This application is a divisional of U.S. patent application Ser. No. 14/214,853 filed Mar. 15, 2014, which claims priority to U.S. Provisional Application No. 61/788,266, filed Mar. 15, 2013, each of which are incorporated herein by reference in their entireties.

›STATEMENT OF GOVERNMENT INTEREST

This invention was made with government support under federal grant numbers R01-A1068804, K24-AI093969, 5U01AI066569-05, 3U01AI066569-05S1 awarded by the National Institutes of Health and N66001-09-C-2082 awarded by Defense Advanced Research Projects Agency of the Department of Defense. The U.S. Government has certain rights to this invention.

›TECHNICAL FIELD

The present invention relates to methods of identifying and treating subjects suffering from bacterial infection.

›BACKGROUND

Septicemia causes substantial morbidity and mortality among patients in the United States, with a rising burden of Staphylococcus aureus infection. Although blood cultures are the diagnostic gold standard for blood stream infection (BSI), sensitivity is limited and results are not rapidly available. Such diagnostic delays can extend the time to administration of effective antibiotics, which is an independent risk factor for mortality. Conversely, diagnostic uncertainty also leads to high rates of empiric overtreatment, fueling the burden of antimicrobial resistance. Thus, novel approaches that are faster and more accurate are needed to differentiate between the major pathogens causing sepsis and BSI.

Whereas conventional diagnostic approaches have focused on identifying the infecting pathogen, a growing body of evidence suggests that the host's inflammatory response to the pathogen also represents a potential diagnostic tool. In vitro and In vivo experiments have revealed fundamental differences in host response to Gram-positive and Gram-negative bacterial infection, including significant differences in Toll-like receptor (TLR) signaling and cytokine production. Distinctive gene expression profiles exist for viral, bacterial, and fungal infections in both animal model systems and ex vivo stimulation of human peripheral blood leukocytes. Peripheral blood mononuclear cell (PBMC) gene expression signatures have also been evaluated in humans for a variety of conditions including severe infection, bacterial vs. viral illness, systemic lupus erythematosus, atherosclerosis, and radiation exposure. Taken together, these studies provide strong evidence that global changes in host blood gene expression patterns can be used to differentiate disease states.

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. Early diagnostic strategies for S. aureus BSI could improve patient care by reducing the time required to establish the diagnosis and provide appropriate treatment while avoiding unnecessary anti-MRSA antibiotics. There is a need in the art to have alternative methods for diagnosing and treating patients with bacterial infection, such as sepsis.

›SUMMARY · 1 of 3

The present invention is directed to a method of developing a diagnostic assay for identifying and/or classifying a bacterial infection in a subject. The method comprising determining the gene expression levels of at least two biomarkers in a subject infected with bacterial infection, wherein the biomarkers are selected from one or more of Tables 3-17; comparing the gene expression levels of the biomarkers in the subject with the gene expression levels of the biomarkers in a control; identifying factors, wherein each factor comprises differentially expressed biomarkers that have the greatest ability to differentiate between gene expression in the subject and the control; providing a weighted value for the differentially expressed biomarkers within the factor; and determining a relationship between the factor and the bacterial infection using the weighted values of the differentially expressed biomarkers with an algorithm, wherein a relationship between the factor and the bacterial infection is used to develop the diagnostic assay. The method may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from Table 8 and Table 10. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9898. The method may distinguish a subject that has a Staphylococcus aureus blood stream infection from a subject that has an Escherichia coli blood stream infection. The biomarkers may be selected from Table 8 and Table 10. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.8372. The method may distinguish a subject that has an Escherichia coli blood stream infection from a healthy subject. The biomarkers may be selected from Table 8 and Table 10. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9229. The method may distinguish a subject that has a gram positive blood stream infection from a subject that has a gram negative blood stream infection. The biomarkers may be selected from Table 9. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.8503. The method may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from Table 7. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9217. The method may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from Tables 3, 4, and 6. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9522. The method may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from Tables 3, 4, 5 and 6. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9964. The method may distinguish a subject that has a Staphylococcus aureus blood stream infection from a subject that has an Escherichia coli blood stream infection. The biomarkers may be selected from Tables 3, 4, 5 and 6. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9935. The method may distinguish a subject that has an Escherichia coli blood stream infection from a healthy subject. The biomarkers may be selected from Tables 3, 4, 5 and 6. The factor may comprise about 5 to about 250 biomarkers. The relationship may have an AUC value of 0.9484. At least one of the differentially expressed biomarkers may have an increased expression level compared to the control. At least one of the differentially expressed biomarkers may have a decreased expression level compared to the control. At least one of the differentially expressed biomarkers may have an increased expression level compared to the control and at least one of the differentially expressed biomarkers may have a decreased expression level compared to the control. The factor may comprise about 10 biomarkers. The method of any one of the preceding claims, wherein the factor may comprise about 20 biomarkers. The factor may comprise about 50 biomarkers. The factor may comprise about 100 biomarkers. The factor may comprise about 150 biomarkers. The factor may comprise about 200 biomarkers. The factor may comprise about 250 biomarkers. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

The present invention is directed to method of identifying and treating a bacterial infection in a subject. The method comprises performing the diagnostic assay as developed by the methods, as described above, and administrating an antibacterial therapy to the subject diagnosed with a bacterial infection. The method further comprising quantifying the amount of at least one biomarker present in a biological sample derived from the subject, wherein the biomarker may be associated with a factor. At least one of the differentially expressed biomarkers may have an increased expression level compared to the control. At least one of the differentially expressed biomarkers may have a decreased expression level compared to the control. At least one of the differentially expressed biomarkers may have an increased expression level compared to the control and at least one of the differentially expressed biomarkers may have a decreased expression level compared to the control. The factor may comprise about 10 biomarkers. The method of any one of the preceding claims, wherein the factor may comprise about 20 biomarkers. The factor may comprise about 50 biomarkers. The factor may comprise about 100 biomarkers. The factor may comprise about 150 biomarkers. The factor may comprise about 200 biomarkers. The factor may comprise about 250 biomarkers. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

›SUMMARY · 2 of 3

The present invention is also directed towards a method of identifying and treating a subject suspected of having a bacterial blood stream infection (BSI). The method comprises determining gene expression levels of at least two biomarkers in a peripheral blood cell sample of the subject, wherein the biomarkers are selected from any one of Tables 3-17; comparing the gene expression levels of the at least two biomarkers to standard gene expression levels wherein the standard gene expression levels correspond to the gene expression levels for the biomarkers in a control; identifying the subject as having a bacterial BSI if the gene expression levels of the biomarkers are different than the standard gene expression levels; and administering an effective amount of antibiotic therapy to treat the subject identified as having a bacterial BSI. The bacterial BSI may be Staphylococcus aureus BSI or Escherichia coli BSI. The bacterial blood stream infection may be S. aureus BSI and the biomarkers may be selected from one of Tables 3-8 or 10. At least about 2 to about 250 biomarkers may be selected from one of Tables 3-8 or 10. The bacterial blood stream infection may be E. coli BSI and the biomarkers may be selected from one of Tables 3-6, 8 or 10. At least about 2 to about 250 biomarkers may be selected from one of Tables 3-6, 8 or 10. The control may be a healthy subject. At least one of the biomarkers may have an increased gene expression level compared to the control. At least one of the biomarkers may have a decreased gene expression level compared to the control. At least one of the biomarkers may have an increased gene expression level compared to the control and at least one of the biomarkers has a decreased gene expression level compared to the control. The gene expression levels of about 10 biomarkers may be determined. The gene expression levels of about 20 biomarkers may be determined. The gene expression levels of about 50 biomarkers may be determined. The gene expression levels of about 100 biomarkers may be determined. The gene expression levels of about 150 biomarkers may be determined. The gene expression levels of about 200 biomarkers may be determined. The gene expression levels of about 250 biomarkers may be determined. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

The present invention is directed to method of distinguishing and treating Staphylococcus aureus blood stream infection (BSI) from Escherichia coli BSI in a subject suspected of having a bacterial infection. The method comprises determining gene expression levels of at least two biomarkers in a peripheral blood cell sample of the subject, wherein the biomarkers are selected from any one of Tables 8 and 10 or Tables 3-6; comparing the gene expression levels of the at least two biomarkers to standard gene expression levels wherein the standard gene expression levels correspond to the gene expression levels for the biomarkers in a control; identifying the subject as having a S. aureus BSI if the gene expression levels of the biomarkers are different than the standard gene expression levels and identifying the subject as having an E. coli BSI if the gene expression levels of the biomarkers are the same as the standard gene expression levels; and administering an effective amount of appropriate antibacterial therapy to treat the subject identified as having a S. aureus BSI or E. coli . The control may be a subject having an E. coli BSI. At least one of the biomarkers may have an increased gene expression level compared to the control. At least one of the biomarkers may have a decreased gene expression level compared to the control. At least one of the biomarkers may have an increased gene expression level compared to the control and at least one of the biomarkers has a decreased gene expression level compared to the control. The gene expression levels of about 10 biomarkers may be determined. The gene expression levels of about 20 biomarkers may be determined. The gene expression levels of about 50 biomarkers may be determined. The gene expression levels of about 100 biomarkers may be determined. The gene expression levels of about 150 biomarkers may be determined. The gene expression levels of about 200 biomarkers may be determined. The gene expression levels of about 250 biomarkers may be determined. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

The present invention is directed to method of distinguishing and treating a gram positive bacterial infection from a gram negative bacterial infection in a subject suspected of having a bacterial infection. The method comprises determining gene expression levels of at least two biomarkers in a peripheral blood cell sample of the subject, wherein the biomarkers are selected from Table 9; comparing the gene expression levels of the at least two biomarkers to standard gene expression levels wherein the standard gene expression levels correspond to the gene expression levels for the biomarkers in a control; identifying the subject as having a gram positive bacterial infection if the gene expression levels of the biomarkers are different than the standard gene expression levels in a control; and administering an effective amount of appropriate antibacterial therapy to treat the subject identified as a gram positive bacterial infection. The gram positive bacterial infection may be Staphylococcus aureus . The control may be a subject having a gram negative bacterial infection. The gram negative bacterial infection may be Escherichia coli . At least one of the biomarkers may have an increased gene expression level compared to the control. At least one of the biomarkers may have a decreased gene expression level compared to the control. At least one of the biomarkers may have an increased gene expression level compared to the control and at least one of the biomarkers has a decreased gene expression level compared to the control. The gene expression levels of about 10 biomarkers may be determined. The gene expression levels of about 20 biomarkers may be determined. The gene expression levels of about 50 biomarkers may be determined. The gene expression levels of about 100 biomarkers may be determined. The gene expression levels of about 150 biomarkers may be determined. The gene expression levels of about 200 biomarkers may be determined. The gene expression levels of about 250 biomarkers may be determined. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

›SUMMARY · 3 of 3

The present invention is directed method of identifying and treating a subject suspected of having a methicillin-resistant Staphylococcus aureus (MRSA) infection. The method comprises determining gene expression levels of at least one biomarker in a peripheral blood cell sample of the subject wherein the biomarker is selected from Table 11; comparing the gene expression levels of the biomarker to a standard gene expression level of the biomarker, wherein the standard gene expression level corresponds to the gene expression level of the biomarker in a subject that has a methicillin-sensitive Staphylococcus aureus (MSSA) infection; identifying the subject as having MRSA if the gene expression levels of the biomarkers are different than the standard gene expression levels; and administering an effective amount of an antibiotic therapy to treat the subject identified as having MRSA. The antibiotic therapy may be mupirocine or vancomycin. At least one of the biomarkers may have an increased gene expression level compared to the control. At least one of the biomarkers may have a decreased gene expression level compared to the control. At least one of the biomarkers may have an increased gene expression level compared to the control and at least one of the biomarkers has a decreased gene expression level compared to the control. The gene expression levels of about 10 biomarkers may be determined. The gene expression levels of about 20 biomarkers may be determined. The gene expression levels of about 50 biomarkers may be determined. The gene expression levels of about 100 biomarkers may be determined. The gene expression levels of about 150 biomarkers may be determined. The gene expression levels of about 200 biomarkers may be determined. The gene expression levels of about 250 biomarkers may be determined. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

The present invention is also directed to a method for determining the efficacy of an anti-bacterial treatment regime in a subject. The method comprises determining a baseline gene expression level for at least one biomarker selected from Tables 3-17; administering to the subject a therapeutic regimen; and redetermining the gene expression level of the at least one biomarker in the subject. A difference in the gene expression level of the at least one biomarker indicates the efficacy of the therapeutic regimen. At least one of the biomarkers may have an increased gene expression level compared to the control. At least one of the biomarkers may have a decreased gene expression level compared to the control. At least one of the biomarkers may have an increased gene expression level compared to the control and at least one of the biomarkers has a decreased gene expression level compared to the control. The gene expression levels of about 10 biomarkers may be determined. The gene expression levels of about 20 biomarkers may be determined. The gene expression levels of about 50 biomarkers may be determined. The gene expression levels of about 100 biomarkers may be determined. The gene expression levels of about 150 biomarkers may be determined. The gene expression levels of about 200 biomarkers may be determined. The gene expression levels of about 250 biomarkers may be determined. The subject may be a mammal. The subject may be a human. The subject may be a mouse. The biological sample may be selected from the group consisting of tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus, and tears. The sample may comprise plasma. The RNA gene expression levels may be determined.

The present invention is also directed to a composition of matter comprising (a) a probe array for determining a biomarker level in a sample, the array comprising of a plurality of probes that hybridizes to one or more biomarkers selected from Tables 3-17; or (b) a kit for determining a biomarker level in a sample, comprising the probe array of (a) and instructions for carrying out the determination of biomarker expression level in the sample. The composition may further comprise a solid support with the plurality of probes attached thereto.

›BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic of derivation and validation cohorts.

FIG. 2 shows that murine S. aureus classifier accurately identifies S. aureus infection under a variety of conditions. Conditions represented include different murine hosts (A), bacterial genetic backgrounds (B), and time from inoculation (C). Animals with S. aureus infection are depicted by a red “x”. Uninfected control mice are depicted by black circles.

FIG. 3 shows that the murine S. aureus classifier differentiates S. aureus from E. coli infection. (A) Inbred mice were tested under three conditions: uninfected controls (black circles), S. aureus infected (red “x”), and E. coli infected (blue triangles). The y-axis represents the predicted probability that a given animal was infected with S. aureus . (B) The murine S. aureus classifier is validated in outbred CD-1 mice where it differentiates S. aureus infection from E. coli infection and uninfected controls.

FIG. 4 shows the performance of the human S. aureus classifier. (A) The human S. aureus classifier differentiates S. aureus BSI from both uninfected controls and E. coli BSI. (B) A separate classifier was generated using only S. aureus and E. coli -infected human subjects and tested using leave-one-out cross-validation.

FIG. 5 shows the projection of the mouse S. aureus classifier onto human subjects. The murine S. aureus classifier identifies humans with S. aureus BSI, but does not differentiate S. aureus from E. coli BSI.

FIG. 6 shows validation in an independent human cohort. (A) The murine S. aureus classifier differentiates between S. aureus infection and healthy. (B) The human S. aureus classifier differentiates between S. aureus infection and healthy.

FIG. 7 shows bacterial challenge experiments. (A) Survival curves for A/J and C57BL/6J mice following an intra-peritoneal infection with S. aureus (16107 CFU/g) or E. coli (66104 CFU/g). Principal Components Analysis plots of the samples in the dataset. Samples are colored by infection status and pathogen. (B) S. aureus infection by time after inoculation (n=10 animals/time point). (C) E. coli infection by time after inoculation (n=10 animals/time point). (D) PCA differentiated by pathogen.

FIG. 8 shows heat maps of genes contributing to the murine S. aureus classifier. (A) Genes within the top five factors contributing to the murine S. aureus classifier were identified and ranked by p-value after Bonferroni correction. A subset of genes (393 after removing duplicates) is depicted here, stratified by pathogen. (B) The same genes depicted in part (A) are categorized first pathogen and then by time since infection.

FIG. 9 shows a Venn diagram demonstrating the number of overlapping probes in each murine experimental group pairwise comparison. Probes were included that had significantly different levels of expression after Bonferroni correction.

FIGS. 10A-10P show that sixteen murine factors independently associated with S. aureus infection projected onto healthy controls (left panel, black circles), animals with E. coli infection (middle panel, blue triangles), and animals with S. aureus infection (right panel, red “x”). The y-axis represents the factor score.

FIG. 11 shows that a factor-based classifier distinguishes MRSA from MSSA infection in mice. An ROC curve is shown for this classification.

FIG. 12 shows Venn diagram demonstrating the number of overlapping probes in each human experimental group pairwise comparison. Probes were included that had significantly different levels of expression after Bonferroni correction. No probes met this cutoff for the S. aureus vs. E. coli comparison.

FIGS. 13A-13S show seventeen human factors independently associated with S. aureus BSI projected onto healthy controls (left panel, black circles), subjects with E. coli BSI (middle panel, blue triangles), and subjects with S. aureus BSI (right panel, red “x”). The y-axis represents the factor score.

FIG. 14 shows heat map of genes contributing to the human S. aureus classifier. Genes within the top two factors contributing to the human S. aureus classifier were identified and ranked by p-value after Bonferroni correction. A subset of genes (86 after removing duplicates) is depicted here, stratified by pathogen.

›DETAILED DESCRIPTION · 1 of 12

The present disclosure provides biomarkers useful for identifying and/or classifying a bacterial infection a subject. S. aureus and Escherichia coli were used as prototypical Gram-positive and Gram-negative bacteria due to their prevalence and clinical relevance. Host gene expression was measured in mice with bacterial infection across multiple conditions. From these data, a molecular classifier was derived for S. aureus infection in inbred mice and validated in a cohort of outbred mice. Host gene expression data from a well-characterized cohort of septic human subjects was used to identify a molecular classifier that accurately distinguished S. aureus BSI from E. coli BSI or uninfected controls. Murine and human S. aureus classifiers exhibited significant similarity particularly in comparing S. aureus infection to the healthy state. Furthermore, both murine and human classifiers were validated in an independent human cohort. The present disclosure demonstrates that the in vivo host response to Gram-positive infections is conserved from mouse to human and can be harnessed as a novel diagnostic strategy in patients with bacterial sepsis.

This study takes significant steps forward on multiple levels in the ongoing effort to understand this pathogen; the host response to it; and identify new diagnostic and therapeutic avenues. A diagnostic modality capable of differentiating infection from health across species is described. Host gene expression classifiers can differentiate infection due to S. aureus from that of E. coli but this effect is less pronounced in the complex human host. The approach described here also affords great insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Evidence to support the paradigm shift in how diagnostics are thought about is provided as well as new areas for research into the pathways that subserve sepsis pathophysiology have been identified.

For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to preferred embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alteration and further modifications of the disclosure as illustrated herein, being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

Articles “a” and “an” are used herein to refer to one or to more than one (i.e. at least one) of the grammatical object of the article. By way of example, “an element” means at least one element and can include more than one element.

Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

1. Definitions

The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “and” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments “comprising,” “consisting of” and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not.

For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present invention. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting.

“About” is used to provide flexibility to a numerical range endpoint by providing that a given value may be “slightly above” or “slightly below” the endpoint without affecting the desired result.

The term “antibiotic” as used herein refers to an agent that either kills or inhibits the growth of a microorganism. Antibiotics may include beta-lactam antibiotics, such as penicillin, which are produced by fungi in the genus Penicillium , cephalosporins, carbapenems, aminoglycosides, sulfonamides, quinolones, oxazolidinones, fluoroquinolone, marcolide, ketolide, rifampin, chloramphenicol, glycopeptide, and trimethoprim. The antibiotics may be ciproflaxacin, levofloxacin, gatifloxacin, moxifloxacin, ofloxacin, norflaxacin, erythromycin, azithromycin, clarithromycin, telithromycin, rifamipin, tetracycline, minocycline, chloramphenicol, gentamicin, linezolid, penicillin, amoxicillin, ceftriaxone, imipenem, vancomycin, teicoplainin, sulfamethoxazole, isoniazid, ethambutol, para-aminosalicylic acid, mupicorin, or cycloserine.

The “area under curve” or “AUC” refers to area under a ROC curve. AUC under a ROC curve is a measure of accuracy. An area of 1 represents a perfect test, whereas an area of 0.5 represents an insignificant test. A preferred AUC may be at least approximately 0.700, at least approximately 0.750, at least approximately 0.800, at least approximately 0.850, at least approximately 0.900, at least approximately 0.910, at least approximately 0.920, at least approximately 0.930, at least approximately 0.940, at least approximately 0.950, at least approximately 0.960, at least approximately 0.970, at least approximately 0.980, at least approximately 0.990, or at least approximately 0.995.

›DETAILED DESCRIPTION · 2 of 12

As used herein, the term “biomarker” refers to a naturally occurring biological molecule present in a subject at varying concentrations useful in identifying and/or classifying a disease or a condition, such as a bacterial infection. For example, the biomarker can be a gene that is upregulated or downregulated in a subject that has a disease, such as a bacterial infection. The biomarker can include genes, proteins, nucleic acids, ribonucleic acids, or a polypeptide used as an indicator or marker for bacterial infection. In some embodiments, the biomarker is a gene. In one embodiment where the bacterial infection comprises S. aureus , the biomarker is selected from the group consisting of the biomarkers provided in Tables 3-17, and combinations thereof. In another embodiment where the bacterial infection comprises E. coli , the biomarker is selected from the group consisting of the biomarkers provided in Tables 3-17, and combinations thereof.

As used herein, the term “bacterial infection” refers to those disease states characterized by the presence of a pathogenic bacteria. Such bacteria may be gram-positive or gram-negative. Examples of gram-positive bacteria include, but are not limited to, S. aureus . Examples of gram-negative bacteria include, but are not limited to, E. coli . A bacterial infection may be sepsis.

As used herein, the term “factor” refers to a group of co-expressed genes. A factor becomes a term in binary regression model to distinguish or predict subjects with and without infection, or distinguish the type of infection

“Sample,” “test sample,” “specimen,” “sample from a subject,” and “patient sample” as used herein may be used interchangeable and may be a sample of blood, tissue, urine, serum, plasma, amniotic fluid, cerebrospinal fluid, placental cells or tissue, endothelial cells, leukocytes, or monocytes. The sample can be used directly as obtained from a patient or can be pre-treated, such as by filtration, distillation, extraction, concentration, centrifugation, inactivation of interfering components, addition of reagents, and the like, to modify the character of the sample in some manner as discussed herein or otherwise as is known in the art.

As used herein, the term “subject” and “patient” are used interchangeably herein and refer to both human and nonhuman animals. The term “nonhuman animals” of the disclosure includes all vertebrates, e.g., mammals and non-mammals, such as nonhuman primates, sheep, dog, cat, horse, cow, chickens, amphibians, reptiles, and the like. Preferably, the subject is a human patient that has a bacterial infection.

The term “biological sample” as used herein includes, but is not limited to, a sample containing tissues, cells, and/or biological fluids isolated from a subject. Examples of biological samples include, but are not limited to, tissues, cells, biopsies, blood, lymph, serum, plasma, urine, saliva, mucus and tears. In one embodiment, the biological sample is a blood sample (such as a plasma sample). A biological sample may be obtained directly from a subject (e.g., by blood or tissue sampling) or from a third party (e.g., received from an intermediary, such as a healthcare provider or lab technician).

Any cell type, tissue, or bodily fluid may be utilized to obtain a sample. Such cell types, tissues, and fluid may include sections of tissues such as biopsy and autopsy samples, frozen sections taken for histologic purposes, blood (such as whole blood), plasma, serum, sputum, stool, tears, mucus, saliva, bronchoalveolar lavage (BAL) fluid, hair, skin, red blood cells, platelets, interstitial fluid, ocular lens fluid, cerebral spinal fluid, sweat, nasal fluid, synovial fluid, menses, amniotic fluid, semen, etc. Cell types and tissues may also include lymph fluid, ascetic fluid, gynecological fluid, urine, peritoneal fluid, cerebrospinal fluid, a fluid collected by vaginal rinsing, or a fluid collected by vaginal flushing. A tissue or cell type may be provided by removing a sample of cells from an animal, but can also be accomplished by using previously isolated cells (e.g., isolated by another person, at another time, and/or for another purpose). Archival tissues, such as those having treatment or outcome history, may also be used. Protein or nucleotide isolation and/or purification may not be necessary.

“Sepsis” as used herein is a condition characterized by a whole-body inflammatory state that is triggered by either a proven (on the basis of sampling or radiology) or probable (considering the patient's clinical presentation, white cell count, CRP, radiology) infection. The infection may be caused by bacteria, virus or fungi. Triggers of sepsis include pneumonia, such as ventilator-associated pneumonia, abdominal infection, kidney infection, and bloodstream infection. The body may develop this inflammatory response by the immune system to microbes in the blood, urine, lungs, skin, or other tissues. A lay term for sepsis is blood poisoning, also used to describe septicaemia. Septicaemia is a related medical term referring to the presence of pathogenic organisms in the bloodstream, leading to sepsis.

Symptoms related to the provoking infection, sepsis is characterized by presence of acute inflammation present throughout the entire body, and is, therefore, frequently associated with fever and elevated white blood cell count (leukocytosis) or low white blood cell count (leukopenia) and lower-than-average temperature, and vomiting. The modern concept of sepsis is that the host's immune response to the infection causes most of the symptoms of sepsis, resulting in hemodynamic consequences and damage to organs. This immunological response causes widespread activation of acute-phase proteins, affecting the complement system and the coagulation pathways, which then cause damage to the vasculature as well as to the organs. Various neuroendocrine counter-regulatory systems are then activated as well, often compounding the problem. Even with immediate and aggressive treatment, this may progress to multiple organ dysfunction syndrome and eventually death.

›DETAILED DESCRIPTION · 3 of 12

“Subject” and “patient” as used herein interchangeably refers to any vertebrate, including, but not limited to, a mammal (e.g., cow, pig, camel, llama, horse, goat, rabbit, sheep, hamsters, guinea pig, cat, dog, rat, and mouse, a non-human primate (for example, a monkey, such as a cynomolgous or rhesus monkey, chimpanzee, etc.) and a human). In some embodiments, the subject may be a human or a non-human. The subject or patient may be undergoing other forms of treatment.

As used herein, “treatment,” “therapy” and/or “therapy regimen” refer to the clinical intervention made in response to a disease, disorder or physiological condition manifested by a patient or to which a patient may be susceptible. The aim of treatment includes the alleviation or prevention of symptoms, slowing or stopping the progression or worsening of a disease, disorder, or condition and/or the remission of the disease, disorder or condition. In certain embodiments, the treatment comprises anti-bacterial therapy, such as the administration of antibiotics.

The term “effective amount” or “therapeutically effective amount” refers to an amount sufficient to effect beneficial or desirable biological and/or clinical results.

2. Factors and Biomarkers for Bacterial Infection

One aspect of the present disclosure provides biomarkers useful for the identification and/or classification of a bacterial infection. In one embodiment, the present disclosure provides biomarkers that are differentially expressed, such as upregulated, down-regulated, or disregulated in a bacterial infection, as compared to normal populations who do not have the condition, such a bacterial infection.

In some embodiments, the bacterial infection comprises a gram-positive bacteria, such as S. aureus . In those embodiments where the bacterial infection comprises S. aureus , the biomarker is selected from the group consisting of the biomarkers provided in Tables 3-17, and combinations thereof. In other embodiments, the bacterial infection comprises a gram-negative bacteria, such as E. coli . In those embodiments where the bacterial infection comprises E. coli , the biomarker is selected from the group consisting of the biomarkers provided in Tables 3-17, and combinations thereof.

In some embodiments, the biomarkers are selected from one or more biomarkers that are up-regulated, down-regulated or over-expressed in a subject suffering from a bacterial infection.

In some specific embodiments, the biomarkers are selected from one or more biomarkers up-regulated, down-regulated or over-expressed more than 50-fold, 40-fold, 30-fold, 20-fold, 15-fold, 10-fold, 9-fold, 8-fold, 7-fold, 6-fold, 5-fold, 4-fold, 3-fold, 2-fold, or 1-fold in a subject suffering from a bacterial infection, when compared to a control. In some embodiments, the biomarker comprises one or more biomarkers found in Tables 3-17, wherein the up-regulation, down-regulating or over-expression of one or more of the biomarker in the subject's biological sample, when compared to a control, indicates that the subject is suffering from a bacterial infection comprising S. aureus . In other embodiments, the biomarker comprises one or more biomarkers found in Tables 3-17, wherein the up-regulation, down-regulation, or over-expression of one or more of the biomarkers indicates the subject is suffering from a bacterial infection comprising E. coli.

In some embodiments, at least about one of the differentially expressed biomarkers may have an increased expression level compared to the control. In some embodiments, at least about one of the differentially expressed biomarkers may have a decreased expression level compared to the control. In some embodiments, at least about one of the differentially expressed biomarkers may have an increased expression level compared to the control and at least about one of the differentially expressed biomarkers may have a decreased expression level compared to the control.

3. Methods Using Biomarkers of the Present Disclosure

The present disclosure describes how different hosts respond differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. Bayesian sparse factor modeling and penalized binary regression were used to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli -infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances the understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

A series of genes or biomarkers may be selected from Tables 3-17 and optimized for diagnosis. The number of genes may be at least 1 gene, at least 5 genes, at least 10 genes, at least 25 genes, at least 30 genes, at least 35 genes, at least 40 genes, at least 45 genes, at least 50 genes, at least 55 genes, at least 60 genes, at least 65 genes, at least 70 genes, at least 75 genes, at least 80 genes, at least 85 genes, at least 90 genes, at least 95 genes, at least 100 genes, at least 125 genes, at least 150 genes, at least 175 genes, at least 200 gene, or at least 250 genes selected from Tables 3-17. RNA probes may be developed for the selected genes. A patient sample may be obtained and examined. For example, RNA may be examined after extraction from the sample or directed from the sample without extraction. The RNA may be measured by PCR or another RNA detection platform. The RNA expression may be measure and compared to control level for these selected genes. An algorithm may be used to produce a probability or score. Cut-off values or scores may be established and used to make a definitive diagnosis. For example, if the patient's gene expression levels are above the cut-off value or score, the patient is diagnosed as having infection. After the diagnosis is made, the subject may be treated for the infection.

›DETAILED DESCRIPTION · 4 of 12

In one embodiment, the present disclosure provides a method for identifying and/or classifying a bacterial infection in a subject comprising, consisting of, or consisting essentially of:

(a) determining a biomarker expression profile (expression level) in a biological sample from the subject;

(b) characterizing the subject's biomarker profile; and

(c) comparing the subject's biomarker profile with the biomarker profile of a control from subjects not suffering from a bacterial infection (e.g., a healthy subject); and

(d) administering an appropriate ant-bacterial therapy if one or more of the biomarkers are upregulated, down-regulated or overexpressed.

In one embodiment, the method further includes obtaining the biological sample from the subject. In one embodiment, the identification and/or classification of a condition such as a bacterial infection can be determined by comparing the subjects biomarker profile to a reference biomarker profile, such as one that corresponds to biological samples obtained from a normal population (e.g., healthy population) that do not have a condition such as a bacterial infection, or that corresponds to biological samples obtained from a population that have a condition such as a bacterial infection. Optionally, the reference profile comprises multiple biomarker expression profiles, with each corresponding to a type of a condition such as a bacterial infection with a gram-negative or gram-positive bacteria.

