlation coefficient between the sample probe intensities and a reference probeintensity. The tentative phenotype is accepted as the phenotype of the sample if the linear correlation coefficient isgreater than a threshold value. In addition, the present invention includes a microarray for identifying a human subjectwith melanoma. A microarray includes the detection of expression of two or more metastatic melanoma genes listed inmodules M1.1 and M1.2 of Table 12 into a dataset. The present invention provides a method of distinguishing betweenmetastatic melanoma and immunosuppression associated with transplants by determining the level of expression ofone or more genes, and calculating one or more gene expression vectors. The melanoma-specific transcriptome-ex-pression vectors may include values for the upregulation or downregulation of six or more genes listed in modules M1.1and M1.2 of Table 12. The present invention provides a method of identifying a subject with immunosuppression asso-ciated with transplants by determining the level of expression of one or more immunosuppression associated expressionvectors. The immunosuppression-specific transcriptome-expression vectors may include values for the upregulation ordownregulation of six or more genes listed in modules M1.1 and M1.2 of Table 13.[0023] A computer readable medium is also included that has computer-executable instructions for performing themethod for determining the phenotype of a sample. The method for determining the phenotype of a sample includesobtaining a plurality of sample probe intensities and diagnosing immunosuppression based upon the sample probeintensities for two or more immunosuppression associated expression vectors. A linear correlation coefficient is calculatedbetween the sample probe intensities and a reference probe intensity and a tentative phenotype is accepted as thephenotype of the sample if the linear correlation coefficient is greater than a threshold value. The present invention alsoincludes a system for diagnosing immunosuppression including an expression level detector for determining the expres-sion level of two or more immunosuppression expression vectors selected from the 1, 2, 3, 4, 5, 6, 8, 10, 15, 20, 25 ormore genes. The melanoma-specific transcriptome-expression vectors that are used to generate expression data foreach gene, which is saved into a dataset, may include values for the upregulation or downregulation of six or more geneslisted in modules M1.1 and M1.2 of Table 12. The immunosuppression-specific transcriptome-expression vectors may

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include values in a dataset that includes the upregulation or downregulation of six or more genes listed in modules M1.1and M1.2 of Table 13. The presence of melanoma or immunosuppression is determined based on the presence of twoor more positive indicators in a gene expression vector dataset.[0024] The arrays, methods and systems of the present invention may even be used to select patients for a clinicaltrial by obtaining the transcriptome of a prospective patient; comparing the transcriptome to one or more transcriptionalmodules that are indicative of a disease or condition that is to be treated in the clinical trial; and determining the likelihoodthat a patient is a good candidate for the clinical trial based on the presence, absence or level of one or more genesthat are expressed in the patient’s transcriptome within one or more transcriptional modules that are correlated withsuccess in a clinical trial. Generally, for each module a vector that correlates with a sum of the proportion of transcriptsin a sample may be used, e.g., when each module includes a vector and wherein one or more diseases or conditionsis associated with the one or more vectors. Therefore, each module may include a vector that correlates to the expressionlevel of one or more genes within each module.[0025] Herein arrays, e.g., custom microarrays are described that include nucleic acid probes immobilized on a solidsupport that includes sufficient probes from one or more expression vectors to provide a sufficient proportion of differ-entially expressed genes to distinguish between one or more diseases. For example, an array of nucleic acid probesimmobilized on a solid support, in which the array includes at least two sets of probe modules, wherein the probes inthe first probe set have one or more interrogation positions respectively corresponding to one or more diseases. Thearray may have between 100 and 100,000 probes, and each probe may be, e.g., 9-21 nucleotides long. When separatedinto organized probe sets, these may be interrogated separately.[0026] One or more nucleic acid probes immobilized on a solid support to form a module array are also described thatincludes at least one pair of first and second probe groups, each group having one or more probes as defined by Table1. The probe groups are selected to provide a composite transcriptional marker vector that is consistent across microarrayplatforms. In fact, the probe groups may even be used to provide a composite transcriptional marker vector that isconsistent across microarray platforms and displayed in a summary for regulatory approval. The skilled artisan willappreciate that using the modules of the present invention it is possible to rapidly develop one or more disease specificarrays that may be used to rapidly diagnose or distinguish between different disease and/or conditions.[0027] A method for displaying transcriptome vector data from a transcriptome vector dataset by separating one ormore genes into one or more modules to visually display an aggregate gene expression vector value for each of themodules; and displaying the aggregate gene expression vector value for overexpression, underexpression or equalexpression of the aggregate gene expression vector value in each module is also described. In one example, overex-pression is identified with a first identifier and underexpression is identified with a second identifier. Examples of identifiersinclude colors, shapes, patterns, light/dark, on/off, symbols and combinations thereof. For example, overexpression isidentified with a first identifier and underexpression is identified with a second identifier, wherein the first identifier is afirst color and the second identifier is a second color, and wherein first and second identifiers are superimposed toprovide a combined color.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] For a more complete understanding of the features and advantages of the present invention, reference is nowmade to the detailed description of the invention along with the accompanying figures and in which:

FIGURES 1A to 1C show the basic microarray data mining strategy steps involved in accepted gene-level microarraydata analysis (FIGURE 1A), the modular mining strategy of the present invention FIGURE 1B and a full size repre-sentation of the module extraction algorithm FIGURE 1C to generate on or more datasets used to create theexpression vectors;

FIGURE 2 is a graph representing transcriptional profiles showing levels of modular gene expression profiles acrossan independent group of samples;

FIGURE 3 is a distribution of keyword occurrence in the literature obtained for four sets of coordinately expressedgenes;

FIGURE 4 illustrates a modular microarray analysis strategy for characterization of the transcriptional system;

FIGURE 5 is an analysis of patient blood leukocyte transcriptional profiles;

FIGURE 6 illustrates module maps of transcriptional changes caused by disease;

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FIGURE 7 illustrates the identification of a blood leukocyte transcriptional signature in patients with metastaticmelanoma;

FIGURE 8 illustrates the validation of microarray results in an independent set of samples;

FIGURE 9 illustrates the identification of a blood leukocyte transcriptional signature in transplant recipients underimmunosuppressive drug therapy

FIGURE 10-13 illustrate detailed results of the module-level analysis;

FIGURE 14 illustrates the module-level analysis for distinctive transcriptional signatures in blood from patients withmetastatic melanoma and from liver transplant recipients;

FIGURE 15 illustrates the mapping transcriptional changes in patient blood leukocytes at the module level;

FIGURE 16 illustrates the module-level analysis for common transcriptional signatures in blood from patients withmetastatic melanoma and from liver transplant recipients;

FIGURE 17 illustrates the analysis of significance patterns with genes expressed at higher levels in both melanomaand liver transplant patients compared to healthy volunteers;

FIGURE 18 illustrates the modular distribution of ubiquitous and specific gene signatures common to melanomaand transplant groups;

FIGURE 19 illustrates the transcriptional signature of immunosuppression;

FIGURE 20 shows a statistical group comparison between patients and their respective controls;

FIGURE 21 shows the analysis of significance patterns for genes over-expressed in SLE patients but not in patientswith acute Influenza A infection;

FIGURE 22 shows the patterns of significance for genes common to Influenza A and SLE; and

FIGURE 23 is a functional analysis of genes shared by patients with Influenza infection and Lupus grouped accordingto significance patterns.

DETAILED DESCRIPTION OF THE INVENTION

[0029] While the making and using of various embodiments of the present invention are discussed in detail below, itshould be appreciated that the present invention provides many applicable inventive concepts that can be embodied ina wide variety of specific contexts. The specific embodiments discussed herein are merely illustrative of specific waysto make and use the invention and do not delimit the scope of the invention.[0030] To facilitate the understanding of this invention, a number of terms are defined below. Terms defined hereinhave meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention.Terms such as "a", "an" and "the" are not intended to refer to only a singular entity, but include the general class of whicha specific example may be used for illustration. The terminology herein is used to describe specific embodiments of theinvention, but their usage does not delimit the invention, except as outlined in the claims. Unless defined otherwise, alltechnical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to whichthis invention belongs. The following references provide one of skill with a general definition of many of the terms usedin this invention: Singleton, et al., Dictionary Of Microbiology And Molecular Biology (2d ed. 1994); The CambridgeDictionary Of Science And Technology (Walker ed., 1988); The Glossary Of Genetics, 5th Ed., R. Rieger et al. (eds.),Springer Verlag (1991); and Hale & Marham, The Harper Collins Dictionary Of Biology (1991).[0031] Various biochemical and molecular biology methods are well known in the art. For example, methods of isolationand purification of nucleic acids are described in detail in WO 97/10365, WO 97/27317, Chapter 3 of Laboratory Tech-niques in Biochemistry and Molecular Biology: Hybridization With Nucleic Acid Probes, Part I. Theory and Nucleic AcidPreparation, (P. Tijssen, ed.) Elsevier, N.Y. (1993); Chapter 3 of Laboratory Techniques in Biochemistry and MolecularBiology: Hybridization With Nucleic Acid Probes, Part 1. Theory and Nucleic Acid Preparation, (P. Tijssen, ed.) Elsevier,N.Y. (1993); and Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, N.Y., (1989);

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and Current Protocols in Molecular Biology, (Ausubel, F. M. et al., eds.) John Wiley & Sons, Inc., New York (1987-1999),including supplements such as supplement 46 (April 1999).

BIOINFORMATICS DEFINITIONS

[0032] As used herein, an "object" refers to any item or information of interest (generally textual, including noun, verb,adjective, adverb, phrase, sentence, symbol, numeric characters, etc.). Therefore, an object is anything that can forma relationship and anything that can be obtained, identified, and/or searched from a source. "Objects" include, but arenot limited to, an entity of interest such as gene, protein, disease, phenotype, mechanism, drug, etc. In some aspects,an object may be data, as further described below.[0033] As used herein, a "relationship" refers to the co-occurrence of objects within the same unit (e.g., a phrase,sentence, two or more lines of text, a paragraph, a section of a webpage, a page, a magazine, paper, book, etc.). It maybe text, symbols, numbers and combinations, thereof[0034] As used herein, "meta data content" refers to information as to the organization of text in a data source. Metadata can comprise standard metadata such as Dublin Core metadata or can be collection-specific. Examples of metadataformats include, but are not limited to, Machine Readable Catalog (MARC) records used for library catalogs, ResourceDescription Format (RDF) and the Extensible Markup Language (XML). Meta objects may be generated manually orthrough automated information extraction algorithms.[0035] As used herein, an "engine" refers to a program that performs a core or essential function for other programs.For example, an engine may be a central program in an operating system or application program that coordinates theoverall operation of other programs. The term "engine" may also refer to a program containing an algorithm that can bechanged. For example, a knowledge discovery engine may be designed so that its approach to identifying relationshipscan be changed to reflect new rules of identifying and ranking relationships.[0036] As used herein, "semantic analysis" refers to the identification of relationships between words that representsimilar concepts, e.g., though suffix removal or stemming or by employing a thesaurus. "Statistical analysis" refers to atechnique based on counting the number of occurrences of each term (word, word root, word stem, n-gram, phrase,etc.). In collections unrestricted as to subject, the same phrase used in different contexts may represent different concepts.Statistical analysis of phrase co-occurrence can help to resolve word sense ambiguity. "Syntactic analysis" can be usedto further decrease ambiguity by part-of-speech analysis. As used herein, one or more of such analyses are referred tomore generally as "lexical analysis." "Artificial intelligence (AI)" refers to methods by which a non-human device, suchas a computer, performs tasks that humans would deem noteworthy or "intelligent." Examples include identifying pictures,understanding spoken words or written text, and solving problems.[0037] As used herein, the term "database" or "dataset" refer to repositories for raw or compiled data, even if variousinformational facets can be found within the data fields. A database is typically organized so its contents can be accessed,managed, and updated (e.g., the database is dynamic). The term "database" and "source" are also used interchangeablyin the present invention, because primary sources of data and information are databases. However, a "source database"or "source data" refers in general to data, e.g., unstructured text and/or structured data, which are input into the systemfor identifying objects and determining relationships. A source database may or may not be a relational database.However, a system database usually includes a relational database or some equivalent type of database or datasetwhich stores or includes stored values relating to relationships between objects.[0038] As used herein, a "system database" and "relational database" are used interchangeably and refer to one ormore collections of data organized as a set of tables containing data fitted into predefined categories. For example, adatabase table may comprise one or more categories defined by columns (e.g. attributes), while rows of the databasemay contain a unique object for the categories defined by the columns. Thus, an object such as the identity of a genemight have columns for its presence, absence and/or level of expression of the gene. A row of a relational databasemay also be referred to as a "set" and is generally defined by the values of its columns. A "domain" in the context of arelational database is a range of valid values a field such as a column may include.[0039] As used herein, a "domain of knowledge" refers to an area of study over which the system is operative, forexample, all biomedical data. It should be pointed out that there is advantage to combining data from several domains,for example, biomedical data and engineering data, for this diverse data can sometimes link things that cannot be puttogether for a normal person that is only familiar with one area or research/study (one domain). A "distributed database"refers to a database that may be dispersed or replicated among different points in a network.[0040] Terms such "data" and "information" are often used interchangeably, as are "information" and "knowledge." Asused herein, "data" is the most fundamental unit that is an empirical measurement or set of measurements. Data iscompiled to contribute to information, but it is fundamentally independent of it. Information, by contrast, is derived frominterests, e.g., data (the unit) may be gathered on ethnicity, gender, height, weight and diet for the purpose of findingvariables correlated with risk of cardiovascular disease. However, the same data could be used to develop a formula orto create "information" about dietary preferences, i.e., likelihood that certain products in a supermarket have a higher

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likelihood of selling.[0041] As used herein, "information" refers to a data set that may include numbers, letters, sets of numbers, sets ofletters, or conclusions resulting or derived from a set of data. "Data" is then a measurement or statistic and the fundamentalunit of information. "Information" may also include other types of data such as words, symbols, text, such as unstructuredfree text, code, etc. "Knowledge" is loosely defined as a set of information that gives sufficient understanding of a systemto model cause and effect. To extend the previous example, information on demographics, gender and prior purchasesmay be used to develop a regional marketing strategy for food sales while information on nationality could be used bybuyers as a guideline for importation of products. It is important to note that there are no strict boundaries between data,information, and knowledge; the three terms are, at times, considered to be equivalent. In general, data comes fromexamining, information comes from correlating, and knowledge comes from modeling.[0042] As used herein, "a program" or "computer program" refers generally to a syntactic unit that conforms to therules of a particular programming language and that is composed of declarations and statements or instructions, divisibleinto, "code segments" needed to solve or execute a certain function, task, or problem. A programming language isgenerally an artificial language for expressing programs.[0043] As used herein, a "system" or a "computer system" generally refers to one or more computers, peripheralequipment, and software that perform data processing. A "user" or "system operator" in general includes a person, thatuses a computer network accessed through a "user device" (e.g., a computer, a wireless device, etc) for the purposeof data processing and information exchange. A "computer" is generally a functional unit that can perform substantialcomputations, including numerous arithmetic operations and logic operations without human intervention.[0044] As used herein, "application software" or an "application program" refers generally to software or a programthat is specific to the solution of an application problem. An "application problem" is generally a problem submitted byan end user and requiring information processing for its solution.[0045] As used herein, a "natural language" refers to a language whose rules are based on current usage withoutbeing specifically prescribed, e.g., English, Spanish or Chinese. As used herein, an "artificial language" refers to alanguage whose rules are explicitly established prior to its use, e.g., computer-programming languages such as C, C++,Java, BASIC, FORTRAN, or COBOL.[0046] As used herein, "statistical relevance" refers to using one or more of the ranking schemes (O/E ratio, strength,etc.), where a relationship is determined to be statistically relevant if it occurs significantly more frequently than wouldbe expected by random chance.[0047] As used herein, the terms "coordinately regulated genes" or "transcriptional modules" are used interchangeablyto refer to grouped, gene expression profiles (e.g., signal values associated with a specific gene sequence) of specificgenes. Each transcriptional module correlates two key pieces of data, a literature search portion and actual empiricalgene expression value data obtained from a gene microarray. The set of genes that is selected into a transcriptionalmodule based on the analysis of gene expression data (using the module extraction algorithm described above). Addi-tional steps are taught by Chaussabel, D. & Sher, A. Mining microarray expression data by literature profiling. GenomeBiol 3, RESEARCH0055 (2002), (http://genomebiology.com/2002/3/10/research/0055) and expression data obtainedfrom a disease or condition of interest, e.g., Systemic Lupus erythematosus, arthritis, lymphoma, carcinoma, melanoma,acute infection, autoimmune disorders, autoinflammatory disorders, etc.).[0048] The Table below lists examples of keywords that were used to develop the literature search portion or contributionto the transcription modules. The skilled artisan will recognize that other terms may easily be selected for other conditions,e.g., specific cancers, specific infectious disease, transplantation, etc. For example, genes and signals for those genesassociated with T cell activation are described hereinbelow as Module ID "M 2.8" in which certain keywords (e.g.,Lymphoma, T-cell, CD4, CD8, TCR, Thymus, Lymphoid, IL2) were used to identify key T-cell associated genes, e.g.,T-cell surface markers (CD5, CD6, CD7, CD26, CD28, CD96); molecules expressed by lymphoid lineage cells (lympho-toxin beta, IL2-inducible T-cell kinase, TCF7; and T-cell differentiation protein mal, GATA3, STAT5B). Next, the completemodule is developed by correlating data from a patient population for these genes (regardless of platform, presence/absence and/or up or downregulation) to generate the transcriptional module. In some cases, the gene profile does notmatch (at this time) any particular clustering of genes for these disease conditions and data, however, certain physiologicalpathways (e.g., cAMP signaling, zinc-finger proteins, cell surface markers, etc.) are found within the "Underdetermined"modules. In fact, the gene expression data set may be used to extract genes that have coordinated expression prior tomatching to the keyword search, i.e., either data set may be correlated prior to cross-referencing with the second data set.

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Table 1. Examples of Genes within Distinct Modules

Module I.D. Number of probe sets Keyword selection Assessment

M 1.1 76 Ig, Immunoglobulin, Bone, Marrow, PreB, IgM,Mu.

Plasma cells. Includes genes coding for Immunoglobulin chains (e.g. IGHM, IGJ, IGLL1, IGKC, IGHD) and the plasma cell marker CD38.

M 1.2 130 Platelet, Adhesion, Aggregation, Endothelial, Vascular

Platelets. Includes genes coding for platelet glycoproteins (ITGA2B, ITGB3, GP6, GP1A/B), and platelet-derived immune mediators such as PPPB (pro-platelet basic protein) and PF4 (platelet factor 4).

M 1.3 80 Immunoreceptor, BCR, B-cell, IgG B-cells. Includes genes coding for B-cell surface markers (CD72, CD79A/B, CD19, CD22) and other B-cell associated molecules: Early B-cell factor (EBF), B-cell linker (BLNK) and B lymphoid tyrosine kinase (BLK).

M 1.4 132 Replication, Repression, Repair, CREB, Lymphoid, TNF-alpha

Undetermined. This set includes regulators and targets of cAMP signaling pathway (JUND, ATF4, CREM, PDE4, NR4A2, VIL2), as well as repressors of TNF-alpha mediated NF-KB activation (CYLD, ASK, TNFAIP3).

M 1.5 142 Monocytes, Dendritic, MHC, Costimulatory, TLR4, MYD88

Myeloid lineage. Includes molecules expressed by cells of the myeloid lineage (CD86, CD163, FCGR2A), some of which being involved in pathogen recognition (CD14, TLR2, MYD88). This set also includes TNF family members (TNFR2, BAFF).

M 1.6 141 Zinc, Finger, P53, RAS Undetermined. This set includes genes coding for signaling molecules, e.g. the zinc finger containing inhibitor of activated STAT (PIAS1 and PIAS2), or the nuclear factor of activated T-cells NFATC3.

M 1.7 129 Ribosome, Translational, 40S, 60S, HLA

MHC/Ribosomal proteins. Almost exclusively formed by genes coding MHC class I molecules (HLA-A,B,C,G,E)+ Beta 2-microglobulin (B2M) or Ribosomal proteins (RPLs, RPSs).

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(continued)

Module I.D. Number of probe sets Keyword selection Assessment

M 1.8 154 Metabolism, Biosynthesis, Replication, Helicase

Undetermined. Includes genes encoding metabolic enzymes (GLS, NSF1, NAT1) and factors involved in DNA replication (PURA, TERF2, EIF2S1).

M 2.1 95 NK, Killer, Cytolytic, CD8, Cell-mediated, T-cell, CTL, IFN-g

Cytotoxic cells. Includes cytotoxic T-cells amd NK-cells surface markers (CD8A, CD2, CD160, NKG7, KLRs), cytolytic molecules (granzyme, perforin, granulysin), chemokines (CCL5, XCL1) and CTL/NK-cell associated molecules (CTSW).

M 2.2 49 Granulocytes, Neutrophils, Defense, Myeloid, Marrow

Neutrophils. This set includes innate molecules that are found in neutrophil granules (Lactotransferrin: LTF, defensin: DEAF1, Bacterial Permeability Increasing protein: BPI, Cathelicidin antimicrobial protein: CAMP...).

M 2.3 148 Erythrocytes, Red, Anemia, Globin, Hemoglobin

Erythrocytes. Includes hemoglobin genes (HGBs) and other erythrocyte-associated genes (erythrocytic alkirin:ANK1, Glycophorin C: GYPC, hydroxymethylbilane synthase: HMBS, erythroid associated factor: ERAF).

M 2.4 133 Ribonucleoprotein, 60S, nucleolus, Assembly, Elongation

Ribosomal proteins. Including genes encoding ribosomal proteins (RPLs, RPSs), Eukaryotic Translation Elongation factor family members (EEFs) and Nucleolar proteins (NPM1, NOAL2, NAP1L1).

M 2.5 315 Adenoma, Interstitial, Mesenchyme, Dendrite, Motor

Undetermined. This module includes genes encoding immune-related (CD40, CD80, CXCL12, IFNA5, IL4R) as well as cytoskeleton-related molecules (Myosin, Dedicator of Cytokenesis, Syndecan 2, Plexin C1, Distrobrevin).

M 2.6 165 Granulocytes, Monocytes, Myeloid, ERK, Necrosis

Myeloid lineage. Includes genes expressed in myeloid lineage cells (IGTB2/CD18, Lymphotoxin beta receptor, Myeloid related proteins 8/14 Formyl peptide receptor 1), such as Monocytes and Neutrophils.

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Module I.D. Number of probe sets Keyword selection Assessment

M 2.7 71 No keywords extracted. Undetermined. This module is largely composed of transcripts with no known function. Only 20 genes associated with literature, including a member of the chemokine-like factor superfamily (CKLFSF8).

M 2.8 141 Lymphoma, T-cell, CD4, CD8, TCR, Thymus, Lymphoid, IL2

T-cells. Includes T-cell surface markers (CD5, CD6, CD7, CD26, CD28, CD96) and molecules expressed by lymphoid lineage cells (lymphotoxin beta, IL2-inducible T-cell kinase, TCF7, T-cell differentiation protein mal, GATA3, STAT5B).

M 2.9 159 ERK, Transactivation, Cytoskeletal, MAPK, JNK

Undetermined. Includes genes encoding molecules that associate to the cytoskeleton (Actin related protein 2/3, MAPK1, MAP3K1, RAB5A). Also present are T-cell expressed genes (FAS, ITGA4/CD49D, ZNF1A1).

M2.10 106 Myeloid, Macrophage, Dendritic, Inflammatory, Interleukin

Undetermined. Includes genes encoding for Immune-related cell surface molecules (CD36, CD86, LILRB), cytokines (IL15) and molecules involved in signaling pathways (FYB, TICAM2-Toll-like receptor pathway).

M 2.11 176 Replication, Repress, RAS, Autophosphorylation, Oncogenic

Undetermined. Includes kinases (UHMK1, CSNK1G1, CDK6, WNK1, TAOK1, CALM2, PRKCI, ITPKB, SRPK2, STK17B, DYRK2, PIK3R1, STK4, CLK4, PKN2) and RAS family members (G3BP, RAB14, RASA2, RAP2A, KRAS).

M 3.1 122 ISRE, Influenza, Antiviral, IFN-gamma, IFN-alpha, Interferon

Interferon-inducible. This set includes interferon-inducible genes: antiviral molecules (OAS1/2/3/L, GBP1, G1P2, EIF2AK2/PKR, MX1, PML), chemokines (CXCL10/IP-10), signaling molecules (STAT1, STAt2, IRF7, ISGF3G).

M 3.2 322 TGF-beta, TNF, Inflammation I. Includes genes encoding molecules involved

Inflammatory, Apoptotic, Lipopolysaccharide

in inflammatory processes (e.g. IL8, ICAM1, C5R1, CD44, PLAUR, IL1A, CXCL16), and regulators of apoptosis (MCL1, FOX03A, RARA, BCL3/6/2A1, GADD45B).

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[0049] As used herein, the term "array" refers to a solid support or substrate with one or more peptides or nucleic acidprobes attached to the support. Arrays typically have one or more different nucleic acid or peptide probes that are coupledto a surface of a substrate in different, known locations. These arrays, also described as "microarrays", "gene-chips" orDNA chips that may have 10,000; 20,000, 30,000; or 40,000 different identifiable genes based on the known genome,e.g., the human genome. These pan-arrays are used to detect the entire "transcriptome" or transcriptional pool of genesthat are expressed or found in a sample, e.g., nucleic acids that are expressed as RNA, mRNA and the like that maybe subjected to RT and/or RT-PCR to made a complementary set of DNA replicons. Arrays may be produced usingmechanical synthesis methods, light directed synthesis methods and the like that incorporate a combination of non-

(continued)

Module I.D. Number of probe sets Keyword selection Assessment

M 3.3 276 Inflammatory, Defense, Lysosomal, Oxidative, LPS

Inflammation II. Includes molecules inducing or inducible by inflammation (IL18, ALOX5, ANPEP, AOAH, HMOX1, SERPINB1), as well as lysosomal enzymes (PPT1, CTSB/S, NEU1, ASAH1, LAMP2, CAST).

M 3.4 325 Ligase, Kinase, KIP1, Ubiquitin, Chaperone

Undetermined. Includes protein phosphatases (PPP1R12A, PTPRC, PPP1CB, PPM1B) and phosphoinositide 3-kinase (PI3K) family members (PIK3CA, PIK32A, PIP5K3).

M 3.5 22 No keyword extracted Undetermined. Composed of only a small number of transcripts. Includes hemoglobin genes (HBA1, HBA2, HBB).

M 3.6 288 Ribosomal, T-cell, Beta-catenin Undetermined. This set includes mitochondrial ribosomal proteins (MRPLs, MRPs), mitochondrial elongations factors (GFM1/2), Sortin Nexins (SN1/6/14) as well as lysosomal ATPases (ATP6V1C/D).

M 3.7 301 Spliceosome, Methylation, Ubiquitin

Undetermined. Includes genes encoding proteasome subunits (PSMA2/5, PSMB5/8); ubiquitin protein ligases HIP2, STUB1, as well as components ofubiqutin ligase complexes (SUGT1).

M 3.8 284 CDC, TCR, CREB, Glycosylase Undetermined. Includes genes encoding enzymes: aminomethyltransferase, arginyltransferase, asparagines synthetase, diacylglycerol kinase, inositol phosphatases, methyltransferases, helicases...

M 3.9 260 Chromatin, Checkpoint, Replication, Transactivation

Undetermined. Includes genes encoding kinases (IBTK, PRKRIR, PRKDC, PRKCI) and phosphatases (e.g. PTPLB, PPP2CB/3CB, PTPRC, MTM1, MTMR2).

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lithographic and/or photolithographic methods and solid phase synthesis methods. Bead arrays that include 50-meroligonucleotide probes attached to 3 micrometer beads may be used that are, e.g., lodged into microwells at the surfaceof a glass slide or are part of a liquid phase suspension arrays (e.g., Luminex or Illumina) that are digital beadarrays inliquid phase and uses "barcoded" glass rods for detection and identification.[0050] Various techniques for the synthesis of these nucleic acid arrays have been described, e.g., fabricated on asurface of virtually any shape or even a multiplicity of surfaces. Arrays may be peptides or nucleic acids on beads, gels,polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate. Arrays may be packaged insuch a manner as to allow for diagnostics or other manipulation of an all inclusive device, see for example, U.S. Pat.No. 6,955,788.

