HK1237382B - Diagnostic for sepsis - Google Patents

Description

For the diagnosis of sepsis

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 61/953,458, filed on March 14, 2014. The entire contents of U.S. Provisional Patent Application No. 61/953,458 are incorporated herein by reference.

Field of the Invention

The present invention relates to the field of diagnostics and, in particular, to sets of unique DNA sequences, the combination of which enables early diagnosis of sepsis and prediction of severe sepsis and/or organ failure.

Background of the Invention

Sepsis remains the leading cause of infection-related death worldwide, accounting for an estimated 8.5% of deaths (5 million) annually [Angus D et al. Critical Care Medicine 2001;29(7):1303-10; Kumar G, Kumar N, Taneja A et al. Chest 2011;140:1223-31]. Despite advances in modern medicine, including new antibiotics and vaccines, early recognition and best-practice treatment, and efficient, well-equipped intensive care units [Angus D et al], the high mortality rate of approximately 30% has remained relatively unchanged for decades [Daniels R. J Antimicrobial Chemotherapy 2011;66(Suppl 2):ii11-ii23].

Bacterial endotoxins, including LPS, are potent inducers of inflammation and have been proposed to be triggers of sepsis, responsible for the early, life-threatening cytokine storm and septic shock [Opal SM. Contributions to Nephrology 2010;167:14-24; Salomao R et al. Shock 2012;38:227-42]. In contrast, LPS can also produce the opposite effect known as endotoxin tolerance, which is defined as a severely reduced ability of cells to respond to LPS and other bacterial products during a second exposure to the stimulus [Otto GP et al. Critical Care 2011;15:R183]. It is important to note that endotoxin tolerance, also known as cellular reprogramming because it can be induced by other microbial molecules, is not an anti-inflammatory state of cells, but rather a reprogramming of cells such that they are no longer able to respond to a variety of microbial signatures, including endotoxins.

It has been proposed that endotoxin tolerance may be related to the immunosuppressive state that has been initially observed during late severe sepsis [Otto GP et al. 2011; Cavaillon J et al. J Endotoxin Res 2005;11(5):311-20; Cavaillon J, Adib-Conquy M. Critical Care Medicine 2006;10:233]. However, characterization of this relationship remains insufficient, in part due to the limitations of the ex vivo cytokine assays used to date. Despite these observations, the clinical rule is to identify and treat sepsis, especially in its early stages, as an excessive inflammatory response. However, the unique immunosuppressive state characteristic of sepsis is intrinsically linked to the prognosis of the disease. Indeed, understanding the relative balance between excessive inflammation and immunosuppression—especially at each stage of the clinical course of the disease—is an important step in improving the outcome of sepsis.

Biomarkers for diagnosing sepsis have been proposed in U.S. Patent No. 7,767,395; U.S. Patent Application Publication No. 2011/0312521; U.S. Patent Application Publication No. 2011/0076685; International Patent Application Publication No. WO 2014/209238; and International Patent Application Publication No. WO 2013/152047.

This background information is provided to disclose information that the applicant believes may be relevant to the present invention, but it is not intended or should be construed as constituting any prior art that conflicts with the present invention.

SUMMARY OF THE INVENTION

The present invention generally relates to the diagnosis of early-stage severe sepsis. In one aspect, the present invention relates to a method for diagnosing sepsis in a subject, the method comprising: determining the expression level of each of a plurality of endotoxin tolerance signature genes in a biological sample obtained from the subject to provide a sample gene signature, and comparing the sample gene signature to a reference gene signature, wherein the reference gene signature represents a standard expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject has sepsis.

In another aspect, the present invention relates to a method for identifying a subject at risk of developing severe sepsis, the method comprising determining the expression level of each of a plurality of endotoxin tolerance signature genes in a biological sample obtained from the subject to provide a sample gene signature, and comparing the sample gene signature to a reference gene signature, wherein the reference gene signature represents a standard expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject is at risk of developing severe sepsis.

In another aspect, the present invention relates to a method for identifying a subject at risk of organ failure, comprising: determining the expression level of each of a plurality of endotoxin tolerance signature genes in a biological sample obtained from the subject to provide a sample gene signature, and comparing the sample gene signature with a reference gene signature, wherein the reference gene signature represents a standard expression level of each of the plurality of genes; wherein the difference between the sample gene signature and the reference gene signature indicates that the subject is at risk of organ failure.

In certain embodiments, the plurality of genes is selected from the group consisting of: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP , IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT 1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROC R, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S10 0A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention relates to a method for diagnosing endotoxin tolerance in a subject, the method comprising: a) determining the expression level of each of a plurality of genes selected from the group consisting of ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CS in a biological sample obtained from the subject. T6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNM B. GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA119 9. LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLI G2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S1 00A12, S100A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN to provide a sample gene signature, and b) comparing the sample gene signature with a reference gene signature, wherein the reference gene signature represents a standardized expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject has endotoxin tolerance.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention relates to a method for treating sepsis, comprising administering an effective amount of one or more antibiotics to a subject diagnosed with sepsis by the above method.

In another aspect, the present invention relates to a method for treating sepsis in a subject, the method comprising: a) determining whether the subject has sepsis or is at risk of having sepsis by: (i) determining the expression level of each of a plurality of genes selected from the group consisting of ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPV3, VEGF, F3K ... L, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILR A5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLA UR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1, and VCAN to provide a sample gene signature, and (ii) comparing the sample gene signature to a reference gene signature, wherein the reference gene signature represents a standardized expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject has sepsis or is at risk of having sepsis, and b) if the subject has sepsis or is at risk of having sepsis, administering to the subject an effective amount of one or more antibiotics.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In some embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention relates to a method for reducing the risk of organ failure in a subject, the method comprising administering an effective amount of one or more antibiotics to a subject diagnosed as having sepsis or at risk of having sepsis by the above method.

In another aspect, the present invention relates to a method for reducing the risk of organ failure in a subject, the method comprising: a) determining whether the subject is at risk of organ failure by: (i) determining the expression level of each of a plurality of genes selected from the group consisting of ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, in a biological sample obtained from the subject. CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB , GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDL IM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S100A9 , SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1, and VCAN to provide a sample gene signature; and (ii) comparing the sample gene signature to a reference gene signature, wherein the reference gene signature represents a standardized expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject is at risk of organ failure, and b) if the subject is at risk of organ failure, administering to the subject an effective amount of one or more antibiotics.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention relates to a method for reducing the risk of developing severe sepsis in a subject, the method comprising administering an effective amount of an agent that counteracts endotoxin tolerance to a subject diagnosed as being at risk of developing severe sepsis by the above method.

In another aspect, the present invention relates to a method for reducing the risk of organ failure in a subject, the method comprising administering to a subject diagnosed as being at risk of organ failure by the above method an effective amount of an agent that counteracts endotoxin tolerance.

In another aspect, the present invention relates to a method for reducing the risk of severe sepsis or organ failure in a subject, the method comprising administering to the subject an effective amount of an agent that resists endotoxin tolerance. In certain embodiments, the method may further comprise determining that the subject is at risk of severe sepsis or organ failure by: (a) determining the expression levels of a plurality of genes selected from the following in a biological sample obtained from the subject: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST 6. CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LI LRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PAN X2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S1 00A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN to provide a gene signature for the sample, and (b) comparing the sample gene signature with a reference gene signature, wherein the reference gene signature represents a standardized expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject is at risk of developing severe sepsis or organ failure.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention is directed to a method for identifying a candidate agent for treating sepsis, the method comprising: a) contacting an endotoxin-tolerant cell with a test agent; b) determining the expression level of each of a plurality of endotoxin-tolerant signature genes in the endotoxin-tolerant cell to provide an expression signature; c) comparing the expression signature to a reference expression signature, wherein the reference signature represents the expression levels of the plurality of genes in normal cells; and d) selecting the test agent as a candidate agent for treating sepsis when the expression signature substantially corresponds to the reference signature.

In certain embodiments, the plurality of genes is selected from the group consisting of: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP , IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT 1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROC R, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S10 0A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention relates to a kit for determining the expression level of each of a plurality of genes selected from the group consisting of ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL 10. CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, G PR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KI AA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, N QO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1 , RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN, the kit comprising gene-specific reagents and instructions for use, each gene-specific reagent being capable of detecting the expression product of a corresponding one of multiple genes or their complements.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In another aspect, the present invention relates to a microarray for detecting the expression of multiple endotoxin tolerance signature genes in a sample, the microarray comprising multiple polynucleotide probes attached to a solid support, each polynucleotide probe being capable of specifically hybridizing to the expression product of a corresponding one of the multiple genes or their complements.

In certain embodiments, the plurality of genes is selected from the group consisting of: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP , IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT 1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROC R, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S10 0A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the plurality of genes is selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes includes: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of genes consists of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the present invention will become more apparent from the following detailed description taken with reference to the accompanying drawings.

Fig. 1 shows the schematic diagram of the method for defining endotoxin tolerance label and inflammatory label.Endotoxin tolerance label is defined as 99 kinds of genes (fold change>2, p value<0.05) that are uniquely differentially expressed in endotoxin tolerance PBMC rather than inflammatory PBMC compared with control.Inflammatory label is defined as the label of 93 kinds of genes by selecting the gene that is consistently differentially expressed in endotoxemia data set in vivo.

Figure 2 shows that re-analysis of differential expression of genes in sepsis patients from public datasets showed a close association with the endotoxin tolerance signature. The gene set test method ROAST was used to characterize the enrichment of "endotoxin tolerance" in sepsis patients relative to controls from 9 previously published datasets. All datasets included sepsis patients recruited on day 1 or 3 after ICU admission and compared with "healthy" controls. The ROAST gene set test was run at 99999 revolutions so that the most significant p-value obtained from the test was 0.00001. The p-values from the ROAST gene set test were plotted as logarithms (1/p-value), but untransformed p-values are shown for ease of visualization.

Figure 3 shows that based on public datasets, sepsis patients generally show a less significant association with inflammatory signatures. The gene set test method ROAST was used to characterize the enrichment of the inflammatory signature (white) relative to the "endotoxin tolerance" signature (gray) in sepsis patients relative to controls in 9 previously published datasets. All datasets included sepsis patients recruited on day 1 and/or 3 after ICU admission and compared with "healthy" controls. The ROAST gene set test was run at 99999 revolutions so that the most significant p-value obtained from the test was 0.00001. The p-values from the ROAST gene set test were plotted as logarithms (1/p-value), but untransformed p-values are shown for ease of visualization.

Figure 4 shows that the association between endotoxin tolerance and sepsis is independent of the specific method used to define the endotoxin tolerance signature. Different endotoxin tolerance-associated signatures were identified based on genes that were uniquely differentially expressed in endotoxin-tolerant PBMCs, but not inflammatory PBMCs, at various fold changes (FCs) and p-value cut-offs compared to controls. The datasets are described in the legend to Figure 3, with the exception of the RNA sequencing dataset for day 0, which is described here. The final endotoxin tolerance signature was defined with fold changes (FCs) and p-value cut-offs of 2 and 0.05, respectively.

Figure 5 shows that the endotoxin tolerance signature is associated with the initial clinical presentation of sepsis patients. A gene set testing approach was used to characterize the enrichment of endotoxin tolerance and inflammatory signatures in prospective sepsis patients recruited with a unique in-house cohort of patients suspected of first clinical sepsis (i.e., typically in the emergency room before ICU admission). Patient groups were then defined based on retrospective clinical characterization of "sepsis" or "no sepsis" consistent with current sepsis criteria (Table 3) (Table 3). Analysis was performed comparing the "sepsis" and "no sepsis" groups to the control (a) and the "sepsis" group to the "no sepsis" group (b). In addition, the enrichment of the signature was analyzed based on the microbial culture results in the "sepsis" group (c) and the "no sepsis" group (d).

Figure 6 shows that the endotoxin tolerance signature is closely associated with the initial clinical presentation of sepsis patients and is related to the severity of disease and organ failure. A gene set test was used to characterize the enrichment of endotoxin tolerance and inflammatory signatures in prospective sepsis patients (referenced to surgical controls), as described in Figure 5. (a) Patients were grouped into single, combined (3+), single type of organ failure, and no organ failure groups. (b) Patients were grouped into those who required transfer to the ICU and those who did not.

Figure 7 shows a core set of characteristic endotoxin tolerance genes for sepsis patients. A core set of 31 genes from the 99 genes in the endotoxin tolerance signature was determined based on the most frequently observed differentially expressed genes in all sepsis patient studies (literature and internal datasets). For better visual comparison between different studies, each individual dataset was further transformed by dividing the gene expression values into 6 equal bins. The data are displayed as a heatmap with the brightest and darkest shades, which represent larger and smaller expression changes, respectively. The differences are more obvious in the colored heatmap.

Figure 8 shows the identification of gene subnetworks from the endotoxicity tolerance signature using the j-activation module plug-in in Cytoscape. First, a network was constructed by including the first level interactors of the genes listed in Table 1, and then analyzed using the j-activation module, which specifically identifies dense (i.e., highly interconnected) subnetworks. Dark nodes (genes) are highly dysregulated, bright nodes are direct interactors of dysregulated genes, and lines represent "edges" and indicate experimentally proven interactions. The fact that 60 of the 99 genes of this signature are closely interconnected in human cells suggests a biologically meaningful relationship between these genes; that is, these genes are co-regulated or participate in a common purpose in the cell. Prominent in the network were hub proteins (central, highly interconnected proteins involved in cell signaling and trafficking), including Serpin A1, the transcription factors CEBPα, β, EGR2, HNF4A, CXCL10, and FCER2, and the prominent innate immune transcription factors NFKB1, IRF1, STAT6, JUN, and FOS, and the receptor TLR4 (not dysregulated per se), suggesting their possible involvement in endotoxin tolerance.

