Markers of resistance and systemic inflammation and uses thereof

Abstract

The present disclosure relates to a methods, compositions and kits for the diagnosis and prognosis of sepsis and / or associated conditions, as well as tailoring personalized treatment regimens based on the balance between systemic inflammation and resistance state of subjects in need. The disclosed methods involve calculating the resistance (R) level and the systemic inflammation (SI) level of a subject to determine a R / SI balance score that enables classification of the subject.

Description

[0001] MARKERS OF RESISTANCE AND SYSTEMIC INFLAMMATION AND USES THEREOF

[0002] The present disclosure relates to the field of personalized medicine. More specifically, the disclosure provides compositions, kits and methods for the diagnosis and prognosis of sepsis and / or associated conditions, as well as tailoring personalized treatment regimens based on the balance between systemic inflammation and resistance state of subjects in need.

[0003] BACKGROUND ART

[0004] References considered to be relevant as background to the presently disclosed subject matter are listed below:

[0005] 1. Leventogiannis, K. et al. Toward personalized immunotherapy in sepsis: The PROVIDE randomized clinical trial. Cell Rep Med 3, 100817 (2022).

[0006] 2. Davenport, E. E. et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir Med 4, 259-271 (2016).

[0007] 3. Scicluna, B. P. et al. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir Med 5, 816-826 (2017).

[0008] 4. Tom van der Poll, et al. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol. 17(7):407-420 (2017).

[0009] 5. Cohn, O. et al. Distinct gene programs underpinning disease tolerance and resistance in influenza virus infection. Cell Syst 13, 1002-1015.e9 (2022).

[0010] 6. WO 2023 / 017524.

[0011] 7. Frishberg, A. et al. An integrative model of cardiometabolic traits identifies two types of metabolic syndrome. eLife 10, e61710 (2021).

[0012] Acknowledgement of the above references herein is not to be inferred as meaning that these are in any way relevant to the patentability of the presently disclosed subject matter.

[0013] BACKGROUND

[0014] Sepsis is a pathological condition resulting from dysregulated immune responses in patients with infections, leading to severe symptomatology, organ dysfunction, and often death [Singer, M. et al. JAMA 315, 801-810 (2016)]. Sepsis is currently one of the most important causes of morbidity and mortality in both developed and developing countries, with estimated 49 million cases and 11 million deaths globally every year [Rudd, K. E. et al. Lancet 395, 200-211 (2020)]. Antibiotics and intensive care units have significantly decreased sepsis mortality during the 20thcentury, but the outcome of sepsis remained largely stable in the last two decades [Rudd, K. E. et al. Lancet 395, 200-211 (2020); Kaukonen, K.M. et al. JAMA 311, 1308-1316 (2014)]. Septic shock, a subtype of sepsis, is characterized by more severe cardiovascular abnormalities and a higher risk of death of around 40% to 50% [Shankar-Hari, M. et al. JAMA 315, 775-787 (2016)]. In fact, sepsis accounts for 19.7% of all global deaths (Fleischmann, C. et al. Am J Respir Crit Care Med 193, 259-272 (2016)). The burden of sepsis is the highest in the sub-Saharan Africa, Southeast Asia and Oceania, but it is still very high also in developed nations in Europe and North America. There is therefore a need to develop and implement improved diagnostics and therapeutic interventions in sepsis. Recent studies revealed that sepsis survivors are associated with increased incidence of poor long-term clinical outcome [Prescott, H.C. et al. Am J Respir Crit Care Med 200, 972-981 (2019); van der Slikke, et al. EBioMedicine 61, 103044 (2020); Manu Shankar-Hari, et al. Intensive Care Medicine volume 46, pages 619-636 (2020]. This includes long-term cognitive impairment, stress disorders, depression, dementia, cardiovascular events, acute renal failure, recurrent infections and sepsis, as well as excessive mortality that cannot be explained by the baseline clinical status. Rehospitalizations occurred in 21.4% of septic patients discharged from the hospital. Long-term immune dysregulation in sepsis survivors, as well as injuries occurring during sepsis that are only partially repaired, likely lead to long-term post-sepsis complications.

[0015] One of the major challenges in sepsis and post-sepsis syndrome is patient heterogeneity. Particularly, sepsis demonstrates a great variety of underlying comorbidities, causative pathogens, infectious disease entities with diverse pathogenesis and pathophysiology. Post sepsis syndrome also contains wide heterogeneity of complications. It is currently believed that due to this wide heterogeneity, the pathophysiology of sepsis and post-sepsis syndrome remains poorly understood. Furthermore, this heterogeneity likely explains the multiple clinical trials that already have been tried, all of which failed to improve outcome.

[0016] It has been hypothesized that immunotherapy will be the next revolution in the treatment of sepsis, yet this has never materialized despite a plethora of clinical trials with anti-inflammatory immunotherapies (e.g., anti-cytokine, anti-complement, anti-receptor antibodies) that all failed to improve the outcome of the patients with sepsis. It is believed that the heterogeneity of sepsis at the level of causal microorganism, source of infection, and especially the type of immune dysregulation has led to the failure of sepsis immunotherapy trials [Ref 3]. Indeed, some sepsis patients display hyperinflammatory characteristics, the so-called macrophage activation-like syndrome (MALS) [Kyriazopoulou, E. et al. BMC Med 15, 172 (2017)], other patients show defects of critical immune functions (immunosuppression) [Ref 4], and in some critically ill sepsis patients these two types of immune dysregulation can occur at the same time or change in time depending on the phase of disease. Understanding the heterogeneity of immune dysregulation in sepsis is of fundamental interest and could facilitate the development of a better therapeutic approach.

[0017] Recent studies have stratified sepsis patients either relying on clinical parameters [Ref 1] or relying on signatures based on high-throughput molecular information such as transcriptomes [Ref 2-4], but none of these approaches proved successful. On the one hand, patient stratifications based on clinical parameters are not able to identify the immune defects that transcend purely clinical phenotypes. On the other hand, transcriptional scoring systems are complex, have technical demands that are often not available in real-life situations, and such machine-learning approaches lack a functional correlate to explain the observed impact on severity and / or mortality. Thus, none of these classifications has been successful in identifying endotypes that can be readily targeted with specific immunotherapies. Moreover, these methods describe each endotype separately, and a common denominator of sepsis remains largely unknown.

[0018] More specifically, there is a little reproducibility (overlap) of subtypes across different sepsis studies, limiting the ability to identify shared targets for immunomodulation [Shankar-Hari, M. et al. Lancet Respir Med S2213-2600(23)00468-X (2024) doi:10.1016 / S2213-2600(23)00468-X]. In addition, there is a lack of consistency between current sepsis datasets (e.g., datasets differ in how sepsis is defined, infection type, and age group) and it is challenging to integrate them. In accordance, most studies do not integrate information from multiple cohorts. Finally, differences between sepsis and infections without sepsis are not commonly exploited in the analysis of sepsis. In particular, current datasets of the differences between infections that result in sepsis versus those that do not are commonly limited to small cohorts and a particular type of infection [Reyes, M. et al. Nat Med 26, 333-340 (2020)], and none have systematically compared these two types of infections at large scale. A new approach is therefore needed to provide patient classification into endotypes that are at the same time: i. biologically relevant, ii. easy to apply in clinical practice, and iii. mediated by immune mechanisms that could be targeted therapeutically. A successful approach should also describe shared and distinct determinants of the various endotypes.

[0019] The inventors have recently described two transcriptional signatures: a signature to assess immune activation that is specifically arising to detect and eliminate the invading pathogen (resistance - R) [Refs 5, 6], and a signature to assess immune activation that is associated with the physiological status of systemic inflammation (systemic inflammation - SI) [Ref 7]. GENERAL DESCRIPTION

[0020] The inventors integrated their previously described resistance (R) [Refs 5, 6] and systemic inflammation (SI) [Ref 7] signatures with clinical and molecular information across multiple cohorts of both sepsis and moderate infections. Sepsis patients differ in their clinical presentations and immune dysregulation in response to infection, but the fundamental processes that determine this heterogeneity have remained elusive. Here the inventors aimed to understand which types of immune dysregulation characterize sepsis patients, and how to identify them. To that end, the inventors dissected sepsis pathogenesis in the context of interaction between two distinct transcriptional states: one represents the immune response to eliminate pathogens (resistance, R) and the other is associated with systemic inflammation (SI). Using integrative analysis across multiple sepsis cohorts, it was found that sepsis patients share a unique molecular fingerprint of a low R-to-SI balance - i.e., a low R relative to the level of SI. Differences between sepsis patients are explained by the wide diversity of R and SI states that fall under this fingerprint, such as high- SI patients, low-R patients, or both. The inventors show how this R / SI framework can be used to guide patient stratification that is relevant to disease prognosis, outperforming existing classifications of sepsis in an independent cohort. Overall, the present disclosure redefines sepsis as an impaired balance between the intracellular states of R and SI in immune cells, providing a framework to describe sepsis heterogeneity with implications on disease stratification and management.

[0021] In a first aspect the present disclosure provides a method for diagnosing, prognosing and / or classifying sepsis and / or associated conditions in a subject by determining the balance between resistance / systemic inflammation (R / SI) transcriptional programs of the subject. More specifically, in some embodiments, the method comprising the following steps. A first step (a) involves determining in at least one biological sample of the subject the expression level of a subset of biomarkers to obtain an expression value for each of these biomarkers. The second step (b) involves determining / calculating the resistance (R) level and the systemic inflammation (SI) level of this subject from the expression values obtained in step (a) and determining the R / SI balance. In some embodiments the disclosed methods further comprise the step of (c), calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (b); and (d), concluding that the R / SI balance is impaired, if the R / SI balance score is negative. The severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score. A further aspect of the present disclosure relates to a prognostic method for determining the susceptibility of a subject to sepsis and / or associated conditions, and / or predicting the outcome of the sepsis and / or associated conditions in this subject. The method comprising the following steps: In step (a), determination of the R / SI balance score of the subject. Step (b), involves classifying the subject as a subject susceptible to sepsis and / or to develop a negative outcome of sepsis, if the R / SI balance of the subject is lower than 0.

[0022] Another aspect of the preset disclosure relates to a prognostic method for predicting and assessing responsiveness of a subject suffering from sepsis and / or associated conditions, to at least one compound or a treatment regimen comprising this compound. The method is optionally for monitoring disease progression. The method comprising the following steps. In step (a), determination of the R / SI balance score in at least one sample of the subject; and In step (b), classification of the subject as: (i), a responder to at least one compound or a treatment regimen comprising this compound, if at least one sample obtained after the initiation of the treatment regimen and / or a sample of the subject contacted with the compound displays elevation in the R / SI balance score, as compared with the R / SI balance score determined for a sample obtained prior to the treatment, or a sample not contacted with the compound, or (ii), a non-responder, if at least one sample obtained after the initiation of the treatment regimen and / or a sample of the subject contacted with the compound displays reduction, or no change in the R / SI balance score, as compared with the R / SI balance score determined for a sample obtained prior to the treatment, or a sample not contacted with the compound.

[0023] Another aspect of the present disclosure relates to a method for determining a personalized treatment regimen for a subject suffering from sepsis and / or associated conditions. The method comprising the following steps. In step (a), determination of the R / SI balance score of the subject. In step (b), classifying the subject as one of: (i), a subject displaying reduced R level; (ii), a subject displaying increased SI level; and (iii), a subject displaying reduced R level and increased SI level. Step (c), involves selecting for the subject a treatment regimen and / or at least one compound that elevates the levels of R / SI balance score.

[0024] Another aspect of the present disclosure relates to a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of sepsis and / or associated conditions in a subject in need thereof. The method comprising the following steps. In step (a), determining the R / SI balance score of the subject by the steps of: (i), determining in at least one biological sample of the subject the expression level of a subset of biomarkers to obtain an expression value for each of the biomarkers; and (ii), determining and / or calculating the R level and the SI level of the subject from the expression values obtained in step (i); and (iii), calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii).

[0025] In step (b), classifying the subject as one of: (i), a subject displaying reduced R level; (ii), a subject displaying increased SI level; or (iii), a subject displaying reduced R level and increased SI level. Step (c), involves administering to the subject a therapeutic compound or subjecting the subject to a treatment regime that elevate the R / SI balance in the subject.

[0026] Another aspect of the present disclosure relates to a screening method for identifying and / or evaluating at least one therapeutic compound for the treatment of sepsis and / or associated conditions. The method comprising the following steps. In step (a), determining the R / SI balance score of at least one biological sample contacted with a candidate compound. In some embodiments, the sample is of a subject suffering from sepsis and / or associated conditions.

[0027] The next step (b), involves determining that the candidate compound is a therapeutic compound for sepsis and / or associated conditions if the candidate compound elevates the R / SI balance score, as compared with the R / SI balance score determined for a control sample.

[0028] Another aspect of the present disclosure relates to a diagnostic composition comprising at least one detecting molecule or any combination or mixture of plurality of detecting molecules, and / or means, specific for determining the level of expression of at least one of: (i), at least one biomarker of R / SI balance score. The at least one biomarker comprise at least one of the biomarkers (gene biomarkers) IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNC13D and WDTC1 or any combination thereof; and / or (ii), at least one biomarker of resistance (R). The at least one biomarker comprise at least one of the biomarkers IFNy, CXCL10, MCP-2, CXCL11, CXCL9 (protein biomarkers), GOLGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ (gene biomarkers) or any combination thereof; and / or (iii), at least one biomarker of the systemic inflammation (SI). The at least one biomarker comprise at least one of the biomarkers CCL4, IL6, CCL3, CCL20, IE8 (protein biomarkers), DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 (gene biomarkers) or any combination thereof.

[0029] In some embodiments, each of the detecting molecules is specific for one of the biomarker / s. These and other aspects of the present disclosure will become apparent by the hand of the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0031] FIGURE 1A-1E. Global transcriptional states of systemic inflammation and resistance in sepsis patients

[0032] Fig. 1A. Schematic of methodology: integrative analysis of sepsis and moderate infections, including the newly generated PBMCs / monocytes data from the FUSE cohort [Ricano-Ponce, I. et al. BMC Infect Dis 22, 778 (2022)]. Personal levels of resistance (R) and systemic inflammation (SI) were calculated for each subject.

[0033] Fig. IB. R and SI are associated with two distinct inflammatory states in sepsis. The scatter plot compares, for each protein (a dot), its correlation with SI levels (x axis) and its correlation with R levels (y axis). Correlations (r) were calculated across sepsis patients from the FUSE cohort; R and SI levels were calculated using expression profiles in PBMCs. Included are selected pro- inflammatory plasma protein markers. IL6 and IFNy are exemplified in Fig. 1C. Findings are consistent with previous studies (Figure 2C).

[0034] Fig. 1C. Associations of the plasma IFNy and IE6 proteins with R and SI levels in PBMCs of sepsis patients. Left: Scatter plots for R or SI levels (x axis) against protein abundance (y axis) across sepsis individuals (dots). Right: Scatter plot for the SI and R levels (x and y axis, respectively) of each sepsis patient (a dot), where each patient is colored by its plasma level of a certain protein (indicated on top). R and SI levels were calculated using the expression profiles in PBMCs from the FUSE cohort.

[0035] Fig. ID- IE. Validation in monocytes. Plots ID and IE are shown as in plots IB and 1C, respectively, but for R / SI levels that were calculated using expression profiles of blood-derived monocytes (rather than PBMCs). Data of sepsis patients from the FUSE cohort.

[0036] FIGURE 2A-2D. Characterization of the resistance (R) and systemic inflammation (SI) programs

[0037] Fig. 2A. Enrichments of R and SI in previously annotated genesets related to bacterial infections. Presented are minus log 10 q-values for the enrichment of R markers (top) or SI markers (bottom) in selected immune genesets (hyper-geometric test, FDR corrected). The plots include two classes of genesets: genesets of sepsis, and geneset of pathogenic-like stimulations. Included are only genesets with significant enrichment. The selection of SI and R markers is detailed in the Experimental procedure. Fig. 2B. Functional properties of the R and SI programs, evaluated in a systematic analysis of the GEO transcriptome compendium. The analysis was performed using the SEEK algorithm, which integrated transcriptomes from 5210 datasets in GEO in a systematic manner. The bar plots present selected functional categories and their enrichment q-value for the R program (top) and SI program (bottom). In this visualization, the indication of enrichment in co-expressed genes or datasets is omitted.

[0038] Fig. 2C-I-2C-II. R and SI levels are linked to a different inflammatory plasma state.

[0039] Fig. 2C-I. It was originally demonstrated that the resistance program is engaged to eliminate the pathogen [Ref 5]. Particularly, it was shown that the level of resistance in lungs is tightly correlated with the viral burden, the mRNA levels of interferon genes and the mRNA levels of CxcllO and Cxcll 1 in murine lungs [Ref 5]. Shown are the Pearson's correlation coefficients between R levels and infection parameters (y axis), calculated in different time points after in vivo IAV infection (x axis). Correlations between R levels and each infection parameter (in each time point) were calculated across 33 CC mouse strains. Shown are correlations of R levels with five parameters of the infection response. The correlations with the viral burden are demonstrated in Example 19. Fig. 2C-II. It was originally demonstrated that the SI levels vary significantly between obese subjects and are tightly correlated with the percentage of lymphocytes in blood, as well as the IL6, IL18 and IL18BP proteins in plasma [Ref 7]. Shown are Pearson's correlation coefficients of SI levels with each of these four parameters. Correlations were calculated from blood parameters across obese human subjects. Data is shown in four distinct groups of subjects. The plots were produced from data in Refs. 5, 6. Overall, the inventors' findings in sepsis patients (Figure IB and ID) are consistent with the original characterization of programs R and SI, as described in the original publications (Ref. 5, 6) and demonstrated in Fig. IB: IFNg, CXCL11 and CXCL10 in sepsis are mainly associated with program R, whereas IL6 and IL18bp in sepsis are mainly associated with program SI.

[0040] Fig. 2D-I-2D-II. SI and R levels in SIRS and sepsis. Data is shown for two datasets: a dataset with blood samples (Fig. 2D-I; GSE13904) and a dataset with samples of isolated neutrophils (Fig. 2D-II; GSE123729). Each dataset consists of three group: healthy controls, SIRS (in which negative blood culture was confirmed for all patients) and sepsis / septic shock (in which positive blood culture was confirmed for all patients). Thus, in each dataset, the SIRS and sepsis groups differ in the presence of pathogen (among other differences). Left: The levels of SI (x axis) and R (y axis) across individuals (dots) from each cohort. Right: Box plots for the SI and R levels in each individual group from each cohort. The plots show that SIRS is linked to a significant increase in SI levels but no increase in R levels. In sepsis, the results were similar to SIRS with a slight elevation of R levels. These results support the general role of SI in any type of inflammation (with or without pathogen) and the specific role of program R in exposure to pathogen, consistent with the notion that R and SI are resistance and systemic inflammation programs, respectively. Indicated are t test p-values; in Fig. 2D-II, due to the small dataset empirical p-values were also calculated using permutation tests (reported in parentheses).

[0041] FIGURE 3A-3D. The healthy organization of R and SI is maintained in sepsis

[0042] Fig. 3A-3B. PBMCS data. Each scatter plot provides the Pearson’s correlation (r) of each gene (a dot) with R levels (Fig. 3A) or SI levels (Fig. 3B) in PBMCs. Correlations were calculated across healthy subjects (x axis) or across sepsis patients (y axis) from the FUSE cohorts.

[0043] Fig. 3C-3D. Monocytes data. Each scatter plot provides the Pearson’s correlation (r) of each gene (a dot) with R levels (Fig. 3C) or SI levels (Fig. 3D) in monocytes. Correlations were calculated across healthy subjects (x axis) or across sepsis patients (y axis) from the FUSE cohorts. The analysis shows how the homeostatic organization of R and SI is maintained in sepsis.

[0044] FIGURE 4A-4C. Shared patterns of response to in vivo infection across datasets. The analysis is focused on in vivo infection datasets with different characteristics - both moderate (IAV, TB), sepsis (5 different datasets), and a mixture of moderate and severe infection in the ‘S. aureus’ dataset . As all datasets include infected and healthy samples, the inventors validated that all datasets manifest a similar response to infection. For each dataset, a “differential expression score” was calculated for each gene biomarker (gene as used herein) (infected versus controls, the score is a signed logio t test statistic; positive / negative values for increasing / decreasing in infection). Next, for each dataset, the inventors defined a geneset of “upregulated genes” as the 200 genes with best differential expression score among the genes whose expression is increasing during infection. Similarly, the “downregulated genes” were defined as the 200 genes with best differential expression score among the genes whose expression is decreasing during infection compared to healthy controls. Finally, for each dataset, the inventors calculated hyper-geometric p-value for the enrichment of the upregulated genes (or downregulated genes) within each geneset from the GO ontology. For each GO geneset, the “enrichment score” is defined as the FDR q values of the over-representation of the regulated genes in the GO geneset.

[0045] Fig. 4A. Consistency of differential expression patterns across datasets. The matrix presents correlations of differential expression scores between each pair of datasets. In particular, each entry compares differential expression scores of all genes between the two datasets. Fig. 4B. Consistency of enrichment scores for upregulated genes across different datasets. The matrix presents correlations of enrichment scores between each pair of datasets. The enrichment scores were calculated for the 200 upregulated genes in each dataset.

[0046] Fig. 4C-I-4C-II. Consistency of enrichment scores for downregulated genes across different datasets.

[0047] Fig. 4C-I. The matrix presents correlations of enrichment scores between each pair of datasets. The enrichment scores were calculated for the 200 downregulated genes in each dataset.

[0048] Fig. 4C-II. Example of one pair of datasets from matrix of Fig. 4C-I, marked as X within matrix 4C-I. In matrices of Fig. 4A-4C, the order of columns is the same as the order of rows. Color coding of matrices is in accordance with the Pearson’s correlation coefficient across all genes (matrix of Fig. 4A) or across all genesets (matrices of Fig. 4B, 4C). In matrices of Fig. 4A-4C, all pairs of datasets were positively correlated. Overall, comparison of the results across datasets confirmed high consistencies of immune responses across datasets, including (i) shared global differential expression patterns across datasets (Fig. 4A) and (ii) shared signaling pathways of regulated genes across pathways (Fig, 4B, 4C).

[0049] FIGURE 5A-5G. Sepsis is marked by low resistance relative to the systemic inflammation level

[0050] Fig. 5A-5E. data from multiple independent cohorts of either blood, PBMCs or monocyte profiling.

[0051] Fig. 5A-5B. The levels of SI (x axis) and R (y axis) across individuals (dots) from all cohorts (Fig. 5A) or specific cohorts (Fig. SB).

[0052] Fig. SC. Bar plots for the means of R and SI levels across the various cohorts. Error bars: 95% confidence intervals.

[0053] Fig. 5D. Differential R and SI levels (disease versus controls, standard t-test statistics) across cohorts (dots).

[0054] Fig. 5E. The “R / SI balance score” is a biomarker of sepsis. Left: The R / SI balance score is defined as R minus SI - that is, the score projects each subject onto the top-left-to-bottom-right diagonal, where positive and negative scores indicate R>SI and R<SI, respectively. Right: The distributions of individuals by their R / SI balance scores, revealing lower balance scores in sepsis (an ‘impaired’ R / SI balance) compared to moderate infections (a ‘good’ R / SI balance).

[0055] Fig. 5F. R and SI levels across time points during infection. Included are time-series dataset of additional cohorts (Table 1). Error bars: 95% confidence intervals, p.i., post infection; p.s., post symptoms. Fig. 5G-I-5G-II. R and SI responses to urogenital tract infection (UTI) at single-cell resolution. Fig. 5G-I. For each single monocyte (a dot), the plot presents its R and SI levels; each plot presents specific monocyte subpopulation (MSI or MS2) for all controls (light gray) or one patient (dark gray). Presented are P-values (t-test) for the bias in single-cell R / SI levels in one patient versus all controls (for a given monocyte subpopulation). These P-value (log-scaled and signed by direction) are referred to as the R (or SI) response. Fig. 5G-II. Each dot provides the R and SI responses for a single patient and a certain monocyte subpopulation. R / SI responses of individuals #l-#4 (indicated in plot Fig. 5G-II are exemplified in Fig. 5G-I.

[0056] FIGURE 6A-6B. Additional evidence

[0057] Fig. 6A. Autoimmune disease. Differential R and SI levels (disease versus controls, t-test statistics) across moderate infections (light gray), sepsis (white) and autoimmune disease (dark gray). The plot indicates that the R / SI balance in autoimmune disease is similar to the balance in moderate infections and is distinct from the balance in sepsis.

[0058] Fig. 6B-I-6B-II. Accounting for age and gender. Differential R and SI levels (disease versus controls) across cohorts (dots). Presented are all cohorts from Figure 5D in which annotation of males and females is available (Fig. 6B-I) or annotation of age is available (Fig. 6B-II). Fig. 6B- I: Females and males were analyzed separately and are presented by different shapes. Differential levels were calculated using standard t-test statistics. Fig. 6B-II: Analysis of each cohort was applied either with covariates (square) or without covariates (circle). Covariates included are age and gender. For septic shock I and II only the age covariate was available. Differential SI / R levels were calculated using the coefficient of the disease term in a linear regression model where R (or SI) is the dependent variable, disease is the independent variable, and age / gender are used as additional covariates.

[0059] FIGURE 7A-7F. Molecular states of systemic inflammation and resistance in sepsis patients Data from scRNA-seq profiling [Reyes, M. et al. Nat Med 26, 333-340 (2020)] of (i) 19 healthy controls, (ii) 10 urinary tract infection (UTI) patients showing clear symptoms of sepsis - i.e., a clear and persistent organ dysfunction (denoted ‘URO’ in the original publication [Reyes, M. et al. Nat Med 26, 333-340 (2020)]), and (iii) 10 UTI patients with moderate infection: a clear leukocytosis but no organ dysfunction (denoted ‘Leuk-UTI’ in the original publication [Reyes, M. et al. Nat Med 26, 333-340 (2020)]). All patients were enrolled within 12 hours of presentation to the emergency department and within 12 hours of antibiotic treatment. The analysis is focused on the four subpopulations of monocytes (MS1-MS4) as previously defined [Reyes, M. et al. Nat Med 26, 333-340 (2020)]. Fig. 7A. Demonstration of the R and SI levels in single monocytes. A two-dimensional space in which each gene (a dot) is located in accordance with its correlation with SI levels (x axis) and R levels (y axis) in PBMCs across individuals of the FUSE cohort. In each panel, the map is colored by the measured expression levels in a single monocyte cell. The white / black scale refer to low / high gene expression levels (with smoothing). For each cell, the R and SI levels were calculated based on the gradients along the x and y axes, respectively; the R and SI levels of each cell are indicated on top. As demonstrated here, high (low) SI levels reflect increasing (decreasing) gradients along the x axis. Similarly, high (low) R levels reflect increasing (decreasing) gradients along the y axis.

[0060] Fig. 7B. A significant molecular R and SI response of monocytes during the course of moderate infection and sepsis. For each individual (a dot), the plot presents its molecular responses (y axis). Responses are reported for both R and SI in each monocyte subpopulation (x axis). Sepsis and moderate UTI-infections are shown in two separate plots. For a large fraction of individuals, there is a significant response (p<0.05, above the horizontal line).

[0061] Fig. 7C. distributions of SI levels (top) and R levels (bottom) across single cells of all UTI-sepsis patients (white) and all UTI no sepsis (gray). Distributions are shown for T cells, B cells, monocytes and NK cells (left to right). P-values for the difference between the sepsis and no-sepsis distributions are indicated on top.

[0062] Fig. 7D-7F. Results are shown as in Fig. 7B but for T cells (Fig. 7D), B cells (Fig. 7E) and NK cells (Fig. 7F).

[0063] FIGURE 8A-8D. R / SI crosstalks between cell subpopulations

[0064] Fig. 8A. A matrix of correlations among cell subpopulations. Each entry presents a Pearson’s correlation coefficient of either R responses (top) or SI responses (bottom) between a pair of cell subpopulations. Correlations were calculated separately for UTI patients without sepsis (moderate infection, left) and for the UTI patients with sepsis (right). Several correlations are exemplified in Fig. 8D.

[0065] Fig. 8B. Hierarchical clustering of one matrix from Fig. 8A (sepsis, R response), revealing two distinct multicellular communities, each community corresponds to a different part of the immune system.

[0066] Fig. 8C. A statistical test for the consistency of correlations between cell subpopulations, reported for R responses (top matrix) and SI responses (bottom matrix) for individuals carrying UTI without sepsis (left matrix) or UTI with sepsis (right matrix). Each entry in each matrix aggregates the relevant correlations from plot A, using the Fisher’s combined test. Particularly, p values are reported for correlations between different subpopulations of monocytes (bottom right entry), correlations between different subpopulations of lymphocytes (top left entry) and correlations between lymphocytes and monocytes subpopulations (top right and bottom left entries). For Fisher’s combined test with p value <0.1 (“significant”), the direction is color coded (red / blue for positive / negative correlation).

[0067] Fig. 8D. Examples of specific correlations between cell subpopulations. The scatter plots present SI response (top) or R response (bottom) of specific cell subpopulations from either UTI patients without sepsis (gray dots) or UTI patients with sepsis (white dots). The resulting Pearson’s correlation coefficients (r) were used for the color coding of the matrices in Fig. 8A and Fig. 8B.

[0068] FIGURE 9A-9C. Temporal patterns of R and SI levels in a murine sepsis model: in vivo LPS stimulation

[0069] Fig. 9A. R and SI levels across time points during moderate infections. Included are three time- series gene expression datasets (Table 1); see additional time series datasets in Fig. 9B-9C. Error bars: 95% confidence intervals, p.i., post infection; p.s., post symptoms.

[0070] Fig. 9B-9C. Temporal patterns of R and SI levels in a murine sepsis model: in vivo LPS stimulation. Data from (Takahama et al. Nat Immunol 25, 226-239. 2024).

[0071] Fig. 9B-I-9B-II. R and SI levels calculated in murine PBMC following in vivo LPS stimulation. Fig. 9B-I. Error bars: 95% confidence intervals. Fig. 9B-II: Scatter plot visualization. Each circle is an individual mouse and time points are color coded.

[0072] Fig. 9C-I-9C-II. R and SI levels calculated in 13 different tissues following in vivo LPS stimulation. Fig. 9C-I : heat map for the temporal response of each tissue. Time points and tissues are indicated on columns and rows, respectively. Fig. 9C-II: Scatter plot visualization, demonstrating the dynamic response in bone marrow and spleen. Each circle is an individual mouse and time points are color coded. Abbreviations are as in Takahama et al. Nat Immunol 25, 226-239. 2024.