BIOLOGICAL DEFINITIONS

[0051] As used herein, the term "disease" refers to a physiological state of an organism with any abnormal biologicalstate of a cell. Disease includes, but is not limited to, an interruption, cessation or disorder of cells, tissues, body functions,systems or organs that may be inherent, inherited, caused by an infection, caused by abnormal cell function, abnormalcell division and the like. A disease that leads to a "disease state" is generally detrimental to the biological system, thatis, the host of the disease. With respect to the present invention, any biological state, such as an infection (e.g., viral,bacterial, fungal, helminthic, etc.), inflammation, autoinflammation, autoimmunity, anaphylaxis, allergies, premalignancy,malignancy, surgical, transplantation, physiological, and the like that is associated with a disease or disorder is consideredto be a disease state. A pathological state is generally the equivalent of a disease state.[0052] Disease states may also be categorized into different levels of disease state. As used herein, the level of adisease or disease state is an arbitrary measure reflecting the progression of a disease or disease state as well as thephysiological response upon, during and after treatment. Generally, a disease or disease state will progress throughlevels or stages, wherein the affects of the disease become increasingly severe. The level of a disease state may beimpacted by the physiological state of cells in the sample.[0053] As used herein, the terms "therapy" or "therapeutic regimen" refer to those medical steps taken to alleviate oralter a disease state, e.g., a course of treatment intended to reduce or eliminate the affects or symptoms of a diseaseusing pharmacological, surgical, dietary and/or other techniques. A therapeutic regimen may include a prescribed dosageof one or more drugs or surgery. Therapies will most often be beneficial and reduce the disease state but in manyinstances the effect of a therapy will have non-desirable or side-effects. The effect of therapy will also be impacted bythe physiological state of the host, e.g., age, gender, genetics, weight, other disease conditions, etc.[0054] As used herein, the term "pharmacological state" or "pharmacological status" refers to those samples that willbe, are and/or were treated with one or more drugs, surgery and the like that may affect the pharmacological state ofone or more nucleic acids in a sample, e.g., newly transcribed, stabilized and/or destabilized as a result of the pharma-cological intervention. The pharmacological state of a sample relates to changes in the biological status before, duringand/or after drug treatment and may serve a diagnostic or prognostic function, as taught herein. Some changes followingdrug treatment or surgery may be relevant to the disease state and/or may be unrelated side-effects of the therapy.Changes in the pharmacological state are the likely results of the duration of therapy, types and doses of drugs prescribed,degree of compliance with a given course of therapy, and/or un-prescribed drugs ingested.[0055] As used herein, the term "biological state" refers to the state of the transcriptome (that is the entire collectionof RNA transcripts) of the cellular sample isolated and purified for the analysis of changes in expression. The biologicalstate reflects the physiological state of the cells in the sample by measuring the abundance and/or activity of cellularconstituents, characterizing according to morphological phenotype or a combination of the methods for the detection oftranscripts.[0056] As used herein, the term "expression profile" refers to the relative abundance of RNA, DNA or protein abun-dances or activity levels. The expression profile can be a measurement for example of the transcriptional state or thetranslational state by any number of methods and using any of a number of gene-chips, gene arrays, beads, multiplexPCR, quantitiative PCR, run-on assays, Northern blot analysis, Western blot analysis, protein expression, fluorescenceactivated cell sorting (FACS), enzyme linked immunosorbent assays (ELISA), chemiluminescence studies, enzymaticassays, proliferation studies or any other method, apparatus and system for the determination and/or analysis of geneexpression that are readily commercially available.[0057] As used herein, the term "transcriptional state" of a sample includes the identities and relative abundances ofthe RNA species, especially mRNAs present in the sample. The entire transcriptional state of a sample, that is thecombination of identity and abundance of RNA, is also referred to herein as the transcriptome. Generally, a substantialfraction of all the relative constituents of the entire set of RNA species in the sample are measured.[0058] As used herein, the term "transcriptional vectors," "expression vectors," "genomic vectors" (used interchange-ably) refers to transcriptional expression data that reflects the "proportion of differentially expressed genes." For example,for each module the proportion of transcripts differentially expressed between at least two groups (e.g., healthy subjects

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versus patients). This vector is derived from the comparison of two groups of samples. The first analytical step is usedfor the selection of disease-specific sets of transcripts within each module. Next, there is the "expression level." Thegroup comparison for a given disease provides the list of differentially expressed transcripts for each module. It wasfound that different diseases yield different subsets of modular transcripts. With this expression level it is then possibleto calculate vectors for each module(s) for a single sample by averaging expression values of disease-specific subsetsof genes identified as being differentially expressed. This approach permits the generation of maps of modular expressionvectors for a single sample, e.g., those described in the module maps disclosed herein. These vector module mapsrepresent an averaged expression level for each module (instead of a proportion of differentially expressed genes) thatcan be derived for each sample. These composite "expression vectors" are formed through successive rounds of se-lection: 1) of the modules that were significantly changed across study groups and 2) of the genes within these moduleswhich are significantly changed across study groups (Figure 2, step II). Expression levels are subsequently derived byaveraging the values obtained for the subset of transcripts forming each vector (Figure 2, step III). Patient profiles canthen be represented by plotting expression levels obtained for each of these vectors on a graph (e.g. on a radar plot).Therefore a set of vectors results from two round of selection, first at the module level, and then at the gene level. Vectorexpression values are composite by construction as they derive from the average expression values of the transcriptforming the vector.[0059] Using the present invention it is possible to identify and distinguish diseases not only at the module-level, butalso at the gene-level; i.e., two diseases can have the same vector (identical proportion of differentially expressedtranscripts, identical "polarity"), but the gene composition of the expression vector can still be disease-specific. Thisdisease-specific customization permits the user to optimize the performance of a given set of markers by increasing itsspecificity.[0060] Using modules as a foundation grounds expression vectors to coherent functional and transcriptional unitscontaining minimized amounts of noise. Furthermore, the present invention takes advantage of composite transcriptionalmarkers. As used herein, the term "composite transcriptional markers" refers to the average expression values of multiplegenes (subsets of modules) as compared to using individual genes as markers (and the composition of these markerscan be disease-specific). The composite transcriptional markers approach is unique because the user can developmultivariate microarray scores to assess disease severity in patients with, e.g., SLE, or to derive expression vectorsdisclosed herein. The fact that expression vectors are composite (i.e. formed by a combination of transcripts) furthercontributes to the stability of these markers. Most importantly, it has been found that using the composite modulartranscriptional markers of the present invention the results found herein are reproducible across microarray platform,thereby providing greater reliability for regulatory approval. Indeed, vector expression values proved remarkably robust,as indicated by the excellent reproducibility obtained across microarray platforms; as well as the validation resultsobtained in an independent set of pediatric lupus patients. These results are of importance since improving the reliabilityof microarray data is a prerequisite for the widespread use of this technology in clinical practice.[0061] Gene expression monitoring systems for use with the present invention may include customized gene arrayswith a limited and/or basic number of genes that are specific and/or customized for the one or more target diseases.Unlike the general, pan-genome arrays that are in customary use, the present invention provides for not only the use ofthese general pan-arrays for retrospective gene and genome analysis without the need to use a specific platform, butmore importantly, it provides for the development of customized arrays that provide an optimal gene set for analysiswithout the need for the thousands of other, non-relevant genes. One distinct advantage of the optimized arrays andmodules of the present invention over the existing art is a reduction in the financial costs (e.g., cost per assay, materials,equipment, time, personnel, training, etc.), and more importantly, the environmental cost of manufacturing pan-arrayswhere the vast majority of the data is irrelevant. The modules of the present invention allow for the first time the designof simple, custom arrays that provide optimal data with the least number of probes while maximizing the signal to noiseratio. By eliminating the total number of genes for analysis, it is possible to, e.g., eliminate the need to manufacturethousands of expensive platinum masks for photolithography during the manufacture of pan-genetic chips that providevast amounts of irrelevant data. Using the present invention it is possible to completely avoid the need for microarraysif the limited probe set(s) of the present invention are used with, e.g., digital optical chemistry arrays, ball bead arrays,beads (e.g., Luminex), multiplex PCR, quantitiative PCR, run-on assays, Northern blot analysis, or even, for proteinanalysis, e.g., Western blot analysis, 2-D and 3-D gel protein expression, MALDI, MALDI-TOF, fluorescence activatedcell sorting (FACS) (cell surface or intracellular), enzyme linked immunosorbent assays (ELISA), chemiluminescencestudies, enzymatic assays, proliferation studies or any other method, apparatus and system for the determination and/oranalysis of gene expression that are readily commercially available.[0062] The "molecular fingerprinting system" of the present invention may be used to facilitate and conduct a com-parative analysis of expression in different cells or tissues, different subpopulations of the same cells or tissues, differentphysiological states of the same cells or tissue, different developmental stages of the same cells or tissue, or differentcell populations of the same tissue against other diseases and/or normal cell controls. In some cases, the normal orwild-type expression data may be from samples analyzed at or about the same time or it may be expression data obtained

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or culled from existing gene array expression databases, e.g., public databases such as the NCBI Gene ExpressionOmnibus database.[0063] As used herein, the term "differentially expressed" refers to the measurement of a cellular constituent (e.g.,nucleic acid, protein, enzymatic activity and the like) that varies in two or more samples, e.g., between a disease sampleand a normal sample. The cellular constituent may be on or off (present or absent), upregulated relative to a referenceor downregulated relative to the reference. For use with gene-chips or gene-arrays, differential gene expression ofnucleic acids, e.g., mRNA or other RNAs (miRNA, siRNA, hnRNA, rRNA, tRNA, etc.) may be used to distinguish betweencell types or nucleic acids. Most commonly, the measurement of the transcriptional state of a cell is accomplished byquantitative reverse transcriptase (RT) and/or quantitative reverse transcriptase-polymerase chain reaction (RT-PCR),genomic expression analysis, post-translational analysis, modifications to genomic DNA, translocations, in situ hybrid-ization and the like.[0064] For some disease states it is possible to identify cellular or morphological differences, especially at early levelsof the disease state. The present invention avoids the need to identify those specific mutations or one or more genesby looking at modules of genes of the cells themselves or, more importantly, of the cellular RNA expression of genesfrom immune effector cells that are acting within their regular physiologic context, that is, during immune activation,immune tolerance or even immune anergy. While a genetic mutation may result in a dramatic change in the expressionlevels of a group of genes, biological systems often compensate for changes by altering the expression of other genes.As a result of these internal compensation responses, many perturbations may have minimal effects on observablephenotypes of the system but profound effects to the composition of cellular constituents. Likewise, the actual copiesof a gene transcript may not increase or decrease, however, the longevity or half-life of the transcript may be affectedleading to greatly increases protein production. The present invention eliminates the need of detecting the actual messageby, in one embodiment, looking at effector cells (e.g., leukocytes, lymphocytes and/or sub-populations thereof) ratherthan single messages and/or mutations.[0065] The skilled artisan will appreciate readily that samples may be obtained from a variety of sources including,e.g., single cells, a collection of cells, tissue, cell culture and the like. In certain cases, it may even be possible to isolatesufficient RNA from cells found in, e.g., urine, blood, saliva, tissue or biopsy samples and the like. In certain circumstances,enough cells and/or RNA may be obtained from: mucosal secretion, feces, tears, blood plasma, peritoneal fluid, interstitialfluid, intradural, cerebrospinal fluid, sweat or other bodily fluids. The nucleic acid source, e.g., from tissue or cell sources,may include a tissue biopsy sample, one or more sorted cell populations, cell culture, cell clones, transformed cells,biopies or a single cell. The tissue source may include, e.g., brain, liver, heart, kidney, lung, spleen, retina, bone, neural,lymph node, endocrine gland, reproductive organ, blood, nerve, vascular tissue, and olfactory epithelium.[0066] The present invention includes the following basic components, which may be used alone or in combination,namely, one or more data mining algorithms; one or more module-level analytical processes; the characterization ofblood leukocyte transcriptional modules; the use of aggregated modular data in multivariate analyses for the moleculardiagnostic/prognostic of human diseases; and/or visualization of module-level data and results. Using the present in-vention it is also possible to develop and analyze composite transcriptional markers, which may be further aggregatedinto a single multivariate score.[0067] The present inventors have recognized that current microarray-based research is facing significant challengeswith the analysis of data that are notoriously "noisy," that is, data that is difficult to interpret and does not compare wellacross laboratories and platforms. A widely accepted approach for the analysis of microarray data begins with theidentification of subsets of genes differentially expressed between study groups. Next, the users try subsequently to"make sense" out of resulting gene lists using pattern discovery algorithms and existing scientific knowledge.[0068] Rather than deal with the great variability across platforms, the present inventors have developed a strategythat emphasized the selection of biologically relevant genes at an early stage of the analysis. Briefly, the method includesthe identification of the transcriptional components characterizing a given biological system for which an improved datamining algorithm was developed to analyze and extract groups of coordinately expressed genes, or transcriptionalmodules, from large collections of data.[0069] The biomarker discovery strategy described herein is particularly well adapted for the exploitation of microarraydata acquired on a global scale. Starting from ~44,000 transcripts a set of 28 modules was defined that are composedof nearly 5000 transcripts. Sets of disease-specific composite expression vectors were then derived. Vector expressionvalues (expression vectors) proved remarkably robust, as indicated by the excellent reproducibility obtained acrossmicroarray platforms. This finding is notable, since improving the reliability of microarray data is a prerequisite for thewidespread use of this technology in clinical practice. Finally, expression vectors can in turn be combined to obtainunique multivariate scores, therefore delivering results in a form that is compatible with mainstream clinical practice.Interestingly, multivariate scores recapitulate global patterns of change rather than changes in individual markers. Thedevelopment of such "global biomarkers" can be used for both diagnostic and pharmacogenomics fields.[0070] In one example, twenty-eight transcriptional modules regrouping 4742 probe sets were obtained from 239 bloodleukocyte transcriptional profiles. Functional convergence among genes forming these modules was demonstrated

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through literature profiling. The second step consisted of studying perturbations of transcriptional systems on a modularbasis. To illustrate this concept, leukocyte transcriptional profiles obtained from healthy volunteers and patients wereobtained, compared and analyzed. Further validation of this gene fingerprinting strategy was obtained through theanalysis of a published microarray dataset. Remarkably, the modular transcriptional apparatus, system and methods ofthe present invention using pre-existing data showed a high degree of reproducibility across two commercial microarrayplatforms.[0071] The present invention includes the implementation of a widely applicable, two-step microarray data miningstrategy designed for the modular analysis of transcriptional systems. This novel approach was used to characterizetranscriptional signatures of blood leukocytes, which constitutes the most accessible source of clinically relevant infor-mation.[0072] As demonstrated herein, it is possible to determine, differential and/or distinguish between two disease basedon two vectors even if the vector is identical (+/+) for two diseases - e.g. M1.3 = 53% down for both SLE and FLU becausethe composition of each vector can still be used to differentiate them. For example, even though the proportion andpolarity of differentially expressed transcripts is identical between the two diseases for M1.3, the gene composition canstill be disease-specific. The combination of gene-level and module-level analysis considerably increases resolution.Furthermore, it is possible to use 2, 3, 4, 5, 10, 15, 20, 25, 28 or more modules to differentiate diseases.[0073] The term "gene" refers to a nucleic acid (e.g., DNA) sequence that includes coding sequences necessary forthe production of a polypeptide (e.g., ), precursor, or RNA (e.g., mRNA). The polypeptide may be encoded by a fulllength coding sequence or by any portion of the coding sequence so long as the desired activity or functional property(e.g., enzymatic activity, ligand binding, signal transduction, immunogenicity, etc.) of the full-length or fragment is retained.The term also encompasses the coding region of a structural gene and the sequences located adjacent to the codingregion on both the 5’ and 3’ ends for a distance of about 2 kb or more on either end such that the gene corresponds tothe length of the full-length mRNA and 5’ regulatory sequences which influence the transcriptional properties of the gene.Sequences located 5’ of the coding region and present on the mRNA are referred to as 5’-untranslated sequences. The5’-untranslated sequences usually contain the regulatory sequences. Sequences located 3’ or downstream of the codingregion and present on the mRNA are referred to as 3’-untranslated sequences. The term "gene" encompasses bothcDNA and genomic forms of a gene. A genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed "introns" or "intervening regions" or "intervening sequences." Introns are segments of a genethat are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Intronsare removed or "spliced out" from the nuclear or primary transcript; introns therefore are absent in the messenger RNA(mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascentpolypeptide.[0074] As used herein, the term "nucleic acid" refers to any nucleic acid containing molecule, including but not limitedto, DNA, cDNA and RNA. In particular, the terms "a gene in Table X" refers to at least a portion or the full-length sequencelisted in a particular table, as found hereinbelow. The gene may even be found or detected a genomic form, that is, itincludes one or more intron(s). Genomic forms of a gene may also include sequences located on both the 5’ and 3’ endof the coding sequences that are present on the RNA transcript. These sequences are referred to as "flanking" sequencesor regions. The 5’ flanking region may contain regulatory sequences such as promoters and enhancers that control orinfluence the transcription of the gene. The 3’ flanking region may contain sequences that influence the transcriptiontermination, post-transcriptional cleavage, mRNA stability and polyadenylation.[0075] As used herein, the term "wild-type" refers to a gene or gene product isolated from a naturally occurring source.A wild-type gene is that which is most frequently observed in a population and is thus arbitrarily designed the "normal"or "wild-type" form of the gene. In contrast, the term "modified" or "mutant" refers to a gene or gene product that displaysmodifications in sequence and/or functional properties (i.e., altered characteristics) when compared to the wild-typegene or gene product. It is noted that naturally occurring mutants can be isolated; these are identified by the fact thatthey have altered characteristics (including altered nucleic acid sequences) when compared to the wild-type gene orgene product.[0076] As used herein, the term "polymorphism" refers to the regular and simultaneous occurrence in a single inter-breeding population of two or more alleles of a gene, where the frequency of the rarer alleles is greater than can beexplained by recurrent mutation alone (typically greater than 1%).[0077] As used herein, the terms "nucleic acid molecule encoding," "DNA sequence encoding," and "DNA encoding"refer to the order or sequence of deoxyribonucleotides along a strand of deoxyribonucleic acid. The order of thesedeoxyribonucleotides determines the order of amino acids along the polypeptide protein) chain. The DNA sequencethus codes for the amino acid sequence.[0078] As used herein, the terms "complementary" or "complementarity" are used in reference to polynucleotides (i.e.,a sequence of nucleotides) related by the base-pairing rules. For example, the sequence "A-G-T," is complementary tothe sequence "T-C-A." Complementarity may be "partial," in which only some of the nucleic acids’ bases are matchedaccording to the base pairing rules. Or, there may be "complete" or "total" complementarity between the nucleic acids.

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The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength ofhybridization between nucleic acid strands. This is of particular importance in amplification reactions, as well as detectionmethods that depend upon binding between nucleic acids.[0079] As used herein, the term "hybridization" is used in reference to the pairing of complementary nucleic acids.Hybridization and the strength of hybridization (i.e., the strength of the association between the nucleic acids) is impactedby such factors as the degree of complementarity between the nucleic acids, stringency of the conditions involved, theTm of the formed hybrid, and the G:C ratio within the nucleic acids. A single molecule that contains pairing of comple-mentary nucleic acids within its structure is said to be "self-hybridized."[0080] As used herein the term "stringency" is used in reference to the conditions of temperature, ionic strength, andthe presence of other compounds such as organic solvents, under which nucleic acid hybridizations are conducted.Under "low stringency conditions" a nucleic acid sequence of interest will hybridize to its exact complement, sequenceswith single base mismatches, closely related sequences (e.g., sequences with 90% or greater homology), and sequenceshaving only partial homology (e.g., sequences with 50-90% homology). Under "medium stringency conditions," a nucleicacid sequence of interest will hybridize only to its exact complement, sequences with single base mismatches, andclosely related sequences (e.g., 90% or greater homology). Under "high stringency conditions," a nucleic acid sequenceof interest will hybridize only to its exact complement, and (depending on conditions such a temperature) sequenceswith single base mismatches. In other words, under conditions of high stringency the temperature can be raised so asto exclude hybridization to sequences with single base mismatches.[0081] As used herein, the term "probe" refers to an oligonucleotide (i.e., a sequence of nucleotides), whether occurringnaturally as in a purified restriction digest or produced synthetically, recombinantly or by PCR amplification, that iscapable of hybridizing to another oligonucleotide of interest. A probe may be single-stranded or double-stranded. Probesare useful in the detection, identification and isolation of particular gene sequences. Any probe used in the presentinvention may be labeled with any "reporter molecule," so that it is detectable in any detection system, including, but notlimited to enzyme (e.g., ELISA, as well as enzyme-based histochemical assays), fluorescent, radioactive, luminescentsystems and the like. It is not intended that the present invention be limited to any particular detection system or label.[0082] As used herein, the term "target," refers to the region of nucleic acid bounded by the primers. Thus, the "target"is sought to be sorted out from other nucleic acid sequences. A "segment" is defined as a region of nucleic acid withinthe target sequence.[0083] As used herein, the term "Southern blot" refers to the analysis of DNA on agarose or acrylamide gels tofractionate the DNA according to size followed by transfer of the DNA from the gel to a solid support, such as nitrocelluloseor a nylon membrane. The immobilized DNA is then probed with a labeled probe to detect DNA species complementaryto the probe used. The DNA may be cleaved with restriction enzymes prior to electrophoresis. Following electrophoresis,the DNA may be partially depurinated and denatured prior to or during transfer to the solid support. Southern blots area standard tool of molecular biologists (Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring HarborPress, NY, pp 9.31-9.58, 1989).[0084] As used herein, the term "Northern blot" refers to the analysis of RNA by electrophoresis of RNA on agarosegels, to fractionate the RNA according to size followed by transfer of the RNA from the gel to a solid support, such asnitrocellulose or a nylon membrane. The immobilized RNA is then probed with a labeled probe to detect RNA speciescomplementary to the probe used. Northern blots are a standard tool of molecular biologists (Sambrook, et al., supra,pp 7.39-7.52, 1989).[0085] As used herein, the term "Western blot" refers to the analysis of protein(s) (or polypeptides) immobilized ontoa support such as nitrocellulose or a membrane. The proteins are run on acrylamide gels to separate the proteins,followed by transfer of the protein from the gel to a solid support, such as nitrocellulose or a nylon membrane. Theimmobilized proteins are then exposed to antibodies with reactivity against an antigen of interest. The binding of theantibodies may be detected by various methods, including the use of radiolabeled antibodies.[0086] As used herein, the term "polymerase chain reaction" ("PCR") refers to the method of K. B. Mullis (U.S. Pat.Nos. 4,683,195 4,683,202, and 4,965,188), which describe a method for increasing the concentration of a segment ofa target sequence in a mixture of genomic DNA without cloning or purification. This process for amplifying the targetsequence consists of introducing a large excess of two oligonucleotide primers to the DNA mixture containing the desiredtarget sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The twoprimers are complementary to their respective strands of the double stranded target sequence. To effect amplification,the mixture is denatured and the primers then annealed to their complementary sequences within the target molecule.Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands.The steps of denaturation, primer annealing and polymerase extension can be repeated many times (i.e., denaturation,annealing and extension constitute one "cycle"; there can be numerous "cycles") to obtain a high concentration of anamplified segment of the desired target sequence. The length of the amplified segment of the desired target sequenceis determined by the relative positions of the primers with respect to each other, and therefore, this length is a controllableparameter. By virtue of the repeating aspect of the process, the method is referred to as the "polymerase chain reaction"

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(hereinafter "PCR"). Because the desired amplified segments of the target sequence become the predominant sequences(in terms of concentration) in the mixture, they are said to be "PCR amplified".[0087] As used herein, the terms "PCR product," "PCR fragment," and "amplification product" refer to the resultantmixture of compounds after two or more cycles of the PCR steps of denaturation, annealing and extension are complete.These terms encompass the case where there has been amplification of one or more segments of one or more targetsequences.[0088] As used herein, the term "real time PCR" as used herein, refers to various PCR applications in which amplificationis measured during as opposed to after completion of the reaction. Reagents suitable for use in real time PCR embod-iments of the present invention include but are not limited to TaqMan probes, molecular beacons, Scorpions primers ordouble-stranded DNA binding dyes.[0089] As used herein, the term "transcriptional upregulation" as used herein refers to an increase in synthesis ofRNA, by RNA polymerases using a DNA template. For example, when used in reference to the methods of the presentinvention, the term "transcriptional upregulation" refers to an increase of least 1 to 2 fold, 2 to 3 fold, 3 to 10 fold, andeven greater than 10 fold, in the quantity of mRNA corresponding to a gene of interest detected in a sample derivedfrom an individual predisposed to SLE as compared to that detected in a sample derived from an individual who is notpredisposed to SLE. However, the system and evaluation is sufficiently specific to require less that a 2 fold change inexpression to be detected. Furthermore, the change in expression may be at the cellular level (change in expressionwithin a single cell or cell populations) or may even be evaluated at a tissue level, where there is a change in the numberof cells that are expressing the gene. Particularly useful differences are those that are statistically significant.[0090] Conversely, the term "transcriptional downregulation" refers to a decrease in synthesis of RNA, by RNA polymer-ases using a DNA template. For example, when used in reference to the methods of the present invention, the term"transcriptional downregulation" refers to a decrease of least 2 fold, 2 to 3 fold, 3 to 10 fold, and even greater than 10fold, in the quantity of mRNA corresponding to a gene of interest detected in a sample derived from an individualpredisposed to SLE as compared to that detected in a sample derived from an individual who is not predisposed to sucha condition or to a database of information for wild-type and/or normal control, e.g., fibromyalgia. Again, the system andevaluation is sufficiently specific to require less that a 2 fold change in expression to be detected. Particularly usefuldifferences are those that are statistically significant.[0091] Both transcriptional "upregulation"/overexpression and transcriptional "downregulation"/underexpression mayalso be indirectly monitored through measurement of the translation product or protein level corresponding to the geneof interest. The present invention is not limited to any given mechanism related to upregulation or downregulation oftranscription.[0092] The term "eukaryotic cell" as used herein refers to a cell or organism with membrane-bound, structurally discretenucleus and other well-developed subcellular compartments. Eukaryotes include all organisms except viruses, bacteria,and bluegreen algae.[0093] As used herein, the term "in vitro transcription" refers to a transcription reaction comprising a purified DNAtemplate containing a promoter, ribonucleotide triphosphates, a buffer system that includes a reducing agent and cations,e.g., DTT and magnesium ions, and an appropriate RNA polymerase, which is performed outside of a living cell ororganism.[0094] As used herein, the term "amplification reagents" refers to those reagents (deoxyribonucleotide triphosphates,buffer, etc.), needed for amplification except for primers, nucleic acid template and the amplification enzyme. Typically,amplification reagents along with other reaction components are placed and contained in a reaction vessel (test tube,microwell, etc.).[0095] As used herein, the term "diagnosis" refers to the determination of the nature of a case of disease. In someembodiments of the present invention, methods for making a diagnosis are provided which permit determination of SLE.[0096] The present invention may be used alone or in combination with disease therapy to monitor disease progressionand/or patient management. For example, a patient may be tested one or more times to determine the best course oftreatment, determine if the treatment is having the intended medical effect, if the patient is not a candidate for thatparticular therapy and combinations thereof. The skilled artisan will recognize that one or more of the expression vectorsmay be indicative of one or more diseases and may be affected by other conditions, be they acute or chronic.[0097] As used herein, the term "pharmacogenetic test" refers to an assay intended to study interindividual variationsin DNA sequence related to, e.g., drug absorption and disposition (pharmacokinetics) or drug action (pharmacodynamics),which may include polymorphic variations in one or more genes that encode the functions of, e.g., transporters, metab-olizing enzymes, receptors and other proteins.[0098] As used herein, the term "pharmacogenomic test" refers to an assay used to study interindividual variations inwhole-genome or candidate genes, e.g., single-nucleotide polymorphism (SNP) maps or haplotype markers, and thealteration of gene expression or inactivation that may be correlated with pharmacological function and therapeutic re-sponse.[0099] As used herein, an "expression profile" refers to the measurement of the relative abundance of a plurality of

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cellular constituents. Such measurements may include, e.g., RNA or protein abundances or activity levels. The expressionprofile can be a measurement for example of the transcriptional state or the translational state. See U.S. Pat. Nos.6,040,138, 5,800,992, 6,020,135, 6,033,860. The gene expression monitoring system, include nucleic acid probe arrays,membrane blot (such as used in hybridization analysis such as Northern, Southern, dot, and the like), or microwells,sample tubes, gels, beads or fibers (or any solid support comprising bound nucleic acids). See, e.g., U.S. Pat. Nos.5,770,722, 5,874,219, 5,744,305, 5,677,195 and 5,445,934. The gene expression monitoring system may also comprisenucleic acid probes in solution.[0100] The gene expression monitoring system according to the present invention may be used to facilitate a com-parative analysis of expression in different cells or tissues, different subpopulations of the same cells or tissues, differentphysiological states of the same cells or tissue, different developmental stages of the same cells or tissue, or differentcell populations of the same tissue.[0101] As used herein, the term "differentially expressed: refers to the measurement of a cellular constituent varies intwo or more samples. The cellular constituent can be either up-regulated in the test sample relative to the reference ordown-regulated in the test sample relative to one or more references. Differential gene expression can also be used todistinguish between cell types or nucleic acids. See U.S. Pat. No. 5,800,992.[0102] Therapy or Therapeutic Regimen: In order to alleviate or alter a disease state, a therapy or therapeutic regimenis often undertaken. A therapy or therapeutic regimen, as used herein, refers to a course of treatment intended to reduceor eliminate the affects or symptoms of a disease. A therapeutic regimen will typically comprise, but is not limited to, aprescribed dosage of one or more drugs or surgery. Therapies, ideally, will be beneficial and reduce the disease statebut in many instances the effect of a therapy will have non-desirable effects as well. The effect of therapy will also beimpacted by the physiological state of the sample.[0103] Modules display distinct "transcriptional behavior". It is widely assumed that co-expressed genes are functionallylinked. This concept of "guilt by association" is particularly compelling in cases where genes follow complex expressionpatterns across many samples. The present inventors discovered that transcriptional modules form coherent biologicalunits and, therefore, predicted that the co-expression properties identified in the initial dataset would be conserved inan independent set of samples. Data were obtained for PBMCs isolated from the blood of twenty-one healthy volunteers.These samples were not used in the module selection process described above.[0104] The present invention includes the following basic components, which may be used alone or in combination,namely, one or more data mining algorithms; one or more module-level analytical processes; the characterization ofblood leukocyte transcriptional modules; the use of aggregated modular data in multivariate analyses for the moleculardiagnostic/prognostic of human diseases; and/or visualization of module-level data and results. Using the present in-vention it is also possible to develop and analyze composite transcriptional markers, which may be further aggregatedinto a single multivariate score.[0105] The present inventors have recognized that current microarray-based research is facing significant challengeswith the analysis of data that are notoriously "noisy," that is, data that is difficult to interpret and does not compare wellacross laboratories and platforms. A widely accepted approach for the analysis of microarray data begins with theidentification of subsets of genes differentially expressed between study groups. Next, the users try subsequently to"make sense" out of resulting gene lists using pattern discovery algorithms and existing scientific knowledge.[0106] Rather than deal with the great variability across platforms, the present inventors have developed a strategythat emphasized the selection of biologically relevant genes at an early stage of the analysis. Briefly, the method includesthe identification of the transcriptional components characterizing a given biological system for which an improved datamining algorithm was developed to analyze and extract groups of coordinately expressed genes, or transcriptionalmodules, from large collections of data.[0107] The biomarker discovery strategy that we have developed is particularly well adapted for the exploitation ofmicroarray data acquired on a global scale. Starting from ~44,000 transcripts the inventors defined 28 modules composedof nearly 5000 transcripts. Sets of disease-specific composite expression vectors were then derived. Vector expressionvalues proved remarkably robust, as indicated by the excellent reproducibility obtained across microarray platforms.This finding is notable, since improving the reliability of microarray data is a prerequisite for the widespread use of thistechnology in clinical practice. Finally, vectors can in turn be combined to obtain unique multivariate scores, thereforedelivering results in a form that is compatible with mainstream clinical practice. Interestingly, multivariate scores reca-pitulate global patterns of change rather than changes in individual markers. The development of such "global biomarkers"constitutes therefore a promising prospect for both diagnostic and pharmacogenomics fields.[0108] In one example, twenty-eight transcriptional modules regrouping 4742 probe sets were obtained from 239 bloodleukocyte transcriptional profiles. Functional convergence among genes forming these modules was demonstratedthrough literature profiling. The second step consisted of studying perturbations of transcriptional systems on a modularbasis. To illustrate this concept, leukocyte transcriptional profiles obtained from healthy volunteers and patients wereobtained, compared and analyzed. Further validation of this gene fingerprinting strategy was obtained through theanalysis of a published microarray dataset. Remarkably, the modular transcriptional apparatus, system and methods of