Detailed Description of the Invention

A unique gene signature characteristic of endotoxin tolerance (i.e., an "endotoxin tolerance signature") is defined herein as being useful for diagnosing sepsis. The endotoxin tolerance signature can differentiate patients suspected of sepsis from those who subsequently developed sepsis and those who did not, and also predict organ failure.

Thus, certain embodiments of the present invention relate to diagnosing endotoxin tolerance in a subject using the endotoxin tolerance signature described herein, such as a patient known or suspected of having sepsis. Demonstrating the presence of endotoxin tolerance indicates that the patient has sepsis and, in addition, indicates that the patient is at risk of developing severe sepsis and/or organ failure. Certain embodiments of the present invention relate to methods for diagnosing sepsis in a subject using the endotoxin tolerance signature described herein. In certain embodiments, sepsis is severe sepsis. Certain embodiments relate to methods for confirming sepsis in a subject suspected of having sepsis using the endotoxin tolerance signature described herein. Some embodiments relate to methods for predicting whether a subject is at risk of developing severe sepsis and/or organ failure using the endotoxin tolerance signature described herein.

As described herein, endotoxin tolerance-mediated immune dysfunction has been identified as present in a predominant manner after initial presentation and throughout the clinical course of the disease. The data presented herein redefine sepsis as a disease characterized by endotoxin tolerance-mediated immune dysfunction at all stages of clinical illness and thus define endotoxin tolerance as a potential therapeutic target for both early and late sepsis.

Thus, certain embodiments of the present invention relate to methods for reducing the risk of sepsis, severe sepsis, and/or organ failure in patients identified as having endotoxin tolerance, for example, by treating them using the diagnostic methods described herein. Certain embodiments relate to treating sepsis, including severe sepsis, with agents that counteract endotoxin tolerance.

Certain embodiments of the present invention relate to methods of identifying candidate agents for treating sepsis using the endotoxin resistance signatures described herein.

Certain embodiments relate to a method for diagnosing sepsis in a subject, the method comprising: determining the expression level of each of a plurality of endotoxin tolerance signature genes in a biological sample obtained from the subject to provide a sample gene signature, and comparing the sample gene signature to a reference gene signature, wherein the reference gene signature represents a standard expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject has sepsis.

Certain embodiments relate to a method for identifying a subject at risk for developing severe sepsis, the method comprising: determining the expression level of each of a plurality of endotoxin tolerance signature genes in a biological sample obtained from the subject to provide a sample gene signature, and comparing the sample gene signature to a reference gene signature, wherein the reference gene signature represents a standard expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject is at risk for developing severe sepsis.

Certain embodiments relate to a method for identifying a subject at risk of organ failure, the method comprising: determining the expression level of each of a plurality of endotoxin tolerance signature genes in a biological sample obtained from the subject to provide a sample gene signature, and comparing the sample gene signature with a reference gene signature, wherein the reference gene signature represents a standard expression level of each of the plurality of genes; wherein a difference between the sample gene signature and the reference gene signature indicates that the subject is at risk of organ failure.

definition

For convenience, the meanings of certain terms and phrases used in the specification, examples, and appended claims are provided below. The definitions are not meant to be limiting in nature but are merely helpful for understanding certain aspects of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly used by one of ordinary skill in the art to which this invention belongs.

As used herein, the term "plurality" refers to more than one, for example, two or more, three or more, four or more, etc.

The term "gene" refers to a nucleic acid sequence comprising a sequence encoding a polypeptide or precursor. In some cases, the term "gene" also encompasses control sequences that instruct and/or control the expression of a coding sequence. A polypeptide or precursor can be encoded by a full-length coding sequence or a portion of a coding sequence. A gene can comprise one or more modifications in a coding region or untranslated region that may affect the biological activity or chemical structure, expression rate, or expression control mode of a polypeptide or precursor. Such modifications include, but are not limited to, mutations, insertions, deletions, and substitutions of one or more nucleotides, including single nucleotide polymorphisms naturally occurring in a population. The gene can constitute a continuous coding sequence, or it can include one or more subsequences. The term "gene" as used herein includes the gene variants identified in Table 1.

The term "gene expression profile" or "gene signature" refers to the set of genes expressed by a particular cell or tissue type, where the expression of the genes occurs together or such genes are differentially expressed, which is indicative and/or predictive of certain conditions such as sepsis.

As used herein, the term "nucleic acid" refers to a molecule consisting of one or more nucleotides, such as ribonucleotides, deoxyribonucleotides, or both. The term includes monomers and polymers of nucleotides, in which the sequence of nucleotides is typically bound together by a 5'-3' bond, but alternative bonds are also encompassed in some embodiments. Nucleotide polymers can be single-stranded or double-stranded. Nucleotides can be naturally occurring or synthetically produced analogs that can form base pair relationships with natural base pairs. Examples of non-natural bases that can form base pairing relationships include, but are not limited to, aza- and deaza-pyrimidine analogs, aza- and deaza-purine analogs, and other heterocyclic base analogs, in which one or more carbon atoms and nitrogen atoms of the pyrimidine ring are replaced by heteroatoms such as oxygen, sulfur, selenium, phosphorus, etc.

As used herein, the term "corresponding to" and its grammatical variants relative to a nucleic acid sequence indicate that the nucleic acid sequence is identical to all or a portion of a reference nucleic acid sequence. In contrast, as used herein, the term "complementary" indicates that the nucleic acid sequence is identical to all or a portion of the complementary strand of a reference nucleic acid sequence. For illustration, the nucleic acid sequence "TATAC" corresponds to the reference sequence "TATAC" and is complementary to the reference sequence "GTATA". As used herein, "its complement" refers to a nucleic acid whose nucleotide sequence is complementary to a reference nucleic acid. The complement of an mRNA can be an RNA polynucleotide sequence or a DNA polynucleotide sequence. The complement of a DNA polynucleotide can be an RNA polynucleotide or a DNA polynucleotide.

The term "differential expression" refers to the qualitative and/or quantitative differences in the expression of genes or proteins in diseased tissues or cells compared to normal tissues or cells. For example, differentially expressed genes can activate their expression or completely inactivate it under normal conditions compared to diseased conditions, or can be raised (overexpressed) or lowered (underexpressed) under diseased conditions compared to normal conditions. In other words, when the expression of the gene or protein occurs in the diseased tissues or cells of the patient at a higher or lower level relative to its expression level in the patient's normal (disease-free) tissues or cells and/or control tissues or cells, the gene or protein is differentially expressed.

The term "biological sample" refers to a sample obtained from an organism (e.g., a human patient) or a component (e.g., a cell) of an organism. The sample can be any relevant biological tissue or fluid. The sample can be a "clinical sample", which is a sample derived from a patient. Such samples include, but are not limited to, sputum, blood, blood cells (e.g., leukocytes), amniotic fluid, plasma, semen, bone marrow and tissue or fine needle biopsy samples, urine, peritoneal fluid and pleural fluid, or cells therein. A biological sample can also include tissue sections, such as frozen sections obtained for histological purposes. A biological sample can also be referred to as a "patient sample".

As used herein, the terms "comprising," "having," "including," and "containing," and grammatical variations thereof, are inclusive or open-ended and do not exclude additional, unrecited elements and/or method steps. The term "consisting essentially of," when used herein in combination with a composition, use, or method, indicates that additional elements and/or method steps may be present, but that these additions do not materially affect the manner in which the composition, method, or use stated therein functions. The term "consisting of," when used herein in combination with a composition, use, or method, excludes the presence of additional elements and/or method steps. Compositions, uses, or methods described herein that comprise certain elements and/or steps may also, in certain embodiments, consist essentially of these elements and/or steps, and in other embodiments, consist of these elements and/or steps, whether or not these embodiments are specifically mentioned.

It is contemplated that any embodiment discussed herein can be implemented with respect to any disclosed method, use, or composition of the present invention, and vice versa.

Sepsis

"Sepsis" generally refers to the clinical response to suspected or confirmed infection. Sepsis can be defined, for example, as including two or more of the following symptoms: tachypnea or tachycardia; leukocytosis or leukopenia; and hyperthermia or hypothermia, and can present as a complex infectious and immune dysfunction. Many other symptoms may or may not occur and have been defined by a consensus meeting of physicians (see Bone RC, Balk RA, Cerra FB, et al. Chest 2009; 136(Suppl 5):e28), but none of these symptoms are specific for sepsis. Sepsis can be complicated by organ failure and may require admission to an intensive care unit, in which case it is referred to as "severe sepsis." When patients—usually those in the emergency room—acquire early symptoms associated with sepsis, they are often considered suspected sepsis patients, which triggers specialized hospital procedures for treatment. However, patients are considered to have "early-stage sepsis" only after infection is confirmed by microbiological testing or they develop more severe symptoms, including failure of one of more organs, 24 to 48 hours later (reviewed by Lyle NH et al., Annals of the New York Academy of Sciences 2014, 1323:101-14).

Endotoxin-resistant labeling

In one aspect, the present invention relates to a plurality of genes regulated during sepsis, the expression profiles of which are used to determine the endotoxin tolerance of a subject. A gene signature indicating endotoxin tolerance ("endotoxin tolerance signature") is defined based on the differential expression of these genes based on upregulation or downregulation of the genes compared to a control. Non-limiting examples of endotoxin tolerance signature genes (ETSGs) included in an endotoxin tolerance signature according to certain embodiments of the present invention are provided in Table 1.

The sequences of these genes can be readily obtained by those skilled in the art from publicly available databases, such as the GenBank database maintained by the National Center for Biotechnology Information (NCBI), for example, by searching using the provided gene symbols. These gene symbols are recognized by all databases, including HGNC, Entrez Gene, UniProtKB/Swiss-prot, OMIM, GeneLoc, and Ensembl; all aliases are defined by the GeneCard database. Non-limiting examples of representative gene sequences available from GenBank are provided in Table 1.

Table 1: Representative endotoxin tolerance signature genes (ETSG)

The endotoxin tolerance signature can include all of the endotoxin tolerance signature genes (ETSGs) shown in Table 1, or it can include a subset of these genes. In certain embodiments, the endotoxin tolerance signature can include as few as two ETSGs and as many as 99 ETSGs shown in Table 1. In some embodiments, the endotoxin tolerance signature includes at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, or at least fifteen ETSGs of Table 1. In some embodiments, the endotoxin tolerance signature includes 15 or more ETSGs, e.g., 20 or more, 25 or more, or 30 or more ETSGs. In some embodiments, the endotoxin tolerance signature includes about 31 ETSGs of Table 1. In some embodiments, the endotoxin tolerance signature includes about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 99 ETSGs.

In certain embodiments, the endotoxin resistance tag comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99 of the ETSGs in Table 1.

In certain embodiments, the endotoxin tolerance signature comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ETSGs selected from C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In some embodiments, the endotoxin tolerance signature comprises at least 15, at least 20, at least 25, or at least 30 ETSGs selected from C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the endotoxin tolerance signature comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 selected from C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK , HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR, and may optionally contain one or more additional ETSGs from Table 1.

In some embodiments, the endotoxin tolerance signature comprises the ETSGs: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR, and may optionally comprise one or more additional ETSGs from Table 1.

Changes in ETSG expression can be defined by the direction of the change in expression, which indicates whether the gene is upregulated or downregulated in subjects with endotoxin tolerance compared to the expression of ETSG in a control (or reference) sample. Referring to the ETSG shown in Table 1, for example, subjects with endotoxin tolerance will exhibit upregulation of one or more of the following: ADAMDEC1, ANKRD1, C19orf59, CA12, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, upregulation of CD93, CDK5RAP2, CYP1B1, CYP27B1, DDIT4, DPYSL3, EGR2, EMR1 , EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT 1M, MT1X, MXD1, MYADM, NEFH, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PPBP, PROCR, PTGES, PTGR1, R AB13, RETN, RHBDD2, S100A12, S100A8S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TMEM158, TREM1, UPP1, or VCAN, and downregulation of one or more of the following: ADAM15, ALCAM, ALDH1A1, CAMP, CPVL, CST3, CST6, CTSK, CXCL10, DHRS9, GPNMB, HTRA1, IL18BP, LIPA, LY86, NQO1, PLD3, PSTPIP2, RARRES1, RNASE1, S100A4, TLR7, or TSPAN4.

The expression change of ETSG can optionally be further defined by the minimum fold change of expression level relative to the control. In certain embodiments, the upregulation or downregulation of a given ETSG can be defined as a change of at least 1.5-fold in gene expression level compared to the control. In some embodiments, the upregulation or downregulation of a given ETSG can be defined as a change of 2-fold or greater in gene expression level compared to the control. The control (or standard or reference) expression level can be, for example, the expression level of ETSG in a sample from a healthy subject or the expression level of ETSG in a non-endotoxin tolerant cell.

method

Diagnostic methods

Certain embodiments of the present invention relate to methods of using an endotoxin tolerance signature to determine whether a subject having or suspected of having sepsis is endotoxin tolerant and, therefore, at risk for one or more of sepsis, severe sepsis, and/or organ failure.