[0073] FIGURE 10A-10F. The heterogeneity of genes and plasma proteins within sepsis is associated with the resistance and systemic inflammation levels

[0074] The inter-individual variation in sepsis, either in plasma protein concentrations (Fig. 10A-10B) or in PBMCs / monocytes mRNA levels (Fig. 10C-10F), is associated with the R / SI-balance state. Fig. 10A-10D. Data from the FUSE cohort.

[0075] Fig. 10A-10F. Box plots for the percentage of inter-individual variance in proteins (Fig. 10A) or in mRNA (Fig. 10C) that is explained by a linear combination of R and SI, using either real (white) or permuted (gray) data. R / SI levels were calculated either using transcriptomes from PBMCs (left) or monocytes (right). Fig. 10B. Protein markers of immunopathology in sepsis are associated with the impaired R / SI balance. The scatter plot compares, for each protein (a dot), its correlation with SI levels (x axis) and R levels (y axis) across sepsis patients. R and SI levels were calculated using expression profiles in either PBMCs (left) or monocytes (right). Included are markers for immune dysfunctions that have a known up- (light gray) or down-regulation (dark gray) in sepsis. Fig. 10D-10F. Transcriptional signatures of immunopathology in sepsis are associated with the impaired R / SI balance. Included are previously reported pathways that are up- or down-regulated in sepsis.

[0076] Fig. 10D. The scatter plots compare, for the expression of each gene (a dot), its correlation with SI levels (x axis) and R levels (y axis). Correlations were calculated using data in monocytes across sepsis patients.

[0077] Fig. 10E. Correlations (color-coded) between each gene (a row) and the SI or R levels (columns), calculated based on transcriptomes in each cohort (columns; Table 1). Abbreviations: SS-I / II - septic shock cohort I and II.

[0078] Fig. 10F. Examples of selected genes from Fig. 10E, shown as in Fig. 1C-1D. Genes are indicated on top (see additional genes in Figure 12B).

[0079] FIGURE 11A-11B. The relations of plasma proteins with R and SI levels

[0080] Fig. 11A-I-11A-II. Plots demonstrating that the findings in Figures 10A and 10B are reproduced in both females and males (Fig. 11A-I) and when adding the age and gender as covariates (Fig. 11A-II).

[0081] Fig. 11B. A reduction in R-to-SI balance following exposure to septic plasma. In this experiment [Khaenam, P. et al. J Transl Med 12, 65 (2014)], plasma samples were derived from several sepsis patients (‘septic plasma’) and several healthy subjects (‘healthy plasma’). Next, cells from healthy patients were stimulated with each of the plasma samples; both granulocytes, PBMCs and DCs were stimulated. Finally, transcriptomes were generated for each cell type after stimulation with the plasma of each individual subject. Presented are scatter plots of SI and R levels in granulocytes, PBMCs and DCs, for stimulation with septic plasma or healthy plasma. Each dot presents R / SI levels following stimulation with the plasma of a different individual subject. Bottom: t-test p values for the differences between healthy and septic plasma in either R levels, SI levels, or the R / SI-balance score. Overall, the impaired R / SI balance is observed in both DCs, granulocytes and PBMCs following the exposure to septic plasma. FIGURE 12A-12B. Transcriptional signatures of sepsis

[0082] Fig. 12A-I-12A-II. The findings in Figure 10D are reproduced in both females and males (Fig. 12A-I) and when adding the age and gender as covariates (Fig. 12A-II).

[0083] Fig. 12B. Shown are examples of selected genes, demonstrated as in Figure 10F.

[0084] FIGURE 13A-13E. R and SI are informative for sepsis pathology in the PROVIDE cohort

[0085] Fig. 13A. The plasma signature of sepsis reflects the impaired R / SI balance. Plot is shown as in Figure 13B but using the PROVIDE data. In general, associations of plasma proteins with R and SI that were detected in the FUSE cohort (Figure 13B) were also detected in PROVIDE.

[0086] Fig. 13B. Validation of FGF-23 as a marker of the R / SI-balance score. Based on the FUSE cohort, FGF-23 is the best protein marker of the R / SI balance score (Table 2A). The scatter plot demonstrates that it is also a marker of the R / SI-balance score in the PROVIDE cohort.

[0087] Fig. 13C-13D. Relations of R and SI with clinical parameters across patients in the PROVIDE cohort. For each clinical parameter, the inventors calculated linear regression where the clinical parameter is the dependent variable, and the SI (or R) is the independent variable. Fig. 13C. For each clinical parameter (a row), presented is the logio p- value for the SI (or R) term. The loglO p- value is signed: a positive / negative sign for a positive / negative coefficient of the term. For p<0.05, the p-value is reported; for p>0.05, the entry is empty and white. Grey entry: p-value was not calculated (too few data points). Regressions were calculated either with females only (column 2), males only (column 3), females and males together without any covariate (column 4), and females and males with age as co variate (column 5). The pathologies in lines 1-8 are known to be higher in sepsis, and the pathologies in lines 9-10 (mHLA-DR and %lymphocytes) are known to have reduced values in sepsis; this is consistent with the opposite associations of these pathologies with R and SI levels.

[0088] Fig. 13D. A scatter plot for the relations of each clinical parameter (a dot) with SI and R (x and y axis, respectively), shown for the regression of females and males together without covariates. Presented is the signed logio p-value for the SI and R terms (a positive / negative sign for a positive / negative coefficient of the term). The analysis was conducted twice: using all sepsis individuals of the PROVIDE cohort (n=223) and when excluding the high- -SI patients (n= 169). In Fig. 13C-13D, the analysis indicates significant associations of both R and SI, and further shows that R and SI present opposite directions of associations: in all cases, pathophysiological parameters are associated with either high SI, low R, or both.

[0089] Fig. 13E. Relations with disease severity. The bar plot indicates the relations of each parameter (y axis) with sepsis severity (using the SOFA score). The x axis presents the percentage of variance explained for regressions that were calculated with females and males together when using either all individuals or only low-SI individuals. The plots present comparison of the R / SI balance score to known inflammatory markers (top) and a separate regressions of R levels and SI levels (bottom). FIGURE 14A-14B. The heterogeneity of clinical parameters withing sepsis is associated with the resistance and systemic inflammation levels

[0090] Data is shown for eleven physiological phenotypes across the PROVIDE clinical trial: SOFA scores in day 1 of hospitalization, quantity of the mHLA-DR protein in day 1 of hospitalization, the percentage of circulatory lymphocytes in day 2 of hospitalization, septic shock, CO, %neutrophils, NLR, WBC, Lactate, CRP and renal failure in day 1 of hospitalization.

[0091] Fig. 14A. Box plots for the percentages of inter-individual variance in phenotypes that are explained by either SI (left), R (middle), or the linear combination of R and SI (right), using either real (white) and permuted (gray) data. Each phenotype is presented by a big / small dot for a significant / insignificant (empirical p <0.05) percentage of explained variation.

[0092] Fig. 14B. Scatter plots for SI and R levels (x and y axis, respectively) of each sepsis patient (a dot), where each patient is colored by its level of a certain clinical parameter (indicated on top). The plots demonstrate the utility of the R and SI levels as biomarkers for the pathophysiology of sepsis.

[0093] FIGURE 15A-15D. The R / SI balance is informative for sepsis severity in the FUSE cohort

[0094] The FUSE patients were grouped into severe and non-severe sepsis according to the clinical manifestation of disease (the qSOFA score, Experimental Procedures). The plots present a comparison of severe (n=29) versus non-severe (n=94) sepsis in this cohort, using R and SI levels in PBMCs.

[0095] Fig. 15A-15B. Presented are a scatter plot (Fig. ISA) and distributions (Fig. 15B) of severe and non-severe sepsis patients, revealing lower R / SIL-alance scores in severe sepsis compared to non- severe sepsis (P <0.006, t test).

[0096] Fig. 15C-15D. The performance of the R / SI-balance score as a classifier of sepsis severity. Fig. 15C: Shown are ROC curves presenting the predictive performance of the R / SI-balance score (AUC = 0.75) and a currently established marker (ferritin, AUC = 0.54) for the prediction of severe versus non-severe sepsis. The inventors observed better performance of the R / SLbalance score in PBMCs throughout the entire ROC range compared to ferritin. Performing this analysis with age correction did not affect the results, although the p-values were less significant (P < 0.01 with age covariate in the linear regression). As shown in Fig. 15D, neither an R-only score nor an Si-only score performed as well as the R / SI-balance score for stratifying disease severity (Si-only AUC = 0.69, R-only AUC = 0.62). FIGURE 16A-16E. Functional characterization of the impaired R / SI-balance state

[0097] Fig. 16A. The R / SI-balance mRNA markers show consistent associations in monocytes, PBMCs and blood samples. For the selected 300 markers of good R / SI-balance scores and the selected 300 markers of impaired R / SI-balance scores (columns), presented are the correlation of each mRNA marker with the R / SI-balance score (color-coded), either in PBMCs, monocytes or whole blood (rows).

[0098] Fig. 16B. An unbiased functional characterization of the R / SI balance. An enrichment test was used to evaluate the overrepresentation of each functional class in the 300 markers from A (hyper- geometric test, -log FDR q- values; right / left directions indicate over-representation of gene sets in markers of the good / impaired-balance score).

[0099] Fig. 16C. A scatter plot of SI and R levels in PBMCs (x and y axis, respectively) in each individual patient (a dot; including all sepsis patients from the FUSE cohort). The plot is colored by the averaged gene expression of 85 quiescence genes.

[0100] Fig. 16D-I-16D-II.

[0101] Fig. 16D-I. Correlation of each gene with SI (x axis) or R levels (y axis), which were calculated in PBMCs (left) or monocytes (right). Presented are all genes (gray) and quiescence genes (dark gray).

[0102] Fig. 16D-II. Scatter plots of SI and R levels (x and y axis, respectively) in each individual patient (a dot; including all sepsis patients from the FUSE cohort). Plots are colored by the gene expression of specific quiescence genes (indicated on top of each panel). SI and R levels were calculated in PBMCs (left panels) or monocytes (right panels).

[0103] Fig. 16E. Correlations (color-coded) between each quiescence gene (column) and the R or SI levels (indicated on right). Correlations were calculated using transcription profiles in each cohort (rows). Abbreviations: SS-I / II - septic shock cohort I and II; cohorts are detailed in Table 1.

[0104] FIGURE 17. A unified model of sepsis based on R and SI states

[0105] The molecular state of a low R-to-SI balance is a unique fingerprint of sepsis. The combination of R and SI states explains the heterogeneity in sepsis at the population and single-patient level. At the population level, the heterogeneity is explained by the wide diversity of R / SI levels that fall under the broad fingerprint of sepsis. At the single -patient level, the combined state of low R and high SI explains the heterogeneity of seemingly contradicting immune dysfunctions within the same patient. The model suggests that each sepsis patient should be treated either with a pro-R drug, an anti-SI drug, or both. FIGURE 18A-18B. Clinical implications of the R / SI framework

[0106] Fig. 18A. Modulation of the sepsis-related cell state in monocytes. R and SI levels were evaluated using transcription profiles of monocytes following different treatments (two repeats for each treatment, data from Hu et al. (2021) [Hu, G. et al. Nat Commun 12, 773 (2021)]. Shown are the R and SI levels following IFNy treatment, control (DMSO) treatment, and treatment with eight anti-inflammatory drugs that were identified by Hu et al. (2021) [Hu, G. et al. Nat Commun 12, 773 (2021)]. Horizontal dashed lines: 1.5 standard deviations, based on controls. Confidence intervals of R / SI levels were calculated by bootstrapping, using resampling of 50% of the genes. Abbreviations: DC - Diphenyleneiodonium chloride. For example, the plot suggests that IFNg leads to increased R (but not SI) levels - that is, a pro-R treatment. Multiple anti-inflammatory such as Sorafenib and Bosutinib lead to a beneficial decrease in SI levels, but these drugs should be treated with caution in sepsis because these drugs exhibit an undesired decrease in R levels.

[0107] Fig. 18B. An added value of the R / SI-based endotypes within each endotype of a previous classification. Comparison between a previously described classification [Ref. 1 ; Karakike, E. et al. ESCAPE: An Open-Label Trial of Personalized Immunotherapy in Critically 111 COVID-19 Patients. J Innate Immun 14, 218-228 (2022); Giamarellos-Bourboulis, E. J. et al. Cell Host Microbe 27, 992-1000.e3 (2020)] (columns) and the new R / SI-based classification (rows) in the PROVIDE cohort. For each combination of current and new classes, indicated is the overall mortality rate (top, in bold), as well as the number of deceased patients out of the total number of patients (bottom). Included are only patients that were classified by the current and previous classifications.

[0108] FIGURE 19. Differences in relations to program R between sepsis and moderate bacterial infections

[0109] Presented are -log t test p-values, for the difference between correlations with R in sepsis versus moderate infections. X axis: sepsis: 5 cohorts, moderate infection: 5 cohorts. Y axis: excluding blood cohorts in sepsis. TGFBR3 and TGFB1 are exemplified in Figure 20.

[0110] FIGURE 20A-20B. Correlation between the R state and the expression of TGFBR3 or TGFB1 in cohorts of sepsis and moderate infection

[0111] Presented are the correlation between the R state and the expression of TGFBR3 (Fig. 20A) or TGFB1 (Fig. 20B) (y axis) in each cohort (x axis), either a sepsis or a moderate infection cohort.

[0112] FIGURE 21. IRF1 dysregulation as a plausible candidate for sepsis pathology. R and SI levels are shown in a murine sepsis model: in vivo LPS stimulation. Data from (Takahama et al. Nat Immunol 25, 226-239. 2024). R and SI levels (y and x axes, respectively) are from bone marrow of LPS-stimulated WT mice vs. LPS-stimulated IRF1K0 mice (30 minutes after LPS-stimulation; 3 biological repeats for each experiment) Of note, the IRFIKOmice demonstrate the response signature of sepsis (reduced R and elevated SI) compared to the WT response.

[0113] FIGURE 22. Stratification of sepsis patients using the R / SI framework

[0114] Analysis of sepsis patients from the PROVIDE clinical trial [Ref 1].

[0115] Three R / SI-based endotypes are indicated. The prognostic capacity of these endotypes is demonstrated in Fig. 23A-23E.

[0116] FIGURE 23A-23E. Stratification of sepsis patients using the R / SI framework

[0117] Analysis of sepsis patients from the PROVIDE clinical trial [Ref 1].

[0118] Fig. 23A. The prognostic capacity of the R / SI-based endotypes. Kaplan-Meier survival curves for the endotypes.

[0119] Fig. 23B-23D. The R / SI-based classification adds prognostic information beyond the current classification.

[0120] Fig. 23B. Prognostic capacity of the R / SI-based endotypes within previously defined immune states [Ref 9][Karakike, E. et al. J Innate Immun 14, 218-228 (2022); Giamarellos-Bourboulis, E. J. et al. Cell Host Microbe 27, 992-1000.e3 (2020)]. Presented are Kaplan-Meier survival curves for the R / SI-based endotypes. Plots are shown as in Fig. 23A, but each plot shows the survival curve within one previously defined endotype (indicated on top).

[0121] Fig. 23C.The percentage of 28-days mortality of each R / SI-based endotype within previously defined subset of patients (x axis).

[0122] Fig. 23D. The percentage of 28-days mortality of each previously defined subset within each of the newly defined R / SI-based endotypes (x axis).

[0123] Fig. 23E. 28-days prognostic capacity of the R / SI-based endotypes when using biomarkers of R and SI. Results are calculated and presented as in Fig. 23A (for the same individuals and endotypes), except from R and SI that were assessed using biomarkers: averaging CXCL11 and IFNγ plasma protein levels for R, and averaging IL6 and IL8 plasma protein levels for SI. In Fig. 23A-23E, comparison P-values were calculated using the log-rank test and insignificant results (p > 0.1) were excluded for simplicity.

[0124] FIGURE 24. The effect of Anakinra in Schnitzler syndrome (SchS)

[0125] Figure shows the R and SI levels calculated for individuals subjected to the indicated treatment (PBMCs data). Each dot represents data in a single individual. FIGURE 25A-25B. The effects of Anakinra and empagliflozin in postprandial hypoglycemia Figure represents blood-derived monocytes data. Each dot represents data in a single individual. Fig. 25A. SI and R levels for each individual.

[0126] Fig. 25B. SI and R response to meal for each individual. Si-response is calculated as the SI post meal minus the SI pre meal. R-response is calculated as the R post meal minus the R pre meal.

[0127] FIGURE 26. The effect of Anakinra in SJIA (whole-blood data). Each dot represents data in a single individual.

[0128] FIGURE 27A-27B. Individual variation after recovery is linked to the molecular R / SI state during sepsis

[0129] Compared are R / SI levels at day 1 of sepsis diagnosis (x axis) and at 1-3 months after recovery (y axis), for each individual patient (dot). R / SI levels were calculated using profiles of blood-derived monocytes. The plots show consistency of SI (Fig 27 A) and R (Fig 27B) levels between sepsis and post-sepsis conditions (Pearson’s p<0.12, 0.004, respectively).

[0130] FIGURE 28A-28D. Calculation of the resistance state

[0131] Fig. 28A. The pipeline of the analysis.

[0132] Fig. 28B. The resulting gene-expression map.

[0133] Fig. 28C. A personalized view of specific individuals across the map.

[0134] Fig. 28D. The R and T states of each individual.

[0135] The figure is based on Figure 1 of Cohn et al 2022 [Ref. 5].

[0136] FIGURE 29A-29E. Calculation of the systemic inflammation state

[0137] Fig. 29A. The pipeline of the analysis.

[0138] Fig. 29B. The resulting clinical / metabolomics map.

[0139] Fig. 29C. A personalized view of specific individuals across the clinical / metabolomics map.

[0140] Fig. 29D. The MetS and SI states of each individual.

[0141] Fig. 29E. The resulting gene-expression map.

[0142] The figure is based on Figures 1 and 2 of Frishberg et al 2022 [Ref 7].

[0143] FIGURE 30A-30C. Pipeline for the analysis of R and SI states

[0144] Fig. 30A. Selection of genes based on the reference map. For the calculation of R states, the relevant map is the gene-expression R / T map. For the calculation of SI states, the relevant map is the gene-expression SI / MetS map.

[0145] Fig. 30B. A scatter plot for the relations between the R (or SI) states when using all genes (y axis) compared to the R or SI states when using only 4k selected genes (x axis) as indicated in Fig. 30A. Shown are the results for 4k =40, 200, 400, 2000, 4000. Pearson’s correlations and p-values are indicated on top.

[0146] Fig. 30C. A summary of the correlations (Pearson’s r) demonstrated in Fig. 30B.

[0147] FIGURE 31A-31E. Accuracy of the inferred R and SI levels

[0148] Fig. 31A. An example for the evaluation of quality. Comparison between true (x axis) and predicted (y axis) R levels across individuals (sepsis patients; dots). Synthetic data was generated using a constant noise a2= 15. The quality metrics are indicated on top. Fig. 31B-31D. The slope, R2and minus logio p-value (y axis) for each synthetic data collection (x axis), calculated for SI and R levels (left and right panels). Results are shown for sepsis and control subjects. The level of noise that is similar to the biological level of noise is indicated with black arrows.

[0149] Fig. 31E. The real distribution of o across individuals in the FUSE cohort, using gene expression of PBMCs.

[0150] FIGURE 32A-32B. Validity of conversion between human and mouse

[0151] Presented are R and SI levels (panels) for each individual subject (a dot) from the human cohort (Fig. 32A) or the mouse cohort (Fig. 32B). The R / SI levels of each individual were calculated either using all genes (x axis) or using the functional homologs only (y axis). For the human (FUSE) cohort in Fig. 32A, results are shown for PBMCs (top) and monocytes (bottom); sepsis and controls are color coded. For the mouse cohort in Fig. 32B, infected and controls are color coded. Numbers of genes used for each R / SI calculation are indicated in each axis.

[0152] FIGURE 33A-33B. Robustness to missing data

[0153] Fig. 33A. An example. For each individual (a dot), the scatter plot presents its R levels (top) or SI levels (bottom) when calculated using all genes (x axis) or only 80% of the genes, selected from all genes (y axis). The Pearson’s correlation coefficient is indicated on top.

[0154] Fig. 33B. A summary of Pearson’s correlation coefficient (r) between R (or SI) levels that were calculated in two ways: using all genes versus using only selected genes. The x axis indicates the percentages of selected genes (e.g., 5% selected genes is 95% missing genes); light gray or black colors indicate the program R or SI, respectively. Top: genes were selected from all genes. Bottom: genes were selected only from genes with low expression level. In all cases, the standard deviation of r values (calculated across the 10 repeats) were lower than 0.001 (data not shown).

[0155] FIGURE 34A-34E. Prediction of gene expression in held-out data

[0156] Fig. 34A-34B. Examples. Box plots for the minus log p-value metric (Fig. 34A) and the R2metric (Fig. 34B) across all genes. The plots present the analysis of real data and permuted data (x axis), for the joint model, using the cohort of PBMCs from sepsis patients. (Fig. 34A) The horizontal line indicates empirical p = 0.05. The percentage of genes in which the model presents empirical p <0.05 is specified (reported for all cohorts in Table 9). Fig. 34B Cutoff of f?2= 0.05 is indicate as a horizonal line. Genes above this line are referred to as true positive (real data) and false positives (permuted data). The fractions of true and false positive genes are reported for all cohorts in Table 10.

[0157] Fig. 34C. Precision, recall and Fl values (y axis) across datasets (x axis) for a cutoff of R2= 0.05, using the joint model.

[0158] Fig. 34D-34E. Comparison of TPR and FPR. Each dot represents a single dataset from Table 8 and a specific cutoff (R2= 0.01, 0.05, 0.1, 0.5). (Fig. 34D) The tradeoff between TPR and FPR across the six datasets and four R2cutoffs, using the joint model. (Fig. 34E) Comparison of TPR and FPR between the R-only model and the Si-only models.

[0159] FIGURE 35. Examples: analysis of inter-gene variation within individuals

[0160] Box plots for the regression’s R2using the SI / MetS program across all sepsis individuals (PBMCs, left) and for the regression’s R2using the R / T model across all individuals with S. aureus infection (blood samples, right). The plots present the analysis of real data and permuted data (x axis). The horizontal dashed lines indicate empirical p = 0.05 level. The fraction of individuals with empirical p <0.05 in real data (true positives, TP) and permuted data (false positives, FP) is specified.

[0161] FIGURE 36. Shown are ROC curves presenting the predictive performance of the R levels (black) and SI levels (gray) for the prediction of infected versus healthy patients. The inventors observed a high performance of both R and SI throughout the entire ROC range.

[0162] FIGURE 37. Co-expression of each candidate program during infections in general and sepsis in particular. For each candidate program (a dot), the scatter plot shows the ‘covariation in sepsis’ score (x axis) and the ‘covariation in infections’ score (y axis). The scores are detailed in Example 18. Selected programs are indicated.

[0163] FIGURE 38A-38B. Response of candidate programs to infections and SIRS.

[0164] Fig. 38A. The effect of disease on program level (red / blue indicates upregulation / downregulation, Wilcoxon signed rank test statistic) is color coded for each program (column) and each condition (row).

[0165] Fig. 38B. The effect of disease on program level (Wilcoxon signed rank test statistic) is detailed for four selected programs (marked with arrows in Fig. 38A).

[0166] FIGURE 39. Correlations of clinical parameters with the SI levels. For each clinical parameter (a dot), its correlation with IM2 (referred to SI) levels were calculated across individuals from the 300-OB cohort. Reproduced from [Ref 7]. FIGURE 40. Correlation of the R level with the pathogen load, in lungs. Shown are the R levels in lungs (x axis) against the influenza viral load in lungs (y axis), in different time points after in vivo IAV infection. R levels and viral load were calculated in each individual mouse, using mice from 33 recombinant inbred mouse strains. The plot was generated using data from [Ref 5].

[0167] DETAILED DESCRIPTION OF EMBODIMENTS

[0168] Sepsis is a heterogeneous clinical condition characterized by complex immune dysregulation during severe infections, in which various combinations of immunosuppression and exaggerated inflammation can be found dependent of the etiology or phase of the disease. This wide heterogeneity of sepsis poses two key challenges. First, it significantly impedes the identification of the most important drivers of immune defects in sepsis. Second, it requires the stratification of patients into immune endotypes that may respond to specific immunotherapeutic approaches.

[0169] In the present disclosure, the inventors found that the transcriptional states of two cell programs, resistance (R) and systemic inflammation (SI), capture the complexity of sepsis (Figure 17): (1) The balance between the states of R and SI (namely the ‘R / SI balance’) reliably separated patients with sepsis and moderate infections: patients with sepsis are characterized by low R relative to the SI level, whereas moderate infections are characterized by the opposite state (Figure 5). (2) R and SI explain the observed differences between sepsis patients at multiple biological layers. In particular, sepsis pathology, severity and mortality are associated with a low R / SI balance (Figure 10, 14, 22, 23). Finally, (3) the uncoupling between induction of SI activity and repression of R activity at the cell-intrinsic level may explain how both hyperinflammation and immunosuppression coexist in the same patient (Figures 5G).

[0170] One important aspect to underline is the fact that initially the R and SI cell programs have been derived from a very distinct context (R - influenza; SI - low-grade chronic inflammation) (Example 19). However, the high co-variation of these programs in sepsis and infection cohorts, as well as the link between these programs to clinical measured in different models of sepsis and moderate infections (Figures 5, 10, 14, 9), strongly argues for their generalizability for understanding sepsis. It was noted that R and SI are cell programs - that is, the R and SI states vary at the cell-intrinsic level (Figures SB, 5G, 10) - opening the way to ex vivo testing of therapeutic interventions. This is unlike recent studies of immune programs that rely on whole- blood transcriptomes without determining a relevance at the cell-intrinsic level. Overall, by leveraging the programs from one context in another context, the inventors improved the understanding of sepsis. This suggests potential reusage of regulatory programs in response to a changing environment, which can be exploited to enhance model generalization in future studies. The R / SI model disclosed herein, has a potential to guide future development of novel anti-sepsis strategies. First, a low R / SI balance allowed the identification of sepsis-related pathways, such as Bone Morphogenetic Protein 2 (BMP2) signaling and elastic fiber formation (Figure 16), which indicate the direction for the development of novel therapeutic approaches. Second, the present disclosure shows that both high systemic inflammation and deficit in resistance are associated with sepsis severity and mortality. These are actionable guidelines of blocking inflammation (several drug candidates already being available, such as anti-cytokine antibodies), or amplifying resistance (several candidates such as recombinant interferon gamma (rlFNy), recombinant interleukin -7 (rIL-7), Granulocyte-macrophage colony-stimulating factor (GM-CSF), etc.), depending on the source of dysfunction in a particular patient (Figure 17). Third, the transcriptional signatures of R and SI can be used to evaluate immunotherapies of sepsis in an ex vivo setting (Figure 18). An important aspect needs to be underscored at this point. While the R / SI model can guide the selection of a tailored therapy for each R / SI state, this does not exclude the possibility of using different types of immunotherapeutic approaches depending on the pathophysiological process that has led to a specific R / SI state. For example, it can be envisaged that a state characterized by a strong suppression of resistance without hyperinflammation can be induced through different mechanisms: e.g., a defective IL-12 / IFNy pathway, or overexpression of inhibitory molecules. Such particular immunotypes may respond better to either treatment with recombinant IFNy or checkpoint inhibitors, to give just one example. Follow-up studies are warranted to define the immunotypes characterizing each of the R / SI states and their respective target immunotherapy treatments.

[0171] Finally, the present disclosure demonstrates the advantage of the R / SI framework in patient stratification. In particular, the inventors classified sepsis patients into immune endotypes based on their R / SI levels, and then compare this classification with a former stratification of two extreme endotypes (immunosuppression and MALS). Using an independent validation cohort (PROVIDE), the inventors show that the R / SI-based classification presents strong prognostic capacity within each of these former endotypes, highlighting the added prognostic value of the R / SI-based criteria. Furthermore, using the former stratification, many sepsis patients did not reach the criteria for either immunosuppression or MALS, and were therefore described as ‘unclassified’ despite a poor outcome of the disease. This led to a failure to characterize immunologically a large proportion of sepsis patients and left physicians with the incapacity to propose appropriate immune-based treatment. Using the R / SI-based classification, the present disclosure enables the provision of a significant prognosis of these previously unclassified patients. The present disclosure demonstrated that there are plasma protein biomarkers that can identify the R / SI-based endotypes of sepsis and related conditions. Taken together, the inventors demonstrated that the R / SI-based endotypes are 1) clinically relevant, 2) could be targeted using therapeutic interventions, and 3) there are plasma protein markers for these endotypes. These results suggest translatability of the R / SI-stratification framework. Overall, whereas the personal R / SI-based endotype determines the general type of therapy (either pro-R, anti-SI, or both; Figure 17), the particular therapy should be selected in accordance with the pathophysiological process that has led to a specific R / SI state. Thus, in a first aspect the present disclosure provides a method for diagnosing, prognosing and / or classifying sepsis and / or associated conditions in a subject by determining the resistance / systemic inflammation (R / SI) balance of this subject. More specifically, in some embodiments, the method comprising the following steps. A first step (a) involves determining in at least one biological sample of the subject the expression level of a subset of biomarkers (e.g., gene biomarkers) to obtain an expression value for each of these biomarkers. The second step (b) involves determining and / or calculating the resistance (R) level and the systemic inflammation (SI) level of this subject from the expression values obtained in step (a) and determining the R / SI balance. In some embodiments, a R / SI balance that is smaller than 0, is indicative of sepsis and / or associated conditions in this subject.

[0172] Step (a) of all of the disclosed methods, as well as in the compositions and kits disclosed herein, involve the use of one or more 'biomarker / s' or any subset thereof. The term 'biomarker / s', as used herein, refers to a measurable biological characteristic or indicator that is objectively evaluated and serves as a marker for a specific physiological state, particularly for sepsis. Generally, biomarkers can include, but are not limited to, genes, proteins, or metabolites. It should be understood that the terms 'biomarker / s', 'gene / s', 'gene biomarker / s', 'gene marker / s', and 'biomarker gene / s' are used interchangeably throughout the present disclosure and should be interpreted broadly. This includes nucleic acid molecules such as genes, coding or non-coding sequences, RNA molecules, and similar entities, as well as protein or peptide biomarkers.