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the present invention using pre-existing data showed a high degree of reproducibility across two commercial microarrayplatforms.[0109] The present invention includes the implementation of a widely applicable, two-step microarray data miningstrategy designed for the modular analysis of transcriptional systems. This novel approach was used to characterizetranscriptional signatures of blood leukocytes, which constitutes the most accessible source of clinically relevant infor-mation.[0110] As demonstrated herein, it is possible to determine, differential and/or distinguish between two disease basedon two vectors even if the vector is identical (+/+) for two diseases - e.g. M1.3 = 53% down for both SLE and FLU becausethe composition of each vector can still be used to differentiate them. For example, even though the proportion andpolarity of differentially expressed transcripts is identical between the two diseases for M1.3, the gene composition canstill be disease-specific. The combination of gene-level and module-level analysis considerably increases resolution.Furthermore, it is possible to use 2, 3, 4, 5, 10, 15, 20, 25, 28 or more modules to differentiate diseases.[0111] Material and methods. Processing of blood samples. All blood samples were collected in acid citrate dextrosetubes (BD Vacutainer) and immediately delivered at room temperature to the Baylor Institute for Immunology Research,Dallas, TX, for processing. Peripheral blood mononuclear cells (PBMCs) from 3-4 ml of blood were isolated via Ficollgradient and immediately lysed in RLT reagent (Qiagen, Valencia, CA) with beta-mercaptoethanol (BME) and stored at-80°C prior to the RNA extraction step.[0112] Microarray analysis. Total RNA was isolated using the RNeasy kit (Qiagen) according to the manufacturer’sinstructions and RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA).[0113] Affymetrix GeneChips: These microarrays include short oligonucleotide probe sets synthesized in situ on aquartz wafer. Target labeling was performed according to the manufacturer’s standard protocol (Affymetrix Inc., SantaClara, CA). Biotinylated cRNA targets were purified and subsequently hybridized to Affymetrix HG-U133A and U133BGeneChips (>44,000 probe sets). Arrays were scanned using an Affymetrix confocal laser scanner. Microarray Suite,Version 5.0 (MAS 5.0; Affymetrix) software was used to assess fluorescent hybridization signals, to normalize signals,and to evaluate signal detection calls. Normalization of signal values per chip was achieved using the MAS 5.0 globalmethod of scaling to the target intensity value of 500 per GeneChip. A gene expression analysis software program,GeneSpring, Version 7.1 (Agilent), was used to perform statistical analysis and hierarchical clustering.[0114] Illumina BeadChips: These microarrays include 50mer oligonucleotide probes attached to 3mm beads, whichare lodged into microwells at the surface of a glass slide. Samples were processed and acquired by Illumina Inc. (SanDiego, CA) on the basis of a service contract. Targets were prepared using the Illumina RNA amplification kit (Ambion,Austin, TX). cRNA targets were hybridized to Sentrix HumanRef8 BeadChips (>25,000 probes), which were scannedon an Illumina BeadStation 500. Illumina’s Beadstudio software was used to assess fluorescent hybridization signals.[0115] Literature profiling. The literature profiling algorithm employed in this study has been previously described indetail (Chaussabel, D. & Sher, A. Mining microarray expression data by literature profiling. Genome Biol 3,RESEARCH0055 (2002)). This approach links genes sharing similar keywords. It uses hierarchical clustering, a popularunsupervised pattern discovery algorithm, to analyze patterns of term occurrence in literature abstracts. Step 1: A gene:literature index identifying pertinent publications for each gene is created. Step 2: Term occurrence frequencies werecomputed by a text processor. Step 3: Stringent filter criteria are used to select relevant keywords (i.e., eliminate termswith either high or low frequency across all genes and retain the few discerning terms characterized by a pattern of highoccurrence for only a few genes). Step 4: Two-way hierarchical clustering groups of genes and relevant keywords basedon occurrence patterns, providing a visual representation of functional relationships existing among a group of genes.[0116] Modular data mining algorithm. First, one or more transcriptional components are identified that permit thecharacterization of biological systems beyond the level of single genes. Sets of coordinately regulated genes, or tran-scriptional modules, were extracted using a novel mining algorithm, which was applied to a large set of blood leukocytemicroarray profiles (Figure 1). Gene expression profiles from a total of 239 peripheral blood mononuclear cells (PBMCs)samples were generated using Affymetrix U133A&B GeneChips (>44,000 probe sets). Transcriptional data were obtainedfor eight experimental groups (systemic juvenile idiopathic arthritis, systemic lupus erythematosus, type I diabetes, livertransplant recipients, melanoma patients, and patients with acute infections: Escherichia coli, Staphylococcus aureusand influenza A). For each group, transcripts with an absent flag call across all conditions were filtered out. The remaininggenes were distributed among thirty sets by hierarchical clustering (clusters C1 through C30). The cluster assignmentfor each gene was recorded in a table and distribution patterns were compared among all the genes. Modules wereselected using an iterative process, starting with the largest set of genes that belonged to the same cluster in all studygroups (i.e. genes that were found in the same cluster in eight of the eight experimental groups). The selection was thenexpanded from this core reference pattern to include genes with 7/8, 6/8 and 5/8 matches. The resulting set of genesformed a transcriptional module and was withdrawn from the selection pool. The process was then repeated startingwith the second largest group of genes, progressively reducing the level of stringency. This analysis led to the identificationof 5348 transcripts that were distributed among twenty-eight modules (a complete list is provided as supplementarymaterial). Each module is assigned a unique identifier indicating the round and order of selection (i.e. M3.1 was the first

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module identified in the third round of selection).[0117] Analysis of "significance patterns" was performed on gene expression data generated from PBMCs obtainedfrom patients and healthy volunteers using Affymetrix HG-U133A GeneChips that were run on the same Affymetrixsystem, using standard operating procedures. P values were obtained by comparing 7 groups of patients to their re-spective healthy control groups (Mann-Whitney rank test). The groups were composed of pediatric patients with: 1)Systemic Lupus Erythomatosus (SLE, 16 samples), 2) Influenza A (16 samples), 3) Staphylococcus aureus (16 samples),4) Escherichia coli (16 samples) and 5) Streptococcus pneumoniae (14 samples); as well as adult transplant recipients:6) Liver transplant patients that have accepted the graft under immunosuppressive therapy (16 samples) and 7) bonemarrow transplant recipients undergoing graft versus host disease (GVHD, 12 samples). Control groups were alsoformed taking into account age, sex and project (10 samples in each group). Genes significantly changed (p<0.01) inthe "study group" (Influenza A and/or SLE) were divided in two sets: over-expressed versus control and under-expressedversus control. P-values of the genes forming the over-expressed set were obtained for the "reference groups" (infectionswith E. coli, S. aureus, S. pneumoniae, Liver transplant recipients and graft versus host disease). P-values of the referencegroups were set to 1 when genes were under-expressed. The same procedure was used in the set of genes under-expressed in study group, only this time P-values of the reference group were set to 1 when genes were over-expressed.P-value data were processed with a gene expression analysis software program, GeneSpring, Version 7.1 (Agilent),that was used to perform hierarchical clustering and group genes based on significance patterns.[0118] Modules display distinct "transcriptional behavior". It is widely assumed that co-expressed genes are functionallylinked. This concept of "guilt by association" is particularly compelling in cases where genes follow complex expressionpatterns across many samples. The present inventors discovered that transcriptional modules form coherent biologicalunits and, therefore, predicted that the co-expression properties identified in the initial dataset would be conserved inan independent set of samples. Data were obtained for PBMCs isolated from the blood of twenty-one healthy volunteers.These samples were not used in the module selection process described above.[0119] FIGURE 2 shows gene expression profiles of four different modules are shown (Figure 2: M1.2, M1.7, M2.11and M2.1). In the graphs of Figure 2, each line represents the expression level (y-axis) of a single gene across multiplesamples (21 samples on the x-axis). Differences in gene expression in this example represent inter-individual variationbetween "healthy" individuals. It was found that within each module genes display a coherent "transcriptional behavior".Indeed, the variation in gene expression appeared to be consistent across all the samples (for some samples theexpression of all the genes was elevated and formed a peak, while in others levels were low for all the genes whichformed a dip). Importantly, inter-individual variations appeared to be module-specific as peaks and dips formed fordifferent samples in M1.2, M2.11 and M2.1. Furthermore, the amplitude of variation was also characteristic of eachmodule, with levels of expression being more variable for M1.2 and M2.11 than M2.1 and especially M1.7. Thus, wefind that transcriptional modules constitute independent biological variables.[0120] Functional characterization of transcriptional modules. Next, the modules were characterized at a functionallevel. A text mining approach was employed to extract keywords from the biomedical literature collected for each gene(described in 18). The distribution of keywords associated to the four modules that were analyzed is clearly distinct (Figure3). The following is a list of keywords that may be associated with certain modules.

• Keywords highly specific for M1.2 included Platelet, Aggregation or Thrombosis, and were associated with genessuch as ITGA2B (Integrin alpha 2b, platelet glycoprotein IIb), PF4 (platelet factor 4), SELP (Selectin P) and GP6(platelet glycoprotein 6).

• Keywords highly specific for M1.3 included B-cell, Immunoglobulin or IgG and were associated with genes such asCD19, CD22, CD72A, BLNK (B cell linker protein), BLK (B lymphoid tyrosine kinase) and PAX5 (paired box gene5, a B-cell lineage specific activator).

• Keywords highly specific for M1.5 included Monocyte, Dendritic, CD14 or Toll-like and were associated with genessuch as MYD88 (myeloid differentiation primary response gene 88), CD86, TLR2 (Toll-like receptor 2), LILRB2(leukocyte immunoglobulin-like receptor B2) and CD163.

• Keywords highly specific for M3.1 included Interferon, IFN-alpha, Antiviral, or ISRE and were associated with genessuch as STAT1 (signal transducer and activator of transcription 1), CXCL10 (CXC chemokine ligand 10, IP-10),OAS2 (oligoadenylate synthetase 2) and MX2 (myxovirus resistance 2).

[0121] This contrasted pattern of term occurrence denotes the remarkable functional coherence of each module.Information extracted from the literature for all the modules that have been identified permit a comprehensive functionalcharacterization of the PBMC system at a transcriptional level. Table 2 provides an example of genes that may be usedto distinguish between immune responses to, e.g., melanoma and liver transplant.

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Table 2: Genes in Module 1.4 Used to Distinguish Immune ResponsesmoduleID Entrez ID Gene Symbol Gene Title1.4 55544 RNPC1 RNA-binding region (RNP1, RRM) containing 11.4 5930 RBBP6 retinoblastoma binding protein 61.4 80273 GRPEL1 GrpE-like 1, mitochondrial (E. coli)

1.4 57162 PELI1 pellino homolog 1 (Drosophila)1.4 9921 RNF10 ring finger protein 10 /// ring finger protein 101.4 90637 LOC90637 hypothetical protein LOC906371.4 80314 EPC1 Enhancer of polycomb homolog 1 (Drosophila)1.4 --- --- Full length insert cDNA clone ZB81B121.4 5756 PTK9 PTK9 protein tyrosine kinase 9

1.4 55038 CDCA4 cell division cycle associated 41.4 5187 PER1 period homolog 1 (Drosophila)1.4 9205 ZNF237 zinc finger protein 2371.4 25976 TIPARP TCDD-inducible poly(ADP-ribose) polymerase1.4 57018 CCNL1 cyclin L11.4 64061 TSPYL2 TSPY-like 2

1.4 81488 GRINL1A glutamate receptor, ionotropic, N-methyl D-aspartate-like 1A1.4 22850 KIAA0863 KIAA0863 protein1.4 23764 MAFF v-maf musculoaponeurotic fibrosarcoma oncogene homolog F (avian)1.4 29035 PRO0149 PRO0149 protein1.4 7803 PTP4A1 protein tyrosine phosphatase type IVA, member 1

1.4 11171 STRAP serine/threonine kinase receptor associated protein1.4 5814 PURB purine-rich element binding protein B

1.4 5142 PDE4Bphosphodiesterase 4B, cAMP-specific (phosphodiesterase E4 dunce homolog, Drosophila)

1.4 30836 ERBP estrogen receptor binding protein1.4 6782 STCH stress 70 protein chaperone, microsome-associated, 60kDa

1.4 10950 BTG3 BTG family, member 31.4 7037 TFRC transferrin receptor (p90, CD71)1.4 54934 FLJ20436 hypothetical protein FLJ20436

1.4 5144 PDE4Dphosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)

1.4 9929 KIAA0063 KIAA0063 gene product

1.4 143187 VTI1AVesicle transport through interaction with t-SNAREs homolog 1A (yeast)

1.4 440309 --- LOC4403091.4 150094 SNF1LK SNF1-like kinase /// SNF1-like kinase1.4 1850 DUSP8 dual specificity phosphatase 81.4 9584 RNPC2 RNA-binding region (RNP1, RRM) containing 2

1.4 140735 Dlc2 dynein light chain 21.4 54542 MNAB membrane associated DNA binding protein1.4 9262 STK17B serine/threonine kinase 17b (apoptosis-inducing)1.4 7128 TNFAIP3 tumor necrosis factor, alpha-induced protein 31.4 3183 HNRPC heterogeneous nuclear ribonucleoprotein C (C1/C2)

1.4 5144 PDE4DPhosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)

1.4 80311 KLHL15 kelch-like 15 (Drosophila)1.4 22850 KIAA0863 KIAA0863 protein1.4 5996 RGS1 ---

1.4 468 ATF4activating transcription factor 4 (tax-responsive enhancer element B67)

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(continued)moduleID Entrez ID Gene Symbol Gene Title1.4 --- --- ---1.4 7430 VIL2 villin 2 (ezrin)1.4 6627 SNRPA1 small nuclear ribonucleoprotein polypeptide A’

1.4 7750 ZNF198 zinc finger protein 1981.4 1390 CREM cAMP responsive element modulator1.4 10291 SF3A1 splicing factor 3a, subunit 1, 120kDa1.4 9308 CD83 CD83 antigen (activated B lymphocytes, immunoglobulin superfamily)1.4 63935 C20orf67 ---1.4 10049 DNAJB6 DnaJ (Hsp40) homolog, subfamily B, member 6

1.4 51526 C20orf111 chromosome 20 open reading frame 1111.4 55500 ETNK1 ethanolamine kinase 1 /// ethanolamine kinase 11.4 79441 C4orf15 chromosome 4 open reading frame 151.4 11236 RNF139 ring finger protein 1391.4 246243 RNASEH1 ribonuclease H11.4 3727 JUND jun D proto-oncogene

1.4 6500 SKP1A S-phase kinase-associated protein 1A (p19A)1.4 4204 MECP2 Methyl CpG binding protein 2 (Rett syndrome)1.4 3189 HNRPH3 heterogeneous nuclear ribonucleoprotein H3 (2H9)

1.4 222161DKFZp586I1420 hypothetical protein DKFZp586I1420

1.4 266812 NAP1L5 nucleosome assembly protein 1-like 51.4 9908 G3BP2 Ras-GTPase activating protein SH3 domain-binding protein

21.4 10425 ARIH2 ---1.4 55422 ZNF331 Zinc finger protein 3311.4 8454 CUL1 Cullin 1

1.4 51119 SBDS Shwachman-Bodian-Diamond syndrome1.4 6309 SC5DL sterol-C5-desaturase (ERG3 delta-5-desaturase homolog, fungal)-like

1.4 5277 PIGA

phosphatidylinositol glycan, class A (paroxysmal nocturnal hemoglobinuria) /// phosphatidylinositol glycan, class A (paroxysmal nocturnal hemoglobinuria)

1.4 3422 IDI1 isopentenyl-diphosphate delta isomerase

1.4 63935 C20orf67 chromosome 20 open reading frame 671.4 7975 MAFK v-maf musculoaponeurotic fibrosarcoma oncogene homolog K (avian)1.4 7456 WASPIP Wiskott-Aldrich syndrome protein interacting protein1.4 55975 KLHL7 kelch-like 7 (Drosophila)1.4 7128 TNFAIP3 tumor necrosis factor, alpha-induced protein 31.4 388796 LOC388796 hypothetical LOC388796

1.4 25852 ARMC8 armadillo repeat containing 81.4 54542 MNAB Membrane associated DNA binding protein1.4 55422 ZNF331 zinc finger protein 3311.4 1390 CREM cAMP responsive element modulator1.4 10209 SUI1 putative translation initiation factor1.4 10049 DNAJB6 DnaJ (Hsp40) homolog, subfamily B, member 6

1.4 4929 NR4A2 nuclear receptor subfamily 4, group A, member 21.4 1540 CYLD cylindromatosis (turban tumor syndrome)1.4 4929 NR4A2 nuclear receptor subfamily 4, group A, member 21.4 5805 PTS 6-pyruvoyltetrahydropterin synthase1.4 10926 ASK activator of S phase kinase

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[0122] Yet another group that may be used alone or in combination with the genes listed in supplementary table thatincludes the data shown in Figure 17, denoted P2, and which may include one or more the following genes that areoverexpressed, e.g., WARS; IFI53; IFP53; GAMMA-2; FAM46C; FLJ20202; H3F3B; H3.3B; FOXK2; ILF; ILF1; ILF-1;

(continued)moduleID Entrez ID Gene Symbol Gene Title

1.4 10923 PC4

activated RNA polymerase II transcription cofactor 4 /// similar to Activated RNA polymerase II transcriptional coactivator p15 (Positive cofactor 4) (PC4) (p14)

1.4 388796 RNU71A Hypothetical LOC3887961.4 133746 JMY junction-mediating and regulatory protein1.4 90634 CG018 Hypothetical gene CG0181.4 10209 SUI1 putative translation initiation factor1.4 1847 DUSP5 dual specificity phosphatase 51.4 7088 TLE1 Transducin-like enhancer of split 1 (E(sp1) homolog, Drosophila)

1.4 84275 MGC4399 mitochondrial carrier protein1.4 --- --- ---1.4 7803 PTP4A1 protein tyrosine phosphatase type IVA, member 11.4 55422 ZNF331 zinc finger protein 3311.4 --- --- CDNA clone IMAGE:30332316, partial cds1.4 3609 ILF3 interleukin enhancer binding factor 3, 90kDa

1.4 --- --- Homo sapiens, clone IMAGE:4753714, mRNA1.4 6651 SON SON DNA binding protein1.4 11276 AP1GBP1 AP1 gamma subunit binding protein 11.4 84124 ZNF394 zinc finger protein 3 941.4 63935 C20orf67 ---

1.4 1983 EIF5 eukaryotic translation initiation factor 51.4 80063 ATF7IP2 Activating transcription factor 7 interacting protein 21.4 285831 LOC285831 hypothetical protein LOC2858311.4 81873 ARPC5L actin related protein 2/3 complex, subunit 5-like1.4 144438 LOC144438 hypothetical protein LOC1444381.4 10209 SUI1 putative translation initiation factor

1.4 3021 H3F3B H3 histone, family 3B (H3.3B)1.4 25948 KBTBD2 kelch repeat and BTB (POZ) domain containing 2

1.4 --- ---CDNA FLJ40725 fis, clone TKIDN1000001, highly similar to Translocase of inner mitochondrial membrane 23

1.4 1540 CYLD cylindromatosis (turban tumor syndrome)

1.4 5144 PDE4Dphosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)

1.4 51182 HSPA14 heat shock 70kDa protein 141.4 29080 HSPC128 HSPC128 protein1.4 8731 RNMT RNA (guanine-7-) methyltransferase1.4 3423 IDS iduronate 2-sulfatase (Hunter syndrome)1.4 283991 MGC29814 hypothetical protein MGC29814

1.4 1454 CSNK1E Casein kinase 1, epsilon1.4 26051 PPP1R16B protein phosphatase 1, regulatory (inhibitor) subunit 16B1.4 3422 IDI1 isopentenyl-diphosphate delta isomerase1.4 5887 RAD23B RAD23 homolog B (S. cerevisiae)

1.4 5144 PDE4DPhosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)

1.4 49854 ZNF295 zinc finger protein 2951.4 60493 FLJ13149 hypothetical protein FLJ131491.4 10950 BTG3 BTG family, member 3

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DUSP5; HVH3; ARF6; DKFZp762C186; BRD2; NAT; RNF3; FSRG1; RING3; D6S113E; KIAA9001; RORA; ROR1;ROR2; ROR3; RZRA; NR1F1; DKFZp762C186; DNAJB1; SUI1; CXCR4; HM89; LAP3; NPYR; WHIM; LESTR; NPY3R;HSY3RR; NPYY3R; D2S201E; GRINL1A; CTSB; TRIP-Br2; PDE4B; DPDE4; PDEIVB; PMAIP1; APR; NOXA; BTG2;PC3; TIS21; ASAHL; SON; SUI1; A121; ISO1; HERPUD1; SUP; Mif1; KIAA0025; DUSP2; PAC1; PAC-1; RNF139;RCA1; TRC8; HRCA1; MGC31961; TNFAIP3; A20; TNFA1P2; ARS2; HNRPL; hnRNP-L; P/OKc1.14; C20orf67;C20orf111; HSPC207; dJ1183I21.1; ZNF331; RITA; ZNF361; ZNF463; C20orf67; IER5; SBBI48; ; SUI1; JUN; AP1;CD69; TOB1; H3F3B; H3.3B; FOLR1; TNFAIP3; TCF8; BZP; ZEB; ZEB1; AREB6; ZFHEP; NIL-2A; ZFHX1A; NIL-2-A;DUSP10; MKP5; MKP-5; GGTLA4; MGC50550; dJ831C21.2; PMAIP1; ZC3HAV1; ZAP; FLB6421; ZC3HDC2;FLJ13288; MGC48898; DSIPI; DIP; GILZ; hDIP; TSC-22R; MCL1; TM; EAT; MCL1L; MCL1S; MGC1839; SH3TC1;FLJ20356; CIAS1; FCU; MWS; FCAS; NALP3; Clorf7; PYPAF1; AII/AVP; AGTAVPRL; SLC15A3; PHT2; PTR3; hPTR3;PTDSR; PSR; PTDSR1; KIAA0585; BHLHB2; DEC1; STRA13; Stra14; HMGE; KIAA0063; NR4A2; NOT; RNR1; HZF-3; NURR1; TINUR; NR4A2; NOT; RNR1; HZF-3; NURR1; TINUR; PTS; PTPS; HEAB; CLP1; hClp1; AREG; SDGF;CRDGF; MGC13647; EDG4; LPA2; EDG-4; LPAR2; CREM; ICER; MGC17881; MGC41893; CD83; BL11; HB15;ZNF394; FLJ12298, and combinations thereof.[0123] Module-based microarray data mining strategy. Results from "traditional" microarray analyses are notoriouslynoisy and difficult to interpret. A widely accepted approach for microarray data analyses includes three basic steps: 1)Use of a statistical test to select genes differentially expressed between study groups; 2) Apply pattern discovery algo-rithms to identify signatures among the resulting gene lists; and 3) Interpret the data using knowledge derived from theliterature or ontology databases.[0124] The present invention uses a novel microarray data mining strategy emphasizing the selection of biologicallyrelevant transcripts at an early stage of the analysis. This first step can be carried out using for instance the modularmining algorithm described above in combination with a functional mining tool used for in-depth characterization of eachtranscriptional module (FIGURE 4: top panel, Step 1). The analysis does not take into consideration differences in geneexpression levels between groups. Rather, the present invention focuses instead on complex gene expression patternsthat arise due to biological variations (e.g., inter-individual variations among a patient population). After defining thetranscriptional components associated to a given biological system the second step of the analysis includes the analysisof changes in gene expression through the comparison of different study groups (FIGURE 4: bottom panel, Step 2).Group comparison analyses are carried out independently for each module. Changes at the module level are expressedas the proportion of genes that meet the significance criteria (represented by a pie chart in FIGURE 5 or a spot in FIGURE6). Notably, carrying out comparisons at the modular level permits to avoid the noise generated when thousands of testsare performed on "random" collections of genes.[0125] Perturbation of modular PBMC transcriptional profiles in human diseases. To illustrate the second step of themicroarray data mining strategy described above (FIGURE 4), gene expression data for PBMC samples obtained fromtwo pediatric patient populations composed of eighteen children with systemic lupus erythematosus (SLE) and sixteenchildren with acute influenza A infection was obtained, compared and analyzed. Each patient cohort was matched to itsrespective control group (healthy volunteers: eleven and ten donors were matched to the SLE and influenza groups,respectively). Following the analytical scheme depicted in FIGURE 4, a statistical group comparisons between patientand healthy groups for each individual module and measured the proportion of genes significantly changed in eachmodule (FIGURE 5) was performed. The statistical group comparison approach allows the user to focus the analysison well defined groups of genes that contain minimal amounts of noise and carry identifiable biological meaning. A keyto the graphical representation of these results is provided in Figure 4.[0126] The following findings were made: (1) that a large proportion of genes in M3.1 ("interferon-associated") metthe significance level in both Flu and SLE groups (84% and 94%, respectively). This observation confirms earlier workwith SLE patients 19 and identifies the presence of an interferon signature in patients with acute influenza infection. (2)Equivalent proportions of genes in M1.3 ("B-cell-associated") were significantly changed in both groups (53%), with over50% overlap between the two lists. This time, genes were consistently under-expressed in patient compared to healthygroups. (3) Modules were also found that differentiate the two diseases. The proportion of genes significantly changedin Module 1.1 reaches 39% in SLE patients and is only 7% in Flu patients, which at a significance level of 0.05 is veryclose to the proportion of genes that would be expected to be differentially expressed only by chance. Interestingly, thismodule is almost exclusively composed of genes encoding immunoglobulin chains and has been associated with plasmacells. However, this module is clearly distinct from the B-cell associated module (M1.3), both in terms of gene expressionlevel and pattern (not shown). (4) As illustrated by module M1.5, gene-level analysis of individual modules can be usedto further discriminate the two diseases. It is also the case for M1.3, where, despite the absence of differences at themodule-level (FIGURE 4: 53% under-expressed transcripts), differences between Flu and SLE groups could be identifiedat the gene-level (only 51% of the under-expressed transcripts in M1.3 were common to the two disease groups). Theseexamples illustrate the use of a modular framework to streamline the analysis and interpretation of microarray results.[0127] Mapping changes in gene expression at the modular level. Data visualization is paramount for the interpretationof complex datasets and the present invention includes a comprehensive graphical illustration of changes that occur at

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the modular level. Changes in gene expression levels caused by different diseases were represented for the twenty-eightPBMC transcriptional modules (FIGURE 6). Each disease group is compared to its respective control group composedof healthy donors who were matched for age and sex (eighteen patients with SLE, sixteen with acute influenza infection,sixteen with metastatic melanoma and sixteen liver transplant recipients receiving immunosuppressive drug treatmentwere compared to control groups composed of ten to eleven healthy subjects). Module-level data were representedgraphically by spots aligned on a grid, with each position corresponding to a different module (See Table 1 for functionalannotations on each of the modules).[0128] The spot intensity indicates the proportion of genes significantly changed for each module. The spot colorindicates the polarity of the change (red: proportion of over-expressed genes, blue: proportion of under-expressed genes;modules containing a significant proportion of both over- and under-expressed genes would be purple-though none wereobserved). This representation permits a rapid assessment of perturbations of the PBMC transcriptional system. Such"module maps" were generated for each disease. When comparing the four maps, we found that diseases were char-acterized by a unique modular combination. Indeed, results for M1.1 and M1.2 alone sufficed to distinguish all fourdiseases (M1.1/M1.2: SLE = +/+; FLU=0/0; Melanoma=-/+; transplant=-/-). A number of genes in M3.2 ("inflammation")were over-expressed in all diseases (particularly so in the transplant group), while genes in M3.1 (interferon) were over-expressed in patients with SLE, influenza infection and, to some extent, transplant recipients. "Ribosomal protein" modulegenes (M1.7 and M2.4) were under-expressed in both SLE and Flu groups. The level of expression of these genes wasrecently found to be inversely correlated to disease activity in SLE patients (Bennett et al., submitted). M2.8 includes T-cell transcripts which are under-expressed in lymphopenic SLE patients and transplant recipients treated with immuno-suppressive drugs targeting T-cells.[0129] Interestingly, differentially expressed genes in each module were predominantly either under-expressed orover-expressed (FIGURE 5 and FIGURE 6). Yet, modules were purely selected on the basis of similarities in geneexpression profiles, not changes in expression levels between groups. The fact that changes in gene expression appearhighly polarized within each module denotes the functional relevance of modular data. Thus, the present inventionenables disease fingerprinting by a modular analysis of patient blood leukocyte transcriptional profiles.[0130] Validation of PBMC modules in a published dataset. Next, the validity of the PBMC transcriptional modulesdescribed above in a "third-party" dataset was tested. The study from Connolly, et al., who investigated the effects ofexercise on gene expression in human PBMCs20 was tested.[0131] Blood samples were obtained from 35 patients with metastatic melanoma enrolled in three phase I/II clinicaltrials designed to test the efficacy of a dendritic cell therapeutic vaccine as seen in the table below. Gene expressionsignatures were generated from blood samples collected prior to the initiation of vaccine therapy, and at least 4 weeksafter the last systemic therapy if a patient had undergone such.

Table 3: Clinical and demographic characteristics of 35 patients with metastatic melanoma

ID Sex Age Stage Diagnosis

Time from Dx to Blood

Draw (Months)

Blood Draw Status

Blood Draw to Time of

Death (Months)

MEL 23 F 61 M1a 02/14/00 16 06/28/01 Deceased 28MEL 24 M 53 M1c 09/01/99 22 07/05/01 Deceased 5

MEL 26 F 52 M1c 10/01/97 45 07/18/01 Deceased 2MEL 27 F 54 M1a 07/10/93 98 09/14/01 Deceased 5MEL 29 M 41 M1c 10/26/94 83 09/26/01 Deceased 14MEL 30 F 58 M1c 10/04/99 23 09/25/01 Deceased 11MEL 32 M 56 M1b 01/17/94 95 12/17/01 Deceased 24MEL 34 F 28 M1b 04/01/01 10 02/05/02 Deceased 42

MEL 35 M 29 M1a 08/25/98 43 03/12/02 Deceased 12MEL 36 F 69 M1b 01/01/85 205 02/19/02 Deceased 7MEL 40 F 43 M1c 07/02/91 132 07/19/02 Deceased 19MEL 43 M 60 M1a 08/14/94 100 12/04/02 Alive 05/16/05 *MEL 44 F 68 M1c 05/01/99 44 01/28/03 Deceased 12MEL 45 F 53 M1a 02/01/02 10 12/17/02 Alive 5/5/05 *

MEL 46 F 47 M1c 11/18/97 61 12/27/02 Deceased 22MEL 47 F 35 M1c 03/02/02 10 01/09/03 Deceased 2MEL 48 M 68 M1b *1992 >120 03/12/03 Alive 05/10/05 *

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[0132] The Table provides both clinical and demographic characteristics of 35 patients with metastatic melanoma.[0133] A second group of patients included 39 liver transplant recipients maintaining their graft under pharmacologicalimmunosuppressive therapy, time from transplant had a median of 729 days and a range of between 338 and 1905days. Outpatients coming for routine exams were recruited for this study. All patients received standard treatmentregimens with calcineurin inhibitors (e.g., Tacrolimus: n=25; Cyclosporin A: n=13). The main indications for liver transplantwere hepatitis C (n = 19) and Laennec’s cirrhosis (n = 7). The table below provides both clinical and demographiccharacteristics of 39 liver transplant recipients.