In certain embodiments, the subject is suspected of having sepsis, and the method identifies the patient as having sepsis. In some embodiments, the subject is suspected of having sepsis, and the method identifies the subject as being at risk for severe sepsis and/or organ failure. In certain embodiments, the subject is suspected of having sepsis, and the method identifies the patient as having severe sepsis.

Typically, the diagnostic method comprises detecting the expression of genes contained in an endotoxin tolerance signature in a biological sample obtained from a subject. The differential expression of these genes is determined compared to a control. A differential expression of at least two of these genes in a defined direction of expression change indicates that the subject has one or more of sepsis, severe sepsis, and/or organ failure, or is at risk of one or more of sepsis, severe sepsis, and/or organ failure.

In certain embodiments, the differential expression of three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, or fifteen or more ETSGs in the endotoxin tolerance signature as compared to a control sample indicates that the subject has one or more of sepsis, severe sepsis, and/or organ failure, or is at risk of one or more of sepsis, severe sepsis, and/or organ failure. In some embodiments, the differential expression of at least 15, at least 20, at least 25, or at least 30 ETSGs in the endotoxin tolerance signature as compared to a control sample indicates that the subject has one or more of sepsis, severe sepsis, and/or organ failure, or is at risk of one or more of sepsis, severe sepsis, and/or organ failure. In some embodiments, differential expression of about 31 ETSGs in the endotoxin tolerance signature compared to the control sample indicates that the subject has or is at risk for one or more of sepsis, severe sepsis, and/or organ failure.

In alternative embodiments, compared to the control sample, at least 20% of the ETSG expression differences in the endotoxin tolerance signature indicate that the subject has sepsis, severe sepsis, and/or organ failure, or is at risk of sepsis, severe sepsis, and/or organ failure. In some embodiments, compared to the control sample, 20% or more, 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, or 90% or more of the ETSG expression differences in the endotoxin tolerance signature indicate that the subject has sepsis, severe sepsis, and/or organ failure, or is at risk of sepsis, severe sepsis, and/or organ failure. In some embodiments, compared to the control sample, at least 35% of the ETSG expression differences in the endotoxin tolerance signature indicate that the subject has sepsis, severe sepsis, and/or organ failure, or is at risk of sepsis, severe sepsis, and/or organ failure.

In some embodiments, differential expression of each ETSG in the endotoxin tolerance signature compared to the control sample indicates that the subject has, or is at risk for, one or more of sepsis, severe sepsis, and/or organ failure, wherein the endotoxin tolerance signature can comprise from 2 to about 99 ETSGs, e.g., about 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, or 30 and about 99 ETSGs.

In certain embodiments, compared to the control sample, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 1, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99 ETSGs indicates that the subject has or is at risk for one or more of sepsis, severe sepsis, and/or organ failure.

In certain embodiments, differential expression of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of the following ETSGs, as compared to a control sample, indicates that the subject has or is at risk for one or more of sepsis, severe sepsis, and/or organ failure: C19orf59, C CL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDL IM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86 and PROCR.

The biological sample may include, for example, blood, plasma, serum, tissue, amniotic fluid, saliva, urine, feces, bronchoalveolar fluid, cerebrospinal fluid, or cells (such as skin cells) or cell extracts.

The expression of ETSG contained in the endotoxin tolerance tag can be determined by detecting the expression product of each gene. The expression product can be, for example, RNA, cDNA prepared from RNA, or protein. When the expression product is RNA or cDNA, the entire sequence of the gene can be detected, or any specific portion of the gene, for example, a sequence of 10 or more nucleotides, can be detected.

Methods for detecting and quantifying gene expression are well known in the art (see, e.g., Current Protocols in Molecular Biology, 1987 & updates, Ausubel et al. (eds.), Wiley & Sons, New York, NY) and include the use of detectably labeled polynucleotide probes, antibodies, aptamers, and the like.

In certain embodiments, one or more of polymerase chain reaction (PCR), reverse transcriptase (RT) PCR, Q-beta replicase amplification, ligase chain reaction, nucleic acid sequence amplification, Ampliprobe, light cycle, differential display, RNA analysis, hybridization, microarray, RNA sequencing, nucleic acid sequencing, MassArray analysis, and MALDI-TOF mass spectrometry can be used to determine the expression of ETSG.

In certain embodiments, the diagnostic method uses a detectably labeled polynucleotide to detect the expression of ETSG. The method may also include one or more of nucleic acid isolation from the sample, nucleic acid purification, reverse transcription of RNA, and/or nucleic acid amplification. In some embodiments, the polynucleotide probes used to determine ETSG expression can be immobilized on a solid support, for example, as an array or microarray that allows for faster processing of samples. Methods for preparing arrays and microarrays are well known in the art. In addition, many standard microarrays are commercially available, including probes for detecting some of the genes identified herein as ETSG and therefore suitable for use in the disclosed diagnostic methods. For example, the Affymetrix U133 GeneChip ^™ array (Affymetrix, Inc., Santa Clara, CA), the Agilent Technologies genomic cDNA microarray (Santa Clara, CA), and arrays available from Illumina, Inc. (San Diego, CA). These arrays have probe sets targeting the entire human genome immobilized on the chip and can be used to determine upregulation and downregulation of genes in a test sample. Custom arrays and microarrays for detecting preselected genes are also commercially available from many companies. Instruments and reagents for performing gene expression analysis are commercially available (e.g., Affymetrix Affymetrix GeneChip ^™ System). The expression data obtained from this analysis can then be input into an appropriate database for further analysis, if necessary or desired.

In some embodiments, differentially expressed genes can be detected after conversion to cDNA using matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry using, for example, the Sequenom system (see, e.g., Kricka LJ. Clin Chem 1999; 45:453-458).

The expression of certain genes, referred to as "reference genes," "control genes," or "housekeeping genes," can also be determined in the sample in a manner that ensures the accuracy of the expression profile. Reference genes are genes that are consistently expressed across many tissue types, including cancer cells and normal tissues, and can therefore be used to normalize gene expression profiles. Parallel determination of the expression of reference genes and genes in the endotoxin tolerance signature provides further assurance that the technology for determining gene expression profiles is working properly. Appropriate reference genes (also referred to herein as control genes and housekeeping genes) can be readily selected by those skilled in the art.

The expression level of ETSG determined for the endotoxin tolerance tag is compared with a suitable reference or control, such as the expression level of ETSG in a biological sample from a healthy individual or the expression level of ETSG in non-endotoxin tolerant cells. This comparison can include, for example, visual inspection and/or arithmetic or statistical comparison of measurements and can take into account the expression of any reference gene. Suitable comparison methods for determining differences in gene expression levels are well known in the art.

In certain embodiments, the diagnostic method can be used as a confirmatory diagnosis for standard sepsis diagnostic procedures. In some embodiments, the diagnostic method can be used as a stand-alone diagnostic.

In certain embodiments, the diagnostic methods can be used to confirm sepsis in a subject suspected of having sepsis. The subject may have already undergone one or more assessments to determine whether they meet standard diagnostic criteria for sepsis, such as microbial culture analysis, blood pressure measurement, white blood cell count measurement, temperature measurement, respiratory rate measurement, and/or heart rate measurement. In certain embodiments, the diagnostic methods can be used to confirm sepsis in a patient who has been diagnosed with sepsis by standard diagnostic criteria. In certain embodiments, the diagnostic methods can be used to diagnose a patient with sepsis as having severe sepsis and/or being at risk for organ failure.

In certain embodiments, determining the expression level of ETSG in a biological sample comprises detecting the presence of multiple mRNAs encoded by multiple ETSGs in the biological sample. In some embodiments, detecting the presence of mRNAs encoded by the ETSGs in the sample comprises performing a reverse transcription reaction using mRNA obtained from the biological sample to produce a cDNA product, and contacting the cDNA product with a nucleic acid probe capable of hybridizing to the cDNA, wherein the cDNA comprises a nucleotide sequence complementary to the mRNA encoded by the ETSG.

In some embodiments, the method comprises contacting cDNA generated by a reverse transcription reaction using mRNA obtained from a biological sample with a microarray containing nucleic acid probes capable of hybridizing to cDNA containing nucleotide sequences complementary to mRNAs encoded by a plurality of ETSGs.

Treatment

In certain embodiments, the present invention relates to methods of treating patients identified as endotoxin tolerant, for example, by using the diagnostic methods described herein, to reduce their risk of developing sepsis, severe sepsis, and/or organ failure. In certain embodiments, early identification of the immune status of sepsis patients by the methods described herein helps guide the selection of appropriate therapy.

In certain embodiments, when a patient is identified as endotoxin tolerant and at risk for sepsis, severe sepsis, and/or organ failure, the treatment method comprises administering to the patient a therapeutically effective dose of at least one antibiotic for treating severe sepsis.

Examples of suitable antibiotics for treating severe sepsis include, but are not limited to, glycopeptides (e.g., vancomycin, oritvancin, or televancin), ceflasporins (e.g., ceftriaxone, cefotaxime, or cefepime), β-lactam/β-lactamase inhibitors (e.g., piperacillin-tazobactam, ticarcillin-clavulanic acid), carbapenems (e.g., imipenem or meropenem), quinolones and quinolones (e.g., ciprofloxacin, moxifloxacin, or levofloxacin), aminoglycosides (e.g., gentamicin, tobramycin, or amikacin), macrolides (e.g., azithromycin, clarithromycin, or erythromycin), and monocyclics (e.g., aztreonam), and various combinations thereof. Typically, the combination includes antibiotics from different classes.

As demonstrated herein, sepsis can be defined as a disease characterized by an immune dysfunction mediated by endotoxin tolerance. Thus, resisting endotoxin tolerance in septic patients is a potential treatment method for preventing or reducing the likelihood of patients developing severe sepsis and/or organ failure. Therefore, in some embodiments, the present invention relates to a method for treating a septic patient, the method comprising administering to the patient an agent that resists endotoxin tolerance. In some embodiments, the present invention relates to a method for preventing or reducing a patient's risk of developing severe sepsis and/or organ failure, the method comprising administering to the patient an agent that resists endotoxin tolerance. In certain embodiments, a patient is identified as being at risk of developing sepsis, severe sepsis, and/or organ failure by the diagnostic methods described herein.

The agent that resists endotoxin tolerance can be, for example, an immunotherapy. In some embodiments, the agent that resists endotoxin tolerance comprises an immune cell. Other examples of agents that resist endotoxin tolerance include, but are not limited to, interferon-γ, CpG oligonucleotides alone or in combination with IL-10, anti-CD40 antibodies, STAT3 inhibitors, STAT6 inhibitors, p50 inhibitors, NFκB inhibitors, IKKβ inhibitors, imidazole quinolones, and zoledronic acid.

Endotoxin resistance may cause macrophages to be "locked" into the M2 state. In certain embodiments, agents that counteract endotoxin resistance are capable of changing the macrophage phenotype from M2 to M1 or M2 to M0 (which represents uncommitted macrophages).

In some embodiments, the present invention relates to a method for treating a patient with sepsis, comprising administering to the patient an agent that changes the macrophage phenotype from M2 to M1. In some embodiments, the present invention relates to a method for preventing or reducing the risk of a patient developing severe sepsis and/or organ failure, comprising administering to the patient an agent that changes the macrophage phenotype from M2 to M1. In certain embodiments, the patient is identified as being at risk of sepsis, severe sepsis, and/or organ failure by the diagnostic methods described herein.

In certain embodiments, the agent capable of changing the phenotype of macrophages from M2 to M1 is selected from an immunotherapeutic agent, an immune cell, interferon-γ, a CpG oligonucleotide alone or in combination with IL-10, an anti-CD40 antibody, a STAT3 inhibitor, a STAT6 inhibitor, a p50 inhibitor, an NFκB inhibitor, an IKKβ inhibitor, an imidazoquinolone, and zoledronic acid.

Certain embodiments of the present invention relate to a method for reducing the risk of severe sepsis in a subject, the method comprising administering to a subject in need thereof an effective amount of an agent that counteracts endotoxin tolerance. In some embodiments, the subject is diagnosed as being at risk for severe sepsis by the methods disclosed herein.

Certain embodiments of the present invention relate to a method for reducing the risk of organ failure in a subject, the method comprising administering to a subject in need thereof an effective amount of an agent that counteracts endotoxin tolerance. In some embodiments, the subject is diagnosed as being at risk of organ failure by the methods disclosed herein.

Certain embodiments of the present invention relate to methods for treating sepsis, comprising administering to a subject in need thereof an effective amount of an agent that counteracts endotoxin tolerance. In some embodiments, the subject is diagnosed as having sepsis by the methods disclosed herein.

In one embodiment, the agent that resists endotoxin tolerance and finds use in the methods disclosed herein can be an immunotherapeutic agent. In one embodiment, the agent that resists endotoxin tolerance and finds use in the methods disclosed herein comprises an immune cell. In one embodiment, the immune cell is a syngeneic immune cell, for example, the cell can be derived from the subject to which the immune cell is administered. In another embodiment, the immune cell is an allogeneic immune cell, i.e., derived from an individual other than the subject to whom the immune cell is administered.