[0173] The term "balance" between two parameters, specifically, the resistance and the systemic inflammation, provides insights into the relative values of these parameters within the diagnostic framework. It is typically represented mathematically as the quotient of the two parameters, allowing for a comparative analysis of their respective levels or contribution. For instance, considering the two programs, the resistant program and the systemic inflammation, their balance can be expressed as follows: Balance= the substruction of the systemic inflammation (SI) level from the resistance (R) level of the subject. In some embodiments, the R / SI balance of the sample may be equal to 0, where the R and the SI values are equal. In yet some embodiments, the R / SI balance may be larger than 0, when the R level is larger than the SI level, specifically, from about 0.0000001 to about 10000, or more, for example, about 0.001, 0.005, 0.010, 0.020, 0.040, 0.05,

[0174] 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24,

[0175] 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42,

[0176] 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6,

[0177] 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.2, 1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3, 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.38, 1.39, 1.4, 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69, 1.7, 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8, 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88, 1.89, 1.9, 1.91, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2.0, 2.01, 2.02, 2.03, 2.04, 2.05, 2.06, 2.07, 2.08, 2.09, 2.1, 2.11, 2.12, 2.13, 2.14, 2.15, 2.16, 2.17, 2.18, 2.19, 2.2, 2.21, 2.22, 2.23, 2.24, 2.25, 2.26, 2.27, 2.28, 2.29, 2.3, 2.31, 2.32, 2.33, 2.34, 2.35, 2.36, 2.37, 2.38, 2.39, 2.4, 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5, 2.51, 2.52, 2.53, 2.54, 2.55, 2.56, 2.57, 2.58, 2.59, 2.6, 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69, 2.7, 2.71, 2.72, 2.73, 2.74, 2.75, 2.76, 2.77, 2.78, 2.79, 2.8, 2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87, 2.88, 2.89, 2.9, 2.91, 2.92, 2.93, 2.94, 2.95, 2.96, 2.97, 2.98, 2.99, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 8.0, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9.0, 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 10. In yet some further embodiments, the R / SI balance may be smaller than 0, when the R level is smaller than the SI level, specifically, from about - 0.0000001 to about -10000, or less, for example, about - 0.001, -0.005, -0.010, -0.020, -0.040, - 0.05, -0.06, -0.07, -0.08, -0.09, -0.1, -0.12, -0.13, -0.14, -0.15, -0.16, -0.17, -0.18, -0.19, -0.2, - 0.21, -0.22, -0.23, -0.24, -0.25, -0.26, -0.27, -0.28, -0.29, -0.3, -0.31, -0.32, -0.33, -0.34, -0.35, - 0.36, -0.37, -0.38, -0.39, -0.4, -0.41, -0.42, -0.43, -0.44, -0.45, -0.46, -0.47, -0.48, -0.49, -0.5, - 0.51, -0.52, -0.53, -0.54, -0.55, -0.56, -0.57, -0.58, -0.59, -0.6, -0.61, -0.62, -0.63, -0.64, -0.65, - 0.66, -0.67, -0.68, -0.69, -0.7, -0.71, -0.72, -0.73, -0.74, -0.75, -0.76, -0.77, -0.78, -0.79, -0.8, - 0.81, -0.82, -0.83, -0.84, -0.85, -0.86, -0.87, -0.88, -0.89, -0.9, -0.91, -0.92, -0.93, -0.94, -0.95, - 0.96, -0.97, -0.98, -0.99, -1.0, -1.01, -1.02, -1.03, -1.04, -1.05, -1.06, -1.07, -1.08, -1.09, -1.1, - 1.11, -1.12, -1.13, -1.14, -1.15, -1.16, -1.17, -1.18, -1.19, -1.2, -1.21, -1.22, -1.23, -1.24, -1.25, - 1.26, -1.27, -1.28, -1.29, -1.3, -1.31, -1.32, -1.33, -1.34, -1.35, -1.36, -1.37, -1.38, -1.39, -1.4, - 1.41, -1.42, -1.43, -1.44, -1.45, -1.46, -1.47, -1.48, -1.49, -1.5, -1.51, -1.52, -1.53, -1.54, -1.55, - 1.56, -1.57, -1.58, -1.59, -1.6, -1.61, -1.62, -1.63, -1.64, -1.65, -1.66, -1.67, -1.68, -1.69, -1.7, - 1.71, -1.72, -1.73, -1.74, -1.75, -1.76, -1.77, -1.78, -1.79, -1.8, -1.81, -1.82, -1.83, -1.84, -1.85, - 1.86, -1.87, -1.88, -1.89, -1.9, -1.91, -1.92, -1.93, -1.94, -1.95, -1.96, -1.97, -1.98, -1.99, -2.0, - 2.01, -2.02, -2.03, -2.04, -2.05, -2.06, -2.07, -2.08, -2.09, -2.1, -2.11, -2.12, -2.13, -2.14, -2.15, - 2.16, -2.17, -2.18, -2.19, -2.2, -2.21, -2.22, -2.23, -2.24, -2.25, -2.26, -2.27, -2.28, -2.29, -2.3, - 2.31, -2.32, -2.33, -2.34, -2.35, -2.36, -2.37, -2.38, -2.39, -2.4, -2.41, -2.42, -2.43, -2.44, -2.45, - 2.46, -2.47, -2.48, -2.49, -2.5, -2.51, -2.52, -2.53, -2.54, -2.55, -2.56, -2.57, -2.58, -2.59, -2.6, - 2.61, -2.62, -2.63, -2.64, -2.65, -2.66, -2.67, -2.68, -2.69, -2.7, -2.71, -2.72, -2.73, -2.74, -2.75, - 2.76, -2.77, -2.78, -2.79, -2.8, -2.81, -2.82, -2.83, -2.84, -2.85, -2.86, -2.87, -2.88, -2.89, -2.9, - 2.91, -2.92, -2.93, -2.94, -2.95, -2.96, -2.97, -2.98, -2.99, -3.0, -3.1, -3.2, -3.3, -3.4, -3.5, -3.6, -3.7,

[0178] -3.8, -3.9, -4.0, -4.1, -4.2, -4.3, -4.4, -4.5, -4.6, -4.7, -4.8, -4.9, -5.0, -5.1, -5.2, -5.3, -5.4, -5.5, -5.6,

[0179] -5.7, -5.8, -5.9, -6.0, -6.1, -6.2, -6.3, -6.4, -6.5, -6.6, -6.7, -6.8, -6.9, -7.0, -7.1, -7.2, -7.3, -7.4, -7.5,

[0180] -7.6, -7.7, -7.8, -7.9, -8.0, -8.1, -8.2, -8.3, -8.4, -8.5, -8.6, -8.7, -8.8, -8.9, -9.0, -9.1, -9.2, -9.3, -9.4,

[0181] -9.5, -9.6, -9.7, -9.8, -9.9, -10. In some embodiments, the disclosed method further comprises the steps of calculation and classification. More specifically, step (c) involves calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (b); and step (d) involves concluding or in other words, classifying the balance score, specifically, concluding that the R / SI balance is impaired, if the R / SI balance score is negative, specifically, smaller than 0, or alternatively, that the R / SI balance is good if the R / SI balance score is positive, specifically, greater than 0 (zero). More specifically, an "impaired R / SI balance score" as used herein, refers to a balance score that falls outside the optimal or normal range, indicating a disruption or deficiency in the equilibrium of the measured parameters, specifically the R and the SI levels. This disruption signifies a deviation from the healthy or desired state, e.g., any state that does not involve sepsis or related conditions, often pointing to underlying pathological conditions or system malfunctions. In some embodiments, an "impaired R / SI balance score" refers to a reduced, decreased, diminished, lowered, lessened, minimized, curtailed, attenuated, abated balance score, as compared with the R / SI balance score of an appropriate control. For example, the balance score determined for a sample and / or subject that is not affected by sepsis, for example, a healthy subject, or alternatively, a subject affected by a disease other than sepsis or related disorders. A good balance score, as used herein, refers to a balance score that falls within the optimal or normal range. In health a good balance score is around 0. During normal infections, a good balance score is above 0. In some embodiments, a good balance score is a positive R / SI balance score, as used herein that is either around 0, or that is greater than 0. It should be noted that the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score. Specifically, negatively correlated as used herein, is meant that the two variables, specifically, the R / SI balance score and the severity of sepsis, are said to be "negatively correlated" when they have an inverse relationship. This means that as one variable increases, e.g., the R / SI balance score, the other variable tends to decrease, e.g., severity of sepsis and vice versa. More specifically, in the context of sepsis and its associated conditions, a negative correlation between the severity of sepsis and the R / SI balance score indicates that as the severity of sepsis increases, the R / SI balance score tends to decrease, and vice versa.

[0182] Still further, the term "R / SI balance score" as used herein, is a quantitative metric used to evaluate the equilibrium or proportion between the R level and the SI levels as calculated and defined in step (b). Unlike a simple ratio, a balance score often integrates multiple dimensions and may apply weighting factors to different variables to provide a more comprehensive assessment of balance. In some embodiments, the R / SI balance score is calculated by subtraction of the SI level from the R level, as calculated and defined in step (b), to obtain the R / SI balance score.

[0183] The present disclosure provides diagnostic methods for detecting and classifying sepsis and associated disorders. 'Sepsis' (or septicaemia or blood poisoning) as used herein is a pathological condition resulting from dysregulated immune responses in patients with infections, leading to severe symptomatology (including fever, increased heart rate, increased breathing rate, and confusion as well as symptoms related to a specific infection, such as a cough with pneumonia, or painful urination with a kidney infection), organ dysfunction, and often death. Sepsis is caused by many organisms and pathogens including bacteria, viruses, fungi and parasites. Common locations for the primary infection include the lungs, brain, urinary tract, skin, and abdominal organs. Risk factors include being very young or old, a weakened immune system from conditions such as cancer or diabetes, major trauma, and burns. Severe sepsis causes poor organ function or blood flow. The presence of low blood pressure, high blood lactate, or low urine output may suggest poor blood flow. The most updated consensus definition of sepsis and septic shock (Sepsis- 3), published in 2016, defined sepsis as a life-threatening organ dysfunction caused by a dysregulated host response to infection. The current Sepsis-3 definition is reflected in an increase in the Sequential Organ Failure Assessment (SOFA) score as a validated measure of organ dysfunction associated with subsequent mortality. The Quick SOFA Score (qSOFA) was also defined in 2016 as a simplified version of the SOFA Score that can be applied quickly and easily on the patient. Sepsis is associated with a mortality rate of 10% to 20%. Septic shock, a subgroup of sepsis characterized by more severe cardiovascular abnormalities (i.e. requiring vasopressors to maintain a mean arterial pressure above 65 mmHg, associated with a lactate concentration higher than 2 mmol / L), is characterized by a higher risk of death in the range of 40% to 50%. Sepsis-3 definitions, by applying strict clinical severity criteria, was an improvement over the 1992 and 2001 sepsis definitions which relied on the concept of a systemic inflammatory response syndrome (SIRS) induced by infection and the concept that sepsis was a continuum ranging from sepsis to septic shock through severe sepsis. More specifically, the term "severity of sepsis", as used herein refers to classification of the subject with respect to the extent and impact of the sepsis, using the quantitative score, specifically, either the SOFA score or qsOFA score as defined herein. It is characterized by a spectrum of conditions ranging from mild sepsis to severe sepsis and septic shock, with increasing levels of organ dysfunction and risk of mortality. More specifically, the Sequential Organ Failure Assessment (SOFA) score, formerly known as the Sepsis-related Organ Failure Assessment score, is a tool used in intensive care units (ICUs) to monitor and assess the extent of a patient’s organ dysfunction or failure (Mervyn Singer, MD, et al., JAMA. 2016: 315(8): 801-810. doi:10.1001 / jama. 2016.0287). It evaluates six organ systems: respiratory, cardiovascular, hepatic, coagulation, renal, and neurological. The scoring system assigns points based on specific physiological parameters within these systems, ranging from 0 to 4 for each system, with a total score ranging from 0 (indicating best function) to 24 (indicating worst function). In cases where physiological parameters do not align with any predefined criteria, a score of 0 is given. If parameters match more than one category, the highest score is selected. The SOFA score is especially valuable for predicting clinical outcomes, with studies showing a mortality rate of over 50% when the score increases within the first 96 hours of ICU admission. In contrast, a mortality rate of 27% to 35% occurs if the score remains stable, and less than 27% if the score decreases. Additionally, a simplified version, the quick SOFA (qSOFA), is used to estimate the risk of morbidity and mortality due to sepsis. The scoring parameters for each organ system involve specific metrics, such as the Glasgow Coma Scale for the central nervous system, mean arterial pressure and vasopressor administration for the cardiovascular system, PaO2 / FiO2 ratios for the respiratory system, platelet count for coagulation, bilirubin levels for liver function, and creatinine levels or urine output for renal function. More specifically, for the central nervous system, the score is based on the Glasgow Coma Scale (GCS), where a GCS of 15 scores 0 points, and a GCS below 6 scores 4 points. The cardiovascular system is evaluated by mean arterial pressure (MAP) and the need for vasopressors, with a MAP > 70 mmHg scoring 0 points, and the use of high-dose vasopressors scoring 4 points. The respiratory system is assessed using the PaO2 / FiO2 ratio, where a ratio > 400 mmHg scores 0 points, and a ratio < 100 mmHg with mechanical ventilation scores 4 points. Coagulation is measured by platelet count, with counts > 150 x 103 / pl scoring 0 points, and counts < 20 x 103 / pl scoring 4 points. Liver function is determined by bilirubin levels, where levels < 1.2 mg / dl score 0 points, and levels > 12.0 mg / dl score 4 points. Renal function is based on creatinine levels or urine output, with creatinine < 1.2 mg / dl or normal urine output scoring 0 points, and creatinine > 5.0 mg / dl or urine output < 200 ml / day scoring 4 points. Thus, according to some embodiments, the term severity of sepsis as used herein is determined using the SOFA score and may be presented in a score of any one of: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, as defined above. Still further, in some alternative or additional embodiments, the severity of sepsis may be determined using the qSOFA. More specifically, the Quick SOFA Score (quickSOFA or qSOFA) is a simplified tool to quickly identify patients at high risk for poor outcomes due to infection, specifically sepsis. The qSOFA score consists of three clinical criteria: low blood pressure (systolic blood pressure < 100 mmHg), high respiratory rate (> 22 breaths / min) and altered mentation (Glasgow Coma Scale score < 14). Each criterion scores 1 point, resulting in a total score ranging from 0 to 3 points. A score of 2 or more points is associated with a higher risk of death or prolonged intensive care unit stay, indicating a likelihood of sepsis.

[0184] The present disclosure is particularly applicable for the diagnosis, prognostic and treatment of sepsis and any associated conditions. More specifically, 'associated conditions' of sepsis may be any condition or disorder "associated", “linked” and "related" to sepsis. More specifically, when referring to pathologies herein, these terms that are interchangeably used terms mean diseases, disorders, conditions, or any pathologies which at least one of: share causalities, co-exist at a higher than coincidental frequency with sepsis, or where sepsis causes or alternatively, is caused by at least one of these associated disease, disorder condition or pathology. In some embodiments, sepsis associated conditions may comprise but are not limited to at least one of the following: any immune dysregulation (ranging from hyperinflammation to immunoparalysis), hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure (hemodialysis), acute renal failure (ARF), cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis. As shown by the present disclosure, the R / SI balance and specifically, the R / SI balance score calculated herein, distinguish between patients with sepsis and those with moderate infections. Patients with sepsis are characterized by low R relative to the SI level, thereby displaying an R / SI balance score that is smaller than 0, whereas moderate infections are characterized by the opposite state. Namely, R / SI balance that is negative (lower than 0), is indicative of sepsis and / or associated conditions and R / SI balance that is positive (higher than 0) is indicative of moderate infection. Moreover, as indicated above, the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score, meaning that decreasing R / SI balance scores are indicative of increasing severity state of sepsis.

[0185] The disclosed methods and compositions are aimed at evaluating and calculating each of the R level and the SI level of an individual. Each of these calculated values reflect various parameters influencing the immune state of a given individual, and each, are defined based on two categories, also referred to herein as programs. More specifically, the term program, as generally used, describe co-regulation of genes or proteins in one or several contexts (e.g., pathologic and / or physiological conditions). Specifically, a set of genes / proteins whose expression is co-regulated, or coordinated, in a certain condition (or a set of conditions). As used herein in the present disclosure, the term program is the mathematical presentation of this co-regulation of gene biomarkers in an axis (e.g., R and / or SI axis), in accordance with vector of weights across the biomarkers (e.g., gene biomarkers), for either R, SI, MetS or T.

[0186] Resistance (R) for example is defined based on evaluation of the resistance when accounting for potential disease-tolerance (T) confounders, and Systemic inflammation (SI) is defined based on the systemic inflammation when accounting for the confounding immunometabolic state of metabolic syndrome (MetS).

[0187] More specifically, when referring to resistance (R), it should be understood that the host defense strategies have been classified into two broad categories: resistance strategies that sense and react to eradicate an invading pathogen, and disease-tolerance strategies, that enable the host to promote health in the presence of pathogen.

[0188] The term 'resistance' (R) as referred to herein, relates to the ability to eliminate or restrict the replication of an invading pathogen. For instance, in the case of the influenza virus, infection induces a unique spectrum of host defense genes, including interferon-stimulated genes (ISGs) and genes encoding other proteins with antiviral potential. Cellular proteins with putative antiviral activity (hereafter referred to as “restriction factors”) can target various steps in the virus life- cycle. In the context of influenza virus infection, restriction factors are those that target virus entry, genomic replication, translation and virus release. The resistance of an individual is associated with various disease severity phenotypes that may be quantified by analyzing of various parameters such as viral burden, weight loss, breathing dysfunction, expression of specific markers, e.g., Ifnbl and Ccl2, the quantity of immune cells, and tissue damage.

[0189] It should be understood that the term 'disease tolerance ' (T) refers to the capacity to bear, endure, or tolerate a state of disease, by limiting the negative impact of infection on host health and fitness without exerting a direct impact on pathogens. Disease tolerance is a physiological term, referring to the relations between the level of health and the pathogen. The physiological status of disease tolerance is defined herein as relative health, or alternatively, the relative tissue damage. More specifically, the relative tissue damage as used herein, is defined through reaction norms plot the level of damage for an individual at each pathogen burden. This term reflects the slope of the damage-to-pathogen regression in this plot, such that a shallower slope indicates a better ability to tolerate the pathogen. Since disease tolerance (T) and resistance (R) are distinct but closely intertwined during infection, the calculation of R level takes into consideration the status of T.

[0190] The R level is calculated from the expression level of a subset of genes as detailed in the 'Experimental Procedures' section of the current application, which is based on the previous publication by the present inventors [Ref 5 (Cohn et al. (2022))], as detailed herein after.

[0191] Systemic inflammation (SI) is the other value that is indicative of sepsis and when is higher than the resistance level, reflects the severity of sepsis. More specifically, "Systemic inflammation" or "SI" in short, refers to the immunometabolism state of an individual. More specifically, systemic inflammation refers to immune activation that may arise in the absence of invading pathogens. Systemic inflammation (SI) is equivalent to the IM2 program described in the previous publication by the inventors [Ref 7 (Frishberg et al. (2021))], described herein after. As detailed by Frishberg et al [Ref 7], one component of the whole-body chronic inflammatory state represents ‘metabolic syndrome’ (referred to herein as IM1 or MetS), a conventional way to determine the cardiometabolic state. The clinical definition of MetS is a small cluster of metabolic and hemodynamic risk factors that are associated with cardiovascular disease, including abdominal obesity, high blood pressure, impaired fasting glucose, high triglyceride levels, and low HDE cholesterol levels (Huang PE, Disease Models & Mechanisms 2:231-237. 2009). The second component (IM2 or SI) is decoupled from the MetS and is characterized by a different set of clinical parameters, including dysregulated lipoprotein parameters (e.g. low free cholesterol in small high-density lipoproteins) and attenuated cytokine responses of immune cells to ex vivo stimulations. Both immunometabolic components (MetS and SI) are associated with disease but differ in their particular associations. Since systemic inflammation (SI) and metabolic syndrome (MetS) are distinct but closely intertwined (namely, both have multiple shared genes and pathways), the calculation of SI level (“SI state”) takes into consideration the status of MetS.

[0192] The SI level is calculated from the expression level of a subset of genes (also referred to herein as biomarkers, or gene biomarkers) as detailed in the 'Experimental Procedures' section of the current application, which is based on Ref 7.

[0193] In some further embodiments an impaired R / SI balance score comprises at least one of the following: (i) reduced R level, as compared with a suitable control or standard, which refers to strong suppression of resistance with no hyperinflammation, (ii) increased SI level, as compared with a suitable control or standard, which refers to high systemic inflammation with no suppression of resistance, and (iii) reduced R level and increased SI level, as compared with a suitable control or standard, which refers to suppression of resistance and presence of hyperinflammation. A suitable control and / or standard, as used herein and throughout the present disclosure in connection with any embodiment and / or aspect / s thereof, when referred to the R level and SI levels of the subject, that may reduce or increase, refers to the R level and / or SI levels of at least one biological sample and / or at least one subject or at least one population of subjects predetermined as not being affected by sepsis. For example, healthy subjects of any age, gender, ethnicity, geographic location, health status, and of any / or life-style factor (smokers, non-smokers, alcohol consumers or not, education, physical activity, and the like), or alternatively, any non-healthy subject affected by a disease and / or disorder other than sepsis, of any age, gender, ethnicity, geographic location, health status, and / or of any life-style factor.

[0194] In some further embodiments, the subject diagnosed with sepsis and / or related conditions is further classified by the disclosed methods as belonging to one of the following groups based on the R / SI balance score:

[0195] (i), a subject displaying a non-substantial, moderate and / or minor reduction in R / SI balance, as compared with a suitable control or standard, is classified as having a moderate R / SI imbalance.

[0196] (ii), a subject displaying a substantial reduction in R / SI balance with high SI level, as compared with a suitable control or standard, is classified as having a high-SI.

[0197] (iii), a subject displaying a substantial reduction in R / SI balance without exceptionally high SI, as compared with a suitable control or standard, is classified as having a severe R / SI imbalance.

[0198] In some embodiments, the term "reduced", specifically when referred to the R levels or the RI / SI balance score, encompass any “reduction”, "inhibition", "moderation", "decrease" or "attenuation" with respect to the R levels and / or the R / SI balance score, in accordance with the present disclosure (e.g., either due to reduction of R, elevation of SI, and / or reduction of R and elevation of SI, all reduce the R / SI balance score), relate to the act of becoming progressively smaller in size, amount, number, or intensity. More specifically, this term relates to the retardation, restraining or reduction of the indicated values by any one of about 1% to 99.9%, specifically, about 1% to about 5%, about 5% to 10%, about 10% to 15%, about 15% to 20%, about 20% to 25%, about 25% to 30%, about 30% to 35%, about 35% to 40%, about 40% to 45%, about 45% to 50%, about 50% to 55%, about 55% to 60%, about 60% to 65%, about 65% to 70%, about 75% to 80%, about 80% to 85% about 85% to 90%, about 90% to 95%, about 95% to 99%, or about 99% to 99.9%, 100% or more. Still further, the term "increased", specifically when referred to the SI levels, encompass any "increase", "augmentation" and "enhancement", and relate to the act of becoming progressively greater in size, amount, number, or intensity. More specifically, the term increase, as used herein, in connection with the SI levels, is meant that such increase or enhancement may be an increase or elevation of the indicated levels, of between about 1% to 100%, specifically, 5% to 100% of the indicated parameter, more specifically, about 1% to about 5%, about 5% to 10%, about 10% to 15%, about

[0199] 15% to 20%, about 20% to 25%, about 25% to 30%, about 30% to 35%, about 35% to 40%, about

[0200] 40% to 45%, about 45% to 50%, about 50% to 55%, about 55% to 60%, about 60% to 65%, about

[0201] 65% to 70%, about 75% to 80%, about 80% to 85% about 85% to 90%, about 90% to 95%, about

[0202] 95% to 99%, or about 99% to 99.9%, 100% or more.

[0203] Still further "substantial reduction" as used herein refers to a significant decrease in the quantity, amount, or level of the R, and / or the R / SI balance score. In the context of quantitative measurements, "substantial reduction" can typically be defined as a decrease within a specific range, often expressed as a percentage. For example, a substantial reduction might be characterized as a reduction of at least about or more, for example, 50% to about 90%, or more, relative to the original or baseline level, specifically, substantial reduction, is of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more of the level of the R, and / or the R / SI balance score.

[0204] It should be noted that classifications as high-SI or severe R / SI imbalance are indicative of a negative prognosis.

[0205] As indicated above, in case the R / SI balance score is negative (e.g., smaller than 0), the balance is specified as an "Impaired R / SI balance This term refers to any imbalance between R levels and SI levels, which might be caused as a result of either reduced R level (and no change in SI level), increased SI levels (and no change in R level) or reduced R level combined with increased SI level as compared to a suitable control sample and / or subject and / or population of subjects and / or standard samples and / or a previous R / SI balance result for sample / s of the same individual. 'Moderate R / SI imbalance ' refers to patients with a limited reduction in R / SI balance. In some embodiments, moderate versus severe R / SI imbalance, the cutoff is 50% of sepsis patients. Still further, for high-SI, the cutoff for SI is in the 30% sepsis patients with highest SI.

[0206] For example, a limited reduction in R and / or a moderate elevation in SI. Formally, the moderate- R / SI-imbalance group refers to patients with R / SI balance score >50thpercentile among sepsis patients by the sepsis-3 definition, as described herein before. 'High-SI' refers to patients with substantial reduction in R / SI balance whose SI level is exceptionally high. Formally, the high-SI group refers to patients with R / SI balance score <50lhpercentile that also carry SI level >70thpercentile among sepsis patients by the sepsis-3 definition. 'Severe R / SI imbalance' refers to patients with substantial reduction in R / SI balance whose SI level is not exceptionally high. Formally, the sever-R / SI-imbalance group refers to patients with R / SI balance score <50thpercentile that also carry SI level <70thpercentile among sepsis patients by the sepsis-3 definition. The nature and extend of impaired R / SI balance as well as the change direction (i.e. increase vs. reduce vs. no change) of its components (i.e R and SI) may direct to a proper treatment selection. As indicated above, a high-SI or severe R / SI imbalance are indicative of a negative prognosis of the subject. The disclosed methods, compositions and kits are therefore directed at diagnosis and prognosis of subjects. "Prognosis" is defined as a forecast of the future course of a disease or disorder, based on medical knowledge. It should be understood that as used herein, the term "positive prognosis", may be also referred to as "excellent, good, fair prognosis", and indicates a positive forecast of the course of a disease that may be reflected by reduced chances for relapse, increased disease-free survival (DFS), reduced disease symptoms, e.g., reduced sepsis severity, increased responsiveness to treatment, and even cure. In yet some further embodiments, the "positive prognosis" or "positive outcome", as used herein, refers to a not severe form of the disease, a mild form, early stage or grade, enhanced chances for recovery, extended survival, remission, extended disease-free period and the like.

[0207] A "negative prognosis" as used herein, also referred to herein as "poor prognosis" or "bad prognosis", refers to patients who have low likelihood of response to treatment, increased chances for relapse, reduced disease-free survival and reduced or no chance of cure. More specifically, a "Negative prognosis" or "negative outcome" as used herein refers to a severe form, relapse, any worsening of existing symptoms or conditions of sepsis, deterioration of overall condition of the subject (e.g., body weight, tissue and organ integrity and function, physical and / or mental functioning), involvement of long-term symptoms and / or complications and associated disorders and or even decreased survival and death of the subject etc. In some embodiments, a "Negative prognosis" further refers herein to high-SI or severe R / SI imbalance.

[0208] Still further a return of a disease or illness, specifically, sepsis and related conditions, following partial recovery or a period of apparent improvement, or in other words, relapse, forms a major part of a negative prognosis, and may be prognosed by the disclosed methods, kits and compositions. The term 'relapse', as used herein, relates to the re-occurrence of a condition, disease or disorder that affected a person in the past. Specifically, the term relates to the re- occurrence of a disease being treated a regimen.

[0209] As indicated above, the disclosed methods involve in the first step determination of the expression level of biomarkers to obtain the R / SI balance of a subject and / or calculate the R / SI balance score. The next step involves determination if the R / SI balance is positive or negative. It should be understood that determination of a "positive" or alternatively "negative" R / SI balance and / or the R / SI balance score, reflects in some embodiments, the comparison of each of the R and SI determined levels with respect to a standard value or a control value may involve in some embodiments comparison of the R and / or SI levels of the examined sample as obtained in steps (b) of the disclosed methods calculated from the expression levels of the various biomarkers, with the R and / or SI levels calculated for a control sample, or from any established or predetermined value (e.g., a standard value) obtained from a known control (either healthy controls or of subjects suffering from sepsis and / or associated conditions defined herein before).

[0210] As used herein, 'healthy controls' or 'healthy population' may refer to a population of subjects that do not suffer from the discussed disease, or refer to a population before appearance of the discussed disease. In some embodiments, the R / SI balance of a control population refers to a baseline level of resistance and / or systemic inflammation of a healthy population or to a baseline level of resistance and / or systemic inflammation before appearance of sepsis and or associated conditions in a studied population. In some other embodiment, a “healthy control” or “control” may refer to a baseline level of resistance and / or systemic inflammation before appearance of the discussed disease in a specific patient.

[0211] In some further embodiments, determining and / or calculating the resistance (R) level according to step (b) of the disclosed methods is performed by the following calculation:

[0212] The R level is calculated in conjugation with the tolerance (T) level of the sample using the formula wherein: Zi is the vector of relative measured levels across all genes (also referred to herein as biomarkers, or gene biomarkers) of individual i. Relative expression levels are calculated in three steps, first (i) Log2-transformation. (ii) Sample-level standardization. Each sample is centered and divided by standard deviation across genes, (iii) Gene-level standardization. Each gene is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0213] VTand VRare the pre-defined vectors of gene biomarker weights of all gene biomarkers for the T and R programs, respectively; is the T level of individual t; sRlis the R level of individual t; and bi is a constant. It should be understood that the s and sRlare the T and R levels of individual i, and are therefore extracted from the above formula. More specifically, the inventors infer T and R levels for which the expression of each gene biomarker is approximated well by the weighted sum of T and R levels (for each gene biomarker, using different T and R weights that were predefined for the relevant gene biomarker).

[0214] In some embodiments, the method further involves, standardizing by subtracting from the calculated R level the mean level of a standard or control samples and dividing the result of the substruction by the standard deviation of the standard or control samples.