(continued)

ID Sex Age Stage Diagnosis

Time from Dx to Blood

Draw (Months)

Blood Draw Status

Blood Draw to Time of

Death (Months)

MEL 49 M 71 M1b 12/05/97 64 04/10/03 Alive 07/05/05 *MEL 50 M 52 M1c 07/08/97 69 04/11/03 Deceased 18MEL 51 M 56 M1c 10/01/01 18 04/16/03 Alive 05/17/05 *MEL 52 M 42 M1c 03/01/02 13 04/17/03 Deceased 9MEL 54 M 50 M1b 07/20/90 153 04/25/03 Alive 04/08/05 *

MEL 56 M 71 M1c 03/01/01 26 05/29/03 Deceased 9MEL 57 F 36 M1b 07/01/02 11 06/05/03 Deceased 20MEL 58 M 67 M1c 10/01/99 45 07/18/03 Deceased 10MEL 59 M 61 M1c unknown * 07/25/03 Alive 06/22/05 *MEL 60 M 41 M1c 11/01/02 9 08/14/03 Deceased 7MEL 61 F 54 M1a 03/03/99 54 09/10/03 Alive 05/18/05 *

MEL 62 M 46 M1b 12/01/01 22 10/09/03 Deceased 5MEL 63 M 75 M1b 12/01/00 34 10/29/03 Alive 03/16/05 *MEL 64 F 53 M1b 04/01/00 42 10/30/03 Alive 03/05/04 *MEL 65 M 62 M1b 08/14/94 111 11/14/03 Alive 05/16/05 *MEL 68 M 74 M1b 06/09/04 1 07/29/04 Deceased 9

MEL 70 M 67 M1b 04/06/04 5 09/23/04Alive 8/18/2005

*

MEL 72 M 50 M1c 09/23/04 2 11/10/04 Deceased *

Table 4: Clinical and demographic characteristics of 39 liver transplant recipients

Patient ID Age Sex Transpl. to Blood Draw (Days) TAC CsA Primary Dx

R1292 47 M 1854 yes Hepatitis C

R1297 48 M 1868 yes Hepatitis CR1308 66 F 1905 yes Primary Biliary CirrhosisR1322 32 F 1821 yes Hepatitis CR1323 45 M 1828 yes Hepatitis CR1325 50 M 1801 yes Hepatitis CR1329 51 F 1781 yes Laennec’s Cirrhosis

R1340 50 M 1829 yes Laennec’s CirrhosisR1348 64 F 1802 yes Fulminant Hepatic FailureR1355 61 M 1780 yes CryptogenicR1364 42 M 1756 yes Hepatitis CR1413 52 M 1856 yes Laennec’s CirrhosisR1673 60 F 756 yes Hepatitis B

R1674 45 M 729 yes Hepatitis CR1684 65 F 721 yes Mx. CarcinoidR1686 59 M 704 yes Hepatitis BR1689 43 M 692 yes Hepatitis CR1700 53 M 732 yes Hepatitis C

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[0134] Blood samples were also obtained from 25 healthy donors that constituted the control groups. The table belowprovides the demographic characteristics of the 25 healthy donors.

(continued)Patient ID Age Sex Transpl. to Blood Draw (Days) TAC CsA Primary Dx

R1701 45 M 737 yes Hepatitis CR1702 48 M 726 yes Hepatitis C

R1706 57 M 721 yes Laennec’s CirrhosisR1710 50 F 736 yes CryptogenicR1714 63 F 718 yes Nonalcoholic SteatohepatitisR1718 53 F 707 yes Hepatitis CR1754 51 F 589 yes Primary Sclerosing CholangitisR1771 42 F 812 Hepatitis C

R1787 60 F 794 yes Laennec’s Cirrhosis

R1805 42 M 735 yesLaennec’s Cirrhosis & Postnecrotic Cirrhosis Type C

R1814 45 M 427 yes Hepatitis CR1838 66 F 360 yes Autoimmune HepatitisR1839 56 M 354 yes Cryptogenic

R1841 52 M 363 yes PSC-UCR1843 48 M 361 yes Hepatitis CR1845 44 F 338 yes Primary Biliary CirrhosisR1846 41 F 338 yes Hepatitis CR1847 53 M 358 yes Laennec’s Cirrhosis

R1854 50 M 350 yes Hepatitis C

R1971 46 M 367 yesHepatitis C; Hepatocellular Carcinoma with Cirrhosis

R1974 54 M 341 yes Hepatitis C

Table 5: Demographic characteristics of 25 healthy donorsHealthy Volunteers Sex Age

D-001 M 41D-002 F 53D-005 F 40D-007 F 44D-008 M 40

D-010 M 46D-011 F 43D-013 M 58D-014 F 47D-015 M 42D-016 F 40

D-017 M 25D-018 M 46D-019 F 40D-020 M 39D-021 M 45D-022 M 50

D-024 F 44D-025 F 48D-027 F 43D-028 F 43

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[0135] Identification of disease-associated blood leukocyte transcriptional signatures. Blood leukocyte gene expressionsignatures were identified in patients with metastatic melanoma and liver transplant recipients. Each set of patients wascompared to a control group of healthy volunteers. Patient samples were divided into a training set, used to identifydisease-associated and predictive expression signatures, and an independent test set. This step-wise analysis allowedvalidation of results in samples that were not used to establish the disease signature. Stringent criteria were employedto select the samples forming training sets in order to avoid confounding the analysis with biological and/or technicalfactors. The table below illustrates the composition of sample sets taking into account age, gender and sample processingmethod used for the identification (training) and validation (testing) of expression signatures associated with metastaticmelanoma.

[0136] Similarly, the table below provides the composition of sample sets used taking into account age, gender andsample processing method for the identification (training) and validation (testing) of expression signatures associatedwith liver transplant recipients undergoing immunosuppressive drug therapy.

(continued)Healthy Volunteers Sex Age

D-029 M 43D-031 M 35

D-032 F 43D-033 F 43

Table 6: Composition of sample sets associated with metastatic melanoma.

Training Set Test Total nHV Fresh Frozen Fresh Frozen

Male 7 7 14 Male 5 0 5

Female 0 9 9 Female 8 0 8

7 16 23 13 0 13 36

Melanoma Fresh Frozen Fresh Frozen

Male 3 9 12 Male 0 11 11Female 0 10 10 Female 0 5 5

3 19 22 0 16 16 38Total 45 Total 29

Table 7: Composition of sample sets associated with liver transplant recipients undergoing immunosuppressive drug therapy.

Training Set Test Total nHV Fresh Frozen Fresh Frozen

Male 12 5 17 Male 0 2 2Female 8 2 10 Female 0 7 7

20 7 27 0 9 9 36

LTx Fresh Frozen Fresh Frozen

Male 9 3 12 Male 15 0 15Female 9 1 10 Female 6 0 6

18 4 22 21 0 21 43

Total 49 Total 30

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[0137] Table 8 lists the genes differentially expressed in patients with metastatic melanoma in comparison to healthyvolunteers. Statistical comparison of a group of twenty-two patients with metastatic melanoma versus twenty-threehealthy volunteers, training set, identified 899 differentially expressed genes (p<0.01, non parametric Mann-Whitneyrank test and >1.25 fold change; 218 overexpressed and 681 underexpressed genes). The Tables provide a correlationto the modules from which there were identified, their expression level, various nomenclatures for their individual iden-tification.[0138] FIGURES 7A-7D are images of the hierarchical clustering of genes. FIGURE 7A illustrates the hierarchicalclustering of genes that produce a reciprocal expression pattern; which was confirmed in an independent test set shownin FIGURE 7B. FIGURE 7B displays results from 13 healthy volunteers versus 16 patients. Next, class prediction algo-rithms were applied to the initial training set. These algorithms yielded 81 genes with the best ability to classify healthyvolunteers and patients based on their differential expression as shown in FIGURE 7C and Table 9). Table 9 illustratesthe expression levels of a set of transcripts discriminating patients with melanoma from healthy volunteers. Using these81 genes, an independent test set was classified with 90% accuracy; the class of only three was indeterminate asillustrated in FIGURE 7D.[0139] FIGURES 8A and 8B are plots of the microarray results in an independent set of samples to confirm the reliabilityof the results by correlating expression levels obtained for the training and test sets. Genes with the best ability todiscriminate between patients and healthy volunteers were identified in a training set using a class prediction algorithm(k-Nearest Neighbors). Fold change expression levels between healthy controls and patients were measured for dis-criminative genes in the training set and an independent test set. Fold change values obtained in training and test setswere correlated: FIGURE 8A illustrates results for metastatic melanoma and produced 81 genes with a Pearson corre-lation of r2=0.83 and a p<0.0001 and FIGURE 8B illustrates results for liver transplant recipients and produced 65 geneswith a Pearson correlation of r2=0.94 and a p<0.0001.[0140] FIGURES 9A-9D are images of the hierarchical clustering of genes for the identification of a blood leukocytetranscriptional signature in transplant recipients under immunosuppressive drug therapy. Samples were divided into atraining set (27 healthy, 22 patients) used to identify differentially expressed genes in liver transplant recipients versushealthy volunteers as seen in FIGURE 9A and FIGURE 9C respectively. A test set of nine healthy and 21 patients wereused to independently validate this signature as seen in FIGURE 9B and FIGURE 9D. Class comparison identified 2,589differentially expressed genes (Mann-Whitney test p < 0.01, fold change > 1.25). FIGURE 9A illustrates a similar signaturein the test set and FIGURE 9B illustrates the class prediction that identified 81 genes. FIGURE 9C shows that thediscrimination of the independent test set with 90% accuracy. FIGURE 9D illustrates the class could not be identifiedfor two samples out of 30; one sample was incorrectly predicted (i.e. transplant recipient classified as healthy).[0141] The same analysis strategy was applied to Liver transplant patients. Statistical comparison of a group of 22transplant recipients versus 27 healthy volunteers identified 2,589 differentially expressed genes (p<0.01, non-parametricMann-Whitney rank test and >1.25 fold change; 938 overexpressed and 1651 underexpressed genes; Table 10). Table10 illustrates genes that are expressed differentially in liver transplant recipients under treatment with immunosuppressivedrugs in comparison to healthy volunteers. Hierarchical clustering of genes produced a reciprocal expression patternthat was observed in both training as seen in FIGURE 9A and independent test sets as seen in FIGURE 9B. Sixty-fiveclassifier genes were established in the training set and are illustrates in FIGURE 9C and Table 11. Table 11 illustratesthe expression level of a set of transcripts discriminating liver transplant recipients under treatment with immunosup-pressive drugs from healthy volunteers. The sixty-five classifier genes were applied to an independent test set of 9healthy donors and 21 patients. Samples were correctly classified 90% of the time: class could not be determined intwo cases and one sample was misclassified as seen in FIGURE 9D. The results obtained for both of these sets werehighly correlated, e.g., pearson correlation, r2=0.94, p<0.0001, as seen in FIGURE 8B. Thus, the blood leukocyte tran-scriptional signatures associated with patients with metastatic melanoma and in liver transplant recipients have beenidentified and validated.[0142] Module-level analysis of patients PBMC transcriptional profiles was performed. A custom microarray datamining strategy was used to further characterize disease-associated gene expression patterns. The analysis of a set of239 blood leukocytes transcriptional signatures identified 28 transcriptional modules regrouping 4,742 probe sets. These"transcriptional modules" are sets of genes that follow similar expression patterns across a large number of samples inmultiple studies, identified through co-expression meta-analysis. Each module is associated with a unique identifier thatindicates the round of selection and order (e.g., M2.8 designates the eighth module of the second round of selection).Upon extraction each transcriptional module is functionally characterized with the help of a literature profiling algorithm(Chaussabel and Sher, 2002).[0143] FIGURES 10 to 13 illustrate detailed statistical comparisons between healthy and disease groups of the mod-ule-level analysis. For example, twenty-eight sets of coordinately expressed genes, or transcriptional modules, wereidentified through the analysis of 239 PBMC microarray profiles. For each of these modules changes in expression levelsbetween groups of healthy volunteers and either patients with metastatic melanoma or transplant recipients were tested.A pie chart indicates the proportion of genes that were significantly changed in each module, where red indicates

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overexpressed genes and blue illustrates underexpressed genes, with a p<0.05 for the Mann-Whitney test. For eachmodule, keywords extracted from the literature are listed in green along with a functional assessment of relationshipsexisting among the genes.[0144] FIGURE 14 is a plot of the changes observed in a few representative modules represented by a transcriptionalprofile. Each differentially expressed gene is represented by a line that indicates relative levels of expression acrosshealthy volunteer and patient samples. Peaks and dips respectively indicate relatively higher and lower gene expressionin a given patient. Genes that were not significantly different are not represented. The levels of expression of genesassociated with a platelet signature changed in opposite directions: 28% of the genes forming this signature (M1.2) wereoverexpressed in patients with melanoma and 27% were underexpressed in transplant recipients. Furthermore, half ofthe genes belonging to module M2.1 (cytotoxic cell signature) were underexpressed in transplant recipients. This trendwas not observed in patients with melanoma (7% overexpressed with p<0.05 where 5% of changes are expected bychance only). Similarly, a massive down-regulation of genes associated to T cells was observed in transplant recipients(74% of genes in M2.8). This finding most likely reflects pharmacological immunosuppression. In patients with melanoma29% of these T-cell related genes were down-regulated. In addition, 44% of interferon-inducible genes that form moduleM3.1 were overexpressed in transplant recipients, while 26% were underexpressed in patients with melanoma. Lists ofdifferentially expressed genes in each module are available in Tables 12 and 13.[0145] Patients with metastatic melanoma and transplant recipients display common transcriptional profiles at themodular level. This analysis identified similarities as well as differences in blood leukocyte transcriptional signatures ofpatients with metastatic melanoma and liver transplant recipients.[0146] FIGURE 15 is an image of the modular changes observed in both groups of patients vs. their respective healthycontrol group. The proportion of differentially expressed genes for each module is indicated by a spot of variable intensity.For example, in the overlay a change in transplant is represented by a yellow, while a change in melanoma is indicatedby a blue color and a change in both is indicated by a green color.[0147] Proportions of underexpressed and overexpressed transcripts are represented on separate grids. Modules thatwere common between the two groups of patients include M1.4 (regulator of cAMP and NF-kB signaling pathways),M2.6 (including genes expressed in myeloid lineage cells), M3.2 and M3.3 (both M3.2 and 3.3 include factors involvedin inflammation; as seen in FIGURES 10-13).[0148] FIGURE 16 is an image illustrating the module-level analysis of the present invention. Common transcriptionalsignatures in blood from patients with metastatic melanoma and from liver transplant recipients. Expression profiles ofgenes belonging to blood leukocyte transcriptional modules M1.1, M1.3, M1.4 and M3.2. The total number of probes isindicated for each module (U133A), along with a brief functional interpretation. Keywords extracted by literature profilingare indicated in green. From the total number in each module, the proportion of genes that were significantly changed(Mann-Whitney test, p<0.05) in patients compared to the appropriate healthy control group is indicated in a pie chartwith overexpressed genes are expressed in red and underexpressed genes are expressed in blue. Graphs representtranscriptional profiles of the genes that were significantly changed, with each line showing levels of expression on they-axis of a single transcript across multiple conditions (samples, x-axis).[0149] The association between metastatic melanoma and liver transplant phenotype was strongest for M1.4 andM3.2 as seen in FIGURE 16. Interestingly, a majority of underexpressed modules were common to melanoma andtransplant groups, with the most striking similarities in the case of M1.1 (including plasma cell associated genes), M1.3(including B-cell associated genes) and M1.8 (including genes coding for metabolic enzymes and factors involved inDNA replication).[0150] Identification of a common transcriptional signature that is unique to metastatic melanoma and liver transplantpatients. The extent of the similarities between patients with metastatic melanoma and liver transplant recipients werespecific to these two groups of patients were examined. Statistical group comparison was carried out between patientsand healthy controls across all samples (e.g., thirty-eight melanoma, forty-three transplant, thirty-six healthy). Briefly,323 transcripts were identified that were significantly overexpressed and 918 that were significantly underexpressed inboth liver transplant recipients and patients with metastatic melanoma (Mann-Whitney test, p<0.01, filtered >1.25 foldchange). Next, group comparisons for these transcripts were carried using samples from patients with Systemic LupusErythematosus ("SLE"), acute infections (S. pneumoniae, S. aureus, E. coli, and Influenza A) and Graft versus HostDisease ("GVHD") compared to relevant healthy controls. This analysis yielded p-values whose hierarchical clusteringidentified distinct significance patterns among transcripts common to the melanoma and transplant groups. This analysisidentified sets of genes that changed across all diseases as seen in FIGURE 17 with P1 being ubiquitously overexpressed;P3 being ubiquitously underexpressed, while others were associated more specifically with the melanoma and transplantgroups as seen in FIGURE 18 with P2 being overexpressed; and P4 being underexpressed. Table 14 illustrates thesignificance levels across 8 diseases of the genes forming patterns P1, P2, P3 and P4.[0151] FIGURE 17 is an image of the analysis of significance patterns. Genes expressed at higher levels in both stageIV melanoma or liver transplant patients compared to healthy volunteers were selected. P-values were similarly obtainedfrom gene expression profiles generated in other disease models: in PBMCs obtained from patients suffering from

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systemic lupus erythematosus (SLE), Graft versus Host Disease (GVHD), or acute infections with influenza virus (Influ-enza A), Escherichia coli (E. coli), Streptococcus pneumoniae (Strep. Pneumo.) or Staphylococcus aureus (Staph.aureus). Each of these cohorts was compared to the appropriate control group of healthy volunteers accrued in thecontext of these studies. The genes expressed at significantly higher or lower levels in PBMCs obtained from bothpatients with melanoma and liver transplant recipients (OVER-XP and UNDER-XP, respectively) were ranked by hier-archical clustering of p-values generated for all the conditions listed above. P-values are represented according to acolor scale: Green represents low p-value/significant, while white represents high p-value/not significant. Distinct signif-icant patterns are identified, where P1 and P3 are ubiquitous and P2 and P4 are most specific to melanoma and livertransplant groups.[0152] FIGURE 18 is a chart of the modular distribution of ubiquitous and specific gene signatures common to melanomaand transplant groups. Distribution among 28 PBMC transcriptional modules was determined for genes that form ubiq-uitous (P1) and specific (P2) transcriptional signatures common to the melanoma and transplant groups. Gene lists ofeach of the modules were compared in turn to the 109 and 69 transcripts that form P1 and P2. For each module, theproportion of genes shared with either P1 or P2 was recorded. These results are represented by a bar graph of FIGURE 18.[0153] Thus, genes forming transcriptional signatures common to the melanoma and transplant groups can be parti-tioned into distinct sets based on two properties: (1) coordinated expression as seen in the transcriptional modules ofFIGURE 13; and (2) change in expression across diseases as seen in the significance patterns of FIGURE 17. Theresults from these two different mining strategies were recouped by examining the modular distribution of ubiquitous(P1) and specific (P2) PBMC transcriptional signatures. FIGURE 18 clearly shows that the distribution of P1 and P2across the 28 PBMC transcriptional modules that have been identified to date is not random. Indeed, P1 transcripts arepreferentially found among M3.2 (characterized by transcripts related to inflammation), whereas M1.4 transcripts almostexclusively belonged to P2, which includes genes that are more specifically overexpressed in patients with melanomaand liver transplant recipients.[0154] FIGURE 19 is an illustration of the transcriptional signature of immunosuppression. Transcripts overexpressedmost specifically in patients with melanoma and transplant recipients (P1) include repressors of immune responses thatinhibit: 1) NF-kB translocation; 2) Interleukin 2 production and signaling; 3) MAPK pathways and 4) cell proliferation.Some of these factors are well characterized anti-inflammatory molecules and others are expressed in anergic T-cells.[0155] Molecular signature of immunosuppression. The genes that were most specifically overexpressed in melanomaand transplant groups (P1) were examined. From the 69 probe sets, 55 unique gene identifiers were identified. A queryagainst a literature database indexed by gene, have developed to aid the interpretation of microarray gene expressiondata, identified 6527 publications associated with 47 genes, 30 of which were associated with more than ten publications.FIGURE 19 illustrates a remarkable functional convergence among the genes forming this signature and includes genesencoding molecules that possess immunoregulatory functions (e.g., anti-proliferative genes: BTG2, TOB1, AREG, SUI1or RNF139; anti-inflammatory genes: TNFAIP3); inhibitors of transcription: (SON, ZC3HAV1, ZNF394); stress-inducedmolecules (HERPUD1); while others possess well established immunosuppressive properties. For example, dual spe-cificity phosphatases 2, 5 and 10 (DUSP2, 5, 10) interfere with the MAP kinases ERK1/2, which are known targets ofcalcineurin inhibitors such as Tacrolimus/FK506. DUSP10 selectively dephosphorylates stress activated kinases(The-odosiou et al., 1999). Interestingly, DUSP5 was found to have a negative feedback role in IL2 signaling in T-cells(Kovanenet al., 2003). CREM, FOXK2 and TCF8 directly bind the IL2 promoter and can contribute to the repression of IL-2production in T cell anergy(Powell et al., 1999). BHLHB2 (Stra13) negatively regulates lymphocyte development andfunction in vivo(Seimiya et al., 2004). CIAS1 codes for the protein Cryopyrin, which regulates NF-kappa B activationand production of proinflammatory cytokines. Mutations of this gene have been identified in several inflammatory dis-orders(Agostini et al., 2004). DSIPI, a leucine zipper protein, is known to mediate the immunosuppressive effects ofglucocorticoids and IL10 by interfering with a broad range of signaling pathways (NF-kappa B, NFAT/AP-1, MEK, ERK1/2), leading to the general inhibition of inflammatory responses in macrophages and down-regulation of the IL2 receptorin T cells. Noatably, the expression of DSIPI in immune cells was found to be augmented after drug treatment (dexam-ethasone)(D’Adamio et al., 1997) or long term exposure to tumor cells (Burkitt Lymphoma)(Berrebi et al., 2003).[0156] Other immunosuppressive molecules, which did not belong to P1, were also found overexpressed in melanomaand transplant groups. Notably, DDIT4, another dexamethasone-induced gene which was recently found to inhibit mTOR,the mammalian target for rapamycin (Corradetti et al., 2005). Thus, this endogenous factor appears capable of repro-ducing the action of potent immunosuppressive drugs. HMOX1, a cytoprotective molecule that also demonstrates anti-inflammatory properties. Most recently, HMOX1 expression was found to be induced by FOXP3 and to mediate immu-nosuppressive effects of CD4+ CD25+ regulatory T cells (Choi et al., 2005). Accordingly, an increase in transcriptionalactivity of HMOX1 has been correlated with favorable outcomes in experimental transplant models (Soares et al., 1998).Both DDIT4 and HMOX1 genes were also overexpressed in patients with acute E. coli or S. aureus infections. Theimmunophilin FKBP1A (FKBP12), a member of the FK506-binding protein family, is a key mediator of T-cell immuno-suppression by the drugs FK506 (tacrolimus) and rapamycin (Xu et al., 2002). Expression of this gene was elevated incomparison to healthy donors in all patient groups.

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[0157] Blood is an accessible tissue and lends itself to comparative analyses across multiple diseases. Parmacologicaland tumor-mediated immunosuppression would produce a common transcriptional signature in blood leukocytes. Met-astatic melanoma and transplant recipients disease-associated transcriptional signatures were identified in the blood ofpatients. These signatures were identified and confirmed through several analytical approaches. Analysis of transcrip-tional modules identified alterations in blood leukocytes transcriptional components associated to cell types (e.g., Plasmacells, B-cells, T-cells, Cytotoxic cells) and to immune reactions (e.g., Inflammation, Interferon). Furthermore, using bothtranscriptional modules and gene expression levels similarities between blood transcriptional signatures in patients withmetastatic melanoma and liver transplant recipients were identified. However, this common transcriptional signaturecould not be entirely attributed to immunosuppression. For instance, expression levels of B-cell associated genes (M1.3)were not only decreased in the melanoma and transplant groups, but also in patients with acute influenza infection andsystemic lupus erythematosus (SLE) (53% of genes were underexpressed, in comparison to healthy controls; Chaussabelet al.). Conversely, nearly 40% of the genes associated with plasma cells (M1.1) were overexpressed in SLE patientsand there was no change in patients with acute influenza infection (7% of the genes were overexpressed at p<0.05),whereas expression levels were significantly decreased in both patients with melanoma and transplant recipients (61% and 62% of the genes in M1.1, respectively). In order to select the most specific transcripts common between melanomaand transplant signatures a gene-level analysis was carried out across a total of eight groups of patients. This led to theidentification of a set of transcripts that was most specifically overexpressed in immunosuppressed patients. The identifiedset of genes showed marked functional convergence and included genes coding for repressors of Interleukin-2 tran-scription, inhibitors of NF-kB or MAPK pathways, and anti-proliferative molecules. Interestingly, these signatures areconsistent with the mechanism of action of drugs used for pharmacological immunosuppression, which inhibit the activityof calcineurin, a calcium-dependent serine threonine protein phosphatase responsible for the nuclear translocation ofNF-AT and NF-kappaB upon T-cell activation. Indicating a functional convergence between immunosuppressive mech-anisms operating in patients with advanced melanoma and pharmacologically treated transplant recipients. The factthat the transcripts more specifically induced in immunosuppressed patients include glucocorticoids-inducible genes(e.g., DSIPI, CXCR4, JUN) and hormone nuclear receptors (NR4A2 and RORA)(Winoto and Littman, 2002) suggest apossible role for steroid hormones in tumor-mediated immunosuppression.[0158] Patients with metastatic melanoma display an endogenous transcriptional signature of immunosuppressionsimilar to that induced by pharmacological treatments in patients who underwent liver transplant. The present inventionprovides a method and apparatus to identify patients at high risk of melanoma progression. In addition the presentinvention also provides a method and apparatus for monitoring indicators of immunosuppression could help adjustingthe dosage of immunosuppressive drugs and balance risks of rejection and side effects for liver transplant recipients.[0159] Examples of patient information and processing of blood samples include the following. Blood was obtainedafter informed consent as approved by the institutional IRB (Liver transplant recipients: 002-1570199-017; patients withmelanoma: 000-048, 002-094; 003-187). Blood samples were obtained in acid citrate dextrose yellow-top tubes (BDVaccutainer) at the Baylor University Medical Center in Dallas, TX. Samples were immediately delivered at room tem-perature to the Baylor Institute for Immunology Research, Dallas, TX, for processing. Fresh PBMCs isolated via Ficollgradient were either stored in liquid nitrogen (e.g., viable freezing) or immediately lysed in RLT buffer, containing β-mercaptoethanol (Qiagen, Valencia, CA). Total RNA was extracted from cells previously frozen in liquid nitrogen ("frozen")or from cells that were lysed immediately after isolation ("fresh"), using the RNEASY® Mini Kit according to the manu-facturer’s recommended protocol (Qiagen, Valencia, CA). This parameter was taken into account in the experimentaldesign taking into account the age, gender and sample processing method used for the identification (training) andvalidation (testing) of expression signatures associated with metastatic melanoma and liver transplant recipients under-going immunosuppressive drug therapy.[0160] Microarray assays. Total RNA was isolated using the RNEASY® kit (Qiagen, Valencia, CA) according to themanufacturer’s instructions and the RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto,CA). Although, the skilled artisan will recognize that other methods of isolation may be used. From 2-5 micrograms oftotal RNA, double-stranded cDNA containing the T7-dT (24) promoter sequence (Operon Biotechnologies, Huntsville,AL) was generated. This cDNA was then used as a template for in vitro transcription single round amplification withbiotin labels (Enzo BioArray HighYield RNA Transcript Labeling Kit from Affymetrix Inc, Santa Clara, CA). BiotinylatedcRNA targets were purified using the Sample Cleanup Module and subsequently hybridized to human U133A GeneChips(Affymetrix Inc, Santa Clara, CA) according to the manufacturer’s standard protocols. Affymetrix U133A GeneChips thatcontain 22,283 probe sets, represented by ten to twenty unique probe pairs (perfect match and its corresponding mis-match), which allow detection of 14,500 different genes and expressed sequence tags (ESTs). Arrays were scannedusing a laser confocal scanner (Agilent). The samples were processed by the same team, at the same core facility, andwere randomized between each array run. Raw data are deposited with GEO (www.ncbi.nltn.nih.gov/geo/).[0161] Data analysis. For each Affymetrix U133A GENE CHIP® raw intensity data were normalized to the meanintensity of all measurements on that array and scaled to a target intensity value of 500 (TGT) in Affymetrix MicroarraySuite 5.0. With the aid of GeneSpring software, version 7.2, the measurement for each gene per patient sample array