In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is interferon gamma. In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is CpG-oligonucleotide (ODN). In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is a combination of CpG ODN and interleukin-10 (IL-10). In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is an anti-CD40 antibody. In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is a STAT3 inhibitor. In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is a STAT-6 inhibitor. In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is a p50 inhibitor. In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is an NFκB inhibitor. In one embodiment, the reagent that resists endotoxin tolerance and finds use in the methods disclosed herein is an Iκκβ inhibitor. In one embodiment, the agent that counteracts endotoxin tolerance and finds use in the methods disclosed herein is an imidazoquinolone. In one embodiment, the agent that counteracts endotoxin tolerance and finds use in the methods disclosed herein is zoledronic acid.

Screening methods

Certain embodiments of the present invention relate to methods for identifying candidate agents for treating sepsis by evaluating the effect of a test agent on the expression of ETSG, which is comprised of an endotoxin resistance signature. The ability of a test compound to affect ETSG expression can be evaluated, for example, by contacting cells with the test compound ex vivo, determining the expression of ETSG in the cells, and comparing the expression of ETSG in the cells to the expression level of the same ETSG in control cells.

Expression of ETSG can be assessed by various methods known in the art as described herein and elsewhere.

In certain embodiments, the test cells may be endotoxin-tolerant cells and the control cells may be non-endotoxin-tolerant (normal) cells. According to this embodiment, if the expression pattern (or gene signature) of the cells treated with the test agent substantially corresponds to the expression pattern (or gene signature) of the control cells, the test agent is a candidate agent for treating sepsis. "Substantially corresponding to" in this context means that the expression of those ETSGs that are upregulated in endotoxin-tolerant cells is reduced, while the expression of those ETSGs that are downregulated in endotoxin-tolerant cells is increased.

In some embodiments, the expression level of at least one of the ETSGs in the treated cells is within a predetermined margin of the expression level of the same ETSG in the control cells, for example, within about ±25%, within about ±20%, within about ±15%, or within about ±10% of the expression level of the same ETSG in the control cells.

In some embodiments, the method can also include contacting cells with endotoxins for a sufficiently long period of time to induce endotoxin tolerance before contacting cells with test reagents. Endotoxins can be, for example, bacterial lipopolysaccharide (LPS) or lipoteichoic acid or a combination thereof. LPS or lipoteichoic acid can be isolated forms, or can be provided by contacting cells with natural bacteria containing lipopolysaccharide and/or lipoteichoic acid. The amount of time required for inducing endotoxin tolerance can be easily determined by technicians. It may be necessary to carry out more than one treatment to induce endotoxin tolerance with endotoxins. Generally speaking, about 12 hours to about 24 hours, for example, about 14, about 16, about 18 or about 20 hours and one to three treatments with endotoxins can be adopted. When adopting multiple treatments, endotoxins used in each treatment can be identical or different.

In some embodiments, the screening method may include assessing the ability of the restored endotoxin-tolerant cells to react with endotoxin, thereby indicating that the test agent is capable of disrupting the tolerance of the cells. In some embodiments, the screening method further includes contacting a second cell with an agent known to resist endotoxin tolerance, such as interferon-γ, CpG-oligonucleotides (with or without IL-10), anti-CD40 antibodies, STAT3 inhibitors, STAT6 inhibitors, p50 inhibitors, NFκB inhibitors, IKKβ inhibitors, imidazoquinolones, or zoledronic acid, and determining the expression of the same ETSG in the second cell.

In certain embodiments, the method may further comprise determining the ability of the test agent to change the macrophage phenotype from M2 to M1.

Kits and microarrays

Certain embodiments of the present invention relate to kits comprising one or more reagents for determining the expression of multiple (e.g., two or more) ETSGs. Typically, the kit will comprise a collection of reagents, e.g., two or more reagent collections used together to perform a diagnostic method, or one or more steps of a diagnostic method as described herein, and which are typically provided together in common packaging.

The one or more reagents for determining ETSG expression may include a gene-specific probe capable of detecting an expression product (nucleic acid or protein) of ETSG or the complement of a nucleic acid expression product, a polynucleotide primer for reverse transcribing mRNA encoded by ETSG and/or for amplifying a nucleic acid sequence of cDNA prepared from ETSG or mRNA encoded by ETSG.

In certain embodiments, the kit comprises gene-specific probes for a plurality of ETSGs selected from the ETSGs listed in Table 1. In some embodiments, the plurality of ETSGs comprises C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98 or 99 gene-specific probes.

In certain embodiments, the gene-specific probe of the kit specific for ETSG comprises a probe for an ETSG selected from Table 1.

In certain embodiments, the gene-specific probes of the kit specific for ETSG consist of probes to ETSG selected from Table 1.

In certain embodiments, the gene-specific probes of the kit specific for ETSG consist of probes for all of ETSG in Table 1.

In certain embodiments, the gene-specific probes of the kit specific for ETSG comprise a probe for ETSG selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the gene-specific probes of the kit specific for ETSG consist of probes to ETSG selected from the group consisting of C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the gene-specific probes of the kit specific for ETSG consist of a probe for each of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the kit comprises antibodies against the following ETSGs: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, R Gene-specific probes for 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of HBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the kit may comprise or consist of a microarray comprising a plurality of ETSG-specific polynucleotide probes immobilized on a solid support. The microarray may also comprise a control polynucleotide probe specific for a control sequence (e.g., a housekeeping gene).

The kit may optionally include one or more other reagents required for performing the biological procedure, such as buffers, salts, enzymes, enzyme cofactors, substrates, detection reagents, detergents, etc. The kit may also include other components, such as buffers and solutions for isolating and/or treating the test sample. The kit may also include one or more control sequences or samples.

One or more components of the kit may optionally be lyophilized, and the kit may further comprise reagents suitable for reconstitution of the lyophilized components.

The various components of the test kit are provided in suitable containers. In some embodiments, the container itself can be a suitable vessel for implementing a biological procedure, for example, a microtiter plate. Where appropriate, the test kit can also optionally contain reaction vessels, mixing vessels and other components that contribute to the preparation of reagents or test samples or the implementation of a biological procedure. The test kit can also include instruments for assisting in obtaining test samples, such as syringes, pipettes, tweezers, etc.

In some embodiments, the reagents contained in the kit or their containers can be color-coded to facilitate their use. When the reagents are color-coded, adding one reagent to another in a particular step can, for example, result in a color change in the mixture, thereby providing a prompt that the step has been performed.

The kit may optionally include instructions for use, which may be provided in paper form, in computer-readable form (e.g., CD, DVD, USB stick, etc.), or in the form of instructions or instructions for accessing a website. The kit may also include a computer-readable medium containing software or instructions or instructions for accessing a website that provides software to assist in interpreting the results obtained from using the kit.

Certain embodiments of the present invention relate to a microarray for detecting a variety of ETSGs. In one embodiment, the microarray includes a plurality of polynucleotide probes connected to a solid support, each polynucleotide probe being capable of hybridizing with each expression product (or its complement) in a variety of ETSGs. The microarray may optionally include one or more control probes, for example, probes capable of detecting the expression of housekeeping genes. In some embodiments, the microarray may also include probes for a plurality of inflammatory tag genes, for example, genes selected from those identified in Table 4. For microanalysis, the length of the probe sequence is generally about 15 to about 100 nucleotides, for example, a length of about 15 to about 90 nucleotides, a length of about 15 to about 80 nucleotides, a length of about 15 to about 70 nucleotides, a length of about 15 to about 60 nucleotides, or a length of about 20 to about 60 nucleotides. By way of example only and without intent to limit, the probe sequence generally comprises about 25nt in the Affymetrix array and about 60nt in the Agilent array.

In certain embodiments, the microarray comprises a plurality of nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a plurality of ETSGs.

In some embodiments, the microarray consists essentially of nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs. In some embodiments, the microarray consists essentially of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, and (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of non-ETSGs. In some embodiments, the microarray consists essentially of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, and (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of housekeeping genes. In some embodiments, the microarray consists essentially of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, and (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple inflammatory signature genes. In some embodiments, the microarray consists essentially of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a plurality of ETSGs, (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a plurality of inflammatory signature genes, and (iii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of housekeeping genes. In some embodiments, the microarray consists essentially of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a plurality of ETSGs, (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a plurality of inflammatory signature genes, and (iii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of non-ETSGs.

In some embodiments, the microarray is composed of nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs. In some embodiments, the microarray is composed of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, and (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of non-ETSGs. In some embodiments, the microarray is composed of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, and (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of housekeeping genes. In some embodiments, the microarray is composed of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, and (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple inflammatory signature genes. In some embodiments, the microarray is composed of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple inflammatory signature genes, and (iii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of housekeeping genes. In some embodiments, the microarray is composed of: (i) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs, (ii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple inflammatory signature genes, and (iii) nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by a partial set of non-ETSGs.

In some embodiments, the number of nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by multiple ETSGs is greater than the number of other nucleic acid probes in the microarray. In some embodiments, the number of nucleic acid probes capable of hybridizing to cDNAs comprising nucleotide sequences complementary to mRNAs encoded by ETSGs plus the number of nucleic acid probes capable of hybridizing to mRNAs encoded by inflammatory signature genes is greater than the number of other nucleic acid probes in the microarray.

In some embodiments, the plurality of ETSGs are selected from the ETSGs listed in Table 1. In some embodiments, the plurality of ETSGs include C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR. In some embodiments, the plurality of inflammatory signature genes are selected from the genes listed in Table 4.

In some embodiments, the microarray includes a target for 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 , 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99 probes.

In certain embodiments, the plurality of probes in the microarray specific for ETSG includes probes for ETSG selected from Table 1.

In certain embodiments, the plurality of probes in the microarray specific for ETSG consists of probes to ETSG selected from Table 1.

In certain embodiments, the plurality of probes in the microarray specific for ETSG consists of probes for all of the ETSGs in Table 1.

In some embodiments, the plurality of probes in the microarray specific for ETSG include a probe for ETSG selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of probes in the microarray specific for ETSG consists of probes to ETSG selected from the group consisting of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In certain embodiments, the plurality of probes in the microarray specific for ETSG consists of a probe for each of: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In some embodiments, the microarray includes probes for 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of the following ETSGs: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

Other aspects and implementations

Further aspects and embodiments of the invention are disclosed herein as follows:

In one embodiment, a method of identifying a patient having severe sepsis or at high risk for severe sepsis is provided, the method comprising obtaining a biological sample from the individual and determining the expression levels of at least two or more genes from an endotoxin tolerance signature, thereby indicating a risk of sepsis, severe sepsis, or organ failure by altered expression of the endotoxin tolerance signature genes relative to the expression of the same genes in non-septic individuals.

In one aspect, the present invention provides a method for identifying a patient with severe sepsis or at high risk of developing severe sepsis, the method comprising obtaining a biological sample from the patient and determining the expression levels of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different endotoxin tolerance signature genes (ETSGs) in the biological sample, whereby the expression levels of the ETSGs indicate a high risk or presence of severe sepsis. In one embodiment, the expression levels of more than 15 different ETSGs are determined. In one embodiment, the expression levels of more than 20 different ETSGs are determined. In one embodiment, the expression levels of more than 25 different ETSGs are determined. In one embodiment, the expression levels of more than 30 different ETSGs are determined. In one embodiment, the expression levels of more than 31 different ETSGs are determined.

In one embodiment, at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or up to 31 ETSGs are selected from RNASE1, ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL 22. CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHR S9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2 AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY8 6. MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDL IM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A 4. S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the determination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209

In certain embodiments, the expression level of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of the following ETSGs is determined: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In one embodiment, the method further comprises determining the expression level of the same ETSG in a control sample from an individual who does not have sepsis. When the expression levels of ETSG from the patient sample and the control sample are different, the patient is identified as having severe sepsis or at risk for severe sepsis.

In one embodiment, the patient has not been clearly diagnosed with severe sepsis. In another embodiment, the patient has been diagnosed with severe sepsis.

In one embodiment, the biological sample is selected from the group consisting of blood, tissue, amniotic fluid, saliva, urine, amniotic fluid, bronchoalveolar lavage fluid, and skin cells.

In one embodiment, identification of patients with severe sepsis is used to guide optimal treatment of the patient.

In one embodiment, the expression level of ETSG is determined by assessing the level of RNA or cDNA in a biological sample. In one embodiment, the expression level of ETSG is determined using one or more methods selected from the group consisting of polymerase chain reaction (PCR), reverse transcriptase (RT) PCR, Q-beta replicase amplification, ligase chain reaction, nucleic acid sequence amplification, signal amplification (Ampliprobe), light cycle and other variations based on PCR or non-PCR amplification methods, differential display, RNA analysis, hybridization, microarray, DNA sequencing, RNA sequencing, nucleic acid sequencing, MassArray analysis, and MALDI-TOF mass spectrometry.

In one aspect, the present invention provides a method for identifying an individual at risk of organ failure, the method comprising obtaining a biological sample from the individual and determining the expression levels of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different ETSGs in the biological sample, whereby the expression levels of the ETSGs indicate the risk of organ failure. In one embodiment, the expression levels of more than 15 different ETGSs are determined. In one embodiment, the expression levels of more than 20 different ETGSs are determined. In one embodiment, the expression levels of more than 25 different ETGSs are determined. In one embodiment, the expression levels of more than 30 different ETGSs are determined. In one embodiment, the expression levels of more than 31 different ETGSs are determined.