[0215] The gene biomarker weights (VTand VR) were previously defined and are used as constants in all embodiments. These weights were originally created by reducing the multi-dimensionality of gene biomarker expression values of a dataset of acute infection into a two-dimensional map, wherein resistance (denoted R) and disease tolerance (denoted T) are the vertical (axis R) and the horizontal (axis T), respectively, two axes of a two-dimensional “R / T map”, as also shown in Figure 28. For each gene biomarker, its gene (biomarker) weight of program T and its gene (biomarker) weight of program R are defined as the coordinates of this gene biomarker on the T and R axes of the R / T map. Details on how the gene biomarker weights were originally defined through dimension reduction are in disclosed Cohn et al. (2022) [Ref. 5], and further described in the ‘Experimental Procedures’ section herein after. It should be further understood that the vector of weights across genes (for either R or T) is referred to as an axis (e.g., R axis). The vector of weights (that is, axis) is a model for the concept of a ‘program’ , as defined herein before.

[0216] Similarly, in some further embodiments, determining / calculating the systemic inflammation (SI) level according to step (b) of the disclosed methods is performed by the following calculation: SI level is calculated in conjugation with the MetS level of the sample using the formula wherein:

[0217] Ziis the vector of relative measured levels across all gene biomarkers of individual i. Relative expression levels are calculated in three steps: (i) Log2-transformation. (ii) Sample-level standardization. Each sample is centered and divided by standard deviation across genes, (iii) Gene-level standardization. Each gene is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0218] Rwets and Vs / are the pre-defined vectors of gene biomarker weights of all gene biomarkers for the MetS and SI programs, respectively; sMets isthe MetS level of individual i; s$fis the SI level of individual i; and b( is a constant.

[0219] It should be understood that the are the MetS and SI levels of individual i, and are therefore extracted from the above formula. More specifically, the inventors infer MetS and SI levels for which the expression of each gene biomarker is approximated well by the weighted sum of MetS and SI levels (for each gene biomarker, using different MetS and SI weights that were predefined for the relevant gene biomarker).

[0220] In some embodiments, the method further involves (iii), standardizing by subtracting from the calculated SI level the mean level of a standard or control samples and dividing the result of the substruction by the standard deviation of the standard or control samples.

[0221] The gene biomarker weights (VMetsand VS / ) were previously defined and are used as constants in all embodiments. These weights were originally created by reducing the multi-dimensionality of biomarker expression and EMR values of a cohort of individuals into a two-dimensional map. It was shown that systemic inflammation (denoted IM2 or SI) and metabolic syndrome (denoted IM1 or MetS) are the vertical (axis SI) and the horizontal (axis MetS), respectively, two axes of the two-dimensional “SI / MetS map”. For each gene biomarker, its gene (biomarker) weight of program MetS and its gene biomarker weight of program SI are defined as the coordinates of this gene biomarker on the SI and MetS axes of the SI / MetS map, as also shown in Figure 29. A detailed on how the gene biomarker weights were originally defined through dimension reduction (PC A) are disclosed in Frishberg et al. (2020) [Ref. 7], and further described in the ‘Experimental Procedures’ section herein after. It should be further understood that the vector of weights across genes (for either SI or MetS) is referred to as an axis (e.g., SI axis). The vector of weights (that is, axis) is a model for the concept of a ‘program’ , as defined herein before.

[0222] Dimensionality reduction techniques are designed to reduce the number of variables in a dataset, while retaining as much of the relevant information as possible. In computational biology, these techniques are used to reduce the dimensionality of gene-biomarker expression data, protein- protein interaction networks, and other large-scale biological datasets. One of the most commonly used dimensionality reduction techniques in computational biology is principal component analysis (PC A). PC A is a mathematical method that transforms a dataset into a set of new variables, known as principal components, which are linear combinations of the original variables. These principal components are ordered in terms of their variance, with the first component explaining the most variance in the data, followed by the second component explaining the most variance when the first component is removed, and so on.

[0223] In some embodiments, PCA can be used to visualize high-dimensional data in a two- or three- dimensional space, allowing researchers to identify clusters or patterns in the data. It can also be used to identify gene-biomarkers or proteins that are driving the variation in the dataset. For example, PCA can be used to identify the gene-biomarkers that are most strongly associated with a particular disease or phenotype. Another commonly used dimensionality reduction technique in computational biology is autoencoder based on neural network (or a deep neural network). Autoencoding is a nonlinear dimensionality reduction technique that is particularly effective at visualizing high-dimensional data in a low-dimensional space.

[0224] Still further, 'Multi-dimensionality of gene expression' as used herein refers to high dimensionality datasets that have a large number of gene-biomarker expression features. High dimensionality datasets pose a number of problems, the most common being overfitting, which reduces the ability to generalize beyond what is in the training set. As such, dimensionality reduction techniques should be employed to reduce the number of features in the dataset. As mentioned above, in some non-limiting embodiments, a 'Principal Component Analysis (PCA)' and Autoencoder' are two such techniques that may be applicable in the methods and compositions of the present disclosure.

[0225] 'Two-dimensional R / T map' as used herein refers to ‘disease tolerance' (T) as one component of the two-dimensional map, which is expressed in the horizonal axis (also known as the x axis), and is referred herein as 'axis T', and 'resistance' (R) as the second component of the map, which is expressed in the vertical axis (also known as the y axis), and is referred herein as 'axis R' (e.g., Figure 28). 'Two-dimensional SI / MetS map' as used herein refers to ‘metabolic syndrome’ (MetS or IM1) as one component of the two-dimensional map, which is expressed in the horizonal axis (also known as the x axis), and is referred herein as 'axis MetS' and IM2 or systemic inflammation (SI) as the second component of the map, which is expressed in the vertical axis (also known as the y axis) and is referred herein as 'axis SI' (e.g., Figure 29).

[0226] A 'vector' is a quantity or phenomenon that has two independent properties: magnitude and direction. The term also denotes the mathematical or geometrical representation of such a quantity. Zi as used herein is the vector of relative measured levels across all gene biomarker expression parameters of individual i, for example, the diagnosed subject.

[0227] One of the initial steps of the disclosed methods involved determination of the expression level of the biomarkers, e.g., at least one subset of biomarkers. Thus, in some embodiments, the expression level of a subset of biomarkers in at least one sample of the subject is determined at the nucleic acid and / or the protein level.

[0228] It should be further understood that the level of expression of the subset of biomarkers and / or biomarkers as disclosed herein may be performed in the entire sample, that includes a bulk of nucleic acids or proteins, or at the single cell level.

[0229] The terms “level of expression” or “expression level” are used interchangeably and generally refer to a numerical representation of the amount (quantity) of nucleic acid product or an amino acid product or polypeptide or protein in a biological sample. In yet some further embodiments, the “level of expression” or “expression level” refers to the numerical representation of the amount (quantity) of polynucleotide or polypeptide in a biological sample. It should be however understood that the present disclosure encompasses the total level of the biomarker / s in a sample or any body fluid, cell or tissue, that reflects biomarker expression, biomarker activity and functionality, and also the biomarker / gene stability. “Expression” generally refers to the process by which biomarker-encoded information is converted into the structures present and operating in the cell. For example, the expression may be measured in the nucleic acid level, for example using RNA-sequencing and / or Real-Time Polymerase Chain Reaction, sometimes also referred to as RT- PCR or quantitative PCR (qPCR). The luminosity in case of RT-PCR, or any other tag is captured by a detector that converts the signal intensity into a numerical representation which is said expression value, in terms of biomarker or a gene product. Therefore, according to the present disclosure “expression” of a biomarker of a subset of biomarkers (e.g. gene biomarkers), specifically, any biomarker encoding any of the biomarkers of the invention may refer to transcription into a polynucleotide and translation into a polypeptide. Fragments of the transcribed polynucleotide, the translated protein, or the post-translationally modified protein shall also be regarded as expressed whether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a post-translational processing of the protein, e.g., by proteolysis. Methods for determining the level of expression of the biomarkers of the disclosure will be described in more detail herein after.

[0230] The expression level of the biomarkers, that may be biomarker proteins / genes (expression either at the nucleic acid, specifically, mRNA level or the protein level) of the invention is determined to obtain an expression value. The term "expression value” refers to the result of a calculation, that uses as an input the “level of expression” or "expression level” obtained experimentally. It should be appreciated that in some optional embodiments, determination of the expression value may further involves normalizing the “level of expression” or "expression level” by at least one normalization step as detailed herein, where the resulting calculated value termed herein "expression value” is obtained.

[0231] More specifically, as used herein, "normalized values" in some embodiments, are the quotient of raw expression values of biomarker / s, specifically, gene and any product thereof, e.g., mRNA, protein, divided by the expression value of a control reference biomarker from the same sample. Any assayed sample may contain more or less biological material than is intended, due to human error and equipment failures. Importantly, the same error or deviation applies to both the biomarker protein / gene of the invention and to the control reference biomarker and any product thereof, e.g., mRNA or protein, whose expression is essentially constant. Thus, division of the biomarker raw expression value by the control reference protein raw expression value yields a quotient which is essentially free from any technical failures or inaccuracies (except for major errors which destroy the sample for testing purposes) and constitutes a normalized expression value of the biomarker. This normalized expression value may then be compared with normalized cutoff values, i.e., cutoff values calculated from normalized expression values. In certain embodiments, the control reference biomarker, specifically, biomarker and any product thereof, e.g., mRNA or protein, may be a protein / gene that maintains stable in all samples analyzed.

[0232] Normalized biomarker expression (either the nucleic acid molecule, (mRNA) and / or the protein) level values that are higher (positive) or lower (negative) in comparison with a corresponding predetermined standard expression value or a cut-off value in a control sample predict to which population of subjects, either having high / low levels of resistance and / or high / low levels of systemic inflammation, the tested sample belongs. It should be appreciated that an important step in the method of the present disclosure is determining whether the expression value of any one of the biomarkers is changed or different when compared to a pre-determined cut off or is within the range of expression of such cutoff. Alternatively, or in addition, the expression value may be compared to the expression value of a control sample, for example, a sample obtained from a healthy population.

[0233] More specifically, in some embodiments, the expression level of a subset of biomarkers in at least one sample of the subject is determined at the nucleic acid level by an RNA sequencing, e.g., genome-wide RNA sequencing. Accordingly, in some embodiments, the subset of biomarkers as indicated herein, may comprise the entire arsenal of all transcribed biomarkers in the sample. In yet some further embodiments RNA sequencing is performed in the entire RNA of the whole genome. Thus, according to some embodiments, a subset of biomarkers as used herein, is meant the entire repertoire of the transcribed genes of the examined subject.

[0234] The term "Sequencing”, as used herein is the process of determining the nucleotide order of a given RNA or DNA fragment. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) provide the sequence of RNA or DNA bases across the genome and exome, respectively. 'RNA-Seq' (named as an abbreviation of 'RNA sequencing') is a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence, sequence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome.

[0235] The 'transcriptome' is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment.

[0236] Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post- transcriptional modifications, gene fusion, mutations / SNPs and changes in gene expression over time, or differences in gene expression in different groups or treatments In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling. RNA-Seq can also be used to determine exon / intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. RNA- Seq include single cell sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.

[0237] Still further, 'Whole-transcriptome sequencing' or 'genome-wide RNA-Seq' or 'bulk RNA-Seq' provides sequence information about coding and multiple noncoding forms of RNA to assess variations and gene (biomarkers) expression levels across the entire transcriptome. 'Single-cell transcriptomics or Single-cell RNA-Seq' examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration (conventionally only messenger RNA (mRNA)) of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics, all previously masked in bulk RNA sequencing.

[0238] In some embodiments, RNA-sequencing to determine the level of expression of the genes (biomarkers) in the subset of biomarkers may be performed by NGS. 'Next generation sequencing (NGS)' analysis can extend from a small number of biomarkers to an entire genome. NGS can be grouped into two major categories: sequencing by hybridization (SBH) and sequencing by synthesis (SBS).

[0239] Sequencing by synthesis (SBS) methods are a further development of Sanger sequencing, without the dideoxy terminators, in combination with repeated cycles of synthesis, imaging, and methods to incorporate additional nucleotides in the growing chain. These methods differ from Sanger sequencing in that they rely on much shorter reads (currently up to about 300-500 bases) but with high sequence coverage (“massively parallel sequencing”) of millions to billions of sequences reads as a way to obtain an accurate sequence based upon the identification of a consensus sequence. Sequencing by hybridization (SBH) uses arrayed RNA or DNA oligonucleotides of known sequence on filters that are hybridized to labeled fragments of the RNA or DNA to be sequenced. By repeatedly hybridizing and washing away the unwanted non-hybridized RNA or DNA, it is possible to determine whether the hybridizing labeled fragments match the sequence of the RNA or DNA probes on the filter. It is thus possible to build larger contiguous sequence information, based upon overlapping information from the probe hybridization spots.

[0240] Additional sequencing methodologies enable sequencing of long RNA (and DNA) molecules and may be also applicable in the disclosed methods, kits and compositions. The current commercialized technology leader in this area is Pacific Biosciences (PacBio) PacBio sequencing, also referred to as SMRT (Singe Molecule Real Time) sequencing, enables very long fragments to be sequenced, up to 30-50 kb, or longer. The SMRT method involves binding an engineered DNA or RNA polymerase, with bound DNA or RNA to be sequenced, to the bottom of a well (zero- mode waveguide (ZMW) in a SMRT flow cell). A ZMW is small chamber that guides light energy into an area whose dimensions are small, relative to the wavelength of the illuminating light. Because of the ZMW design and wavelength of light utilized, imaging occurs only at the bottom of the ZMW where the DNA polymerase, bound to the DNA, incorporates each base in a growing chain. The four nucleotides are labeled with different phospho-linked fluorophores for differential detection. When a nucleotide is incorporated into the growing chain, imaging occurs on the millisecond time scale as the correct fluorescently-labeled nucleotide is bound. After incorporation, the phosphate-linked fluorescent moiety is released, which “floats away” from the bottom of the ZMW and can no longer be detected. The next nucleotide can then be incorporated. Imaging is timed with the rate of nucleotide incorporation so that each base is identified as it is incorporated into the growing DNA or RNA chain. This simultaneously occurs in parallel in up to one million zeptoliter ZMWs, present on a single chip within the SMRT cells. Nanopore sequencing is based on the possibility to pass long DNA or RNA molecules through small diameter “holes” and measure differing currents as each nucleotide passes by a linked detector. Two types of nanopore systems for DNA or RNA sequencing are being developed, biological membrane systems and solid-state sensor technology. Biological nanopore sequencing relies on the use of transmembrane proteins embedded in a lipid membrane to produce the pores. Two proteins that have been utilized to generate pores have been extensively studied: alpha hemolysin and Mycobacterium smegmatis porin A (MspA). The rate of passage through the pores is regulated by the addition of motor proteins, such as a highly processive DNA polymerase (phi29) that ratchets DNA through upon nucleotide addition. Other accessory proteins, such as a DNA helicase, exonuclease I, or oligonucleotides to bind DNA strands, enable unwinding and “ratcheting” of the DNA nucleotides through the nanopore for detection. DNA can be moved through the pores at a constant rate for tens of thousands of nucleotides. Solid state sensor technology uses various metal or metal alloy substrates with nanometer sized pores that allow DNA or RNA to pass through. MinlON, benchtop GridlON, and a high throughput PromethlON are examples of such sequencers.

[0241] As indicated above, one of the initial steps of the disclosed methods involved determination of the expression level of the biomarkers, e.g., at least one subset of biomarkers. A subset of genes or a subset of biomarkers (or gene biomarkers) , that in most cases are used interchangeably herein, in accordance with the present disclosure may comprise at least one gene or biomarker, and in other embodiments, may comprise all gene biomarkers of the entire transcriptome. More specifically, a subset of gene biomarkers in accordance with the present disclosure may include either individual gene biomarkers used herein as a biomarker, or alternatively, dozens, hundreds, thousands of gene biomarkers. In some specific embodiments, for determining the R levels, 40 to 4000 gene biomarkers may be used. Thus in some embodiments, a subset of biomarkers, e.g., a subset of gene biomarkers may comprise 40 to 4000. Specifically, a subset of biomarkers, e.g., a subset of gene biomarkers may comprise 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1500, 2000, 2500, 3000, 3500, 4000 gene biomarkers.

[0242] More specifically in some specific embodiments, the subset of gene biomarkers for R calculation may comprise: k gene biomarkers with most-positive weight in the R axis (or most-positive position in the R axis), k gene biomarkers with most-negative weight in the R axis (or most- negative position in the R axis), k gene biomarkers with most-positive weight in the T axis (or most-positive position in the T axis), and k gene biomarkers with most-negative weight in the T axis (or most-negative position in the T axis) of the R / T gene biomarker-expression map for a total of 4k gene biomarkers, and wherein k is an integer between 10 to 1,000, as also shown in Figure 30A (right). It should be understood that the terms "positions" and " weight" are used interchangeably throughout the present disclosure.

[0243] More specifically, in some embodiments, K, the number of gene biomarkers, may be 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950 or 1000.

[0244] Similarly, in yet some further embodiments, the subset of biomarkers for SI calculation comprises: k biomarkers with most-positive weight in the SI axis (or most-positive position in the SI axis), k biomarkers with most-negative weight in the SI axis (or most-negative position in the SI axis), k biomarkers with most-positive weight in the MetS axis (or most-positive position in the MetS axis), and k biomarkers with most-negative weight in the MetS axis (or most-positive position in the MetS axis), of the SI / MetS map for a total of 4k biomarkers, and wherein k is an integer between 10 to 1,000, as also shown in Figure 30A (left). In some embodiments, K, the number of biomarkers, may be 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950 or 1000.

[0245] In some further specific and non-limiting embodiments, the subset of biomarkers may comprise biomarker characterized by an averaged correlation to the R / SI balance score that is equal or greater than 0.95, 0.90, 0.85, 0.80, 0.75, 0.70, 0.65, 0.60, 0.55, 0.50, 0.45, 0.4, 0.35, specifically, 0.65, and / or a correlation to the R / SI balance score that is equal or smaller than -0.15, -0.20, -0.25, -0.30, -0.35, -0.40, -0.45, -0.50, -0.55, -0.60, -0.65, -0.70, -0.75, specifically, -0.45. More specifically, in some further specific and non-limiting embodiments, the subset of biomarkers may comprise biomarker characterized by an averaged correlation to the R / SI balance score that is equal or greater than 0.65 and / or a correlation to the R / SI balance score that is equal or smaller than - Non-limiting embodiments for specific gene biomarkers that exhibit a R / SI balance score that is equal or greater than 0.65, are the gene biomarkers listed in Table 4A. It should be understood that in some embodiments, the methods and compositions of the present disclosure may use at least one of the 300 gene biomarkers listed in the table. In yet some further embodiments, the disclosed methods and compositions may use any subset of the 300 gene biomarkers disclosed in Table 4A, specifically, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, of the gene biomarkers disclosed in Table 4A. These gene biomarkers are associated with a good R / SI balance.

[0246] Still further, in some additional and / or alternative embodiments, the disclosed methods and compositions may use as a subset of gene biomarkers, any gene biomarker having an R / SI balance score that is equal or smaller than -0.45. These gene biomarkers may be associated with, and reflect, an impaired balance score. Non-limiting embodiments for specific gene biomarkers that exhibit a R / SI balance score that is equal or smaller than -0.45, are the gene biomarkers listed in Table 4B. It should be understood that in some embodiments, the methods and compositions of the present disclosure may use at least one of the 300 gene biomarkers listed in the table. In yet some further embodiments, the disclosed methods and compositions may use any subset of the 300 gene biomarkers disclosed in Table 4B, specifically, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, of the gene biomarkers disclosed in Table 4B. These gene biomarkers are associated with an impaired R / SI balance.

[0247] It should be understood that a subset of gene biomarkers as used herein, may comprise any combination of the gene biomarkers listed in Table 4A and Table 4B, in other embodiments, the subset of gene biomarkers may comprise the gene biomarkers listed in Table 4A and Table 4B. In yet some further embodiments, the subset of gene biomarkers may comprise any combinations of the gene biomarkers listed in the biomarkers listed in Table 4A and / or Table 4B, and any other gene biomarkers that are characterized as being one of the 4K gene biomarkers as indicated above. Thus, in some embodiments the R levels and / or the SI levels are calculated using any of the biomarkers (e.g., genes) indicated in Tables 4A, 4B, or any subset thereof.

[0248] More specifically, for the generation of Tables 4A,4B and 4C the following analysis was performed. For each dataset (each of the columns in the Table represents a database):

[0249] First (i), the SI / R-balance score was calculated for each individual subject.

[0250] Next (ii), for each biomarker (row) the correlation between the biomarker and the R / SI-balance score was calculated across individuals in the dataset. The entries in all tables 4ABC are Pearson's correlation coefficients values. Next (iii), For each gene biomarker (row), the correlations across all datasets were averaged (the average appears in column 2) . The selection of gene biomarkers for each table was different, as indicated herein:

[0251] Table 4A: gene biomarkers whose average correlation > 0.65 (300 gene biomarkers);

[0252] Table 4A: gene biomarkers whose average correlation < -0.45 (300 gene biomarkers);

[0253] Table 4C: this tables includes selected positive and negative markers as follows:

[0254] Positive markers: gene biomarkers whose average correlation across datasets > 0.6 and the minimal correlation across datasets > 0.4. Negative markers: gene biomarkers whose average correlation across datasets < -0.6 and the maximal correlation across datasets < -0.4.

[0255] Because additional requirements of max / min were added in Table 4C, a smaller list of gene biomarkers compared to the lists in Tables 4A, 4B was obtained.

[0256] Thus, in some embodiments, smaller gene biomarker subsets may be used in the disclosed methods and compositions. For the representative gene biomarkers the following selection strategy may be used. For either R or SI: (i) positive markers are gene biomarkers with average correlation that is greater than > 0.6 and minimal correlation is greater than > 0.4 across all sepsis datasets, (ii) negative markers are gene biomarkers with average correlation is smaller than < -0.6 and maximal correlation is smaller than < -0.4 across all sepsis datasets in Table 4D and 4E.

[0257] Still further, in some embodiments, additional gene biomarkers that do not follow the disclosed role may be also included in a smaller subset of gene biomarkers. For example, HLA-DRA, may be also used in the disclosed methods, kits and compositions, as it is the best marker of monocytes, although it has weaker correlations at the whole-blood level.

[0258] In some particular and non-limiting embodiments, a subset of gene biomarkers that may be used in accordance with the present disclosure may comprise any of the R / SI gene biomarkers disclosed in Table 4C.

[0259] More specifically, the subset of gene biomarkers for R / SI balance score calculation comprise at least one of: IQ calmodulin-binding motif containing 1 (IQCB1), SET Nuclear Proto-Oncogene (SET), WD repeat-containing protein 89 (WDR89), zinc finger protein 559 (ZNF559), NDC1 Transmembrane Nucleoporin (NDC1), Solute Carrier Family 25 Member 32 (SLC25A32), Nuclear Cap Binding Protein Subunit 2(NCBP2), ATP Binding Cassette Subfamily E Member 1 (ABCE1), NOP58 Ribonucleoprotein (NOP58), Zinc Finger ZZ-Type Containing 3 (ZZZ3), Biogenesis Of Ribosomes BRX1 (BRIX1), DEAD-Box Helicase 18 (DDX18), Pre-MRNA Processing Factor 39 (PRPF39), Phosducin Like 3 (PDCL3), ADP Ribosylation Factor Like GTPase 5A (ARL5A), Ubiquitin Like Modifier Activating Enzyme 2 (UB A2), Heat Shock Protein Family A (Hsp70) Member 9 (HSPA9), Major Histocompatibility Complex, Class II, DR Alpha (HLA-DRA), Kinesin Family Member 3C (KIF3C), Furin, Paired Basic Amino Acid Cleaving Enzyme (FURIN), Unc-13 Homolog D (UNC13D) and WD And Tetratricopeptide Repeats 1 (WDTC1) or any combination thereof. It should be noted that in some embodiments, the gene biomarkers as disclosed herein as relevant for the R / SI balance score calculation, relate to the human genes. In yet some further specific embodiments, the nucleic acid sequences of the genes and the amino acid sequences of the protein products thereof, are as in the accession numbers specified and disclosed for each of these gene biomarkers in Table (I), herein after.

[0260] In yet some further embodiments, the subset of biomarkers may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty one, at least twenty two biomarkers of any one of the gene biomarkers disclosed in Table 4C, specifically, of IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNC13D and WDTC1. In yet some further optional embodiments, at least one more biomarker may be used, specifically, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,

[0261] 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,

[0262] 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,

[0263] 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,

[0264] 99, 100 or more, specifically, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450 and 500 at the most, additional biomarkers. In some embodiments, the at least one additional gene biomarker may be any of the biomarkers disclosed in any on of Tables 4A, 4B, 4D and 4E. In yet some further embodiments, the at least one additional biomarker may be any gene biomarker characterized by any of the parameters disclosed herein above, specifically, any of the 4K gene biomarkers as disclosed herein.

[0265] In some embodiments, the following gene biomarkers are "positive" and are specifically upregulated: IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9 and HLA-DRA. In yet some further embodiments, the following biomarker genes are "negative", and are specifically downregulated: KIF3C, FURIN, UNCI 3D and WDTC1.

[0266] In yet some certain embodiments, the subset of biomarkers (specifically, gene biomarkers) for R calculation comprise at least one of: Golgin A5 (GOLGA5), Heterogeneous Nuclear Ribonucleoprotein A2 / B1 (HNRNPA2B1), ADP Ribosylation Factor Like GTPase 8B (ARL8B), Seel Family Domain Containing 1 (SCFD1), COPI Coat Complex Subunit Beta 1 (COPB1), Cyclin Dependent Kinase 7 (CDK7), Protein Phosphatase 2 Catalytic Subunit Alpha (PPP2CA), Vesicle Trafficking 1 (VTA1), UDP-Glucose Pyrophosphorylase 2 (UGP2), Signal Recognition Particle 54 (SRP54), COPI Coat Complex Subunit Beta 2 (COPB2), Phosphofurin Acidic Cluster Sorting Protein 2 (PACS2), GRB10 Interacting GYF Protein 1 (GIGYF1), KRAB-A Domain Containing 1 (KRBA1) and Phosphatidylinositol Glycan Anchor Biosynthesis Class Q (PIGQ) biomarkers, or any combination thereof. It should be noted that in some embodiments, the gene biomarkers as disclosed herein as relevant for the R calculation, relate to the human genes. In yet some further specific embodiments, the nucleic acid sequences of the genes and the amino acid sequences of the protein products thereof, are as in the accession numbers specified and disclosed for each of these gene biomarkers in Table (I), herein after.

[0267] In yet some further embodiments, the subset of biomarkers may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen biomarkers of any one of the biomarker genes disclosed in Table 4D, specifically, of G0LGA5, HNRNPA2B 1 , ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ biomarker genes. In yet some further optional embodiments, at least one more biomarker gene may be used, specifically, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

[0268] 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,

[0269] 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,

[0270] 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,

[0271] 95, 96, 97, 98, 99, 100 or more, specifically, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450 and 500 at the most, additional biomarkers. In some embodiments, the at least one additional biomarker gene may be any of the biomarkers disclosed in any on of Tables 4A, 4B, 4C and 4E. In yet some further embodiments, he at least one additional biomarker gene may be any gene biomarker characterized by any of the parameters disclosed herein above, specifically, any of the 4K gene biomarkers as disclosed herein.

[0272] In some embodiments, the following biomarker genes are "positive", and are specifically upregulated: GOLGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54 and COPB2. In yet some further embodiments, the following biomarker genes are "negative" and are specifically downregulated: PACS2, GIGYF1, KRBA1 and PIGQ.

[0273] In certain embodiments, the subset of gene biomarkers for SI calculation comprises at least one of: dachshund family transcription factor 1 (DACH1), Dysferlin (DYSF), Glycogenin 1 (GYG1), Cysteine Rich Transmembrane Module Containing 1 (CYSTM1), Alkaline Phosphatase, Biomineralization Associated (ALPL), Flotillin 1 (FLOT1), CD82 Molecule (CD82), Glucosylceramidase Beta 1 (GBA), Leucine Rich Alpha-2-Glycoprotein 1 (LRG1), TAR (HIV- 1) RNA Binding Protein 1 (TARBP1), Anaphase Promoting Complex Subunit 1 (ANAPC1), N- Myristoyltransferase 2 (NMT2) and Zinc Finger Protein 337 (ZNF337) biomarker genes, or any combination thereof. It should be noted that in some embodiments, the gene biomarkers as disclosed herein as relevant for the SI calculation, relate to the human genes. In yet some further specific embodiments, the nucleic acid sequences of the genes and the amino acid sequences of the protein products thereof, are as in the accession numbers specified and disclosed for each of these gene biomarkers in Table (I), herein after.

[0274] In some further embodiments, the subset of gene biomarkers may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen biomarkers of any one of the biomarker genes disclosed in Table 4E, specifically, of DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 biomarker genes. In yet some further optional embodiments, at least one more biomarker gene may be used, specifically, at least 1, 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,

[0275] 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,

[0276] 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,

[0277] 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or more, specifically, 110,

[0278] 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450 and 500 at the most, additional biomarkers. In certain embodiments, the total amount of the biomarkers and any additional reference is 500 at the most. In some embodiments, the at least one additional biomarker gene may be any of the biomarkers disclosed in any on of Tables 4A, 4B, 4C and 4D. In yet some further embodiments, he at least one additional biomarker gene may be any gene biomarker characterized by any of the parameters disclosed herein above, specifically, any of the 4K gene biomarkers as disclosed herein.

[0279] In some embodiments, the following biomarker genes are "positive" and are specifically upregulated: DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1. In yet some further embodiments, the following biomarker genes are "negative" and are specifically downregulated: TARBP1, ANAPC1, NMT2 and ZNF337. Simply put, a "positive" expression value as used herein refers to high expression value that reflects upregulation, overexpression, elevated expression, high expression and even in some embodiments, moderate expression value.

[0280] A "negative" expression value reflects downregulation, a repressed, low, reduced, or non-existing expression (lack of expression).

[0281] Still further, in some embodiments, the subset of gene biomarkers used in the disclosed methods and composition comprise the following biomarker genes for R calculation, specifically, G0LGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ biomarker genes, or any combination thereof. In yet some further embodiments, the subset of gene biomarkers used in the disclosed methods and composition comprise the following biomarker genes for SI calculation, specifically, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 biomarker genes.