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was divided by the median of that gene’s measurement from the cohort of healthy volunteers. A filter was applied basedon Affymetrix flag calls: probe sets were selected if "Present" in at least 75% of samples in either group (healthy controlsor patients). This step insured a more reliable intensity measurement of the genes used in downstream analyses. Classcomparison was performed using a non-parametric ranking statistical analysis test (Mann-Whitney) applied to the selectedset of genes. In the vertical direction, hierarchical clusters of genes were generated using the Pearson correlation aroundzero, Genespring’s standard correlation measure. Normalized gene expression data were examined with a nonparametricunivariate analysis (Fisher’s exact test) to identify genes potentially discriminating two different groups. A supervisedlearning algorithm, the K-Nearest Neighbors Method, was applied that assigned a sample to pre-defined classes in threesteps: 1) identification of genes (observations) that have strong correlations to classes to be distinguished; 2) confirmationthat identified genes distinguish pre-defined classes; and 3) validation with "unknown samples".[0162] Identification of transcriptional modules. A total of 239 blood leukocyte gene expression profiles were generatedusing Affymetrix U133A&B GENECHIPS (>44K probe sets). Transcriptional data were obtained for 8 groups includingSystemic Juvenile Idiopathic Arthritis, SLE, liver transplant recipients, melanoma patients, and patients with acute in-fections: Escherichia coli, Staphylococcus aureus and Influenza A. For each group, transcripts that were present in atleast 50% of all conditions were segregated into 30 clusters (k-means clustering: clusters C1 through C30). The clusterassignment for each gene was recorded in a table and distribution patterns were compared among all the genes. Moduleswere selected using an iterative process, starting with the largest set of genes that belonged to the same cluster in allstudy groups (i.e. genes that were found in the same cluster in 8 of the 8 groups). The selection was then expandedfrom this core reference pattern to include genes with 7/8, 6/8 and 5/8 matches. The resulting set of genes formed atranscriptional module and was withdrawn from the selection pool. The process was then repeated starting with thesecond largest group of genes, progressively reducing the level of stringency. This analysis led to the identification of4742 transcripts that were distributed among 28 modules. Each module is attributed a unique identifier indicating theround and order of selection (e.g., M3.1 was the first module identified in the third round of selection).[0163] Analysis of significance patterns. Gene expression data were generated for PBMCs obtained from patientsand healthy volunteers using Affymetrix HG-U133A GENECHIPS. P values were obtained for six reference datasets bycomparing groups of patients to their respective healthy control groups (Mann-Whitney rank test). The groups werecomposed of patients with: 1) Systemic Lupus Erythematosus (SLE, 16 samples), 2) Influenza A (16 samples), 3)Escherichia coli (16 samples), 4) Staphylococcus aureus (16 samples), and 5) Streptococcus pneumoniae (14 samples);and 7) bone marrow transplant recipients undergoing graft versus host disease (GVHD, 12 samples). Control groupswere also formed taking into account age, sex and project (10 samples in each group). Genes significantly changed(p<0.01) in the "study group" (Melanoma and Transplant) were divided in two sets: overexpressed versus control andunderexpressed versus control. P-values of the genes forming the overexpressed set were obtained for the "referencegroups" (SLE, GVHD and infections with influenza virus, E. coli, S. aureus, S. pneumoniae). P-value data were processedwith a gene expression analysis software program, GeneSpring, Version 7.2 (Agilent), which was used to performhierarchical clustering and group genes based on significance patterns.[0164] Example 2. Determination and Analysis of Patterns of Significance are used to identify ubiquitous and dis-ease-specific gene expression signatures in patient peripheral blood leukocytes.[0165] The use of gene expression microarrays in patient-based research creates new prospects for the discovery ofdiagnostic biomarkers and the identification of genes or pathways linked to pathogenesis. Gene expression signatureswere generated from peripheral blood mononuclear cells isolated from over one hundred patients with conditions pre-senting a strong immunological component (patient with autoimmune, graft versus host and infectious diseases, as wellas immunosuppressed transplant recipients). This dataset permitted the opportunity to carry out comparative analysesand define disease signatures in a broader context. It was found that nearly 20% of overlap between lists of genessignificantly changed versus healthy controls in patients with Systemic Lupus Erythematosus (SLE) and acute influenzainfection. Transcriptional changes of 22,283 probe sets were evaluated through statistical group comparison performedsystematically for 7 diseases versus their respective healthy control groups. Patterns of significance were generated byhierarchical clustering of p-values. This "Patterns of Significance" approach led to the identification of a SLE-specific"diagnostic signature", formed by genes that did not change compared to healthy in the other 6 diseases. Conversely,"sentinel signatures" were characterized that were common to all 7 diseases. These findings allow for the use of bloodleukocyte expression signatures for diagnostic and early disease detection.[0166] Briefly, blood is a reservoir and migration compartment for immune cells exposed to infectious agents, allergens,tumors, transplants or autoimmune reactions. Leukocytes isolated from the peripheral blood of patients constitute anaccessible source of clinically-relevant information and a comprehensive molecular phenotype of these cells can beobtained by microarray analysis. Gene expression microarrays have been extensively used in cancer research, andproof of principle studies analyzing Peripheral Blood Mononuclear Cell (PBMC) samples isolated from patients withSystemic Lupus Erythematosus (SLE) lead to a better understanding of mechanisms of disease onset and responsesto treatment. Two main applications have been found for gene expression microarrays in the context of patient-basedresearch: (1) the discovery of biomarkers and establishment of diagnosis / prognosis signatures (e.g. prediction of

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survival of breast cancer patients) (2) the identification of genes/pathways involved in pathogenesis, leading for instanceto the discovery of the role of interleukin-1 in the pathogenesis of systemic onset juvenile idiopathic arthritis. However,the analysis of microarray data still constitutes a considerable challenge. The ability to simultaneously acquire data fortens of thousands of features in a single test is one of the most appealing characteristic of microarrays, but it can alsobe a major shortcoming7. This ’curse of dimensionality’ is compounded by the fact that the numbers of samples analyzedis usually small. The imbalance between the numbers of genes and conditions analyzed considerably weakens datainterpretation capabilities. A microarray gene expression database was created that constitutes samples obtained frompatients with diseases that possess a strong immune component. The meta-analysis strategy of the present inventionallows for the identification of ubiquitous as well as disease-specific signatures.[0167] Processing of Blood Samples. Blood samples were collected by venipuncture and immediately delivered atroom temperature to the Baylor Institute for Immunology Research, Dallas, TX, for processing. Peripheral blood mono-nuclear cells (PBMCs) from 3-4 ml of blood were isolated via Ficoll gradient and immediately lysed in RLT reagent(Qiagen, Valencia, CA) with beta-mercaptoethanol (BME) and stored at -80°C prior to the RNA extraction step.[0168] Microarray analysis. Total RNA was isolated using the RNeasy kit (Qiagen, Valencia, CA) according to themanufacturer’s instructions and RNA integrity was assessed by using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto,CA). Target labeling was performed according to the manufacturer’s standard protocol (Affymetrix Inc, Santa Clara, CA).Biotinylated cRNA targets were purified and subsequently hybridized to Affymetrix HG-U133A GeneChips (22,283 probesets). Arrays were scanned using an Affymetrix confocal laser scanner. Microarray Suite, Version 5.0 (MAS 5.0; Affyme-trix) software was used to assess fluorescent hybridization signals, to normalize signals, and to evaluate signal detectioncalls. Normalization of signal values per chip was achieved using the MAS 5.0 global method of scaling to the targetintensity value of 500 per GeneChip. A gene expression analysis software program, GeneSpring, Version 7.1 (Agilent),was used to perform statistical analysis, hierarchical clustering and classification of samples.[0169] Development and Analysis of Patterns of Significance. Gene expression data were generated for PBMCsobtained from patients and healthy volunteers using Affymetrix HG-U133A GeneChips that were run on the sameAffymetrix system, using standard operating procedures. P values were obtained by comparing 7 groups of patients totheir respective healthy control groups (Mann-Whitney rank test). The groups were composed of pediatric patients with:1) Systemic Lupus Erythomatosus (SLE, 16 samples), 2) Influenza A (16 samples), 3) Staphylococcus aureus (16samples), 4) Escherichia coli (16 samples) and 5) Streptococcus pneumoniae (14 samples); as well as adult transplantrecipients: 6) Liver transplant patients that have accepted the graft under immunosuppressive therapy (16 samples) and7) bone marrow transplant recipients undergoing graft versus host disease (GVHD, 12 samples). Control groups werealso formed taking into account age, sex and project (10 samples in each group). Genes significantly changed (p<0.01)in the "study group" (Influenza A and/or SLE) were divided in two sets: over-expressed versus control and under-expressed versus control. P-values of the genes forming the over-expressed set were obtained for the "reference groups"(infections with E .coli, S. aureus, S. pneumoniae, Liver transplant recipients and graft versus host disease). P-valuesof the reference groups were set to 1 when genes were under-expressed. The same procedure was used in the set ofgenes under-expressed in study group, only this time P-values of the reference group were set to 1 when genes wereover-expressed. P-value data were processed with a gene expression analysis software program, GeneSpring, Version7.1 (Agilent), that was used to perform hierarchical clustering and group genes based on significance patterns.[0170] Identification of blood leukocytes transcriptional signatures associated with acute Influenza A infection andSLE. Microarray gene expression data obtained from pediatric patients with either SLE or acute influenza A infectionswere used to identify transcriptional signatures characteristic of these two diseases (Figure 20). Statistical comparisonof a similar number of patients (18 samples) to their respective control group (10 samples) identified: (1) 1826 differentiallyexpressed genes that formed the Influenza signature (of those 703 were over-expressed (red) relative to controls and1123 were under-expressed (blue), see Figure (20A); 2) 3382 differentially expressed genes formed the SLE signature(of those 1019 were over-expressed relative to controls and 2363 were under-expressed, see Figure 20B).[0171] Figure 20 shows a statistical group comparison between patients and their respective controls. Figure 20A.Microarray expression obtained for PBMC isolated from 16 children with acute Influenza A infection (FLU) and 10 healthyvolunteers (HV) were compared (Mann-Whitney rank test, p<0.01). Out of 1826 differentially expressed genes, 703 wereover-expressed and 1123 under-expressed in patients. Figure 20B. An equivalent number of children with SystemicLupus Erythomatosus (SLE) were compared to their respective set of 10 healthy volunteers (HV) (Mann-Whitney ranktest, p<0.01). Out of 3382 differentially expressed genes, 1019 were over-expressed and 2363 under-expressed inpatients. Figure 20C. Comparison of over-expressed and under-expressed gene lists obtained for SLE and FLU samplesrelative to their respective control groups (healthy volunteers).[0172] Transformed expression levels are indicated by color scale, with red representing relatively high expressionand blue indicating relatively low expression compared to the median expression for each gene across all donors.[0173] Analysis of significance patterns. Next, the specificity of these signatures for each disease was determined. Asubstantial overlap was found between the sets of genes that were differentially expressed in FLU and SLE (Figure20C), with 279 over-expressed and 490 under-expressed genes common to both diseases (19% and 16% of similarities,

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respectively). This observation was used to determine whether a specific disease signature could be obtained in thecontext of a broader set of diseases.[0174] In order to address this question the analysis was extended to PBMC transcriptional datasets obtained forpatients with acute infections caused by bacteria (E. coli, S. aureus and S. pneumoniae) as well as transplant recipients(liver recipients who have accepted the allograft under pharmacological immunosuppressive therapy and bone marrowrecipients with graft versus host disease). Patterns of significance were analyzed for genes that were specifically over-expressed in SLE compared to Influenza (Figure 1, 740 genes). This approach allowed the visualization of the significanceof changes in levels of gene expression for each disease compared to its respective control group (age and sex matchedhealthy volunteers). Genes were arranged according to significance patterns by hierarchical clustering.[0175] Of the 4 patterns identified, 2 were found to be largely specific to SLE (Figure 21: P1 - 98 genes, and P3 - 193genes). In conclusion, the method was used to identify sets of genes, particularly among P3, that displayed a high degreeof specificity for SLE when compared to 6 other diseases.[0176] Figure 21 is an analysis of patterns of significance for genes over-expressed in SLE patients but not in patientswith acute Influenza A infection. The genes used for this analysis were significantly over-expressed in patients with SLEcompared to their respective control group (Mann-Whitney P<0.05) and not in patients with acute influenza A infectionwere selected for this analysis (740 genes). P values were obtained for five additional groups of patients: E. coli, S.aureus, S. pneumoniae, Liver transplant recipients and patients with graft vs host disease. The values were importedinto a microarray data analysis software package (see methods for details). Four patterns were identified: SLE-1 to 4.Significance levels are indicated by color scale, with darker green representing lower P-values and white indicating aP-value of 1.[0177] Identification of a common disease signature. An important proportion of genes from Figure 21 were inducedubiquitously (P2 - 222 genes and P4 - 225 genes). This finding suggests that these different diseases may share commontranscriptional components in the blood constituting a "sickness" signature. In order to investigate this possibility setsof genes were analyzed that were shared between Influenza and SLE signatures (Figure 20C: 279 genes over-expressed,and 490 under-expressed).[0178] Figure 22 shows Patterns of Significance for genes common to Influenza A and SLE. Genes overexpressed(left panel, OVER) and underexpressed (right panel, UNDER) in both patients with Influenza A (FLU) and SLE wereexamined in the context of other diseases: acute infections with E. coli, S. aureus, S. pneumoniae, liver transplantrecipients (transplant) and bone marrow recipients with graft versus host disease (GVHD). Significance levels are indi-cated by color scale, with dark green representing lower P-values and white indicating a P-value of 1.[0179] Patterns of significance were generated for these genes across all 7 diseases as described above. Threesubsets were identified among the genes that were over-expressed in patients with Influenza A infection and SLE: onechanging in most diseases, another presenting significant differences in all diseases, while the third was more specificto Influenza and SLE (Figure 22A, respectively P1, P2 and P3). Equivalent patterns can be found upon analysis of a setof under-expressed genes common to Influenza and SLE (Figure 22B, P4-7). Interestingly, the group of patient withsignificance patterns that were the most similar to Influenza and SLE had Graft Versus Host Disease. The parallelismwas particularly striking for the set of under-expressed genes (Figure 22B).[0180] Functional analysis of significance patterns. Finally, functional annotations associated to the patterns identifiedon Figure 22 were extracted. Genes associated with "defense response" were preferentially found in two patterns (P2-3on Figures 3 & 4; Fisher’s test for over-representation of this functional category: p<0.0005). These genes were expressedat higher levels compared to healthy. The list includes Defensin alpha 3, Azurocidin 1, Stabilin 1 (P2); the tumor necrosisfactor family member TRAIL, and Galectin 3 binding protein (P3). Conversely under-expressed genes belonging topatterns P4-6 were preferentially associated with "structural constituent of ribosome" (Fisher’s test for over-representationof this functional category in P4-6: p<0.0001). These genes include multiple ribosomal protein family members (e.g.RPS10, RPL37, and RPL13). Genes belonging to the set of over-expressed genes the most specific to Influenza andSLE (P3) were preferentially associated to "interferon response" (p<0.0001, e.g. myxovirus resistance 1, interferonalpha-inducible protein 16, double stranded RNA inducible protein kinase), while genes in P1 were uniquely associatedto "heavy metal binding" (p<0.0001, reflecting an overabundance of members of the metallothionein family).[0181] Figure 23 is a functional analysis of genes shared by patients with Influenza infection and Lupus groupedaccording to significance patterns. Sets of genes forming the different patterns indicated on Figure 22 (P1-7) weresubjected to functional analyses. The histograms indicate the percentage of genes associated to a given annotation foreach of the sets. Over-expressed genes = red, under-expressed = blue. P1 n= 71 genes; P2 n=118; P3 n=85; P4 n=117;P5 n=184; P6 n=120; P7 n=46.[0182] The comparative analysis of PBMC transcriptional patterns identified disease-specific as well as ubiquitousexpression signatures. Different degrees of disease specificity were observed among the genes found to be commonbetween the transcriptional profiles of PBMCs obtained from patients with Influenza infection and SLE. Differences insignificance patterns were translated into distinct functional associations. Indeed, the genes that were most specific toInfluenza and SLE relative to 5 other diseases were the most strongly associated to biological themes such as: "Interferon

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induction" (over-expressed genes; Figures 22 and 23: P3) or "structural constituent of ribosome" (under-expressedgenes; Figures 22 and 24: P4). These observations permit to validate the relevance of this approach. This analysisfacilitates the interpretation of microarray data by placing disease signatures in a much broader context.[0183] In addition to contributing to a better understanding of disease processes the meta-analysis of PBMC transcrip-tional datasets has important implications for clinical diagnostic with: (1) the identification of discriminatory disease-spe-cific signatures; as the screening of tens of thousands of potential markers will in most cases permit to pinpoint a limitednumber of transcripts that uniquely characterize a disease; and (2) the identification of a sentinel signature; as sets ofgenes for which expression changes in a wide range of health disorders could potentially be used in a screening assayfor early disease detection.[0184] Patients with metastatic melanoma display an endogenous transcriptional signature of immunosuppressionsimilar to that induced by pharmacological treatments in patients who underwent liver transplant. The present inventionprovides a method and apparatus to identify patients at high risk of melanoma progression. In addition, the presentinvention also provides a method and apparatus for monitoring indicators of immunosuppression could help adjustingthe dosage of immunosuppressive drugs and balance risks of rejection and side effects for liver transplant recipients.[0185] It will be understood that particular embodiments described herein are shown by way of illustration and not aslimitations of the invention. The principal features of this invention can be employed in various embodiments withoutdeparting from the scope of the invention. Those skilled in the art will recognize, or be able to ascertain using no morethan routine experimentation, numerous equivalents to the specific procedures described herein. Such equivalents areconsidered to be within the scope of this invention and are covered by the claims.

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[0186]

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Tab

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1.56

6902

822

27.7

456

0.03

51O

vere

xpre

ssed

2169

56_s

_at

0.78

2921

320

4.13

481.

5463

859

415.

9181

80.

0331

Ove

rexp

ress

ed20

1121

_s_a

t1.

0322

056

1613

.434

71.

2225

119

1934

.831

80.

0243

Ove

rexp

ress

ed21

8771

_s_a

t0.

9968

0185

431.

4999

71.

4255

858

1.89

545

0.02

28O

vere

xpre

ssed

2011

20_s

_at

0.94

0104

8434

0.50

867

1.42

432

520.

250.

0214

Ove

rexp

ress

ed20

8546

_x_a

t0.

8914

968

153.

6913

1.37

0589

622

8.33

636

0.01

53O

vere

xpre

ssed

2063

90_x

_at

0.92

7746

6556

43.8

003

1.34

5358

881

65.2

554

0.01

43O

vere

xpre

ssed

EP 2 080 140 B1

43

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Hea

lth

y N

orm

aliz

edR

awM

elan

om

a N

orm

aliz

edR

awp

-val

ue

2212

11_s

_at

0.91

1625

2772

8.28

687

1.57

0396

812

08.6

091

0.01

33O

vere

xpre

ssed

2154

92_x

_at

1.01

6330

244

0.34

348

1.25

6318

754

9.95

450.

0124

Ove

rexp

ress

ed20

0665

_s_a

t0.

8895

5754

1178

.260

71.

3896

513

1828

.436

20.

0124

Ove

rexp

ress

ed20

4081

_at

1.00

1743

146

11.0

264

1.34

5987

257

16.1

274

0.01

Ove

rexp

ress

ed20

9301

_at

0.87

1541

352

7.35

211.

6178

412

996.

9409

0.00

867

Ove

rexp

ress

ed20

9911

_x_a

t1.

0457

131

499.

3782

71.

4152

079

703.

6182

0.00

835

Ove

rexp

ress

ed

2061

10_a

t0.

9189

6015

598.

5217

1.57

0021

391

5.52

264

0.00

692

Ove

rexp

ress

ed20

3585

_at

0.97

5421

998

5.78

711.

2642

365

1237

.199

80.

0043

1O

vere

xpre

ssed

2040

69_a

t0.

9768

9235

183.

3608

91.

3654

811

258.

0636

60.

0023

9O

vere

xpre

ssed

2034

14_a

t0.

9157

6725

1835

.547

71.

4408

619

2731

.363

50.

0016

8O

vere

xpre

ssed

2048

38_s

_at

1.07

2475

735

6.96

957

1.80

2362

579.

2273

0.00

168

Ove

rexp

ress

ed20

8527

_x_a

t0.

9986

5276

315.

1652

81.

2899

728

411.

1136

0.00

153

Ove

rexp

ress

ed

2066

55_s

_at

0.95

7713

3771

0.26

086

1.75

8377

313

52.2

181

0.00

031

Ove

rexp

ress

ed20

2708

_s_a

t1.

0207

2264

1.77

405

1.62

6882

110

41.6

364

0.00

001

Ove

rexp

ress

edM

od

ule

-by-

mo

du

le c

om

par

iso

n o

f g

enes

exp

ress

ion

leve

ls in

pat

ien

ts w

ith

met

asta

tic

mel

ano

ma

vs. h

ealt

hy

volu

nte

ers.

Pat

ien

ts w

ith

mel

ano

ma

(n=

22)

vs.

Hea

lth

y V

olu

nte

er (

n=

23)

- tr

ain

ing

tes

t M

ann

Wh

itn

ey U

tes

t p

-val

ue<

0.05

, no

mtc

M1.

1T

otal

= 6

9 tr

ansc

ripts

1O

vere

xpre

ssed

41U

nder

expr

esse

d

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2217

39_a

tIL

27w

AL5

2409

356

005

2171

48_x

_at

IGLV

imm

unog

lobu

lin la

mbd

a va

riabl

e re

gion

AJ2

4937

728

831

2210

04_s

_at

ITM

2C; E

25; B

RI3

; E

25C

; IT

M3;

B

RIC

D2C

inte

gral

mem

bran

e pr

otei

n 3

NM

_030

926

8161

8

2149

73_x

_at

IGV

H3

imm

unog

lobu

lin h

eavy

cha

in v

aria

ble

regi

onA

J275

469

3502

2172

27_x

_at

IGL@

imm

unog

lobu

lin la

mbd

a lig

ht c

hain

VJC

reg

ion

X93

006

3535

2172

81_x

_at

IGH

Vim

mun

oglo

bulin

hea

vy c

hain

var

iabl

e re

gion

AJ2

3938

335

0222

1253

_s_a

tT

XN

DC

5; E

RP

46;

UN

Q36

4; E

ndoP

DI;

MG

C31

78

thio

redo

xin

dom

ain

cont

aini

ng 5

isof

orm

2; t

hior

edox

in d

omai

n co

ntai

ning

5

isof

orm

1N

M_0

3081

081

567

EP 2 080 140 B1

44

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2147

68_x

_at

IGK

CB

G54

0628

3514

2149

16_x

_at

IGH

MB

G34

0548

3507

2162

07_x

_at

IGK

V1D

-13

AW

4081

9428

902

2165

57_x

_at

A1V

H3

rear

rang

ed im

mun

oglo

bulin

hea

vy c

hain

U92

706

3502

2116

35_x

_at

IGH

@M

2467

035

0721

1634

_x_a

tIG

H@

M24

669

3507

2117

98_x

_at

IGLJ

3si

ngle

-cha

in a

ntib

ody

AB

0017

3328

831

2052

67_a

tP

OU

2AF

1; B

OB

1;

OB

F1;

OC

AB

; OB

F-1

PO

U d

omai

n, c

lass

2, a

ssoc

iatin

g fa

ctor

1N

M_0

0623

554

50

2152

14_a

tIG

L@H

5368

935

3521

6401

_x_a

tIG

KV

imm

unog

lobu

lin k

appa

cha

in v

aria

ble

regi

onA

J408

433

2148

36_x

_at

IGK

CB

G53

6224

3514

2118

81_x

_at

IGLJ

3V

EG

F s

ingl

e ch

ain

antib

ody

AB

0143

4128

831

2116

50_x

_at

IGH

ML3

4164

3507

2170

22_a

_at

IGH

Mim

mun

oglo

bulin

A1-

A2

lam

bda

hybr

id G

AU

hea

vy c

hain

S55

735

3507

2168

53_x

_at

IGLJ

3im

mun

oglo

bulin

ligh

t cha

in v

aria

ble

regi

onA

F23

4255

2883

121

7179

_x_a

tIG

L@im

mun

oglo

bulin

lam

bda

light

cha

inX

7978

235

3521

5121

_x_a

tIG

LJ3

AA

6803

0228

831

2091

38_x

_at

IGLJ

3M

8779

028

831

2116

45_x

_at

IgK

imm

unog

lobu

lin k

appa

-cha

in V

K-1

M85

256

3514

2119

08_x

_at

IGH

MIg

MM

8726

835

0721

5176

_x_a

tIG

KC

AW

4048

9435

1422

1651

_x_a

tIG

KC

Unk

now

n (p

rote

in fo

r M

GC

:124

18)

BC

0053

3235

1421

6984

_x_a

tIG

LJ3

imm

unog

lobu

lin li

ght c

hain

V-J

reg

ion

D84

143

2883

121

7258

_x_a

tIG

Lim

mun

oglo

bulin

lam

bda

chai

nA

F04

3583

2125

92_a

tIG

JA

V73

3266

3512

2146

77_x

_at

IGLJ

3X

5781

228

831

2216

71_x

_at

IGK

CM

6343

835

1421

4669

_x_a

tIG

KC

BG

4851

3535

1421

1644

_x_a

tIG

KC

L144

5835

1421

5379

_x_a

tIG

LJ3

AV

6986

4735

35

2135

02_x

_at

IGLL

3; 1

6.1

lam

bda

L-ch

ain

C r

egio

nX

0352

991

316

EP 2 080 140 B1

45

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2159

46_x

_at

LOC

9131

6A

L022

324

9131

6

2114

30_s

_at

IGH

G3

M87

789

3502

2151

18_s

_at

AW

5191

6821

4777

_at

IGK

CB

G48

2805

3514

M1.

2 T

otal

= 9

6 tr

ansc

ripts

27O

vere

xpre

ssed

0U

nder

expr

esse

dS

yste

mat

icC

omm

on _

Affy

Pro

duct

Gen

bank

Locu

sLin

k

2152

40_a

tIT

GB

3A

I189

839

3690

2145

25_x

_at

MLH

3; H

NP

CC

; S

240I

I117

mut

L ho

mol

og 3

AB

0396

6727

030

2011

08_a

_at

TH

BS

1A

I812

030

7057

2025

55_s

_at

MY

LK; K

RP

; MLC

K;

MLC

K10

8; M

LCK

210;

F

LJ12

216

myo

sin

light

cha

in k

inas

e is

ofor

m 6

; myo

sin

light

cha

in k

inas

e is

ofor

m 1

; m

yosi

n lig

ht c

hain

kin

ase

isof

orm

2; m

yosi

n lig

ht c

hain

kin

ase

isof

orm

3A

; m

yosi

n lig

ht c

hain

kin

ase

isof

orm

3B

; myo

sin

light

cha

in k

inas

e is

ofor

m 4

; m

yosi

n lig

ht c

hain

kin

ase

isof

o

NM

_005

965

4638

2087

92_s

_at

CLU

; CLI

; AP

OJ;

S

GP

2; S

GP

-2; S

P-4

0;

TR

PM

2; T

RP

M-2

; M

GC

2490

3

clus

terin

isof

orm

1; c

lust

erin

isof

orm

2M

2591

511

91

3440

8_at

RT

N2;

NS

P2;

NS

PL1

retic

ulon

2 is

ofor

m A

; ret

icul

on 2

isof

orm

B; r

etic

ulon

2 is

ofor

m C

; ret

icul

on

2 is

ofor

m D

AF

0042

2262

53

2179

63_s

_at

NG

FR

AP

1; B

ex;

BE

X3;

NA

DE

; HG

R74

; D

XS

6984

E

nerv

e gr

owth

fact

or r

ecep

tor

(TN

FR

SF

16)

asso

ciat

ed p

rote

in 1

isof

orm

b;

nerv

e gr

owth

fact

or r

ecep

tor

(TN

FR

SF

16)

asso

ciat

ed p

rote

in 1

isof

orm

aN

M_0

1438

027

018

2169

56_s

_at

ITG

A2B

; GT

A; C

D41

; G

P2B

; CD

41B

; GP

IIbin

tegr

in a

lpha

2b

prec

urso

rA

F09

8114

3674

2011

21_s

_at

PG

RM

C1;

MP

R;

HP

R6.

6pr

oges

tero

ne r

ecep

tor

mem

bran

e co

mpo

nent

1N

M_0

0666

710

857

2187

11_s

_at

SD

PR

; SD

R; P

S-p

68se

rum

dep

rivat

ion

resp

onse

pro

tein

NM

_004

657

8436

2011

21_s

_at

PG

RM

C1

AL5

4794

610

857

2085

46_x

_at

HIS

T1H

2BH

; H2B

/j;

H2B

FJ

H2B

his

tone

fam

ily, m

embe

r J

NM

_003

524

8345

EP 2 080 140 B1

46

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2063

90_x

_at

PF

4; C

XC

L4; S

CY

B4

plat

elet

fact

or 4

(ch

emok

ine

(C-X

-C m

otif)

liga

nd 4

)N

M_0

0261

951

96

2212

11_s

_at

C21

orf7

; TA

K1L

chro

mos

ome

21 o

pen

read

ing

fram

e 7

NM

_020

152

5691

121

5492

_x_a

tP

TC

RA

AL0

3558

717

155

2006

65_s

_at

SP

AR

C; O

Nse

cret

ed p

rote

in, a

cidi

c, c

yste

ine-

rich

(ost

eone

ctin

)N

M_0

0311

866

7820

4081

_at

NR

GN

; RC

3; h

ngne

urog

rani

nN

M_0

0617

649

0020

9301

_at

CA

2; C

A-I

Ica

rbon

ic a

nhyd

rase

IIM

3653

276

020

9911

_x_a

tH

IST

1H2B

D; H

2B/b

; H

2BF

B; H

2B.1

B;

HIR

IP2;

dJ2

21C

16.6

H2B

his

tone

fam

ily, m

embe

r B

BC

0028

4230

17

2061

10_a

tH

IST

1H3H

; H3/

k;

H3F

K; H

3F1K

H3

hist

one

fam

ily, m

embe

r K

NM

_003

536

8357

2035

85_a

tZ

NF

185

zinc

fing

er p

rote

in 1

85 (

LIM

dom

ain)

NM

_007

150

7739

2040

69_a

tM

EIS

1M

eis1

hom

olog

NM

_002

398

4211

2034

14_a

tM

MD

; MM

A; P

AQ

R11

mon

ocyt

e to

mac

roph

age

diffe

rent

iatio

n-as

soci

ated

pre

curs

orN

M_0

1232

923

531

2048

38_s

_at

MLH

3; H

NP

CC

; S

240I

I117

mut

L ho

mol

og 3

NM

_014

381

2703

0

2085

27_x

_at

HIS

T1H

2BE

; H2B

.h;

H2B

/h; H

2BF

H;

dJ22

1C16

.8

H2B

his

tone

fam

ily, m

embe

r H

NM

_003

523

8344

2066

55_s

_at

GP

1BB

; CD

42c

glyc

opro

tein

Ib b

eta

poly

pept

ide

prec

urso

rN

M_0

0040

728

1220

2708

_s_a

tH

IST

2H2B

E; G

L105

; H

2B.1

; H2B

/q; H

2BF

QH

2B h

isto

ne fa

mily

, mem

ber

QN

M_0

0352

883

49

M1.