In one embodiment, the method further comprises determining the expression level of the same ETSG in a control sample from an individual not suffering from sepsis. When the expression levels of ETSG from the patient sample and the control sample are different, the patient is identified as being at risk of organ failure.

In one embodiment, at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 ETSGs are selected from the group consisting of RNASE1, ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL 22. CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHR S9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2 AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY8 6. MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDL IM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A 4. S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the determination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or 99 ETSG expression levels.

In certain embodiments, the expression level of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of the following ETSGs is determined: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In one aspect, the present invention provides a method for treating severe sepsis, comprising identifying a patient suffering from severe sepsis or at high risk for severe sepsis and treating the patient with at least one potential antibiotic indicated for treating severe sepsis. In one embodiment, patient identification comprises obtaining a biological sample from the patient and determining the expression levels of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different ETSGs in the biological sample, whereby the expression levels of at least two ETSGs indicate severe sepsis or a high risk for severe sepsis. In one embodiment, the expression levels of more than 15 different ETSGs are determined. In one embodiment, the expression levels of more than 20 different ETGSs are determined. In one embodiment, the expression levels of more than 25 different ETGSs are determined. In one embodiment, the expression levels of more than 30 different ETGSs are determined. In one embodiment, the expression levels of more than 31 different ETGSs are determined.

In one embodiment, at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 ETSGs are selected from RNASE1, ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL2, or the like. 2. CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHR S9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2 AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY8 6. MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDL IM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A 4. S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the determination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 99 ETSG expression levels.

In certain embodiments, the expression level of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of the following ETSGs is determined: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR.

In one embodiment, the method further comprises determining the expression level of the same ETSG in a control sample from an individual not suffering from sepsis. When the expression levels of ETSG from the patient sample and the control sample are different, the patient is identified as suffering from severe sepsis, and a therapeutically effective dose of at least one potent antibiotic indicated for treating severe sepsis is administered to the patient.

In one aspect, the present invention provides a test kit for identifying severe sepsis, the kit comprising at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different nucleic acids, each of which comprises a nucleotide sequence corresponding to a different ETSG or a nucleotide sequence complementary thereto. In one embodiment, the kit comprises more than 15 different nucleic acids. In one embodiment, the kit comprises more than 20 different nucleic acids. In one embodiment, the kit comprises more than 25 different nucleic acids. In one embodiment, the kit comprises more than 30 different nucleic acids. In one embodiment, the kit comprises more than 31 different nucleic acids.

In one embodiment, the kit comprises at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to a different ETSG. Among them, ETSG is selected from: RNASE1, ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROC R, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S10 0A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to one of the following ETSGs: C19orf59, CCL22, CD14, CD300LF , CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR , PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86 and PROCR.

In one aspect, the present invention provides a test kit for identifying individuals at high risk for severe sepsis, the kit comprising at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to a different ETSG. In one embodiment, the kit comprises more than 15 different nucleic acids. In one embodiment, the kit comprises more than 20 different nucleic acids. In one embodiment, the kit comprises more than 25 different nucleic acids. In one embodiment, the kit comprises more than 30 different nucleic acids. In one embodiment, the kit comprises more than 31 different nucleic acids.

In one embodiment, the kit comprises at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to a different ETSG, wherein the ETSG is selected from the group consisting of RNASE1, ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANK RD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR13 7B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILR A3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PAN X2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12 , S100A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to one of the following ETSGs: C19orf59, CCL22, CD14, CD300LF , CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR , PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86 and PROCR.

In one aspect, the present invention provides a test kit for identifying an individual at risk of organ failure, the kit comprising at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different nucleic acids, each of which comprises a nucleotide sequence corresponding to a different ETSG or a nucleotide sequence complementary thereto. In one embodiment, the kit comprises more than 15 different nucleic acids. In one embodiment, the kit comprises more than 20 different nucleic acids. In one embodiment, the kit comprises more than 25 different nucleic acids. In one embodiment, the kit comprises more than 30 different nucleic acids. In one embodiment, the kit comprises more than 31 different nucleic acids.

In one embodiment, the kit comprises at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to a different ETSG, wherein the ETSG is selected from the group consisting of RNASE1, ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKR D1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, C XCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B , HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3 , LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2 , PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S 100A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN.23.

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100

In certain embodiments, the kit comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 different nucleic acids, each of which comprises a nucleotide sequence corresponding to or complementary to one of the following ETSGs: C19orf59, CCL22, CD14, CD300LF , CYP1B1, DHRS9, FCER1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR , PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86 and PROCR.

In one embodiment, the test kit of the present invention further comprises instructions for use, a sample collection device, one or more reagents for sample preparation, and a positive control sample.

In one embodiment, the test kit of the present invention further comprises instructions for use, a sample collection device, one or more reagents for sample preparation, and a negative control sample.

In one embodiment, the test kit of the present invention further comprises instructions for use, a sample collection device, one or more reagents for sample preparation, and a negative control sample and a positive control sample.

In one aspect, the invention provides a method of treating a patient with severe sepsis by altering the expression of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in cells from an individual, the method comprising administering to the patient a therapeutically effective amount of an agent that counteracts endotoxin tolerance.

In one embodiment, the agent is selected from the group consisting of: interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50, NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the agent is an immune cell.

In one aspect, the invention provides a method of preventing or delaying severe sepsis in a patient by altering the expression of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or up to 99 different ETSGs in cells from the patient, the method comprising administering to the patient an effective amount of an agent that counteracts endotoxin tolerance.

In one embodiment, the agent is selected from the group consisting of: interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50, NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the agent is an immune cell.

In one aspect, the invention provides a method of treating severe sepsis in a patient by altering the expression of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in cells from the patient, the method comprising administering to the patient an effective amount of an agent that counteracts endotoxin tolerance, and further comprising monitoring the at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in samples taken from the patient during treatment.

In one embodiment, the agent is selected from the group consisting of: interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50, NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the agent is an immune cell.

In one aspect, the invention provides a method of treating or prolonging severe sepsis in a patient by altering the expression of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in cells from the patient, the method comprising administering to the patient an effective amount of an agent that counteracts endotoxin tolerance, and further comprising monitoring the at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in samples taken from the patient during treatment.

In one embodiment, the agent is selected from the group consisting of: interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50, NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the agent is an immune cell.

In one aspect, the invention provides a method of preventing or delaying organ failure in a patient by altering the expression of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in cells from the patient, the method comprising administering to the patient an effective amount of an agent that counteracts endotoxin tolerance, and further comprising monitoring the at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least thirty-one different ETSGs in samples taken from the patient during treatment.

In one embodiment, the agent is selected from the group consisting of: interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50, NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the agent is an immune cell.

In one aspect, the present invention provides a method for treating severe sepsis, comprising administering to a patient a therapeutically effective amount of an agent selected from the group consisting of interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50 NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the method further comprises monitoring samples taken from the patient during treatment for at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different ETSGs.

In one aspect, the present invention provides a method for preventing or delaying severe sepsis, the method comprising administering to a patient an effective amount of an agent selected from the group consisting of interferon gamma; CpG-ODN with or without IL-10; anti-CD40; STAT3, STAT6, p50 NFκB, and IKKβ inhibitors; imidazoquinolones; and zoledronic acid. In one embodiment, the method further comprises monitoring at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different ETSGs in samples taken from the patient during treatment.

In one aspect, the present invention provides a method for treating or delaying organ failure, comprising administering to a patient an effective amount of an agent selected from the group consisting of interferon gamma, CpG-ODN with or without IL-10, anti-CD40, inhibitors of STAT3, STAT6, p50NFκB, and IKKβ, imidazoquinolones, and zoledronic acid. In one embodiment, the method further comprises monitoring samples taken from the patient during treatment for at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different ETSGs.

In one aspect, the invention provides a method of identifying an agent capable of treating sepsis, the method comprising contacting a cell with an agent and assaying the cell for expression of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen different ETSGs.

In one embodiment, the cell is an endotoxin tolerant cell. In one embodiment, the method further comprises contacting the cell with an endotoxin followed by contacting the cell with an agent. In one embodiment, the endotoxin is a bacterial lipopolysaccharide or lipoteichoic acid. In one embodiment, the bacterial lipopolysaccharide or lipoteichoic acid is present in bacteria.

In one embodiment, the agents of the invention are obtained by exposing cells to an appropriate dose of endotoxin, waiting 18 hours, and then exposing the cells again to a similar dose of the same or another endotoxin to produce endotoxin-tolerant cells, and then incubating the endotoxin-tolerant cells with the agents of the invention and examining the cells for recovery of their ability to interact with a third dose of endotoxin (breaking tolerance).

In one embodiment, the method further comprises contacting a second cell with interferon gamma; CpG-ODN with or without IL-10; anti-CD40; an inhibitor of STAT3, STAT6, p50 NFκB, and IKKβ; an imidazoquinolone; and zoledronic acid, and determining the expression of the same ETSG in the second cell.

In one embodiment, the method further comprises determining the ability of the agent to change the macrophage phenotype from M2 to M1.

The reagents of the present invention can be obtained by contacting cells with an appropriate dose of endotoxin, waiting 18 hours, and then contacting the cells again with a similar dose of the same or another endotoxin to produce endotoxin-tolerant cells, then incubating the endotoxin-tolerant cells with the reagents of the present invention, and examining the cells for restoration of their ability to interact with a third dose of endotoxin (breaking tolerance). In one embodiment, the endotoxin is bacterial lipopolysaccharide or lipoteichoic acid.

In one aspect, the method provides an agent capable of treating sepsis, said agent being identified by the method of the invention. In one embodiment, said agent is capable of changing the macrophage phenotype from M2 to M1.

In one aspect, the present invention provides a method for treating sepsis by inhibiting endotoxin tolerance. In one embodiment, an agent is used that can alter the expression of at least one, at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least 31 different ETSGs in cells from a patient.

In order to obtain a better understanding of the invention described herein, the following examples are given. It should be understood that these examples are intended to describe exemplary embodiments of the invention and are not intended to limit the scope of the invention in any way.

Example

method

Overview: The endotoxin tolerance and inflammatory LPS gene signatures were derived from a published [Pena OM et al. Journal of Immunology 2011;186:7243-54] microarray analysis of human PBMCs that identified differentially expressed genes compared to control PBMCs. To enable a more direct comparison between the signatures, the differentially expressed inflammatory genes were further reduced from 178 to 93 by overlaying with an experimental endotoxemia microarray dataset (GSE3284) [Calvano et al. Nature, 2005, 437:1032-1037] from PBMCs of healthy individuals stimulated in vivo with low doses of LPS for 2 to 6 hours. Analysis of the "endotoxin tolerance" and "inflammatory" signatures in patients and controls was performed using the statistically rigorous gene set test ROAST [Wu D et al. Bioinformatics 2010;26:2176-82]. Dataset selection was based on the following exclusion criteria: 1) cross-sectional or longitudinal cohort studies. 2) Whole blood or purified leukocyte populations. 3) Pediatric or adult patients. 4) Healthy subjects used as controls. 5) Datasets published only in the scientific literature. Standardized datasets were downloaded from NCBI GEO using the Bioconductor package GEOquery [Davis S, Meltzer PS. Bioinformatics 2007;23:1846-7]. All data processing was performed in R using Bioconductor [Gentleman RC et al. Genome Biology 2004;5:R80]. For the RNA Seq study reported here, 73 patients (aged 60 ± 17; 46 males, 27 females) were recruited with deferred consent at the time of their initial visit to the emergency room under UBC ethics approval based on the physician's opinion that their symptoms might progress to sepsis. After the initial blood draw, total RNA was prepared from whole blood, converted to cDNA, sequenced on an Illumia Genome Analyzer IIx, mapped to the human genome, and converted to expression tables using standard methods. Normalization was performed using the Limma package function voom. All other clinical parameters based on routine tests were obtained by reviewing the patients' charts.

Meta-analysis datasets. A search was conducted for sepsis datasets in the public repositories NCBI GEO and EBI Array Express. Datasets (Table 2) were selected based on the following exclusion criteria: 1) cross-sectional or longitudinal cohort studies; 2) whole blood or purified leukocyte populations; 3) pediatric or adult patients; 4) healthy subjects used as controls; and 5) datasets published only as part of studies in the scientific literature. A list of the datasets obtained is provided in Table 2.

Table 2: Description of the reanalyzed publicly available sepsis datasets ^*

Table 2 footnotes:

*Microarray data were downloaded from the Gene Expression Omnibus (GEO). The relevant paper (PubMed ID), number of patients analyzed, and specific study details are provided. A study description is included as a footnote. Array platforms: A. GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array; B. GPL6947 Illumina Human HT-12 V3.0 Expression Bead Core.

Study design by study in column 1 of Table 2:

1. GSE 28750. This is a cross-sectional, multicenter, prospective clinical trial conducted at four tertiary acute care sites in Australia. Patients with sepsis were enrolled if they met the 1992 informed consent criteria and had clinical evidence of systemic infection based on microbiological diagnosis. Healthy subjects served as normal controls.

2. GSE 13015. Patients with sepsis due to Burkholderia pseudomallei and sepsis due to other organisms were studied with positive blood cultures compared to uninfected controls.