[0282] In some additional and non-limiting embodiments, the subset of gene biomarkers used in the disclosed methods and composition comprise the following biomarker genes for R calculation, specifically, G0LGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ biomarker genes and for SI calculation, specifically, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 biomarker genes. Still further, in some optional embodiments, at least one additional biomarker gene may be used, provided that the number of biomarker genes is 500 at the most. In certain embodiments, the total amount of the biomarkers and any additional reference is 500 at the most. Such biomarker genes may be selected from any one of the 4k gene biomarkers, or any one of the gene biomarkers disclosed in Tables 4A, 4B and / or 4C.

[0283] In some further embodiments, the expression level of a subset of gene biomarkers in at least one sample of the subject, is determined at the nucleic acid level. The method comprises the step of contacting at least one detecting molecule or any combination or mixture of plurality of detecting molecules with a biological sample of the subject, or with any nucleic acid product obtained therefrom, and wherein each of the detecting molecules is specific for one of the biomarkers.

[0284] In some embodiments, the detecting molecules used in the disclosed diagnostic methods and compositions and kits, may be nucleic-acid-based detecting molecules.

[0285] According to such embodiments, the nucleic acid detecting molecules may comprise at least one of: (a), at least one oligonucleotide / s specific for the at least one of the biomarker genes. Specifically, for R calculation, at least one of G0LGA5 , HNRNPA2B 1 , ARL8B , SCFD 1 , COPB 1 , CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ, and / or for SI calculation, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337, and / or for R / SI score IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNCI 3D and WDTC1, (b), the detecting molecule may be at least one nucleic acid aptamer / s specific for the at least one biomarker. In some embodiments, each oligonucleotide specifically hybridizes to a nucleic acid sequence of the gene.

[0286] Table (I): Accession numbers for the biomarkers of R / SI, R and SI

[0287] Still further, in certain alternative or additional embodiments, the subset of biomarkers for R calculation comprise at least two of: IFNy, CXCL10, MCP-2, CXCL11 and CXCL9 biomarkers. More specifically, in some embodiments, at least two, at least three, at least four, at least five, of IFNy, CXCL10, MCP-2, CXCL11 and CXCL9 biomarkers may be determined by the disclosed method. In yet some further optional embodiments, at least one more biomarker gene may be used, specifically, at least 1, 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, 99, 100 or more, specifically, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450 and 500 at the most, additional biomarkers. In certain embodiments, the total amount of the biomarkers and any additional reference is 500 at the most. In some embodiments, the at least one additional biomarker gene may be any of the biomarkers disclosed in any on of Tables 4A, 4B, 4C, 4D and 4E. In yet some further embodiments, he at least one additional biomarker gene may be any biomarker characterized by any of the parameters disclosed herein above, specifically, any of the 4K biomarkers as disclosed herein. In certain additional embodiments, the subset of biomarkers for SI calculation comprises at least two of: IL6, IL8, CCL3, CCL20 and CCL4 biomarkers.

[0288] More specifically, in some embodiments, at least two, at least three, at least four, at least five, of IL6, IL8, CCL3, CCL20 and CCL4 biomarkers may be determined by the disclosed method. In yet some further optional embodiments, at least one more biomarker gene may be used, specifically, at least 1, 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, 99, 100 or more, specifically, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450 and 500 at the most, additional biomarkers. In certain embodiments, the total amount of the biomarkers and any additional reference is 500 at the most. In some embodiments, the at least one additional biomarker gene may be any of the biomarkers disclosed in any on of Tables 4A, 4B, 4C, 4D and 4E. In yet some further embodiments, the at least one additional biomarker gene may be any biomarker characterized by any of the parameters disclosed herein above, specifically, any of the 4K biomarkers as disclosed herein.

[0289] In some embodiments, IFNy may be used as a biomarker protein in the disclosed methods, compositions and kits. 'IFN-y', or type II interferon, is a cytokine that is critical for innate and adaptive immunity against viral, some bacterial and protozoan infections. IFN-y is an important activator of macrophages and inducer of major histocompatibility complex class II molecule expression. Aberrant IFN-y expression is associated with a number of autoinflammatory and autoimmune diseases. The importance of IFN-y in the immune system stems in part from its ability to inhibit viral replication directly, and most importantly from its immunostimulatory and immunomodulatory effects. IFN-y is produced predominantly by natural killer cells (NK) and natural killer T cells (NKT) as part of the innate immune response, and by CD4 Thl and CD8 cytotoxic T lymphocyte (CTF) effector T cells once antigen-specific immunity develops as part of the adaptive immune response. IFN-y is also produced by non- cytotoxic innate lymphoid cells (IFC), a family of immune cells. In some embodiments, IFNy as used herein is the human IFNy. In yet some further embodiments, the human IFNy disclosed herein may comprise the amino acid sequence as denoted by the accession no. P01579. Still further, the disclosed human IFNy is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_000619. Still further, in some embodiments, the human IFNy comprises the amino acid sequence as denoted by SEQ ID NO: 1. In some embodiments, CXCL10 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'C-X-C motif chemokine ligand 10' or 'CXCL10' also known as Interferon gamma-induced protein 10 (IP- 10) or small-inducible cytokine B10 is an 8.7 kDa protein that in humans is encoded by the CXCL10 gene. C-X-C motif chemokine 10 is a small cytokine belonging to the CXC chemokine family. CXCL10 is secreted by several cell types in response to IFN-y. These cell types include monocytes, endothelial cells and fibroblasts. CXCL10 has been attributed to several roles, such as chemoattraction for monocytes / macrophages, T cells, NK cells, and dendritic cells, promotion of T cell adhesion to endothelial cells, antitumor activity, and inhibition of bone marrow colony formation and angiogenesis.

[0290] This chemokine elicits its effects by binding to the cell surface chemokine receptor CXCR3. In some embodiments, CXCL10 as used herein is the human CXCL10. In yet some further embodiments, the huma CXCL10 disclosed herein may comprise the amino acid sequence as denoted by the accession no. P02778. Still further, the disclosed human CXCL10 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_001565. Still further, in some embodiments, the human CXCL10 comprises the amino acid sequence as denoted by SEQ ID NO: 2.

[0291] In some embodiments, MCP-2 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Monocyte chemotactic protein (MCP)-2 ' or 'MCP-2 ', a member of the C- C chemokine subfamily, is chemotactic for and activates a many different immune cells, including mast cells, eosinophils and basophils, (that are implicated in allergic responses), and monocytes, T cells, and NK cells that are involved in the inflammatory response. In some embodiments, MCP- 2 as used herein is the human MCP-2. In yet some further embodiments, the huma MCP-2 disclosed herein may comprise the amino acid sequence as denoted by the accession no. P80075. Still further, the disclosed human MCP-2 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_005623. Still further, in some embodiments, the human MCP-2 comprises the amino acid sequence as denoted by SEQ ID NO: 3.

[0292] In some embodiments, CXCL11 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'C-X-C motif chemokine 11' or 'CXCL11' is a protein that in humans is encoded by the CXCL11 gene. C-X-C motif chemokine 11 is a small cytokine belonging to the CXC chemokine family that is also called Interferon-inducible T-cell alpha chemoattractant (I- TAC) and Interferon-gamma-inducible protein 9 (IP-9). It is highly expressed in peripheral blood leukocytes, pancreas and liver, with moderate levels in thymus, spleen and lung and low expression levels were in small intestine, placenta and prostate. Gene expression of CXCL11 is strongly induced by IFN-y and IFN- , and weakly induced by IFN-a. This chemokine elicits its effects on its target cells by interacting with the cell surface chemokine receptor CXCR3, with a higher affinity than do the other ligands for this receptor, CXCL9 and CXCL10. CXCL11 is chemotactic for activated T cells. Its gene is located on human chromosome 4 along with many other members of the CXC chemokine family. In some embodiments, CXCL11 as used herein is the human CXCL11. In yet some further embodiments, the huma CXCL11 disclosed herein may comprise the amino acid sequence as denoted by the accession no. 014625. Still further, the disclosed human CXCL11 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_005409. Still further, in some embodiments, the human CXCL11 comprises the amino acid sequence as denoted by SEQ ID NO: 4.

[0293] In some embodiments, CXCL9 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Chemokine (C-X-C motif) ligand 9' or 'CXCL9' is a small cytokine belonging to the CXC chemokine family that is also known as monokine induced by gamma interferon (MIG). The CXCL9 is one of the chemokine which plays role to induce chemotaxis, promote differentiation and multiplication of leukocytes, and cause tissue extravasation. The CXCL9 / CXCR3 receptor regulates immune cell migration, differentiation, and activation. Immune reactivity occurs through recruitment of immune cells, such as cytotoxic lymphocytes (CTLs), natural killer (NK) cells, NKT cells, and macrophages. Thl polarization also activates the immune cells in response to IFN-y. It is closely related to two other CXC chemokines called CXCL10 and CXCL11, whose genes are located near the gene for CXCL9 on human chromosome 4. In some embodiments, CXCL9 as used herein is the human CXCL9. In yet some further embodiments, the huma CXCL9 disclosed herein may comprise the amino acid sequence as denoted by the accession no. Q07325. Still further, the disclosed human CXCL9 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_002416. Still further, in some embodiments, the human CXCL9 comprises the amino acid sequence as denoted by SEQ ID NO: 5.

[0294] In some embodiments, IL-6 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Interleukin 6' or 'IL-6' is an interleukin that acts as both a pro- inflammatory cytokine and an anti-inflammatory myokine. In humans, it is encoded by the IL6 gene. In addition, osteoblasts secrete IL-6 to stimulate osteoclast formation. Smooth muscle cells in the tunica media of many blood vessels also produce IL-6 as a pro- inflammatory cytokine. IL-6's role as an anti-inflammatory myokine is mediated through its inhibitory effects on TNF-alpha and IL-1 and its activation of IL-lra and IL-10. IL-6 is secreted by macrophages in response to specific microbial molecules, referred to as pathogen-associated molecular patterns (PAMPs). These PAMPs bind to an important group of detection molecules of the innate immune system, called pattern recognition receptors (PRRs), including Toll-like receptors (TLRs). These are present on the cell surface and intracellular compartments and induce intracellular signaling cascades that give rise to inflammatory cytokine production. IL-6 is an important mediator of fever and of the acute phase response. IL-6 is responsible for stimulating acute phase protein synthesis, as well as the production of neutrophils in the bone marrow. It supports the growth of B cells and is antagonistic to regulatory T cells. In some embodiments, IL- 6 as used herein is the human IL-6. In yet some further embodiments, the huma IL-6 disclosed herein may comprise the amino acid sequence as denoted by the accession no. P05231. Still further, the disclosed human IL-6 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_000600. Still further, in some embodiments, the human IL-6 comprises the amino acid sequence as denoted by SEQ ID NO: 6.

[0295] In some embodiments, CXCL8 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Interleukin 8' or 'IL-8' (or chemokine (C-X-C motif) ligand 8, CXCL8) is a member of the CXC chemokine family, produced by macrophages and other cell types such as epithelial cells, airway smooth muscle cells and endothelial cells. Endothelial cells store IL-8 in their storage vesicles, the Weibel-Palade bodies. In humans, the interleukin-8 protein is encoded by the CXCL8 gene. IL- 8 is initially produced as a precursor peptide of 99 amino acids which then undergoes cleavage to create several active IL-8 isoforms. In culture, a 72 amino acid peptide is the major form secreted by macrophages. There are many receptors on the surface membrane capable of binding IL- 8; the most frequently studied types are the G protein-coupled serpentine receptors CXCR1 and CXCR2. IL- 8 is secreted and is an important mediator of the immune reaction in the innate immune system response. IL-8, also known as neutrophil chemotactic factor , has two primary functions. It induces chemotaxis in target cells, primarily neutrophils but also other granulocytes, causing them to migrate toward the site of infection. IL-8 also stimulates phagocytosis once they have arrived. IL-8 is also known to be a potent promoter of angiogenesis. In target cells, IL-8 induces a series of physiological responses required for migration and phagocytosis, such as increases in intracellular Ca2+, exocytosis (e.g. histamine release), and the respiratory burst. In some embodiments, CXCL8 as used herein is the human CXCL8. In yet some further embodiments, the human CXCL8 disclosed herein may comprise the amino acid sequence as denoted by the accession no. P10145. Still further, the disclosed human CXCL8 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_000584. Still further, in some embodiments, the human CXCL8 comprises the amino acid sequence as denoted by SEQ ID NO: 7.

[0296] In some embodiments, CCL3 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Chemokine (C-C motif) ligand 3 ' or 'CCL3' also known as macrophage inflammatory protein 1 -alpha (MIP-1- alpha) is a protein that in humans is encoded by the CCL3 gene. CCL3 is a cytokine belonging to the CC chemokine family that is involved in the acute inflammatory state in the recruitment and activation of polymorphonuclear leukocytes through binding to the receptors CCR1, CCR4 and CCR5. In some embodiments, CCL3 as used herein is the human CCL3. In yet some further embodiments, the huma CCL3 disclosed herein may comprise the amino acid sequence as denoted by the accession no. P10147. Still further, the disclosed human CCL3 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_002983. Still further, in some embodiments, the human CCL3 comprises the amino acid sequence as denoted by SEQ ID NO: 8.

[0297] In some embodiments, CCL20 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Chemokine (C-C motif) ligand 20' or 'CCL20' or liver activation regulated chemokine (LARC) or Macrophage Inflammatory Protein-3 (MIP3A) is a small cytokine belonging to the CC chemokine family. It is strongly chemotactic for lymphocytes and weakly attracts neutrophils. CCL20 is implicated in the formation and function of mucosal lymphoid tissues via chemoattraction of lymphocytes and dendritic cells towards the epithelial cells surrounding these tissues. CCL20 elicits its effects on its target cells by binding and activating the chemokine receptor CCR6. Gene expression of CCL20 can be induced by microbial factors such as lipopolysaccharide (LPS), and inflammatory cytokines such as tumor necrosis factor and interferon-y, and down-regulated by IL- 10. CCL20 is expressed in several tissues with highest expression observed in peripheral blood lymphocytes, lymph nodes, liver, appendix, and fetal lung and lower levels in thymus, testis, prostate and gut. The gene for CCL20 (scya20) is located on chromosome 2 in humans. In some embodiments, CCL20 as used herein is the human CCL20. In yet some further embodiments, the huma CCL20 disclosed herein may comprise the amino acid sequence as denoted by the accession no. P78556. Still further, the disclosed human CCL20 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_004591. Still further, in some embodiments, the human CCL20 comprises the amino acid sequence as denoted by SEQ ID NO: 9. In some embodiments, CCL4 may be used as a biomarker protein in the disclosed methods, compositions and kits. 'Chemokine (C-C motif) ligands 4' or 'CCL4' previously known as macrophage inflammatory protein (MIP-1β), is a small cytokine that belongs to the CC chemokine subfamily, which in humans is encoded by the CCL4 gene. CCL4 belongs to a cluster of genes located on 17ql l-q21 of the chromosomal region. CCL4 is being secreted under mitogenic signals and antigens and hereby acts as a chemoattractant for natural killer cells, monocytes and various other immune cells in the site of inflamed or damaged tissue. CCL4 is produced during inflammation, damage or other important dynamic processes as an angiogenesis to attract immune cells as leukocytes transgress the vascular endothelium and migrate into peripheral tissues. In some embodiments, CCL4 as used herein is the human CCL4. In yet some further embodiments, the huma CCL4 disclosed herein may comprise the amino acid sequence as denoted by the accession no. Pl 3236. Still further, the disclosed human CCL4 is encoded by the nucleic acid sequence as denoted by Genbank accession no. NM_002984. Still further, in some embodiments, the human CCL4 comprises the amino acid sequence as denoted by SEQ ID NO: 10.

[0298] In some embodiments, the expression level of a subset of biomarkers in at least one sample of the subject, is determined at the protein level. When using protein markers for the calculation of R (e.g., at least one of IFNy, CXCL10, MCP-2, CXCL11 and CXCL9), calculation of SI (IL6, IL8, CCL3, CCL20 and CCL4), and the calculation of R / SI, the calculation is performed as follows. (1) Calculate the relative measured level: measurements of each protein are centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples. (2) The SI state (SI level) is the average of the relative measured levels of the SI protein markers. (3) The R state (R level) is the average of the relative measured levels of the R protein marker. (4) Calculation of the R / SI balance was done as described in Figure 5E but using the R and SI states from that were calculated based on protein markers (in steps 2,3) rather than gene markers.

[0299] More specifically, using protein markers, the R level is calculated using the formula wherein:

[0300] Ziis the 1 -length vector that includes the average of relative measured levels across all protein markers of individual t; Relative protein-expression levels are calculated as follows: each protein is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0301] VTand VRare pre-defined 1-length vectors, VR= (1) and VT= (0). sRlis the R level of individual t; and bi is a constant.

[0302] Still further, using protein markers, the SI level is calculated using the formula wherein:

[0303] Z[ is the 1-length vector that includes the average of relative measured levels across all protein markers of individual t; Relative protein-expression levels are calculated as follows: each protein is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0304] RMets and VS / pre-defined 1-length vectors, is the SI level of individual t; and biis a constant.

[0305] In some embodiments, where the biomarkers used for determining the R, SI and R / SI balance levels, the methods may comprise the step of contacting at least one detecting molecule or any combination or mixture of plurality of detecting molecules with a biological sample of the subject, or with any protein product obtained therefrom, wherein each of the detecting molecules is specific for one of the biomarkers.

[0306] In yet some further embodiments, the detecting molecule / s may be amino acid detecting molecules and / or nucleic acid detecting molecules.

[0307] In more specific embodiments, amino acid detecting molecule / s that may be applicable in the present disclosure may comprise at least two of: (a), at least one antibody or aptamer specific for at least one of (i) at least one of IFNy, CXCL10, MCP-2, CXCL11 and CXCL9 for R; and (ii) IL6, IL8, CCL3, CCL20 and CCL4 for SI; and any combination thereof; (b), the detecting molecules may be at least one labeled or tagged protein / s or any fragment / s, peptide / s or mixture / s thereof, of at least one of the at least two biomarker proteins disclosed herein, (c), the detecting molecules may be at least one protein or peptide aptamer / s specific for at least one of the at least two biomarker proteins.

[0308] As indicated above, in some embodiments, the level and / or the level of expression of at least one biomarker of the at least one subset of biomarkers is determined by the disclosed methods, compositions and kits, at the nucleic acid level. In some specific embodiments, the at least one detecting molecule used for determining the level of expression may be at least one nucleic acid- based detecting molecule / s.

[0309] More specifically, in some embodiments, nucleic acid detecting molecule / s useful in the methods disclosed herein may comprise at least one of: (a) at least one oligonucleotide / s, each oligonucleotide specifically hybridizes to a nucleic acid sequence encoding the at least one biomarker, or any parts or fragments thereof; and / or (b) at least one nucleic acid aptamer / s specific for the at least one of the biomarkers. In some specific embodiments, when the expression level of the biomarkers discussed herein is determined at the nucleic acid level (e.g., mRNA), useful detecting molecules may be in some embodiments, nucleic acid detecting molecule / s. In yet some further specific embodiments, the nucleic acid detecting molecules may be at least one oligonucleotide / s that specifically hybridizes to a nucleic acid sequence encoding the at least one biomarker or any fragment / s, or mixture / s thereof. According to such embodiments, the determination of the expression level of the at least one biomarker / s may be performed by any nucleic acid-based method. In addition to the RNA-sequencing procedure discussed herein before, non-limiting examples for such nucleic acid-based procedures include, but are not limited to, Reverse transcription (RT)- Polymerase Chain Reaction (PCR), Real-Time PCT and / or quantitative PCR (qPCR). These methods will be described in more detail herein after.

[0310] As indicated herein, the detecting molecules used in the disclosed methods, compositions and kits may be nucleic acid-based molecule. As used herein, "nucleic acid molecules" or “nucleic acid sequence” are interchangeable with the term "polynucleotide(s)" and it generally refers to any polyribonucleotide or poly-deoxyribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA or any combination thereof. "Nucleic acids" include, without limitation, single- and double-stranded nucleic acids. As used herein, the term "nucleic acid(s)" also includes DNAs or RNAs as described above that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are "nucleic acids". The term "nucleic acids" as it is used herein embraces such chemically, enzymatically or metabolically modified forms of nucleic acids, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including for example, simple and complex cells. A "nucleic acid" or "nucleic acid sequence" may also include regions of single- or double- stranded RNA or DNA or any combinations.

[0311] More specifically, as indicated above, in some other specific embodiments, the nucleic acid detecting molecules may comprise at least one isolated oligonucleotide / s, each oligonucleotide specifically hybridizes to a nucleic acid sequence encoding one of the at least one biomarker / s, or any parts or fragments of such encoding sequence / s. In an optional embodiment, where the expression levels of the biomarkers of the invention are normalized, the method of the invention may use nucleic acid detecting molecules specific for a nucleic acid sequence encoding the control reference protein / s. As used herein, the term "oligonucleotide" is defined as a molecule comprised of two or more deoxyribonucleotides and / or ribonucleotides, and preferably more than three. Its exact size will depend upon many factors which in turn, depend upon the ultimate function and use of the oligonucleotide. The oligonucleotides may be from about 3 to about 1 ,000 nucleotides long. Although oligonucleotides of 5 to 100 nucleotides are useful in the invention, preferred oligonucleotides range from about 5 to about 15 bases in length, from about 5 to about 20 bases in length, from about 5 to about 25 bases in length, from about 5 to about 30 bases in length, from about 5 to about 40 bases in length or from about 5 to about 50 bases in length. More specifically, the detecting oligonucleotides molecule used by the disclosed methods, compositions and kits may comprise any one of 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, 35, 40, 45, 50 bases in length. It should be further noted that the term “oligonucleotide” refers to a single stranded or double stranded oligomer or polymer of ribonucleic acid (RNA) or deoxyribonucleic acid (DNA) or mimetics thereof. This term includes oligonucleotides composed of naturally-occurring bases, sugars and covalent internucleoside linkages (e.g., backbone) as well as oligonucleotides having non-naturally-occurring portions which function similarly. Such oligonucleotide / s include aptamers, probes and / or primers.

[0312] In yet some other alternative embodiments, the detection molecule may be or may comprise at least one primer, at least one pair of primers, nucleotide probes and any combinations thereof. Thus, it should be further appreciated that the methods, as well as the compositions and kits of the invention may comprise, as an oligonucleotide-based detection molecule, both primers and probes. The term, "primer”, as used herein refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest, or produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand, is induced, i.e., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH. The primer may be single- stranded or double-stranded and must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer depend upon many factors, including temperature, source of primer and the method used. For example, for diagnostic applications, depending on the complexity of the target sequence, the oligonucleotide primer typically contains about 10 to about 30 or more nucleotides, although it may contain fewer nucleotides. More specifically, the primer used by the methods, as well as the compositions and kits of the present disclosure may comprise 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 or 30 nucleotides or more. In certain embodiments, such primers may comprise 30, 40, 50, 60, 70, 80, 90, 100 nucleotides or more. In specific embodiments, the primers used by the methods, compositions and kits of the present disclosure may have a stem and loop structure. The factors involved in determining the appropriate length of primer are known to one of ordinary skill in the art and information regarding them is readily available.

[0313] Still further, the detecting molecules according to some embodiments may be or may comprise at least one probe. As used herein, the term "probe" means oligonucleotides and analogs thereof and refers to a range of chemical species that recognize polynucleotide target sequences through hydrogen bonding interactions with the nucleotide bases of the target sequences. The probe or the target sequences may be single- or double-stranded RNA or single- or double- stranded DNA or a combination of DNA and RNA bases. A probe may be 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 and up to 30 or more nucleotides in length as long as it is less than the full length of the target mRNA or any gene encoding said mRNA. Probes can include oligonucleotides modified so as to have a tag which is detectable by fluorescence, chemiluminescence and the like. The probe can also be modified so as to have both a detectable tag and a quencher molecule, for example TaqMan(R) and Molecular Beacon(R) probes.

[0314] The oligonucleotides and analogs thereof may be RNA or DNA, or analogs of RNA or DNA, commonly referred to as antisense oligomers or antisense oligonucleotides used in the probes and primers. Such RNA or DNA analogs comprise, but are not limited to, 2-'0-alkyl sugar modifications, methylphosphonate, phosphorothiate, phosphorodithioate, formacetal, 3- thioformacetal, sulfone, sulfamate, and nitroxide backbone modifications, and analogs, for example, LNA analogs, wherein the base moieties have been modified. In addition, analogs of oligomers may be polymers in which the sugar moiety has been modified or replaced by another suitable moiety, resulting in polymers which include, but are not limited to, morpholino analogs and peptide nucleic acid (PNA) analogs. Probes may also be mixtures of any of the oligonucleotide analog types together or in combination with native DNA or RNA. At the same time, the oligonucleotides and analogs thereof may be used alone or in combination with one or more additional oligonucleotides or analogs thereof. As noted above, in cases where the detecting molecules used by the methods, compositions and kits of the present disclosure comprise primers and / or probs, and / or where the detection of the level of the biomarker is performed at the nucleic acid level, a suitable method for determining the expression level of the biomarker gene / s may comprise RT-PCR. Reverse transcription polymerase chain reaction (RT-PCR) is a laboratory technique combining reverse transcription of RNA into DNA (in this context called complementary DNA or cDNA) and amplification of specific DNA targets using polymerase chain reaction (PCR). It is primarily used to measure the amount of a specific RNA. This is achieved by monitoring the amplification reaction using fluorescence, a technique called real-time PCR or quantitative PCR (qPCR). Combined RT-PCR and qPCR are routinely used for analysis of biomarker expression and quantification of viral RNA in research and clinical settings. The close association between RT- PCR and qPCR has led to metonymic use of the term qPCR to mean RT-PCR. Such use may be confusing, as RT-PCR can be used without qPCR, for example to enable molecular cloning, sequencing or simple detection of RNA. Conversely, qPCR may be used without RT- PCR, for example to quantify the copy number of a specific piece of DNA.

[0315] "Polymerase chain reaction" or "PCR" refers to an in vitro method for amplifying a specific nucleic acid template sequence. The PCR reaction involves a repetitive series of temperature cycles and is typically performed in a volume of 50-100 microliter. The reaction mix comprises dNTPs (each of the four deoxynucleotides dATP, dCTP, dGTP, and dTTP), primers, buffers, DNA polymerase, and nucleic acid template. The PCR reaction comprises providing a set of polynucleotide primers wherein a first primer contains a sequence complementary to a region in one strand of the nucleic acid template sequence and primes the synthesis of a complementary DNA strand, and a second primer contains a sequence complementary to a region in a second strand of the target nucleic acid sequence and primes the synthesis of a complementary DNA strand, and amplifying the nucleic acid template sequence employing a nucleic acid polymerase as a template-dependent polymerizing agent under conditions which are permissive for PCR cycling steps of (i) annealing of primers required for amplification to a target nucleic acid sequence contained within the template sequence, (ii) extending the primers wherein the nucleic acid polymerase synthesizes a primer extension product. "A set of polynucleotide primers", "a set of PCR primers" or "pair of primers" can comprise two, three, four or more primers.

[0316] Real time nucleic acid amplification and detection methods are efficient for sequence identification and quantification of a target since no pre-hybridization amplification is required. Amplification and hybridization are combined in a single step and can be performed in a fully automated, large- scale, closed-tube format.

[0317] Methods that use hybridization-triggered fluorescent probes for real time PCR are based either on a quench-release fluorescence of a probe digested by DNA Polymerase (e.g., methods using TaqMan(R), MGB- TaqMan(R)), or on a hybridization- triggered fluorescence of intact probes (e.g., molecular beacons, and linear probes). In general, the probes are designed to hybridize to an internal region of a PCR product during annealing stage (also referred to as amplicon). For those methods utilizing TaqMan(R) and MGB-TaqMan(R) the 5'-exonuclease activity of the approaching DNA Polymerase cleaves a probe between a fluorophore and a quencher, releasing fluorescence. Thus, a "real time PCR" assay provides dynamic fluorescence detection of amplified biomarkers of the present disclosure, or any control reference gene produced in a PCR amplification reaction. During PCR, the amplified products created using suitable primers hybridize to probe nucleic acids (TaqMan(R) probe, for example), which may be labeled according to some embodiments with both a reporter dye and a quencher dye. When these two dyes are in close proximity, i.e., both are present in an intact probe oligonucleotide, the fluorescence of the reporter dye is suppressed. However, a polymerase, such as AmpliTaq GoldTM, having 5'-3' nuclease activity can be provided in the PCR reaction. This enzyme cleaves the Anorogenic probe if it is bound specifically to the target nucleic acid sequences between the priming sites. The reporter dye and quencher dye are separated upon cleavage, permitting Auorescent detection of the reporter dye. Upon excitation by a laser provided, e.g., by a sequencing apparatus, the Auorescent signal produced by the reporter dye is detected and / or quantified. The increase in Auorescence is a direct consequence of amplification of target nucleic acids during PCR.

[0318] More particularly, QRT-PCR or "qPCR" (Quantitative RT-PCR), which is quantitative in nature, can also be performed to provide a quantitative measure of biomarker expression levels. In QRT- PCR reverse transcription and PCR can be performed in two steps, or reverse transcription combined with PCR can be performed. One of these techniques, for which there are commercially available kits such as TaqMan(R) (Perkin Elmer, Foster City, CA), is performed with a transcript- specific antisense probe. This probe is specific for the PCR product (e.g. a nucleic acid fragment derived from a gene) and is prepared with a quencher and Auorescent reporter probe attached to the 5' end of the oligonucleotide. Different Auorescent markers are attached to different reporters, allowing for measurement of at least two products in one reaction. When Taq DNA polymerase is activated, it cleaves off the Auorescent reporters of the probe bound to the template by virtue of its 5-to-3' exonuclease activity. In the absence of the quenchers, the reporters now Auoresce. The color change in the reporters is proportional to the amount of each specific product and is measured by a fluorometer; therefore, the amount of each color is measured, and the PCR product is quantified. The PCR reactions can be performed in any solid support, for example, slides, microplates, 96 well plates, 384 well plates and the like so that samples derived from many individuals are processed and measured simultaneously. The TaqMan(R) system has the additional advantage of not requiring gel electrophoresis and allows for quantification when used with a standard curve. An additional technique useful for detecting PCR products quantitatively to determine the levels of the disclosed biomarker / s, is to use an intercalating dye such as the commercially available QuantiTect SYBR Green PCR (Qiagen, Valencia California). Real time PCR is performed using SYBR green as a fluorescent label which is incorporated into the PCR product during the PCR stage and produces fluorescence proportional to the amount of PCR product. Both TaqMan(R) and QuantiTect SYBR systems can be used subsequent to reverse transcription of RNA. Reverse transcription can either be performed in the same reaction mixture as the PCR step (one-step protocol) or reverse transcription can be performed first prior to amplification utilizing PCR (two-step protocol).