3T

otal

= 4

7 tr

ansc

ripts

0O

vere

xpre

ssed

38U

nder

expr

esse

dS

yste

mat

icC

omm

on _

Affy

Pro

duct

Gen

bank

Locu

sLin

k

2024

31_s

_at

MY

C; c

-Myc

v-m

yc m

yelo

cyto

mat

osis

vira

l onc

ogen

e ho

mol

ogN

M_0

0246

746

0921

3674

_x a

tIG

HG

3A

I858

004

3502

2042

08_a

tR

NG

TT

; HC

E; H

CE

1;

hCA

P; C

AP

1AR

NA

gua

nyly

ltran

sfer

ase

and

5’-p

hosp

hata

seN

M_0

0380

087

32

2027

59_s

_at

AK

AP

2B

E87

9367

1121

7

EP 2 080 140 B1

47

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2159

25_s

_at

CD

72; L

YB

2C

D72

ant

igen

AF

2837

7797

1

3931

8_at

TC

L1A

T-c

ell l

ymph

oma-

1X

8224

081

1521

9471

_at

C13

orf1

8; F

LJ21

562

chro

mos

ome

13 o

pen

read

ing

fram

e 18

NM

_025

113

8018

321

2827

_at

IGH

M; M

UX

1711

535

0720

6896

_s_a

tG

NG

7gu

anin

e nu

cleo

tide

bind

ing

prot

ein

(G p

rote

in),

gam

ma

7N

M_0

0514

527

8821

8781

_at

SM

C6L

1; F

LJ22

116

SM

C6

prot

ein

NM

_024

624

7967

720

6398

_s_a

tC

D19

; B4;

MG

C12

802

CD

19 a

ntig

enN

M_0

0177

093

0

2016

89_s

_at

TP

D52

BE

9740

9871

6320

5297

_s_a

tC

D79

B; B

29; I

GB

CD

79B

ant

igen

isof

orm

1 p

recu

rsor

; CD

79B

ant

igen

isof

orm

2 p

recu

rsor

NM

_000

626

974

2108

89_s

_at

FC

GR

2B; C

D32

; F

CG

2; IG

FR

2F

c fr

agm

ent o

f IgG

, low

affi

nity

IIb,

rece

ptor

for (

CD

32) i

sofo

rm 2

; Fc

frag

men

t of

IgG

, low

affi

nity

IIb,

rec

epto

r fo

r (C

D32

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m 3

; Fc

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gG,

low

affi

nity

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rece

ptor

for (

CD

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ffini

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M31

933

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2179

79_a

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1439

927

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2064

78_a

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KIA

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25 g

ene

prod

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NM

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792

9834

2040

04_a

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AW

RA

I336

206

5074

2200

68_a

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EB

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HS

20;

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C7-

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3N

M_0

1337

829

802

2045

81_a

tC

D22

; SIG

LEC

-2C

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igen

NM

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933

2067

59_a

tF

CE

R2;

CD

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CE

2;

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23A

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EB

FF

c fr

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f IgE

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affi

nity

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for

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564

020

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2283

721

3772

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GA

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F19

6572

2306

2

2095

83_s

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CD

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c; C

D20

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tigen

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a

prec

urso

rA

F06

3591

4345

2123

86_a

tA

K02

1980

EP 2 080 140 B1

48

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Sys

tem

atic

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mon

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2194

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CL1

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LJ10

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IAA

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; B-c

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335

2194

97_s

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CL1

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3N

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353

335

4479

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931

2077

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NM

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1126

2

2138

91_s

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AI9

2706

722

0059

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BR

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TA

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TA

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1X

1253

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1

2058

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PIB

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I-B

Spi

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n fa

ctor

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pi-1

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elat

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NM

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121

6689

2076

55_s

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BLN

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760

2190

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rote

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rote

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2196

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LJ20

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B-c

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rote

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ith a

nkyr

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NM

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935

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1.4

Tot

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87

tran

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Ove

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Und

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EP 2 080 140 B1

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5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

Sys

tem

atic

Com

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923

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19_a

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NM

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651

5716

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NF

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AL1

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177

50

2124

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8027

320

8881

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ula

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1390

2008

70_a

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TR

AP

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T-W

D; U

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soci

ated

pro

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NM

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178

1117

1

6008

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CY

LDA

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099

1540

2026

44_s

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FA

IP3;

A20

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NF

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nec

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s fa

ctor

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NM

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290

7128

2183

99_s

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CD

CA

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EP

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FLJ

2076

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ass

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555

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2044

40_a

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D83

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CD

83 a

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activ

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mph

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imm

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uper

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NM

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233

9308

2037

08_a

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DE

4B; D

PD

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P

DE

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phos

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MP

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cific

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hosp

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E4

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mol

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Dro

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NM

_002

600

5142

2131

34_x

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6544

510

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2126

65_a

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KF

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5643

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976

2119

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NM

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324

3021

2088

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like

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N

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P76

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SP

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NF

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AK

0223

6084

124

EP 2 080 140 B1

50

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

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Sys

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Com

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OB

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FA

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GC

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610

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3277

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4929

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NM

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641

5277

2007

31_s

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PT

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F57

6710

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2037

52_s

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NM

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354

3727

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BB

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NM

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5597

5

2192

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NF

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31N

M_0

1855

555

422

2017

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KIA

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prod

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NM

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876

9929

2090

20_a

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5120

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NM

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7037

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NM

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186

4929

2124

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NP

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555

544

EP 2 080 140 B1

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5

10

15

20

25

30

35

40

45

50

55

(con

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5169

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2007

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0389

7106

EP 2 080 140 B1

52

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2112

84_s

_at

GR

N; P

EP

I; P

CD

GF

gran

ulin

BC

0003

2428

9620

4393

_s_a

tA

CP

P; P

AP

; AC

P3;

A

CP

-3pr

osta

tic a

cid

phos

phat

ase

prec

urso

rN

M_0

0109

955

2217

31_x

_at

CS

PG

2; V

ER

SIC

AN

chon

droi

tin s

ulfa

te p

rote

ogly

can

2 (v

ersi

can)

J028

1414

6220

9949

_at

NC

F2;

NO

XA

2;

p67p

hox;

P67

-PH

OX

neut

roph

il cy

toso

lic fa

ctor

2B

C00

1606

4688

2087

02_x

_at

AP

LP2;

AP

PH

; A

PP

L2; C

DE

BP

amyl

oid

beta

(A

4) p

recu

rsor

-like

pro

tein

2B

C00

0373

334

2035

08_a

tT

NF

RS

F1B

; p75

; T

BP

II; T

NF

BR

; T

NF

R2;

CD

120b

; T

NF

R80

; TN

F-R

75;

p75T

NF

R; T

NF

-R-I

I

tum

or n

ecro

sis

fact

or r

ecep

tor

2 pr

ecur

sor

NM

_001

066

7133

2222

18_s

_at

PIL

RA

; FD

F03

paire

d im

mun

oglo

bulin

-like

type

2 re

cept

or a

lpha

isof

orm

1 p

recu

rsor

; pai

red

imm

unog

lobu

lin-li

ke ty

pe 2

rec

epto

r al

pha

isof

orm

2 p

recu

rsor

; pai

red

imm

unog

lobu

lin-li

ke ty

pe 2

rec

epto

r al

pha

isof

orm

3 p

recu

rsor

AJ4

0084

329

992

2017

43_a

tC

D14

CD

14 a

ntig

en p

recu

rsor

NM

_000

591

929

2013

60_a

tC

ST

3; A

D8

cyst

atin

C p

recu

rsor

NM

_000

099

1471

2178

65_a

tR

NF

130;

GP

; G

1RZ

FP

; GO

LIA

TH

ring

finge

r pr

otei

n 13

0N

M_0

1843

455

819

2007

43_s

_at

CLN

2; T

PP

1; L

INC

L;

TP

P I;

TP

P-I

trip

eptid

yl-p

eptid

ase

I pre

curs

orN

M_0

0039

112

00

2178

97_a

tF

XY

D6

FX

YD

dom

ain-

cont

aini

ng io

n tr

ansp

ort r

egul

ator

6N

M_0

2200

353

826

2029

02_s

_at

CT

SS

; MG

C38

86ca

thep

sin

S p

repr

opro

tein

NM

_004

079

1520

2182

17_a

tS

CP

EP

1; R

ISC

; H

SC

P1

serin

e ca

rbox

ypep

tidas

e 1

prec

urso

r pr

otei

nN

M_0

2162

659

342

2177

64_s

_at

RA

B31

; Rab

22B

RA

B31

, mem

ber

RA

S o

ncog

ene

fam

ilyA

F18

3421

1103

120

8248

_x_a

tA

PLP

2; A

PP

H;

AP

PL2

; CD

EB

Pam

yloi

d be

ta (

A4)

pre

curs

or-li

ke p

rote

in 2

NM

_001

642

334

2081

30_s

_at

TB

XA

S1;

TS

; TX

S;

CY

P5;

TH

AS

; TX

AS

; C

YP

5A1

thro

mbo

xane

A s

ynth

ase

1 (p

late

let,

cyto

chro

me

P45

0, fa

mily

5, s

ubfa

mily

A

) is

ofor

m T

XS

-I; t

hrom

boxa

ne A

syn

thas

e 1

(pla

tele

t, cy

toch

rom

e P

450,

fa

mily

5, s

ubfa

mily

A)

isof

orm

TX

S-I

I

NM

_030

984

6916

2150

49_x

_at

CD

163;

M13

0; M

M13

0C

D16

3 an

tigen

isof

orm

a; C

D16

3 an

tigen

isof

orm

bZ

2296

993

3220

0838

_at

CT

SB

; AP

PS

; CP

SB

cath

epsi

n B

pre

prop

rote

inN

M_0

0190

815

08

EP 2 080 140 B1

53

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2028

33_s

_at

SE

RP

INA

1; A

1A;

AA

T; P

I1; A

1AT

; M

GC

9222

; M

GC

2333

0

serin

e (o

r cy

stei

ne)

prot

eina

se in

hibi

tor,

cla

de A

(al

pha-

1 an

tipro

tein

ase,

an

titry

psin

), m

embe

r 1

NM

_000

295

5265

2150

51_x

_at

AIF

1B

F21

3829

199

2184

54_a

tF

LJ22

662

hypo

thet

ical

pro

tein

FLJ

2266

2N

M_0

2482

979

887

2052

37_a

tF

CN

1; F

CN

Mfic

olin

1 p

recu

rsor

NM

_002

003

2219

2037

73_x

_at

BLV

RA

; BV

RA

biliv

erdi

n re

duct

ase

AN

M_0

0071

264

421

1729

_x_a

tB

LVR

A; B

VR

Abi

liver

din

redu

ctas

e A

BC

0059

0264

420

9901

_x_a

tA

IF1;

IBA

1; A

IF-1

; IR

T-1

allo

graf

t inf

lam

mat

ory

fact

or 1

isof

orm

3; a

llogr

aft i

nfla

mm

ator

y fa

ctor

1

isof

orm

2; a

llogr

aft i

nfla

mm

ator

y fa

ctor

1 is

ofor

m 1

U19

713

199

2019

95_a

tE

XT

1ex

osto

sin

1N

M_0

0012

721

31

M1.

6T

otal

= 2

8 tr

ansc

ripts

0O

vere

xpre

ssed

1U

nder

expr

esse

dS

yste

mat

icC

omm

on _

Affy

Pro

duct

Gen

bank

Locu

sLin

k21

5221

_at

AK

0250

64M

1.7

Tot

al =

127

tran

scrip

ts14

Ove

rexp

ress

ed0

Und

erex

pres

sed

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2144

59_x

_at

HLA

-C; D

6S20

4;

HLA

-Cw

7; H

LA-J

Y3

maj

or h

isto

com

patib

ility

com

plex

, cla

ss I,

C p

recu

rsor

M12

679

5135

3

2177

40_x

_at

RP

L7A

; TR

UP

; S

UR

F3

ribos

omal

pro

tein

L7a

NM

_000

972

6130

2016

65_x

_at

RP

S17

; RP

S17

L1;

RP

S17

L2rib

osom

al p

rote

in S

17N

M_0

0102

162

18

2007

16_x

_at

RP

L13A

ribos

omal

pro

tein

L13

aN

M_0

1242

323

521

2119

42_x

_at

BF

9794

1920

1254

_x_a

tR

PS

6rib

osom

al p

rote

in S

6N

M_0

0101

061

9421

1296

_x_a

tU

BC

; HM

G20

ubiq

uitin

CA

B00

9010

7316

2127

88_x

_at

FT

LB

G53

7190

2512

2217

00_s

_at

UB

A52

; CE

P52

; R

PL4

0; H

UB

CE

P52

ubiq

uitin

and

rib

osom

al p

rote

in L

40 p

recu

rsor

AF

3487

0073

11

2087

29_x

_at

HLA

-BD

8304

331

0620

0905

_x_a

tH

LA-E

; HLA

-6.2

maj

or h

isto

com

patib

ility

com

plex

, cla

ss I,

E p

recu

rsor

NM

_005

516

3133

EP 2 080 140 B1

54

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2041

02_s

_at

EE

F2;

EE

F-2

euka

ryot

ic tr

ansl

atio

n el

onga

tion

fact

or 2

NM

_001

961

1938

2125

81_x

_at

GA

PD

BE

5614

7925

9721

1528

_x_a

tH

LA-G

2.2

b2 m

icro

glob

ulin

M90

685

3135

M1.

8T

otal

= 8

6 tr

ansc

ripts

0O

vere

xpre

ssed

73U

nder

expr

esse

dS

yste

mat

icC

omm

on _

Affy

Pro

duct

Gen

bank

Locu

sLin

k

2014

47_a

tT

IA1

TIA

1 pr

otei

n is

ofor

m 1

; TIA

1 pr

otei

n is

ofor

m 2

NM

_022

037

2210

81_s

_at

FLJ

2245

7hy

poth

etic

al p

rote

in F

LJ22

457

NM

_024

901

7996

121

3405

_at

N95

443

2218

65_a

tD

KF

Zp5

47P

234

BF

9699

8620

319

2021

84_s

_at

NU

P13

3; h

NU

P13

3;

FLJ

1081

4;

MG

C21

133

nucl

eopo

rin 1

33kD

aN

M_0

1823

055

746

2024

53_s

_at

GT

F2H

1; B

TF

2; T

FIIH

gene

ral t

rans

crip

tion

fact

or II

H, p

olyp

eptid

e 1,

62k

Da

NM

_005

316

2965

2192

43_a

tH

IMA

P4;

IAN

1; h

IAN

1;

MS

TP

062;

FLJ

1111

0im

mun

ity a

ssoc

iate

d pr

otei

n 4

NM

_018

326

5530

3

2022

27_s

_at

BR

D8;

SM

AP

; p12

0br

omod

omai

n co

ntai

ning

8 is

ofor

m 1

; bro

mod

omai

n co

ntai

ning

8 is

ofor

m 2

; br

omod

omai

n co

ntai

ning

8 is

ofor

m 3

NM

_006

696

1090

2

2184

55_a

tN

FS

1; N

IFS

; H

US

SY

-08

NF

S1

nitr

ogen

fixa

tion

1 is

ofor

m a

pre

curs

or;

NM

_021

100

9054

NF

S1

nitr

ogen

fixa

tion

1 is

ofor

m b

pre

curs

or20

8121

_a_a

tP

TP

RO

; PT

PU

2;

GLE

PP

1; P

TP

-U2

rece

ptor

-typ

e pr

otei

n ty

rosi

ne p

hosp

hata

se O

isof

orm

b p

recu

rsor

; re

cept

or-t

ype

prot

ein

tyro

sine

pho

spha

tase

O is

ofor

m a

pre

curs

or; r

ecep

tor-

type

pro

tein

tyro

sine

pho

spha

tase

O is

ofor

m d

pre

curs

or; r

ecep

tor-

type

pr

otei

n ty

rosi

ne p

hosp

hata

se O

isof

orm

c p

r

NM

_002

848

5800

2123

78_a

tG

AR

TB

E96

6876

2618

2183

03_x

_at

LOC

5131

5hy

poth

etic

al p

rote

in L

OC

5131

5N

M_0

1661

851

315

2186

14_a

tF

LJ10

652

hypo

thet

ical

pro

tein

FLJ

1065

2N

M_0

1816

955

196

2091

75_a

tS

EC

23IP

; P12

5;

MS

TP

053

Sec

23-in

tera

ctin

g pr

otei

n p1

25A

K00

1135

1119

6

2138

38_a

tR

AN

BP

9A

A19

1426

5140

621

0053

_at

TA

F5

AW

1388

2768

7721

9146

_at

FLJ

2272

9hy

poth

etic

al p

rote

in F

LJ22

729

NM

_024

683

7973

6

EP 2 080 140 B1

55

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2128

72_s

_at

US

P49

; TR

FP

; P

R00

213;

TR

F-p

roxi

mal

pro

tein

AK

0230

9225

862

2011

21_s

_at

PU

M2;

PU

MH

2;

PU

ML2

; KIA

A02

35pu

mili

o ho

mol

og 2

D87

078

2336

9

2183

71_s

_at

PS

PC

1; P

SP

1;

FLJ

1095

5pa

rasp

eckl

e pr

otei

n 1

NM

_018

282

5526

9

2031

59_a

tG

LS; G

LS1;

K

IAA

0838

glut

amin

ase

CN

M_0

1490

527

44

2105

41_s

_at

RF

P; R

NF

76; T

RIM

27re

t fin

ger

prot

ein

isof

orm

alp

ha; r

et fi

nger

pro

tein

isof

orm

bet

aA

F23

0394

5987

2121

16_a

tR

FP

; RN

F76

; TR

IM27

ret f

inge

r pr

otei

n is

ofor

m a

lpha

; ret

fing

er p

rote

in is

ofor

m b

eta

NM

_006

510

5987

2141

29_a

tP

DE

4DIP

AI8

2179

196

5921

8716

_x_a

tM

TO

1; C

GI-

02m

itoch

ondr

ial t

rans

latio

n op

timiz

atio

n 1

hom

olog

; mito

chon

dria

l tra

nsla

tion

optim

izat

ion

1 ho

mol

og is

ofor

m IV

NM

_012

123

2582

1

2124

05_s

_at

CG

I-01

; KIA

A08

59C

GI-

01 p

rote

in is

ofor

m 2

; CG

I-01

pro

tein

isof

orm

1A

L049

669

5160

320

9828

_s_a

tIL

16; L

CF

; IL-

16; p

rIL-

16; H

sT19

289

inte

rleuk

in 1

6 is

ofor

m 1

pre

curs

or; i

nter

leuk

in 1

6 is

ofor

m 2

M90

391

3603

2185

88_s

_at

C5o

rf3;

133

K02

chro

mos

ome

5 op

en r

eadi

ng fr

ame

3N

M_0

1869

110

827

2121

70_a

tR

BM

12; S

WA

N;

KIA

A07

65;

HR

IHF

B20

91

RN

A b

indi

ng m

otif

prot

ein

12A

B01

8308

1013

7

2073

38_s

_at

ZN

F20

0zi

nc fi

nger

pro

tein

200

NM

_003

454

7752

2046

76_a

tD

KF

ZP

564K

2062

DK

FZ

P56

4K20

62 p

rote

inN

M_0

1542

125

880

2193

03_a

tC

13or

f7; F

LJ13

449

chro

mos

ome

13 o

pen

read

ing

fram

e 7

NM

_024

546

7959

664

418_

atA

I472

320

2136

26_a

tC

BR

4; F

LJ14

431

carb

onic

red

ucta

se 4

AL0

4944

284

869

2126

32_a

tA

F13

1808

2067

34_a

tJR

KL;

HH

MJG

jerk

y ho

mol

og-li

keN

M_0

0377

286

9020

8968

_s_a

tZ

FP

64; Z

NF

338

zinc

fing

er p

rote

in 6

4 is

ofor

m a

; zin

c fin

ger

prot

ein

64 is

ofor

m b

; zin

c fin

ger

prot

ein

64 is

ofor

m c

; zin

c fin

ger

prot

ein

64 is

ofor

m d

NM

_018

197

5573

4

5396

8 at

KIA

A16

98A

I869

988

8078

9

2127

40_a

tP

IK3R

4B

F74

0111

3084

920

6332

_s_a

tIF

I16;

IFN

GIP

1in

terf

eron

, gam

ma-

indu

cibl

e pr

otei

n 16

NM

_005

531

3428

2179

07_a

tM

RP

L18;

HS

PC

071;

M

RP

-L18

mito

chon

dria

l rib

osom

al p

rote

in L

18N

M_0

1416

129

074

EP 2 080 140 B1

56

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2035

84_a

tK

IAA

0103

KIA

A01

03N

M_0

1467

396

9420

3614

_at

UT

P14

C; K

IAA

0266

; 27

0006

6J21

Rik

UT

P14

, U3

smal

l nuc

leol

ar r

ibon

ucle

opro

tein

, hom

olog

CN

M_0

2164

597

24

2185

34_s

_at

VG

5Q; F

LJ10

283;

H

SU

8497

1;

HS

U84

971

angi

ogen

ic fa

ctor

VG

5QN

M_0

1804

655

109

2031

43_s

_at

KIA

A00

40T

7995

396

7420

5140

_at

FP

GT

; GF

PP

fuco

se-1

-pho

spha

te g

uany

ltran

sfer

ase

NM

_003

838

8790

2191

30_a

tF

LJ10

287;

FLJ

1121

9hy

poth

etic

al p

rote

in

FLJ

1028

7N

M_0

1908

354

482

2191

23_a

tZ

NF

232

zinc

fing

er p

rote

in 2

32N

M_0

1451

977

7521

4440

_at

NA

T1;

AA

C1

N-a

cety

ltran

sfer

ase

1N

M_0

0066

29

2220

28_a

tZ

NF

45A

I967

981

7596

2124

53_a

tK

IAA

1279

; D

KF

ZP

586B

0923

KIA

A12

79A

B03

3105

2612

8

2020

60_a

tS

H2B

P1;

TS

BP

; p1

50T

SP

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ain

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1N

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2181

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5714

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2983

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760

6596

2209

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Clo

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3093

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14

2011

42_a

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7698

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2194

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125

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5N

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1767

654

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1665

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340

2023

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GP

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NM

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837

9453

2034

27_a

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IA;

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NM

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2584

2

2197

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765

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2055

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558

NM

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880

5498

0

EP 2 080 140 B1

57

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2008

54_a

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CO

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NM

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311

9611

2096

62_a

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M

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5383

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C00

5968

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4988

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C; S

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A-b

indi

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m F

X63

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6651

2075

13_s

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ZN

F18

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nc fi

nger

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189

NM

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452

7743

2180

56_a

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FA

R; B

AR

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NM

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2402

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5685

2309

121

8242

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20 h

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hom

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NM

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635

5111

1

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1T

otal

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2 tr

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1O

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4U

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d

Sys

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Com

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2046

55_a

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CLS

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CY

AS

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AN

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CP

228;

D

17S

136E

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GC

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4

smal

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prec

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563

52

2169

20_s

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TC

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6965

2098

13_x

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TR

GV

9; V

2; T

CR

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1676

869

6521

5806

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TC

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T

RG

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2X);

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M13

231

6967

2077

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NK

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2-E

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Pro

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cold

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NM

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651

8531

2072

05_a

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EA

CA

M4;

CG

M7

carc

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mbr

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l adh

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M_0

0181

710

8921

9281

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MS

RA

met

hion

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xide

red

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se A

NM

_012

331

4482

EP 2 080 140 B1

58

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2125

31_a

tLC

N2;

NG

AL

lipoc

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2 (

onco

gene

24p

3)N

M_0

0556

439

3420

8650

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D24

BG

3278

6393

420

4881

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GC

G; G

CS

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mid

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ucos

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eN

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873

5720

8651

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5866

493

426

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3930

934

2043

51_a

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100P

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NM

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980

6286

2163

79_x

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CD

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934

2097

71_x

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1181

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4 tr

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12O

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Pro

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Gen

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Locu

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7748

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QR

1;

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DC

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CG

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1N

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1599

951

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2188

47_a

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P-2

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VIC

KZ

2IG

F-I

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NA

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pro

tein

2N

M_0

0654

810

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2029

47_s

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GY

PC

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Cgl

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C is

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C is

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m 2

NM

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101

2995

2218

24_s

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MG

C26

766

AA

7701

7022

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2048

48_x

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HB

G1;

HB

GA

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GR

A-g

amm

a gl

obin

NM

_000

559

3047

2090

18_s

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PIN

K1

BF

4324

7865

018

2078

27_x

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SN

CA

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AC

P;

PA

RK

1; P

AR

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alph

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CP

140;

alp

ha-s

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isof

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CP

112

L366

7566

22

2000

75_s

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GU

K1;

GM

Kgu

anyl

ate

kina

se 1

BC

0062

4929

8722

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BX

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XD

C2

UB

X d

omai

n co

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ning

1N

M_0

2524

180

700

2194

58_s

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NS

UN

3; F

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609

NO

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mily

3N

M_0

2207

263

899

2076

67_s

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3; M

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B; m

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rote

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inas

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nase

3

isof

orm

C

NM

_002

756

5606

2011

78_a

tF

BX

O7;

FB

X7;

FB

X07

F-b

ox o

nly

prot

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7N

M_0

1217

925

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2142

73_x

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TH

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7043

5381

3121

1475

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AG

1B

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ne is

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m 1

LA

F11

6273

573

M2.

4T

otal

= 1

18 tr

ansc

ripts

10O

vere

xpre

ssed

1U

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expr

esse

dS

yste

mat

icC

omm

on _

Affy

Pro

duct

Gen

bank

Locu

sLin

k21

7906

_at

KLH

DC

2; L

CP

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CLP

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lch

dom

ain

cont

aini

ng 2

NM

_014

315

2358

8

EP 2 080 140 B1

59

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2019

93_x

_at

HN

RP

DL;

HN

RN

P;

JKT

BP

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TB

P2;

la

AU

F1

hete

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neou

s nu

clea

r rib

onuc

leop

rote

in D

-like

NM

_005

463

9987

2006

31_s

_at

SE

T; 2

PP

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GA

AD

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PP

2A; P

HA

PII;

T

AF

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ET

A

SE

T tr

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ion

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loid

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-ass

ocia

ted)

NM

_003

011

6418

2012

58_a

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PS

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osom

al p

rote

in S

16N

M_0

0102

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1721

1666

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PL3

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P-B

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L3

L224

5361

2222

2099

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KF

ZP

434D

1335

AW

5938

5926

065

2012

30_s

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AR

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M_0

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RP

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15A

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9903

6138

2177

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RP

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NM

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6203

2071

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04

2000

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Tot

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tran

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Ove

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Sys

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655

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NM

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2115

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172

9114

EP 2 080 140 B1

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5

10

15

20

25

30

35

40

45

50

55

(con

tinue

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2029

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462

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2089

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3958

2087

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2021

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0589

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NM

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106

2207

2023

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GS

2; G

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359

97

2014

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M

GC

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NM

_000

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217

2059

22_a

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YR

O

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kin

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bind

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NM

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332

7305

2114

74_s

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SE

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C00

4948

5269

2131

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0031

6479

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1429

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9873

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2036

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3220

0839

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TS

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PP

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PS

Bca

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sin

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NM

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908

1508

EP 2 080 140 B1

61

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2087

03_s

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2189

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EP 2 080 140 B1

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20

25

30

35

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45

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tinue

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NM

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6446

2115

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221

6268

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56

EP 2 080 140 B1

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5

10

15

20

25

30

35

40

45

50

55

(con

tinue

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2080

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NM

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2095

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RIP

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NM

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1236

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7832

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EP 2 080 140 B1

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5

10

15

20

25

30

35

40

45

50

55

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tinue

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6348

2011

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NM

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NM

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2022

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NM

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9587

2014

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83

EP 2 080 140 B1

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5

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15

20

25

30

35

40

45

50

55

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M_0

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435

76

2093

04_x

_at

GA

DD

45B

; MY

D11

8;

GA

DD

45B

ET

A;

DK

FZ

P56

6B13

3

grow

th a

rres

t and

DN

A-d

amag

e-in

duci

ble,

bet

aA

F08

7853

4616

2188

81_s

_at

FO

SL2

FO

S-li

ke a

ntig

en 2

NM

_024

530

7957

920

0919

_at

PH

C2;

ED

R2;

HP

H2

poly

hom

eotic

2-li

ke is

ofor

m b

; pol

yhom

eotic

2-li

ke is

ofor

m a

NM

_004

427

1912

2032

34_a

tU

PP

1; U

PA

SE

; U

DR

PA

SE

urid

ine

phos

phor

ylas

e 1

NM

_003

364

7378

2146

96_a

tM

GC

1437

6;

DK

FZ

p686

0061

59hy

poth

etic

al p

rote

in

MG

C14

376

AF

0705

6984

981

2093

45_s

_at

PI4

KII

AL5

6193

055

361

2119

24_s

_at

PLA

UR

; CD

87; U

PA

R;

UR

KR

plas

min

ogen

act

ivat

or, u

roki

nase

rece

ptor

isof

orm

2 p

recu

rsor

; pla

smin

ogen

ac

tivat

or, u

roki

nase

rec

epto

r is

ofor

m 3

pre

curs

or; p

lasm

inog

en a

ctiv

ator

, ur

okin

ase

rece

ptor

isof

orm

1 p

recu

rsor

AY

0291

8053

29

2102

64_a

tG

PR

35G

pro

tein

-cou

pled

rec

epto

r 35

AF

0890

8728

5920

0730

_s_a

tP

TP

4A1

BF

5767

1078

0320

8937

_s_a

tID

1in

hibi

tor

of D

NA

bin

ding

1 is

ofor

m a

; inh

ibito

r of

DN

A b

indi

ng 1

isof

orm

bD

1388

933

9721

8880

_at

FO

SL2

N36

408

2355

2127

22_s

_at

PT

DS

R; P

SR

; P

TD

SR

1; K

IAA

0585

phos

phat

idyl

serin

e re

cept

orA

K02

1780

2321

0

2196

22_a

tR

AB

20; F

LJ20

429

RA

B20

, mem

ber

RA

S o

ncog

ene

fam

ilyN

M_0

1781

755

647

2088

69_s

_at

GA

BA

RA

PL1

; GE

C1;

A

PG

8LG

AB

A(A

) re

cept

or-a

ssoc

iate

d pr

otei

n lik

e 1

AF

0878

4723

710

2135

24_s

_at

G0S

2pu

tativ

e ly

mph

ocyt

e G

0/G

1 sw

itch

gene

NM

_015

714

5048

6

2016

31_s

_at

IER

3; D

IF2;

IEX

1;

PR

G1;

DIF

-2; G

LY96

; IE

X-1

; IE

X-1

L

imm

edia

te e

arly

res

pons

e 3

imm

edia

te e

arly

res

pons

e 3

isof

orm

sho

rt;

isof

orm

long

NM

_003

897

8870

M3.