3. GSE 9692. Cross-sectional. Children younger than 10 years were admitted to a pediatric intensive care unit (PICU) with pediatric-specific criteria for septic shock. Normal control patients were recruited from participating institutions using the following exclusion criteria: recent febrile illness (within 2 weeks), recent use of inflammatory medications (within 2 weeks), or a history of any chronic or acute illness related to inflammation.

4. GSE 26378. Cross-sectional. Expression data were generated for children with septic shock using whole blood-derived RNA samples, representing the first 24 hours of PICU admission. Healthy subjects (children) were used as normal controls in the study.

5. GSE 26440. Cross-sectional. Expression data were generated for children with septic shock using whole blood-derived RNA samples, representing the first 24 hours of PICU admission. Healthy subjects (children) were used as normal controls in the study.

6. GSE 4607. Longitudinal. Children younger than 10 years of age who were admitted to a pediatric intensive care unit and met criteria for SIRS or septic shock were eligible for this study. Control patients were recruited from the outpatient or inpatient settings of participating institutions using the following exclusion criteria: recent febrile illness (within 2 weeks), recent use of anti-inflammatory medications (within 2 weeks), or any history of chronic or acute illness related to inflammation.

7. GSE 8121. Longitudinal. Genome-wide expression profiles were generated from whole blood-derived RNA from children with septic shock on days 1 and 3 of septic shock, respectively. Control patients were recruited from participating institutions using the following exclusion criteria: recent febrile illness (within 2 weeks), recent use of anti-inflammatory medications (within 2 weeks), or any history of chronic or acute illness associated with inflammation.

8. GSE 11755. Longitudinal. Prospective case-control study involving six children with meningococcal sepsis. Blood was drawn at four time points (t = 0, t = 8, t = 24, and t = 72 hours after admission to the pediatric intensive care unit). Healthy subjects (children) served as normal controls in the study.

9. GSE 13904. Longitudinal. Genome-level expression profiles of clinically ill children showing the spectrum of systemic inflammatory response syndrome (SIRS), sepsis, and septic shock on day 1 and day 3 after admission. Healthy subjects (children) were used as normal controls in the study.

Patient selection and study design. In a blinded, observational, controlled cohort study, patients with suspected sepsis were enrolled from St. Paul's Hospital in Vancouver, Canada, at the time of first clinical suspicion of sepsis, based on the opinion of the attending physician. To determine the appropriate sample size for the study, a standard power calculation was used for appropriate sensitivity [Jones SR, S Carley and M Harrison. Emergency Medicine Journal 2003; 20, 453-458, 2003]. To obtain a sensitivity of at least 0.9 at a 95% confidence level, a required sample size of 35 patients with sepsis and a total of 70 patients was estimated (assuming that 50% of patients with suspected sepsis actually had sepsis). A total of 72 patients were recruited, including 37 patients who were later confirmed to have sepsis. The only inclusion criterion for the study was suspicion of sepsis after observation by the attending physician. The majority of patients (83%) were enrolled from the emergency department. As shown in Table 3, these individuals were heterogeneous. UBC ethical approval procedures authorized extended consent, allowing early recruitment of patients in the cohort across non-infection to septic shock. As controls, informed healthy individuals with no documented infection who were scheduled for non-urgent surgery were recruited. Blood was collected in EDTA tubes at the time of initial blood culture and immediately placed on ice. Plasma was separated from the buffy coat and two 1 ml aliquots were transferred to -20°C barcoded cryovials until they were transferred to a secure, alarmed -80°C freezer. The registration form based on these guarantees specified the study identification number and was used during all subsequent analyses; thus, the researchers analyzing gene expression for these patients were blinded to patient identity or clinical course, which was only disclosed during the final data analysis. Clinical data were stored in an ORACLE-based database on a firewalled, RSS-encrypted server at the Hospital de San Paolo.

Clinical data were collected retrospectively by physician investigators who were blinded to the RNA-Seq data. Novel organ dysfunction was defined based on laboratory values collected in the electronic medical record system. Acute organ failure was assessed as the presence of shock (treated with vasopressors), acute respiratory distress syndrome (requiring mechanical ventilation), coagulation (platelet count <80 cells/μL), liver failure (total bilirubin >34 μmol/L), and acute kidney injury (increase in serum creatinine ≥26.5 μmol/L or ≥1.5-fold relative to baseline). Initial vital signs were retrospectively extracted from paper records.

Table 3: Details of individual patients recruited for the controlled cohort study

Table 3 Footnotes: *Within 48 hours of suspected sepsis. ^§Most within 48 hours of suspected sepsis. ¹ Indicates whether the patient was transferred to the ICU after the first clinical examination. ² Diagnostic criteria for sepsis according to [Bone RC, RA Balk, FB Cerra, RP Dellinger, AM Fein, WA Knaus, RM Schein, WJ Sibbald, ASCC Committee. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies insepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Soc Critical Care Medicine. 1992. Chest 2009;136:e28; Hotchkiss, RS, IE Karl, The pathophysiology and treatment of sepsis. N Engl J Med 2003;348,138-150]. Respiratory rate and partial _CO2 are no longer criteria but are added as additional information. ³ NA: Not available. ⁴ Ventilate the patient. ⁵ None: No culture requested.

RNA sequencing. According to the TruSeq chain total RNA sample preparation kit, Ribo-Zero sample preparation guide (Illumina) is used to prepare cDNA libraries from total RNA. During sample preparation, a unique adapter index (adapterindex) (Illumina) is attached, and the sample is run, pooled, and loaded into a single flow cell lane to reduce technical variations. 63bp long sequence reads (+ adapter/index sequence) are used to read and run RNA-Seq on a GAIIx instrument (Illumina). Offline Basecaller 1.9.4 (Ilumina) and custom Perl scripts are used to convert the original primary (basecall) data into FASTQ sequence files. Read values are compared with the hg19 human genome using TopHat version 2.06 and Bowtie2 2.0.0-beta6. Initially, read values are mapped to Ensembl transcripts to search for new disabled junctions. Then, genomic coordinates are converted into counts of protein-encoding Ensembl genes. To this end, a chimeric gene model was first defined by fusing all protein-coding transcripts for a given gene. Transcripts with reads in less than 50% of their exons across all samples were defined as unexpressed and excluded from the chimeric transcriptome. Reads overlapping with chimeric genes were counted using the htseq count script in the intersection non-null model (see EMBL website). This script discards multiply mapped reads and reads that overlap multiple different genes to generate a file of counts for a specific mapped gene.

Data Analysis. All data processing was performed in R using Bioconductor. For meta-analysis, normalized datasets were downloaded from NCBI GEO using the Bioconductor package GEOquery. If the data required further normalization, a quantile normalization step was also included. For RNA-Seq analysis, data were normalized using the Voom function in the Limma package, which converts read counts to weighted logarithms with a count per million of 2. For both meta-analysis and RNA-Seq analysis, data were summarized using linear models in the Limma package.

Gene signature definition and analysis. Endotoxin tolerance and inflammatory gene signatures were obtained from previously published [Pena et al. 2011] microarray analysis of human PBMCs that identified differentially expressed genes compared to control PBMCs. In order to enable a more direct comparison between the signatures, the experimental endotoxemia microarray dataset (GSE3284) obtained from PBMCs of healthy individuals stimulated in vivo with low doses of LPS for 2 and 6 hours was used to further reduce the differentially expressed inflammatory genes from 178 to 93. Importantly, the two main gene expression datasets (GSE22248 & GSE3284) used to obtain the signature were then excluded from subsequent gene set inflammation tests. The analysis of the presence or absence of endotoxin tolerance and inflammatory signatures in patients and controls was performed using the established statistically rigorous gene set test ROAST. The gene set test essentially asks whether a given gene/signature is a signature enriched in the dataset. ROAST also allows the expression direction of genes to be considered when calculating enrichment, which increases the accuracy of the test when the direction of gene expression is known (Wu et al. Bioinformatics 2010126(17):2176-82). ROAST was performed at 99999 revolutions, so the most significant p-value obtained from this test was 0.00001. Additional endotoxin tolerance-related signatures were also defined with essentially the same results from a previously published dataset (Figure 4) and from an alternative independent endotoxin tolerance dataset [Del Fresno C et al. Journal of Immunology 2009; 182:6494-507] using multiple significance cutoffs.

Random Forest Analysis of Diagnosis Prediction. Each dataset was divided into a training set (containing 75% sepsis patients and controls) and a test set (containing 25% sepsis patients and controls) using random sampling. Datasets GSE13015 and GSE11755 were omitted from this analysis due to the low number of controls in each dataset (N=3). For each of the remaining 8 datasets, the RandomForest package [Liaw A, Wiener M.R News 2002;2:18-22] was used with ntree set to 1000, and the model was defined based on the training set and then evaluated on the test set. This procedure was repeated 100 times, and the average prediction accuracy for each dataset was recorded.

Example 1: Definition and characterization of tags.

Confirmed sepsis patients express an "endotoxin tolerance signature". In order to characterize the development of the immunosuppressive stage of sepsis and ultimately determine its association with endotoxin tolerance, a robust bioinformatics approach was performed. In order to define the endotoxin tolerance gene signature, human peripheral blood mononuclear cells (PBMCs) were treated with LPS once to model inflammatory signaling or twice to model endotoxin tolerance using microarray analysis. Compared to the control, an "endotoxin tolerance signature" (Table A below) comprising 99 genes was identified based on genes uniquely differentially expressed in endotoxin-tolerant PBMCs rather than inflammatory PBMCs. For comparison, we defined the "inflammatory signature" from previous PBMC microarray data (Pena et al., 2011) and in vivo experimental endotoxemia data sets (Calvano et al., 2005) (Fig. 1, Table 4). Having defined a genetic signature for endotoxin tolerance, we performed a global meta-analysis of nine open, independent, and blinded clinical sepsis cohorts, including 536 patients with early sepsis (1 or 3 days after ICU admission) and 142 healthy controls (Table 2; Figures 2, 3, and 4). In all of these reanalyzed datasets, patients were recruited within 1 or 3 days of ICU admission.

Table A: Endotoxin tolerance signature genes and their relative expression in endotoxin tolerant PBMC vs. controls

Table 4: Inflammatory signature genes and their relative expression in inflammatory PBMCs vs. controls

To assess the relative expression of endotoxin tolerance and inflammatory signatures in sepsis patients versus healthy controls, a gene set testing approach was used that examines whether a given signature (gene set) is significantly enriched between groups in a dataset. It was found that all nine cohorts of sepsis patients showed an immune expression profile that was closely associated with the endotoxin tolerance signature compared to controls (Figure 2). These results were independent of the fold change/statistical cutoff used to define the endotoxin tolerance signature (Figure 4). Although the inflammatory signature was significantly associated with most datasets, this association was consistently weaker than the endotoxin tolerance signature (Figure 3). In contrast to previous reports that have only associated endotoxin tolerance in late sepsis (Cavaillon J, C Adrie, C Fitting, M Adib-Conquy. J Endotoxin Res 2005; 11: 311-320; Otto, GP, M Sossdorf, RA Claus, J Rodel, K Menge, K Reinhart, M Bauer, NC Riedemann. Critical Care 2011; 15: R183; Schefold JC, D Hasper, HD Volk, P Reinke. Medical hypotheses 2008; 71: 203-208), the association with the "endotoxin tolerance signature" was present in septic patients as early as day 1 after ICU admission and was maintained on day 3, consistent with the early development of a "stable" endotoxin tolerance profile in septic patients (Figure 2). Thus, the immune dysfunction in sepsis appears to be characterized by endotoxin tolerance.

The "endotoxin tolerance signature" appears very early in sepsis patients and can be detected before diagnosis. A limitation of all nine previously published datasets used in the previous meta-analysis was that the transcriptomes of sepsis patients were analyzed after a "confirmed diagnosis" of sepsis, rather than at initial presentation. Therefore, a specific cohort of patients was generated at the earliest possible stage of clinical illness to better understand the timing of endotoxin tolerance development and the diagnostic utility of the signature identified herein. Patients were recruited immediately after clinical suspicion of sepsis based on an analysis of the patient's medical history, physical examination, and status request for microbial culture testing by the attending physician. RNA isolated from an initial blood sample obtained for culture was subjected to RNA sequencing to assist in sepsis diagnosis/microbial identification. An appropriate power calculation was performed, and based on this, 72 patients with very early suspected sepsis were recruited (Table 3) along with an additional 11 control patients without underlying complications, recruited before elective surgery. The potential for early differential diagnostic approaches in this clinically challenging cohort of patients initially presenting with variable and severe physiological derangements potentially caused by sepsis has major clinical implications.

Based on the earliest documented clinical assessment after sample isolation (Table 3), 72 patients in the suspected sepsis cohort were retrospectively classified as "sepsis" (n = 37) or "no sepsis" (n = 35), consistent with current sepsis diagnostic criteria (R. C. Bone et al., 2009). Strikingly, even at the earliest stage of clinical sepsis, the endotoxin tolerance signature was significantly enriched compared to healthy controls only in patients who were subsequently confirmed to have sepsis, but not in patients who received other diagnoses (Figure 5A). When combined with the results from the previous meta-analysis, sepsis appears to be closely associated with endotoxin tolerance throughout all initial stages of clinical illness (Figures 2 & 5A). Furthermore, while the inflammatory signature did not reach statistical significance in the "no sepsis" group, the relative enrichment of endotoxin tolerance compared to the inflammatory signature in the two groups may represent a fundamental difference in the balance of endotoxin tolerance and inflammation that is unique to patients with sepsis. Finally, the endotoxin resistance signature was also enriched in the "sepsis" group when directly compared to the "no sepsis" group (Figure 5B), supporting the specificity of endotoxin resistance to sepsis and not just to "sick" patients.