In some embodiments, the present disclosure provides methods for identifying and/or classifying a condition such as bacterial infection by characterizing a biomarker found in Tables 3-17.

4. Methods of Developing a Diagnostic Assay

The diagnostic assay may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from human factor 20 (56 genes) and/or human factor 74 (137 genes), which are shown in Tables 8 and 10, respectively. The factor may comprise about 1 to about 193 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, or at least about 193 of the biomarkers listed in Tables 8 and 10. The relationship may have an AUC value of about 0.9500 to about 0.9999. For example, the AUC value may be at least about 0.9500, at least about 0.9550, at least about 0.9600, at least about 0.9650, at least about 0.9750, at least about 0.9800, at least about 0.9850, at least about 0.9860, at least about 0.9870, at least about 0.9880, at least about 0.9885, at least about 0.9890, at least about 0.9898, at least about 0.9900, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9898.

The diagnostic assay may distinguish a subject that has a Staphylococcus aureus blood stream infection from a subject that has an Escherichia coli blood stream infection. The biomarkers may be selected from human factor 20 (56 genes) and/or human factor 74 (137 genes), which are shown in Tables 8 and 10, respectively. The factor may comprise about 1 to about 193 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, or at least about 193 of the biomarkers listed in Tables 8 and 10. The relationship may have an AUC value of about 0.8100 to about 0.9999. For example, the AUC value may be at least about 0.8100, at least about 0.8150, at least about 0.8200, at least about 0.8250, at least about 0.8300, at least about 0.8350, at least about 0.8360, at least about 0.8370, at least about 0.8380, at least about 0.8400, at least about 0.8500, at least about 0.8550, at least about 0.8600, at least about 0.8650, at least about 0.8700, at least about 0.8750, at least about 0.8800, at least about 0.8850, at least about 0.8900, at least about 0.8950, at least about 0.9000, at least about 0.9100, at least about 0.9200, at least about 0.9300, at least about 0.9400, at least about 0.9500, at least about 0.9600, at least about 0.9700, at least about 0.9800, at least about 0.9900, or at least about 0.9999. The relationship may have an AUC value of at least 0.8372.

›DETAILED DESCRIPTION · 5 of 12

The diagnostic assay may distinguish a subject that has an Escherichia coli blood stream infection from a healthy subject. The biomarkers may be selected from human factor 20 (56 genes) and/or human factor 74 (137 genes), which are shown in Tables 8 and 10, respectively. The factor may comprise about 1 to about 193 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least bout 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, or at least about 193 of the biomarkers listed in Tables 8 and 10. The relationship may have an AUC value of about 0.9000 to about 0.9999. For example, the AUC value may be at least about 0.9000, at least about 0.9050, at least about 0.9100, at least about 0.9150, at least about 0.9200, at least about 0.9210, at least about 0.9220, at least about 0.9230, at least about 0.9240, at least about 0.9250, at least about 0.9260, at least about 0.9270, at least about 0.9280, at least about 0.9300, at least about 0.9350, at least about 0.9400, at least about 0.9500, at least about 0.9600, at least about 0.9700, at least about 0.9800, at least about 0.9900, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9229.

The diagnostic assay may distinguish a subject that has a gram positive blood stream infection from a subject that has a gram negative blood stream infection. The biomarkers may be selected from human factor 40 (26 genes), as shown in Table 9. The factor may comprise about 1 to about 26 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 11, at least about 12, at least about 13, at least about 14, at least about 15, at least about 16, at least about 17, at least about 18, at least about 19, at least about 20, at least about 21, at least about 22, at least about 23, at least about 24, at least about 25, or at least about 26 of the biomarkers listed in Table 9. The relationship may have an AUC value of about 0.8100 to about 0.9999. For example, the AUC value may be 0.8100, at least about 0.8150, at least about 0.8200, at least about 0.8250, at least about 0.8300, at least about 0.8350, at least about 0.8400, at least about 0.8450, at least about 0.8480, at least about 0.8490, at least about 0.8500, at least about 0.8510, at least about 0.8520, at least about 0.8550, at least about 0.8600, at least about 0.8650, at least about 0.8700, at least about 0.8750, at least about 0.8800, at least about 0.8850, at least about 0.8900, at least about 0.8950, at least about 0.9000, at least about 0.9100, at least about 0.9200, at least about 0.9300, at least about 0.9400, at least about 0.9500, at least about 0.9600, at least about 0.9700, at least about 0.9800, at least about 0.9900, or at least about 0.9999. The relationship may have an AUC value of at least about 0.8503.

The diagnostic assay may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from human factor 4 (349 genes), as shown in Table 7. The factor may comprise about 1 to about 349 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, at least about 195, at least about 200, at least about 205, at least about 210, at least about 215, at least about 220, at least about 225, at least about 230, at least about 235, at least about 240, at least about 245, at least about 250, at least about 255, at least about 260, at least about 265, at least about 270, at least about 275, at least about 280, at least about 285, at least about 290, at least about 295, at least about 300, at least about 305, at least about 310, at least about 315, at least about 320, at least about 325, at least about 330, at least about 335, at least about 340, at least about 345, at least about 349 of the biomarkers listed in Table 7. The relationship may have an AUC value of about 0.9000 to about 0.9999. For example, the AUC value may be at least about 0.9000, at least about 0.9050, at least about 0.9100, at least about 0.9150, at least about 0.9200, at least about 0.9210, at least about 0.9215, at least about 0.9220, at least about 0.9230, at least about 0.9240, at least about 0.9250, at least about 0.9260, at least about 0.9270, at least about 0.9280, at least about 0.9300, at least about 0.9350, at least about 0.9400, at least about 0.9500, at least about 0.9600, at least about 0.9700, at least about 0.9800, at least about 0.9900, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9217.

›DETAILED DESCRIPTION · 6 of 12

The diagnostic assay may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from mouse factors 7, 15, and/or 26, of which the top 200 of each factor are shown in Tables 3, 4, and 6. The factor may comprise about 1 to about 250 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, at least about 195, at least about 200, at least about 205, at least about 210, at least about 215, at least about 220, at least about 225, at least about 230, at least about 235, at least about 240, at least about 245, or at least about 250 of the biomarkers listed in Tables 3, 4, and 6. The relationship may have an AUC value of about 0.9200 to about 0.9999. For example, the AUC value may be at least about 0.9200, at least about 0.9250, at least about 0.9300, at least about 0.9350, at least about 0.9400, at least about 0.9450, at least about 0.9500, at least about 0.9510, at least about 0.9520, at least about 0.9530, at least about 0.9540, at least about 0.9550, at least about 0.9600, at least about 0.9650, at least about 0.9700, at least about 0.9750, at least about 0.9800, at least about 0.9850, at least about 0.9900, at least about 0.9950, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9522.

The diagnostic assay may distinguish a subject that has a Staphylococcus aureus blood stream infection from a healthy subject. The biomarkers may be selected from mouse factors 7, 15, 23, and/or 26, of which the top 200 of each factor are shown in Tables 3, 4, 5, and 6. The factor may comprise about 1 to about 250 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, at least about 195, at least about 200, at least about 205, at least about 210, at least about 215, at least about 220, at least about 225, at least about 230, at least about 235, at least about 240, at least about 245, or at least about 250 of the biomarkers listed in Tables 3, 4, 5, and 6. The relationship may have an AUC value of about 0.9500 to about 0.9999. For example, the AUC value may be at least about 0.9500, at least about 0.9550, at least about 0.9600, at least about 0.9650, at least about 0.9700, at least about 0.9750, at least about 0.9800, at least about 0.9850, at least about 0.9900, at least about 0.9910, at least about 0.9920, at least about 0.9930, at least about 0.9940, at least about 0.9950, at least about 0.9960, at least about 0.9970, at least about 0.9980, at least about 0.9990, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9964.

The diagnostic assay may distinguish a subject that has a Staphylococcus aureus blood stream infection from a subject that has an Escherichia coli blood stream infection. The biomarkers may be selected from mouse factors 7, 15, 23, and/or 26, of which the top 200 of each factor are shown in Tables 3, 4, 5, and 6. The factor may comprise about 1 to about 250 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, at least about 195, at least about 200, at least about 205, at least about 210, at least about 215, at least about 220, at least about 225, at least about 230, at least about 235, at least about 240, at least about 245, or at least about 250 of the biomarkers listed in Tables 3, 4, 5, and 6. The relationship may have an AUC value of about 0.9500 to about 0.9999. For example, the AUC value may be at least about 0.9500, at least about 0.9550, at least about 0.9600, at least about 0.9650, 0.9700, at least about 0.9750, at least about 0.9800, at least about 0.9850, at least about 0.9900, at least about 0.9910, at least about 0.9920, at least about 0.9930, at least about 0.9940, at least about 0.9950, at least about 0.9960, at least about 0.9970, at least about 0.9980, at least about 0.9990, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9935.

›DETAILED DESCRIPTION · 7 of 12

The diagnostic assay may distinguish a subject that has an Escherichia coli blood stream infection from a healthy subject. The biomarkers may be selected from mouse factors 7, 15, 23, and/or 26, of which the top 200 of each factor are shown in Tables 3, 4, 5, and 6. The factor may comprise about 1 to about 250 biomarkers. For example, the factor may comprise at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, at least about 105, at least about 110, at least about 115, at least about 120, at least about 125, at least about 130, at least about 135, at least about 140, at least about 145, at least about 150, at least about 155, at least about 160, at least about 165, at least about 170, at least about 175, at least about 180, at least about 185, at least about 190, at least about 195, at least about 200, at least about 205, at least about 210, at least about 215, at least about 220, at least about 225, at least about 230, at least about 235, at least about 240, at least about 245, or at least about 250 of the biomarkers listed in Tables 3, 4, 5, and 6. The relationship may have an AUC value of about 0.9200 to about 0.9999. For example, the AUC value may be at least about 0.9200, at least about 0.9250, at least about 0.9300, at least about 0.9350, at least about 0.9400, at least about 0.9440, at least about 0.9450, at least about 0.9460, at least about 0.9470, at least about 0.9480, at least about 0.9490, at least about 0.9500, at least about 0.9510, at least about 0.9520, at least about 0.9530, at least about 0.9540, at least about 0.9550, at least about 0.9600, at least about 0.9650, at least about 0.9700, at least about 0.9750, at least about 0.9800, at least about 0.9850, at least about 0.9900, at least about 0.9950, or at least about 0.9999. The relationship may have an AUC value of at least about 0.9484.

5. Methods of Identifying and Treating a Bacterial Infection

The present invention is directed to method of distinguishing and treating Staphylococcus aureus blood stream infection (BSI) from Escherichia coli BSI in a subject suspected of having a bacterial infection. The method comprises determining gene expression levels of at least about two biomarkers in a peripheral blood cell sample of the subject, wherein the biomarkers are selected from any one of Tables 8 and 10 or Tables 3-6; comparing the gene expression levels of the at least about two biomarkers to standard gene expression levels wherein the standard gene expression levels correspond to the gene expression levels for the biomarkers in a control; identifying the subject as having a S. aureus BSI if the gene expression levels of the biomarkers are different than the standard gene expression levels and identifying the subject as having an E. coli BSI if the gene expression levels of the biomarkers are the same as the standard gene expression levels; and administering an effective amount of appropriate antibacterial therapy to treat the subject identified as having a S. aureus BSI or E. coli . The control may be a subject having an E. coli BSI. The present invention is directed to method of distinguishing and treating a gram positive bacterial infection from a gram negative bacterial infection in a subject suspected of having a bacterial infection. The method comprises determining gene expression levels of at least about two biomarkers in a peripheral blood cell sample of the subject, wherein the biomarkers are selected from Table 9; comparing the gene expression levels of the at least about two biomarkers to standard gene expression levels wherein the standard gene expression levels correspond to the gene expression levels for the biomarkers in a control; identifying the subject as having a gram positive bacterial infection if the gene expression levels of the biomarkers are different than the standard gene expression levels in a control; and administering an effective amount of appropriate antibacterial therapy to treat the subject identified as a gram positive bacterial infection. The gram positive bacterial infection may be Staphylococcus aureus . The control may be a subject having a gram negative bacterial infection. The gram negative bacterial infection may be Escherichia coli.

›DETAILED DESCRIPTION · 8 of 12

6. Methods of Determining Efficacy of Treatment Using Biomarkers

Another aspect of the present disclosure provides for methods for monitoring the treatment of conditions such as a bacterial infection. In one embodiment, the method comprises a method of determining the efficacy of treatment regime (e.g., anti-bacterial therapy) in a subject comprising, consisting of, or consisting essentially of: (a) determining a baseline value for the expression of one or more biomarkers associated with bacterial infection; (b) administering to the subject an anti-bacterial therapy regime; and (c) redetermining the expression levels of one or more biomarkers in the subject, wherein observed changes in one or more or the biomarker expression levels as compared to a control is correlated with the efficacy of the therapeutic regimen.

In instances where a change in the biomarker expression is not seen, a change in treatment may be warranted. Such a determination, and the different type of treatment to employ, can be made readily determined by one skilled in the art.

7. Probabilitya And Threshold

A probability score could be produced using various methods, such as those methods using a ENet score as described in Chen et al., IEEE Transactions on Biomedical Engineering 58: 468-479 (2011). For example, one method of determining the probability score is the following: Let X be a p×n matrix of observed data in the real number domain, where each column corresponds to one of n samples, quantifying the associated gene-expression values for all p genes under investigation. To address the “large p, small n” problem in an unsupervised setting the data are assumed to satisfy X=AS+E, where A is a p×r matrix, S is r×n and E is p×n. The columns of A represent the factor “loadings” and each column of S represents factor “scores” for the associated sample (column of X); the rows of S are called factors. E is the usual error matrix.

Thresholds may be defined based on how the classifier performs using the final testing platform that would be implemented clinically. This will require a balance of sensitivity, specificity, and input from end-users. An alternative to a threshold is determining the probability that the patient in question has a S. aureus infection.

8. Treatment

Treatment may include being administered oxygen, either by a tube that is placed near the nose or through a clear plastic mask. Depending on the results of the tests, the physician may order medications. These medications may include antibiotics given intravenously (given directly into the vein). Initially, the antibiotics may be those that kill many different bacteria because the exact kind of infection the patient has is not known. Once the blood culture results show the identity of the bacteria, the doctor may select a different antibiotic that kills the specific organism responsible for the infection. The doctor may also order IV salt solution saline and medications to increase the blood pressure it is too low. The patient may be admitted to the hospital at least until the blood culture results are known. If the patient is very ill and with low blood pressure, the doctor may admit the patient to the intensive care unit (ICU) and may consult specialist doctors to help in the management of the illness. If results show an infection in the abdomen, either drainage of the infection by the placement of tubes or surgery may be necessary. The physician will administer anti-autoimmune drugs or biologics as well to modify the body's aggressive immune response to microbes, which leads to sepsis.

Treatment for sepsis or severe sepsis/septic shock may further include early goal directed therapy, antibiotic, a vasopressor, such as norepinephrine and dopamine, a steroid, such as corticosteroids, insulin, painkillers, sedatives, oxygen, cerebrospinal fluid, and intravenous fluid to the subject. For application of these therapies, a central venous catheter and an arterial catheter may be used. Other hemodynamic variables (such as cardiac output, mixed venous oxygen saturation, or stroke volume variation) may also be used.

Treatment of organ dysfunction may include hemodialysis in kidney failure, mechanical ventilation in pulmonary dysfunction, transfusion of blood products, and drug and fluid therapy for circulatory failure. Ensuring adequate nutrition may further be required by enteral feeding, but if necessary by parenteral nutrition during a prolonged illness.

a. S. aureus

S. aureus bacterial infection may be treated with an antibiotic, such as penicillin and penicillinase-resistant β-lactam antibiotics, such as methicillin, dicloxacillin, nafcillin, oxacillin, and flucloxacillin, cephalosporin, gentamicin, or combinations thereof. S. aureus infection may also be treated with a combination therapy of a penicillinase-resistant penicillin or cephalosporin (in case the organism is MSSA) and clindamycin or a quinolone. Other therapies include clindamycin, trimethoprim-sulfamethoxazole (TMP-SMX), rifampin, doxycycline, or a quinolone. Combination of TMP-SMX and rifampin may also be used

›DETAILED DESCRIPTION · 9 of 12

i. MRSA

In some embodiments, the subject has MRSA and is resistant to β-lactam antibiotic, such as methicillin. MRSA is also called oxacillin-resistant S. aureus . MRSA may be treated with mupirocine or vancomycin.

b. E. coli

E. coli bacterial infection may be treated with antibiotics.

9. Composition of Matter

Another aspect of the present disclosure provides a composition of matter comprising, consisting of, or consisting essentially of: (a) a probe array for determining a biomarker level in a sample, the array comprising of a plurality of probes that hybridizes to one or more biomarkers that are associated with bacterial infection; or (b) a kit for determining a biomarker level in a sample, comprising the probe array of (a) and instructions for carrying out the determination of biomarker expression level in the sample. In certain embodiments the probe array of (a) further comprises a solid support with the plurality of probes attached thereto.

10. Sample

The present disclosure provides a method of determining the identification and/or classification of a bacterial infection on at least one sample obtained from an individual. The individual may be any mammal, but is preferably a human.

The present disclosure may involve obtaining more than one sample, such as two samples, such as three samples, four samples or more from individuals, and preferably the same individual. This allows the relative comparison of expression both as in the presence or absence of at least one biomarker between the two samples. Alternatively, a single sample may be compared against a “standardized” sample, such a sample comprising material or data from several samples, preferably also from several individuals.

11. Sample Preparation

Before analyzing the sample, it will often be desirable to perform one or more sample preparation operations upon the sample. Typically, these sample preparation operations will include such manipulations as concentration, suspension, extraction of intracellular material, e.g., nucleic acids from tissue/whole cell samples and the like, amplification of nucleic acids, fragmentation, transcription, labeling and/or extension reactions.

Any method required for the processing of a sample prior to detection by any of the methods noted herein falls within the scope of the present disclosure. These methods are typically well known by a person skilled in the art.

12. Detection

It is within the general scope of the present disclosure to provide methods for the detection of gene expression as a biomarker. An aspect of the present disclosure relates to the detection of the gene expression as described in the plots and graphs of the figures contained herein. As used herein, the term “detect” or “determine the presence of” refers to the qualitative measurement of undetectable, low, normal, or high concentrations of one or more biomarkers such as, for example, nucleic acids, ribonucleic acids, or polypeptides and other biological molecules. Detection may include 1) detection in the sense of presence versus absence of one or more biomarkers as well as 2) the registration/quantification of the level or degree of expression of one or more biomarkers, depending on the method of detection employed. The term “quantify” or “quantification” may be used interchangeable, and refer to a process of determining the quantity or abundance of a substance in a sample (e., a biomarker), whether relative or absolute. For example, quantification may be determined by methods including but not limited to, micro-array analysis, qRT-PCR, band intensity on a Northern or Western blot, or by various other methods known in the art.

The detection of one or more biomarker molecules allows for the identification and/or classification of a condition such as a bacterial infection. The classification of such conditions is of relevance both medically and scientifically and may provide important information useful for the diagnosis, prognosis and treatment of the condition. The diagnosis of a condition such as a bacterial infection is the affirmation of the presence of the condition, as is the object of the present disclosure, on the expression of at least one biomarker herein. Prognosis is the estimate or prediction of the probable outcome of a condition such as a bacterial infection and the prognosis of such is greatly facilitated by increasing the amount of information on the particular condition. The method of detection is thus a central aspect of the present disclosure.

Any method of detection falls within the general scope of the present disclosure. The detection methods may be generic for the detection of gene expression, nucleic acids, polypeptides and the like. The detection methods may be directed towards the scoring of a presence or absence of one or more biomarker molecules or may be useful in the detection of expression levels.

The detection methods can be divided into two categories herein referred to as in situ methods or screening methods. The term in situ method refers to the detection of nucleic acid and/or protein molecules in a sample wherein the structure of the sample has been preserved. This may thus be a biopsy wherein the structure of the tissue is preserved. In situ methods are generally histological i.e. microscopic in nature and include but are not limited to methods such as: in situ hybridization techniques and in situ PCR methods.

Screening methods generally employ techniques of molecular biology and most often require the preparation of the sample material in order to access the nucleic acid and/or polypeptide molecules to be detected. Screening methods include, but are not limited to methods such as: Array systems, affinity matrices, Northern blotting and PCR techniques, such as real-time quantitative RT-PCR.

13. Probe

One aspect of the present disclosure is to provide a probe which can be used for the detection of a gene, a nucleic acid and/or polypeptide molecule as defined herein. A probe as defined herein is a specific sequence of a nucleic acid and/or polypeptide used to detect nucleic acids and/or polypeptides by hybridization. For example, a nucleic acid is also here any nucleic acid, natural or synthetic such as DNA, RNA, LNA or PNA. A probe may be labeled, tagged or immobilized or otherwise modified according to the requirements of the detection method chosen. A label or a tag is an entity making it possible to identify a compound to which it is associated. It is within the scope of the present disclosure to employ probes that are labeled or tagged by any means known in the art such as but not limited to: radioactive labeling, fluorescent labeling and enzymatic labeling. Furthermore the probe, labeled or not, may be immobilized to facilitate detection according to the detection method of choice and this may be accomplished according to the preferred method of the particular detection method.

›DETAILED DESCRIPTION · 10 of 12

14. Detection Methods

Another aspect of the present disclosure regards the detection of nucleic acid and/or polypeptide molecules by any method known in the art. In the following are given examples of various detection methods that can be employed for this purpose, and the present disclosure includes all the mentioned methods, but is not limited to any of these. In some embodiments, the RNA gene expression levels may be determined.

c. In Situ Hybridization

In situ hybridization (ISH) applies and extrapolates the technology of nucleic acid and/or polypeptide hybridization to the single cell level, and, in combination with the art of cytochemistry, immunocytochemistry and immunohistochemistry, permits the maintenance of morphology and the identification of cellular markers to be maintained and identified, allows the localization of sequences to specific cells within populations, such as tissues and blood samples. ISH is a type of hybridization that uses a complementary nucleic acid to localize one or more specific nucleic acid sequences in a portion or section of tissue (in situ), or, if the tissue is small enough, in the entire tissue (whole mount ISH). DNA ISH can be used to determine the structure of chromosomes and the localization of individual genes and optionally their copy numbers. Fluorescent DNA ISH (FISH) can for example be used in medical diagnostics to assess chromosomal integrity. RNA ISH is used to assay expression and gene expression patterns in a tissue/across cells, such as the expression of miRNAs/nucleic acid molecules. Sample cells are treated to increase their permeability to allow the probe to enter the cells, the probe is added to the treated cells, allowed to hybridize at pertinent temperature, and then excess probe is washed away. A complementary probe is labeled with a radioactive, fluorescent or antigenic tag, so that the probe's location and quantity in the tissue can be determined using autoradiography, fluorescence microscopy or immunoassay, respectively. The sample may be any sample as herein described. The probe is likewise a probe according to any probe based upon the biomarkers mentioned herein.

An aspect of the present disclosure includes the method of detection by in situ hybridization as described herein.

d. In Situ PCR

In situ PCR is the PCR based amplification of the target nucleic acid sequences prior to ISH. For detection of RNA, an intracellular reverse transcription (RT) step is introduced to generate complementary DNA from RNA templates prior to in situ PCR. This enables detection of low copy RNA sequences.

Prior to in situ PCR, cells or tissue samples are fixed and permeabilized to preserve morphology and permit access of the PCR reagents to the intracellular sequences to be amplified. PCR amplification of target sequences is next performed either in intact cells held in suspension or directly in cytocentrifuge preparations or tissue sections on glass slides. In the former approach, fixed cells suspended in the PCR reaction mixture are thermally cycled using conventional thermal cyclers. After PCR the cells are cytocentrifugated onto glass slides with visualization of intracellular PCR products by ISH or immunohistochemistry. In situ PCR on glass slides is performed by overlaying the samples with the PCR mixture under a coverslip which is then sealed to prevent evaporation of the reaction mixture. Thermal cycling is achieved by placing the glass slides either directly on top of the heating block of a conventional or specially designed thermal cycler or by using thermal cycling ovens. Detection of intracellular PCR-products is achieved by one of two entirely different techniques. In indirect in situ PCR by ISH with PCR-product specific probes, or in direct in situ PCR without ISH through direct detection of labeled nucleotides (e.g. digoxigenin-11-dUTP, fluorescein-dUTP, 3H-CTP or biotin-16-dUTP) which have been incorporated into the PCR products during thermal cycling.

An embodiment of the present disclosure concerns the method of in situ PCR as mentioned herein above for the detection of nucleic acid molecules as detailed herein.

e. Microarray

A microarray is a microscopic, ordered array of nucleic acids, proteins, small molecules, cells or other substances that enables parallel analysis of complex biochemical samples. A DNA microarray consists of different nucleic acid probes, known as capture probes that are chemically attached to a solid substrate, which can be a microchip, a glass slide or a microsphere-sized bead. Microarrays can be used e.g. to measure the expression levels of large numbers of polypeptides/proteins/nucleic acids simultaneously.

Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing, or electrochemistry on microelectrode arrays.

An aspect of the present disclosure regards the use of microarrays for the expression profiling of biomarkers in conditions such as bacterial infection. For this purpose, and by way of example, RNA is extracted from a cell or tissue sample, the small RNAs (18-26-nucleotide RNAs) are size-selected from total RNA using denaturing polyacrylamide gel electrophoresis (PAGE). Then oligonucleotide linkers are attached to the 5′ and 3′ ends of the small RNAs and the resulting ligation products are used as templates for an RT-PCR reaction with 10 cycles of amplification. The sense strand PCR primer has a Cy3 fluorophore attached to its 5′ end, thereby fluorescently labeling the sense strand of the PCR product. The PCR product is denatured and then hybridized to the microarray. A PCR product, referred to as the target nucleic acid that is complementary to the corresponding RNA capture probe sequence on the array will hybridize, via base pairing, to the spot at which the capture probes are affixed. The spot will then fluoresce when excited using a microarray laser scanner. The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular biomarker, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular biomarker.

›DETAILED DESCRIPTION · 11 of 12

Several types of microarrays can be employed such as spotted oligonucleotide microarrays, pre-fabricated oligonucleotide microarrays or spotted long oligonucleotide arrays.

In spotted oligonucleotide microarrays the capture probes are oligonucleotides complementary to nucleic acid sequences. This type of array is typically hybridized with amplified.

PCR products of size-selected small RNAs from two samples to be compared that are labeled with two different fluorophores. Alternatively, total RNA containing the small RNA fraction is extracted from the abovementioned two samples and used directly without size-selection of small RNAs, and 3′ end labeled using T4 RNA ligase and short RNA linkers labeled with two different fluorophores. The samples can be mixed and hybridized to one single microarray that is then scanned, allowing the visualization of up-regulated and down-regulated biomarker genes in one go. The downside of this is that the absolute levels of gene expression cannot be observed, but the cost of the experiment is reduced by half. Alternatively, a universal reference can be used, comprising of a large set of fluorophore-labelled oligonucleotides, complementary to the array capture probes.

In pre-fabricated oligonucleotide microarrays or single-channel microarrays, the probes are designed to match the sequences of known or predicted biomarkers. There are commercially available designs that cover complete genomes from companies such as Affymetrix, or Agilent. These microarrays give estimations of the absolute value of gene expression and therefore the comparison of two conditions requires the use of two separate microarrays.

Spotted long oligonucleotide arrays are composed of 50 to 70-mer oligonucleotide capture probes, and are produced by either ink-jet or robotic printing. Short Oligonucleotide Arrays are composed of 20-25-mer oligonucleotide probes, and are produced by photolithographic synthesis (Affymetrix) or by robotic printing. More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes. Arrays can contain up to 390,000 spots, from a custom array design.

An embodiment of the present disclosure concerns the method of microarray use and analysis as described herein.

f. PCR

The terms “PCR reaction”, “PCR amplification”, “PCR”, “pre-PCR”, “Q-PCR”, “real-time quantitative PCR” and “real-time quantitative RT-PCR” are interchangeable terms used to signify use of a nucleic acid amplification system, which multiplies the target nucleic acids being detected. Examples of such systems include the polymerase chain reaction (PCR) system and the ligase chain reaction (LCR) system. Other methods recently described and known to the person of skill in the art are the nucleic acid sequence based amplification and Q Beta Replicase systems. The products formed by said amplification reaction may or may not be monitored in real time or only after the reaction as an end-point measurement.

g. Real-Time Quantitative RT-PCR

Real-time quantitative RT-PCR is a modification of polymerase chain reaction used to rapidly measure the quantity of a product of polymerase chain reaction. It is preferably done in real-time, thus it is an indirect method for quantitatively measuring starting amounts of DNA, complementary DNA or ribonucleic acid (RNA). This is commonly used for the purpose of determining whether a genetic sequence is present or not, and if it is present the number of copies in the sample. There are 3 methods which vary in difficulty and detail. Like other forms of polymerase chain reaction, the process is used to amplify DNA samples, using thermal cycling and a thermostable DNA polymerase.

The three commonly used methods of quantitative polymerase chain reaction are through agarose gel electrophoresis, the use of SYBR Green, a double stranded DNA dye, and the fluorescent reporter probe. The latter two of these three can be analysed in real-time, constituting real-time polymerase chain reaction method.

Agarose gel electrophoresis is the simplest method, but also often slow and less accurate then other methods, depending on the running of an agarose gel via electrophoresis. It cannot give results in real time. The unknown sample and a known sample are prepared with a known concentration of a similarly sized section of target DNA for amplification. Both reactions are run for the same length of time in identical conditions (preferably using the same primers, or at least primers of similar annealing temperatures). Agarose gel electrophoresis is used to separate the products of the reaction from their original DNA and spare primers. The relative quantities of the known and unknown samples are measured to determine the quantity of the unknown. This method is generally used as a simple measure of whether the probe target sequences are present or not, and rarely as ‘true’ Q-PCR.

Using SYBR Green dye is more accurate than the gel method, and gives results in real time. A DNA binding dye binds all newly synthesized double stranded (ds)DNA and an increase in fluorescence intensity is measured, thus allowing initial concentrations to be determined. However, SYBR Green will label all dsDNA including any unexpected PCR products as well as primer dimers, leading to potential complications and artifacts. The reaction is prepared as usual, with the addition of fluorescent dsDNA dye. The reaction is run, and the levels of fluorescence are monitored; the dye only fluoresces when bound to the dsDNA. With reference to a standard sample or a standard curve, the dsDNA concentration in the PCR can be determined.