3T

otal

= 2

30 tr

ansc

ripts

54O

vere

xpre

ssed

6U

nder

expr

esse

dS

yste

mat

icC

omm

on _

Affy

Pro

duct

Gen

bank

Locu

sLin

k

EP 2 080 140 B1

71

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2145

00_a

tH

2AF

Y; H

2A.y

; H2A

/y;

H2A

FJ;

mH

2A1;

H

2AF

12M

; M

AC

RO

H2A

1.1;

M

AC

RO

H2A

1.2

H2A

his

tone

fam

ily, m

embe

r Y

isof

orm

2; H

2A h

isto

ne fa

mily

, mem

ber

Y

isof

orm

1; H

2A h

isto

ne fa

mily

, mem

ber

Y is

ofor

m 3

AF

0442

8695

55

2163

38_s

_at

C6o

rf10

9; K

LIP

1;

dJ33

7H4.

3;

DK

FZ

P56

6C24

3

chro

mos

ome

6 op

en r

eadi

ng fr

ame

109

AK

0214

3325

844

2179

95_a

tS

QR

DL;

CG

I-44

sulfi

de d

ehyd

roge

nase

like

NM

_021

199

5847

221

1742

_s_a

tE

VI2

B; E

VD

B;

D17

S37

6ec

otro

pic

vira

l int

egra

tion

site

2B

BC

0059

2621

24

2183

88_a

tP

GLS

; 6P

GL

6-ph

osph

oglu

cono

lact

onas

eN

M_0

1208

825

796

2180

17_s

_at

FLJ

2224

2N

M_0

2507

080

140

EP 2 080 140 B1

72

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2071

68_s

_at

H2A

FY

; H2A

.y; H

2A/y

; H

2AF

J; m

H2A

1;

H2A

F12

M;

MA

CR

OH

2A1.

1;

MA

CR

OH

2A1.

2

H2A

his

tone

fam

ily, m

embe

r Y

isof

orm

2; H

2A h

isto

ne fa

mily

, mem

ber

Y

isof

orm

1; H

2A h

isto

ne fa

mily

, mem

ber

Y is

ofor

m 3

NM

_004

893

9555

2209

90_s

_at

VM

P1;

D

KF

ZP

566I

133

hypo

thet

ical

pro

tein

DK

FZ

p566

I133

NM

_030

938

8167

1

2056

39_a

tA

OA

Hac

ylox

yacy

l hyd

rola

se p

recu

rsor

NM

_001

637

313

2064

88_s

_at

CD

36; F

AT

; GP

4;

GP

3B; G

PIV

; PA

SIV

; S

CA

RB

3

CD

36 a

ntig

enN

M_0

0007

294

8

2014

63_s

_at

TA

LDO

1; T

AL-

H;

TA

LDO

Rtr

ansa

ldol

ase

1N

M_0

0675

568

88

2046

20_s

_at

CS

PG

2; V

ER

SIC

AN

chon

droi

tin s

ulfa

te p

rote

ogly

can

2 (v

ersi

can)

NM

_004

385

1462

2137

33_a

tM

YO

1FB

F74

0152

4542

2141

52_a

tP

IGB

AU

1442

4394

8821

0715

_s_a

tS

PIN

T2;

Kop

; HA

I2;

HA

I-2

serin

e pr

otea

se in

hibi

tor,

Kun

itz ty

pe, 2

AF

0272

0510

653

2128

07_s

_at

SO

RT

1B

E74

2268

6272

2178

37_s

_at

VP

S24

; NE

DF

; C

GI-

149

vacu

olar

pro

tein

sor

ting

24N

M_0

1607

951

652

2008

08_s

_at

ZY

Xzy

xin

NM

_003

461

7791

2095

75_a

tIL

10R

B; C

RF

B4;

C

RF

2-4;

D21

S58

; D

21S

66; C

DW

210B

; IL

-10R

2

inte

rleuk

in 1

0 re

cept

or, b

eta

prec

urso

rB

C00

1903

3588

2200

34_a

tIR

AK

3; IR

AK

-Min

terle

ukin

-1 r

ecep

tor-

asso

ciat

ed k

inas

e 3

NM

_007

199

1121

320

2788

_at

MA

PK

AP

K3;

3P

K;

MA

PK

AP

3m

itoge

n-ac

tivat

ed p

rote

in k

inas

e-ac

tivat

ed p

rote

in k

inas

e 3

NM

_004

635

7867

2051

47_x

_at

NC

F4;

MG

C38

10;

P40

PH

OX

neut

roph

il cy

toso

lic fa

ctor

4 (

40kD

) is

ofor

m 1

; neu

trop

hil c

ytos

olic

fact

or 4

(4

0kD

) is

ofor

m 2

NM

_000

631

4689

2094

73_a

tE

NT

PD

1A

V71

7590

953

2122

68_a

tS

ER

PIN

B1;

EI;

LEI;

PI2

; MN

EI;

M/N

EI;

ELA

NH

2

serin

e (o

r cy

stei

ne)

prot

eina

se in

hibi

tor,

cla

de B

(ov

albu

min

), m

embe

r 1

NM

_030

666

1992

EP 2 080 140 B1

73

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2096

16_s

_at

CE

S1;

CE

H; C

ES

2;

HM

SE

; SE

S1;

HM

SE

1ca

rbox

yles

tera

se 1

(m

onoc

yte/

mac

roph

age

serin

e es

tera

se 1

)S

7375

110

66

2028

88_s

_at

AN

PE

P; C

D13

; LA

P1;

P

EP

N; g

p150

mem

bran

e al

anin

e am

inop

eptid

ase

prec

urso

rN

M_0

0115

029

0

2082

70_s

_at

RN

PE

P;

DK

FZ

P54

7H08

4ar

giny

l am

inop

eptid

ase

(am

inop

eptid

ase

B)

NM

_020

216

6051

2011

18_a

tP

GD

; 6P

GD

phos

phog

luco

nate

deh

ydro

gena

seN

M_0

0263

152

2620

0824

_at

GS

TP

1; P

I; D

FN

7;

GS

T3;

FA

EE

S3

glut

athi

one

tran

sfer

ase

NM

_000

852

2950

2126

57_s

_at

IL1R

NA

W08

3357

3557

8947

6_r_

atN

PE

PL1

AA

3980

6279

716

2010

95_a

tD

AP

deat

h-as

soci

ated

pro

tein

NM

_004

394

1611

2087

00_s

_at

TK

T; T

KT

1tr

ansk

etol

ase

L127

1170

8620

1483

_s_a

tS

UP

T4H

1; S

PT

4Hsu

ppre

ssor

of T

y 4

hom

olog

1B

C00

2802

6827

2031

75_a

tR

HO

G; A

RH

Gra

s ho

mol

og g

ene

fam

ily, m

embe

r G

NM

_001

665

391

2014

70_a

tG

ST

01; P

28;

GS

TT

Lp28

glut

athi

one-

S-t

rans

fera

se o

meg

a 1

NM

_004

832

9446

2157

06_x

_at

ZY

Xzy

xin

BC

0023

2377

91

2154

89_x

_at

HO

ME

R3

AI8

7128

794

5420

8699

_x_a

tT

KT

BF

6968

4070

8621

8945

_at

MG

C26

54; F

LJ12

433

hypo

thet

ical

pro

tein

MG

C26

54N

M_0

2410

979

091

2009

41_a

tH

SB

P1

heat

sho

ck fa

ctor

bin

ding

pro

tein

1A

K02

6575

3281

2020

96_s

_at

BZ

RP

; MB

R; P

BR

; P

BR

-Spe

riphe

ral b

enzo

diaz

apin

e re

cept

or is

ofor

m P

BR

; per

iphe

ral

benz

odia

zapi

ne r

ecep

tor

isof

orm

PB

R-S

NM

_000

714

706

2120

43_a

tT

GO

LN2;

TG

N38

; T

GN

46; T

GN

48;

TG

N51

; TT

GN

2;

MG

C14

722

tran

s-go

lgi n

etw

ork

prot

ein

2A

K02

5557

2080

74_s

_at

AP

2S1;

AP

17;

CLA

PS

2;

AP

17-D

ELT

A

adap

tor-

rela

ted

prot

ein

com

plex

2, s

igm

a 1

subu

nit i

sofo

rm A

P17

; ad

apto

r-re

late

d pr

otei

n co

mpl

ex 2

, sig

ma

1 su

buni

t iso

form

AP

17de

ltaN

M_0

2157

511

75

2192

56_s

_at

SH

3TC

1; F

LJ20

356

SH

3 do

mai

n an

d te

trat

ricop

eptid

e re

peat

s 1

NM

_018

986

5443

620

2671

_s_a

tP

DX

K; P

KH

; PN

K;

C21

orf9

7py

ridox

al k

inas

eN

M_0

0368

185

66

EP 2 080 140 B1

74

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2110

47_x

_at

AP

2S1;

AP

17;

CLA

PS

2;

AP

17-D

ELT

A

adap

tor-

rela

ted

prot

ein

com

plex

2, s

igm

a 1

subu

nit i

sofo

rm A

P17

; ad

apto

r-re

late

d pr

otei

n co

mpl

ex 2

, sig

ma

1 su

buni

t iso

form

AP

17de

ltaB

C00

6337

1175

2186

06_a

tZ

DH

HC

7; Z

NF

370;

F

LJ10

792;

FLJ

2027

9zi

nc fi

nger

, DH

HC

dom

ain

cont

aini

ng 7

NM

_017

740

5562

5

2040

99_a

tS

MA

RC

D3;

Rsc

6p;

BA

F60

C; C

RA

CD

3S

WI/S

NF

rela

ted,

mat

rix a

ssoc

iate

d, a

ctin

dep

ende

nt re

gula

tor o

f chr

omat

in,

subf

amily

d, m

embe

r 3 is

ofor

m 2

; SW

I/SN

F re

late

d, m

atrix

ass

ocia

ted,

act

in

depe

nden

t reg

ulat

or o

f chr

omat

in, s

ubfa

mily

d, m

embe

r 3

isof

orm

1

NM

_003

078

6604

2078

09_s

_at

AT

P6A

P1;

16A

; CF

2;

OR

F; A

c45;

XA

P3;

X

AP

-3; A

TP

6S1;

V

AT

PS

1; A

TP

6IP

1

AT

Pas

e, H

+ tr

ansp

ortin

g, ly

soso

mal

acc

esso

ry p

rote

in 1

pre

curs

orN

M_0

0118

353

7

2210

59_s

_at

CH

ST

6; M

CD

C1

carb

ohyd

rate

(N

-ace

tylg

luco

sam

ine

6-O

) su

lfotr

ansf

eras

e 6

NM

_021

615

4166

2058

63_a

tS

100A

12; p

6; C

GR

P;

MR

P6;

CA

AF

1;

EN

RA

GE

S10

0 ca

lciu

m-b

indi

ng p

rote

in A

12N

M_0

0562

162

83

2013

79_s

_at

TP

D52

L2; D

54; h

D54

tum

or p

rote

in D

52-li

ke 2

isof

orm

e; t

umor

pro

tein

D52

-like

2 is

ofor

m f;

tum

or

prot

ein

D52

-like

2 is

ofor

m a

; tum

or p

rote

in D

52-li

ke 2

isof

orm

b; t

umor

pro

tein

D

52-li

ke 2

isof

orm

c; t

umor

pro

tein

D52

-like

2 is

ofor

m d

NM

_003

288

7165

2139

02_a

tA

SA

H1

AI3

7933

842

720

6130

_s_a

tA

SG

R2;

L-H

2;

AS

GP

-R; H

s.12

59as

ialo

glyc

opro

tein

rec

epto

r 2

isof

orm

a; a

sial

ogly

copr

otei

n re

cept

or 2

is

ofor

m b

; asi

alog

lyco

prot

ein

rece

ptor

2 is

ofor

m c

NM

_001

181

433

2195

49_s

_at

RT

N3;

AS

YIP

; NS

PL2

; N

SP

LII

retic

ulon

3 is

ofor

m a

; ret

icul

on 3

isof

orm

b; r

etic

ulon

3 is

ofor

m c

; ret

icul

on 3

is

ofor

m d

NM

_006

054

1031

3

2042

14_s

_at

RA

B32

RA

B32

, mem

ber

RA

S o

ncog

ene

fam

ilyN

M_0

0683

410

981

2030

81_a

tC

TN

NB

IP1;

ICA

Tbe

ta-c

aten

in-in

tera

ctin

g pr

otei

n IC

AT

NM

_020

248

5699

820

0736

_s_a

tG

PX

1; G

SH

PX

1;

MG

C14

399

glut

athi

one

pero

xida

se 1

isof

orm

1; g

luta

thio

ne p

erox

idas

e 1

isof

orm

2N

M_0

0058

128

76

2008

66_s

_at

PS

AP

; GLB

A; S

AP

1pr

osap

osin

(va

riant

Gau

cher

dis

ease

and

var

iant

met

achr

omat

ic

leuk

odys

trop

hy)

M32

221

5660

M3.

4T

otal

= 3

23 tr

ansc

ripts

1O

vere

xpre

ssed

107

Und

erex

pres

sed

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2088

41_s

_at

G3B

P2

Ras

-GT

Pas

e ac

tivat

ing

prot

ein

SH

3 do

mai

n-bi

ndin

g pr

otei

n 2

isof

orm

a;

Ras

-GT

Pas

e ac

tivat

ing

prot

ein

SH

3 do

mai

n-bi

ndin

g pr

otei

n 2

isof

orm

bA

B01

4560

9908

EP 2 080 140 B1

75

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2013

85_a

tD

HX

15; D

BP

1; H

RH

2;

DD

X15

; PR

P43

; P

rPp4

3p

DE

AH

(A

sp-G

lu-A

la-H

is)

box

poly

pept

ide

15N

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R3C

1; G

R; G

CR

; G

RL

nucl

ear

rece

ptor

sub

fam

ily 3

, gro

up C

, mem

ber

1 (g

luco

cort

icoi

d re

cept

or)

X03

348

2908

2052

69_a

tLC

P2

AI1

2325

139

3722

1229

_s_a

tF

LJ20

628;

D

KF

Zp5

64I2

178

hypo

thet

ical

pro

tein

FLJ

2062

8N

M_0

1791

055

006

2130

00_a

tZ

CW

CC

3; N

XP

2;

ZC

W5;

KIA

A01

36zi

nc fi

nger

, CW

-typ

e w

ith c

oile

d-co

il do

mai

n 3

AP

0006

9323

515

2035

31_a

tB

F43

5809

2180

96_a

tLP

AA

T-e

; FLJ

1121

0ac

id a

cyltr

ansf

eras

e-ep

silo

nN

M_0

1836

155

326

EP 2 080 140 B1

80

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2033

78_a

tP

CF

11; K

IAA

0824

pre-

mR

NA

cle

avag

e co

mpl

ex II

pro

tein

Pcf

11A

B02

0631

5158

521

2450

_at

KIA

A02

56K

IAA

0256

gen

e pr

oduc

tD

8744

597

2820

3347

_s_a

tM

96; M

TF

2pu

tativ

e D

NA

bin

ding

pro

tein

NM

_007

358

2282

320

1699

_at

PS

MC

6; P

44; p

42;

SU

G2;

CA

DP

44;

MG

C12

520

prot

easo

me

26S

AT

Pas

e su

buni

t 6N

M_0

0280

657

06

2152

45_x

_at

FM

R1

AA

8308

8423

3221

2633

_at

KIA

A07

76A

W29

8092

2337

621

8577

_at

FLJ

2033

1hy

poth

etic

al p

rote

in F

LJ20

331

NM

_017

768

5563

121

8878

_s_a

tS

IRT

1; S

IR2L

1si

rtui

n 1

NM

_012

238

2341

1M

3.5

Tot

al =

19

tran

scrip

ts3

Ove

rexp

ress

ed0

Und

erex

pres

sed

Sys

tem

atic

Com

mon

_A

ffyP

rodu

ctG

enba

nkLo

cusL

ink

2170

52_x

_at

AK

0241

0820

8749

_x_a

tF

LOT

1flo

tillin

1A

F08

5357

1021

120

0630

_x_a

tS

ET

AV

7028

1064

18M

3.6

Tot

al =

233

tran

scrip

ts2

Ove

rexp

ress

ed76

Und

erex

pres

sed

Sys

tem

atic

Com

mo

Pro

duct

Gen

bank

Locu

s20

2603

_at

AD

AM

10N

5137

010

221

4895

_s_a

tA

DA

M10

AU

1351

5410

220

0723

_s_a

tM

11S

1; G

PIA

P1

mem

bran

e co

mpo

nent

, chr

omos

ome

11, s

urfa

ce m

arke

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isof

orm

1;

mem

bran

e co

mpo

nent

, chr

omos

ome

11, s

urfa

ce m

arke

r 1

NM

_005

898

4076

2189

73_a

tE

FT

UD

1; F

AM

42A

; F

LJ13

119;

HsT

1929

4el

onga

tion

fact

or T

u G

TP

bin

ding

dom

ain

cont

aini

ng 1

NM

_024

580

7963

1

2050

78_a

tP

IGF

; MG

C32

646;

M

GC

3313

6ph

osph

atid

ylin

osito

l gly

can,

cla

ss F

isof

orm

1; p

hosp

hatid

ylin

osito

l gly

can,

cl

ass

F is

ofor

m 2

NM

_002

643

5281

2178

15_a

tS

UP

T16

H; F

AC

T;

CD

C68

; FA

CT

P14

0;

FLJ

1085

7; F

LJ14

010;

chro

mat

in-s

peci

fic tr

ansc

riptio

n el

onga

tion

fact

or la

rge

subu

nit

NM

_007

192

1119

8

2042

15_a

tC

7orf

23; M

GC

4175

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M-T

RA

G

SP

T16

/CD

C68

chro

mos

ome

7 op

en r

eadi

ng fr

ame

23N

M_0

2431

579

161

2131

49_a

tD

LAT

AW

2997

4017

37

EP 2 080 140 B1

81

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2029

70_a

tD

YR

K2

dual

-spe

cific

ity ty

rosi

ne-(

Y)-

phos

phor

ylat

ion

regu

late

d ki

nase

2 is

ofor

m 1

; du

al-s

peci

ficity

tyro

sine

-(Y

)-ph

osph

oryl

atio

n re

gula

ted

kina

se 2

isof

orm

2Y

0921

6

2016

33_s

_at

CY

B5-

MA

W23

5051

8077

720

3073

_at

CO

G2;

LD

LCco

mpo

nent

of o

ligom

eric

gol

gi c

ompl

ex 2

NM

_007

357

2279

620

9600

_s_a

tA

CO

X1;

MG

C11

98;

PA

LMC

OX

acyl

-Coe

nzym

e A

oxi

dase

isof

orm

a; a

cyl-C

oenz

yme

A o

xida

se is

ofor

m b

S69

189

51

2006

87_s

_at

SF

3B3;

RS

E1;

S

AP

130;

SF

3b13

0;

ST

AF

130;

KIA

A00

17

splic

ing

fact

or 3

b, s

ubun

it 3,

130

kDa

NM

_012

426

2345

0

2065

72_x

_at

ZN

F85

; HP

F4;

HT

F1

zinc

fing

er p

rote

in 8

5 (H

PF

4, H

TF

1)N

M_0

0342

976

3921

7939

_s_a

tA

FT

IPH

ILIN

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LJ20

080;

FLJ

2379

3;

MG

C33

965

aftip

hilin

pro

tein

isof

orm

c; a

ftiph

ilin

prot

ein

isof

orm

b; a

ftiph

ilin

prot

ein

isof

orm

aN

M_0

1765

754

812

2032

01_a

tP

MM

2; C

DG

1; C

DG

Sph

osph

oman

nom

utas

e 2

NM

_000

303

5373

2087

16_s

_at

LOC

5449

9pu

tativ

e m

embr

ane

prot

ein

AB

0209

8054

499

2041

85_x

_at

PP

ID; C

YP

D; C

YP

-40;

M

GC

3309

6pe

ptid

ylpr

olyl

isom

eras

e D

NM

_005

038

5481

2122

16_a

tK

IAA

0436

; FLJ

1662

7pu

tativ

e pr

olyl

olig

opep

tidas

eA

B00

7896

9581

2113

37_s

_at

76P

gam

ma

tubu

lin r

ing

com

plex

pro

tein

(76

p ge

ne)

BC

0009

6627

229

2041

72_a

tC

PO

X; C

PX

; HC

Pco

prop

orph

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ogen

ox

idas

eN

M_0

0009

713

71

2050

55_a

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GA

E; C

D10

3;

HU

MIN

AE

inte

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ha E

(an

tigen

CD

103,

hum

an m

ucos

al ly

mph

ocyt

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tigen

1;

alph

a po

lype

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0220

836

82

2033

06_s

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SLC

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1; C

ST

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CS

T; C

MP

ST

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solu

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35

(CM

P-s

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aci

d tr

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orte

r), m

embe

r A

1N

M_0

0641

610

559

2046

83_a

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AM

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adhe

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mol

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0087

333

8420

2654

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XO

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AR

CH

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D

KF

ZP

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1122

axot

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M_0

2282

664

844

2076

27_s

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TF

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SF

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TF

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ctor

CP

2N

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0565

370

24

2093

66_x

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CY

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chro

me

b-5

M22

865

1528

2026

63_a

tA

I005

043

2141

79_s

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NF

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1H

9301

347

7921

7921

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H97

940

2190

17_a

tE

TN

K1;

EK

I; E

KI1

etha

nola

min

e ki

nase

1N

M_0

1863

855

500

EP 2 080 140 B1

82

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2011

28_s

_at

AC

LY; A

TP

CL;

C

LAT

PA

TP

citr

ate

lyas

e is

ofor

m 1

; AT

P c

itrat

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ase

isof

orm

2N

M_0

0109

647

2122

64_s

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KIA

A02

61; F

OE

KIA

A02

61D

8745

023

063

2046

42_a

tE

DG

1; E

CG

F1;

E

DG

-1; S

1PR

1;

CH

ED

G1;

D1S

3362

endo

thel

ial d

iffer

entia

tion,

sph

ingo

lipid

G-p

rote

in-c

oupl

ed r

ecep

tor,

1N

M_0

0140

019

01

2006

83_s

_at

UB

E2L

3; E

2-F

1; L

-U

BC

; UB

CH

7; U

bcM

4ub

iqui

tin-c

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gatin

g en

zym

e E

2L 3

isof

orm

1; u

biqu

itin-

conj

ugat

ing

enzy

me

E2L

3 is

ofor

m 2

NM

_003

347

7332

2015

00_s

_at

PP

P1R

11; H

CG

V;

HC

G-V

; TC

TE

5;

TC

TE

X5

prot

ein

phos

phat

ase

1, r

egul

ator

y (in

hibi

tor)

sub

unit

11 is

ofor

m 1

; pro

tein

ph

osph

atas

e 1,

reg

ulat

ory

(inhi

bito

r) s

ubun

it 11

isof

orm

2N

M_0

2195

969

92

2183

13_s

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GA

LNT

7; G

alN

AcT

7;

GA

LNA

C-T

7po

lype

ptid

e N

-ace

tylg

alac

tosa

min

yltr

ansf

eras

e 7

NM

_017

423

5180

9

2074

90_a

tT

UB

A4;

FLJ

1394

0tu

bulin

, alp

ha 4

NM

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019

8008

620

0979

_at

BF

7399

7921

1547

_s_a

tP

AF

AH

1B1;

LIS

1;

MD

CR

plat

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fact

or a

cety

lhyd

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se, i

sofo

rm Ib

, alp

ha s

ubun

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5kD

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3387

5048

2145

82_a

tP

DE

3Bph

osph

odie

ster

ase

3B, c

GM

P-in

hibi

ted

NM

_000

753

5140

2046

34_a

tN

EK

4; N

RK

2; S

TK

2N

IMA

(ne

ver

in m

itosi

s ge

ne a

)-re

late

d ki

nase

4N

M_0

0315

767

8721

9283

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C1G

ALT

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re 1

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sam

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alph

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bet

a 1,

3-ga

lact

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tran

sfer

ase

2N

M_0

1415

829

071

2052

63_a

tB

CL1

0; C

LAP

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10;

CIP

ER

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10;

CA

RM

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LL/ly

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F08

2283

8915

2052

02_a

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CM

T1

prot

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D-a

spar

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) O

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hyltr

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eras

eN

M_0

0538

951

1020

1746

_at

TP

53; p

53; T

RP

53tu

mor

pro

tein

p53

NM

_000

546

7157

2069

89_s

_at

SF

RS

2IP

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1;

CA

SP

11; S

RR

P12

9sp

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g fa

ctor

, arg

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rich

2, in

tera

ctin

g pr

otei

nN

M_0

0471

991

69

2185

38_s

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MR

S2L

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TM

RS

2-lik

e, m

agne

sium

hom

eost

asis

fact

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M_0

2066

257

380

2186

46_a

tF

LJ20

534

hypo

thet

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pro

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FLJ

2053

4N

M_0

1786

754

969

(con

tinue

d)20

0703

_at

DN

CL1

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8; P

IN;

DLC

1; D

LC8;

hdl

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olyp

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M_0

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55

2132

46_a

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AI3

4650

426

175

2130

63_a

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LJ11

806

N64

802

7988

2

2032

84_s

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HS

2ST

1; K

IAA

0448

hepa

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-O-s

ulfo

tran

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ase

1N

M_0

1226

296

53

EP 2 080 140 B1

83

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2024

51_a

tG

TF

2H1;

BT

F2;

TF

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l tra

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n fa

ctor

IIH

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2kD

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C00

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2965

2013

59_a

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OP

B;

DK

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rote

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M_0

1645

113

15

2093

84_a

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RO

SC

AA

1768

3311

212

2138

53_a

tZ

CS

L3zi

nc fi

nger

, CS

L do

mai

n co

ntai

ning

3A

L050

199

2022

97_s

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RE

R1

RE

R1

hom

olog

AF

1573

2411

079

2125

07_a

tR

W1;

PR

O10

48;

KIA

A02

57D

8744

623

505

2046

13_a

tP

LCG

2ph

osph

olip

ase

C, g

amm

a 2

(pho

spha

tidyl

inos

itol-s

peci

fic)

NM

_002

661

5336

2016

34_s

_at

CY

BS

-Mcy

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out

er m

itoch

ondr

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embr

ane

prec

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rN

M_0

3057

980

777

2012

40_s

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KIA

A01

02K

IAA

0102

gen

e pr

oduc

tN

M_0

1475

297

8920

1295

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SB

1; S

WIP

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CS

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pro

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1 is

ofor

m 3

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S

OC

S-b

ox p

rote

in 1

isof

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2N

M_0

1562

626

118

2009

96_a

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CT

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AR

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AR

P3

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pro

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60_s

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4; V

AM

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C00

5974

8674

2009

94_a

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RA

NB

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7A

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1052

720

1985

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KIA

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IAA

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M_0

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698

9720

1518

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TA

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1Hs-

beta

chro

mob

ox h

omol

og 1

(H

P1

beta

hom

olog

Dro

soph

ila)

NM

_006

807

1095

1

2010

98_a

tC

OP

B2;

bet

a’-C

OP

coat

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pro

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com

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beta

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M_0

0476

692

7620

1479

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DK

C1;

NA

P57

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OLA

4; X

AP

101;

dy

sker

in

dysk

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NM

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363

1736

2028

11_a

tS

TA

MB

P; A

MS

HS

TA

M b

indi

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rote

inN

M_0

0646

310

617

2000

50_a

tZ

NF

146;

OZ

Fzi

nc fi

nger

pro

tein

146

NM

_007

145

7705

2014

72_a

tV

BP

1; P

FD

3; P

FD

N3;

V

BP

-1vo

n H

ippe

l-Lin

dau

bind

ing

prot

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1N

M_0

0337

274

11

EP 2 080 140 B1

84

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2058

98_a

tC

X3C

R1;

V28

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CR

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PR

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RV

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C m

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1524

2131

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CT

R3

Z78

330

1009

620

1661

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tA

CS

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CS

3;

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CL3

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0219

4ac

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oA s

ynth

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e lo

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hain

fam

ily m

embe

r 3

NM

_004

457

2181

2124

12_a

tA

V71

5767

2030

08_x

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TX

ND

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AP

AC

DA

TP

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l diff

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190

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2O

vere

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Pro

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k

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F r

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asso

ciat

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rote

inN

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1661

451

567

2140

96_s

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SH

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6472

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H d

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5109

621

1061

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C

DG

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GN

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6390

4247

2000

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1231

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ched

cha

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2, m

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NM

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587

2216

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rote

in S

22N

M_0

2019

156

945

EP 2 080 140 B1

85

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2025

20_s

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CC

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H

NP

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4292

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2956

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NM

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375

1353

2157

43_a

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PP

38A

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489

1055

721

7902

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ER

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8;

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8924

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4896

2121

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1282

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255

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ome

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M_0

0704

911

120

EP 2 080 140 B1

86

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2212

64_s

_at

TA

RD

BP

TA

R D

NA

bin

ding

pro

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NM

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214

8192

721

8557

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256

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4056

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GC

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NM

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371

5147

8

2183

73_a

tF

TS

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1; F

LJ13

258

fuse

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es h

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2247

664

400

2022

20_a

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IAA

0907

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949

2288

922

0035

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NU

P21

0; G

P21

0;

PO

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0; F

LJ22

389;

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IAA

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10N

M_0

2492

323

225

2090

07_s

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2; N

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1A

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7856

5703

5

2183

43_s

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GT

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3; T

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2;

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T

Fiii

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102

gene

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fact

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IC, p

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NM

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9330

2134

83_a

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KIA

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5679

2339

820

4352

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TR

AF

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NF

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C:3

9780

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F r

ecep

tor-

asso

ciat

ed fa

ctor

5N

M_0

0461

971

88

EP 2 080 140 B1

87

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

M3.