One challenge in diagnosing sepsis is confirming infection due to the low sensitivity of bacterial culture (RC Bone et al., 2009). Indeed, due to these sensitivity issues, current sepsis diagnostic criteria are based on "suspected infection" rather than confirmed infection (RC Bone et al., 2009). This concept is highlighted by comparing the enrichment of signatures from microbial culture in the two patient groups. Consistent with previous results, the endotoxin-tolerant signature was higher in the "sepsis group" (Figure 5C), while the inflammatory signature of the "no sepsis" group remained high regardless of the microbial culture results (Figure 5D), further highlighting the interesting inverse trend of the signatures of these two "sick" patient groups. As expected, due to sensitivity issues with microbial culture, the endotoxin-tolerant signature showed more significant enrichment in the culture-positive group and was also strongly enriched in the culture-negative group, which is consistent with the possible incorrect identification of "infection-negative" patients in this group (Figure 5C). Because RNA-Seq analysis was performed on the same blood samples used for diagnostic microbial cultures, the strong correlation between sepsis and the endotoxin tolerance signature suggests that the endotoxin tolerance signature may provide a more sensitive diagnostic tool than microbial cultures.

Together, these data suggest that endotoxin tolerance persists throughout the initial clinical course of sepsis, is detectable before “diagnosis,” and can be used to distinguish patients with sepsis from those suspected of having sepsis.

Table 5: Statistics on organ failure and infection sites in patient groups

Next, we investigated the association of the endotoxin tolerance-driven immunosuppressive state (detected by the endotoxin tolerance signature) with sepsis severity, as defined by the subsequent development of organ dysfunction in a cohort of patients with suspected sepsis. Independent of sepsis diagnosis, subsequent organ dysfunction development was assessed within 48 hours of study enrollment, which retrospectively stratified patients into groups positive for and negative for organ dysfunction (cardiovascular, coagulation, renal, hepatic, and respiratory; Tables 4 & 5). These groups were then subjected to the same gene set analysis as described above. Interestingly, the endotoxin tolerance signature was found to be significantly associated with individual or multiple (3+) organ dysfunction (Figure 6A; with the exception of coagulation failure). Although ICU admission can depend on the inherent subjectivity of hospital regulations, such as the number of available space or beds per department, patients transferred to the ICU are generally in a worse condition with an increased risk of mortality. Therefore, the requirement for ICU admission was also assessed as a second, less precise measure of illness severity, and the endotoxin tolerance signature was again shown to be associated with increased illness severity (Figure 6B). These results suggest that endotoxin tolerance is associated with sepsis severity and, specifically, the subsequent development of organ failure.

Due to the significant association between endotoxin tolerance and sepsis in more than 600 patients from 10 independent data sets, the use of an endotoxin tolerance signature as a sepsis diagnostic tool was investigated. Although the full 99-gene endotoxin tolerance signature can be used to characterize immune dysfunction in sepsis, a smaller number of genes is more useful in diagnostic testing. Thus, the 99-gene signature was further analyzed to identify a usable subset of this initial signature, which can then be tested for diagnostic utility. To this end, genes with differential expression greater than 1.5 times between sepsis patients and controls were selected from the majority (7+) of the 9 literature data sets. This identified a subset of 31 genes from the original 99-gene endotoxin tolerance signature (Figure 7).

The ability of genes in the identified subset of 31 genes to classify sepsis patients from controls was assessed using the classification algorithm Random Forest. Each data set (external and internal) was divided into a training set and a test set, and random forest classification was performed independently for each data set. The 31-gene subset showed excellent accuracy when distinguishing sepsis patients from controls with an accuracy of 95.7% across all data sets (Table 6). The 31-gene subset also showed strong performance in predicting sepsis and single/combined organ failure in a group of patients suspected of sepsis, with an accuracy of 62.4% to 87.4% (Table 6). This same analysis was also performed using the full 99-gene endotoxin tolerance signature, and it was found that the gene set provided equivalent performance supporting the suitability of the gene reduction strategy (Table 7). The area under the curve of the receiver operating characteristic (AUC) assessment was similarly performed. The strong performance of the 31-gene subset across multiple different data sets and at clinically relevant time points (current diagnostic cultures) supports the use of the 31-gene subset for diagnosing sepsis. This strong association between endotoxin tolerance and sepsis was identified in 10 different data sets and was independent of location, method, sex, age, race, and sepsis diagnostic criteria variables. Therefore, both the full 99-gene endotoxin tolerance signature and the 31-gene subset will have utility as diagnostic tools for sepsis. An accurate diagnostic tool for sepsis would be of high clinical priority due to the importance of early intervention for sepsis and the lack of clinical features unique to sepsis [Hotchkiss RS, Monneret G, Payen D. Lancet Infectious Diseases 2013; 13: 260-8]. An additional benefit of using biomarkers associated with endotoxin tolerance is that they also provide information about the patient's immune function status.

Table 6: Diagnostic potential of endotoxin tolerance signatures*

Table 6 Footnotes: * Each dataset was divided into a training set (containing 2/3 septic patients and controls) and a test set (containing 1/3 septic patients and controls) using random sampling. Datasets GSE13015 and GSE11755 were omitted from this analysis due to the low number of controls (N=3) in each dataset. For each of the remaining 8 datasets, the randomForest package was used with ntree set to 1000, and the model was defined based on the training set and then evaluated on the test set. This procedure was repeated 1000 times, and the average prediction accuracy for each dataset was recorded. This analysis was repeated for the datasets to classify patients with initial suspected sepsis who did or did not go on to develop sepsis or organ failure.

The endotoxin-tolerant signature showed excellent accuracy when discriminating septic patients from controls (overall accuracy: Random Forest = 95%; Area Under the Curve = 98.9%) and demonstrated similar accuracy in a separate study of 19-227 patients (Table 6).

The association between the endotoxin tolerance signature and confirmed sepsis was strong and statistically significant in 10 different data sets (Figures 2 & 5, Table 6), and was independent of sample size, location, method, sex, age, and race. These results confirm that the endotoxin tolerance signature is closely associated with very early sepsis. The endotoxin tolerance signature also correlates with disease severity, initially measured by the development of organ dysfunction. Thus, a new model of sepsis pathogenesis mediated by endotoxin tolerance-mediated immune dysfunction is shown. In addition, the results show that immune dysfunction can be detected at clinically relevant diagnostic time points, providing unique information about the patient's functional status. Therefore, the endotoxin tolerance signature or subset may help define a subset of patients who may benefit from immunomodulation (e.g., anti-endotoxin tolerance) and supportive therapy.

Sepsis is typically categorized as a hyperinflammatory response (early phase) followed by a transition to a dominant anti-inflammatory/immunosuppressive phase (Hotchkiss et al., 2013). However, the nature and timing of this latter phase are not well characterized. In contrast to previous reports linking endotoxin tolerance only to late-stage sepsis, the results described here reveal that all 10 cohorts of sepsis patients displayed immune expression profiles strongly associated with the endotoxin tolerance signature and subsets, and throughout all stages of early clinical disease (Figures 2, 5, and 6). From a general clinical perspective, characterizing the nature and timing of the hyperinflammatory and anti-inflammatory/immunosuppressive phases is crucial when considering how to treat this disease. This may be particularly important given that different therapeutic approaches have been largely unsuccessful to date, perhaps due to a lack of knowledge about the patient's immunological status. The data presented here also demonstrate that the signature is able to predict the development of sepsis, suggesting the utility of the endotoxin tolerance signature and subsets as a diagnostic tool.

Most importantly, this study was able to clearly demonstrate the association of the endotoxin tolerance signature and subsets with disease severity and organ dysfunction (Figure 6). Organ dysfunction is considered a major factor contributing to patient deterioration and ultimately death. Importantly, the endotoxin tolerance signature was present up to 48 hours before the onset of organ dysfunction, suggesting that this signature or subset could also be used as a screening method to assess which patients are at higher risk of deterioration.

It is important to note that although the data show that the endotoxin tolerance state is dominant during early sepsis, inflammatory markers are also significantly enriched, but at relatively low levels in many comparisons conducted in this study. From a biological perspective, these observations show that in individuals with localized infection (such as patients in the non-sepsis group), when initial damage occurs, a brief inflammatory response rapidly subsides to balance inflammation and allow the system to maintain homeostasis. However, in sepsis, in the presence of uncontrolled sources of infection and possible genetic influences (Murkin JM and KR Walley, The Journal of Extra-Corporeal Technology 41, P43-49, 2009), the immune balance between inflammation and endotoxin tolerance becomes an unfavorable imbalance towards a state where endotoxin tolerance is increasingly dominant. Thus, the findings herein show that there is an initial (uncontrolled) infection, during which resting immune cells (such as neutrophils and monocytes/macrophages) become activated, causing the first strong clinical symptoms to occur in patients. In sepsis patients, the second endotoxin stimulation may lead to the rapid activation of endotoxin tolerance characteristics. At initial hospital admission, this endotoxin-tolerant signature predominates systemically in peripheral blood mononuclear cells, while residual neutrophilic inflammation remains rapidly developing in this rapidly transforming population.

Thus, although an inflammatory component to sepsis remains, the endotoxin tolerance-driven state contributes to the overall immune dysfunction of sepsis and, thus, the severity of the disease.

At the cellular level, the primary cause of immune dysfunction in sepsis may be the rapid accumulation of tolerant monocytes/macrophages, which lock the system into an M2-like state (Pena, OM et al., J Immunol 2011, 186:7243-7254) in an attempt to reduce excessive neutrophil inflammation and its consequences, such as vascular leakage, coagulation, and lymphocyte death. However, weakening the patient's monocyte/macrophage response can also lead to an inability to clear the primary infection and increased susceptibility to secondary infections, despite the continued activation of other immune cell populations such as neutrophils. Due to their continuous recruitment from the bone marrow, neutrophils may be the primary driver of proinflammatory cytokine responses, although they may also eventually enter an endotoxin-tolerant state (Parker LC et al., J Leukocyte Biology 2005; 78:1301-1305).

In addition, it was demonstrated herein that the endotoxin tolerance signature and subset had a higher association with positive cultures in the sepsis group and a higher trend similar to those with negative cultures (Figure 5C). In contrast, the inflammatory signature dominated in the "no sepsis" patients, with a stronger presence in those with positive cultures (Figure 5D). Interestingly, different trends were observed in each group of patients, which is consistent with the points discussed previously: in the "no sepsis" group, the initial inflammatory signature that increased in the negative to positive culture direction indicated an early increase in inflammation during a possible localized infection or initial sterilizing inflammatory process. Subsequently, in patients with extremely rapid deterioration of condition, such as in the case of those patients in the "sepsis" group, a rapid shift to a tolerance state caused by systemic infection resulted in a stronger and increased presence of an endotoxin tolerance state, as observed in the results, indicating a trend from culture negative to culture positive.

Characterizing the contribution of inflammatory and immunosuppressive programs during clinical illness is crucial when considering host-directed therapies for treatment. The results described herein suggest that an endotoxin-tolerant state predominates throughout the early stages of clinical sepsis and has the potential to drive the immune dysfunction of sepsis. Therefore, if an immune phase characterized solely by excessive inflammation exists, it is likely to occur preclinically. However, given that inflammatory signatures are significantly enriched in many groups of sepsis patients, the combination of endotoxin tolerance and inflammation may contribute to a unique pattern of sepsis pathogenesis. These findings move away from a two-stage model and toward a clinically relevant immunopathology characterized by immune dysfunction driven by endotoxin tolerance in the early stages of clinical illness. Detection of a dominant endotoxin tolerance signature supports suspected sepsis and will therefore guide the treatment team in considering appropriate supportive and immunomodulatory therapies to balance the immune response.

Together, these studies provide the first description of a unique endotoxin tolerance signature that is present very early in the course of sepsis, is relevant to sepsis pathogenesis, and is strongly associated with the risk of organ dysfunction.

Example 2: New Therapy Based on Endotoxin Tolerance Signature

Network analysis of endotoxin tolerance genes revealed that most of the genes formed a very tight subnetwork, which strongly suggested that the signature reflects key mechanisms that may be associated with immune dysfunction in sepsis patients ( FIG. 8 ).

One clue that a patient is about to develop sepsis is that appropriate antibiotic therapy, including a cocktail of the most effective drugs, can be administered. Current clinical guidelines dictate that patients should be started on intravenous ceftriaxone and azithromycin while awaiting culture results. The goal of this approach is to minimize the need for major resistant tissue infection, as only a subset of patients considered likely to develop sepsis actually require this (see, e.g., Table 3). Knowing that a patient is developing sepsis very early in the disease process allows physicians to prescribe the most aggressive therapy to minimize the impact of the infection.

The second treatment strategy is to try to break tolerance and reverse the immunosuppressive state of macrophages. Almost all therapies attempted to treat sepsis to date have attempted to do the opposite: suppress the hyperinflammatory state, which has the potential to make patients less able to resist sepsis. Consistently, in more than 31 clinical trials designed to suppress the hyperinflammatory state, this approach has failed.