The fluorescent reporter probe method is the most accurate and most reliable of the methods. It uses a sequence-specific nucleic acid based probe so as to only quantify the probe sequence and not all double stranded DNA. It is commonly carried out with DNA based probes with a fluorescent reporter and a quencher held in adjacent positions, so-called dual-labeled probes. The close proximity of the reporter to the quencher prevents its fluorescence; it is only on the breakdown of the probe that the fluorescence is detected. This process depends on the 5′ to 3′ exonuclease activity of the polymerase involved. The real-time quantitative PCR reaction is prepared with the addition of the dual-labeled probe. On denaturation of the double-stranded DNA template, the probe is able to bind to its complementary sequence in the region of interest of the template DNA (as the primers will too). When the PCR reaction mixture is heated to activate the polymerase, the polymerase starts synthesizing the complementary strand to the primed single stranded template DNA. As the polymerization continues it reaches the probe bound to its complementary sequence, which is then hydrolyzed due to the 5′-3′ exonuclease activity of the polymerase thereby separating the fluorescent reporter and the quencher molecules. This results in an increase in fluorescence, which is detected. During thermal cycling of the real-time PCR reaction, the increase in fluorescence, as released from the hydrolyzed dual-labeled probe in each PCR cycle is monitored, which allows accurate determination of the final, and so initial, quantities of DNA.

›DETAILED DESCRIPTION · 12 of 12

Any method of PCR that can determine the expression of a nucleic acid molecule as defined herein falls within the scope of the present disclosure. A preferred embodiment of the present disclosure includes the real-time quantitative RT-PCR method, based on the use of either SYBR Green dye or a dual-labeled probe for the detection and quantification of nucleic acids according to the herein described.

h. Northern Blot Analysis

An aspect of the present disclosure includes the detection of the nucleic acid molecules herein disclosed by techniques such as Northern blot analysis. Many variations of the protocol exist.

The following examples are offered by way of illustration and not by way of limitation.

The present invention has multiple aspects, illustrated by the following non-limiting examples.

15. Examples

The foregoing may be better understood by reference to the following examples, which are presented for purposes of illustration and are not intended to limit the scope of the invention.

›Examples17

›EXAMPLE 1

Materials and Methods

Preparation of Bacterial Cells. One methicillin-susceptible S. aureus (Sanger 476) and three methicillin-resistant S. aureus genetic backgrounds (USA100, USA300, and MW2) were used. Overnight S. aureus cultures were inoculated into fresh tryptic soy broth and incubated aerobically at 30° C. to log-phase growth (optical density 600 nm of ˜1.0). Cells were harvested by centrifugation, rinsed, and resuspended in phosphate-buffered saline (PBS). E. coli O 18:K1:H7 was grown at 30° C. overnight in Luria-Bertani broth. Cultures were then diluted with fresh medium and grown for an additional 1 to 2 hours. Upon reaching log phase, cells were treated as described for S. aureus.

Human Subjects. Subjects were enrolled at Duke University Medical Center (DUMC; Durham, N.C.), Durham VAMC (Durham, N.C.), UNC Hospitals (Chapel Hill, N.C.), and Henry Ford Hospital (Detroit, Mich.) as part of a prospective, NIH-sponsored study to develop novel diagnostic tests for severe sepsis and community acquired pneumonia (ClinicalTrials.gov NCT00258869). Enrolled patients had a known or suspected infection and exhibited two or more Systemic Inflammatory Response Syndrome criteria. Patients were excluded if they had an imminently terminal co-morbid condition, advanced AIDS (CD4 count, 50), were being appropriately treated with an antibiotic pre-enrollment, or were enrolled in another clinical trial. Blood was drawn for microarray analysis on the day of hospital presentation with the exception of two subjects (S19 and S29). In these latter two cases, blood was not available for microarray preparation from that time point. However, blood drawn 24 hours into the hospitalization was available and so was used. Subjects in the current report had culture-confirmed monomicrobial BSI due to S. aureus (n=32; median age 58 years; range 24-91) or E. coli (n=19; median age 58; range 25-91). Uninfected controls (n=43; median age 30 years; range 23-59) were enrolled at DUMC as part of a study on the effect of aspirin on platelet function among healthy volunteers. Subjects were recruited through advertisements posted on the Duke campus. Blood used to derive gene expression data in these healthy controls was drawn prior to aspirin challenge.

Murine Sepsis Experiments. Except where noted, mice were purchased from The Jackson Laboratory (Bar Harbor, Me.) and allowed to acclimate for 7 days. All experiments were performed on 6-8 week old mice. For the murine S. aureus classifier, seven inbred mouse strains (3 mice/strain: 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, C57BL/6J, C3H/HeJ, and NOD/LtJ) were IP inoculated with 10 7 CFU/g of S. aureus Sanger476, euthanized at 2 h after injection, and bled. This was repeated using the four different S. aureus genetic backgrounds (USA100, USA300, MW2, and Sanger476) in A/J mice (n=3 per S. aureus background). For time series experiments, both A/J and C57BL/6J mouse strains were IP inoculated with S. aureus Sanger476 as above, and sacrificed at 2, 4, 6, and 12 h after injection (n=5 per mouse strain at each time point). For survival experiments, mice were monitored twice daily after injection and culled upon reaching a moribund state. Animal sacrifice was carried out by carbon dioxide inhalation. Blood was collected by intracardiac puncture and stored in RNAlater at −70° C. for microarray experiments.

The murine E. coli infection model was carried out as described above except a smaller inoculum (6×10 4 CFU/g) was used. Furthermore, the time at which animals were sickest but still alive was 24 hours for E. coli inoculation, which is later than for S. aureus . Consequently, A/J and C57BL/6J mice inoculated with E. coli were sacrificed 24 h after challenge (n=5 per mouse strain). Control mice were not injected.

Outbred CD-1 mice were purchased from Charles River Laboratories (Wilmington, Mass.) to validate the murine S. aureus classifier. CD-1 mice were IP inoculated with 10 7 CFU/g of S. aureus (USA300 and Sanger 476) and 6×10 4 CFU/g of E. coli . Animals including controls were sacrificed at 2 and 24 h postinfection (n=10 mice per pathogen at each time point). Blood was collected and stored as described for the derivation cohort.

›EXAMPLE 2

Microarray Preparation

Total RNA was extracted from mouse blood using the Mouse RiboPure Blood RNA kit (Ambion, Austin, Tex.) according to the manufacturer's instructions. Globin mRNA was removed from whole blood RNA using the Globinclear kit (Ambion, Austin, Tex.). All samples passed the quality criteria of the Agilent Bioanalyzer and were used for microarray analysis. Since the total RNA yield of many samples was low, one round of linear amplification was performed for all samples using the MessageAmp Premier kit (Ambion, Austin, Tex.). RNA integrity numbers were calculated for all samples and found to be within tolerance limits. Microarrays were normalized using Robust Multichip Average (RMA). Affymetrix GeneChip Mouse Genome 430 2.0 Arrays were used (Santa Clara, Calif.). Biotin-labeled cDNA was hybridized to the arrays for 16 hours at 45° C. according to the manufacturer's instructions. Arrays were then washed and labeled with streptavidin-phycoerythrin (strep-PE), and the signal was amplified using biotinylated antistreptavidin followed by another round of staining with strep-PE. These steps were performed on the Affymetrix fluidics station according to the recommended protocol. Amplification and microarray hybridization were performed at the Duke University Microarray Core. Labeled gene chips were scanned using an Affymetrix Genechip Scanner 7G (Santa Clara, Calif.). This array contains 45,101 probe sets to analyze the expression level of over 39,000 transcripts and variants from over 34,000 mouse genes.

Human microarrays were prepared by first extracting total RNA from human blood using the PAXgene Blood RNA Kit (Qiagen, Valencia, Calif.) according to the manufacturer's recommended protocol including DNase treatment. Following isolation, RNA quantity was determined via a Nanodrop UV-Vis Spectrophotometer (Thermo Fisher Scientific, Pittsburgh, Pa.) and quality via capillary electrophoresis using the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, Calif.). RNA quantity and quality was assessed using the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, Calif.). RNA integrity numbers were calculated for all samples and found to be within tolerance limits. Microarrays were normalized using RMA. Hybridization and microarray data collection was then performed at Expression Analysis (Durham, N.C.) using the GeneChip® Human Genome U133A 2.0 Array (Affymetrix, Santa Clara, Calif.) according to the “Affymetrix Technical Manual.”

Target was prepared and hybridized according to the “Affymetrix Technical Manual”. A set of four peptide nucleic acid (PNA) oligomers (Applied Biosystems, Foster City, Calif.) with sequences complimentary to globin mRNA were added to 2.5 ug of total RNA to reduce globin RNA transcription, then converted into cDNA using Reverse Transcriptase (Invitrogen) and a modified oligo(dT)24 primer that contains T7 promoter sequences (GenSet). After first strand synthesis, residual RNA was degraded by the addition of RNaseH and a double-stranded cDNA molecule was generated using DNA Polymerase I and DNA Ligase. The cDNA was then purified and concentrated using a phenol:chloroform extraction followed by ethanol precipitation. The cDNA products were incubated with T7 RNA Polymerase and biotinylated ribonucleotides using an In vitroTranscription kit (Affymetrix). The resultant cRNA product was purified using an RNeasy column (Qiagen) and quantified with a spectrophotometer. The cRNA target (20 ug) was incubated at 94° C. for 35 minutes in fragmentation buffer (Tris, MgOAc, KOAc). The fragmented cRNA was diluted in hybridization buffer (MES, NaCl, EDTA, Tween 20, Herring Sperm DNA, Acetylated BSA) containing biotin-labeled OligoB2 and Eukaryotic Hybridization Controls (Affymetrix). The hybridization cocktail was denatured at 99° C. for 5 minutes, incubated at 45° C. for 5 minutes and then injected into a GeneChip cartridge. The GeneChip array was incubated at 42° C. for at least 16 hours in a rotating oven at 60 rpm. GeneChips were washed with a series of nonstringent (25° C.) and stringent (50° C.) solutions variable amounts of MES, Tween20 and SSPE. The microarrays were then stained with Streptavidin Phycoerythrin and the fluorescent signal was amplified using a biotinylated antibody solution. Fluorescent images were detected in a GeneChip® Scanner 3000 and expression data was extracted using the GeneChip Operating System v 1.1 (Affymetrix). All GeneChips were scaled to a median intensity setting of 500.

Fluorescent images were detected in a GeneChip Scanner 3000 and expression data was extracted using the GeneChip Operating System v 1.1 (Affymetrix). All Gene-Chips were scaled to a median intensity setting of 500. Murine and human microarray data have been deposited in the NCBI GEO (accession # GSE33341).

›EXAMPLE 3

Deriving the Murine and Human S. aureus Classifiers

Microarray data was analyzed in two steps following the analysis strategy previously outlined and utilized. First, a Bayesian sparse factor model was fit to the expression data without regard to phenotype. Second, factors were then used as independent variables to build a penalized binary regression with variable selection model trained to identify S. aureus infection. In order to minimize issues with overfitting, batch was not included in the regression models. A Bayesian penalized regression technique was used for variable selection which allows for weighted model averaging of the resultant models, such that weights are computed from model fit on the training data. The model averaging approach incorporates uncertainty in choice of model as well as regression coefficient. This has been shown to lead to out of sample predictive accuracies that are superior to penalized maximum likelihood approaches. Assumptions for this approach are typical of probit regression including a linear response surface between predictors and the transformed latent probability variable. Genes were filtered for analysis using nonspecific filtering for genes with high mean expression and high variance across samples. Samples with a high number of outlying genes were removed during the factor analysis. Mice were batched into discrete experiments with each experiment containing the relevant controls to avoid confounding. The development and application of this methodological approach has been previously described. Using the same murine experimental data, another classifier was derived to classify methicillin resistant vs. methicillin-sensitive S. aureus infection. The methodology was otherwise the same as that described above.

A factor model was fitted on the human data independently from the mouse data. The factor model was fit to 9,109 genes after nonspecific filtering to remove unexpressed and uniformly expressed genes. Z-scores were computed independently for each gene without regard to experimental design. Subjects with absolute zscores greater than 3 in more than 5% of the genes on the array were identified as outliers and were not used to fit the factor model. The factor model was trained on the 91 samples (after removal of three outliers) from three batches of expression data, and this resulted in 79 factors. These 79 factors were then projected onto the full data set (including the three subjects removed for validation) with the goal of distinguishing S. aureus BSI from healthy controls or E. coli BSI. Leave-one-out cross-validation was utilized in order to control for overfitting of the penalized binary regression model. In order to minimize issues with overfitting, batch was not included in the regression models. Matlab (Natick, Mass., USA) scripts to perform these operations are available. Nonparametric testing was used to evaluate model performance (Wilcoxon rank sum for 2-group comparisons or Kruskal-Wallis for 3 or more-group comparisons) unless otherwise indicated.

One limitation of this approach is that the marginal significance of genes within the factor-based classifier cannot be defined. Instead, gene lists were created to identify genes with differential expression between specified groups with respect to gene-level and factor-level analyses. For 3-group comparisons ( S. aureus vs. E. coli vs. Healthy controls) one-way analysis of variance (ANOVA) was used. For pairwise comparisons, Student's t-test was used. Results were statistically significant at p<0.05 after Bonferroni correction for multiple testing. Spreadsheets of gene/factor lists are provided as supplemental material.

›EXAMPLE 4

Creating a Human Ortholog of the Murine S. aureus Classifier

Chip Comparer (available from URL:chipcomparer.genome.duke.edu/) was used to identify human orthologs for all possible mouse genes. When there were multiple orthologs, the anti-sense target probes that shared the fewest probes with other genes as identified by the probe label. Chip Comparer identified 17,600 probe sets on the Affymetrix GeneChip Human Genome U133A 2.0 Array that have orthologs in the Affymetrix GeneChip Mouse Genome 430 2.0 Array. Factor scores from the mouse factor model were estimated using this set of 17,600 genes as follows: Given a matrix of expression values, X, and a factor model X=BF+e, missing values were first replaced by mean expression levels for those genes. Step 2: Inverse regression was utilized to compute F*, to estimate the factor scores. Step 3: X was estimated by computing BF* and replaced missing values with the corresponding values from this matrix. Steps 2 and 3 were then repeated until the estimates for the missing values converged.

›EXAMPLE 5

External Validation in an Independent Cohort

To externally validate the murine and human S. aureus classifiers, publically available expression data from a pediatric cohort with S. aureus infection and healthy controls were used. Hospitalized children with invasive S. aureus infection were enrolled with sample collection occurring after microbiological confirmation. Healthy controls included children undergoing elective surgical procedures and at healthy outpatient clinic visits. This dataset includes multiple expression platforms. For the purposes of consistency, subjects with Affymetrix U133A data yielding 46 S. aureus -infected patients and 10 healthy controls were included. Given the absence of subjects with E. coli infection in the validation cohort, new murine and human S. aureus classifiers were derived that excluded animals or subjects with E. coli infection. These classifiers were derived and then projected onto the 56-sample validation cohort as described heretofore.

›EXAMPLE 6

Heat Map Generation

In order to generate heat maps of gene expression, the factors from the murine and human S. aureus classifiers were used. Probes from each factor were identified and tested for differential expression in a one-way ANOVA. Probes with significantly different levels of expression after Bonferroni correction were retained. For the murine data, there were thousands of probes (˜1000-3000, typically) meeting these criteria. Consequently, the p-values were sorted in ascending order and the 100 most significant probes from each factor were retained. Duplicate probes across the factors were removed. The human expression heat map was created in the same manner except all significant probes are presented considering there were fewer factors and genes in the human S. aureus classifier as compared to the murine classifier. Heat maps were generated using Matlab (Natick, Mass., USA).

›EXAMPLE 7

Pathway Analysis

Pathway analysis for functional annotation of genes was performed with the MetaCore tool of the GeneGO package (GeneGo, Inc., St. Joseph, Mich., USA) (available from URL:www/genego.com). P-values were assigned to pathways based on the number of genes mapping to a particular pathway relative to the total number of genes in that pathway. Statistically significant pathways were defined as a p-value <0.05 (False Discovery Rate [FDR]-adjusted) based on hypergeometric distributions.

›EXAMPLE 8

Murine Sepsis Due to S. aureus and E. coli

Clinically relevant S. aureus infections in humans typically arise from a primary focus with secondary dissemination. To mimic this process, mice were inoculated via the intraperitoneal (IP) route. Infection-susceptible and infection-resistant inbred mouse strains (A/J and C57BL/6J, respectively) were inoculated with S. aureus (Sanger476) or E. coli (018:K1:H7) (n=5 per mouse strain and bacterial species). A survival analysis was carried out to determine the optimal duration of infection for subsequent experiments ( FIG. 7A ). Based on this data, A/J and C57BL/6J mice were infected with S. aureus (sacrificed at t=0, 2, 4, 6, and 12 hours post-infection; n=10 animals/time point) or E. coli (t=0, 2, 6, 12, and 24 hours post-infection; n=10 animals/time point). The effect of infection status, bacterial pathogen, and duration of infection on global patterns of gene expression was assessed using principal component analysis (PCA) (Partek Genomics Suite) ( FIG. 7B-D ). Gene expression patterns clustered by infection status and by pathogen ( S. aureus vs. E. coli ). Animals infected with S. aureus demonstrated a time-dependent change in gene expression that first manifested at two hours, by which time bacteremia has occurred. This pattern remained stable through 12 hours, when most animals have succumbed to sepsis. E. coli -infected animals did not reveal this time-dependent progression based on the time points sampled, but had a distinctly different pattern of gene expression that was evident at 2 hours and persisted through 24 hours following infection. A heat map depicting the time-dependent nature of these gene expression changes is presented in FIG. 8 .

›EXAMPLE 9

Peripheral Blood Gene Expression Signatures Classify S. aureus -Infected from Uninfected Mice

To create a host gene expression-based classifier for S. aureus infection, mice from a variety of experimental conditions were utilized (n=187 total). Seven strains of inbred mice were challenged with 4 S. aureus genetic backgrounds via IP inoculation and sacrificed at various time points as described in Experimental Procedures. The comparator group for model derivation included 50 A/J or C57BL/6J mice inoculated with E. coli (O18:K1:H7) as well as 54 non-inoculated mice. Whole blood mRNA was used to generate microarray expression data. A list of differentially expressed genes is presented in Tables 3-17. FIG. 9 presents the number of overlapping genes in each pairwise comparison. Patterns of co-expressing genes were defined using sparse latent factor regression in an unsupervised manner (i.e. without knowledge of the source animal's infection status). Factor models are a well-known technique for describing correlation structure in high dimension, low sample size data sets. The sparse latent factor model works by collecting genes that are highly correlated into groups. Predictive models are then built from the latent factors—vectors that describe the aggregate behavior of the group. Subsequently, these factors served as independent variables in a variable selection, binary regression model to distinguish animals with and without S. aureus infection. This approach was taken in lieu of using individual gene expression changes for several reasons. A given gene with biological relevance may be differentially expressed in response to S. aureus infection but not to the degree that would meet statistical significance. Considering this altered gene expression exists amid a network of other such changes, the collective perturbations in that particular pathway would be more easily detected using factor analysis. Furthermore, changes across multiple biological pathways will be reflected across multiple factors. These can then be collectively harnessed for their diagnostic potential using a binary regression model.

Thirty factors were identified, of which 16 demonstrated a pattern of expression significantly associated with infection status (mFactors 15, 7, 23, 13, 9, 29, 28, 2, 17, 16, 21, 1, 5, 4, 26, and 19 in order of greatest significance; ANOVA; p<0.0017 for S. aureus vs. control vs. E. coli after Bonferroni correction; FIG. 10 ). These 30 factors were fitted into a penalized binary regression model, termed the “murine S. aureus classifier”. The best performing model, as defined by the model with the largest log likelihood value, included four factors (mFactors 7, 15, 23, and 26). Other models may be just as adequate, but this “top” model is referred to. Leave-one-out cross-validation was used to control overfitting and to estimate the model's performance in subgroups of experimental conditions as described below (mouse strain, S. aureus genetic background, duration of infection, and bacterial species [ S. aureus vs. E. coli ]). A schematic of the derivation and validation experiments is depicted in FIG. 1 .

The Murine Derivation Cohort includes S. aureus infection (n=83), healthy control mice (n=54), and E. coli infection (n=50). It served as a validation cohort to assess Mouse Strain Effect, S. aureus Genetic Background Effect, Time Course, and to compare S. aureus vs. E. coli and E. coli vs. Healthy. The murine S. aureus classifier was externally validated in Outbred Mice (n=30) and the CAPSOD Human Cohort. The CAPSOD Human Cohort includes S. aureus BSI (n=32), healthy volunteers (n=43), and E. coli BSI (n=19). It served as a validation cohort to compare S. aureus vs. Healthy, S. aureus vs. E. coli , and E. coli vs. Healthy. Model derivation and validation using the entire cohort of animals or humans is depicted by the blue outline and arrows. An independent classifier was generated using only subjects with S. aureus or E. coli BSI (green outline). This classifier was validated using leave one out cross validation (green arrow). The Human Pediatric Cohort (n=46 S. aureus, 10 Healthy) used for external validation does not include patients with E. coli infection. Therefore, S. aureus classifiers were generated from the murine and CAPSOD cohorts that excluded E. coli data (red outline and thick red arrow). The Human Pediatric Cohort was used to derive a Human S. aureus vs. Healthy classifier which was validated in the S. aureus -infected and Healthy populations within the murine and CAPSOD human cohorts (thin red arrow).

The ability of the murine-derived host gene expression classifier to identify S. aureus infection was tested in 7 inbred mouse strains of varying infection susceptibilities. In all 7 strains, the murine S. aureus classifier accurately differentiated S. aureus -infected from control mice (p=4.89×10 −16 ; AUC=0.9964) ( FIG. 2A ). The ability to characterize S. aureus infection persisted when A/J mice (infection-susceptible) were challenged with four different S. aureus backgrounds: USA100 (the predominant US nosocomial methicillin resistant S. aureus [MRSA] genetic background); USA300 (the predominant US community-acquired MRSA genetic background); USA400 (MW2); and Sanger 476 (a methicillin susceptible genetic background) (p=1.92×10 −10 vs. control mice; AUC=1.00) ( FIG. 2B ). Furthermore, the murine S. aureus classifier consistently discriminated S. aureus infected mice from controls at 2, 4, 6, and 12 hours post-inoculation (p=4.41×10 −16 vs. uninfected mice; AUC 1.00) ( FIG. 2C ). This time interval was selected because two hours is the earliest time point at which S. aureus can be cultured from blood; while 12 hours was the point at which animals began to die of S. aureus sepsis ( FIG. 7A ). In summary, a classifier based on murine-derived host gene expression accurately identified the presence of S. aureus infection in mice under a variety of host, pathogen, and temporal conditions.

›EXAMPLE 10

Murine S. aureus Classifier Distinguishes S. aureus Infected from E. coli -Infected Mice

Next, it was determined whether the murine S. aureus classifier could differentiate S. aureus from E. coli infection. Both the infection-susceptible A/J and infection-resistant C57BL/6J strains were infected with either S. aureus (Sanger 476) or E. coli (O18:K1:H7). Animals were sacrificed at 2, 6, and 12 hours after inoculation. The murine S. aureus classifier correctly identified 50 of 53 (94.3%) animals as either infected with S. aureus or not at 2 hours (50/53), 100% of animals at 6 hours (n=20), and 96.7% of animals at 12 hours (29/30) ( FIG. 3A ). This corresponded to an overall p-value of 7.94×10 −26 by Kruskal-Wallis test (comparing S. aureus vs. E. coli vs. Healthy controls) with an AUC of 0.9935 across all time points. Next, the murine S. aureus classifier was independently validated in outbred CD-1 mice with S. aureus infection (Sanger 476 or USA300), E. coli infection (018:K1:H7), or uninfected controls (10 animals per condition). The murine derived S. aureus model accurately classified 95% of all animals where the reference standard was the known experimental condition (38/40; p=1.47×10 −6 ; 90% sensitivity and 100% specificity; AUC=0.9775) ( FIG. 3B ).

The murine S. aureus classifier was generated to identify S. aureus infection within a population including both healthy and E. coli infected animals. However, it is possible this classifier is primarily distinguishing “sick” from “not-sick” phenotypes. In such a case, it would be expected that the classifier would still differentiate animals with E. coli infection from uninfected controls. However, this was not observed (AUC 0.5089; p=0.8785) demonstrating the specificity of this classifier for S. aureus infection. Thus, a murine derived host gene expression classifier accurately distinguished S. aureus -infected from E. coli -infected or uninfected mice across multiple host strains, pathogens, post-infection time points, and was validated in outbred mice.

Given this ability to discriminate infection due to different bacterial species, the potential for a factor based classifier was further explored to distinguish infection due to methicillin-resistant (MRSA) or methicillin-sensitive S. aureus (MSSA), which have been shown to differ in their pathogenicity and virulence. The same 30 factors described above were fitted into a penalized binary regression model with the specific aim of differentiating MRSA from MSSA infection. Leave-one-out cross-validation was used to control overfitting and to estimate the model's performance in a population of 19 MRSA-infected and 84 MSSA-infected mice ( FIG. 11 ). Despite some overlap, this classifier accurately differentiated infection due to MRSA or MSSA (AUC 0.8396; p=4.14×10 −6 ). Genes discriminating infection due to MRSA or MSSA that remained significant after adjusting for multiple tests are presented in Table 11. Table 11 shows the probes and corresponding genes that were differentially expressed (after Bonferroni correction) in mice with MRSA vs. MSSA infection.

›EXAMPLE 11

Human S. aureus Classifier

It was determined whether peripheral blood gene expression in humans could yield a classifier for S. aureus BSI. Peripheral whole blood mRNA from 32 patients with S. aureus BSI, 19 patients with E. coli BSI, and 43 healthy control subjects were used to generate microarray data (Table 1). Also presented is the average probe expression in each comparator group and the fold-change within the pairwise comparison. A list of differentially expressed genes is presented in Tables 7-10. FIG. 12 presents the number of overlapping genes in each pairwise comparison. Seventy-nine factors were defined and fitted into a linear regression model trained to identify the presence of S. aureus BSI. Although 17 factors were independently associated with S. aureus BSI ( FIG. 13 ), only two factors remained in the best performing model (hFactors 20 and 74). Similar to the murine S. aureus classifier, the human S. aureus classifier was generated blind to microbiological diagnosis in an unsupervised manner. Gender was controlled for in the model's derivation considering the predilection for female sex in E. coli BSI (Table 2). The model's performance in phenotypic subgroups was estimated using leave-one-out cross-validation. The classifier accurately differentiated those with S. aureus BSI from healthy controls (72/75 correctly classified; AUC=0.9898; p=5.41×10 −13 ) ( FIG. 4A ). The human S. aureus classifier also correctly distinguished S. aureus from E. coli BSI in 82% (42/51) of cases (AUC=0.8372; p=6.77×10 −4 ). When the human S. aureus classifier was applied to subjects with E. coli BSI vs. healthy controls, an intermediate level of discrimination (56/62 correctly classified; AUC 0.9229; p=1.38×10 −7 ) was observed. This suggests that the human classifier is partially pathogen specific since E. coli BSI could also be distinguished from healthy controls but not with the same degree of accuracy as S. aureus BSI. A heat map depicting these gene expression changes is presented in FIG. 14 .

In the human S. aureus classifier described above, it is the inclusion of healthy controls that drives the discrimination from S. aureus BSI. Considering the clinical importance of differentiating Gram-positive from Gram-negative infections, rather than sick vs. healthy, a penalized binary regression model was created with the specific aim of differentiating human S. aureus (n=32) from E. coli (n=19) BSI. In this cohort, 52 factors were identified (different from the 79 factors identified when Healthy was included) of which only hFactor 40 remained in the top performing model after controlling for gender. Using leave-one-out cross-validation ( FIG. 4B ), this model had a sensitivity of 62.5% (20/32 S. aureus BSIs correctly classified) but a specificity of 94.7% (18/19 E. coli BSIs correctly classified). This corresponds to an AUC of 0.8503 (p=3.47×10 −5 ).

›EXAMPLE 11

A Murine S. aureus Classifier Identifies S. aureus Infection in Humans

It was determined whether the murine S. aureus classifier could identify S. aureus BSI in humans. To accomplish this, the murine S. aureus classifier was projected onto human gene expression data. Specifically, Chip Comparer (available from URL:chipcomparer.genome.duke.edu/) provided a modified representation of the Affymetrix Mouse Genome 430 2.0 Array that only included orthologs of transcripts represented on the Affymetrix Human Genome U133A 2.0 Array. This resulted in a murine S. aureus classifier consisting only of genes with human orthologs (68.6% of the total array representation). This classifier was evaluated in the human cohort. To account for potential species specific variation in gene expression, predicted probabilities were plotted on a logit rather than a probabilistic scale. Using this murine S. aureus classifier, human patients with S. aureus BSI were distinguished from healthy controls (AUC=0.9484; p=4.00×10 −11 ) ( FIG. 5 ). Thus, the host response to S. aureus infection was sufficiently conserved that a predictive model generated in one species ( Mus musculus ) identified S. aureus BSI in another ( Homo sapiens ). However, the murine-derived S. aureus classifier did not differentiate between S. aureus and E. coli BSI in humans (AUC=0.5905; p=0.2883).

›EXAMPLE 11

Validation of Murine and Human Classifiers in an Independent Pediatric Population

The murine and human S. aureus classifiers were externally validated in an independent human cohort. This validation cohort consisted of pediatric patients hospitalized due to invasive S. aureus infection (n=46) and healthy controls (n=10) who had gene expression data generated on a compatible platform (U133A array) with that used in this study. This cohort did not enroll children with E. coli infections. For this reason, E. coli infection was excluded from both classifiers. New murine and human S. aureus classifiers were developed in the same manner described above but without E. coli -related expression data. This modified murine S. aureus classifier was comprised of mFactors 7, 15, and 26 but not mFactor23. The modified human S. aureus classifier only contained hFactor4. Both the murine and human S. aureus classifiers differentiated children with S. aureus infection from healthy controls in this validation cohort (murine classifier AUC=0.9522, p-value=9.03×10 −6 ( FIG. 6A ); human classifier AUC 0.9217, p-value 3.48×10 −5 ( FIG. 6B )). The converse was also true. A S. aureus classifier trained on this independent pediatric cohort accurately discriminated S. aureus infection from healthy controls in the CAPSOD human cohort (70/75 correctly classified; AUC=0.9775, p-value=2.03610212) and murine cohort (123/137 correctly classified; AUC=0.9255; p=4.56×10 −17 ).

›EXAMPLE 11

S. aureus Infection Induces Similar Host Gene-Expression Responses in Mouse and Human

Pairwise comparisons were performed to identify genes with significantly different levels of expression (after Bonferroni correction). Comparisons included S. aureus infection vs. Healthy, E. coli infection vs. Healthy, and S. aureus vs. E. coli infection in mice and humans. Genes from each pairing were entered into the GeneGo pathway map database. The 50 most significant biological pathways arising from the pairwise comparisons are presented in Tables 12-16, which show the pathway analysis for the genes from pairwise comparisons in the mouse and human study. The top 50 ranked pathways from GeneGo MetaCore pathway analysis based upon p-value are shown. Pathways that are present in both the mouse and human response to the specified pathogen are indicated with an asterisk (*). The genes represented within common pathways are presented in Table 17. Table 17 shows the genes in pathways common to murine and human responses to infection. Human genes and murine genes are separately indicated.