9T

otal

= 2

61 tr

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0O

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xpre

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135

Und

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1320

1501

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bind

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fact

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NM

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092

2926

2138

83_s

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BB

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2917

8394

1

2036

29_s

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3410

466

2091

99_s

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4208

2082

89_s

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TP

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4N

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0487

995

3820

4333

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GA

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part

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idas

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sor

NM

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175

2093

82_a

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PC

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5520

4274

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9166

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5048

5

2126

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AL0

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254

505

2009

88_s

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3; K

i; P

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G-G

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sub

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NM

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789

1019

7

2097

86_a

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NH

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HM

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GC

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high

mob

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gro

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ucle

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indi

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omai

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BC

0012

8210

473

2099

43_a

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BX

L4; F

BL4

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and

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235

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NM

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310

1389

2184

60_a

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LJ20

397

hypo

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pro

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2039

7N

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2178

58_s

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SN

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fact

or X

NM

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999

7257

2010

34_a

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DD

3B

E54

5756

120

EP 2 080 140 B1

88

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2057

71_s

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KA

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5716

2190

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UP

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42;

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1328

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bA35

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2464

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2329

9

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NM

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939

2287

820

9247

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BC

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3620

1036

_s_a

tH

AD

HS

C; S

CH

AD

L-3-

hydr

oxya

cyl-C

oenz

yme

A d

ehyd

roge

nase

, sho

rt c

hain

NM

_005

327

3033

EP 2 080 140 B1

89

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2035

66_s

_at

AG

L; G

DE

amyl

o-1,

6-gl

ucos

idas

e, 4

-alp

ha-g

luca

notr

ansf

eras

e is

ofor

m 1

; am

ylo-

1,6-

gluc

osid

ase,

4-a

lpha

-glu

cano

tran

sfer

ase

4-al

phis

ofor

m 1

; am

ylo-

1,6-

gluc

osid

ase,

4-a

lpha

-glu

cano

tran

sfer

ase

isof

orm

2; a

myl

o-1,

6-gl

ucos

idas

e,

4-al

pha-

gluc

anot

rans

fera

se is

ofor

m 3

NM

_000

645

178

2185

93_a

tR

BM

28; F

LJ10

377

RN

A b

indi

ng m

otif

prot

ein

28N

M_0

1807

755

131

2023

18_s

_at

SE

NP

6; S

SP

1;

SU

SP

1; K

IAA

0797

SU

MO

1/se

ntrin

spe

cific

pro

teas

e 6

AF

3065

0826

054

2188

38_s

_at

FLJ

1278

8hy

poth

etic

al p

rote

in F

LJ12

788

NM

_022

492

6442

721

2150

_at

KIA

A01

43A

A80

5651

2316

721

8147

_s_a

tA

D-0

17; F

LJ14

611

glyc

osyl

tran

sfer

ase

AD

-017

NM

_018

446

5583

020

0760

_s_a

tJW

AN

9249

410

550

2119

71_s

_at

LRP

PR

C; L

SF

C;

GP

130;

LR

P13

0;

CLO

NE

-239

70

leuc

ine-

rich

PP

R m

otif-

cont

aini

ng p

rote

inA

F05

2133

1012

8

2186

84_a

tLR

RC

5; F

LJ10

470

leuc

ine

rich

repe

at c

onta

inin

g 5

NM

_018

103

5514

420

1624

_at

DA

RS

aspa

rtyl

-tR

NA

syn

thet

ase

NM

_001

349

1615

2141

98_s

_at

DG

CR

2A

U15

0824

9993

2189

89_x

_at

SLC

30A

5; Z

NT

5;

ZT

L1; Z

NT

L1; Z

nT-5

; M

GC

5499

; FLJ

1249

6;

FLJ

1275

6

zinc

tran

spor

ter

ZT

L1N

M_0

2290

264

924

2008

60_s

_at

KIA

A10

07; A

D-0

05K

IAA

1007

pro

tein

isof

orm

a; K

IAA

1007

pro

tein

isof

orm

bB

C00

0779

2301

921

7886

_at

EP

S15

BF

2135

7520

6020

8953

_at

KIA

A02

17K

IAA

0217

pro

tein

BC

0033

8123

185

2050

52_a

tA

UH

AU

RN

A-b

indi

ng p

rote

in/e

noyl

-Coe

nzym

e A

hyd

rata

se p

recu

rsor

NM

_001

698

549

2022

15_s

_at

NF

YC

; HS

M; C

BF

C;

HA

P5;

CB

F-C

; NF

-YC

; H

1TF

2A

nucl

ear

tran

scrip

tion

fact

or Y

, gam

ma

NM

_014

223

4802

2020

38_a

tU

BE

4A; U

FD

2;

KIA

A01

26ub

iqui

tinat

ion

fact

or E

4AN

M_0

0478

893

54

2125

30_a

tN

EK

7N

IMA

(ne

ver

in m

itosi

s ge

ne a

)-re

late

d ki

nase

7A

L080

111

1406

021

2499

_s_a

tC

14or

f32;

MIS

S;

MG

C23

138;

c1

4_53

46

MA

PK

-inte

ract

ing

and

spin

dle-

stab

ilizi

ng p

rote

inA

K02

5580

2013

75_s

_at

PP

P2C

Bpr

otei

n ph

osph

atas

e 2

(for

mer

ly 2

A),

cat

alyt

ic s

ubun

it, b

eta

isof

orm

NM

_004

156

5516

EP 2 080 140 B1

90

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2094

86_a

tS

AS

10di

srup

ter

of s

ilenc

ing

10B

C00

4546

5705

021

8491

_s_a

tT

HY

28; M

Y10

5;

MD

S01

2; H

SP

C14

4;

MG

C12

187

thym

ocyt

e pr

otei

n th

y28

isof

orm

1; t

hym

ocyt

e pr

otei

n th

y28

isof

orm

2N

M_0

1417

429

087

2182

12_s

_at

MO

CS

2; M

PT

S;

MC

BP

E; M

OC

O1

mol

ybdo

pter

in s

ynth

ase

larg

e su

buni

t MO

CS

2B; m

olyb

dopt

erin

syn

thas

e sm

all s

ubun

it M

OC

S2A

NM

_004

531

4338

2109

62_s

_at

AK

AP

9; P

RK

A9;

C

G-N

AP

; YO

TIA

O;

AK

AP

350;

AK

AP

450;

H

YP

ER

ION

; K

IAA

0803

A-k

inas

e an

chor

pro

tein

9 is

ofor

m 2

; A-k

inas

e an

chor

pro

tein

9 is

ofor

m 4

; A

-kin

ase

anch

or p

rote

in 9

isof

orm

1; A

-kin

ase

anch

or p

rote

in 9

isof

orm

3A

B01

9691

1014

2

2035

13_a

tF

LJ21

439;

KIA

A18

40hy

poth

etic

al p

rote

in F

LJ21

439

NM

_025

137

8020

8

2130

27_a

tA

U14

6655

3889

2_at

KIA

A02

40D

8707

723

506

2036

90_a

tT

UB

GC

P3;

Spc

98p

spin

dle

pole

bod

y pr

otei

nN

M_0

0632

210

426

2095

51_a

tM

GC

1106

1hy

poth

etic

al p

rote

in M

GC

1106

1B

C00

4875

8427

220

2850

_at

AB

CD

3; A

BC

43;

PM

P70

; PX

MP

1A

TP

-bin

ding

cas

sette

, sub

-fam

ily D

, mem

ber

3N

M_0

0285

858

25

2037

38_a

tF

LJ11

193

AI4

2119

255

322

2092

00_a

tM

EF

2CN

2246

842

0820

1326

_at

CC

T6A

BE

7370

3090

821

8128

_at

NF

YB

AI8

0411

848

0121

8962

_s_a

tF

LJ13

576

hypo

thet

ical

pro

tein

FLJ

1357

6N

M_0

2248

464

418

2091

43_s

_at

CLN

S1A

; CLC

I; IC

ln;

CLN

S1B

chlo

ride

chan

nel,

nucl

eotid

e-se

nsiti

ve, 1

AA

F00

5422

1207

2043

14_s

_at

CR

EB

1; M

GC

9284

cAM

P r

espo

nsiv

e el

emen

t bin

ding

pro

tein

1 is

ofor

m A

; cA

MP

res

pons

ive

elem

ent b

indi

ng p

rote

in 1

isof

orm

BN

M_0

0437

913

85

2017

78_s

_at

KIA

A04

94N

M_0

1477

498

1320

2664

_at

AI0

0504

320

4354

_at

PO

T1;

hP

ot1;

D

KF

ZP

586D

211

PO

T1

prot

ectio

n of

telo

mer

es 1

hom

olog

NM

_015

450

2591

3

2126

40_a

tLO

C20

1562

AV

7126

0220

3787

_at

SS

BP

2; H

SP

C11

6si

ngle

-str

ande

d D

NA

bin

ding

pro

tein

2N

M_0

1244

623

635

2023

46_a

tH

IP2;

LIG

; HY

PG

hunt

ingt

in in

tera

ctin

g pr

otei

n 2

NM

_005

339

3093

EP 2 080 140 B1

91

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2155

48_s

_at

SC

FD

1; R

A41

0;

KIA

A09

17;

ST

XB

P1L

2;

C14

orf1

63

vesi

cle

tran

spor

t-re

late

d pr

otei

n is

ofor

m a

; ves

icle

tran

spor

t-re

late

d pr

otei

n is

ofor

m b

AB

0207

2423

256

2125

13_s

_at

US

P33

; VD

U1;

K

IAA

1097

; M

GC

1686

8

ubiq

uitin

spe

cific

pro

teas

e 33

isof

orm

1; u

biqu

itin

spec

ific

prot

ease

33

isof

orm

2; u

biqu

itin

spec

ific

prot

ease

33

isof

orm

3A

B02

9020

2303

2

2124

86_s

_at

N20

923

2129

04_a

tK

IAA

1185

KIA

A11

85 p

rote

inA

B03

3011

5747

020

4020

_at

PU

RA

BF

7399

4358

1320

3465

_at

MR

PL1

9; R

LX1;

R

PM

L15;

MR

P-L

15;

KIA

A01

04;

MG

C20

675

mito

chon

dria

l rib

osom

al p

rote

in L

19N

M_0

1476

398

01

2129

43_a

tK

IAA

0528

KIA

A05

28 g

ene

prod

uct

AB

0111

0098

4720

1296

_s_a

tW

SB

1; S

WIP

1;

WS

B-1

WD

SO

CS

-box

pro

tein

1 is

ofor

m 1

; WD

SO

CS

-box

pro

tein

1 is

ofor

m 3

; WD

S

OC

S-b

ox p

rote

in 1

isof

orm

2N

M_0

1562

626

118

2022

14_s

_at

CU

L4B

; KIA

A06

95cu

llin

4BN

M_0

0358

884

50

2181

79_s

_at

FLJ

1271

6F

LJ12

716

prot

ein

isof

orm

a; F

LJ12

716

prot

ein

isof

orm

bN

M_0

2194

260

684

2146

70_a

tZ

NF

36A

A65

3300

7586

2222

01_s

_at

CA

SP

8AP

2; C

ED

-4;

FLA

SH

; RIP

25;

FLJ

1120

8; K

IAA

1315

CA

SP

8 as

soci

ated

pro

tein

2A

B03

7736

9994

2012

18_a

tC

TB

P2

C-t

erm

inal

bin

ding

pro

tein

2 is

ofor

m 1

; C-t

erm

inal

bin

ding

pro

tein

2 is

ofor

m 2

NM

_001

329

1488

2049

80_a

tC

LOC

K; K

IAA

0334

cloc

kN

M_0

0489

895

7520

8882

_s_a

tD

D5

U69

567

5136

620

1817

_at

KIA

A00

10N

M_0

1467

196

9022

1196

_x_a

tC

6.1A

c6.1

AN

M_0

2433

279

184

2014

93_s

_at

PU

M2

BE

7780

7823

369

2029

56_a

tA

RF

GE

F1;

BIG

1;

P20

0; A

RF

GE

P1;

D

7300

2801

8Rik

bref

eldi

n A

-inhi

bite

d gu

anin

e nu

cleo

tide-

exch

ange

pro

tein

1N

M_0

0642

110

565

2188

42_a

tF

LJ21

908

hypo

thet

ical

pro

tein

FLJ

2190

8N

M_0

2460

479

657

2032

53_s

_at

KIA

A04

33K

IAA

0433

pro

tein

NM

_015

216

2326

221

4113

_s_a

tR

BM

8AA

I738

479

9939

EP 2 080 140 B1

92

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2122

99_a

tN

EK

9; N

ek8;

NE

RC

C;

NE

RC

C1;

M

GC

1671

4;

DK

FZ

p434

D09

35

NIM

A r

elat

ed k

inas

e9

AL1

1750

291

754

2009

92_a

tIP

O7;

RA

NB

P7

impo

rtin

7A

L137

335

1052

720

3689

_s_a

tF

MR

1A

I743

037

2332

2093

23_a

tP

RK

RIR

; DA

P4;

P

52rI

PK

prot

ein-

kina

se, i

nter

fero

n-in

duci

ble

doub

le s

tran

ded

RN

A d

epen

dent

in

hibi

tor,

rep

ress

or o

f (P

58 r

epre

ssor

)A

F08

1567

5612

2183

22_s

_at

AC

SL5

; AC

S2;

AC

S5;

F

AC

L5ac

yl-C

oA s

ynth

etas

e lo

ng-c

hain

fam

ily m

embe

r 5

isof

orm

a; a

cyl-C

oA

synt

heta

se lo

ng-c

hain

fam

ily m

embe

r 5

isof

orm

bN

M_0

1623

451

703

3829

0_at

RG

S14

regu

lato

r of

G-p

rote

in s

igna

lling

14

AF

0371

9510

636

3403

1_i_

atC

CM

1; C

AM

; KR

IT1

krev

inte

ract

ion

trap

ped

1U

9026

888

9

2130

90_s

_at

TA

F4

AI7

4402

968

7421

8545

_at

FLJ

1108

8; p

56hy

poth

etic

al p

rote

in F

LJ11

088

NM

_018

318

5529

720

9268

_at

VP

S45

A; H

1; V

SP

45;

VP

S45

B; V

PS

54A

; V

SP

45A

; H1V

PS

45

vacu

olar

pro

tein

sor

ting

45A

AF

1655

1311

311

2037

43_s

_at

TD

Gth

ymin

e-D

NA

gly

cosy

lase

NM

_003

211

6996

2180

47_a

tO

SB

PL9

; OR

P9;

F

LJ12

492;

oxys

tero

l-bin

ding

pro

tein

-like

pro

tein

9 is

ofor

mN

M_0

2458

611

488

FLJ

1462

9; F

LJ14

801;

F

LJ32

055;

FLJ

3438

4;

MG

C15

035

e; o

xyst

erol

-bin

ding

pro

tein

-like

pro

tein

9 is

ofor

m a

; oxy

ster

ol-b

indi

ng

prot

ein-

like

prot

ein

9 is

ofor

m b

; oxy

ster

ol-b

indi

ng p

rote

in-li

ke p

rote

in 9

is

ofor

m c

; oxy

ster

ol-b

indi

ng p

rote

in-li

ke p

rote

in 9

isof

or22

1873

at

ZN

F14

3A

W16

2015

7702

2117

84_s

_at

BC

0061

8120

1448

at

TIA

1T

IA1

prot

ein

isof

orm

1; T

IA1

prot

ein

isof

orm

2N

M_0

2203

722

1931

_s_a

tS

EC

13L

AV

7011

7381

929

2219

31_s

_at

KIA

A03

72K

IAA

0372

NM

_014

639

9652

2015

03 a

tG

3BP

BG

5000

6710

146

2037

91 a

tD

MX

L1D

mx-

like

1N

M_0

0550

916

57

2193

63_s

_at

CG

I-12

CG

I-12

pro

tein

NM

_015

942

5100

1

EP 2 080 140 B1

93

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

d)

2024

91_s

_at

IKB

KA

P: F

D; D

YS

; IK

AP

inhi

bito

r of k

appa

ligh

t pol

ypep

tide

gene

enh

ance

r in

B-c

ells

, kin

ase

com

plex

-as

soci

ated

pro

tein

NM

_003

640

8518

2015

89_a

tS

MC

1L1;

SM

CB

; S

B1.

8; D

XS

423E

; K

IAA

0178

; S

MC

1alp

ha

SM

C1

stru

ctur

al m

aint

enan

ce o

f chr

omos

omes

1-li

ke 1

D80

000

8243

2132

38_a

tA

TP

10D

AI4

7814

757

205

2095

23_a

tT

AF

2; T

AF

2B;

CIF

150;

TA

FII1

50T

BP

-ass

ocia

ted

fact

or 2

AK

0016

1868

73

EP 2 080 140 B1

94

5

10

15

20

25

30

35

40

45

50

55

Tab

le 1

3: M

odul

e-by

-mod

ule

com

paris

on o

f gen

es e

xpre

ssio

n le

vels

in li

ver

tran

spla

nt r

ecip

ient

s vs

. hea

lthy

volu

ntee

rs. L

iver

tran

spla

nt r

ecip

ient

s (n

=22

) vs

. Hea

lthy

Vol

unte

er (

n=27

) -

trai

ning

set

Man

n W

hitn

ey U

test

p-v

alue

<0.

05, n

o m

tcM

1.1

Tot

al =

69

tran

scrip

tsp<

0.05

1O

vere

xpre

ssed

42U

nd

erex

pre

ssed

Hea

lth

y N

orm

aliz

edR

awL

iver

Tra

nsp

lan

t N

orm

aliz

edR

awp

-val

ue

2165

10_x

_at

0.95

3594

429

3.88

520.

6892

2555

230.

9773

0.03

7U

nd

erex

pre

ssed

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15

20

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30

35

40

45

50

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var

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256

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EP 2 080 140 B1

98

5

10

15

20

25

30

35

40

45

50

55

(con

tinue

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Claims

1. A method of identifying a subject with melanoma comprising:

determining two or more melanoma vectors of expression, wherein the two or more melanoma vectors ofexpression comprise the expression level of six or more genes over-expressed, under-expressed or combina-tions thereof selected from module M1.1 and module M1.2 of Table 12; anddisplaying each of the melanoma expression vectors with a separate identifier.

2. The method of claim 1, further comprising the step of detecting one or more polymorphisms in the six or more genes.

3. The method of claim 1 further comprising a method for displaying melanoma transcriptome vector data comprising:

separating one or more genes into one or more modules to visually display an aggregate gene expressionvector value for each of the modules; anddisplaying the aggregate gene expression vector value for overexpression, underexpression or equal expressionof the aggregate gene expression vector value in each module.

4. A method of identifying a subject with immunosuppression associated with transplants comprising:

determining two or more immunosuppression vectors of expression; anddisplaying each of the immunosuppression vectors of expression with a separate identifier, wherein each im-munosuppression vector of expression comprises levels of expression of six or more genes selected frommodule M1.1 and module M1.2 of Table 13.

5. The method of claim 4, further comprising the step of detecting one or more polymorphisms in the one or moreimmunosuppression vector of expression.

6. The method claim 4 or 5, wherein the one or more immunosuppression vectors of expression are derived from aleukocyte.

7. The method of any one of claims 4 to 6 comprising:

separating six or more genes into two or more modules to visually display, as an aggregate, a vector of expressionfor each of the modules; anddisplaying the vector of expression for overexpression, underexpression or equal expression of the vector ofexpression in each module.

8. The method of any one of claims 1 to 7, wherein the six or more genes comprise genes related to platelets, plateletglycoproteins, platelet-derived immune mediators, MHC/ribosomal proteins, MHC class I molecules, Beta 2-mi-croglobulin, ribosomal proteins, hemoglobin genes or combinations thereof and/or genes related to interferon-in-ducible genes, signaling molecules, kinases, RAS family members or combinations thereof.

9. The method of any one of claims 1 to 8, wherein the expression level comprises mRNA expression level, proteinexpression level or both mRNA expression level and protein expression level.

10. The method of any one of claims 1 to 9, wherein the expression level-comprises a mRNA expression level and isquantitated by a method selected from the group consisting of polymerase chain reaction, real time polymerasechain reaction, reverse transcriptase polymerase chain reaction, hybridization, probe hybridization and gene ex-pression array.

11. The method of any one of claims 1 to 10, wherein the expression level is determined using at least one techniqueselected from the group consisting of polymerase chain reaction, heteroduplex analysis, single stand conformationalpolymorphism analysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blot-ting, Western blotting, enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing.

12. The method of claim 3 or 8, wherein overexpression is identified with a first identifier and underexpression is identifiedwith a second identifier.

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13. The method of claim 3 or 8, wherein overexpression is identified with a first identifier and underexpression is identifiedwith a second identifier, wherein the first identifier is a first color and the second identifier is a second color, whereinfirst and second identifiers are superimposed to provide a combined color.

14. A computer readable medium comprising computer-executable instructions for performing the method of claim 1.

Patentansprüche

1. Verfahren zur Identifikation eines Subjekts mit einem Melanom, umfassend:das Bestimmen von zwei oder mehrMelanom-Expressionsvektoren, wobei die zwei oder mehr Melanom-Expressionsvektoren das Expressionsniveauvon sechs oder mehr Genen mit Überexpression, Unterexpression oder einer aus diesen beiden kombiniertenExpression umfassen, die ausgewählt sind aus den Modulen M1.1 und M1.2 der Tabelle 12; und das Anzeigeneines jeden der Melanom-Expressionsvektoren mittels eines separaten Identifikators.

2. Verfahren nach Anspruch 1, des Weiteren umfassend den Schritt des Detektierens eines oder mehrerer Polymor-phismen in den sechs oder mehr Genen.

3. Verfahren nach Anspruch 1, des Weiteren umfassend ein Verfahren zum Anzeigen von Melanom-Transkriptom-Vektordaten, umfassend:

das Aufteilen eines oder mehrerer Gene in eines oder mehrere Module zur visuellen Anzeige eines Aggregat-Genexpressionsvektorwerts für jedes der Module; und das Anzeigen des Aggregat-Genexpressionsvektorwertszur Überexpression, Unterexpression oder gleichmä-βigen Expression des Aggregat-Genexpressionsvektor-werts in jedem der Module.

4. Verfahren zur Identifikation eines Subjekts mit einer mit Transplantaten assoziierten Immunsuppression, umfassend:das Bestimmen von zwei oder mehr Immunsuppressions-Expressionsvektoren; und das Anzeigen jedes der Im-munsuppressions-Expressionsvektoren mittels eines separaten Identifikators, wobei jeder der Immunsuppressions-Expressionsvektoren das Expressionsniveau von sechs oder mehr Genen umfasst, die aus den Modulen M1.1 undM1.2 der Tabelle 13 ausgewählt sind.

5. Verfahren nach Anspruch 4, des Weiteren umfassend den Schritt des Detektierens eines oder mehrerer Polymor-phismen in dem einen oder den mehreren Immunsuppressions-Expressionsvektoren.

6. Verfahren nach Anspruch 4 oder 5, wobei der eine oder die mehreren Immunsuppressions-Expressionsvektorenvon einem Leukozyten abgeleitet sind.

7. Verfahren nach einem der Ansprüche 4 bis 6, umfassend: das Aufteilen von sechs oder mehr Genen in zwei odermehr Module zur visuellen Anzeige eines Expressionsvektors für jedes der Module in Form eines Aggregats; unddas Anzeigen des Expressionsvektors zur Überexpression, Unterexpression oder gleichmäßigen Expression desExpressionsvektors in jedem der Module.

8. Verfahren nach einem der Ansprüche 1 bis 7, wobei die sechs oder mehr Gene Gene umfassen, die mit Blutplättchen,Blutplättchen-Glykoproteinen, von Blutplättchen abgeleiteten Immunmediatoren, MHC-/ribosomalen Proteinen, Mo-lekülen der MHC-Klasse I, Beta-2-Mikroglobulin, ribosomalen Proteinen, Hämoglobingenen oder Kombinationenaus diesen in Bezug stehen, und/oder Gene, die mit durch Interferon induzierbaren Genen, Signalmolekülen, Ki-nasen, Vertretern der RAS-Familie oder Kombinationen aus diesen in Bezug stehen.

9. Verfahren nach einem der Ansprüche 1 bis 8, wobei das Expressionsniveau das mRNA-Expressionsniveau, dasProtein-Expressionsniveau oder sowohl das mRNA-Expressionsniveau als auch das Protein-Expressionsniveauumfasst.

10. Verfahren nach einem der Ansprüche 1 bis 9, wobei das Expressionsniveau ein mRNA-Expressionsniveau umfasstund durch ein Verfahren quantifiziert ist, das ausgewählt ist aus der Gruppe bestehend aus Polymerase-Kettenrea-tion, Echtzeit-Polymerase-Kettenreaktion, Reverse-Transkriptase-Polymerase-Kettenreaktion, Hybridisierung,Sondenhybridisierung und einem Genexpressions-Array.

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11. Verfahren nach einem der Ansprüche 1 bis 10, wobei die Bestimmung des Expressionsniveaus unter Verwendungmindestens einer Technik erfolgt, die ausgewählt ist aus der Gruppe bestehend aus Polymerase-Kettenreaktion,Heteroduplex-Analyse, Einzelstrang-Konformations-Polymorphismus-Analyse, Ligase-Kettenreaktion, komparati-ver Genomhybridisierung, Southern-Blotting, Northern-Blottin, Western-Blotting, ELISA (enzyme-linked immunosor-bent assay), Fluoreszenz-Resonanz-Energietransfer und Sequenzierung.

12. Verfahren nach Anspruch 3 oder 8, wobei jeweils eine Überexpression mittels eines ersten Identifikators und eineUnterexpression mittels eines zweiten Identifikators identifiziert wird.

13. Verfahren nach Anspruch 3 oder 8, wobei jeweils eine Überexpression mittels eines ersten Identifikators und eineUnterexpression mittels eines zweiten Identifikators identifiziert wird, wobei jeweils der erste Identifikator eine ersteFarbe und der zweite Identifikator eine zweite Farbe aufweist, wobei der erste und der zweite Identifikator zurBereitstellung einer kombinierten Farbe überlagert sind.

14. Computerlesbares Medium, das Anweisungen umfasst, die von einem Computer zur Durchführung des Verfahrensnach Anspruch 1 ausgeführt werden können.

Revendications

1. Procédé permettant d’identifier un sujet souffrant d’un mélanome comprenant :

la détermination de deux vecteurs d’expression de mélanome ou plus, dans lequel les deux vecteurs d’expres-sion de mélanome ou plus comprennent le taux d’expression de six gènes ou plus surexprimés, sous-exprimés,ou des combinaisons de ceux-ci choisis dans le module M1.1 et le module M1.2 du tableau 12 ; etl’affichage de chacun des vecteurs d’expression demélanome avec un identifiant distinct.

2. Procédé selon la revendication 1, comprenant en outre l’étape consistant à détecter un ou plusieurs polymorphismesdans les six gènes ou plus.

3. Procédé selon la revendication 1 comprenant en outre un procédé d’affichage des données des vecteurs du trans-criptome de mélanome comprenant :

la séparation d’un ou de plusieurs gènes en un ou plusieurs modules pour afficher visuellement une valeur devecteur d’expression d’agrégat de gènes pour chacun des modules ; etl’affichage de la valeur de vecteur d’expression d’agrégat de gènes pour la surexpression, la sous-expressionou une expression égale de la valeur de vecteur d’expression d’agrégat de gènes dans chaque module.

4. Procédé permettant d’identifier un sujet souffrant d’une immunosuppression associée à des transplants comprenant :

la détermination de deux vecteurs d’expression d’immunosuppression ou plus ; etl’affichage de chacun des vecteurs d’expression d’immunosuppression avec un identifiant distinct, dans lequelchaque vecteur d’expression d’immunosuppression comprend des taux d’expression de six gènes ou pluschoisis dans le module M1.1 et le module M1.2 du tableau 13.

5. Procédé selon la revendication 4, comprenant en outre l’étape consistant à détecter un ou plusieurs polymorphismesdans les un ou plusieurs vecteurs d’expression d’immunosuppression.

6. Procédé selon la revendication 4 ou 5, dans lequel les un ou plusieurs vecteurs d’expression d’immunosuppressionsont dérivés d’un leucocyte.

7. Procédé selon l’une quelconque des revendications 4 à 6 comprenant :

la séparation de six gènes ou plus en deux modules ou plus pour afficher visuellement, sous la forme d’unagrégat, un vecteur d’expression pour chacun des modules ; etl’affichage du vecteur d’expression pour la surexpression, la sous-expression ou une expression égale duvecteur d’expression dans chaque module.

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8. Procédé selon l’une quelconque des revendications 1 à 7, dans lequel les six gènes ou plus comprennent des gènesassociés aux plaquettes, à des glycoprotéines plaquettaires, à des médiateurs immunitaires d’origine plaquettaire,à des protéines du CMH/ribosomiales, à des molécules du CMH de classe I, à la bêta-2-microglobuline, à desprotéines ribosomiales, aux gènes de l’hémoglobine ou à des combinaisons de ceux-ci et/ou des gènes associésà des gènes inductibles par l’interféron, à des molécules de signalisation, à des kinase, à des membres de la familleRAS ou à des combinaisons de ceux-ci.

9. Procédé selon l’une quelconque des revendications 1 à 8, dans lequel le taux d’expression comprend le tauxd’expression d’ARNm, le taux d’expression de protéines, dans lequel à la fois le taux d’expression d’ARNm et letaux d’expression de protéines.

10. Procédé selon l’une quelconque des revendications 1 à 9, dans lequel le taux d’expression comprend un tauxd’expression d’ARNm et est quantifié par un procédé choisi dans le groupe constitué par la réaction en chaîne dela polymérase, la réaction en chaîne de la polymérase en temps réel, une transcription inverse suivie d’une réactionen chaîne de la polymérase, l’hybridation, l’hybridation de sondes et une puce d’expression de gènes.

11. Procédé selon l’une quelconque des revendications 1 à 10, dans lequel le taux d’expression est déterminé enutilisant au moins une technique choisie dans le groupe constitué par la réaction en chaîne de la polymérase,l’analyse d’hétéroduplex, l’analyse de polymorphismes conformationnels d’un simple brin, la réaction en chaîne dela ligase, l’hybridation génomique comparative, l’analyse Southern blot, l’analyse Northern blot, l’analyse Westernblot, la technique ELISA (enzyme-linked immunosorbent assay), le transfert d’énergie de fluorescence par résonanceet le séquençage.

12. Procédé selon la revendication 3 ou 8, dans lequel la surexpression est identifiée par un premier identifiant et lasous-expression est identifiée par un second identifiant.

13. Procédé selon la revendication 3 ou 8, dans lequel la surexpression est identifiée par un premier identifiant et lasous-expression est identifiée par un second identifiant, dans lequel le premier identifiant est une première couleuret le second identifiant est une seconde couleur, dans lequel les premier et second identifiants sont superposés etfournissent une couleur combinée.

14. Support lisible par ordinateur comprenant des instructions exécutables par ordinateur pour réaliser le procédé selonla revendication 1.

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