Examples of methods for breaking endotoxin tolerance include immune cells [Heusinkveld M et al. Journal of Immunology 2011; 187: 1157-1165], interferon-γ, CpG-ODN with or without IL-10, anti-CD40, STAT3 inhibitors, STAT6 inhibitors, p50 inhibitors, NFκB inhibitors, IKKβ inhibitors, imidazoquinolones, and zoledronic acid [Sica A, A Mantovani. Journal of Clinical Investigation 2012; 122: 787–795]. Other potential agents include chemical agents, cells, or natural products that inhibit the expression of one or more genes from the endotoxin tolerance signature in M2 polarized macrophages or convert the properties of M2 macrophages to M1 macrophages in vitro or in vivo [Sica and Mantovani, 2012].

The disclosures of all patents, patent applications, publications, and database entries cited in this specification are hereby specifically incorporated by reference in their entirety to the same extent as if each such patent, patent application, publication, and database entry was specifically and individually indicated to be incorporated by reference.

Although the present invention has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the spirit and scope of the invention. It will be apparent to those skilled in the art that all such modifications are intended to be included within the scope of the appended claims.

Claims (63)

1. The use of a substance for determining the expression level of each of at least six endotoxin tolerance tag genes in a biological sample obtained from a subject to provide a sample gene tag for comparison with a reference gene tag in the preparation of a product for diagnosing sepsis in indicated subjects, wherein the reference gene tag represents a standard expression level of each of the multiple genes; wherein the difference between the sample gene tag and the reference gene tag indicates that the subject has sepsis. Furthermore, the aforementioned genes are selected from: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL 3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR , PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S10 0A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN. 2. The application according to claim 1, wherein the sepsis is severe sepsis. 3. The use of a substance for determining the expression level of each of at least six endotoxin tolerance tag genes in a biological sample obtained from a subject to provide a sample gene tag for comparison with a reference gene tag in the preparation of a product for identifying whether the subject is at risk of developing severe sepsis, wherein the reference gene tag represents a standard expression level of each of the multiple genes; wherein the difference between the sample gene tag and the reference gene tag indicates that the subject is at risk of developing severe sepsis. Furthermore, the aforementioned genes are selected from: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL 3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD3, PPBP, PROCR , PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S100A8, S10 0A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN. 4. The application according to any one of claims 1 to 3, wherein the subject is suspected of having sepsis. 5. The application according to any one of claims 1 to 3, wherein the subject is diagnosed with sepsis by a standard procedure. 6. The use of a substance containing a sample gene tag for determining the expression level of at least six genes selected from a plurality of genes, chosen from a plurality of genes, in a biological sample obtained from a subject to provide a comparison with a reference gene tag, in the preparation of a product for diagnosing endotoxin tolerance in said subject: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD1 4. CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2 , EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2 AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, M GST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PL AUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S 100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN, The reference gene tag represents the standard expression level of each of the multiple genes; The difference between the sample gene tag and the reference gene tag indicates that the subject has endotoxin tolerance. 7. The application according to claim 6, wherein the subject has or is suspected of having sepsis. 8. The application according to claim 6, further comprising identifying the subject as being at risk of severe sepsis if there is a difference between the sample gene tag and the reference gene tag. 9. The application according to any one of claims 1-3 and 6-8, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 6 of the plurality of genes in the direction of expression change. 10. The application according to claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 7 of the plurality of genes in the direction of expression change. 11. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 8 of the plurality of genes in the direction of expression change. 12. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least nine of the plurality of genes in the direction of expression change. 13. The application according to claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 10 of the plurality of genes in the direction of expression change. 14. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 15 of the plurality of genes in the direction of expression change. 15. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 20 of the plurality of genes in the direction of expression change. 16. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 25 of the plurality of genes in the direction of expression change. 17. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 30 of the plurality of genes in the direction of expression change. 18. The application of claim 9, wherein the difference between the sample gene tag and the reference gene tag is defined by the expression differences of at least 31 of the plurality of genes in the direction of expression change. 19. The application according to claim 9, wherein the gene is ADAMDEC1, ANKRD1, C19orf59, CA12, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CYP1B1, CYP27B1, DDIT4, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCE1G, FCE R2, FPR1, FPR2, GK, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, IL3 RA, ITGB8, KIAA1199, LILRA3, LILRA5, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, If the gene is NEFH, NRIP3, OLIG2, PANX2, PAPRN, PDLIM7, PLAUR, PPBP, PROCR, PTGES, PTGR1, RAB13, RETN, RHBDD2, S100A12, S100A8, S100A9, SERPINA1, SERPINAB7, SLC16A10, SLC7A11, TGM2, TMEM158, TREM1, UPP1, or VCAN, then the expression change is upregulated; if the gene is ADAM15, ALCAM, ALDH1A1, CAMP, CPVL, CST3, CST6, CTSK, CXCL10, DHRS9, GPNMB, HTRA1, IL18BP, LIPA, LY86, NQO1, PLD3, PSTPIP2, RARRES1, RNASE1, S100A4, TLR7, or TSPAN4, then the expression change is downregulated. 20. The application according to any one of claims 10-18, wherein the gene is ADAMDEC1, ANKRD1, C19orf59, CA12, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CYP1B1, CYP27B1, DDIT4, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER 1G, FCER2, FPR1, FPR2, GK, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B 1. IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MY ADM, NEFH, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PPBP, PROCR, PTGES, PTGR1, RAB13, RETN, RHBDD 2. S100A12, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TMEM158, TREM1, UPP1 or VCA If N is present, the expression change is upregulated; if the gene is ADAM15, ALCAM, ALDH1A1, CAMP, CPVL, CST3, CST6, CTSK, CXCL10, DHRS9, GPNMB, HTRA1, IL18BP, LIPA, LY86, NQO1, PLD3, PSTPIP2, RARRES1, RNASE1, S100A4, TLR7, or TSPAN4, the expression is downregulated. 21. The application according to any one of claims 1-3 and 6-8, wherein the plurality of genes are selected from: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCERT1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86 and PROCR. 22. The application according to any one of claims 1-3 and 6-8, wherein the plurality of genes comprises: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCERT1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR. 23. The application according to any one of claims 1-3 and 6-8, wherein determining the expression level comprises detecting nucleic acids encoded by each of the plurality of genes. 24. The application according to claim 23, wherein determining the expression level comprises one or more of the following: polymerase chain reaction (PCR) amplification, non-PCR amplification, reverse transcriptase-(RT) PCR, Q-β replicase amplification, ligase chain reaction, signal amplification (Ampliprobe), light cycling, differential display, RNA analysis, hybridization, microarray analysis, DNA sequencing, Ref-Seq, MassArray analysis, and MALDI-TOF mass spectrometry. 25. The application of claim 23, wherein detecting the nucleic acid comprises contacting the biological sample with a microarray comprising a plurality of polynucleotide probes capable of hybridizing with the nucleic acid encoded by each of the plurality of genes. 26. The application of claim 23, wherein determining the expression level comprises isolating mRNA from the biological sample, reverse transcribing the mRNA to generate a cDNA product, and contacting the cDNA product with a microarray comprising a plurality of polynucleotide probes capable of hybridizing with a plurality of cDNAs complementary to a plurality of mRNAs expressed by the plurality of genes. 27. The application according to any one of claims 1-3 and 6-8, wherein the assay further comprises the step of obtaining the biological sample from the subject. 28. The application according to any one of claims 1-3 and 6-8, wherein the biological sample comprises blood, plasma, serum, tissue, amniotic fluid, saliva, urine, feces, bronchoalveolar fluid, cerebrospinal fluid, or skin cells. 29. The application according to any one of claims 1-3 and 6-8, wherein the biological sample comprises blood. 30. A kit for determining the expression level of at least six genes selected from a plurality of genes in a sample: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL19, CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT4, DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIST2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11 B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F , MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, PDLIM7, PLAUR, PLD 3. PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100A4, S 100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 The kit contains gene-specific reagents for the at least six genes and instructions for use, each of which is capable of detecting the expression product of a corresponding one of the genes or their complement. 31. The kit according to claim 30, wherein the plurality of genes comprises at least 7 genes. 32. The kit according to claim 30, wherein the plurality of genes comprises at least eight genes. 33. The kit according to claim 30, wherein the plurality of genes comprises at least nine genes. 34. The kit according to claim 30, wherein the plurality of genes comprises at least 10 genes. 35. The kit according to claim 30, wherein the plurality of genes comprises at least 15 genes. 36. The kit according to claim 30, wherein the plurality of genes comprises at least 20 genes. 37. The kit according to claim 30, wherein the plurality of genes comprises at least 25 genes. 38. The kit according to claim 30, wherein the plurality of genes comprises at least 30 genes. 39. The kit according to claim 30, wherein the plurality of genes comprises at least 31 genes. 40. The kit according to any one of claims 30-39, wherein the plurality of genes are selected from: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCERT1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR. 41. The kit according to any one of claims 30-39, wherein the plurality of genes comprises: C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCERT1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR. 42. The kit according to any one of claims 30-39, wherein the expression product or its complement is RNA, cDNA or protein. 43. The kit of claim 42, wherein each expression product is a nucleic acid, and each gene-specific reagent comprises a polynucleotide probe capable of binding specifically to at least one of the nucleic acid expression products or its complement. 44. The kit of claim 43, wherein the kit further comprises polynucleotide primers for amplifying at least a portion of the nucleic acid expression product. 45. The kit according to claim 43 or 44, wherein the polynucleotide probe is provided as a microarray. 46. The kit according to any one of claims 30-39, further comprising one or more controls. 47. The kit according to any one of claims 30-39, wherein the kit is used to diagnose sepsis in a subject or to determine the risk of a subject developing severe sepsis. 48. The kit of claim 42, wherein the kit is used to diagnose sepsis in a subject or to determine the risk of a subject developing severe sepsis. 49. The kit of claim 46, wherein the kit is used to diagnose sepsis in a subject or to determine the risk of a subject developing severe sepsis. 50. A microarray for detecting the expression of at least six of a plurality of endotoxin tolerance tag genes in a sample, the microarray comprising at least six of a plurality of polynucleotide probes attached to a solid support, each of the polynucleotide probes being specifically hybridizing to the expression product of a corresponding one of the plurality of genes or their complement, wherein the plurality of genes are selected from: ADAM15, ADAMDEC1, ALCAM, ALDH1A1, ANKRD1, C19orf59, CA12, CAMP, CCL1, CCL2 9. CCL22, CCL24, CCL7, CD14, CD300LF, CD93, CDK5RAP2, CPVL, CST3, CST6, CTSK, CXCL10, CYP1B1, CYP27B1, DDIT 4. DHRS9, DPYSL3, EGR2, EMR1, EMR3, FBP1, FCER1G, FCER2, FPR1, FPR2, GK, GPNMB, GPR137B, HBEGF, HIST1H1C, HIS T2H2AA3, HIST2H2AC, HK2, HK3, HPSE, HSD11B1, HTRA1, IL18BP, IL3RA, ITGB8, KIAA1199, LILRA3, LILRA5, LIPA, LY86, MARCO, MGST1, MMP7, MT1F, MT1G, MT1H, MT1M, MT1X, MXD1, MYADM, NEFH, NQO1, NRIP3, OLIG2, PANX2, PAPLN, P DLIM7, PLAUR, PLD3, PPBP, PROCR, PSTPIP2, PTGES, PTGR1, RAB13, RARRES1, RETN, RHBDD2, RNASE1, S100A12, S100 A4, S100A8, S100A9, SERPINA1, SERPINB7, SLC16A10, SLC7A11, TGM2, TLR7, TMEM158, TREM1, TSPAN4, UPP1 and VCAN. 51. The microarray of claim 50, wherein the plurality of genes comprises at least seven genes. 52. The microarray of claim 50, wherein the plurality of genes comprises at least eight genes. 53. The microarray of claim 50, wherein the plurality of genes comprises at least nine genes. 54. The microarray of claim 50, wherein the plurality of genes comprises at least 10 genes. 55. The microarray of claim 50, wherein the plurality of genes comprises at least 15 genes. 56. The microarray of claim 50, wherein the plurality of genes comprises at least 20 genes. 57. The microarray of claim 50, wherein the plurality of genes comprises at least 25 genes. 58. The microarray of claim 50, wherein the plurality of genes comprises at least 30 genes. 59. The microarray of claim 50, wherein the plurality of genes comprises at least 31 genes. 60. The microarray according to any one of claims 50-59, wherein the plurality of genes are selected from C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCERT1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR. 61. The microarray according to any one of claims 50-59, wherein the plurality of genes includes C19orf59, CCL22, CD14, CD300LF, CYP1B1, DHRS9, FCERT1G, FPR1, FPR2, GK, HISTH2H2AA3, HK2, HK3, HPSE, LILRA5, MGST1, PDLIM7, PLAUR, PSTPIP2, RAB13, RETN, RHBDD2, S100A4, S100A9, S100A12, SERPINA1, UPP1, CPVL, CST3, LY86, and PROCR. 62. The microarray according to any one of claims 50-59, further comprising one or more control polynucleotide probes. 63. The microarray of claim 62, wherein the microarray comprises essentially the following: (i) the polynucleotide probe capable of specifically hybridizing with the expression product of a corresponding one of the plurality of genes or their complement; and (ii) the one or more control polynucleotide probes.