A similar number of pathways overlapped between the murine and human responses to S. aureus (12 of the top 50) and E. coli (14 of the top 50) infection. Most of the overlapping pathways in the murine and human responses to both S. aureus and E. coli belonged to the broad category of immune response including CD28, ICOS, and the MEF2 pathway. Cytoskeletal remodeling (TGF and WNT) and apoptosis were also common to both infection types in mice and humans. Some pathways were highly significant in the S. aureus vs. Healthy comparison but not manifest in E. coli vs. Healthy such as NF-kB-associated pathways; the CD40 immune response pathway; and clathrin-coated vesicle transport. As expected, these pathways were also differentially manifest in the direct comparison of murine S. aureus and E. coli infection. No statistically significant probes were identified that distinguished human S. aureus from E. coli BSI. One probe, corresponding to the F2RL3 gene, nearly met this statistical cutoff (p-value 5.94×10 −6 with a cutoff of 2.24×10 −6 ). F2RL3 encodes proteinase-activated receptor 4. This molecule is a G-protein coupled receptor activated by thrombin and trypsin but has not previously been implicated in the sepsis or immune response. It is expressed in multiple tissues with high levels in the lung, pancreas, thyroid, testis, and small intestine but not peripheral blood or lymphoid tissues.

›EXAMPLE 12 · 1 of 2

Discussion of Examples 1-11

The current investigation contributes to this goal through three key findings. First, S. aureus infection induces conserved host gene expression responses in mice that can differentiate from E. coli -infected or uninfected mice. This discovery was consistent and robust across multiple inbred mouse strains, S. aureus genetic backgrounds, time points, and was validated in outbred mice. The validation step strengthens generalizability and is an important improvement over previous murine gene-expression based classifiers that were developed and tested in only a single inbred mouse strain including the fields of infectious diseases; cancer progression; and aging. Furthermore, this murine predictor was specific for S. aureus infection and not simply a marker of illness based on the observation that mice with E. coli sepsis could not be distinguished from healthy, uninfected animals. The murine S. aureus classifier performed equally well at multiple time points despite progression of illness lending additional support to the specificity of this classifier. Second, human-derived host gene expression signatures differentiated S. aureus BSI from E. coli BSI or uninfected controls. In contrast to the murine-based classifier, the human-based model was less pathogen specific but still provided a significant degree of differentiation between S. aureus and E. coli BSI. Finally, the responses to S. aureus infection were highly conserved at the transcriptional and pathway level. This conserved response allowed the validation of the murine- and human-derived S. aureus classifiers in an independent cohort of S. aureus -infected patients.

Previous efforts to identify a discriminatory host gene expression signature for Gram-positive versus Gram-negative infections have yielded inconsistent results. This is likely due to the observation that transcriptional data derived from complex phenotypes such as infection do not produce just one predictive gene set, but rather generate multiple gene sets associated with the phenotype in question. In the current investigation, well-established methodologies were utilized to derive predictors for S. aureus infection in both mice and humans from gene expression data. A key component of this methodology was a dimensional reduction step generating sets of co-expressed genes, termed “factors.” Multiple, individual factors differentiated between various infection states were observed although none performed universally well. For example, mFactor15 was associated with the lowest overall p-value during model generation. The AUC was 0.9587 for S. aureus vs. uninfected control mice but only 0.7942 for S. aureus vs. E. coli . In contrast, mFactor23 had an AUC of 0.9800 for S. aureus vs. E. coli but an AUC of 0.5926 for S. aureus vs. uninfected control mice. In order to generate a more robust classifier, factors were used as independent variables to generate a binary regression model. Factor models are an excellent technique for estimating correlation structure in very high dimensional data sets. This comprised the second step in generating the S. aureus predictors. It was only by including all factors to build the classifier that the model could be validated in the broadest set of conditions including different bacterial pathogens. Although redundancy among the genes in a molecular classifier is expected and is a potential limitation, such redundancy can also improve robustness for a specific phenotype as is likely to be the case in discriminating S. aureus from E. coli infection in mice. Comparisons at the individual gene level, as with pairwise comparisons, are likely to reveal differences in relatively simple biological responses. In contrast, dimension reduction with factor modeling as utilized in this study incorporates differences across multiple pathways, allowing for the detection of changes in a more complex pathobiology. Additionally, the factor model construction does not incorporate known biological pathways. This leads to gene groupings that are sometimes difficult to interpret. The advantage of the approach is the extreme dimension reduction which allows for discovery and cross-validation on very small data sets. This is one possible explanation for why the human S. aureus classifier differentiated S. aureus from E. coli whereas no genes met the threshold for differential expression after Bonferroni correction in a pairwise comparison between these two patient populations. The strength of this approach is offset by the possibility that smaller or transient changes in gene expression might be missed. Furthermore, there are likely many combinations of genes and factors that would perform similarly to that described here. This study presents findings related to the best performing classifier using the described methodologies.

The murine model has been effectively used to gain insights into the pathophysiology of sepsis in general and S. aureus in particular. Murine-derived gene expression signatures have also been successfully translated to non-infectious human conditions such as radiation exposure and breast cancer. Here, the robust performance of a murine-derived S. aureus classifier in both mice and humans was described and also offer several lines of evidence supporting a partially conserved host response to S. aureus infection in both host species. First, the murine-based predictor could differentiate human S. aureus BSI from uninfected controls. Second, the genetic pathways were highly conserved. For example, most of the relevant murine pathways were also significantly associated with S. aureus BSI in humans. Finally, the murine-based predictor was highly accurate in classifying S. aureus infection in an independent human cohort.

The data presented here also indicates that the S. aureus classifiers are not being driven by lineage-specific transcript abundance. Specifically, the total leukocyte count and cell lineage distribution (based on routine automated differential measurements) were not different between patients with S. aureus infection and E. coli infection (15.7×10 9 /L with 86.2% neutrophils vs. 14.1×10 9 /L with 85.8% neutrophils, respectively). However, the human S. aureus classifier was still able to differentiate infection due to the two pathogens. The murine S. aureus classifier was highly successful in differentiating S. aureus infection from healthy and from E. coli infection yet was unable to differentiate E. coli from healthy. This result would not be expected if transcript abundance was driving the derivation of the classifier.

›EXAMPLE 12 · 2 of 2

The overlap observed in the gene expression response to S. aureus infection in mouse and human was also consistent with published studies. NF-kB signaling pathways have been identified as a critical component of the murine response to infection, which was mirrored in the murine and human data presented here. Similar gene expression-based analyses of the human response to bacterial infection have also revealed the importance of other biological pathways including MHC class I and II antigen presentation, immunological synapse formation, TGF-b receptor signaling, TGF and WNT-dependent cytoskeleton remodeling, and T-cell receptor signaling, all of which were significantly associated with S. aureus infection in this study. Hence, mice and humans utilize many of the same or overlapping pathways in response to bacterial sepsis supporting the potential utility of murine-based diagnostics for human disease.

›EXAMPLE 13

Gene Subsets for Diagnostic Assay

The mouse factors 7, 15, 23 and 26 together classify mice infected with S. aureus as distinct from healthy mice with an area-under-the-curve (AUC or classification accuracy) of 0.996 (where 1 is perfect). In another scenario, mouse factors 7, 15, and 26 translated to their human equivalent are sufficient to distinguish between humans infected with S. aureus and those who are healthy with an AUC of 0.9484.

In order to determine the subset of genes used in a diagnostic test, the relative contribution each gene makes to the factor's classification performance will be determined. Specifically, the number of genes required to achieve greater than 90%, 95%, 97%, and 99% of the factor's classification performance will be defined. Depending on the number of genes necessary to achieve these performance levels, a more limited gene set for diagnostic test development may be used.

An overview of the steps necessary for diagnostic test development is as follows: the optimal subset of genes will be identified from the presented factors that retains classification performance (as described above). As an example, the 200 top performing genes from each murine factor are presented. mRNA-specific probes will be generated for each. Patients with known diagnoses will be tested to verify the selected gene's mRNA can be detected by PCR or some other detection platform. Target gene expression will be measured relative to internal controls. Subsequently, an algorithm will produce a score or probability of S. aureus infection. Thresholds will be defined, above and below which a diagnosis will be made. This report would then be reported to the user.

It is understood that the foregoing detailed description and accompanying examples are merely illustrative and are not to be taken as limitations upon the scope of the invention, which is defined solely by the appended claims and their equivalents.

Various changes and modifications to the disclosed embodiments will be apparent to those skilled in the art. Such changes and modifications, including without limitation those relating to the chemical structures, substituents, derivatives, intermediates, syntheses, compositions, formulations, or methods of use of the invention, may be made without departing from the spirit and scope thereof

Any patents or publications mentioned in this specification are indicative of the levels of those skilled in the art to which the invention pertains. These patents and publications are herein incorporated by reference to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference.

One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The present examples along with the methods described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses will occur to those skilled in the art which are encompassed within the spirit of the invention as defined by the scope of the claims.

›Tables in the description — 16

TABLE 2 — Characteristics of human subjects used for S. aureus classifier derivation.

	S. aureus (n = 32)	Gram-negative (n = 19)	Healthy (n = 43)
Age in years, mean (range)	58 (24-91)	58 (25-91)	30 (23-59)
Gender, n (%)
Female	6 (19)	14 (74)	21 (49)
Male	26 (81)	5 (26)	22 (51)
Race, n (%)
Black	20 (63)	9 (47)	9 (21)
White	11 (34)	10 (53)	26 (60)
Asian	0	0	6 (14)
Unknown	1 (3)	0	2 (5)
Dialysis, n (%)	12 (38)	0	0
Diabetes, n (%)	13 (41)	3 (16)	0
Immunosuppression, n (%)	2 (6)	2 (11)	0

TABLE 3 — Mouse Factor 7

PROBE ID	SYMBOL	GENE NAME
1415741_at	Tmem165	transmembrane protein 165
1415856_at	Emb	embigin
1415899_at	Junb	Jun-B oncogene
1416010_a_at	Ehd1	EH-domain containing 1
1416035_at	Hif1a	hypoxia inducible factor 1 alpha subunit
1416119_at	Txn1	thioredoxin 1
1416249_at	Nadk	NAD kinase
1416281_at	Wdr45l	Wdr45 like
1416360_at	Snx18	sorting nexin 18
1416378_at	Pnkp	polynucleotide kinase 3′-phosphatase
1416381_a_at	Prdx5	peroxiredoxin 5
1416440_at	Cd164	CD164 antigen
1416442_at	Ier2	immediate early response 2
1416522_a_at	Grcc10	gene rich cluster C10 gene
1416527_at	Rab32	RAB32 member RAS oncogene family
1416654_at	Slc31a2	solute carrier family 31 member 2
1416983_s_at	Foxo1	forkhead box O1
1417068_a_at	Ptpn1	protein tyrosine phosphatase non-receptor type 1
1417190_at	Nampt	nicotinamide phosphoribosyltransferase
1417230_at	Ralgps2	Ral GEF with PH domain and SH3 binding motif 2
1417408_at	F3	coagulation factor III
1417409_at	Jun	Jun oncogene
1417478_a_at	Ppp2r3c	protein phosphatase 2 regulatory subunit B″ gamma
1417564_at	Med7	mediator complex subunit 7
1417730_at	Ext1	exostoses (multiple) 1
1418154_at	N4bp1	NEDD4 binding protein 1
1418300_a_at	Mknk2	MAP kinase-interacting serine/threonine kinase 2
1418465_at	Ncf4	neutrophil cytosolic factor 4
1418539_a_at	Ptpre	protein tyrosine phosphatase receptor type E
1418797_at	Ms4a8a	membrane-spanning 4-domains subfamily A member 8A
1418847_at	Arg2	arginase type II
1418992_at	F10	coagulation factor X
1418993_s_at	F10	coagulation factor X
1419004_s_at	NA	NA
1419006_s_at	Peli2	pellino 2
1419178_at	Cd3g	CD3 antigen gamma polypeptide
1419180_at	Bcl9l	B-cell CLL/lymphoma 9-like
1419208_at	Map3k8	mitogen-activated protein kinase kinase kinase 8
1419627_s_at	Clec4n	C-type lectin domain family 4 member n
1419691_at	Camp	cathelicidin antimicrobial peptide
1419766_at	Sik1	salt inducible kinase 1
1420012_at	Xbp1	X-box binding protein 1
1420197_at	Gadd45b	growth arrest and DNA-damage-inducible 45 beta
1420631_a_at	Blcap	bladder cancer associated protein homolog (human)
1420867_at	Tmed2	transmembrane emp24 domain trafficking protein 2
1420868_s_at	Tmed2	transmembrane emp24 domain trafficking protein 2
1421235_s_at	Recql5	RecQ protein-like 5
1421326_at	Csf2rb	colony stimulating factor 2 receptor beta low-affinity
		(granulocyte-macrophage)
1421411_at	Pstpip2	proline-serine-threonine phosphatase-interacting protein 2
1421547_at	Cd180	CD180 antigen
1422084_at	Bmx	BMX non-receptor tyrosine kinase
1422506_a_at	Cstb	cystatin B
1422508_at	Atp6v1a	ATPase H+ transporting lysosomal V1 subunit A
1422791_at	Pafah1b2	platelet-activating factor acetylhydrolase isoform 1b subunit
		2
1423100_at	Fos	FBJ osteosarcoma oncogene
1423135_at	Thy1	thymus cell antigen 1 theta
1423213_at	Plxnc1	plexin C1
1423326_at	Entpd1	ectonucleoside triphosphate diphosphohydrolase 1
1423346_at	Degs1	degenerative spermatocyte homolog 1 ( Drosophila )
1423411_at	Rbm47	RNA binding motif protein 47
1423722_at	Vmp1	vacuole membrane protein 1
1423904_a_at	Pyr	poliovirus receptor
1423996_a_at	Il4ra	interleukin 4 receptor alpha
1424256_at	Rdh12	retinol dehydrogenase 12
1424302_at	Lilrb3	leukocyte immunoglobulin-like receptor subfamily B (with
		TM and ITIM domains) member 3
1424424_at	Slc39a1	solute carrier family 39 (zinc transporter) member 1
1424444_a_at	1600014C10Rik	RIKEN cDNA 1600014C10 gene
1424509_at	Cd177	CD177 antigen
1424996_at	Cflar	CASP8 and FADD-like apoptosis regulator
1425406_at	Clec4a2	C-type lectin domain family 4 member a2
1425587_a_at	Ptprj	protein tyrosine phosphatase receptor type J
1425674_a_at	Ssu72	Ssu72 RNA polymerase II CTD phosphatase homolog
		(yeast)
1425822_a_at	Dtx1	deltex 1 homolog ( Drosophila )
1426227_s_at	Vps37c	vacuolar protein sorting 37C (yeast)
1426370_at	Far1	fatty acyl CoA reductase 1
1426440_at	Dhrs7	dehydrogenase/reductase (SDR family) member 7
1426575_at	Sgms1	sphingomyelin synthase 1
1426600_at	Slc2a1	solute carrier family 2 (facilitated glucose transporter)
		member 1
1426680_at	Sepn1	selenoprotein N1
1426786_s_at	Dhx38	DEAH (Asp-Glu-Ala-His) box polypeptide 38
1426808_at	Lgals3	lectin galactose binding soluble 3
1427164_at	Il13ra1	interleukin 13 receptor alpha 1
1427327_at	Pilra	paired immunoglobin-like type 2 receptor alpha
1427683_at	Egr2	early growth response 2
1428094_at	Lamp2	lysosomal-associated membrane protein 2
1428191_s_at	Mettl17	methyltransferase like 17
1428381_a_at	Ppdpf	pancreatic progenitor cell differentiation and proliferation
		factor homolog (zebrafish)RIKEN cDNA 2700038C09 gene
1428466_at	Chd3	chromodomain helicase DNA binding protein 3
1428579_at	Fmnl2	formin-like 2
1428663_at	Sgms2	sphingomyelin synthase 2
1428749_at	Dmxl2	Dmx-like 2
1428750_at	Cdc42ep2	CDC42 effector protein (Rho GTPase binding) 2
1428781_at	Dmkn	dermokine
1428902_at	Chst11	carbohydrate sulfotransferase 11
1428985_at	Ints12	integrator complex subunit 12
1429352_at	Mocos	molybdenum cofactor sulfurase
1429503_at	Fam69a	family with sequence similarity 69 member A
1429527_a_at	Plscr1	phospholipid scramblase 1
1429782_at	Grcc10	gene rich cluster C10 gene
1429889_at	Faim3	Fas apoptotic inhibitory molecule 3
1430289_a_at	Wdr77	WD repeat domain 77
1431339_a_at	Efhd2	EF hand domain containing 2
1433634_at	Irf2bp2	interferon regulatory factor 2 binding protein 2
1433699_at	Tnfaip3	tumor necrosis factor alpha-induced protein 3
1433968_a_at	Megf9	multiple EGF-like-domains 9
1434015_at	Slc2a6	solute carrier family 2 (facilitated glucose transporter)
		member 6
1434025_at	NA	NA
1434310_at	Bmpr2	bone morphogenic protein receptor type II (serine/threonine
		kinase)
1434334_at	Prkd2	protein kinase D2
1434378_a_at	Mxd4	Max dimerization protein 4
1434402_at	Samd8	sterile alpha motif domain containing 8
1434418_at	Lass6	LAG1 homolog ceramide synthase 6
1434758_at	Crispld2	cysteine-rich secretory protein LCCL domain containing 2
1434774_at	Rhbdf2	rhomboid 5 homolog 2 ( Drosophila )
1434920_a_at	Evl	Ena-vasodilator stimulated phosphoprotein
1435260_at	Akt3	thymoma viral proto-oncogene 3
1435477_s_at	Fcgr2b	Fc receptor IgG low affinity IIb
1435546_a_at	1810013L24Rik	RIKEN cDNA 1810013L24 gene
1436499_at	Sgms1	sphingomyelin synthase 1
1436590_at	Ppp1r3b	protein phosphatase 1 regulatory (inhibitor) subunit 3B
1436819_at	SEPT6	septin 6
1437152_at	Mex3b	mex3 homolog B ( C. elegans )
1437313_x_at	Hmgb2	high mobility group box 2
1437421_at	6330509M05Rik	RIKEN cDNA 6330509M05 gene
1438796_at	Nr4a3	nuclear receptor subfamily 4 group A member 3
1438855_x_at	Tnfaip2	tumor necrosis factor alpha-induced protein 2
1439814_at	Atp8b4	ATPase class I type 8B member 4
1440458_at	Cmah	cytidine monophospho-N-acetylneuraminic acid
		hydroxylase
1442122_at	AI451458	expressed sequence AI451458
1444122_at	Sycp2	synaptonemal complex protein 2
1445687_at	Gm885	predicted gene 885
1447685_x_at	Ets2	E26 avian leukemia oncogene 2 3′ domain
1448123_s_at	Tgfbi	transforming growth factor beta induced
1448213_at	Anxa1	annexin A1
1448231_at	Fkbp5	FK506 binding protein 5
1448297_a_at	Tnk2	tyrosine kinase non-receptor2
1448333_at	Adprh	ADP-ribosylarginine hydrolase
1448443_at	Serpini1	serine (or cysteine) peptidase inhibitor Glade I member 1
1448462_at	Tdg	thymine DNA glycosylase
1448561_at	Ncf2	neutrophil cytosolic factor 2
1448573_a_at	Ceacam10	carcinoembryonic antigen-related cell adhesion molecule 10
1448618_at	Mvp	major vault protein
1448700_at	G0s2	G0/G1 switch gene 2
1448724_at	Cish	cytokine inducible SH2-containing protein
1448993_at	Atg3	autophagy-related 3 (yeast)
1449028_at	Rhou	ras homolog gene family member U
1449037_at	Crem	cAMP responsive element modulator
1449184_at	Pglyrp1	peptidoglycan recognition protein 1
1449310_at	Ptger2	prostaglandin E receptor 2 (subtype EP2)
1449317_at	Cflar	CASP8 and FADD-like apoptosis regulator
1449336_a_at	S1k	STE20-like kinase (yeast)
1449731_s_at	Nfkbia	nuclear factor of kappa light polypeptide gene enhancer in
		B-cells inhibitor alpha
1450081_x_at	Gpi1	glucose phosphate isomerase 1
1450214_at	Adora2b	adenosine A2b receptor
1450829_at	Tnfaip3	tumor necrosis factor alpha-induced protein 3
1451037_at	Ptpn9	protein tyrosine phosphatase non-receptor type 9
1451201_s_at	Rnh1	ribonuclease/angiogenin inhibitor 1
1451335_at	Plac8	placenta-specific 8
1451340_at	Arid5a	AT rich interactive domain 5A (MRF1-like)
1451507_at	Mef2c	myocyte enhancer factor 2C
1451537_at	Chi3l1	chitinase 3-like 1
1451775_s_at	Il13ra1	interleukin 13 receptor alpha 1
1452197_at	Smc4	structural maintenance of chromosomes 4
1452237_at	Agfg1	ArfGAP with FG repeats 1
1452301_at	Aldh3b1	aldehyde dehydrogenase 3 family member B1
1452732_at	Asprv1	aspartic peptidase retroviral-like 1
1453009_at	Cpm	carboxypeptidase M
1453851_a_at	Gadd45g	growth arrest and DNA-damage-inducible 45 gamma
1454654_at	Dirc2	disrupted in renal carcinoma 2 (human)
1454713_s_at	Hdc	histidine decarboxylase
1454799_at	Agpat9	1-acylglycerol-3-phosphate O-acyltransferase 9
1454897_at	6330509M05Rik	RIKEN cDNA 6330509M05 gene
1455009_at	Cpd	carboxypeptidase D
1455081_at	Txnl4b	thioredoxin-like 4B
1455170_at	2810001G20Rik	RIKEN cDNA 2810001G20 gene
1455197_at	Rnd1	Rho family GTPase 1
1455229_x_at	Pgs1	phosphatidylglycerophosphate synthase 1
1455332_x_at	Fcgr2b	Fc receptor IgG low affinity IIb
1455353_at	Tmcc1	transmembrane and coiled coil domains 1
1455405_at	Pstpip2	proline-serine-threonine phosphatase-interacting protein 2
1455658_at	Cggbp1	CGG triplet repeat binding protein 1
1455660_at	Csf2rb	colony stimulating factor 2 receptor beta low-affinity
		(granulocyte-macrophage)
1455665_at	Lonrf1	LON peptidase N-terminal domain and ring finger 1
1455860_at	Pigh	phosphatidylinositol glycan anchor biosynthesis class H
1456028_x_at	Marcks	myristoylated alanine rich protein kinase C substrate
1456055_x_at	Pold1	polymerase (DNA directed) delta 1 catalytic subunit
1457035_at	AI607873	expressed sequence AI607873
1457708_at	Mbd4	methyl-CpG binding domain protein 4
1457728_at	Fam129c	family with sequence similarity 129 member C
1458206_at	NA	NA
1458351_s_at	NA	NA
1459522_s_at	Gyg	glycogenin
1459903_at	Sema7a	sema domain immunoglobulin domain (Ig) and GPI
		membrane anchor (semaphorin) 7A
1460006_at	Zfhx3	zinc finger homeobox 3
1460116_s_at	Spred1	sprouty protein with EVH-1 domain 1 related sequence
1460227_at	Timp1	tissue inhibitor of metalloproteinase 1
1460251_at	Fas	Fas (TNF receptor superfamily member 6)
1460282_at	Trem1	triggering receptor expressed on myeloid cells 1
1460329_at	B4galt6	UDP-Gal:betaGlcNAc beta 14-galactosyltransferase
		polypeptide 6
1460335_at	Lysmd3	LysM putative peptidoglycan-binding domain containing 3

TABLE 4 — Mouse Factor 15

PROBE ID	SYMBOL	GENE NAME
1415741_at	Tmem165	transmembrane protein 165
1415871_at	Tgfbi	transforming growth factor beta induced
1415922_s_at	Marcksl1	MARCKS-like 1
1416010_a_at	Ehd1	EH-domain containing 1
1416298_at	Mmp9	matrix metallopeptidase 9
1416359_at	Snx18	sorting nexin 18
1416369_at	Hiatl1	hippocampus abundant transcript-like 1
1416381_a_at	Prdx5	peroxiredoxin 5
1416472_at	Syap1	synapse associated protein 1
1416522_a_at	Grcc10	gene rich cluster C10 gene
1416576_at	Socs3	suppressor of cytokine signaling 3
1416908_s_at	Tsn	translin
1417191_at	Dnajb9	DnaJ (Hsp40) homolog subfamily B
1417250_at	Rlim	ring finger protein LIM domain interacting member 9
1417266_at	Cc16	chemokine (C-C motif) ligand 6
1417288_at	Plekha2	pleckstrin homology domain-containing family A
		(phosphoinositide binding specific) member 2
1417291_at	Tnfrsf1a	tumor necrosis factor receptor superfamily member 1a
1417297_at	Itpr3	inositol 14
1417434_at	Gpd2	glycerol phosphate dehydrogenase 2 mitochondrial5-
		triphosphate receptor 3
1417457_at	Cks2	CDC28 protein kinase regulatory subunit 2
1417483_at	Nfkbiz	nuclear factor of kappa light polypeptide gene enhancer in
		B-cells inhibitor zeta
1417508_at	Rnf19a	ring finger protein 19A
1417542_at	Rps6ka2	ribosomal protein S6 kinase polypeptide 2
1417544_a_at	Flot2	flotillin 2
1417566_at	Abhd5	abhydrolase domain containing 5
1417586_at	Timeless	timeless homolog ( Drosophila )
1417588_at	Galnt3	UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-
		acetylgalactosaminyltransferase 3
1417730_at	Ext1	exostoses (multiple) 1
1418054_at	Neurod4	neurogenic differentiation 4
1418133_at	Bcl3	B-cell leukemia/lymphoma 3
1418154_at	N4bp1	NEDD4 binding protein 1
1418300_a_at	Mknk2	MAP kinase-interacting serine/threonine kinase 2
1418398_a_at	Tspan32	tetraspanin 32
1418465_at	Ncf4	neutrophil cytosolic factor 4
1418576_at	Yipf5	Yip1 domain family member 5
1418578_at	Dgka	diacylglycerol kinase alpha
1418992_at	F10	coagulation factor X
1418993_s_at	F10	coagulation factor X
1419208_at	Map3k8	mitogen-activated protein kinase kinase kinase 8
1419406_a_at	Bcl11a	B-cell CLL/lymphoma 11A (zinc finger protein)
1419432_at	Spam1	sperm adhesion molecule 1
1419998_at	NA	NA
1420197_at	Gadd45b	growth arrest and DNA-damage-inducible 45 beta
1420394_s_at	NA	NA
1420498_a_at	Dab2	disabled homolog 2 ( Drosophila )
1420631_a_at	Blcap	bladder cancer associated protein homolog (human)
1421065_at	Jak2	Janus kinase 2
1421326_at	Csf2rb	colony stimulating factor 2 receptor beta
1421479_at	Zfp318	zinc finger protein 318 low-affinity (granulocyte-
		macrophage)
1421895_at	NA	NA
1422491_a_at	Bnip2	BCL2/adenovirus E1B interacting protein 2
1422507_at	Cstb	cystatin B
1422508_at	Atp6v1a	ATPase H+ transporting
1422953_at	Fpr2	formyl peptide receptor 2 lysosomal V1 subunit A
1423053_at	Arf4	ADP-ribosylation factor 4
1423326_at	Entpd1	ectonucleoside triphosphate diphosphohydrolase 1
1423345_at	Degs1	degenerative spermatocyte homolog 1 ( Drosophila )
1423558_at	Ifngr2	interferon gamma receptor 2
1423588_at	Arpc4	actin related protein 2/3 complex subunit 4
1423596_at	Nek6	NIMA (never in mitosis gene a)-related expressed kinase 6
1423612_at	Clp1	CLP1 cleavage and polyadenylation factor I subunit
1423989_at	Tecpr1	tectonin beta-propeller repeat containing 1 homolog ( S.
		cerevisiae )
1424141_at	Hectd1	HECT domain containing 1
1424444_a_at	1600014C10Rik	RIKEN cDNA 1600014C10 gene
1424724_a_at	D16Ertd472e	DNA segment Chr 16
1424727_at	CcrS	chemokine (C-C motif) receptor 5 ERATO Doi 472
		expressed
1424779_at	Reep3	receptor accessory protein 3
1424852_at	Mef2c	myocyte enhancer factor 2C
1424990_at	Orai1	ORAI calcium release-activated calcium modulator 1
1425289_a_at	Cr2	complement receptor 2
1425485_at	Mtmr6	myotubularin related protein 6
1425486_s_at	Mtmr6	myotubularin related protein 6
1425493_at	Bmpr1a	bone morphogenetic protein receptor type 1A
1425611_a_at	Cux1	cut-like homeobox 1
1425706_a_at	Ddb2	damage specific DNA binding protein 2
1426369_at	Far1	fatty acyl CoA reductase 1
1426373_at	Ski	ski sarcoma viral oncogene homolog (avian)
1426377_at	Zfp281	zinc finger protein 281
1426390_a_at	Arf1	ADP-ribosylation factor 1
1426473_at	Dnajc9	DnaJ (Hsp40) homolog subfamily C
1426550_at	Sidt1	SID1 transmembrane family member 1 member 9
1426565_at	Igf1r	insulin-like growth factor I receptor
1426818_at	Arrdc4	arrestin domain containing 4
1426977_at	Usp47	ubiquitin specific peptidase 47
1427164_at	Il13ra1	interleukin 13 receptor alpha 1
1427532_at	Trat1	T cell receptor associated transmembrane adaptor 1
1427689_a_at	Tnip1	TNFAIP3 interacting protein 1
1428141_at	Gga2	golgi associated gamma adaptin ear containing
1428191_s_at	Mettl17	methyltransferase like 17 ARF binding protein 2
1428207_at	Bcl7a	B-cell CLL/lymphoma 7A
1428214_at	Tomm7	translocase of outer mitochondrial membrane 7 homolog
		(yeast)
1428381_a_at	Ppdpf	pancreatic progenitor cell differentiation and proliferation
		factor homolog (zebrafish)RIKEN cDNA 2700038C09
		gene
1428545_at	0610007L01Rik	RIKEN cDNA 0610007L01 gene
1428579_at	Fmnl2	formin-like 2
1428669_at	Bmyc	brain expressed myelocytomatosis oncogene
1429321_at	Rnf149	ring finger protein 149
1429400_at	ClcnS	chloride channel 5
1433864_at	Lrp12	low density lipoprotein-related protein 12
1433939_at	Aff3	AF4/FMR2 family member 3
1433943_at	NA	NA
1434054_at	NA	NA
1434123_at	Fut11	fucosyltransferase 11
1434175_s_at	Tecpr1	tectonin beta-propeller repeat containing 1
1434402_at	Samd8	sterile alpha motif domain containing 8
1434521_at	Rfx7	regulatory factor X7
1434547_at	Cpd	carboxypeptidase D
1434573_at	Traf3ip3	TRAF3 interacting protein 3
1434705_at	Ctbp2	C-terminal binding protein 2
1435260_at	Akt3	thymoma viral proto-oncogene 3
1435517_x_at	Ralb	v-ral simian leukemia viral oncogene homolog B (ras
		related)
1435793_at	Aph1b	anterior pharynx defective 1b homolog ( C. elegans )
1436511_at	BC031781	cDNA sequence BC031781
1436921_at	Atp7a	ATPase Cu++ transporting
1436999_at	Pid1	phosphotyrosine interaction domain containing 1 alpha
		polypeptide
1437152_at	Mex3b	mex3 homolog B ( C. elegans )
1437270_a_at	Clcf1	cardiotrophin-like cytokine factor 1
1437421_at	6330509M05Rik	RIKEN cDNA 6330509M05 gene
1437591_a_at	Wdr1	WD repeat domain 1
1437870_at	Slco4c1	solute carrier organic anion transporter family member 4C1
1437992_x_at	Gja1	gap junction protein alpha 1
1438044_at	1700047M11Rik	RIKEN cDNA 1700047M11 gene
1438670_at	Ptpn1	protein tyrosine phosphatase non-receptor type 1
1439160_at	Pramef8	PRAME family member 8
1439443_x_at	Tkt	transketolase
1439457_x_at	Atg12	autophagy-related 12 (yeast)
1439814_at	Atp8b4	ATPase class I
1440458_at	Cmah	cytidine monophospho-N-acetylneuraminic acid
		hydroxylase type 8B member 4
1443021_at	Mthfs	5 10-methenyltetrahydrofolate synthetase
1444459_at	NA	NA
1448204_at	Sav1	salvador homolog 1 ( Drosophila )
1448443_at	Serpini1	serine (or cysteine) peptidase inhibitor Glade I
1448561_at	Ncf2	neutrophil cytosolic factor 2 member 1
1448573_a_at	Ceacam10	carcinoembryonic antigen-related cell adhesion molecule 10
1448617_at	Cd53	CD53 antigen
1448700_at	G0s2	G0/G1 switch gene 2
1448742_at	Snai1	snail homolog 1 ( Drosophila )
1448758_at	Nrbf2	nuclear receptor binding factor 2
1448786_at	Plbd1	phospholipase B domain containing 1
1449037_at	Crem	cAMP responsive element modulator
1449324_at	Ero1l	ERO1-like ( S. cerevisiae )
1449336_a_at	Slk	STE20-like kinase (yeast)
1449677_s_at	Tmem38b	transmembrane protein 38B
1449947_s_at	Zfhx3	zinc finger homeobox 3
1450116_at	Fam48a	family with sequence similarity 48 member A
1450214_at	Adora2b	adenosine A2b receptor
1450377_at	Thbs1	thrombospondin 1
1450424_a_at	Il18bp	interleukin 18 binding protein
1450459_at	2010106G01Rik	RIKEN cDNA 2010106G01 gene
1451079_at	Adpgk	ADP-dependent glucokinase
1451486_at	Slc46a3	solute carrier family 46 member 3
1451507_at	Mef2c	myocyte enhancer factor 2C
1451767_at	Ncf1	neutrophil cytosolic factor 1
1452093_at	Tmem185b	transmembrane protein 185B
1452197_at	Smc4	structural maintenance of chromosomes 4
1452205_x_at	NA	NA
1452301_at	Aldh3b1	aldehyde dehydrogenase 3 family member B1
1452316_at	Ints12	integrator complex subunit 12
1452414_s_at	Ccdc86	coiled-coil domain containing 86
1452922_at	Ppp1r3d	protein phosphatase 1 regulatory subunit 3D
1453283_at	Pgm1	phosphoglucomutase 1
1454976_at	Sod2	superoxide dismutase 2 mitochondrial
1454984_at	Lifr	leukemia inhibitory factor receptor
1455019_x_at	Ckap4	cytoskeleton-associated protein 4
1455089_at	Gng12	guanine nucleotide binding protein (G protein) gamma 12
1455104_at	Mxd1	MAX dimerization protein 1
1455133_s_at	AI848100	expressed sequence AI848100
1455197_at	Rnd1	Rho family GTPase 1
1455660_at	Csf2rb	colony stimulating factor 2 receptor beta
1455729_at	Gnaq	guanine nucleotide binding protein alpha q polypeptide
		low-affinity (granulocyte-macrophage)
1455899_x_at	Socs3	suppressor of cytokine signaling 3
1456135_s_at	Pxn	paxillin
1456545_at	Il18rap	interleukin 18 receptor accessory protein
1456700_x_at	Marcks	myristoylated alanine rich protein kinase C substrate
1457404_at	Nfkbiz	nuclear factor of kappa light polypeptide gene enhancer in
		B-cells inhibitor zeta
1457579_at	D11Ertd717e	DNA segment Chr 11
1457644_s_at	Cxcl1	chemokine (C-X-C motif) ligand 1 ERATO Doi 717
		expressed
1457753_at	Tlr13	toll-like receptor 13
1457793_a_at	Whsc1l1	Wolf-Hirschhorn syndrome candidate 1-like 1 (human)
1457825_x_at	Tcn2	transcobalamin 2
1457976_at	2010002M12Rik	RIKEN cDNA 2010002M12 gene
1458308_at	Sbno2	strawberry notch homolog 2 ( Drosophila )
1458351_s_at	NA	NA
1458518_at	Cpeb2	cytoplasmic polyadenylation element binding protein 2
1458525_at	NA	NA
1458668_at	Tpd52	tumor protein D52
1458933_at	Slc22a15	solute carrier family 22 (organic anion/cation transporter)
		member 15
1459522_s_at	Gyg	glycogenin
1459718_x_at	Klf6	Kruppel-like factor 6
1459961_a_at	Stat3	signal transducer and activator of transcription 3
1460006_at	Zfhx3	zinc finger homeobox 3
1460033_at	A330023F24Rik	RIKEN cDNA A330023F24 gene
1460197_a_at	Steap4	STEAP family member 4
1460227_at	Timp1	tissue inhibitor of metalloproteinase 1
1460251_at	Fas	Fas (TNF receptor superfamily member 6)
1460282_at	Trem1	triggering receptor expressed on myeloid cells 1
1460338_a_at	Crlf3	cytokine receptor-like factor 3
1460458_at	Crispld2	cysteine-rich secretory protein LCCL domain containing 2
1460510_a_at	Coq10b	coenzyme Q10 homolog B ( S. cerevisiae )
1460573_at	AI848100	expressed sequence AI848100
1460598_at	Il28ra	interleukin 28 receptor alpha