[0319] Still further, in some embodiments, other known systems to quantitatively measure mRNA expression products useful in the disclosed methods, compositions and kits, include Molecular Beacons(R) which uses a probe having a fluorescent molecule and a quencher molecule, the probe capable of forming a hairpin structure such that when in the hairpin form, the fluorescence molecule is quenched, and when hybridized, the fluorescence increases giving a quantitative measurement of biomarker expression. According to such embodiment, the detecting molecule / s may be in the form of probe corresponding and thereby hybridizing to any region or at least one of the nucleic acid sequences encoding or being the biomarker / s or any control reference protein. More particularly, it is important to choose regions which will permit hybridization to the target nucleic acids. Factors such as the Tm of the oligonucleotide, the percent GC content, the degree of secondary structure and the length of nucleic acid are important factors.

[0320] Still further, in some embodiments, a standard Northern blot assay or dot blot can also be used to ascertain an RNA transcript size and the relative amounts of the biomarkers of the present disclosure or any control gene product, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art.

[0321] In yet some further specific embodiments, where the detecting molecules of the invention are nucleic acid-based molecules, optional detecting molecule / s may be at least one nucleic acid aptamer specific for the at least one of the biomarker / s. As used herein the term "aptamer” or “specific aptamers” denotes single-stranded nucleic acid (DNA or RNA) molecules which specifically recognizes and binds to a target molecule. The aptamers according to the disclosure may fold into a defined tertiary structure and can bind a specific target molecule with high specificities and affinities. Aptamers are usually obtained by selection from a large random sequence library, using methods well known in the art, such as SELEX and / or Molinex. In various embodiments, aptamers may include single-stranded, partially single-stranded, partially double- stranded or double-stranded nucleic acid sequences; sequences comprising nucleotides, ribonucleotides, deoxyribonucleotides, nucleotide analogs, modified nucleotides and nucleotides comprising backbone modifications, branch points and non-nucleotide residues, groups or bridges; synthetic RNA, DNA and chimeric nucleotides, hybrids, duplexes, heteroduplexes; and any ribonucleotide, deoxyribonucleotide or chimeric counterpart thereof and / or corresponding complementary sequence. In certain specific embodiments, aptamers used by the present disclosure are composed of deoxyribonucleotides. It should be understood that the use of aptamer / s as detecting molecule / s may be applicable to detection of biomarker gene products at the nucleic acid level, as well as at the amino acid level. According to the present disclosure and as appreciated in the art, the recognition between the aptamer and the target biomarker nucleic acid molecule or protein is specific and may be detected by the appearance of a detectable signal by using a colorimetric sensor or a fluorimetric / lumination sensor, radioactive sensor, or any appropriate means. The aptamers that may be used according to some aspects of the present disclosure may be biotinylated. The aptamers may optionally include a chemically reactive group at the 3' and / or 5' termini. The term reactive group is used herein to denote any functional group comprising a group of atoms which is found in a molecule and is involved in chemical reactions. Some non- limiting examples for a reactive group include primary amines (NH2), thiol (SH), carboxy group (COOH), phosphates (PO4), Tosyl, and a photo-reactive group. In some embodiments, the aptamer that may be applicable herein may optionally comprise a spacer between the nucleic acid sequence and the reactive group. The spacer may be an alkyl chain such as (CH2)e / i2, namely comprising six to twelve carbon atoms.

[0322] In yet some alternative and / or additional embodiments, when the determination of the expression levels of the disclosed specific biomarkers is performed at the protein level, and thus, amino acid based detecting molecules may be used. In yet some further specific embodiments, such amino- acid-based detecting molecule / s comprise at least one of: (a) at least one antibody specific for the at least one of the biomarkers; (b) at least one labeled or tagged biomarker / s or any fragment / s, peptide / s or mixture / s thereof; (c), at least one protein or peptide aptamer / s specific for the at least one of the biomarkers; and (d) any combination of (a), (b) and (c).

[0323] More specifically, in some embodiments, the determination of the level / s, specifically of the expression level of the biomarkers used by the disclosed methods is performed at the protein level. Accordingly, in some embodiments, the detecting molecule / s may be amino-acid-based detecting molecule. The invention thus contemplates the use of amino acid-based molecules such as proteins or polypeptides as detecting molecules disclosed herein and would be known by a person skilled in the art to measure the level of the at least one biomarker disclosed herein. As used herein, the terms "protein" and "polypeptide" are used interchangeably to refer to a chain of amino acids linked together by peptide bonds. In a specific embodiment, a protein is composed of between at least 3 to at least 5000 or more amino acids linked together by peptide bonds. It should be noted that peptide bond as described herein is a covalent amid bond formed between two amino acid residues. Techniques for detection and quantification known to persons skilled in the art (for example, Mass spectrometry (MS) or different immunological techniques such as Western Blotting, Immunoprecipitation, ELISAs, protein microarray analysis, Flow cytometry and the like) can then be used to measure the level of protein products corresponding to the biomarker / s of the present disclosure.

[0324] In specific embodiments, the detecting amino acid molecules applicable for the disclosure may be isolated antibodies, with specific binding selectively to at least one of the biomarker proteins, as demonstrated for example in Figures 1C, IE, Figs. 10A, 10B, Fig. 11B, and Tables 2A and 2B. More specifically, the term “antibody” as used in this invention includes whole antibody molecules as well as functional fragments thereof, such as Fab, F(ab')2, and Fv that are capable of binding with antigenic portions of the target polypeptide, i.e. at least one of the biomarker protein / s. The antibody may be preferably monospecific, e.g., a monoclonal antibody, or antigen-binding fragment thereof. The term "monospecific antibody" refers to an antibody that displays a single binding specificity and affinity for a particular target, e.g., epitope. This term includes a "monoclonal antibody" or "monoclonal antibody composition", which, as used herein, refer to a preparation of antibodies or fragments thereof of single molecular composition.

[0325] It should be recognized that the antibody can be a human antibody, a chimeric antibody, a recombinant antibody, a humanized antibody, a monoclonal antibody, or a polyclonal antibody. The antibody can be an intact immuno globulin, e.g., an IgA, IgG, IgE, IgD, IgM or subtypes thereof. The antibody can be conjugated to a labeling moiety as discussed above. Still further, the antibodies used by the present invention may optionally be covalently or non- covalently linked to a detectable label or tag. In addition, the label and can also refer to indirect labeling of the protein by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of at least one of the biomarker protein / s of the present disclosure using a fluorescently labeled secondary antibody. More specifically, detectable labels suitable for such use include any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means.

[0326] As indicated above, in some other embodiments, the detecting molecules are peptide aptamers specific for the at least one of the biomarker proteins. "Peptide or protein aptamers" as used herein refers to small peptides with a single variable loop region tied to a protein scaffold on both ends that binds to a specific molecular target (e.g. protein), and which are bind to their targets only with said variable loop region and usually with high specificity properties.

[0327] In some embodiments, the detecting molecules used by the disclosed methods may be recombinantly expressed or synthetically prepared. In further embodiments, the recombinantly or synthetically expressed and prepared detecting molecules may be labeled or tagged. It should be noted that in some embodiments, these detecting molecules may be isolated detecting molecules. As used herein, "Recombinant proteins" denotes proteins encoded by a recombinant DNA which is a genetically engineered DNA formed by laboratory methods of genetic recombination to bring together genetic material from multiple sources and thus creating variable sequences.

[0328] It should be appreciated that in certain embodiments, the signature biomarkers, specifically, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty one, at least twenty two or up to 2000 or any protein-fragments thereof may be also detected and quantified without the need for detection molecule / s. Detection can be based on mass spectrometry (MS) approaches using non-targeted or targeted methods such as selected reaction monitoring (SRM) or parallel reaction monitoring (PRM). These analyses can be performed with or without a reference heavy standard and provide quantitative measure of the peptide / protein biomarkers amount. The heavy reference can be a synthetic peptide, or a chemically labeled peptide / protein biomarkers or metabolically labeled proteins. In the absence of a standard, the MS signal can provide the measure of peptide abundance.

[0329] It should be noted that for determining the expression value / s of at least one of the disclosed biomarkers, the methods of the present disclosure may further comprise the step of providing at least one detecting molecule specific for determining the expression of at least one of the biomarkers of the invention. In some embodiments, such detecting molecules may be provided as a mixture, as a composition or as a kit. Thus, in some embodiments, the at least one detecting molecule / s may be provided as a mixture of detecting molecules, wherein each detecting molecule is specific for one biomarker. It should be appreciated however, that for each biomarker, one or several specific detecting molecules may be used and provided. In yet some further alternative embodiments, the detecting molecules may be provided separately for each biomarker, e.g., in specific tube, containers, slot / s, spot / s, well / s, dot / s, bead / s, particle / s, chip / s and the like. It further alternative embodiments, the detecting molecules may be attached or immobilized to a solid support, specifically, in recorded location.

[0330] The present disclosure further encompasses in some embodiments thereof any of the disclosed detecting molecules that may be provided either separated or mixed, either attached or immobilized to a solid support or provided unattached and not immobilized to a solid support.

[0331] Still further, in some embodiments, for determining the levels, e.g., the expression level of the specified biomarkers the sample or any nucleic acid molecules or proteins thereof is contacted with specific detecting molecules for each of the biomarkers.

[0332] The term “contacting” means to bring, put, incubates or mix together. As such, a first item is contacted with a second item when the two items are brought or put together, e.g., by touching them to each other or combining them. In the context of the present disclosure, the term "contacting" includes all measures or steps which allow interaction between the at least one of the detection molecules of at least one of the biomarkers, and optionally, for at least one suitable control reference mRNA / protein of the tested sample. The contacting is performed in a manner so that the at least one of detecting molecule of at least one of the biomarkers for example, can interact with or bind to the at least one of the biomarkers, in the tested sample. The binding will preferably be non-covalent, reversible binding, e.g., binding via salt bridges, hydrogen bonds, hydrophobic interactions or a combination thereof.

[0333] In some embodiments, the biological sample used in the disclosed methods may be any biological sample, specifically, sample obtained from the subject, for example, at least one of a body fluid sample and a cell or tissue sample.

[0334] More specifically, in certain embodiments, the sample applicable in the disclosed methods and compositions, may be at least one of blood sample, plasma sample, serum sample, tissue sample, cell sample and tissue biopsy. In further specific embodiments, the cell sample may comprise at least one of peripheral blood mononuclear cell (PBMC(s)) and monocyte(s). In more specific embodiments, a sample used in the disclosed methods may be a PBMCs sample. In yet some further embodiments, a sample used in the disclosed methods may be a monocyte(s) sample. In yet some further embodiments, a sample useful for the disclosed methods, compositions and kits, may be a plasma sample. Such samples were exemplified herein for biomarkers at the protein level. More specifically, the term 'biological sample ' in the present specification and claims is meant to include samples obtained from at least one mammalian subject. The biological sample may be a bodily fluid (that may either comprise cells or not), and / or any cell sample, cell fractions and / or cell organelles, and / or tissue sample, and / or a tissue biopsy and / or organ sample of the examined subject. It should be understood that any biological sample applicable for use in the disclosed methods, compositions and kits, may be any sample comprising at least one nucleic acid molecule, e.g., at least one gene and / or gene product / s, e.g., at least one RNA molecule or amino acid molecule, of any of the disclosed biomarkers of the biomarker subset used herein. In some embodiments the biological sample is blood sample, plasma sample, tissue sample, cell sample and tissue biopsy.

[0335] In some specific and non-limiting embodiments, the sample of the disclosed methods may be a body fluid sample. More specifically, such sample may be any body fluid such as blood, plasma, lymph, urine, saliva, serum, cerebrospinal fluid, seminal plasma, pancreatic juice, breast milk, uterine, peritoneal cavity, lung lavage, or fluids collected from any organ or tissue cavity. More specifically, in some embodiments the cell sample is at least one of PBMC(s) and monocyte(s). The sample can be fractionated or preselected by a number of known fractionation or pre-selection techniques. A sample can also be any extract of the above. The term also encompasses protein fractions or alternatively, nucleic acid from cells or tissue. Thus, in some specific embodiments, the sample may be any one of a biological sample of organ / s, cell / s or tissue / s and a blood sample, including any blood or hematopoietic cells of any one of the erythroid, myeloid, lymphoid lineages. Specific embodiments relate to hematopoietic cells, for example, T cells, B cells and the like.

[0336] The term "sample" further refers to healthy as well as diseased or pathologically changed cells or tissues. Hence, the term further refers to a body fluid, a cell or a tissue associated with a disease, such as sepsis and / or associated conditions, in particular.

[0337] In some embodiments, the sepsis-associated conditions comprise poor long-term and short-term clinical outcomes. More specifically, the conditions associated with sepsis comprise at least one of: any immune dysregulation (ranging from hyperinflammation to immunoparalysis), hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure (hemodialysis), acute renal failure (ARF), cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis.

[0338] Still further, in certain embodiments, the disclosed diagnostic methods may further comprise an additional therapeutic step. Accordingly, the disclosed methods further comprise administering to a subject diagnosed with sepsis and / or associated conditions, an effective amount of at least one therapeutic compound that elevates the R / SI balance in the subject. Specifically, any compound that elevates the R level, reduces the SI level and / or elevates the R and reduces the SI levels.

[0339] In some embodiments, the diagnostic and classification methods disclosed herein above, and any step thereof as defined by the present disclosure, may be applicable for determining the susceptibility of a subject to sepsis and / or associated conditions, and / or for predicting the outcome of the sepsis and / or associated conditions in a subject.

[0340] In yet some further embodiments, the diagnostic and classification methods disclosed herein above, and any step thereof as defined by the present disclosure, may be applicable for predicting and assessing responsiveness of a subject suffering from sepsis and / or associated conditions, to at least one compound or a treatment regimen comprising the compound, and optionally for monitoring disease progression.

[0341] Still further, in some embodiments, the diagnostic and classification methods disclosed herein above, and any step thereof as defined by the present disclosure, may be applicable for determining a personalized treatment regimen for a subject suffering from sepsis and / or associated conditions. In yet some further embodiments, the diagnostic and classification methods disclosed herein above, and any step thereof as defined by the present disclosure, may be applicable for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of sepsis and / or associated conditions in a subject in need thereof.

[0342] A further aspect of the present disclosure relates to a prognostic method for determining the susceptibility of a subject to sepsis and / or associated conditions, and / or predicting the outcome of the sepsis and / or associated conditions in the subject. The method comprising the following steps. In step (a), calculating the R / SI balance score of the subject. Step (b), involves classifying the subject as a subject susceptible to sepsis and / or to develop a negative outcome of sepsis, if the R / SI balance score of the subject determined in step (a) is smaller than 0 (also referred to herein as negative).

[0343] In some embodiments, the R / SI balance score is determined and / or calculated in step (a), by the following steps. In step (i), determining in at least one biological sample of the subject the expression level of a subset of biomarkers to obtain an expression value for each of the biomarkers. Next step (ii), involves determining and / or calculating the R level and the SI level of the subject from the expression values obtained in step (i). And, step (iii) involves calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii).

[0344] In certain embodiments, a negative R / SI balance score is indicative of an impaired balance score. The impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level; and wherein the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

[0345] In some embodiments of the disclosed prognostic methods, the R / SI balance score is determined / calculated as defined for the diagnostic method of the present disclosure as defined herein in connection with other aspects of the invention. Briefly, the in some further embodiments, determining and / or calculating the resistance (R) level according to step (b) of the disclosed methods is performed by the following calculation. The R level is calculated in conjugation with the tolerance (T) level of the sample using the formula wherein: Ziis the vector of relative measured levels across all genes (biomarkers) of individual i. Relative expression levels are calculated in three steps: (i) Log2-transformation. (ii) Sample-level standardization. Each sample is centered and divided by standard deviation across genes, (iii) Gene-level standardization. Each gene is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples. VTand VRare the pre-defined vectors of gene weights of all genes for the T and R programs, respectively; is the T level of individual t; SR is the R level of individual i; and biis a constant. It should be understood that the are the T and R levels of individual i, are therefore extracted from the above formula. More specifically, the inventors infer T and R levels for which the expression of each gene / biomarker is approximated well by the weighted sum of T and R levels (for each gene / biomarker, using different T and R weights that were predefined for the relevant gene). In some embodiments, the individual i is the diagnosed subject. In some embodiments, the method further involves, standardizing by subtracting from the calculated R level the mean level of a standard or control samples and dividing the result of the substruction by the standard deviation of the standard or control samples. The gene / biomarker weights (VTand VR) were previously defined and are used as constants in all embodiments. These weights were originally created by reducing the multi-dimensionality of gene / biomarker expression values of a dataset of acute infection into a two-dimensional map, wherein resistance (denoted R) and disease tolerance (denoted T) are the vertical (axis R) and the horizontal (axis T), respectively, two axes of a two-dimensional “R / T map”. For each biomarker, its biomarker weight of program T and its biomarker weight of program R are defined as the coordinates of this biomarker on the T and R axes of the R / T map. Details on how the biomarker weights were originally defined through dimension reduction are in disclosed Cohn et al. (2022) [Ref. 5], and further described in the ‘Experimental Procedures’ section herein after. Similarly, in some further embodiments, determining / calculating the systemic inflammation (SI) level according to step (b) of the disclosed methods is performed by the following calculation. SI level is calculated in conjugation with the MetS level of the sample using the formula Zi= wherein: is the vector of relative measured levels across all biomarkers of individual i. Relative expression levels are calculated in three steps: (i) Log2- transformation. (ii) Sample-level standardization. Each sample is centered and divided by standard deviation across genes, (iii) Gene-level standardization. Each gene is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples. VMetsand VS[are the pre-defined vectors of biomarker weights of all biomarkers for the MetS and SI programs, respectively; is the MetS level of individual is the SI level of individual i; and b, is a constant. It should be understood that the s are the MetS and SI levels of individual i, and are therefore extracted from the above formula. More specifically, the inventors infer MetS and SI levels for which the expression of each biomarker is approximated well by the weighted sum of MetS and SI levels (for each biomarker, using different MetS and SI weights that were predefined for the relevant biomarker). In some embodiments, the method further involves, standardizing by subtracting from the calculated SI level the mean level of a standard or control samples and dividing the result of the substruction by the standard deviation of the standard or control samples. The gene-biomarker weights (VMetsand Vs / ) were previously defined and are used as constants in all embodiments. These weights were originally created by reducing the multi-dimensionality of biomarker expression and EMR values of a cohort of individuals into a two-dimensional map. It was shown that systemic inflammation (denoted IM2 or SI) and metabolic syndrome (denoted IM1 or MetS) are the vertical (axis SI) and the horizontal (axis MetS), respectively, two axes of the two-dimensional “SI / MetS map”. For each biomarker, its biomarker weight of program MetS and its biomarker weight of program SI are defined as the coordinates of this biomarker on the SI and MetS axes of the SI / MetS map. A detailed on how the biomarker weights were originally defined through dimension reduction (PCA) are disclosed in Frishberg et al. (2020) [Ref. 7], and further described in the ‘Experimental Procedures’ section herein after. It should be noted that the expression level of a subset of biomarkers in at least one sample of the subject is determined at the nucleic acid and / or the protein level. For example, when determined at the nucleic acid level the determination may be provided by RNA sequencing. Still further, in some embodiments, the subset of biomarkers for R calculation comprises: k biomarkers with most-positive weight on the R axis (or most-positive position in the R axis), k biomarkers with most-negative weight on the R axis (or most-negative position in the R axis), k genes with most-positive weight on the T axis, and k genes with most-negative weight on the T axis of the R / T gene-expression map for a total of 4k genes, and wherein k is an integer between 10 to 1,000. In yet some further embodiments, the subset of biomarkers for SI calculation comprises: k biomarkers with most-positive weight on the SI axis (or most-positive position in the SI axis), k biomarkers with most-negative weight on the SI axis (or most-negative position in the SI axis), k biomarkers with most-positive weight on the MetS axis, and k biomarkers with most-negative weight on the MetS axis of the SI / MetS gene-expression map for a total of 4k biomarkers, and wherein k is an integer between 10 to 1,000. In some embodiments, the subset of biomarkers comprises biomarkers characterized by an averaged correlation to the R / SI balance score that is equal or greater than 0.65 and / or a correlation to the R / SI balance score that is equal or smaller than -0.45. In some particular embodiments, the subset of biomarkers for R / SI balance score calculation comprise at least one of: IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNCI 3D and WDTC1 or any combination thereof. In yet some further embodiments, the subset of biomarkers for R calculation comprise at least one of: GOEGA5, HNRNPA2B1, ARE8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ biomarkers, or any combination thereof. In some further embodiments, the subset of biomarkers for SI calculation comprises at least one of: DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 biomarkers, or any combination thereof. In some alternative embodiments, particularly when the level of the biomarker is determined at the amino acid level (protein), the subset of biomarkers for R calculation comprise at least two of: IFNy, CXCL10, MCP-2, CXCL11 and CXCL9 biomarkers. In some further embodiments, the subset of biomarkers for SI calculation comprises at least two of: IL6, IL8, CCL3, CCL20 and CCL4 biomarkers.

[0346] In some embodiments, using protein markers, the R level is calculated using the formula wherein: Ziis the 1 -length vector that includes the average of relative measured levels across all protein markers of individual t; Relative protein-expression levels are calculated as follows: each protein is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples. VTand VRare pre-defined 1-length vectors, VR= (1) and VT is the R level of individual t; and b, is a constant. As indicated above, in some embodiments, the individual I, is the diagnosed subject.

[0347] Still further, in some embodiments, using protein markers, the SI level is calculated using the formula wherein: Z^ is the 1-length vector that includes the average of relative measured levels across all protein markers of individual t; Relative protein- expression levels are calculated as follows: each protein is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0348] VMetsands / are pre-defined 1-length vectors, is the SI level of individual t; and is a constant. As indicated above, in some embodiments, the individual I, is the diagnosed subject.

[0349] It should be understood that all steps defined for the methods disclosed herein as the previous aspects are applicable for this aspect as well, and also for any aspect of the present disclosure as will be disclosed herein after. Moreover, for brevity reasons, the brief steps disclosed herein above, as well as the steps disclosed in the previous aspect although disclosed only herein, are applicable for each and every aspect of the present disclosure.

[0350] In yet some further embodiments, the prognostic methods disclosed herein, may further comprise the step of administering to the subject an effective amount of at least one therapeutic compound that elevates the R / SI balance in the subject. More specifically, the compound used herein may be any one of: (i), a therapeutic compound that elevates the R level in a subject, for example, such compound may be useful if the subject is classified as having reduced R level; or (ii), a therapeutic compound that reduces the SI levels in a subject, for example, such compound may be useful if the subject is classified as having increased SI level; or (iii), a therapeutic compound that elevates the R level and reduces the SI level in a subject, for example, such compound may be useful if the subject is classified as having reduced R level and increased SI level.

[0351] Another aspect of the preset disclosure relates to a prognostic method for predicting and assessing responsiveness of a subject suffering from sepsis and / or associated conditions, to at least one compound or a treatment regimen comprising the compound. The method is optionally for monitoring disease progression. The method comprising the following steps. In step (a), determining the R / SI balance score in at least one sample of the subject; and In step (b), classifying the subject as: (i), a responder to at least one compound or a treatment regimen comprising the compound, if at least one sample obtained after the initiation of the treatment regimen and / or a sample of the subject contacted with the compound displays elevation in the R / SI balance score, as compared with the R / SI balance score determined for a sample obtained prior to the treatment, or a sample not contacted with the compound, or (ii), a non-responder, if at least one sample obtained after the initiation of the treatment regimen and / or a sample of the subject contacted with the compound displays reduction, or no change in the R / SI balance score, as compared with the R / SI balance score determined for a sample obtained prior to the treatment, or the R / SI balance score determined for a sample not contacted with the compound.

[0352] More specifically, the term 'response' or 'responsiveness' to a certain treatment regimen and / or a certain therapeutic compound, refers to an improvement in at least one relevant clinical parameter as compared with an untreated subject diagnosed with the same pathology (e.g., the same type, stage, degree and / or classification of the pathology), or as compared to the clinical parameters of the same subject prior to interferon treatment with said medicament. In some embodiments, a responder subject is a subject that experience an improvement of one or more of the clinical parameters of sepsis, and further display elevation of the R / SI balance score. Such elevation may be a result of an increase in the R level, a decrease in the SI level and a combination of both.

[0353] The term 'non responder' or "drug resistance" to treatment with a specific medicament, refers to a patient not experiencing an improvement in at least one of the clinical parameter and is diagnosed with the same condition as an untreated subject diagnosed with the same pathology (e.g., the same type, stage, degree and / or classification of the pathology), or experiencing the clinical parameters of the same subject prior to treatment with the specific medicament.

[0354] In some embodiments, the R / SI balance score is determined / calculated in step (a), by the following steps. In step (i), determining in at least one biological sample of the subject the expression level of a subset of biomarkers to obtain an expression value for each of the biomarkers. Step (ii), involves determining and / or calculating the R level and the SI level of the subject from the expression values obtained in step (i). And, step (iii), involves calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii). It should be understood that calculation of the R / SI balance score, is as defined in more detail herein before, in connection with other aspects of the present disclosure.

[0355] In some further embodiments, a negative R / SI balance score (e.g., smaller than 0) is indicative of an impaired balance score. The impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level. The severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

[0356] In some embodiments, the R / SI balance score is determined / calculated in the disclosed prognostic methods as defined herein before, for the diagnostic method of the present disclosure. As indicated above, for brevity reasons, the brief steps disclosed herein above, as well as the detailed steps disclosed in the previous aspects, are for each and every aspect of the present disclosure, as well in the present aspect.

[0357] In certain embodiments, the disclosed prognostic methods may be applicable for monitoring disease progression. More specifically, monitoring, as used herein, comprises at least one of: predicting, determining and / or assessing disease relapse and / or long-term sepsis associated with poor clinical outcome. Accordingly, the method further comprises the following steps. In step (c), repeating step (a) to determine the R / SI balance score in at least one more temporally-separated sample of the subject. And, step (d), involves predicting and / or determining disease relapse and / or long-term sepsis in the subject, if at least one temporally separated sample obtained after the initiation of the treatment regimen displays reduction in the R / SI balance score. It should be understood that such reduction may be a result of increased R levels, reduced SI levels or a combination of both.

[0358] In some embodiments, the sepsis-associated conditions comprise poor long-term and short-term clinical outcome. The conditions comprise at least one of: any immune dysregulation (from hyperinflammation to immune paralysis), hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure (hemodialysis), ARF, cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis.

[0359] In some further embodiments, the prognostic methods may further comprise the following steps. Step (e), that comprises one of: either (i), maintaining the treatment regimen for a subject classified as a responder subject; or (ii), ceasing the treatment regimen for subject classified as a non- responder, and optionally, administering to the non-responder subject an alternative treatment regimen that elevates the R / SI balance score.

[0360] As indicated above, in accordance with some embodiments of the present disclosure, in order to assess the patient condition, or monitor the disease progression, as well as responsiveness to a certain treatment, at least two “temporally-separated” test samples must be collected from the examined patient and compared thereafter, in order to determine if there is any change or difference in the levels of resistance and / or disease-tolerance between the samples. Such change may reflect a change in the responsiveness of the subject. In practice, to detect a change having more accurate predictive value, at least two "temporally-separated" test samples and preferably more, must be collected from the patient. In some embodiments, the at least one more temporally-separated sample may be obtained after the initiation of at least one treatment regimen. It should be understood that in some particular embodiments, at least one sample may be obtained prior to initiation of the treatment. However, in some embodiments, the methods disclosed herein may be applied to subjects already treated by a treatment regimen. Such monitoring may therefore provide a powerful therapeutic tool used for improving and personalizing the treatment regimen offered to the treated subject. The number of samples collected and used for evaluation and classification of the subject either as a responder or alternatively, as a drug resistant (i.e. non-responder) or as a subject that may experience relapse of the disease, may change according to the frequency with which they are collected. For example, the samples may be collected at least every day, every two days, every four days, every week, every two weeks, every three weeks, every month, every two months, every three months every four months, every 5 months, every 6 months, every 7 months, every 8 months, every 9 months, every 10 months, every 11 months, every year or even more. Furthermore, to assess the disease progression according to the present disclosure, it is understood that the change in resistance and / or disease-tolerance levels, may be calculated as an average change over at least three samples taken in different time points, or the change may be calculated for every two samples collected at adjacent time points. It should be appreciated that the sample may be obtained from the monitored patient in the indicated time intervals for a period of several months or several years. More specifically, for a period of 1 year, for a period of 2 years, for a period of 3 years, for a period of 4 years, for a period of 5 years, for a period of 6 years, for a period of 7 years, for a period of 8 years, for a period of 9 years, for a period of 10 years, for a period of 11 years, for a period of 12 years, for a period of 13 years, for a period of 14 years, for a period of 15 years or more.

[0361] Another aspect of the present disclosure relates to a method for determining a personalized treatment regimen for a subject suffering from sepsis and / or associated conditions. The method comprising the following steps. In step (a), determining the R / SI balance score of the subject. In step (b), classifying the subject as one of: (i), a subject displaying reduced R level; (ii), a subject displaying increased SI level; and (iii), a subject displaying reduced R level and increased SI level. Step (c), involves selecting for the subject a treatment regimen and / or at least one compound that elevates the levels of R / SI balance score. In some embodiments, the treatment regimen and / or compound of (c) determined and selected by the disclosed personalized method, may comprise any one of: (i), a treatment regimen and / or compound that elevates the R level in a subject, if the subject is classified as having reduced R level; or (ii), a treatment regimen and / or compound that reduces the SI levels in a subject, if the subject is classified as having increased SI level; or (iii), a treatment regimen and / or compound that elevates the R level and reduces the SI levels in a subject, if the subject is classified as having reduced R level and increased SI level.

[0362] In some further embodiments, the methods for determining personalized therapy may further comprise the following steps. Step (d), that involves determining the responsiveness of the subject to the treatment regimen and / or compound. In some optional embodiments, the responsiveness is determined, by the methods as defined by the present disclosure herein before. The next step (e), further comprises one of: (i), either maintaining the treatment regimen for a subject classified as a responder subject; or (ii), ceasing the treatment regimen for subject classified as a non-responder; and optionally, administering to the non-responder subject an alternative treatment regimen that elevates the R / SI balance score.