TABLE 5 — Mouse Factor 23

PROBE ID	SYMBOL	GENE NAME
1415734_at	Rab7	RAB7 member RAS oncogene family
1415778_at	Morf412	mortality factor 4 like 2
1415826_at	Atp6v1h	ATPase H+ transporting lysosomal V1 subunit H
1415856_at	Emb	embigin
1415871_at	Tgfbi	transforming growth factor beta induced
1416011_x_at	Ehd1	EH-domain containing 1
1416012_at	Ehd1	EH-domain containing 1
1416067_at	Ifrd1	interferon-related developmental regulator 1
1416082_at	Rab1	RAB1 member RAS oncogene family
1416459_at	Arf2	ADP-ribosylation factor 2
1416466_at	Vapa	vesicle-associated membrane protein associated protein A
1416467_at	Ddx3x	DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 3 X-
		linked
1416496_at	Mrfap1	Morf4 family associated protein 1
1417218_at	Calhm2	calcium homeostasis modulator 2
1417268_at	Cd14	CD14 antigen
1417291_at	Tnfrsf1a	tumor necrosis factor receptor superfamily member 1a
1417392_a_at	Slc7a7	solute carrier family 7 (cationic amino acid transporter y+
		system) member 7
1417478_a_at	Ppp2r3c	protein phosphatase 2 regulatory subunit B″ gamma
1417564_at	Med7	mediator complex subunit 7
1417591_at	Ptges2	prostaglandin E synthase 2
1417597_at	Cd28	CD28 antigen
1417730_at	Ext1	exostoses (multiple) 1
1417741_at	Pygl	liver glycogen phosphorylase
1418300_a_at	Mknk2	MAP kinase-interacting serine/threonine kinase 2
1418465_at	Ncf4	neutrophil cytosolic factor 4
1418468_at	Anxa11	annexin All
1418841_s_at	Cdk11b	cyclin-dependent kinase 11B
1418992_at	F10	coagulation factor X
1418993_s_at	F10	coagulation factor X
1419091_a_at	Anxa2	annexin A2
1419180_at	Bcl91	B-cell CLL/lymphoma 9-like
1419607_at	Tnf	tumor necrosis factor
1419609_at	Ccr1	chemokine (C-C motif) receptor 1
1419722_at	K1k8	kallikrein related-peptidase 8
1420012_at	Xbp1	X-box binding protein 1
1420361_at	Slc11a1	solute carrier family 11 (proton-coupled divalent metal ion
		transporters) member 1
1420886_a_at	Xbp1	X-box binding protein 1
1420997_a_at	Gpi1	glucose phosphate isomerase 1
1421291_at	Il18rap	interleukin 18 receptor accessory protein
1421463_at	Siglece	sialic acid binding Ig-like lectin E
1421478_a_at	Zfp318	zinc finger protein 318
1421855_at	Fgl2	fibrinogen-like protein 2
1422002_at	Mxd1	MAX dimerization protein 1
1422013_at	Clec4a2	C-type lectin domain family 4 member a2
1422046_at	Itgam	integrin alpha M
1422573_at	Ampd3	adenosine monophosphate deaminase 3
1422631_at	Ahr	aryl-hydrocarbon receptor
1422791_at	Pafah1b2	platelet-activating factor acetylhydrolase isoform 1b subunit
		2
1422879_at	Sypl	synaptophysin-like protein
1422880_at	Sypl	synaptophysin-like protein
1422887_a_at	Ctbp2	C-terminal binding protein 2
1422931_at	Fosl2	fos-like antigen 2
1423053_at	Arf4	ADP-ribosylation factor 4
1423100_at	Fos	FBJ osteosarcoma oncogene
1423112_at	Ube2d3	ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog
		yeast)
1423135_at	Thy1	thymus cell antigen 1 theta
1423170_at	Taf7	TAF7 RNA polymerase II TATA box binding protein
		(TBP)-associated factor
1423326_at	Entpd1	ectonucleoside triphosphate diphosphohydrolase 1
1423361_at	Yme1l1	YME1-like 1 ( S. cerevisiae )
1423383_a_at	Osbpl9	oxysterol binding protein-like 9
1423466_at	Ccr7	chemokine (C-C motif) receptor 7
1423558_at	Ifngr2	interferon gamma receptor 2
1423596_at	Nek6	NIMA (never in mitosis gene a)-related expressed kinase 6
1423706_a_at	Pgd	phosphogluconate dehydrogenase
1423854_a_at	Rasl11b	RAS-like family 11 member B
1423903_at	Pvr	poliovirus receptor
1424027_at	Pxn	paxillin
1424441_at	Slc27a4	solute carrier family 27 (fatty acid transporter) member 4
1424942_a_at	Myc	myelocytomatosis oncogene
1425128_at	B3gnt8	UDP-GlcNAc:betaGal beta-13-N-
		acetylglucosaminyltransferase 8
1425346_at	Zfp318	zinc finger protein 318
1425347_a_at	Zfp318	zinc finger protein 318
1425485_at	Mtmr6	myotubularin related protein 6
1425486_s_at	Mtmr6	myotubularin related protein 6
1425663_at	Il1rn	interleukin 1 receptor antagonist
1425674_a_at	Ssu72	Ssu72 RNA polymerase II CTD phosphatase homolog
		(yeast)
1426112_a_at	Cd72	CD72 antigen
1426227_s_at	Vps37c	vacuolar protein sorting 37C (yeast)
1426299_at	Snx20	sorting nexin 20
1426312_at	Bre	brain and reproductive organ-expressed protein
1426377_at	Zfp281	zinc finger protein 281
1426396_at	Cd247	CD247 antigen
1426473_at	Dnajc9	DnaJ (Hsp40) homolog subfamily C member 9
1426501_a_at	Tifa	TRAF-interacting protein with forkhead-associated domain
1426554_a_at	Pgam1	phosphoglycerate mutase 1
1426565_at	Igf1r	insulin-like growth factor I receptor
1426648_at	Mapkapk2	MAP kinase-activated protein kinase 2
1426722_at	Slc38a2	solute carrier family 38 member 2
1426755_at	Ckap4	cytoskeleton-associated protein 4
1426798_a_at	Ppp1r15b	protein phosphatase 1 regulatory (inhibitor) subunit 15b
1426808_at	Lgals3	lectin galactose binding soluble 3
1426988_at	KlhdcS	kelch domain containing 5
1427102_at	Slfn4	schlafen 4
1427227_at	Gabrg1	gamma-aminobutyric acid (GABA) A receptor subunit
		gamma 1
1427314_at	Tmed7	transmembrane emp24 protein transport domain containing
		7
1427747_a_at	Lcn2	lipocalin 2
1427899_at	Rnf6	ring finger protein (C3H2C3 type) 6
1428141_at	Gga2	golgi associated gamma adaptin ear containing ARF
		binding protein 2
1428192_at	Kbtbd7	kelch repeat and BTB (POZ) domain containing 7
1428243_at	1700021K19Rik	RIKEN cDNA 1700021K19 gene
1428720_s_at	NA	NA
1428875_at	Golim4	golgi integral membrane protein 4
1428942_at	Mt2	metallothionein 2
1429413_at	Cpm	carboxypeptidase M
1429527_a_at	Plscr1	phospholipid scramblase 1
1429693_at	Dab2	disabled homolog 2 ( Drosophila )
1430443_at	Anxa10	annexin A10
1431339_a_at	Efhd2	EF hand domain containing 2
1431774_a_at	Lyrm1	LYR motif containing 1
1433508_at	Klf6	Kruppel-like factor 6
1433939_at	Aff3	AF4/FMR2 family member 3
1433943_at	NA	NA
1434402_at	Samd8	sterile alpha motif domain containing 8
1434418_at	Lass6	LAG1 homolog ceramide synthase 6
1434432_at	Rffl	ring finger and FYVE like domain containing protein
1434502_x_at	Slc4a1	solute carrier family 4 (anion exchanger) member 1
1434556_at	Tmem170b	transmembrane protein 170B
1435176_a_at	Id2	inhibitor of DNA binding 2
1435260_at	Akt3	thymoma viral proto-oncogene 3
1435449_at	Bc12l11	BCL2-like 11 (apoptosis facilitator)
1435517_x_at	Ralb	v-ral simian leukemia viral oncogene homolog B (ras
		related)
1435644_at	Sh3pxd2b	SH3 and PX domains 2B
1436333_a_at	Synj1	synaptojanin 1
1436590_at	Ppp1r3b	protein phosphatase 1 regulatory (inhibitor) subunit 3B
1436763_a_at	Klf9	Kruppel-like factor 9
1436921_at	Atp7a	ATPase Cu++ transporting alpha polypeptide
1436986_at	Sntb2	syntrophin basic 2
1436999_at	Pid1	phosphotyrosine interaction domain containing 1
1437152_at	Mex3b	mex3 homolog B ( C. elegans )
1437270_a_at	Clcf1	cardiotrophin-like cytokine factor 1
1437271_at	Clcf1	cardiotrophin-like cytokine factor 1
1437313_x_at	Hmgb2	high mobility group box 2
1437363_at	Homer1	homer homolog 1 ( Drosophila )
1437870_at	Slco4c1	solute carrier organic anion transporter family member 4C1
1438021_at	Rbm47	RNA binding motif protein 47
1438044_at	1700047M11Rik	RIKEN cDNA 1700047M11 gene
1438627_x_at	Pgd	phosphogluconate dehydrogenase
1438650_x_at	Gja1	gap junction protein alpha 1
1438657_x_at	NA	NA
1440458_at	Cmah	cytidine monophospho-N-acetylneuraminic acid
		hydroxylase
1441843_s_at	NA	NA
1444402_at	Zc3h12c	zinc finger CCCH type containing 12C
1445687_at	Gm885	predicted gene 885
1448165_at	Casp2	caspase 2
1448190_at	Mrpl33	mitochondrial ribosomal protein L33
1448199_at	Ankrd10	ankyrin repeat domain 10
1448210_at	Rab1	RAB1 member RAS oncogene family
1448231_at	Fkbp5	FK506 binding protein 5
1448291_at	Mmp9	matrix metallopeptidase 9
1448318_at	Plin2	perilipin 2
1448377_at	Slpi	secretory leukocyte peptidase inhibitor
1448511_at	Ptprcap	protein tyrosine phosphatase receptor type C polypeptide-
		associated protein
1448617_at	Cd53	CD53 antigen
1448648_at	Fam114a1	family with sequence similarity 114 member A1
1448728_a_at	Nfkbiz	nuclear factor of kappa light polypeptide gene enhancer in
		B-cells inhibitor zeta
1448795_a_at	NA	NA
1448861_at	Traf5	TNF receptor-associated factor 5
1448898_at	Ccl9	chemokine (C-C motif) ligand 9
1449303_at	Sesn3	sestrin 3
1449310_at	Ptger2	prostaglandin E receptor 2 (subtype EP2)
1449336_a_at	Slk	STE20-like kinase (yeast)
1449342_at	Ptplb	protein tyrosine phosphatase-like (proline instead of
		catalytic arginine) member b
1450513_at	Cd33	CD33 antigen
1450654_a_at	Dhdds	dehydrodolichyl diphosphate synthase
1450858_a_at	Ube2d3	ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog
		yeast)
1451097_at	Vasp	vasodilator-stimulated phosphoprotein
1452045_at	Zfp281	zinc finger protein 281
1452163_at	Ets1	E26 avian leukemia oncogene 1 5′ domain
1452181_at	Ckap4	cytoskeleton-associated protein 4
1452443_s_at	Helz	helicase with zinc finger domain
1452922_at	Ppp1r3d	protein phosphatase 1 regulatory subunit 3D
1453985_at	0610007P08Rik	RIKEN cDNA 0610007P08 gene
1454064_a_at	Rnf138	ring finger protein 138
1454699_at	Sesn1	sestrin 1
1454762_at	Xkrx	X Kell blood group precursor related X linked
1454896_at	Rbpj	recombination signal binding protein for immunoglobulin
		kappa J region
1454976_at	Sod2	superoxide dismutase 2 mitochondrial
1454984_at	Lifr	leukemia inhibitory factor receptor
1455229_x_at	Pgs1	phosphatidylglycerophosphate synthase 1
1455353_at	Tmcc1	transmembrane and coiled coil domains 1
1455729_at	Gnaq	guanine nucleotide binding protein alpha q polypeptide
1456037_x_at	Preb	prolactin regulatory element binding
1456307_s_at	Adcy7	adenylate cyclase 7
1456388_at	Atp11a	ATPase class VI type 11A
1456545_at	Il18rap	interleukin 18 receptor accessory protein
1457035_at	AI607873	expressed sequence AI607873
1457645_at	C130079G13Rik	RIKEN cDNA C130079G13 gene
1457917_at	Lck	lymphocyte protein tyrosine kinase
1458067_at	Fam179b	family with sequence similarity 179 member B
1458518_at	Cpeb2	cytoplasmic polyadenylation element binding protein 2
1458668_at	Tpd52	tumor protein D52
1460197_a_at	Steap4	STEAP family member 4
1460227_at	Timp1	tissue inhibitor of metalloproteinase 1
1460271_at	Trem3	triggering receptor expressed on myeloid cells 3
1460329_at	B4galt6	UDP-Gal:betaGlcNAc beta 14-galactosyltransferase
		polypeptide 6
1460335_at	Lysmd3	LysM putative peptidoglycan-binding domain containing 3
1460408_at	Gabrg1	gamma-aminobutyric acid (GABA) A receptor subunit
		gamma 1
1460510_a_at	Coq10b	coenzyme Q10 homolog B ( S. cerevisiae )
1460573_at	AI848100	expressed sequence AI848100
1460735_at	Svil	supervillin

TABLE 6 — Mouse Factor 26

PROBE ID	SYMBOL	GENE NAME
1415686_at	Rab14	RAB14 member RAS oncogene family
1415800_at	Gja1	gap junction proteinalpha 1
1415830_at	OrcS	origin recognition complex subunit 5
1415871_at	Tgfbi	transforming growth factor beta induced
1416019_at	Dr1	down-regulator of transcription 1
1416234_at	Lrrc59	leucine rich repeat containing 59
1416360_at	Snx18	sorting nexin 18
1416369_at	Hiatl1	hippocampus abundant transcript-like 1
1416435_at	Ltbr	lymphotoxin B receptor
1416502_a_at	Preb	prolactin regulatory element binding
1416522_a_at	Grcc10	gene rich cluster C10 gene
1416527_at	Rab32	RAB32 member RAS oncogene family
1416543_at	Nfe2l2	nuclear factor erythroid derived 2 like 2
1416573_at	Pofut2	protein O-fucosyltransferase 2
1416576_at	Socs3	suppressor of cytokine signaling 3
1416700_at	Rnd3	Rho family GTPase 3
1416881_at	Mcl1	myeloid cell leukemia sequence 1
1416981_at	Foxo1	forkhead box O1
1416983_s_at	Foxo1	forkhead box O1
1417297_at	Itpr3	inositol 145-triphosphate receptor 3
1417392_a_at	Slc7a7	solute carrier family 7 (cationic amino acid transporter y+
		system) member 7
1417446_at	Slc12a4	solute carrier family 12 member 4
1417460_at	Ifitm2	interferon induced transmembrane protein 2
1417509_at	Rnf19a	ring finger protein 19A
1417566_at	Abhd5	abhydrolase domain containing 5
1417661_at	Rdm1	RAD52 motif 1
1417696_at	Soat1	sterol O-acyltransferase 1
1417744_a_at	Ralb	v-ral simian leukemia viral oncogene homolog B (ras
		related)
1417859_at	Gas7	growth arrest specific 7
1417890_at	Pdxp	pyridoxal (pyridoxine vitamin B6) phosphatase
1417985_at	Nrarp	Notch-regulated ankyrin repeat protein
1418133_at	Bcl3	B-cell leukemia/lymphoma 3
1418468_at	Anxa11	annexin A11
1418574_a_at	Shfm1	split hand/foot malformation (ectrodactyly) type 1
1418612_at	Slfn1	schlafen 1
1418798_s_at	Srpk3	serine/arginine-rich protein specific kinase 3
1418901_at	Cebpb	CCAAT/enhancer binding protein (C/EBP) beta
1418930_at	Cxcl10	chemokine (C-X-C motif) ligand 10
1419006_s_at	Peli2	pellino 2
1419178_at	Cd3g	CD3 antigen gamma polypeptide
1419508_at	Ripk1	receptor (TNFRSF)-interacting serine-threonine kinase 1
1419641_at	Purb	purine rich element binding protein B
1419709_at	Stfa3	stefin A3
1419721_at	Niacr1	niacin receptor 1
1419722_at	Klk8	kallikrein related-peptidase 8
1420369_a_at	Csn2	casein beta
1420394_s_at	NA	NA
1420591_at	Gpr84	G protein-coupled receptor 84
1420873_at	Twf1	twinfilin actin-binding protein homolog 1 ( Drosophila )
1421366_at	Clec5a	C-type lectin domain family 5 member a
1421547_at	Cd180	CD180 antigen
1421863_at	Vampl1	vesicle-associated membrane protein 1
1422013_at	Clec4a2	C-type lectin domain family 4 member a2
1422506_a_at	Cstb	cystatin B
1422519_at	Cask	calcium/calmodulin-dependent serine protein kinase
		(MAGUK family)
1422557_s_at	Mt1	metallothionein 1
1423167_at	Mobkl3	MOB1 Mps One Binder kinase activator-like 3 (yeast)
1423313_at	Pde7a	phosphodiesterase 7A
1423383_a_at	Osbpl9	oxysterol binding protein-like 9
1423632_at	Gpr146	G protein-coupled receptor 146
1423670_a_at	Srpr	signal recognition particle receptor (‘docking protein’)
1423743_at	Arcn1	archain 1
1423754_at	Ifitm3	interferon induced transmembrane protein 3
1423838_s_at	2400003C14Rik	RIKEN cDNA 2400003C14 gene
1423989_at	Tecpr1	tectonin beta-propeller repeat containing 1
1424032_at	Hven1	hydrogen voltage-gated channel 1
1424254_at	Ifitm1	interferon induced transmembrane protein 1
1424256_at	Rdh12	retinol dehydrogenase 12
1424564_at	2410001C21Rik	RIKEN cDNA 2410001C21 gene
1424573_at	Tmed5	transmembrane emp24 protein transport domain containing
		5
1424906_at	Pqlc3	PQ loop repeat containing
1425128_at	B3gnt8	UDP-GlcNAc:betaGal beta-13-N-
		acetylglucosaminyltransferase 8
1425407_s_at	NA	NA
1425492_at	Bmpr1a	bone morphogenetic protein receptor type 1A
1425587_a_at	Ptprj	protein tyrosine phosphatase receptor type J
1425933_a_at	Nt5c2	5′-nucleotidase cytosolic II
1425977_a_at	Slk	STE20-like kinase (yeast)
1426063_a_at	Gem	GTP binding protein (gene overexpressed in skeletal muscle)
1426368_at	Rin2	Ras and Rab interactor 2
1426369_at	Far1	fatty acyl CoA reductase 1
1426708_at	Antxr2	anthrax toxin receptor 2
1426865_a_at	Ncam1	neural cell adhesion molecule 1
1426899_at	Tbc1d23	TBC1 domain family member 23
1426978_at	NA	NA
1427243_at	Rell1	RELT-like 1
1428141_at	Gga2	golgi associated gamma adaptin ear containing ARF binding
		protein 2
1428214_at	Tomm7	translocase of outer mitochondrial membrane 7 homolog
		(yeast)
1428231_at	Cpsf6	cleavage and polyadenylation specific factor 6
1428357_at	2610019F03Rik	RIKEN cDNA 2610019F03 gene
1428702_at	Ddx28	DEAD (Asp-Glu-Ala-Asp) box polypeptide 28
1428719_at	NA	NA
1428985_at	Ints12	integrator complex subunit 12
1429115_at	NA	NA
1429321_at	Rnf149	ring finger protein 149
1429466_s_at	NA	NA
1429527_a_at	Plscr1	phospholipid scramblase 1
1429775_a_at	NA	NA
1433454_at	Abtb2	ankyrin repeat and BTB (POZ) domain containing 2
1433815_at	Jakmip1	janus kinase and microtubule interacting protein 1
1434025_at	NA	NA
1434054_at	NA	NA
1434070_at	Jag1	jagged 1
1434126_at	4930402H24Rik	RIKEN cDNA 4930402H24 gene
1434310_at	Bmpr2	bone morphogenic protein receptor type II (serine/threonine
		kinase)
1434311_at	Cnot6l	CCR4-NOT transcription complex subunit 6-like
1434431_x_at	Adora2b	adenosine A2b receptor
1434484_at	1100001G20Rik	RIKEN cDNA 1100001G20 gene
1434487_at	Mef2d	myocyte enhancer factor 2D
1434773_a_at	Slc2a1	solute carrier family 2 (facilitated glucose transporter)
		member 1
1434814_x_at	Gpi1	glucose phosphate isomerase 1
1435077_at	Asxl1	additional sex combs like 1 ( Drosophila )
1435176_a_at	Id2	inhibitor of DNA binding 2
1435231_at	Coq4	coenzyme Q4 homolog (yeast)
1435458_at	Pim1	proviral integration site 1
1435996_at	Card11	caspase recruitment domain family member 11
1436077_a_at	Fcho1	FCH domain only 1
1436103_at	Rab3ip	RAB3A interacting protein
1436666_at	NA	NA
1437111_at	Zc3h12c	zinc finger CCCH type containing 12C
1437225_x_at	Gnai3	guanine nucleotide binding protein (G protein) alpha
		inhibiting 3
1437270_a_at	Clcf1	cardiotrophin-like cytokine factor 1
1437313_x_at	Hmgb2	high mobility group box 2
1438031_at	Rasgrp3	RAS guanyl releasing protein 3
1438627_x_at	Pgd	phosphogluconate dehydrogenase
1439443_x_at	Tkt	transketolase
1439662_at	Homer1	homer homolog 1 ( Drosophila )
1439799_at	NA	NA
1441079_at	Mbd4	methyl-CpG binding domain protein 4
1441962_at	Alox5	arachidonate 5-lipoxygenase
1442122_at	AI451458	expressed sequence AI451458
1444090_at	Pram1	PML-RAR alpha-regulated adaptor molecule 1
1444985_at	NA	NA
1447918_x_at	Iglv1	immunoglobulin lambda variable 1
1448159_at	Rab7	RAB7 member RAS oncogene family
1448306_at	Nfkbia	nuclear factor of kappa light polypeptide gene enhancer in
		B-cells inhibitor alpha
1448333_at	Adprh	ADP-ribosylarginine hydrolase
1448377_at	Slpi	secretory leukocyte peptidase inhibitor
1448543_at	Slmo2	slowmo homolog 2 ( Drosophila )
1448559_at	Flot1	flotillin 1
1448604_at	Uck2	uridine-cytidine kinase 2
1448856_a_at	Msra	methionine sulfoxide reductase A
1448948_at	Slc50a1	solute carrier family 50 (sugar transporter) member 1
1448957_at	Rbpj	recombination signal binding protein for immunoglobulin
		kappa J region
1449028_at	Rhou	ras homolog gene family member U
1449455_at	Hck	hemopoietic cell kinase
1449591_at	Casp4	caspase 4 apoptosis-related cysteine peptidase
1449712_s_at	Atp6v1e1	ATPase H+ transporting lysosomal V1 subunit El
1449829_at	Itgb2l	integrin beta 2-like
1449858_at	Cd86	CD86 antigen
1450295_s_at	Pvr	poliovirus receptor
1450459_at	2010106G01Rik	RIKEN cDNA 2010106G01 gene
1450742_at	Bysl	bystin-like
1450786_x_at	Pdlim5	PDZ and LIM domain 5
1450882_s_at	NA	NA
1450913_at	B4galt6	UDP-Gal:betaGlcNAc beta 14-galactosyltransferase
		polypeptide 6
1450937_at	Lin7c	lin-7 homolog C ( C. elegans )
1450971_at	Gadd45b	growth arrest and DNA-damage-inducible 45 beta
1451015_at	Tkt	transketolase
1451037_at	Ptpn9	protein tyrosine phosphatase non-receptor type 9
1451079_at	Adpgk	ADP-dependent glucokinase
1451171_at	2310008H04Rik	RIKEN cDNA 2310008H04 gene
1451275_at	Uhrf1bp1l	UHRF1 (ICBP90) binding protein 1-like
1451317_at	Ythdf2	YTH domain family 2
1451387_s_at	Cuta	cutA divalent cation tolerance homolog ( E. coli )
1451436_at	Sbno1	sno strawberry notch homolog 1 ( Drosophila )
1451458_at	Tmem2	transmembrane protein 2
1451537_at	Chi3l1	chitinase 3-like 1
1451713_a_at	Fcer2a	Fc receptor IgE low affinity II alpha polypeptide
1451767_at	Ncf1	neutrophil cytosolic factor 1
1452024_a_at	Ldb1	LIM domain binding 1
1452118_at	Rrp1b	ribosomal RNA processing 1 homolog B ( S. cerevisiae )
1452162_at	Wdr48	WD repeat domain 48
1452163_at	Ets1	E26 avian leukemia oncogene 1 5′ domain
1452306_at	Zfyve26	zinc finger FYVE domain containing 26
1452408_at	NA	NA
1452483_a_at	Cd44	CD44 antigen
1452732_at	Asprv1	aspartic peptidase retroviral-like 1
1453009_at	Cpm	carboxypeptidase M
1454197_a_at	Ccdc86	coiled-coil domain containing 86
1454654_at	Dirc2	disrupted in renal carcinoma 2 (human)
1454711_at	Trio	triple functional domain (PTPRF interacting)
1454880_s_at	Bmf	BCL2 modifying factor
1454979_at	Diap1	diaphanous homolog 1 ( Drosophila )
1455000_at	Gpr68	G protein-coupled receptor 68
1455002_at	Ptp4a1	protein tyrosine phosphatase 4a1
1455032_at	Ccnyl1	cyclin Y-like 1
1455300_at	Tet2	tet oncogene family member 2
1455332_x_at	Fcgr2b	Fc receptor IgG low affinity IIb
1456037_x_at	Preb	prolactin regulatory element binding
1456341_a_at	Klf9	Kruppel-like factor 9
1456875_at	Gm19906	predicted gene19906
1457376_at	NA	NA
1457753_at	Tlr13	toll-like receptor 13
1457825_x_at	Tcn2	transcobalamin 2
1459903_at	Sema7a	sema domain immunoglobulin domain (Ig) and GPI
		membrane anchor (semaphorin) 7A
1459916_at	Gm19980	predicted gene19980
1460257_a_at	NA	NA
1460282_at	Trem1	triggering receptor expressed on myeloid cells 1
1460287_at	Timp2	tissue inhibitor of metalloproteinase 2
1460351_at	S100a11	5100 calcium binding protein A11 (calgizzarin)

TABLE 7 — Human Factor 4

PROBE ID	SYMBOL	GENE NAME
200060_s_at	RNPS1	RNA binding protein S1 serine-rich domain
200633_at	UBB	ubiquitin B
200675_at	CD81	CD81 molecule
200693_at	YWHAQ	tyrosine 3-monooxygenase/tryptophan 5-monooxygenase
		activation protein theta polypeptide
200702_s_at	DDX24	DEAD (Asp-Glu-Ala-Asp) box polypeptide 24
200717_x_at	RPL7	ribosomal protein L7
200739_s_at	SUMO3	SMT3 suppressor of mif two 3 homolog 3 ( S . cerevisiae )
200741_s_at	RPS27	ribosomal protein S27
200743_s_at	TPP1	tripeptidyl peptidase I
200764_s_at	CTNNA1	catenin (cadherin-associated protein) alpha 1 102 kDa
200766_at	CTSD	cathepsin D
200767_s_at	FAM120A	family with sequence similarity 120A
200874_s_at	NOP56	NOP56 ribonucleoprotein homolog (yeast)
200875_s_at	NOP56	NOP56 ribonucleoprotein homolog (yeast)
200881_s_at	DNAJA1	DnaJ (Hsp40) homolog subfamily A member 1
200886_s_at	PGAM1	phosphoglycerate mutase 1 (brain)
200947_s_at	GLUD1	glutamate dehydrogenase 1
200951_s_at	CCND2	cyclin D2
200954_at	ATP6V0C	ATPase H+ transporting lysosomal 16 kDa V0 subunit c
200955_at	IMMT	inner membrane protein mitochondrial
200968_s_at	PPIB	peptidylprolyl isomerase B (cyclophilin B)
200969_at	SERP1	stress-associated endoplasmic reticulum protein 1
200984_s_at	CD59	CD59 molecule complement regulatory protein
200985_s_at	CD59	CD59 molecule complement regulatory protein
200998_s_at	CKAP4	cytoskeleton-associated protein 4
201015_s_at	JUP	junction plakoglobin
201055_s_at	HNRNPA0	heterogeneous nuclear ribonucleoprotein A0
201087_at	PXN	paxillin
201243_s_at	ATP1B1	ATPase Na+/K+ transporting beta 1 polypeptide
201260_s_at	SYPL1	synaptophysin-like 1
201332_s_at	STAT6	signal transducer and activator of transcription 6
201353_s_at	BAZ2A	bromodomain adjacent to zinc finger domain 2A
201369_s_at	ZFP36L2	zinc finger protein 36 C3H type-like 2
201395_at	RBMS	RNA binding motif protein 5
201396_s_at	SGTA	small glutamine-rich tetratricopeptide repeat (TPR)-containing
		alpha
201449_at	TIA1	TIA1 cytotoxic granule-associated RNA binding protein
201525_at	APOD	apolipoprotein D
201530_x_at	EIF4A1	eukaryotic translation initiation factor 4A1
201532_at	PSMA3	proteasome (prosome macropain) subunit alpha type 3
201556_s_at	VAMP2	vesicle-associated membrane protein 2 (synaptobrevin 2)
201601_x_at	IFITM1	interferon induced transmembrane protein 1 (9-27)
201602_s_at	PPP1R12A	protein phosphatase 1 regulatory (inhibitor) subunit 12A
201614_s_at	RUVBL1	RuvB-like 1 ( E . coli )
201650_at	KRT19	keratin 19
201699_at	PSMC6	proteasome (prosome macropain) 26S subunit ATPase 6
201700_at	CCND3	cyclin D3
201701_s_at	PGRMC2	progesterone receptor membrane component 2
201737_s_at	6-Mar	membrane-associated ring finger (C3HC4) 6
201751_at	JOSD1	Josephin domain containing 1
201840_at	NEDD8	neural precursor cell expressed developmentally down-regulated 8
201881_s_at	ARIH1	ariadne homolog ubiquitin-conjugating enzyme E2 binding
		protein 1 ( Drosophila )
201903_at	UQCRC1	ubiquinol-cytochrome c reductase core protein I
201960_s_at	MYCBP2	MYC binding protein 2
201984_s_at	EGFR	epidermal growth factor receptor
202007_at	NID1	nidogen 1
202008_s_at	NID1	nidogen 1
202054_s_at	ALDH3A2	aldehyde dehydrogenase 3 family member A2
202056_at	KPNA1	karyopherin alpha 1 (importin alpha 5)
202057_at	KPNA1	karyopherin alpha 1 (importin alpha 5)
202162_s_at	CNOT8	CCR4-NOT transcription complex subunit 8
202234_s_at	SLC16A1	solute carrier family 16 member 1 (monocarboxylic acid
		transporter 1)
202240_at	PLK1	polo-like kinase 1
202273_at	PDGFRB	platelet-derived growth factor receptor beta polypeptide
202329_at	CSK	c-src tyrosine kinase
202360_at	MAML1	mastermind-like 1 ( Drosophila )
202368_s_at	TRAM2	translocation associated membrane protein 2
202382_s_at	GNPDA1	glucosamine-6-phosphate deaminase 1
202426_s_at	RXRA	retinoid X receptor alpha
202508_s_at	SNAP25	synaptosomal-associated protein 25 kDa
202548_s_at	ARHGEF7	Rho guanine nucleotide exchange factor (GEF) 7
202566_s_at	SVIL	supervillin
202623_at	EAPP	E2F-associated phosphoprotein
202624_s_at	CABIN1	calcineurin binding protein 1
202636_at	RNF103	ring finger protein 103
202693_s_at	STK17A	serine/threonine kinase 17a
202694_at	STK17A	serine/threonine kinase 17a
202709_at	FMOD	fibromodulin
202750_s_at	TFIP11	tuftelin interacting protein 11
202756_s_at	GPC1	glypican 1
202787_s_at	MAPKAPK3	mitogen-activated protein kinase-activated protein kinase 3
202795_x_at	TRIOBP	TRIO and F-actin binding protein
202796_at	SYNPO	synaptopodin
202830_s_at	SLC37A4	solute carrier family 37 (glucose-6-phosphate transporter)
		member 4
202859_x_at	IL8	interleukin 8
202972_s_at	FAM13A	family with sequence similarity 13 member A
202973_x_at	FAM13A	family with sequence similarity 13 member A
202985_s_at	BAGS	BCL2-associated athanogene 5
203023_at	NOP16	NOP16 nucleolar protein homolog (yeast)
203199_s_at	MTRR	5-methyltetrahydrofolate-homocysteine methyltransferase
		reductase
203236_s_at	LGALS9	lectin galactoside-binding soluble 9
203376_at	CDC40	cell division cycle 40 homolog ( S . cerevisiae )
203460_s_at	PSEN1	presenilin 1
203610_s_at	TRIM38	tripartite motif containing 38
203693_s_at	E2F3	E2F transcription factor 3
203718_at	PNPLA6	patatin-like phospholipase domain containing 6
203764_at	DLGAP5	discs large ( Drosophila ) homolog-associated protein 5
203794_at	CDC42BPA	CDC42 binding protein kinase alpha (DMPK-like)
203863_at	ACTN2	actinin alpha 2
203864_s_at	ACTN2	actinin alpha 2
204217_s_at	RTN2	reticulon 2
204218_at	C11orf51	chromosome 11 open reading frame 51
204237_at	GULP1	GULP engulfment adaptor PTB domain containing 1
204238_s_at	C6orf108	chromosome 6 open reading frame 108
204242_s_at	ACOX3	acyl-CoA oxidase 3 pristanoyl
204249_s_at	LMO2	LIM domain only 2 (rhombotin-like 1)
204250_s_at	CEP164	centrosomal protein 164 kDa
204287_at	SYNGR1	synaptogyrin 1
204447_at	ProSAPiP1	ProSAPiP1 protein
204464_s_at	EDNRA	endothelin receptor type A
204491_at	PDE4D	phosphodiesterase 4D cAMP-specific
204496_at	STRN3	striatin calmodulin binding protein 3
204497_at	ADCY9	adenylate cyclase 9
204597_x_at	STC1	stanniocalcin 1
204697_s_at	CHGA	chromogranin A (parathyroid secretory protein 1)
205053_at	PRIM1	primase DNA polypeptide 1 (49 kDa)
205064_at	SPRR1B	small proline-rich protein 1B
205067_at	IL1B	interleukin 1 beta
205144_at	NA	NA
205151_s_at	TRIL	TLR4 interactor with leucine-rich repeats
205182_s_at	ZNF324	zinc finger protein 324
205225_at	ESR1	estrogen receptor 1
205243_at	SLC13A3	solute carrier family 13 (sodium-dependent dicarboxylate
		transporter) member 3
205296_at	RBL1	retinoblastoma-like 1 (p107)
205340_at	ZBTB24	zinc finger and BTB domain containing 24
205364_at	ACOX2	acyl-CoA oxidase 2 branched chain
205396_at	SMAD3	SMAD family member 3
205483_s_at	ISG15	ISG15 ubiquitin-like modifier
205590_at	RASGRP1	RAS guanyl releasing protein 1 (calcium and DAG-regulated)
205680_at	MMP10	matrix metallopeptidase 10 (stromelysin 2)
205734_s_at	AFF3	AF4/FMR2 family member 3
205887_x_at	MSH3	mutS homolog 3 ( E . coli )
206099_at	PRKCH	protein kinase C eta
206147_x_at	SCML2	sex comb on midleg-like 2 ( Drosophila )
206266_at	GPLD1	glycosylphosphatidylinositol specific phospholipase D1
206421_s_at	SERPINB7	serpin peptidase inhibitor Glade B (ovalbumin) member 7
206614_at	GDF5	growth differentiation factor 5
206653_at	POLR3G	polymerase (RNA) III (DNA directed) polypeptide G (32 kD)
206931_at	ZNF141	zinc finger protein 141
206932_at	CH25H	cholesterol 25-hydroxylase
207022_s_at	LDHC	lactate dehydrogenase C
207076_s_at	ASS1	argininosuccinate synthase 1
207086_x_at	NA	NA
207160_at	IL12A	interleukin 12A (natural killer cell stimulatory factor 1 cytotoxic
		lymphocyte maturation factor 1 p35)
207174_at	GPC5	glypican 5
207385_at	TFDP3	transcription factor Dp family member 3
207588_at	NA	NA
207759_s_at	NA	NA
207908_at	KRT2	keratin 2
208043_at	NA	NA
208113_x_at	PABPC3	poly(A) binding protein cytoplasmic 3
208114_s_at	ISG20L2	interferon stimulated exonuclease gene 20 kDa-like 2
208334_at	NDST4	N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 4
208340_at	NA	NA
208381_s_at	SGPL1	sphingosine-1-phosphate lyase 1
208556_at	GPR31	G protein-coupled receptor 31
208683_at	CAPN2	calpain 2 (m/II) large subunit
208709_s_at	NRD1	nardilysin (N-arginine dibasic convertase)
208710_s_at	AP3D1	adaptor-related protein complex 3 delta 1 subunit
208729_x_at	HLA-B	major histocompatibility complex class I B
208738_x_at	SUMO2	SMT3 suppressor of mif two 3 homolog 2 ( S . cerevisiae )
208748_s_at	FLOT1	flotillin 1
208750_s_at	ARF1	ADP-ribosylation factor 1
208765_s_at	HNRNPR	heterogeneous nuclear ribonucleoprotein R
208786_s_at	MAP1LC3B	microtubule-associated protein 1 light chain 3 beta
208800_at	SRP72	signal recognition particle 72 kDa
208817_at	COMT	catechol-O-methyltransferase
208820_at	PTK2	PTK2 protein tyrosine kinase 2
208852_s_at	CANX	calnexin
208853_s_at	CANX	calnexin
208856_x_at	RPLP0	ribosomal protein large P0
208857_s_at	PCMT1	protein-L-isoaspartate (D-aspartate) O-methyltransferase
208875_s_at	PAK2	p21 protein (Cdc42/Rac)-activated kinase 2
209021_x_at	ATG13	ATG13 autophagy related 13 homolog ( S . cerevisiae )
209047_at	AQP1	aquaporin 1 (Colton blood group)
209055_s_at	CDCSL	CDCS cell division cycle 5-like ( S . pombe )
209087_x_at	MCAM	melanoma cell adhesion molecule
209088_s_at	UBN1	ubinuclein 1
209169_at	GPM6B	glycoprotein M6B
209170_s_at	GPM6B	glycoprotein M6B
209197_at	SYT11	synaptotagmin XI
209225_x_at	TNPO1	transportin 1
209253_at	SORBS3	sorbin and SH3 domain containing 3
209352_s_at	SIN3B	SIN3 homolog B transcription regulator (yeast)
209393_s_at	EIF4E2	eukaryotic translation initiation factor 4E family member 2
209479_at	CCDC28A	coiled-coil domain containing 28A
209556_at	NCDN	neurochondrin
209623_at	MCCC2	methylcrotonoyl-CoA carboxylase 2 (beta)
209666_s_at	CHUK	conserved helix-loop-helix ubiquitous kinase
209689_at	CCDC93	coiled-coil domain containing 93
209726_at	CA11	carbonic anhydrase XI
209822_s_at	VLDLR	very low density lipoprotein receptor
209865_at	SLC35A3	solute carrier family 35 (UDP-N-acetylglucosamine (UDP-
		GlcNAc) transporter) member A3
209866_s_at	LPHN3	latrophilin 3
209972_s_at	AIMP2	aminoacyl tRNA synthetase complex-interacting multifunctional
		protein 2
209973_at	NFKBIL1	nuclear factor of kappa light polypeptide gene enhancer in B-cells
		inhibitor-like 1
210158_at	ERCC4	excision repair cross-complementing rodent repair deficiency
		complementation group 4
210163_at	CXCL11	chemokine (C-X-C motif) ligand 11
210251_s_at	RUFY3	RUN and FYVE domain containing 3
210344_at	OSBPL7	oxysterol binding protein-like 7
210346_s_at	CLK4	CDC-like kinase 4
210377_at	ACSM3	acyl-CoA synthetase medium-chain family member 3
210470_x_at	NONO	non-POU domain containing octamer-binding
210613_s_at	SYNGR1	synaptogyrin 1
210627_s_at	MOGS	mannosyl-oligosaccharide glucosidase
210677_at	SOAT2	sterol O-acyltransferase 2
210731_s_at	LGALS8	lectin galactoside-binding soluble 8
210732_s_at	LGALS8	lectin galactoside-binding soluble 8
210823_s_at	PTPRS	protein tyrosine phosphatase receptor type S
210919_at	PHLPP1	PH domain and leucine rich repeat protein phosphatase 1
211054_at	INVS	inversin
211055_s_at	INVS	inversin
211070_x_at	DBI	diazepam binding inhibitor (GABA receptor modulator acyl-CoA
		binding protein)
211165_x_at	EPHB2	EPH receptor B2
211190_x_at	CD84	CD84 molecule
211224_s_at	ABCB11	ATP-binding cassette sub-family B (MDR/TAP) member 11
211334_at	MRE11A	MRE11 meiotic recombination 11 homolog A ( S . cerevisiae )
211375_s_at	ILF3	interleukin enhancer binding factor 3 90 kDa
211380_s_at	PRKG1	protein kinase cGMP-dependent type I
211413_s_at	PADI4	peptidyl arginine deiminase type IV
211555_s_at	GUCY1B3	guanylate cyclase 1 soluble beta 3
211775_x_at	MGC13053	hypothetical MGC13053
211794_at	FYB	FYN binding protein
211833_s_at	BAX	BCL2-associated X protein
211954_s_at	IPO5	importin 5
211964_at	COL4A2	collagen type IV alpha 2
211995_x_at	ACTG1	actin gamma 1
211996_s_at	NA	NA
212037_at	PNN	pinin desmosome associated protein
212075_s_at	CSNK2A1	casein kinase 2 alpha 1 polypeptide
212152_x_at	ARID1A	AT rich interactive domain 1A (SWI-like)
212154_at	SDC2	syndecan 2
212274_at	LPIN1	lipin 1
212282_at	TMEM97	transmembrane protein 97
212307_s_at	OGT	O-linked N-acetylglucosamine (GlcNAc) transferase (UDP-N-
		acetylglucosamine:polypeptide-N-acetylglucosaminyl transferase)
212328_at	LIMCH1	LIM and calponin homology domains 1
212387_at	TCF4	transcription factor 4
212439_at	IP6K1	inositol hexakisphosphate kinase 1
212440_at	SNRNP27	small nuclear ribonucleoprotein 27 kDa (U4/U6.U5)
212738_at	ARHGAP19	Rho GTPase activating protein 19
212854_x_at	NA	NA
212938_at	COL6A1	collagen type VI alpha 1
212951_at	GPR116	G protein-coupled receptor 116
212966_at	HIC2	hypermethylated in cancer 2
213022_s_at	UTRN	utrophin
213039_at	ARHGEF18	Rho/Rac guanine nucleotide exchange factor (GEF) 18
213067_at	MYH10	myosin heavy chain 10 non-muscle
213073_at	ZFYVE26	zinc finger FYVE domain containing 26
213135_at	TIAM1	T-cell lymphoma invasion and metastasis 1
213168_at	SP3	Sp3 transcription factor
213185_at	KIAA0556	KIAA0556
213188_s_at	MINA	MYC induced nuclear antigen
213334_x_at	HAUS7	HAUS augmin-like complex subunit 7
213351_s_at	TMCC1	transmembrane and coiled-coil domain family 1
213395_at	MLC1	megalencephalic leukoencephalopathy with subcortical cysts 1
213397_x_at	NA	NA
213436_at	CNR1	cannabinoid receptor 1 (brain)
213439_x_at	RUNDC3A	RUN domain containing 3A
213659_at	ZNF75D	zinc finger protein 75D
213687_s_at	RPL35A	ribosomal protein L35a
213816_s_at	MET	met proto-oncogene (hepatocyte growth factor receptor)
213874_at	SERPINA4	serpin peptidase inhibitor clade A (alpha-1 antiproteinase
		antitrypsin) member 4
214319_at	FRY	furry homolog ( Drosophila )
214349_at	NA	NA
214380_at	PRPF31	PRP31 pre-mRNA processing factor 31 homolog ( S . cerevisiae )
214387_x_at	SFTPC	surfactant protein C
214565_s_at	NA	NA
214727_at	BRCA2	breast cancer 2 early onset
214915_at	ZNF362	zinc finger protein 362
214952_at	NCAM1	neural cell adhesion molecule 1
215067_x_at	PRDX2	peroxiredoxin 2
215240_at	ITGB3	integrin beta 3 (platelet glycoprotein IIIa antigen CD61)
215254_at	RCAN1	regulator of calcineurin 1
215510_at	ETV2	ets variant 2
215727_x_at	NA	NA
215974_at	HCG4B	HLA complex group 4B (non-protein coding)
216048_s_at	RHOBTB3	Rho-related BTB domain containing 3
216752_at	PIK3R4	phosphoinositide-3-kinase regulatory subunit 4
216971_s_at	PLEC	plectin
217455_s_at	SSTR2	somatostatin receptor 2
217544_at	NA	NA
217640_x_at	SKA1	spindle and kinetochore associated complex subunit 1
217641_at	GPR135	G protein-coupled receptor 135
217740_x_at	RPL7A	ribosomal protein L7a
217799_x_at	UBE2H	ubiquitin-conjugating enzyme E2H
217889_s_at	CYBRD1	cytochrome b reductase 1
217903_at	STRN4	striatin calmodulin binding protein 4
217924_at	C6orf106	chromosome 6 open reading frame 106
217928_s_at	PPP6R3	protein phosphatase 6 regulatory subunit 3
217941_s_at	ERBB2IP	erbb2 interacting protein
217992_s_at	EFHD2	EF-hand domain family member D2
217996_at	PHLDA1	pleckstrin homology-like domain family A member 1
218006_s_at	ZNF22	zinc finger protein 22 (KOX 15)
218028_at	ELOVL1	ELOVL fatty acid elongase 1
218048_at	COMMD3	COMM domain containing 3
218049_s_at	MRPL13	mitochondrial ribosomal protein L13
218358_at	CRELD2	cysteine-rich with EGF-like domains 2
218380_at	NA	NA
218390_s_at	FAM204A	family with sequence similarity 204 member A
218541_s_at	C8orf4	chromosome 8 open reading frame 4
218580_x_at	AURKAIP1	aurora kinase A interacting protein 1
218625_at	NRN1	neuritin 1
218694_at	ARMCX1	armadillo repeat containing X-linked 1
218710_at	TTC27	tetratricopeptide repeat domain 27
218771_at	PANK4	pantothenate kinase 4
218920_at	FAM193B	family with sequence similarity 193 member B
219051_x_at	METRN	meteorin glial cell differentiation regulator
219058_x_at	TINAGL1	tubulointerstitial nephritis antigen-like 1
219388_at	GRHL2	grainyhead-like 2 ( Drosophila )
219485_s_at	PSMD10	proteasome (prosome macropain) 26S subunit non-ATPase 10
219495_s_at	ZNF180	zinc finger protein 180
219528_s_at	BCL11B	B-cell CLL/lymphoma 11B (zinc finger protein)
219829_at	ITGB1BP2	integrin beta 1 binding protein (melusin) 2
219975_x_at	OLAH	oleoyl-ACP hydrolase
219988_s_at	RNF220	ring finger protein 220
220156_at	EFCAB1	EF-hand calcium binding domain 1
220243_at	ZBTB44	zinc finger and BTB domain containing 44
220357_s_at	SGK2	serum/glucocorticoid regulated kinase 2
220403_s_at	TP53AIP1	tumor protein p53 regulated apoptosis inducing protein 1
220650_s_at	SLC9A5	solute carrier family 9 (sodium/hydrogen exchanger) member 5
220663_at	IL1RAPL1	interleukin 1 receptor accessory protein-like 1
220750_s_at	LEPRE1	leucine proline-enriched proteoglycan (leprecan) 1
220769_s_at	WDR78	WD repeat domain 78
220991_s_at	RNF32	ring finger protein 32
221036_s_at	APH1B	anterior pharynx defective 1 homolog B ( C . elegans )
221066_at	RXFP3	relaxin/insulin-like family peptide receptor 3
221080_s_at	DENND1C	DENN/MADD domain containing 1C
221102_s_at	TRPM6	transient receptor potential cation channel subfamily M member 6
221280_s_at	PARD3	par-3 partitioning defective 3 homolog ( C . elegans )
221296_at	TECTA	tectorin alpha
221510_s_at	GLS	glutaminase
221625_at	ZNF506	zinc finger protein 506
221888_at	CC2D1A	coiled-coil and C2 domain containing 1A
221896_s_at	HIGD1A	HIG1 hypoxia inducible domain family member 1A
222014_x_at	MTO1	mitochondrial translation optimization 1 homolog ( S . cerevisiae )
222015_at	CSNK1E	casein kinase 1 epsilon
222062_at	IL27RA	interleukin 27 receptor alpha
222144_at	KIF17	kinesin family member 17
222158_s_at	PPPDE1	PPPDE peptidase domain containing 1
222165_x_at	C9orf16	chromosome 9 open reading frame 16
222226_at	SAA3P	serum amyloid A3 pseudogene
31845_at	ELF4	E74-like factor 4 (ets domain transcription factor)
34063_at	RECQL5	RecQ protein-like 5
35160_at	LDB1	LIM domain binding 1
37943_at	ZFYVE26	zinc finger FYVE domain containing 26
38398_at	MADD	MAP-kinase activating death domain
40359_at	RASSF7	Ras association (RalGDS/AF-6) domain family (N-terminal)
		member 7
41512_at	BRAP	BRCA1 associated protein
54632_at	THADA	thyroid adenoma associated
65635_at	ENGASE	endo-beta-N-acetylglucosaminidase

TABLE 8 — Human Factor 20

PROBE ID	SYMBOL	GENE NAME
200992_at	IPO7	importin 7
201034_at	ADD3	adducin 3 (gamma)
201149_s_at	TIMP3	TIMP metallopeptidase inhibitor 3
201412_at	LRP10	low density lipoprotein receptor-related protein 10
201565_s_at	ID2	inhibitor of DNA binding 2 dominant negative helix-loop-helix
		protein
201651_s_at	PACSIN2	protein kinase C and casein kinase substrate in neurons 2
201662_s_at	ACSL3	acyl-CoA synthetase long-chain family member 3
201721_s_at	LAPTM5	lysosomal protein transmembrane 5
201761_at	MTHFD2	methylenetetrahydrofolate dehydrogenase (NADP+ dependent)
		2 methenyltetrahydrofolate cyclohydrolase
202350_s_at	MATN2	matrilin 2
202628_s_at	SERPINE1	serpin peptidase inhibitor Glade E (nexin plasminogen activator
		inhibitor type 1) member 1
203191_at	ABCB6	ATP-binding cassette sub-family B (MDR/TAP) member 6
203216_s_at	MYO6	myosin VI
203229_s_at	CLK2	CDC-like kinase 2
203879_at	PIK3CD	phosphoinositide-3-kinase catalytic delta polypeptide
203884_s_at	RAB11FIP2	RAB11 family interacting protein 2 (class I)
203950_s_at	CLCN6	chloride channel 6
204025_s_at	PDCD2	programmed cell death 2
204060_s_at	NA	NA
204215_at	C7orf23	chromosome 7 open reading frame 23
204316_at	RGS10	regulator of G-protein signaling 10
204387_x_at	MRP63	mitochondrial ribosomal protein 63
206541_at	KLKB1	kallikrein B plasma (Fletcher factor) 1
208461_at	HIC1	hypermethylated in cancer 1
208742_s_at	SAP18	Sin3A-associated protein 18 kDa
209043_at	PAPSS1	3′-phosphoadenosine 5′-phosphosulfate synthase 1
209115_at	UBA3	ubiquitin-like modifier activating enzyme 3
209146_at	MSMO1	methylsterol monooxygenase 1
209227_at	TUSC3	tumor suppressor candidate 3
209743_s_at	ITCH	itchy E3 ubiquitin protein ligase homolog (mouse)
210545_at	ITSN2	intersectin 2
212027_at	RBM25	RNA binding motif protein 25
212668_at	SMURF1	SMAD specific E3 ubiquitin protein ligase 1
212871_at	MAPKAPK5	mitogen-activated protein kinase-activated protein kinase 5
212969_x_at	EML3	echinoderm microtubule associated protein like 3
212983_at	HRAS	v-Ha-ras Harvey rat sarcoma viral oncogene homolog
213336_at	BAZ1B	bromodomain adjacent to zinc finger domain 1B
214140_at	SLC25A16	solute carrier family 25 (mitochondrial carrier; Graves disease
		autoantigen) member 16
214355_x_at	NA	NA
215169_at	NA	NA
215420_at	IHH	Indian hedgehog
216975_x_at	NPAS1	neuronal PAS domain protein 1
217837_s_at	VPS24	vacuolar protein sorting 24 homolog ( S . cerevisiae )
217891_at	C16orf58	chromosome 16 open reading frame 58
217994_x_at	CPSF3L	cleavage and polyadenylation specific factor 3-like
218136_s_at	SLC25A37	solute carrier family 25 member 37
218478_s_at	ZCCHC8	zinc finger CCHC domain containing 8
218675_at	SLC22A17	solute carrier family 22 member 17
218756_s_at	DHRS11	dehydrogenase/reductase (SDR family) member 11
218882_s_at	WDR3	WD repeat domain 3
221087_s_at	APOL3	apolipoprotein L3
221778_at	JHDM1D	jumonji C domain containing histone demethylase 1 homolog D
		( S . cerevisiae )
221823_at	C5orf30	chromosome 5 open reading frame 30
222255_at	PRX	periaxin
40472_at	LPCAT4	lysophosphatidylcholine acyltransferase 4
44617_at	OGFOD2	2-oxoglutarate and iron-dependent oxygenase domain
		containing 2

TABLE 9 — Human Factor 40

PROBE ID	SYMBOL	GENE NAME
200989_at	HIF1A	hypoxia inducible factor 1 alpha subunit (basic helix-loop-helix
		transcription factor)
201772_at	AZIN1	antizyme inhibitor 1
202116_at	DPF2	D4 zinc and double PHD fingers family 2
202552_s_at	CRIM1	cysteine rich transmembrane BMP regulator 1 (chordin-like)
203607_at	INPP5F	inositol polyphosphate-5-phosphatase F
203643_at	ERF	Ets2 repressor factor
204161_s_at	ENPP4	ectonucleotide pyrophosphatase/phosphodiesterase 4 (putative)
204284_at	PPP1R3C	protein phosphatase 1 regulatory (inhibitor) subunit 3C
204452_s_at	FZD1	frizzled family receptor 1
207583_at	ABCD2	ATP-binding cassette sub-family D (ALD) member 2
208701_at	APLP2	amyloid beta (A4) precursor-like protein 2
209054_s_at	WHSC1	Wolf-Hirschhorn syndrome candidate 1
209593_s_at	TOR1B	torsin family 1 member B (torsin B)
209840_s_at	LRRN3	leucine rich repeat neuronal 3
210736_x_at	DTNA	dystrobrevin alpha
213611_at	AQP5	aquaporin 5
214062_x_at	NFKBIB	nuclear factor of kappa light polypeptide gene enhancer in B-cells
		inhibitor beta
214952_at	NCAM1	neural cell adhesion molecule 1
216222_s_at	MYO10	myosin X
216869_at	PDE1C	phosphodiesterase 1C calmodulin-dependent 70 kDa
220585_at	HKDC1	hexokinase domain containing 1
221402_at	OR1F1	olfactory receptor family 1 subfamily F member 1
221651_x_at	NA	NA
221708_s_at	UNC45A	unc-45 homolog A ( C . elegans )
222296_at	NA	NA
222316_at	NA	NA