[0363] In certain embodiments, the R / SI balance score calculated by the disclosed personalized methods may be determined and / or calculated in step (a), by the following steps:

[0364] In step (i), determining in at least one biological sample of the subject the expression level of a subset of biomarkers to obtain an expression value for each of the biomarkers; and in step (ii), determining and / or calculating the R level and the SI level of the subject from the expression values obtained in step (i); and in step (iii), calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii).

[0365] Still further, in certain additional embodiments, a negative R / SI balance score is indicative of an impaired balance score. The impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level. The severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

[0366] In some embodiments, the R / SI balance score determined / calculated in the personalized methods of the present disclosure, as defined herein before, for the diagnostic method disclosed herein. As indicated above, for brevity reasons, the brief steps disclosed herein above, as well as the detailed steps disclosed in the previous aspects, are for each and every aspect of the present disclosure, as well in the present aspect. Another aspect of the present disclosure relates to a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of sepsis and / or associated conditions in a subject in need thereof. The method comprising the following steps. In step (a), determining the R / SI balance score of the subject by the steps of: (i), determining in at least one biological sample of the subject the expression level of a subset of biomarkers to obtain an expression value for each of the biomarkers; and (ii), determining / calculating the R level and the SI level of the subject from the expression values obtained in step (i); and (iii), calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii). In step (b), classifying the subject as one of: (i), a subject displaying reduced R level; (ii), a subject displaying increased SI level; or (iii), a subject displaying reduced R level and increased SI level. Step (c), involves administering to the subject a therapeutic compound or subjecting the subject to a treatment regime that elevate the R / SI balance in the subject.

[0367] In some embodiments, the treatment regimen and / or compound administered in step (c), comprises any one of: (i), a treatment regimen and / or compound that elevates the R level in a subject. In some embodiments, this may be useful if the subject is classified as having reduced R level; or (ii), a treatment regimen and / or compound that reduces the SI levels in a subject. In some embodiments, this may be useful if the subject is classified as having increased SI level; or (iii), a treatment regimen and / or compound that elevates the R level and reduces SI level, reduces the SI levels in a subject. In some embodiments, this may be useful if the subject is classified as having reduced R level and increased SI level.

[0368] Still further, in some embodiments, the therapeutic methods disclosed herein may further comprise a diagnostic step that may involve the following steps. In step (d), determining the responsiveness of the subject to the treatment regimen and / or compound, optionally, by the prognostic methods of the present disclosure as defined herein before. The therapeutic methods disclosed herein may further comprise in step (e), one of: either option (i), maintaining the treatment regimen for a subject classified as a responder subject; or option (ii), ceasing the treatment regimen for subject classified as a non-responder; and optionally, administering to the non-responder subject an alternative treatment regimen that elevates the R / SI balance score.

[0369] In some embodiments, the R / SI balance score is determined / calculated in the disclosed therapeutic methods as defined for the diagnostic method of the present disclosure herein before.

[0370] In some further embodiments, the sepsis-associated conditions comprise poor long-term and short- term clinical outcome. The conditions comprise at least one of: any immune dysfunction, specifically, ranging from hyperinflammation to immune paralysis, hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure (hemodialysis), acute renal failure (ARF), cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis.

[0371] It should be appreciated that the method, compositions and / or kits of the invention may be suitable for any mammalian subject. By 'patient' or 'subject' it is meant any mammal that may be affected by sepsis and / or associated conditions, and to whom the methods, compositions and / or kits herein described is desired, including human, domestic and non-domestic mammals such as canine and feline subjects, bovine, simian, equine and rodents, livestock and murine subjects. Specifically, said subject is a human. It is to be understood that the terms "treat”, “treating”, “treatment" or forms thereof, as used herein, mean preventing, ameliorating or delaying the onset of one or more clinical indications of disease activity in a subject having a pathologic disorder. Treatment refers to therapeutic treatment. Those in need of treatment are subjects suffering from a pathologic disorder. Specifically, providing a "preventive treatment" (to prevent) or a "prophylactic treatment" is acting in a protective manner, to defend against or prevent something, especially a condition or disease. The term “treatment or prevention” as used herein, refers to the complete range of therapeutically positive effects of administrating to a subject including inhibition, reduction of, alleviation of, and relief from, an immune-related condition and illness, immune- related symptoms or undesired side effects or immune-related disorders. More specifically, treatment or prevention of relapse or recurrence of the disease, includes the prevention or postponement of development of the disease, prevention or postponement of development of symptoms and / or a reduction in the severity of such symptoms that will or are expected to develop. These further include ameliorating existing symptoms, preventing- additional symptoms and ameliorating or preventing the underlying metabolic causes of symptoms. It should be appreciated that the terms "inhibition", "moderation", “reduction”, "decrease" or "attenuation" as referred to herein, relate to the retardation, restraining or reduction of a process by any one of about 1 % to 99.9%, specifically, about 1% to about 5%, about 5% to 10%, about 10% to 15%, about 15% to 20%, about 20% to 25%, about 25% to 30%, about 30% to 35%, about 35% to 40%, about 40% to 45%, about 45% to 50%, about 50% to 55%, about 55% to 60%, about 60% to 65%, about 65% to 70%, about 75% to 80%, about 80% to 85% about 85% to 90%, about 90% to 95%, about 95% to 99%, or about 99% to 99.9%, 100% or more.

[0372] With regards to the above, it is to be understood that, where provided, percentage values such as, for example, 10%, 50%, 120%, 500%, etc., are interchangeable with "fold change" values, i.e., 0.1, 0.5, 1.2, 5, etc., respectively. The term "amelioration" as referred to herein, relates to a decrease in the symptoms, and improvement in a subject's condition brought about by the compositions and methods according to the invention, wherein said improvement may be manifested in the forms of inhibition of pathologic processes associated with the immune-related disorders described herein, a significant reduction in their magnitude, or an improvement in a diseased subject physiological state. The term "inhibit" and all variations of this term is intended to encompass the restriction or prohibition of the progress and exacerbation of pathologic symptoms or a pathologic process progress, said pathologic process symptoms or process are associated with. The term "eliminate" relates to the substantial eradication or removal of the pathologic symptoms and possibly pathologic etiology, optionally, according to the methods of the invention described herein. The terms "delay" , "delaying the onset”, "retard” and all variations thereof are intended to encompass the slowing of the progress and / or exacerbation of a disorder associated with the immune-related disorders and their symptoms slowing their progress, further exacerbation or development, so as to appear later than in the absence of the treatment according to the present disclosure.

[0373] Another aspect of the present disclosure relates to a screening method for identifying and / or evaluating at least one therapeutic compound for the treatment of sepsis and / or associated conditions. The method comprising the following steps. In step (a), determining the R / SI balance score of at least one biological sample contacted with a candidate compound. The sample is of a subject suffering from sepsis and / or associated conditions. The next step (b), involves determining that the candidate compound is a therapeutic compound for sepsis and / or associated conditions if the candidate compound elevates the R / SI balance score, as compared with the R / SI balance score determined for at least one control sample.

[0374] It should be understood that in some embodiments, the screening method of the present disclosure may further analyze the R / SI score in at least one biological sample not contacted with the candidate compound, such that the change in the R / SI balance score caused by the candidate compound may be further evaluated and determined.

[0375] In some embodiments, the candidate compound is at least one of a small molecule, aptamer, a peptide, a nucleic acid molecule and an immunological agent, and any combinations thereof.

[0376] In some embodiments, the candidate molecule is a therapeutic agent / drug. More specifically, a compound to be tested by the disclosed screening methods may be referred to as a test compound or a candidate compound. The candidate compounds may be any known used for a specific disorder, or any unknown drug or compound that is screened herein based on its effect on the R and / or SI levels, and thus, as a candidate compound that may be used in treating sepsis or any related conditions in a given subject. Any compound may be used as a test compound in various embodiments. In some embodiments a library of FDA approved compounds that can be used by humans may be used. Compound libraries are commercially available from a number of companies including but not limited to Maybridge Chemical Co. (Trevillet, Cornwall, UK), Comgenex (Princeton, NJ), Microsource (New Milford, CT), Aldrich (Milwaukee, WI), AKos Consulting and Solutions GmbH (Basel, Switzerland), Ambinter (Paris, France), Asinex (Moscow, Russia), Aurora (Graz, Austria), BioFocus DPI, Switzerland, Bionet (Camelford, UK), ChemBridge, (San Diego, CA), ChemDiv, (San Diego, CA), Chemical Block Lt, (Moscow, Russia), ChemStar (Moscow, Russia), Exclusive Chemistry, Ltd (Obninsk, Russia), Enamine (Kiev, Ukraine), Evotec (Hamburg, Germany), Indofine (Hillsborough, NJ), Interbio screen (Moscow, Russia), Interchim (Montlucon, France), Life Chemicals, Inc. (Orange, CT), Microchemistry Ltd. (Moscow, Russia), Otava, (Toronto, ON), PharmEx Ltd.(Moscow, Russia), Princeton Biomolecular (Monmouth Junction, NJ), Scientific Exchange (Center Ossipee, NH), Specs (Delft, Netherlands), TimTec (Newark, DE), Toronto Research Corp. (North York ON), UkrOrgSynthesis (Kiev, Ukraine), Vitas-M, (Moscow, Russia), Zelinsky Institute, (Moscow, Russia), and Bicoll (Shanghai, China). Combinatorial libraries are available and can be prepared. Libraries of natural compounds in the form of bacterial, fungal, plant and animal extracts are commercially available or can be readily prepared by methods well known in the art. Compounds isolated from natural sources, such as animals, bacteria, fungi, plant sources, and marine samples may be tested for the presence of potentially useful pharmaceutical compounds. It will be understood that the agents to be screened could also be derived or synthesized from chemical compositions or man-made compounds. In some embodiments a library useful in the present invention may comprise at least 10,000 compounds, at least 50,000 compounds, at least 100,000 compounds, at least 250,000 compounds, or more. In yet some further embodiments, the candidate compound may be at least one of a small molecule, aptamer, a peptide, a nucleic acid molecule and an immunological agent, and any combinations thereof. In some specific embodiments, the compound used by the screening methods of the present disclosure, that specifically modulate the R / SI balance score in a subject, may be a small molecule. A "small molecule" as used herein, is an organic molecule that is less than about 2 kilodaltons (kDa) in mass. In some embodiments, the small molecule is less than about 1.5 kDa, or less than about 1 kDa. In some embodiments, the small molecule is less than about 800 daltons (Da), 600 Da, 500 Da, 400 Da, 300 Da, 200 Da, or 100 Da. Often, a small molecule has a mass of at least 50 Da. In some embodiments, a small molecule is non-polymeric. In some embodiments, a small molecule is not an amino acid. In some embodiments, a small molecule is not a nucleotide. In some embodiments, a small molecule is not a saccharide. In some embodiments, a small molecule contains multiple carbon-carbon bonds and can comprise one or more heteroatoms and / or one or more functional groups important for structural interaction with proteins (e.g., hydrogen bonding), e.g., an amine, carbonyl, hydroxyl, or carboxyl group, and in some embodiments at least two functional groups. Small molecules often comprise one or more cyclic carbon or heterocyclic structures and / or aromatic or poly aromatic structures, optionally substituted with one or more of the above functional groups. In some embodiments, the candidate therapeutic compound is a known drug used for the treatment of sepsis and / or any of the associated conditions as discussed above. In such case, the method disclosed herein is used to evaluate if the particular drug is suitable and / or optimal for treating sepsis and associated conditions in the specific subject, thereby providing a personalized therapeutic tool.

[0377] In yet some additional embodiments, the screened compound may be further classified by the disclosed screening method / s as any one of: (i), a compound that elevates the R level in a subject, optionally, with reducing or at least without altering the SI levels. The compound is suitable for a subject classified as having reduced R level. Alternatively (ii), a compound that reduces the SI levels in a subject, optionally while elevating, or at least not reducing the R levels. The compound is suitable for a subject classified having increased SI level; or (iii), a compound that elevates the R level and reduces SI level. The compound is suitable for a subject classified as having reduced R level and increased SI level.

[0378] A further aspect of the present disclosure relates to a diagnostic composition comprising at least one detecting molecule or any combination or mixture of plurality of detecting molecules, and / or means, specific for determining the level of expression of at least one of the following biomarkers: (a), at least one biomarker gene of a subset of genes / biomarkers (or gene biomarkers) for R calculation. It should be understood that the subset of gene biomarkers comprises at least one of: k gene biomarkers with most-positive weight on the R axis (or most-positive position in the R axis), k gene biomarkers with most-negative weight on the R axis (or most-negative position in the R axis), k gene biomarkers with most-positive weight on the T axis (or most-positive position in the T axis), and k gene biomarkers with most-negative weight on the T axis (or most-negative position in the T axis) of the R / T map for a total of 4k gene biomarkers, and wherein k is an integer between 1 to 5,000; and / or (b), at least one biomarker gene of a subset of gene biomarkers for SI calculation. It should be noted that the subset of gene biomarkers comprises at least one of: k gene biomarkers with most-positive weight on the SI axis (or most-positive position in the SI axis), k gene biomarkers with most-negative weight on the SI axis (or most-negative position in the SI axis), k gene biomarkers with most-positive weight on the MetS axis (or most-positive position in the MetS axis), and k gene biomarkers with most-negative weight on the MetS axis (or most- negative position in the MetS axis) of the SI / MctS map for a total of 4k gene biomarkers, and wherein k is an integer between 10 to 1,000. It should be appreciated that the disclosed subset of biomarkers is as defined herein before in connection with other aspects of the present disclosure. In some embodiments, the diagnostic composition may comprise detecting molecules and / or means specific for a subset of gene biomarkers comprising gene biomarkers characterized by a R / SI balance score that is equal or greater than 0.65 and / or a correlation with the R / SI balance score that is equal or smaller than -0.45.

[0379] In some embodiments, the disclosed composition may comprise detecting molecules that are specific for at least one of the biomarker genes as disclosed in any one of Tables 4A and 4B. Still further, in some embodiments, the disclosed composition may comprise detecting molecules specific for t least one of the biomarker genes as disclosed by any one of Tables 4C, 4D, 4E, or any combinations thereof.

[0380] It should be understood that the detecting molecule / s used in the disclosed compositions comprise amino acid detecting molecules and / or nucleic acid detecting molecules.

[0381] Another aspect of the present disclosure relates to a diagnostic composition comprising at least one detecting molecule or any combination or mixture of plurality of detecting molecules, and / or means, specific for determining the level of expression of at least one of: (i) , at least one biomarker of R / SI balance score. The at least one biomarker comprise at least one of IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNC13D and WDTC1 or any combination thereof; and / or (ii), at least one biomarker of resistance (R). The at least one biomarker comprise at least one of IFNy, CXCL10, MCP-2, CXCL11, CXCL9, GOEGA5, , HNRNPA2B1, ARE8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ or any combination thereof; and / or (iii), at least one biomarker of the systemic inflammation (SI). The at least one biomarker comprise at least one of CCL4, IE6, CCL3, CCL20, IE8, DACH1, DYSF, GYG1, CYSTM1, AEPE, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 or any combination thereof.

[0382] In some embodiments, each of the detecting molecules is specific for one of the biomarker / s. In some embodiments the disclosed kit comprises detecting molecules specific for (i), biomarkers for determining the R / SI levels; and / or (ii) biomarkers for determining the R levels; and / or (iii) biomarkers for determining the SI levels.

[0383] In some embodiments, the detecting molecule / s comprise amino acid detecting molecules and / or nucleic acid detecting molecules.

[0384] In more specific embodiments, amino acid detecting molecule / s that may be applicable in the present disclosure may comprise at least one of: (a), at least one antibody specific for at least one of the at least one of IFNy, CXCL10, MCP-2, CXCL11 and CXCL9 for R; and (ii) IL6, IL8, CCL3, CCL20 and CCL4 for SI; and any combination thereof;

[0385] (b), the detecting molecules may be at least one labeled or tagged protein / s or any fragment / s, peptide / s or mixture / s thereof, of at least one of (i)

[0386] (c), the detecting molecules may be at least one protein or peptide aptamer / s specific for at least one of the at least two biomarker proteins.

[0387] According to such embodiments, the nucleic acid detecting molecules may comprise at least one of: (a), at least one oligonucleotide / s (e.g., primers and / or probs). Specifically, for R calculation, at least one of G0LGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ, for SI calculation, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 andZNF337; and for R / SI balance score IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNCI 3D and WDTC1; (b), the detecting molecule may be at least one nucleic acid aptamer / s specific for the at least one of the biomarker genes. In some embodiments, each oligonucleotide specifically hybridizes to a nucleic acid sequence of the gene biomarker.

[0388] Another embodiment of the aspect of the present disclosure relates to a kit comprising: a. at least one detecting molecule specific for determining the level of expression of at least one of: (i), at least one biomarker of R / SI balance. The at least one biomarker is at least one of IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNC13D and WDTC1 or any combination thereof; and (ii), at least one biomarker of resistance. The at least one biomarker is at least one of IFNy, CXCL10, MCP-2, CXCL11, CXCL9, G0LGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ or any combination thereof; and at least one biomarker of systemic inflammation. The at least one biomarker is at least one of CCL4, IL6, CCL3, CCL20, IL8, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 or any combination thereof. It should be understood that each of the detecting molecules is specific for one of the biomarker / s. In some embodiments, the kit optionally further comprises at least one of: b. pre-determined calibration curve / s or predetermined standard / s providing standard expression values of at least one biomarker; and c. at least one control sample.

[0389] In some embodiments, the above-described kit is for use in a method for diagnosing, prognosing and / or classifying sepsis and associated conditions in a subject by determining the R / SI balance of the subject.

[0390] All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0391] The term "about" as used herein indicates values that may deviate up to 1%, more specifically 5%, more specifically 10%, more specifically 15%, and in some cases up to 20% higher or lower than the value referred to, the deviation range including integer values, and, if applicable, non-integer values as well, constituting a continuous range. In some embodiments, the term "about" refers to ± 10 %.

[0392] The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” It must be noted that, as used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise.

[0393] The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and / or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and / or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

[0394] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e., “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of’ “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0395] As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and / or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

[0396] It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.

[0397] Throughout this specification and the Examples and claims which follow, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Specifically, it should understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures. More specifically, the terms "comprises", "comprising", "includes", "including", “having” and their conjugates mean "including but not limited to". The term “consisting of means “including and limited to”. The term "consisting essentially of" means that the composition, method or structure may include additional ingredients, steps and / or parts, but only if the additional ingredients, steps and / or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.

[0398] It should be noted that various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range. Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases "ranging / ranges between" a first indicate number and a second indicate number and "ranging / ranges from" a first indicate number "to" a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals there between. As used herein the term "method" refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.

[0399] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub combination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

[0400] Various embodiments and aspects of the present invention as delineated herein above and as claimed in the claims section below find experimental support in the following examples. Disclosed and described, it is to be understood that this invention is not limited to the particular examples, methods steps, and compositions disclosed herein as such methods steps and compositions may vary somewhat. It is also to be understood that the terminology used herein is used for the purpose of describing particular embodiments only and not intended to be limiting since the scope of the present invention will be limited only by the appended claims and equivalents thereof.

[0401] The following examples are representative of techniques employed by the inventors in carrying out aspects of the present invention. It should be appreciated that while these techniques are exemplary of preferred embodiments for the practice of the invention, those of skill in the art, in light of the present disclosure, will recognize that numerous modifications can be made without departing from the spirit and intended scope of the invention.

[0402] EXAMPLES

[0403] Experimental procedures

[0404] The FUSE cohort

[0405] The FUSE cohort comprises 125 sepsis patients and 284 healthy subjects. Patient characteristics are described in [Ricano-Ponce, I. et al. BMC Infect Dis 22, 778 (2022)]. In brief, included participants enrolled between May 2017 and November 2019 in the Hospital for Infectious Diseases and Pneumology "Victor Babe” Craiova, Romania, and the academic hospital serving Dolj county in south-west Romania; all with an Eastern European ancestry. Inclusion criteria: above 18 years of age, with a diagnosis of sepsis according to the ACCP / SCCM Consensus Conference criteria. Subjects with diagnosis of inherited or acquired immunodeficiency (HIV, chemotherapy or prolonged steroid treatment) were excluded. Healthy controls (>18 years old), with negative medical history and under no prescribed or self-administered medication, were recruited at the Human Genomics Laboratory, University of Medicine and Pharmacy of Craiova. Approval was obtained from the local institutional review boards and all participants signed the informed consent form. Sample collection was performed before initiation of antibiotic therapy. Classification as severe and non-severe sepsis in the FUSE cohort was determined using the quick SOFA (qSOFA) score (GCS < 15, respiratory rate >22, systolic BP < 100). Complete SOFA scores using the Sepsis 3 criteria were not available.

[0406] All samples were collected within 24h from diagnosis between 07:00-10:00 AM, and EDTA plasma was separated within 4h since collection at the Human Genomics Laboratory, University of Medicine and Pharmacy of Craiova. Circulatory inflammatory proteins were measured in plasma using the targeted Olink INFLAMMATION panel (v.3021, 92 proteins) with a proximity extension assay (PEA) used by OLINK proteomics. Protein concentrations were reported as log2 transformation in a normalized protein expression (NPX) scale. Circulating concentrations of ferritin, interleukin IL6, IL1 receptor antagonist (IL1RA), IL18, and IL18 binding protein (IL18BP) were measured with Ella Simple Plex Cartridge Kits (ProteinSimple, San Jose, USA) according to the manufacturer’s protocol.

[0407] Gene expression data from PBMCs includes 125 sepsis and 284 controls, and gene expression data for monocytes includes 36 sepsis and 15 controls from the FUSE cohort. RNA extraction and RNA-sequencing of PBMCs and monocytes were then performed.

[0408] Additional datasets

[0409] The inventors' primary analysis in Figure 5A-5E is based on the following sepsis and moderate infections datasets, as well as moderate inflammation (autoimmunity):

[0410] 1. Sepsis, PBMCs (the FUSE cohort) [Ricano-Ponce, I. et al. BMC Infect Dis 22, 778 (2022)] : data from 284 controls and 125 sepsis individuals, as described above. Data was deposited in GEO accession GSE205672 (secure token: ojkpcoochlgxhez).

[0411] 2. Sepsis, monocytes (the FUSE cohort) [Ricano-Ponce, I. et al. BMC Infect Dis 22, 778 (2022)]: data from 36 sepsis and 15 controls, as described above. Data was deposited in GEO accession GSE205672 (secure token: ojkpcoochlgxhez).

[0412] 3. Sepsis in blood dataset: Whole blood expression data from 18 healthy and 52 sepsis samples (GEO accession GSE13904) [Wong, H. R. et al. Critical Care Medicine 37 , 1558-1566 (2009)].

[0413] 4. Septic shock in blood dataset I (referred to as SS-I): Whole blood expression data from 98 children with septic shock and 32 healthy controls (GSE26440) [Wong, H. R. et al. BMC Med 7, 34 (2009)].

[0414] 5. Septic shock in blood dataset II (referred to as SS-II): Whole blood expression data from 82 children with septic shock and 21 healthy controls (GSE26378) [Wynn, J. L. et al. Mol Med 17, 1146-1156 (2011)].

[0415] 6. Influenza A virus (IAV) dataset: Whole blood expression data from 107 adults with IAV infection (63 moderate, 44 severe) and 52 healthy controls (GSE101702) [Tang, B. M. et al. Nat Commun 10, 3422 (2019)]. 7. Tuberculosis (TB) dataset: Blood transcriptome in a cohort of 26 active TB samples and

[0416] 11 healthy samples that were collected at diagnosis (before the start of treatment) and post- treatment (GSE152532) [Burel, J. G. et al. Tuberculosis 131, 102127 (2021)].

[0417] 8. A dataset of five bacterial and fungal infections: Transcriptomic responses of human PBMCs from 8 individuals with 5 pathogenic stimulations at 4h post infection (32 samples). Stimulations are: A. fumigatus, C. albicans, P. aeruginosa, S. pneumoniae and M. tuberculosis. Stimulation with RPMI was used as a control (8 samples) (GSE131590) [Le, K. T. T. et al. Front. Immunol. 10, 2508 (2019)].

[0418] 9. S. aureus infection dataset: PBMCs data from 46 human individuals with S. aureus infection and 10 healthy controls (GSE16129) [Ardura, M. I. et al. PLoS One 4, e5446 (2009)].

[0419] 10. LPS dataset: Expression in primary macrophages of mouse inbred strains. 89 samples from different strains were exposed to bacterial lipopolysaccharide (LPS) and 86 samples were mock- treated and use as controls (GSE38705) [Orozco, L. D. et al. Cell 151, 658-670 (2012)].

[0420] 11. Autoimmune disease dataset (SLE and SSc): Isolated cells from 26 CD45+ cell types derived from blood samples across 63 healthy subjects, 60 SLE patients and 45 SSc patients (E- GEAD-397) [Ota, M. et al. Cell 184, 3006-3021.el7 (2021)].

[0421] In addition, the inventors used the following datasets of single-cell profiling (dataset 12) and time- series datasets (datasets 13-17) in specific analyses:

[0422] 12. Sepsis and moderate infection: data of single-cells [Reyes, M. et al. Nat Med 26, 333-340 (2020)]. The data includes scRNA-Seq of 19 healthy controls, 10 urinary tract infection (UTI) patients showing clear symptoms of sepsis (i.e., persistent organ dysfunction), and 10 UTI patients that are classified as moderate infection (leukocytosis but no organ dysfunction). The two groups are referred to as URO and Leuk-UTI in the original data publication. All disease patients were enrolled within 12 hours of presentation to the emergency department and within 12 hours of antibiotic treatment, and most recruited subjects are older adults [Reyes, M. et al. Nat Med 26, 333-340 (2020)]. This dataset consists of subpopulations of monocytes, T, B and NK cells. Monocytes: 29,420 single monocytes that were previously categorized into four subpopulations (MS1-MS4). B cells: 6603 cells in two previously-defined subpopulations BS1, BS2. T cells: 25472 single cells in two previously-defined subpopulations TS1,TS2. NK cells: 6101 single cells in two previously-defined subpopulations NS1,NS2. Dendritic cells were not analyzed due to their low numbers of cells. Each subpopulation was analyzed separately in Figures 5G and Figure 7. For each individual and each subpopulation, the molecular response of R (or SI) was calculated by comparison of R levels (or SI levels) of all relevant monocytes from the selected patient against the relevant R levels (or SI levels) of the relevant monocytes from all control subjects (t test p value).

[0423] 13. Time series data of sepsis: Expression profiling of whole blood for up to 5 days for 35 sepsis patients and 18 healthy controls (GSE54514) [Parnell, G. P. et al. Shock 40, 166-174 (2013)].

[0424] 14. Time series data of murine Staphylococcus aureus and Escherichia coli in vivo bacterial infections: Expression profiling of blood for up to 24 hours. For S. aureus, IS infected samples and 29 control samples were included. For E. coli, 40 infected samples and 10 controls samples were included (GSE33341) [Ahn, S. H. et al. PLoS One 8, e48979 (2013)].

[0425] 15. Time series data of IAV and rhinovirus in vivo viral infections: Blood expression data from 70 healthy adults that developed respiratory infection (45 IAV infection and 25 rhinovirus infection) before the infection (controls) and at 4 timepoints over 6 days after the initiation of the infection’s symptoms (GSE68310) [Zhai, Y. et al. PLoS Pathog 11, el004869 (2015)].

[0426] 16. Time series data of in vivo Ebola infection: Transcriptional profiles from liver from 10 Collaborative Cross mice infected with mouse-adapted Ebola virus (MA-EBOV) in 3 time points; a total of 30 infected samples (GSE130629) [Price, A. etal. Cell Reports 30, 1702-1713. e6 (2020)]. Sepsis and sepsis recovery, monocytes'. Monocytes isolated from blood of 8 patients during Gram- negative sepsis and following their recovery (1-3 months post sepsis recovery). Monocytes from 6 healthy donors were also profiled. (GEO accession GSE46955) (Shalova IN, et al., Immunity. 17;42(3):484-98 (2015)). For the FUSE dataset, the inventors mapped reads to reference transcripts using Bowtie2 software and then calculated the gene expression level (FPKM) for each sample with the RSEM software. For the remaining datasets, the preprocessed data was downloaded from the GEO database. All datasets were log2-transformed. For every gene expression data set, the inventors applied standardization of genes, biomarkers (gene biomarkers) based on the distribution in the healthy (no infection; time 0 in cohorts 10-12) individuals. One additional cohort (PROVIDE) is detailed below.

[0427] Validation cohort (PROVIDE)

[0428] PROVIDE is a randomized controlled immunotherapy trial carried out in patients diagnosed with sepsis according to the Sepsis-3 criteria [Singer, M. et al. JAMA 315, 801-810 (2016)], caused by either community-acquired pneumonia, healthcare-associated pneumonia, ventilator-associated pneumonia, acute cholangitis, or primary bloodstream infection. Patients were recruited in 14 study sites in Greece, in accordance with the applicable rules concerning the review of research ethics committees and informed consent (EudraCT 2017-002171-26; approval by the National Ethics Committee of Greece 78 / 17; approval IS 75-17 by the National Organization for Medicines of Greece; Clinicaltrials.gov registration NCT03332225). In order to classify patients into immunological endotypes, serum ferritin concentration and mHLA-DR expression were measured. Patients with serum ferritin concentrations of 4420 ng / mL or above were classified as ‘hyperinflammation’ (MALS) [Kyriazopoulou, E. et al. BMC Med 15, 172 (2017)], regardless of their mHLA-DR expression. Patients with mHLA-DR expression of 5000 antibodies bound per cell (Ab / cell) or less and ferritin concentrations lower than 4420 ng / mL were classified as having ‘immunosuppression [Ref 1; Karakike, E. et al. Innate Immun 14, 218-228 (2022); Giamarellos- Bourboulis, E. J. et al. Cell Host Microbe 27 , 992-1000.e3 (2020); Dbcke, W.-D. et al. Clin Chem 51, 2341-2347 (2005)]. If neither the criteria for MALS nor immunosuppression were fulfilled, patients were categorized as ‘unclassified’ .

[0429] Resistance (R), systemic inflammation (SI) and the R / SI balance

[0430] Calculation of Resistance (R) level

[0431] Experimental design

[0432] To characterize the host defense during infection, the inventors recently collected longitudinal data during in vivo IAV infection across 33 genetically distinct mouse strains of the collaborative cross (CC) cohort [Figure 1A in Ref 5, also incorporated herein by reference]. Infection was performed using the H1N1 influenza virus, particularly the PR8 virus strain. Lung transcriptomes were recorded both in steady-state (before infection) and at multiple time points during infection. Several phenotypes were also recorded for these mice, such as the viral burden in the lungs, whole- body weight loss and breathing dysfunction. The selected time interval encompasses the initial incubation period between exposure to the virus and the onset of systemic symptoms, and the acute stage that is characterized by an exponential increase in viral burden, pronounced symptoms, and robust immune responses.