TABLE 10 — Human Factor 74

PROBE ID	SYMBOL	GENE NAME
1487_at	ESRRA	estrogen-related receptor alpha
201045_s_at	RAB6A	RAB6A member RAS oncogene family
201046_s_at	RAD23A	RAD23 homolog A ( S . cerevisiae )
201751_at	JOSD1	Josephin domain containing 1
201752_s_at	ADD3	adducin 3 (gamma)
201753_s_at	ADD3	adducin 3 (gamma)
201951_at	ALCAM	activated leukocyte cell adhesion molecule
202058_s_at	KPNA1	karyopherin alpha 1 (importin alpha 5)
202579_x_at	HMGN4	high mobility group nucleosomal binding domain 4
202786_at	STK39	serine threonine kinase 39
202813_at	TARBP1	TAR (HIV-1) RNA binding protein 1
202814_s_at	HEXIM1	hexamethylene bis-acetamide inducible 1
202954_at	UBE2C	ubiquitin-conjugating enzyme E2C
203092_at	TIMM44	translocase of inner mitochondrial membrane 44 homolog (yeast)
203100_s_at	CDYL	chromodomain protein Y-like
203276_at	LMNB1	lamin B1
203694_s_at	DHX16	DEAH (Asp-Glu-Ala-His) box polypeptide 16
203763_at	DYNC2LI1	dynein cytoplasmic 2 light intermediate chain 1
203806_s_at	FANCA	Fanconi anemia complementation group A
203848_at	AKAP8	A kinase (PRKA) anchor protein 8
203984_s_at	CASP9	caspase 9 apoptosis-related cysteine peptidase
203991_s_at	KDM6A	lysine (K)-specific demethylase 6A
204056_s_at	MVK	mevalonate kinase
204109_s_at	NFYA	nuclear transcription factor Y alpha
204244_s_at	DBF4	DBF4 homolog ( S . cerevisiae )
204403_x_at	FAM115A	family with sequence similarity 115 member A
204535_s_at	REST	RE1-silencing transcription factor
204632_at	RPS6KA4	ribosomal protein S6 kinase 90 kDa polypeptide 4
205000_at	DDX3Y	DEAD (Asp-Glu-Ala-Asp) box polypeptide 3 Y-linked
205098_at	CCR1	chemokine (C-C motif) receptor 1
205150_s_at	TRIL	TLR4 interactor with leucine-rich repeats
205315_s_at	SNTB2	syntrophin beta 2 (dystrophin-associated protein A1 59 kDa basic
		component 2)
205345_at	BARD1	BRCA1 associated RING domain 1
205513_at	TCN1	transcobalamin I (vitamin B12 binding protein R binder family)
205591_at	OLFM1	olfactomedin 1
205718_at	ITGB7	integrin beta 7
205848_at	GAS2	growth arrest-specific 2
205908_s_at	OMD	osteomodulin
205948_at	PTPRT	protein tyrosine phosphatase receptor type T
205980_s_at	NA	NA
206045_s_at	NOL4	nucleolar protein 4
206173_x_at	GABPB1	GA binding protein transcription factor beta subunit 1
206253_at	DLG2	discs large homolog 2 ( Drosophila )
206302_s_at	NA	NA
206445_s_at	PRMT1	protein arginine methyltransferase 1
206525_at	GABRR1	gamma-aminobutyric acid (GABA) receptor rho 1
206805_at	SEMA3A	sema domain immunoglobulin domain (Ig) short basic domain
		secreted (semaphorin) 3A
206806_at	DGKI	diacylglycerol kinase iota
207029_at	KITLG	KIT ligand
207700_s_at	NCOA3	nuclear receptor coactivator 3
207864_at	SCN7A	sodium channel voltage-gated type VII alpha
207871_s_at	ST7	suppression of tumorigenicity 7
208017_s_at	MCF2	MCF.2 cell line derived transforming sequence
208311_at	GPR50	G protein-coupled receptor 50
208955_at	DUT	deoxyuridine triphosphatase
209192_x_at	KAT5	K(lysine) acetyltransferase 5
209346_s_at	PI4K2A	phosphatidylinositol 4-kinase type 2 alpha
209412_at	TRAPPC10	trafficking protein particle complex 10
209598_at	PNMA2	paraneoplastic antigen MA2
209620_s_at	ABCB7	ATP-binding cassette sub-family B (MDR/TAP) member 7
209621_s_at	PDLIM3	PDZ and LIM domain 3
209658_at	CDC16	cell division cycle 16 homolog ( S . cerevisiae )
209829_at	FAM65B	family with sequence similarity 65 member B
210033_s_at	SPAG6	sperm associated antigen 6
210034_s_at	RPLS	ribosomal protein L5
210078_s_at	KCNAB1	potassium voltage-gated channel shaker-related subfamily beta
		member 1
210386_s_at	MTX1	metaxin 1
210394_x_at	NA	NA
210470_x_at	NONO	non-POU domain containing octamer-binding
210491_at	NA	NA
210493_s_at	MFAP3L	microfibrillar-associated protein 3-like
210697_at	ZNF257	zinc finger protein 257
211238_at	ADAM7	ADAM metallopeptidase domain 7
211691_x_at	NA	NA
212125_at	RANGAP1	Ran GTPase activating protein 1
212338_at	MYO1D	myosin ID
212340_at	YIPF6	Yip1 domain family member 6
212408_at	TOR1AIP1	torsin A interacting protein 1
212412_at	PDLIMS	PDZ and LIM domain 5
212413_at	6-Sep	septin 6
212614_at	ARID5B	AT rich interactive domain 5B (MRF1-like)
212615_at	CHD9	chromodomain helicase DNA binding protein 9
212696_s_at	RNF4	ring finger protein 4
212739_s_at	NME4	non-metastatic cells 4 protein expressed in
212774_at	ZNF238	zinc finger protein 238
212849_at	AXIN1	axin 1
213370_s_at	SFMBT1	Scm-like with four mbt domains 1
213621_s_at	GUK1	guanylate kinase 1
213793_s_at	HOMER1	homer homolog 1 ( Drosophila )
213912_at	TBC1D30	TBC1 domain family member 30
214937_x_at	PCM1	pericentriolar material 1
215367_at	KIAA1614	KIAA1614
215927_at	ARFGEF2	ADP-ribosylation factor guanine nucleotide-exchange factor 2
		(brefeldin A-inhibited)
215945_s_at	TRIM2	tripartite motif containing 2
216064_s_at	AGA	aspartylglucosaminidase
216718_at	LINC00302	long intergenic non-protein coding RNA 302
216753_at	NA	NA
217436_x_at	NA	NA
217440_at	NA	NA
217496_s_at	IDE	insulin-degrading enzyme
217892_s_at	LIMA1	LIM domain and actin binding 1
217996_at	PHLDA1	pleckstrin homology-like domain family A member 1
218111_s_at	CMAS	cytidine monophosphate N-acetylneuraminic acid synthetase
218962_s_at	TMEM168	transmembrane protein 168
218987_at	ATF7IP	activating transcription factor 7 interacting protein
219045_at	RHOF	ras homolog gene family member F (in filopodia)
219086_at	ZNF839	zinc finger protein 839
219136_s_at	LMF1	lipase maturation factor 1
219507_at	RSRC1	arginine/serine-rich coiled-coil 1
219508_at	GCNT3	glucosaminyl (N-acetyl) transferase 3 mucin type
219569_s_at	TMEM22	transmembrane protein 22
219677_at	SPSB1	splA/ryanodine receptor domain and SOCS box containing 1
219680_at	NLRX1	NLR family member X1
219744_at	FN3K	fructosamine 3 kinase
219747_at	NDNF	neuron-derived neurotrophic factor
220068_at	VPREB3	pre-B lymphocyte 3
220077_at	CCDC134	coiled-coil domain containing 134
220385_at	JPH2	junctophilin 2
221240_s_at	B3GNT4	UDP-GlcNAc:betaGal beta-13-N-acetylglucosaminyltransferase 4
221245_s_at	FZD5	frizzled family receptor 5
221428_s_at	TBL1XR1	transducin (beta)-like 1 X-linked receptor 1
221592_at	NA	NA
221607_x_at	ACTG1	actin gamma 1
221608_at	WNT6	wingless-type MMTV integration site family member 6
221630_s_at	DDX4	DEAD (Asp-Glu-Ala-Asp) box polypeptide 4
222150_s_at	PION	pigeon homolog ( Drosophila )
222285_at	NA	NA
222288_at	NA	NA
222289_at	KCNC2	potassium voltage-gated channel Shaw-related subfamily
		member 2
35201_at	HNRNPL	heterogeneous nuclear ribonucleoprotein L
38487_at	STAB1	stabilin 1
38703_at	DNPEP	aspartyl aminopeptidase
39549_at	NPAS2	neuronal PAS domain protein 2
41657_at	STK11	serine/threonine kinase 11
41858_at	PGAP2	post-GPI attachment to proteins 2
52255_s_at	COL5A3	collagen type V alpha 3
74694_s_at	RABEP2	rabaptin RAB GTPase binding effector protein 2

TABLE 11 — MRSA v. MSSA

	Gene		Entrez
Probe Set ID	Symbol	Gene Title	Gene ID	p-value
1420021_s_at	Suz12	suppressor of zeste 12 homolog ( Drosophila )	52615	5.54E−07
1422842_at	Xrn2	5′-3′ exoribonuclease 2	24128	9.49E−07
1429432_at	Bat212	HLA-B associated transcript 2-like 2	226562	7.86E−07
1434391_at	AI503316	expressed sequence AI503316	105860	1.02E−06
1439247_at	Dock10	dedicator of cytokinesis 10	210293	1.02E−06
1444279_at	Huwe1	HECT, UBA and WWE domain containing 1	59026	3.22E−07
1446384_at	—	(Unannotated)	—	1.33E−07
1446512_at	Zc3h15	zinc finger CCCH-type containing 15	69082	2.34E−07
1449578_at	Supt16h	suppressor of Ty 16 homolog ( S . cerevisiae )	114741	2.91E−07
1450051_at	Atrx	alpha thalassemia/mental retardation syndrome	22589	6.07E−07
		X-linked homolog (human)
1451685_at	Mllt6	myeloid/lymphoid or mixed-lineage leukemia	246198	8.09E−07
		(trithorax homolog, Drosophila ); translocated to, 6
1452470_at	Cep350	centrosomal protein 350	74081	1.03E−06
1456112_at	Tpr	translocated promoter region	108989	8.10E−07
1457731_at	Snapc3	small nuclear RNA activating complex,	77634	3.80E−07
		polypeptide 3
1459398_at	Peli1	Pellino 1	67245	7.47E−07

TABLE 12 — Pathway analysis for the genes from pairwise comparisons in the mouse and human study. Asterisk (*) indicates pathways that are present in both the mouse and human response to the specified pathogen. MOUSE S. aureus VS HEALTHY CONTROLS

#	Pathway	p-value
1	Immune response_HMGB1/RAGE signaling pathway	1.58E−14
2	* Immune response_T cell receptor signaling pathway	7.40E−14
3	* Immune response_CD28 signaling	2.75E−13
4	* Transcription_NF-kB signaling pathway	5.45E−13
5	Immune response_Oncostatin M signaling via MAPK in	1.24E−12
	human cells
6	* Transport_Clathrin-coated vesicle cycle	1.29E−12
7	Signal transduction_JNK pathway	5.29E−12
8	Apoptosis and survival_APRIL and BAFF signaling	5.95E−12
9	Immune response_Regulation of T cell function by	6.32E−12
	CTLA-4
10	* Immune response_ICOS pathway in T-helper cell	8.69E−12
11	* Immune response_TCR and CD28 co-stimulation in	1.24E−11
	activation of NF-kB
12	Immune response_IL-3 activation and signaling pathway	1.38E−11
13	Development_Flt3 signaling	2.06E−11
14	Immune response_IL-1 signaling pathway	2.06E−11
15	Immune response_CD137 signaling in immune cell	3.03E−11
16	Immune response_Oncostatin M signaling via MAPK in	3.18E−11
	mouse cells
17	G-protein signaling_Regulation of p38 and JNK signaling	5.86E−11
	mediated by G-proteins
18	Immune response_IL-17 signaling pathways	5.88E−11
19	Reproduction_GnRH signaling	7.27E−11
20	* Immune response_CD40 signaling	1.05E−10
21	Development_TGF-beta-dependent induction of EMT	1.30E−10
	via MAPK
22	Development_Prolactin receptor signaling	1.39E−10
23	Apoptosis and survival_Lymphotoxin-beta receptor	4.08E−10
	signaling
24	Immune response_Gastrin in inflammatory response	6.02E−10
25	* Development_GM-CSF signaling	6.76E−10
26	* Immune response_Function of MEF2 in T lymphocytes	6.76E−10
27	Immune response_IL-22 signaling pathway	7.58E−10
28	Immune response_CCR5 signaling in macrophages and	8.59E−10
	T lymphocytes
29	Immune response_MIF in innate immunity response	9.71E−10
30	Immune response_Fc epsilon RI pathway	1.27E−09
31	* Immune response_Immunological synapse formation	1.36E−09
32	* Immune response_CXCR4 signaling via second	1.52E−09
	messenger
33	Chemotaxis_CXCR4 signaling pathway	1.52E−09
34	Immune response_MIF-mediated glucocorticoid regulation	1.68E−09
35	* Cytoskeleton remodeling_TGF, WNT and cytoskeletal	1.68E−09
	remodeling
36	Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/	1.76E−09
	Bcl-2 pathway
37	Immune response_IL-15 signaling	2.14E−09
38	Immune response_CD16 signaling in NK cells	3.12E−09
39	Cell adhesion_Chemokines and adhesion	3.81E−09
40	Cytokine production by Th17 cells in CF	4.20E−09
41	Mucin expression in CF via TLRs, EGFR signaling	4.38E−09
	pathways
42	G-protein signaling_Ras family GTPases in kinase	4.44E−09
	cascades (scheme)
43	Immune response_HMGB1/TLR signaling pathway	5.51E−09
44	Chemotaxis_Leukocyte chemotaxis	6.15E−09
45	Apoptosis and survival_Regulation of Apoptosis by	6.98E−09
	Mitochondrial Proteins
46	G-protein signaling_N-RAS regulation pathway	6.98E−09
47	* Immune response_NFAT in immune response	7.06E−09
48	Immune response_TREM1 signaling pathway	7.58E−09
49	* Immune response_IL-27 signaling pathway	1.01E−08
50	* Development_NOTCH1-mediated pathway for NF-KB	1.31E−08
	activity modulation

TABLE 13 — Pathway analysis for the genes from pairwise comparisons in the mouse and human study. Asterisk (*) indicates pathways that are present in both the mouse and human response to the specified pathogen. HUMAN S. aureus VS. HEALTHY CONTROLS

#	Pathway	p-value
1	* Cytoskeleton remodeling_TGF, WNT and cytoskeletal	5.45E−11
	remodeling
2	* Immune response_NFAT in immune response	1.13E−10
3	Cytoskeleton remodeling_Cytoskeleton remodeling	1.45E−09
4	Protein folding and maturation_POMC processing	1.79E−09
5	Immune response_IL-4 signaling pathway	1.43E−08
6	Oxidative phosphorylation	1.47E−08
7	* Immune response_CD28 signaling	2.13E−08
8	* Immune response_T cell receptor signaling pathway	3.90E−08
9	* Immune response_ICOS pathway in T-helper cell	4.51E−08
10	* Immune response_TCR and CD28 co-stimulation in	4.70E−08
	activation of NF-kB
11	* Immune response_Function of MEF2 in T lymphocytes	7.12E−08
12	Transcription_Role of heterochromatin protein 1 (HP1)	1.31E−07
	family in transcriptional silencing
13	Signal transduction_Activation of PKC via G-Protein	1.90E−07
	coupled receptor
14	Signal transduction_AKT signaling	2.59E−07
15	Development_TGF-beta receptor signaling	3.47E−07
16	Development_Role of HDAC and calcium/calmodulin-	4.73E−07
	dependent kinase (CaMK) in control of skeletal
	myogenesis
17	Immune response_BCR pathway	4.73E−07
18	Some pathways of EMT in cancer cells	5.51E−07
19	Regulation of degradation of deltaF508 CFTR in CF	8.47E−07
20	Apoptosis and survival_Granzyme A signaling	9.21E−07
21	Immune response_NF-AT signaling and leukocyte	1.16E−06
	interactions
22	Apoptosis and survival_Endoplasmic reticulum stress	1.32E−06
	response pathway
23	Development_WNT signaling pathway. Part 2	1.32E−06
24	Immune response_Antigen presentation by MHC class I	1.67E−06
25	Development_PIP3 signaling in cardiac myocytes	1.83E−06
26	* Immune response_CD40 signaling	2.34E−06
27	Cytoskeleton remodeling_Regulation of actin	2.68E−06
	cytoskeleton by Rho GTPases
28	Immune response_IL-10 signaling pathway	3.01E−06
29	Development_PDGF signaling via STATs and NF-kB	3.09E−06
30	* Immune response_Immunological synapse formation	3.58E−06
31	Immune response_IL-2 activation and signaling pathway	4.34E−06
32	Translation_Regulation of EIF4F activity	5.37E−06
33	* Immune response_IL-27 signaling pathway	5.44E−06
34	Immune response_Bacterial infections in normal airways	6.52E−06
35	Immune response_Th1 and Th2 cell differentiation	7.07E−06
36	Development_Leptin signaling via PI3K-dependent	7.86E−06
	pathway
38	* Development_NOTCH1-mediated pathway for NF-KB	9.05E−06
	activity modulation
37	* Immune response_CXCR4 signaling via second	9.05E−06
	messenger
39	CFTR folding and maturation (norm and CF)	9.76E−06
40	Immune response_CCR3 signaling in eosinophils	1.02E−05
41	PGE2 pathways in cancer	1.14E−05
42	Develosment_IGF-1 receptor signaling	1.41E−05
43	Immune response_Antigen presentation by MHC class II	1.51E−05
44	* Transport_Clathrin-coated vesicle cycle	1.67E−05
45	Translation_Insulin regulation of translation	1.71E−05
46	Signal transduction_IP3 signaling	1.73E−05
47	Apoptosis and survival_nAChR in apoptosis inhibition	1.82E−05
	and cell cycle progression
48	* Transcription_NF-kB signaling pathway	2.03E−05
49	G-protein signaling_Regulation of RAC1 activity	2.37E−05
50	* Development_GM-CSF signaling	2.52E−05

TABLE 14 — Pathway analysis for the genes from pairwise comparisons in the mouse and human study. Asterisk (*) indicates pathways that are present in both the mouse and human response to the specified pathogen. MOUSE E. coli VS. HEALTHY CONTROLS

#	Pathway	p-value
1	* Development VEGF signaling via VEGFR2 - generic	5.51E−14
	cascades
2	Immune response_CCR5 signaling in macrophages and	4.40E−13
	T lymphocytes
3	* Immune response_CD28 signaling	6.16E−12
4	Development_A2A receptor signaling	3.10E−11
5	Cytoskeleton remodeling_Role of PKA in cytoskeleton	7.11E−11
	reorganisation
6	Apoptosis and survival_Granzyme B signaling	1.13E−10
7	* Immune response_Gastrin in inflammatory response	2.52E−10
8	* Development_Prolactin receptor signaling	2.63E−10
9	* Immune response_HMGB1/RAGE signaling pathway	2.97E−10
10	Apoptosis and survival_BAD phosphorylation	1.82E−09
11	Immune response_TREM1 signaling pathway	2.69E−09
12	Apoptosis and survival_Anti-apoptotic action of Gastrin	2.81E−09
13	* Cytoskeleton remodeling_TGF, WNT and cytoskeletal	3.96E−09
	remodeling
14	Immune response_IL-1 signaling pathway	4.27E−09
15	Development_GM-CSF signaling	5.64E−09
16	* Immune response_Function of MEF2 in T lymphocytes	5.64E−09
17	Development_Growth hormone signaling via STATs and	6.47E−09
	PLC/IP3
18	Cytoskeleton remodeling_Reverse signaling by ephrin B	9.11E−09
19	Immune response_HMGB1 release from the cell	1.04E−08
20	Immune response_HMGB1/TLR signaling pathway	1.04E−08
21	Immune response_Inhibitory action of Lipoxins on pro-	1.38E−08
	inflammatory TNF-alpha signaling
22	Blood coagulation_GPCRs in platelet aggregation	1.73E−08
23	* Immune response_Histamine H1 receptor signaling in	2.00E−08
	immune response
24	* Immune response_IFN gamma signaling pathway	2.29E−08
25	* Immune response_IL-22 signaling pathway	2.45E−08
26	Signal transduction_cAMP signaling	2.51E−08
27	* Cell adhesion_Chemokines and adhesion	3.31E−08
28	Development_Flt3 signaling	3.40E−08
29	Signal transduction_Erk Interactions: Inhibition of Erk	3.89E−08
30	G-protein signaling G-Protein alpha-q signaling cascades	3.89E−08
31	Development_Gastrin in cell growth and proliferation	4.41E−08
32	Chemotaxis_Leukocyte chemotaxis	5.03E−08
33	Development_EGFR signaling pathway	5.89E−08
34	Chemotaxis_Lipoxin inhibitory action on fMLP-induced	7.04E−08
	neutrophil chemotaxis
35	* Immune response_ICOS pathway in T-helper cell	7.04E−08
36	* Immune response_IL-15 signaling	7.81E−08
37	Signal transduction_Activation of PKC via G-Protein	7.83E−08
	coupled receptor
38	Regulation of CFTR activity (norm and CF)	8.05E−08
39	Immune response_Fc gamma R-mediated phagocytosis in	9.96E−08
	macrophages
40	G-protein signaling_G-Protein alpha-12 signaling pathway	1.39E−07
41	Cell adhesion_Role of tetraspanins in the integrin-	1.39E−07
	mediated cell adhesion
42	Apoptosis and survival_HTR1A signaling	2.65E−07
43	Immune response_Bacterial infections in normal airways	2.65E−07
44	HIV-1 signaling via CCR5 in macrophages and T	2.99E−07
	lymphocytes
45	Transcription_NF-kB signaling pathway	2.99E−07
46	Immune response_CXCR4 signaling via second	3.39E−07
	messenger
47	Cell adhesion_Ephrin signaling	3.44E−07
48	Cell adhesion_Histamine H1 receptor signaling in the	3.44E−07
	interruption of cell barrier integrity
49	* Immune response_NFAT in immune response	3.60E−07
50	Some pathways of EMT in cancer cells	3.60E−07

TABLE 15 — Pathway analysis for the genes from pairwise comparisons in the mouse and human study. Asterisk (*) indicates pathways that are present in both the mouse and human response to the specified pathogen. HUMAN E. coli VS. HEALTHY CONTROLS

#	Pathway	p-value
1	* Cytoskeleton remodeling_TGF, WNT and cytoskeletal	2.76E−10
	remodeling
2	* Immune response_HMGB1/RAGE signaling pathway	1.15E−09
3	Development_EPO-induced Jak-STAT pathway	6.64E−09
4	Immune response_Oncostatin M signaling via MAPK in	6.64E−09
	mouse cells
5	Cytoskeleton remodeling_Cytoskeleton remodeling	7.59E−09
6	Protein folding and maturation_POMC processing	9.88E−09
7	* Development_Prolactin receptor signaling	1.59E−08
8	Immune response_IL-2 activation and signaling pathway	2.29E−08
9	* Immune response_ICOS pathway in T-helper cell	2.48E−08
10	Immune response_Immunological synapse formation	2.56E−08
11	Immune response_Oncostatin M signaling via MAPK in	2.64E−08
	human cells
12	* Immune response_NFAT in immune response	6.40E−08
13	Transcription_Role of heterochromatin protein 1 (HP1)	8.70E−08
	family in transcriptional silencing
14	Immune response_T cell receptor signaling pathway	1.04E−07
15	* Immune response_Function of MEF2 in T lymphocytes	1.95E−07
16	Immune response_IL-4 signaling pathway	2.55E−07
18	* Immune response__IFN gamma signaling pathway	2.63E−07
19	Immune response_BCR pathway	2.63E−07
17	* Immune response_CD28 signaling	2.63E−07
20	* Immune response_Histamine H1 receptor signaling in	3.65E−07
	immune response
21	Immune response_CD16 signaling in NK cells	3.87E−07
22	PGE2 pathways in cancer	4.07E−07
23	* Development_VEGF signaling via VEGFR2 - generic	4.14E−07
	cascades
24	Development_Thrombopoietin-regulated cell processes	4.21E−07
25	Immune response_IL-4 - antiapoptotic action	5.94E−07
26	Normal and pathological TGF-beta-mediated regulation	5.96E−07
	of cell proliferation
27	Immune response_Th1 and Th2 cell differentiation	8.97E−07
28	Development_TGF-beta receptor signaling	9.11E−07
29	Chemotaxis_CXCR4 signaling pathway	1.07E−06
30	Development_PIP3 signaling in cardiac myocytes	1.09E−06
31	Development_Role of HDAC and calcium/calmodulin-	1.15E−06
	dependent kinase (CaMK) in control of skeletal
	myogenesis
32	* Immune response_Gastrin in inflammatory response	1.44E−06
33	* Cell adhesion_Chemokines and adhesion	1.52E−06
34	Transcription_Role of AP-1 in regulation of cellular	1.69E−06
	metabolism
35	Transcription_Sin3 and NuRD in transcription regulation	1.69E−06
37	Development_Growth hormone signaling via PI3K/AKT	2.40E−06
	and MAPK cascades
36	Translation_Insulin regulation of translation	2.40E−06
38	Signal transduction_IP3 signaling	2.61E−06
39	Translation_Regulation of EIF2 activity	2.78E−06
40	Immune response_NF-AT signaling and leukocyte	3.16E−06
	interactions
43	Immune response_IL-12-induced IFN-gamma production	3.17E−06
42	Immune response_IL-9 signaling pathway	3.17E−06
41	Immune response_Regulation of T cell function by	3.17E−06
	CTLA-4
44	Translation_Regulation of EIF4F activity	3.18E−06
45	* Immune response_IL-15 signaling	3.32E−06
47	G-protein signaling_N-RAS regulation pathway	3.52E−06
46	* Immune response_IL-22 signaling pathway	3.52E−06
48	Immune response_IL-27 signaling pathway	3.77E−06
49	Apoptosis and survival_Granzyme A signaling	3.78E−06
50	Immune response_Signaling pathway mediated by IL-6	3.78E−06
	and IL-1

TABLE 16 — Pathway analysis for the genes from pairwise comparisons in the mouse and human study. MOUSE S . aureus VS. E . coli

#	Pathway	p-value
1	Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling	4.98E−13
2	Development_IGF-1 receptor signaling	1.17E−12
3	Transport_Clathrin-coated vesicle cycle	1.27E−12
4	Development_PIP3 signaling in cardiac myocytes	2.24E−12
5	Immune response_HMGB1/RAGE signaling pathway	2.32E−12
6	Immune response_IL-15 signaling	6.07E−12
7	Immune response_IL-2 activation and signaling pathway	9.24E−12
8	Development_GM-CSF signaling	1.80E−11
9	Immune response_HMGB1/TLR signaling pathway	2.74E−11
10	Apoptosis and survival_APRIL and BAFF signaling	3.22E−11
11	Immune response_IL-3 activation and signaling pathway	3.84E−11
12	Cell adhesion_Chemokines and adhesion	4.55E−11
13	Immune response_CD40 signaling	6.24E−11
14	Signal transduction_AKT signaling	7.25E−11
15	Immune response_Gastrin in inflammatory response	8.44E−11
16	Cytoskeleton remodeling_Cytoskeleton remodeling	3.97E−10
17	Development_NOTCH1-mediated pathway for NF-KB activity modulation	5.11E−10
18	Cell cycle_Influence of Ras and Rho proteins on G1/S Transition	7.37E−10
19	Apoptosis and survival_HTR1A signaling	8.53E−10
20	Immune response_IL-1 signaling pathway	1.06E−09
21	Transcription_NF-kB signaling pathway	2.25E−09
22	Immune response_Regulation of T cell function by CTLA-4	2.27E−09
23	Immune response_IL-17 signaling pathways	4.61E−09
24	Immune response_Bacterial infections in normal airways	5.21E−09
25	Mucin expression in CF via TLRs, EGFR signaling pathways	5.21E−09
26	Development_TGF-beta-dependent induction of EMT via MAPK	6.16E−09
27	Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/Bcl-2 pathway	7.98E−09
28	Development_EPO-induced Jak-STAT pathway	9.02E−09
29	Development_VEGF signaling via VEGFR2-generic cascades	1.01E−08
30	Immune response_Histamine H1 receptor signaling in immune response	1.06E−08
31	Apoptosis and survival_Regulation of Apoptosis by Mitochondrial Proteins	1.80E−08
32	Apoptosis and survival_Endoplasmic reticulum stress response pathway	2.37E−08
33	Immune response_Oncostatin M signaling via MAPK in human cells	3.27E−08
34	Development_Role of HDAC and calcium/calmodulin-dependent kinase	3.79E−08
	(CaMK) in control of skeletal myogenesis
35	Development_FGFR signaling pathway	3.79E−08
36	Immune response_BCR pathway	3.79E−08
37	Development_Flt3 signaling	4.37E−08
38	Immune response_CCR5 signaling in macrophages and T lymphocytes	4.62E−08
39	Apoptosis and survival_Granzyme B signaling	7.07E−08
40	NGF activation of NF-kB	7.15E−08
41	Development_Thrombopoietin-regulated cell processes	7.35E−08
42	Apoptosis and survival_Lymphotoxin-beta receptor signaling	8.88E−08
43	Development_Growth hormone signaling via PI3K/AKT and MAPK cascades	8.88E−08
44	Development_PEDF signaling	9.68E−08
45	Development_A3 receptor signaling	9.68E−08
46	Transcription_Receptor-mediated HIF regulation	1.05E−07
47	IL-1 beta-dependent CFTR expression	1.19E−07
48	Translation_Regulation of EIF4F activity	1.20E−07
49	Immune response_ICOS pathway in T-helper cell	1.21E−07
50	Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway	1.31E−07

TABLE 17 — Genes in pathways common to murine and human responses to infection. Pairwise

comparison	Pathway	Genes
S.	aureus
Cytoskeleton	Human genes:
vs. Healthy	remodeling_TGF, WNT	AKT1 AKT3 ACTG1 ACTR3 ACTR3B ARPC1A ARPC2
	and cytoskeletal	AXIN1 CRK CSNK2A1 CSNK2B COL4A2 COL4A3
	remodeling	DSTN DVL1 DVL2 DVL3 PTK2 FZD3 FZD5 FZD7
		GSK3B CHUK LIMK2 LAMA1 LAMC1 MYL6 MYL6B
		PPP1CB PPP1R12A MMP13 MKNK1 MYL12A MYL12B
		RPS6KA5 NCL PIK3CB PIK3R1 PIK3R3 PLAUR PPARD
		RHEB ROCK2 SMAD3 SOS2 TAB1 TCF7L2 TGFB1
		TGFBR1 TLN2 LEF1 TCF7 TSC2 VAV1 VEGFA VCL
		MYC SRC MTOR CDKN2B MAPK13 MAPK14 TP53
		Murine genes:
		Akt1 Akt3 Acta2 Actb Actg1 Actn4 Actr2 Actr3 Arpc1b
		Arpc4 Arpc5 Axin2 Ctnnb1 Cdc42 Crk Col4a2 Dstn Dvl2
		Mapk1 Fzd1 Fzd7 Grb2 Mdm2 Ppp1r12a Map3k11 Rps6ka5
		Nlk Serpine1 Pak1 Plaur Ppard Pxn Rheb Rhoa Sos1 Sos2
		Map3k7 Tgfbr2 Lef1 Tcf7 Vav1 Jun Myc Raf1 Eif4e
		Cdkn1a Mapk11 Mapk13
	Immune response_NFAT	Human genes:
	in immune response	AKT1 AKT3 BLNK CD28 CD247 CD3D CD3E CD3G
		CD79A CD79B PPP3CC CALM1 CALM3 FCER1A
		FCER1G GSK3B NFKBIA CHUK IKBKB ITPR1 ITPR3
		ITK IGHV3-23 IGKC IGK@ IGL@ IGHM LAT LCK LYN
		HLA-DMA HLA-DMB HLA-DOA HLA-DOB HLA-DPA1
		HLA-DPB1 HLA-DQA1 HLA-DQA2 HLA-DQB1 HLA-
		DRA HLA-DRB1 HLA-DRB3 HLA-DRB4 HLA-DRB5
		NFAT5 NFATC1 NFATC3 REL RELA PIK3CB PIK3R1
		PIK3R3 PRKCQ PLCG1 TRAC TRA@ TRAT1 VAV1
		ZAP70
		Murine genes:
		Akt1 Akt3 Blnk Cd28 Cd247 Cd3d Cd3e Cd3g Cd79a
		Cd79b Cd80 Cd86 Fcer1g Ms4a2 Nfkbia Nfkbib Nfkbie
		Ikbkb Itpr2 Itpr3 Itk Igk Lat Lck H2-Eb2 Nfatc2 Nfatc3 Relb
		Nfkb2 Rel Rela Prkcq Plcg1 Lcp2 Tcrb-J Trat1 Vav1 Zap70
	Immune response_CD28	Human genes:
	signaling	AKT1 AKT3 BAD BCL2L1 CD28 CD247 CD3D CD3E
		CD3G PPP3CC CALM1 CALM3 FYN GRAP2 GSK3B
		NFKBIA CHUK IKBKB ITPR1 ITPR3 ITK LAT LCK
		NFAT5 NFATC1 NFATC3 REL RELA PIK3CB PIK3R1
		PIK3R3 PRKCQ PLCG1 TRAC TRA@ VAV1 ZAP70
		Murine genes:
		Akt1 Akt3 Bad Bcl2l1 Cd28 Cd247 Cd3d Cd3e Cd3g Cd80
		Cd86 Fyn Grap2 Grb2 Nfkbia Nfkbib Nfkbie Ikbkb Itpr2
		Itpr3 Itk Mapk8 Lat Lck Map2k4 Map2k7 Nfatc2 Nfatc3
		Relb Nfkb2 Rel Rela Pak1 Prkcq Plcg1 Lcp2 Tcrb-J Vav1
		Zap70 Jun
	Immune response_T cell	Human genes:
	receptor signaling	CD247 CD3D CD3E CD3G CD4 RASGRP1 PPP3CC
	pathway	CALM1 CALM3 ELK1 FYN NFKBIA CHUK IKBKB
		ITPR1 ITPR3 ITK LAT LCK MALT1 HLA-DMA HLA-
		DMB HLA-DOA HLA-DOB HLA-DPA1 HLA-DPB1
		HLA-DQA1 HLA-DQA2 HLA-DQB1 HLA-DRA HLA-
		DRB1 HLA-DRB3 HLA-DRB4 HLA-DRB5 NFATC1
		NFATC3 REL RELA PRKCQ PLCG1 SOS2 TRAC TRA@
		TRAT1 VAV1 ZAP70
		Murine genes:
		Bcl10 Card11 Cd247 Cd3d Cd3e Cd3g Cd4 Rasgrp1 Mapk1
		Elk1 Fyn Grb2 Nfkbia Nfkbib Nfkbie Ikbkb Itpr2 Itpr3 Itk
		Lat Lck H2-Eb2 Ube2v2 Nfatc2 Nfatc3 Relb Nfkb2 Rel
		Rela Prkcq Plcg1 Sos1 Sos2 Lcp2 Tcrb-J Trat1 Vav1 Zap70
		Fos Raf1
	Immune response_ICOS	Human genes:
	pathway in T-helper cell	AKT1 AKT3 BAD CD28 CD247 CD3D CD3E CD3G
		PPP3CC CALM1 CALM3 GNA15 GNB5 GNB1 GNG10
		GNG3 GNG5 NFKBIA ICOS IKBKB ITPR1 ITPR3 ITK
		LAT LCK HLA-DMA HLA-DMB HLA-DOA HLA-DOB
		HLA-DPA1 HLA-DPB1 HLA-DQA1 HLA-DQA2 HLA-
		DQB1 HLA-DRA HLA-DRB1 HLA-DRB3 HLA-DRB4
		HLA-DRB5 REL RELA PIK3CB PIK3R1 PIK3R3 PRKCQ
		PLCG1 TRAC TRA@ TRAT1 VAV1 ZAP70
		Murine genes:
		Akt1 Akt3 Bad Cd28 Cd247 Cd3d Cd3e Cd3g Cd80 Cd86
		Cdc42 Gnb1 Gnb2 Gng12 Gng2 Gab2 Grb2 Nfkbia Nfkbib
		Nfkbie Ikbkb Itpr2 Itpr3 Itk Lat Lck H2-Eb2 Nfatc2 Relb
		Nfkb2 Rel Rela Pik3r5 Prkcq Plcg1 Lcp2 Tcrb-J Trat1 Vav1
		Zap70
	Immune response_TCR	Human genes:
	and CD28 co-stimulation	AKT1 AKT3 CD28 CD247 CD3D CD3E CD3G CD4
	in activation of NF-kB	GRAP2 NFKBIA CHUK IKBKB ITK LAT LCK MALT1
		HLA-DMA HLA-DMB HLA-DOA HLA-DOB HLA-DPA1
		HLA-DPB1 HLA-DQA1 HLA-DQA2 HLA-DQB1 HLA-
		DRA HLA-DRB1 HLA-DRB3 HLA-DRB4 HLA-DRB5
		REL RELA PIK3CB PIK3R1 PIK3R3 PRKCQ PLCG1
		TRAC TRA@ VAV1 ZAP70
		Murine genes:
		Akt1 Akt3 Bcl10 Card11 Cd28 Cd247 Cd3d Cd3e Cd3g Cd4
		Cd80 Cd86 Grap2 Grb2 Nfkbia Nfkbib Nfkbie Ikbkb Itk Lat
		Lck H2-Eb2 Ube2v2 Relb Nfkb2 Rel Rela Prkcq Plcg1 Lcp2
		Tcrb-J Vav1 Zap70
	Immune	Human genes:
	response_Function of	CABIN1 CALM1 CALM3 CAMK4 CAMKK2 CARM1
	MEF2 in T lymphocytes	CD247 CD3D CD3E CD3G HDAC1 HDAC4 HDAC5
		HDAC9 ITPR1 ITPR3 LAT LCK MAP2K5 MAP2K6
		MAPK14 MEF2A MEF2B MEF2C NCOA2 PLCG1
		PPP3CC PRKCA PRKCG PRKCH PRKCQ PRKCZ
		PRKD3 TRA@ TRAC YWHAQ ZAP70
		Murine genes:
		Cabin1 Camk4 Camkk1 Cd247 Cd3d Cd3e Cd3g Hdac4
		Hdac7 Il2 Itpr2 Itpr3 Jun Lat Lck Map2k6 Map3k3 Mapk11
		Mef2c Mef2d Ncoa2 Nfatc2 Nr4a1 Plcg1 Prkcd Prkce Prkcq
		Prkd2 Prkd3 Tcrb-J Ywhab Ywhae Ywhag Zap70
	Immune response_CD40	Human genes:
	signaling	AKT1 AKT3 BCL2L1 CCND2 CD40LG CFLAR CHUK
		FAS FASLG FCER2 ICAM1 IKBKB JAK2 JAK3 LTA
		LYN MAP3K14 MAPK13 MAPK14 NFKBIA PIK3CB
		PIK3R1 REL RELA TNFAIP3 TP53 TRAF1 TRAF3IP2
		TRAF5
		Murine genes:
		Akt1 Akt3 Bcl2l1 Cbl Ccl12 Cd80 Cd86 Cflar Fas Fcer2a
		Icam1 Ikbkb Il6 Irf1 Jak2 Jun Map2k4 Map3k14 Map3k7
		Mapk11 Mapk13 Mapk8 Nfkb2 Nfkbia Nfkbib Nfkbie Ptgs2
		Rel Rela Relb Smpd1 Stat3 Tnfaip3 Traf1 Traf5 Traf6
	Immune	Human genes:
	response_Immunological	ACTG1 ACTR3 ACTR3B ARF6 ARPC1A ARPC2 CD2
	synapse formation	CD247 CD28 CD3D CD3E CD3G CD4 CD58 CRKL
		CXCR4 CYTH1 DOCK2 FYN GNB1 GNB5 GNG10
		GNG3 GNG5 HLA-DMA HLA-DMB HLA-DOA HLA-
		DOB HLA-DPA1 HLA-DPB1 HLA-DQA1 HLA-DQA2
		HLA-DQB1 HLA-DRA HLA-DRB1 HLA-DRB3 HLA-
		DRB4 HLA-DRB5 ICAM1 ICAM2 ITPR1 ITPR3 PIK3CB
		PIK3R1 PIK3R3 PLCG1 RASGRP2 SKAP1 TLN2 TRA@
		TRAC VAV1 VCL
		Murine genes:
		Actb Actg1 Actn4 Actr2 Actr3 Arf6 Arpc1b Arpc4 Arpc5
		Cbl Cd2 Cd247 Cd28 Cd3d Cd3e Cd3g Cd4 Cd80 Cd86
		Cdc42 Cxcr4 Fyn Gnb1 Gnb2 Gng12 Gng2 Grb2 H2-Eb2
		Icam1 Icam2 Itgb2 Itpr2 Itpr3 Lcp2 Pik3r5 Plcg1 Rac2
		Rasgrp2 Skap1 Tcrb-J Vav1
	Immune response_IL-27	Human genes:
	signaling pathway	CD28 ICAM1 IFNGR1 IL10 IL12RB2 EBI3 IL27RA IL6ST
		JAK1 JAK2 REL RELA SOCS3 STAT1 TBX21 MYC
		Murine genes:
		Cd28 Icam1 Ifng Ifngr2 Il10 Il2 Ebi3 Il27ra Il6st Jak2 Relb
		Nfkb2 Rel Rela Pim1 Socs3 Stat1 Stat3 Itgb2 Myc
	Development_NOTCH1-	Human genes:
	mediated pathway for	ADAM17 HDAC1 RBBP4 RBBP7 H3F3A H3F3B
	NF-KB activity	HIST1H4B HIST1H4D HIST1H4E HIST1H4F HIST1H4H
	modulation	HIST1H4J HIST1H4K HIST2H4A HIST2H4B NFKBIA
		CHUK IKBKB IL1A IL1R1 JAG1 NCOR1 REL RELA
		MAP3K14 RBPJ SAP30 NCOR2 APH1B PSEN1 PSENEN
		Murine genes:
		Adam17 Rbbp4 H3f3b Hist1h4j Nfkbia Nfkbib Nfkbie
		Ikbkb Il1a Irak2 Jag1 Ncor1 Relb Nfkb2 Rel Rela Map3k14
		Notch1 Rbpj Sap30 Traf6 Aph1a Aph1b Aph1c Ncstn Psen1
	Immune	Human genes:
	response_CXCR4	AKT1 AKT3 CXCR4 FYN GNAI3 GNAZ GNB5 GNB1
	signaling via second	GNG10 GNG3 GNG5 NFKBIA CHUK IKBKB ITPR1
	messenger	ITPR3 ITK LAT LCK REL RELA PRKCQ PLCG1 ZAP70
		Murine genes:
		Akt1 Akt3 Cxcr4 Fyn Gnai1 Gnai2 Gnai3 Gnb1 Gnb2
		Gng12 Gng2 Nfkbia Nfkbib Nfkbie Ikbkb Itpr2 Itpr3 Itk Lat
		Lck Relb Nfkb2 Rel Rela Pik3r5 Prkcq Plcb3 Plcg1 Ptk2b
		Lcp2 Zap70
	Transport_Clathrin-	Human genes:
	coated vesicle cycle	AP1G1 AP1S1 AP2M1 ACTG1 BIN1 SAR1A SAR1B
		SEC24A SEC24C SEC24D SEC31A SEC31B CLTC COPA
		COPB1 COPZ1 SH3GLB1 EPN1 HIP1R MYO1E NSF
		PIK3C3 PIK3R4 RABGEF1 RAB11A RAB4A RAB7A
		RAB11FIP1 STX12 STX16 STX7 STX8 PLIN3 VAMP2
		VAMP4 VAMP7 YKT
		Murine genes:
		Ap1b1 Ap1g1 Ap1g2 Ap2a2 Ap2b1 Arf1 Acta2 Actb Actg1
		Bin1 Sar1a Sec23b Sec24b Sec24d Cltc Cltb Arcn1 Copb1
		Dab2 Eea1 Gosr1 Hip1 Hip1r Nsf Picalm Pikfyve Preb
		Rabgef1 Rab4b Rab5a Rab7 Rab8a Rab11fip1 Stx12 Stx16
		Stx6 Stx7 Vamp2 Vamp4 Vamp8 Vti1a Vti1b Ykt6
	Transcription_NF-kB	Human genes:
	signaling pathway	AKT1 AKT3 CD28 NFKBIA IKBKAP CHUK IKBKB
		IL1A IL1R1 IRAK3 LY96 MYD88 REL RELA MAP3K14
		PRKCQ TRAC TRA@ TNFRSF1B LTA TRADD
		Murine genes:
		Akt1 Akt3 Cd14 Cd28 Nfkbia Nfkbib Nfkbie Ikbkb Il1a
		Il1rap Irak2 Irak3 Lbp Ltbr Ly96 Myd88 Relb Nfkb2 Rel
		Rela Map3k14 Prkcq Ripk2 Tcrb-J Tlr4 Tnfrsf1a Tnfrsf1b
		Tnf Traf6
	Development_GM-CSF	Human genes:
	signaling	AKT1 AKT3 BAD BCL2 BCL2L1 CSF2RA CASP3 ELK1
		HCK NFKBIA CHUK IKBKB JAK2 LYN REL RELA
		PIK3CB PIK3R1 GNB2L1 SOS2 STAT5B MYC
		Murine genes:
		Akt1 Akt3 Bad Bcl2 Bcl2l1 Ccl12 Cish Csf2ra Csf2rb Egr1
		Mapk1 Elk1 Grb2 Hck Nfkbia Nfkbib Nfkbie Ikbkb Jak2
		Mcl1 Relb Nflcb2 Rel Rela Pim1 Sos1 Sos2 Stat3 Fos Myc
E. coli vs.	Cytoskeleton	Human genes:
Healthy	remodeling_TGF, WNT	AKT1 AKT3 ACTG1 ARPC1A ARPC2 SERPING1 CRK
	and cytoskeletal	CSNK2A1 CCND1 DVL2 DVL3 MAPK1 PTK2 FZD5
	remodeling	GRB2 GSK3B LIMK2 LAMA1 MYL6 MYL6B PPP1R12A
		MMP13 MKNK1 MYL12A MYL12B RPS6KA5 NCL
		PIK3CB PIK3R1 PIK3R3 PLAUR PPARD SMAD2
		SMAD3 SOS2 SHC1 TCF7L2 TGFB1 TGFBR1 LEF1
		TCF7 TSC2 VAV1 VCL WNT11 WNT8B XIAP JUN MYC
		SRC MTOR CDKN2B MAPK13 MAPK14 TP53
		Murine genes:
		Akt1 Akt2 Akt3 Acta2 Actb Actg1 Actr2 Arpc4 Ctnnb1
		Cdc42 Crk Csnk2a1 Csnk2a2 Cfl2 Col4a2 Dock1 Dstn Dvl3
		Ptk2 Foxo3 Fn1 Fzd1 Fzd3 Grb2 Chuk Limk2 Lamc1 Mdm2
		Ppp1cb Ppp1r12a Map3k11 Mknk1 My12 Rps6ka5 Ncl
		Serpine1 Pik3r3 Plaur Pxn Rheb Rock1 Rock2 Smad2 Sos1
		Sos2 Shc1 Map3k7 Tgfbr1 Vcl Wnt8b Wnt16 Wnt5a Jun
		Myc Raf1 Eif4e Mtor Cdkn1a Mapk13 Mapk14
	Immune	Human genes:
	response_HMGB1/RAGE	AKT1 AKT3 DIAPH1 MAPK1 PTK2 HMGB1 NFKBIA
	signaling pathway	NFKBIB ICAM1 IL1A IL1B IL1RN MAPK9 KRAS
		MEF2A MEF2C MAP2K6 MYOG RELB PIK3CB PIK3R1
		AGER TLR2 TNF JUN SRC NOS2 MAPK13 MAPK14
		Murine genes:
		Akt1 Akt2 Akt3 Cdc42 Diap1 Ptk2 Nflcbia Nfkbib Icam1
		Il1a Il1b Il6 Il1rn Kras Mef2a Mef2c Map2k6 Ccl4 Relb
		Nfkb2 Rel Rela Serpine1 Pxn Scg2 Tlr2 Tlr4 Tnf F3 Vcam1
		Jun Raf1 Mapk13 Mapk14 Rps6ka3
	Development_Prolactin	Human genes:
	receptor signaling	AKT1 AKT3 BCL2 CEBPB CREBBP CCND1 MAPK1
		FYN GRB2 IRS1 JAK2 NEK3 NMI PIK3CB PIK3R1
		PLCG1 PRL PTPN11 SOCS1 SOCS3 SOS2 STAT1
		STAT5B SHC1 VAV1 CBL JUN MYC SRC
		Murine genes:
		Akt1 Akt2 Akt3 Bcl2 Bcl2l1 Csn2 Cebpb Nr3c1 Grb2 Jak2
		Rela Socs1 Socs3 Sos1 Sos2 Stat3 Stat5a Shc1 Vav2 Cbl
		Jun Myc Raf1
	Immune response_ICOS	Human genes:
	pathway in T-helper cell	AKT1 AKT3 BAD CD28 CD247 CD3D CD3E CD3G
		PPP3CC CALM1 GNA15 GNB5 GNB1 GNG10 GNG5
		GRB2 NFKBIA NFKBIB ICOS IKBKB ITPR1 ITPR2
		ITPR3 ITK LAT LCK HLA-DMA HLA-DMB HLA-DOB
		HLA-DPA1 HLA-DPB1 HLA-DQA1 HLA-DQA2 HLA-
		DQB1 HLA-DRA HLA-DRB1 HLA-DRB3 HLA-DRB4
		HLA-DRB5 RELB PIK3CB PIK3CG PIK3R1 PIK3R3
		PRKCQ PLCG1 TRAC TRA@ VAV1 ZAP70
		Murine genes:
		Akt1 Akt2 Akt3 Cd247 Cd3g Cd80 Cd86 Cdc42 Ppp3ca
		Ppp3cb Gnb1 Gnb5 Gng10 Gng12 Gng13 Gngt1 Gngt2
		Gab2 Grb2 Nfkbia Nfkbib Icos1 Ikbkb Itpr3 Lat Lck H2-Eb2
		Nfatc2 Relb Nfkb2 Rel Rela Pdpk1 Pik3r3 Pik3r5 Lcp2
		Tcrb-J Trat1 Zap70
	Immune response_NFAT	Human genes:
	in immune response	AKT1 AKT3 BLNK CD28 CD247 CD3D CD3E CD3G
		CD79A CD79B PPP3CC CALM1 FCER1A FCER1G
		GSK3B NFKBIA NFKBIB IKBKB ITPR1 ITPR2 ITPR3
		ITK IGHV3-23 IGKC IGK@ IGL@ IGHM LAT LCK LYN
		HLA-DMA HLA-DMB HLA-DOB HLA-DPA1 HLA-
		DPB1 HLA-DQA1 HLA-DQA2 HLA-DQB1 HLA-DRA
		HLA-DRB1 HLA-DRB3 HLA-DRB4 HLA-DRB5 NFAT5
		NFATC1 NFATC3 RELB PIK3CB PIK3R1 PIK3R3
		PRKCQ PLCG1 TRAC TRA@ VAV1 ZAP70
		Murine genes:
		Akt1 Akt2 Akt3 Cd247 Cd3g Cd79a Cd79b Cd80 Cd86
		Ppp3ca Ppp3cb Ms4a2 Nfkbia Nfkbib Chuk Ikbkb Itpr3 Igk
		Lat Lck Lyn H2-Eb2 Nfat5 Nfatc2 Relb Nfkb2 Rel Rela
		Pik3r3 Lcp2 Syk Tcrb-J Trat1 Zap70
	Immune	Human genes:
	response_Function of	CABIN1 CALM1 CAMK4 CAMKK2 CD247 CD3D CD3E
	MEF2 in T lymphocytes	CD3G HDAC1 HDAC4 HDAC9 ITPR1 ITPR2 ITPR3 JUN
		LAT LCK MAP2K5 MAP2K6 MAPK14 MEF2A MEF2B
		MEF2C NCOA2 PLCG1 PPP3CC PRKCA PRKCH
		PRKCQ PRKD3 TRA@ TRAC YWHAQ ZAP70
		Murine genes:
		Cd247 Cd3g Hdac2 Hdac7 Itpr3 Jun Kat2b Lat Lck Map2k5
		Map2k6 Mapk14 Mapk7 Mef2a Mef2c Mef2d Ncoa2 Nfatc2
		Ppp3ca Ppp3cb Prkci Prkd2 Tcrb-J Ywhab Ywhae Ywhag
		Ywhaq Ywhaz Zap70
	Immune response__IFN	Human genes:
	gamma signaling pathway	AKT1 AKT3 CALM1 CAMK2G CBL CEBPB CREBBP
		CRKL ICAM1 IFNGR1 IFNGR2 ITPR1 ITPR2 ITPR3
		JAK1 JAK2 MAP2K6 MAP3K4 MAPK1 MAPK13
		MAPK14 MCM5 MYC PIK3CB PIK3R1 PIK3R3 PRKCA
		PTPN11 SMAD7 SOCS1 SRC STAT1 STAT2
		Murine genes:
		Afap1 Akt1 Akt2 Akt3 Cebpb Crk1 Icam1 Itpr3 Stat2 Jak2
		Mcm5 Map2k6 Map3k4 Pdpk1 Pik3r3 Ptk2b Smad7 Socs1
		Cbl Myc Cdkn1a Mapk13 Mapk14
	Immune response_CD28	Human genes:
	signaling	AKT1 AKT3 BAD CALM1 CD247 CD28 CD3D CD3E
		CD3G FYN GRB2 GSK3B IKBKB ITK ITPR1 ITPR2
		ITPR3 JUN LAT LCK MAPK9 NFAT5 NFATC1 NFATC3
		NFKBIA NFKBIB PIK3CB PIK3R1 PIK3R3 PLCG1
		PPP3CC PRKCQ RELB TRA@ TRAC VAV1 ZAP70
		Murine genes:
		Akt1 Akt2 Akt3 Bcl2l1 Cd247 Cd3g Cd80 Cd86 Chuk
		Grap2 Grb2 Ikbkb Itpr3 Jun Lat Lck Lcp2 Map2k4 Nfat5
		Nfatc2 Nfkb2 Nfkbia Nfkbib Pik3r3 Pip5k1a Ppp3ca Ppp3cb
		Rel Rela Relb Tcrb-J Zap70
	Immune	Human genes:
	response_Histamine H1	CALM1 GNA11 GNB1 GNB5 GNG10 GNG5 ICAM1
	receptor signaling in	IKBKB ITPR1 ITPR2 ITPR3 JUN MAPK1 MAPK13
	immune response	MAPK14 MAPK9 MMP1 MMP13 MMP3 MMP9 NFATC1
		NFKBIA NFKBIB NOS2 PLA2G4A PLCB2 PPP3CC
		PRKCA TNF
		Murine genes:
		Chuk F3 Fos Gnaq Gnb1 Gnb5 Gng10 Gng12 Gng13 Gngt1
		Gngt2 Icam1 Ikbkb Il6 Itpr3 Jun Mapk13 Mapk14 Mmp1a
		Nfkbia Nfkbib Nos3 Pla2g4a Pla2g4c Plcb1 Plcb4 Ppp3ca
		Ppp3cb Ppp3r1 Raf1 Rela Tnf Vcam1
	Development_VEGF	Human genes:
	signaling via VEGFR2 -	ACTG1 AKT1 AKT3 FYN GRB2 GSK3B HSP90AA1
	generic cascades	HSPB1 IKBKB ITPR1 ITPR2 ITPR3 JUN MAPK1
		MAPK13 MAPK14 MAPKAPK2 NF1 NFATC1 NFKBIA
		NFKBIB NOS2 PIK3CB PIK3R1 PIK3R3 PLAUR PLCG1
		PPP3CC PRKCA PRKCH PRKCQ PRKD3 PTGS1 PTK2
		SH2D2A SHC1 SOS2 SRC TCF7L2 TRAP1 VCL
		Murine genes:
		Actb Actg1 Akt1 Akt2 Akt3 Cdc42 Chuk Ctnnb1 Fos Grb2
		Hsp90aa1 Hsp90b1 Hspb1 Ikbkb Itpr3 Jun Mapk13 Mapk14
		Mapkapk2 Nck1 Nf1 Nfkbia Nfkbib Nos3 Pak2 Pdpk1
		Pik3r3 Plaur Ppp3ca Ppp3cb Prkci Prkd2 Ptgs2 Ptk2 Pxn
		Raf1 Rela Rock1 Shc1 Sos1 Sos2 Sphk1 Vcl
	Immune	Human genes:
	response_Gastrin in	AKT1 AKT3 ELAVL1 GNA11 GRB2 IKBKB IRS1 ITPR1
	inflammatory response	ITPR2 ITPR3 JUN MAP2K5 MAP2K6 MAP3K14 MAPK1
		MAPK14 MAPK9 MEF2A MEF2B MEF2C MMP3
		NFKBIA NFKBIB PIK3CB PIK3R1 PLCG1 PRKCA PTK2
		SERPINB2 SHC1 SOS2 SRC
		Murine genes:
		Akt1 Akt2 Akt3 Chuk Cxcl1 Elavl1 Elk1 Fos Gnaq Grb2
		Hbegf Ikbkb Itpr3 Jun Map2k4 Map2k5 Map2k6 Map3k14
		Map3k7 Mapk14 Mapk7 Mef2a Mef2c Mef2d Nfkbia
		Nfkbib Pdpk1 Ptgs2 Ptk2 Raf1 Rela Serpinb2 Shc1 Sos1
		Sos2 Traf6
	Cell	Human genes:
	adhesion_Chemokines	ACTG1 AKT1 AKT3 ARPC1A ARPC2 CAV2 CCR1 CD44
	and adhesion	CD47 CRK FLNA FLOT2 GNAI3 GNB1 GNB5 GNG10
		GNG5 GRB2 GSK3B ITGA6 ITGB1 JUN LAMA1 LEF1
		LIMK2 MAPK1 MMP1 MMP13 MSN MYC PIK3CB
		PIK3CG PIK3R1 PIK3R3 PLAUR PTEN PTK2 RAP1GAP
		RELB SDC2 SHC1 SOS2 SRC TCF7 TCF7L2 TRIO VAV1
		VCL
		Murine genes:
		Acta2 Actb Actg1 Actr2 Akt1 Akt2 Akt3 Arpc4 Braf Cav2
		Ccr1 Cd44 Cd47 Cdc42 Cfl2 Col1a2 Col4a2 Crk Ctnnb1
		Cxcl3 Cxcl5 Dock1 Flot2 Fn1 Gnai1 Gnai2 Gnai3 Gnb1
		Gnb5 Gng10 Gng12 Gng13 Gngt1 Gngt2 Grb2 Itgb1 Jun
		Lamc1 Limk2 Mmpla Msn Myc Nfkb2 Pik3r3 Pik3r5 Plaur
		Pten Ptk2 Pxn Raf1 Rap1gap Rel Rela Relb Rock1 Rock2
		Serpine1 Shc1 Sos1 Sos2 Thbs1 Trio Vcl
	Immune response_IL-15	Human genes:
	signaling	ADAM17 AKT1 AKT3 BCL2 FKBP1A GRB2 IKBKB
		IL15RA IL2RB LCK MAP2K6 MAP3K14 MAPK1
		MAPK13 MAPK14 MKNK1 MTOR MYB MYC NFKBIA
		NFKBIB PIK3CB PIK3R1 PLCG1 PTK2 SHC1 SOS2
		Murine genes:
		Adam17 Akt1 Akt2 Akt3 Bcl2 Bcl2l1 Chuk Eif4e Ets1
		Fkbp1a Fos Gab2 Grb2 Ikbkb Il15ra Il6 Lck Map2k4
		Map2k6 Map3k14 Mapk13 Mapk14 Mcl1 Mknk1 Mtor Myc
		Nfkbia Nfkbib Pdpk1 Plcb1 Ptk2 Raf1 Rela Rps6kb1 Shc1
		Sos1 Sos2 Syk
	Immune response_IL-22	Human genes:
	signaling pathway	BCL2 CD28 CD4 HLA-DMA HLA-DMB HLA-DOB HLA-
		DPA1 HLA-DPB1 HLA-DQA1 HLA-DQA2 HLA-DQB1
		HLA-DRA HLA-DRB1 HLA-DRB3 HLA-DRB4 HLA-
		DRB5 IL10RB IL12RB2 IL2RA IL2RB JAK1 JAK2 JAK3
		JUN MAPK1 MAPK13 MAPK14 MAPK9 MYC SOCS3
		STAT1 STAT4 STAT5B TRA@ TRAC
		Murine genes:
		Bcl2 Bcl2l1 Cd86 Fos H2-Eb2 Il10rb Il2ra Jak2 Jak3 Jun
		Mapk13 Mapk14 Mcl1 Myc Socs3 Stat3 Stat5a Tcrb-J

Claims as granted

8 claims

Classifications

4 codes

IPC · International Patent Classification

Section C — Chemistry; metallurgy

C12Q1/14
C12Q1/68
C12Q1/00
C12Q1/04

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Channing S Mahatan

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