[0433] Several lines of evidence confirm the validity of the collected lAV-inf ection data. The inventors found substantial diversity in the host response, such as viral burden and antiviral responses [e.g., Figure IB in Ref 5, also incorporated herein by reference]. The response is largely coordinated with the viral burden, as expected [Figures 1C in Ref 5, also incorporated herein by reference]. Finally, the inventors also observed substantial differences in the ability to tolerate disease. For instance, as shown in Figure 1C in Ref 5, even in the presence of a similar viral burden (viral mRNA expression of around 5, log scaled, at 96h p.i.), some mice lost only 7-10% of their weight (high disease tolerance) while others lost more than 20% of their weight (low disease tolerance).

[0434] Computational pipeline to identify the central gene programs of the host defense

[0435] To model the host defense against infections, the gene (biomarker) expression data was first filtered and Log2 transformed across the CC mice, and then an autoencoder was used to reduce the multi-dimensionality of the lAV-infection data (i.e., multiple gene biomarkers, host genetic backgrounds and time points) into a two-dimensional space (Figure 28A and Figure 28B). Additional methodological details are presented in Supp Figure 10 in Cohn et al. 2022 [Ref 5]. In this arrangement, referred to as a “map”, each gene biomarker is embedded at a certain coordinate within the two-dimensional space. The construction of the map relied on a ‘similarity rule’: The closer two gene biomarkers are to each other in the map, the higher the similarity of their transcriptional responses in all measured strains and at all time points. The horizontal and vertical dimensions of the map were referred as “axis T” and “axis R”, respectively.

[0436] In many individuals, a gradual change in expression levels was observed over the T and R axes (see color coding of several individuals in Figures 28C). Different individuals showed distinct directions and distinct rates of change along their gradients. This organization indicates the existence of two regulatory programs, denoted T and R, which underlie the heterogeneity of gene expression along the T and R axes, respectively.

[0437] To derive quantitative metrics for the activation level of programs T and R in any given individual, the gradients of gene expression were leveraged along the T and R axes. As the gradient of an individual can be decomposed into two gradients that run along the two axes, the personal activation levels of programs T and R can be represented by two quantitative metrics: the T score reflects the gradient along axis T (the 'activation level of T', or 'T state' or 'T level') and the R score reflects the gradient along axis R (the 'activation level of R', or 'R state' or 'R level') calculated using a linear deconvolution approach. The calculation takes as input the positions of all gene biomarkers in the map (see formal details of the deconvolution calculation in the next section: "Formal calculation of the R level"). Positive and negative activation levels indicate increasing and decreasing gradients along the axes, respectively, and a zero level indicates an absence of a gradient. A two-dimensional representation of individual states by their T and R levels is presented in Figure 28D. For instance, an individual with a bottom-left to top-right gradient is scored by positive T and R levels (e.g., individual #3 in Figure 28C and Figure 28D) and an individual with a top-left to bottom-right gradient is scored by negative T and positive R (e.g., individual #1 in Figure 28C and Figure 28D). As the quantitative T and R levels were primarily increasing during inflammation [Figure IE Ref 5], the levels of each program scale from inactivation (negative values) to activation (positive values; Figure 28D).

[0438] The host defense strategies have been classified into two broad categories: resistance strategies that sense and react to eradicate the pathogen, and disease-tolerance strategies that enable the host to promote health in the presence of pathogen [Flerlage, T., et al., Nat. Rev. Microbiol. 19, 425- 441(2021); Martins, R., et al, Annu. Rev. Immunol. 37, 405-437 (2019); Soares, M.P., Teixeira, L., and Moita, L.F. Nat. Rev. Immunol. 17, 83-96 (2017); Soares, M.P., et al, Trends Immunol. 35, 483-494 (2014); Chovatiya, R., and Medzhitov, R. Mol. Cell 54, 281-288 (2014); Medzhitov, R., et al, Science 335, 936-941 (2012); Schneider, D.S., and Ayres, J.S. Nat. Rev. Immunol. 8, 889-895 (2008)].

[0439] Comparisons with established hallmarks of disease-tolerance and resistance strategies - including (i) molecular functions, (ii) response to stressors, and (iii) organismal phenotypes - indicated that the T and R programs manifest the main hallmarks of disease-tolerance and resistance, respectively [see Figure 2 in Ref 5 for details].

[0440] Multiple lines of evidence showed that the identified programs are part of a generic molecular response. Particularly, a highly significant diversity of T and R levels is observed (i) in multiple immune and non-immune cell types, (ii) in both mouse and human, (iii) in both the lung tissue and in human blood samples, and (iv) in multiple types of infections, in which there is activation of these programs during the course of infection [see Figure IE and Figure 3 in Ref 5].

[0441] Formal calculation of the R level.

[0442] The R / T map, as defined above, allows to calculate the R and T levels of each new sample. Formally, for a transcription profile of given individual i, the R and T levels are defined using a linear model that combines genome-wide expression profiling with prior knowledge on the weights of each gene biomarker: where Z^ is the vector of all relative measured

[0443] (log2-transformed) gene-expression levels of individual i, VTand VRare the pre-defined vectors of weights of all gene biomarkers for the T and R programs, respectively [Ref 5]. Particularly, the gene biomarker- weights in VTand VRare the pre-defined coordinates of all gene biomarkers in the T and R axes of the R / T map, respectively, indicates the “T level” of individual i, and indicates the “R level” (“R state”) of individual i. b is a constant. The R level is subsequently used in the analysis of sepsis. After the calculation of R levels, standardization of the calculated levels is applied based on the healthy controls in each data set - i.e., subtracting the mean level (centering) and dividing by the standard deviation of the control subjects. This standardization was done separately for the R, T and SI levels and was done separately in each data set.

[0444] Calculation of Systemic Inflammation (SI) level

[0445] Experimental design

[0446] For the calculation of systemic inflammation states, the inventors recently used two cohorts of healthy individuals with different characteristics and distinct origins [Ref 7]. One cohort consists of healthy young adults with body mass indices (BMIs) in the normal range (75% aged 18 to 40 with BMI 18 to 25 kg / m2; n=473; data from the 500-FG Project) [Rob Ter Horst et al. Cell, 2016. 3;167(4):1111-1124]. For the other cohort, the inventors sampled older adults who are either overweight or obese (aged 53 to 78 with BMI > 27 kg / m2; n=126) and are generally healthy (e.g., did not have diabetes and carotid atherosclerosis) [Rob Ter Horst et al. Cell, 2016. 3 ; 167(4): 1111- 1124]. For simplicity, these two healthy cohorts are referred as “normal BMI” and “obesity” cohorts, respectively. These individuals were extensively phenotyped with clinical and physiological parameters (in short, “parameters”), including metabolomics data consisting of 168 lipoprotein parameters (a total of 292 and 259 parameters in the obesity and normal-BMI cohorts, respectively; referred to as ‘clinical / metabolomics data’). The relative measured levels of each parameter were used - that is, for each parameter, its measured levels were centered and scaled across the entire cohort (for simplicity, relative levels are also referred to as ‘measured levels’). Given the limited number of missing values (an average of 5% missing values across parameters), all analyses were performed without any data imputations. In all individual-level analyses, parameters with more than 65% missing values were omitted.

[0447] A pipeline to identify the central systemic inflammatory states from clinical / metabolomics data

[0448] The input is the clinical / metabolomics data, which is a matrix of measured levels for all clinical metabolomics parameters across individuals in the obesity cohort. The model was constructed in several steps (Figure 29A). Data transformation was first applied in order to amplify signals of similarity among clinical parameters. The transformed data reflects the relations between each clinical parameter k and lipoprotein parameter i while accounting for covariates such as age, BMI and gender. After this transformation, called ‘covariate-corrected lipoprotein-based transformation’, transformed values were then scaled so that the transformed profile of each clinical parameter had unit length. Overall, if two clinical parameters are correlated (after the transformation), it means that they have similar interrelations with lipoproteins. The transformation indeed amplifies the correlations among parameters [see Ref 7 : Figure 1 - figure supplement 3A-C].

[0449] In the next step, the structure was inferred from the transformed data using dimensionality reduction (principal component analysis (PCA)). The result is a low-dimensional space (called the ‘clinical / metabolomics map’) in which each clinical, physiological and metabolomics parameter is embedded in a certain position (referred to as ‘weights’) within the space. The two dimensions of the map are referred to as ‘axis IM1’ and ‘axis IM2’ (Figure 29B). As discussed below, the IM1 axis refer to metabolic syndrome (MetS) and the IM2 axis refer to systemic inflammation. In accordance, IM1 and IM2 are also referred to as MetS and SI, respectively.

[0450] In this map, the various parameters fall in a circular pattern along a nearly continuous spectrum. The circular organization of the map reflects a relatively simple structure of interrelations among parameters: proximal parameters are positively correlated and parameters that are located in opposite sides of the map are negatively correlated. It was confirmed that the positions of parameters in the map of the obesity cohort are similar to the positions of parameters in a map that was constructed using the normal-BMI cohort [Figure 1C, in Ref 7], despite major differences in age and BMI ranges. In addition, the map is also consistent in separate analyses of males and females [Figure 1C, in Ref 7].

[0451] The map allowed to formulate a quantitative metric for the activation level of programs MetS and SI in a given individual. To this end, the inventors leveraged the gradients of gene expression along the MetS and SI axes (Figure 29C). As the gradient of an individual can be decomposed into two gradients that run along the two axes, the personal activation levels of programs MetS and SI can be represented by two quantitative metrics: the MetS score reflects the gradient along axis MetS (the 'activation level of MetS', ‘MetS level’, or the 'MetS state') and the SI score reflects the gradient along axis SI (the 'activation level of SI', ‘SI level’, or 'SI state') (calculated using a linear deconvolution approach). The calculation of states takes as input the position (referred to as ‘weights’) of each clinical / metabolomics parameter in the clinical / metabolomics map (see formal details in the next Section: "Formal calculation of the systemic inflammation (SI) level"). Positive and negative activation levels of MetS or SI indicate increasing and decreasing gradients along the axes, respectively, and a zero level indicates an absence of a gradient. A two-dimensional representation of individual states of MetS and SI are presented in Figure 29D. Subsequent analysis of the two scores with established hallmarks of metabolic syndrome and systemic inflammation indicated that the MetS and SI programs manifest the main hallmarks of metabolic syndrome and systemic inflammation, respectively. Thus, the SI state refer to the level of systemic inflammation and the MetS state refers to the level of metabolic syndrome.

[0452] Formal calculation of the systemic inflammation (SI) level.

[0453] The SI / MetS map, as defined above, allows to calculate the SI and MetS levels of each new sample. Formally, for a given individual i, the SI and Mets states are defined using a linear model combining genome-wide expression profiling with prior knowledge on the weight of each gene (biomarker): where Ziis the vector of relative measured levels across all parameters of individual i, VMetsand are the pre-defined vectors of positions (weights) of all parameters for the MetS and SI programs, respectively,sindicates the “MetS level” (“MetS state”) of individual i, and indicates the “SI level” (“SI state”) of individual i. bi is a constant. The SI level is subsequently used in the analysis of sepsis.

[0454] There are two alternative ways to calculate the SI level (Figure 29 A):

[0455] 1) Using clinical / metabolomics data. In such case the input Ziis the vector of relative measured levels across all clinical and metabolomics parameters of individual i, and VMetsand VS[are the positions of all clinical and metabolomics parameters in the MetS and SI axes of the clinical / metabolomics map.

[0456] 2) Using gene-expression data. In such case the input Ziis the vector of relative (log2- transformed) expression levels across all gene biomarkers of individual i, and VMetsand are the positions of all gene biomarkers in the MetS and SI axes of the gene biomarker- expression SI / MetS map.

[0457] In all embodiments of this patent, the inventors refer always to the ‘ gene biomarker- expression SI / MetS map’, thus for simplicity it is denoted the ‘SI / MetS map’.

[0458] After the calculation of SI levels (either using gene biomarker expression or clinical / metabolomics data), standardization of the calculated levels is applied based on the healthy controls in each datasets - i.e., subtracting the mean level (centering) and dividing by the standard deviation of the control subjects. This standardization is applied separately for the MetS and SI levels and is applied separately for each data set. Calculation of ‘R / SI balance ’ state

[0459] The 'R / SI balance' score was then calculated by the projection on the R-SI diagonal (as illustrated in Figure 5E; performed after the standardization of R and SI levels). This calculation is equivalent to substruction of the SI level from the R level.

[0460] Protein biomarkers of R, SI and R / SI balance

[0461] When using protein markers for the calculation of R, SI, and R / SI, the calculation is performed as follows. 1) Calculate the relative measured level: measurements of each protein are centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples. . 2) The SI state (SI level) is the average of the relative measured levels of the SI protein markers 3) The R state (R level) is the average of the relative measured levels of the R protein marker. 4) Calculation of the R / SI balance was done as described in Figure 5E but using the R and SI states from that were calculated based on protein markers (in steps 2,3) rather than gene markers.

[0462] Using protein markers, the R level is calculated using the formula wherein:

[0463] Zi is the 1 -length vector that includes the average of relative measured levels across all protein markers of individual t; Relative protein-expression levels are calculated as follows: each protein is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0464] VTand VRare pre-defined 1-length vectors, VR= (1) and VT= (0). sRlis the R level of individual t; and bi is a constant.

[0465] Using protein markers, the SI level is calculated using the formula sMletsVMets', wherein:

[0466] Zj is the 1-length vector that includes the average of relative measured levels across all protein markers of individual t; Relative protein-expression levels are calculated as follows: each protein is centered and divided by standard deviation based on the distribution in the healthy samples - that is, only the healthy samples are used for the calculation of the standardization that is subsequently applied on all samples.

[0467] VMetsand Vs / are pre-defined 1-length vectors, Vs / = (1) and VMets= (0). is the SI level of individual i; and b( is a constant.

[0468] Calculation of R and SI levels in PROVIDE

[0469] The R and SI levels were originally calculated using gene biomarker expression data based on a 2D map of gene parameters [Ref 5, 6, 7]. Using the FUSE cohort, the inventors calculated the positions of 92 protein (rather than genes) in a 2D protein map where R and SI as the main axes - particularly, the inventors used the correlations with R and SI levels across the FUSE individuals as the positions of proteins in the R-SI protein map. As independent confirmation for the quality of this FUSE protein map, the inventors calculated the correlation between each clinical parameter and each protein in the PROVIDE cohort. In Figure 14B, the FUSE protein map is presented as a scatter plot where each protein is a dot in a 2D space. The color coding of this map is according to the correlations of each protein with a certain clinical parameter in the PROVIDE cohort. This visualization highlights a clear organization of the map in various directions, confirming the validity of the FUSE protein map in the independent PROVIDE cohort. The FUSE protein map was thus used as the basis for the calculation of R and SI levels of each PROVIDE patient. In the same way that the inventors used transcriptome data relying on the map of gene / biomarkers parameters for the calculation of R and SI levels, in the PROVIDE cohort the inventors used the map of protein parameters for the calculation of R and SI levels.

[0470] Survival analysis for the R / SI-based endotypes

[0471] Kaplan-Meier plots were constructed to evaluate differences in survival rate among the R / SI-based endotypes. Survival was measured from the date of hospital admission to date of discharge (restricted to 28 days). Log-rank test was performed to evaluate the significance of survival differences between endotypes. The COX proportional hazards model estimated the hazard of death (with age and gender as additional covariates), either for all sepsis patients or for specific subtypes of sepsis identified using the ferritin (MALS) and mHLA-DR (immunosuppression) biomarkers.

[0472] Functional analysis

[0473] Gene sets that are increasing / decreasing in sepsis (Figure 10D) were taken from a previous annotation [Cheng, S.-C. et al. Nat Immunol 17, 406-413 (2016)]. Systematic analysis of biological functions (Figure 16, and Tables 4 and Table 5) was applied in several steps. First, the inventors calculated the correlation of every gene biomarker to the R / SI-balance score across all individuals (applied separately in each sepsis dataset). Next, for each gene biomarker, the mean correlation was calculated across all cohorts. This average is the general ranking of genes as markers of the R / SI balance score. The ‘top ranked genes’ are those 300 genes (biomarkers) with average correlation > 0.65 for good balance and 300 genes with average correlation <-0.45 for impaired balance (Tables 4A and 4B, respectively) . Finally, for a given functional class and a given set of 300 R / SI balance markers, the enrichment p- values were calculated using a hyper geometric test (Table 5). The inventors tested all functional classes in the Reactome and MSigDB’s CGP collections. All reported results are FDR-adjusted p-values (q values).

[0474] The quiescence gene set (Figure 16) was taken from [Cuitino, M. C. et al. Cell Reports 27, 3547- 3560.e5 (2019)]. In brief, these genes were found as markers in quiescent (GO) embryonic cells and in cycling (Gl, Gl-S, and S-G2-M) embryonic cells, respectively (embryonic days 10.5 (E10.5), El 1.5, and E13.5).

[0475] Representative genes are presented in Table 4C-4E with the following criteria: either a gene with average correlation >0.6 and minimal correlation >0.4, or a gene with average correlation <-0.6 and maximal correlation <-0.4.

[0476] Data and code availability

[0477] RNA-seq data have been deposited in the GEO database under accession number GSE205672 (https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE205672; secure token: ojkpcoochlgxhez). The analysis is not using custom code.

[0478] EXAMPLE 1

[0479] Characterization of the resistance and systemic inflammation states in sepsis

[0480] The inventors aimed to identify predefined transcriptional immune programs that are relevant for this study. To this end, the inventors constructed an unbiased set of 76 candidate immune programs, and systematically benchmarked each of these programs using two criteria: a high covariation during infections in general, and during sepsis in particular. The analysis identified two best-performing programs: first, a ‘resistance’ (R) program, with high covariation during infections as well as specificity to infections, and second, the ‘systemic inflammation’ (SI) program, with the best covariation during sepsis (Example 18). Given that programs R and SI were originally identified in another context (R - influenza A virus (IAV) infection in mice [Ref 5], SI - chronic systemic inflammation in humans [Ref 7]; Example 19), in the following the inventors confirmed the general relevance of these two programs in human blood samples, during moderate infections and sepsis. Several lines of evidence confirmed the relevance of R and SI in moderate infections. First, analysis of inter-individual and inter-gene variation indicates that both R and SI are valid for the study of human blood, in healthy subjects, during both bacterial and viral infections (Example 17). Second, both SI- and R-associated genes are responding during viral and bacterial infections (Figure 2A, 2B, Example 18, Figure 38). The inventors note that the R and SI were confirmed as distinct programs: each program has significant contribution to the variation during infection (Example 17) [Ref 5, 7], the two programs are linked to a distinct inflammatory plasma state (Figure 2C) [Ref 5, 7], and only SI (but not R) is responding in systemic inflammation with negative blood culture (Figure 2D).

[0481] To comprehensively interrogate sepsis, the inventors measured transcriptomes of PBMCs derived from the blood of sepsis patients (n=125) and healthy control subjects (n=284) (the FUSE cohort, Experimental Procedures). The levels of resistance (R) and systemic inflammation (SI), two major immune programs during infection, were then determined (Figure 1A) using previously defined gene signatures [Ref 5, 7]. Several lines of evidence support the validity of the inferred R and SI levels in PBMCs. First, using measurements of plasma proteins in the FUSE cohort, the inventors confirmed that the induction of both R and SI is positively correlated with a variety of immune activation markers, consistent with the notion that each of these two programs is part of the host immune response (Figure IB and Figure 1C). Second, in line with previous reports [Ref 5, 7], R and SI differed in their associations to plasma biomarkers: the plasma concentrations of IFNy, CXCL10 and CXCL11 proteins in sepsis were mainly correlated with R levels of PBMCs (Pearson's r =0.46, 0.47, 0.21, P <10"5, 10"5, 0.07, respectively), whereas plasma concentrations of IL6 and IL18bp proteins in sepsis were mainly correlated with SI levels of PBMCs (Pearson's r = 0.71, 0.34, P < 1015, 103) (Figure IB and Figure 2C). Finally, R and SI levels explain a large fraction of the global transcriptional response in sepsis (Example 17), validating the applicability of the inventors' approach.

[0482] As an additional support, the inventors tested the relevance of R and SI in monocytes from sepsis patients. As monocytes are an abundant cell type in PBMCs of sepsis patients who often suffer from lymphopenia [Reyes, M. et al. Nat Med 26, 333-340 (2020)], while being a first-responder immune cell during infections, the inventors anticipated that much of the R / SI signal in PBMCs stems for the molecular states of monocytes. The inventors therefore generated transcription profiles of isolated monocytes derived from the blood of 36 sepsis and 15 healthy subjects from the FUSE cohort (Experimental Procedures), each transcription profile was subsequently used to quantify the R and SI levels of monocytes in an individual subject. Indeed, the associations of plasma proteins with R and SI were similar in PBMCs and monocytes (Figures IB and Figure 1C versus Figure ID and Figure IE), supporting the hypothesis that the R / SI levels of PBMCs are consistent with R / SI levels of monocytes. Additionally, the monocytes’ R and SI levels explain a significant fraction of the individual variation in thousands of genes (biomarkers) (Example 17), indicating that R and SI are two global programs that together govern transcriptional states of monocytes during sepsis.

[0483] Finally, the associations of gene biomarkers with R and SI in healthy subjects were maintained in sepsis (Figure 3), validating the applicability of the inventor's approach.

[0484] The inventors therefore focused on the R and SI programs to investigate sepsis throughout this study.

[0485] EXAMPLE 2

[0486] A disbalance between the states of resistance and systemic-inflammation is a fingerprint of sepsis

[0487] To investigate sepsis in a systematic and unbiased manner, the FUSE data was combined with multiple additional expression-profiling datasets of varying patient and sample characteristics (Table 1), including: three sepsis and septic shock datasets of gene biomarker expression in blood [Wong, H. R. et al. Critical Care Medicine 37 , 1558-1566 (2009); Wong, H. R. et al. BMC Med 7 , 34 (2009); Wynn, J. L. et al. Mol Med 17, 1146-1156 (2011)], and in addition, several independent datasets of moderate (non-sepsis) infection: gene biomarker expression of human whole-blood or PBMCs during influenza A virus (IAV) infection [Tang, B. M. et al. Nat Commun 10, 3422 (2019)], tuberculosis (TB) infection [Burel, J. G. et al. Tuberculosis 131, 102127 (2021)] in humans, gene biomarker expression of PBMCs during Staphylococcus aureus infection in humans [Ardura, M. I. et al. PLoS One 4, e5446 (2009)], ex-vivo stimulations of human PBMCs with A. fumigatus, C. albicans, M. tuberculosis, P. aeruginosa and S. pneumoniae [Le, K. T. T. et al. Front. Immunol. 10, 2508 (2019)] infections, and an ex-vivo LPS stimulation of murine macrophages [Orozco, L. D. et al. Cell 151, 658-670 (2012)]. Cohorts with blood-derived CD45+ cells of autoimmune disease (SLE and SSc [Ota, M. et al. Cell 184, 3006-3021. el7 (2021)]) were also analyzed. All cohorts include disease subjects and healthy controls, and all datasets of infections had consistent patterns of immune responses (Figure 4A-4C).

[0488] In a systematic dissection of the R and SI states across all individuals from all datasets, the inventors found a unique signature of R and SI in sepsis. In individuals with infections of low or moderate severity or with autoimmune conditions, both R and SI levels are elevated, but R tends to be activated at higher levels compared to the SI levels. However, this response is hampered in sepsis, such that R is low relatively to the SI in these patients (Figure 5A). To further demonstrate the relevance of these finding, each dataset was analyzed independently. In datasets of moderate inflammator...

Claims

CLAIMS:

1. A method for diagnosing, prognosing and / or classifying sepsis and / or associated conditions in a subject by determining the resistance / systemic inflammation (R / SI) balance of said subject, the method comprising the steps of:(a) determining in at least one biological sample of said subject the expression level of a subset of biomarkers to obtain an expression value for each of said biomarkers; and(b) calculating the resistance (R) level and the systemic inflammation (SI) level of said subject from the expression values obtained in step (a), and determining the R / SI balance; wherein a R / SI balance that is smaller than 0, is indicative of sepsis and / or associated conditions in said subject.

2. The method according to claim 1, further comprising:(c) calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (b); and(d) concluding that the R / SI balance is impaired, if the R / SI balance score is negative, wherein the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

3. The method according to claim 2, wherein an impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level.

4. The method according to any one of claims 1 to 3, wherein a subject diagnosed with sepsis and / or related conditions is further classified, wherein:(i) a subject displaying a non-substantial reduction in R / SI balance as compared with a control, is classified as having a moderate R / SI imbalance;(ii) a subject displaying a substantial reduction in R / SI balance with high SI level as compared with a control, is classified as having a high-SI imbalance; and(iii) a subject displaying a substantial reduction in R / SI balance without exceptionally high SI as compared with a control, is classified as having a severe R / SI imbalance;Wherein classifications as high-SI or severe R / SI imbalance are indicative of a negative prognosis.

5. The method according to any one of claims 1 to 4, wherein calculating the resistance (R) level according to step (b) is performed by using the formulaWherein:Ziis the vector of all relative measured (log2-transformed) biomarker-expression levels of individual t;VTand VRare the pre-defined vectors of weights of all biomarkers for the tolerance (T) and R programs, respectively; is the T level of individual t; is the R level of individual t; andbiis a constant, wherein said individual is said subject.

6. The method according to any one of claims 1 to 5, wherein determining / calculating the systemic inflammation (SI) level according to step (b) is performed by using the formula Zi=wherein:Ziis the vector of relative measured (log2-transformed) biomarker expression levels across all biomarkers for individual t;VMetsands / are the pre-defined vectors of weights of all biomarkers for the metabolic syndrome (MetS) and SI programs, respectively;is the MetS level of individual i; is the SI level of individual i; and b is a constant, wherein said individual is said subject.

7. The method according to any one of claims 1 to 6, wherein the expression level of a subset of biomarkers in at least one sample of the subject is determined at the nucleic acid and / or the protein level.

8. The method according to claim 7, wherein the expression level of a subset of biomarkers in at least one sample of the subject is determined at the nucleic acid level by RNA sequencing.

9. The method of any one of claims 1 to 8, wherein the subset of biomarkers for R calculation comprises: k biomarkers with most-positive weight on the R axis, k biomarkers with most-negative weight on the R axis, k biomarkers with most-positive weight on the T axis, and k biomarkers withmost-negative weight on the T axis of the R / T biomarker-expression map for a total of 4k biomarkers, and wherein k is an integer between 10 to 1,000.

10. The method of any one of claims 1 to 9, wherein the subset of biomarkers for SI calculation comprises: k biomarkers with most-positive weight on the SI axis, k biomarkers with most- negative weight on the SI axis, k biomarkers with most-positive weight on the MetS axis, and k biomarkers with most-negative weight on the MetS axis of the SI / MetS biomarker-expression map for a total of 4k biomarkers, and wherein k is an integer between 10 to 1,000.

11. The method of any one of claims 1 to 10, wherein the subset of biomarkers comprises biomarkers characterized by an averaged correlation to the R / SI balance score that is equal or greater than 0.65 and / or a correlation to the R / SI balance score that is equal or smaller than -0.45.

12. The method according to claim 11, wherein said subset of biomarkers for R / SI balance score calculation comprise at least one of: IQ calmodulin-binding motif containing 1 (IQCB1), SET Nuclear Proto- Oncogene (SET), WD repeat-containing protein 89 (WDR89), zinc finger protein 559 (ZNF559), NDC1 Transmembrane Nucleoporin (NDC1), Solute Carrier Family 25 Member 32 (SLC25A32), Nuclear Cap Binding Protein Subunit 2 (NCBP2), ATP Binding Cassette Subfamily E Member 1 (ABCE1), NOP58 Ribonucleoprotein (NOP58), Zinc Finger ZZ- Type Containing 3 (ZZZ3), Biogenesis Of Ribosomes BRX1 (BRIX1), DEAD-Box Helicase 18 (DDX18), Pre-MRNA Processing Factor 39 (PRPF39), Phosducin Like 3 (PDCL3), ADP Ribosylation Factor Like GTPase 5 A (ARL5A), Ubiquitin Like Modifier Activating Enzyme 2 (UBA2), Heat Shock Protein Family A (Hsp70) Member 9 (HSPA9), Major Histocompatibility Complex, Class II, DR Alpha (HLA-DRA), Kinesin Family Member 3C (KIF3C), Furin, Paired Basic Amino Acid Cleaving Enzyme (FURIN), Unc-13 Homolog D (UNC13D) and Tetratricopeptide Repeats 1 (WDTC1) or any combination thereof.

13. The method of any one of claims 1 to 12, wherein the subset of biomarkers for R calculation comprise at least one of: golgin A5 (GOLGA5), Heterogeneous Nuclear Ribonucleoprotein A2 / B1 (HNRNPA2B1), ADP Ribosylation Factor Like GTPase 8B (ARL8B), Seel Family Domain Containing 1 (SCFD1), COPI Coat Complex Subunit Beta 1 (COPB1), Cyclin Dependent Kinase 7 (CDK7), Protein Phosphatase 2 Catalytic Subunit Alpha (PPP2CA), Vesicle Trafficking 1 (VTA1), UDP-Glucose Pyrophosphorylase 2 (UGP2), Signal Recognition Particle 54 (SRP54),COPI Coat Complex Subunit Beta 2 (COPB2), Phosphofurin Acidic Cluster Sorting Protein 2 (PACS2), GRB10 Interacting GYF Protein 1 (GIGYF1), KRAB-A Domain Containing 1 (KRBA1) and Phosphatidylinositol Glycan Anchor Biosynthesis Class Q (PIGQ) biomarkers, or any combination thereof.

14. The method of any of claims 1 to 12, wherein the subset of biomarkers for SI calculation comprises at least one of: dachshund family transcription factor 1 (DACH1), Dysferlin (DYSF), Glycogenin 1 (GYG1), Cysteine Rich Transmembrane Module Containing 1 (CYSTM1), Alkaline Phosphatase, Biomineralization Associated (ALPL), Flotillin 1 (FLOT1), CD82 Molecule (CD82), Glucosylceramidase Beta 1 (GBA), Leucine Rich Alpha-2-Glycoprotein 1 (LRG1), TAR (HIV-1) RNA Binding Protein 1 (TARBP1), Anaphase Promoting Complex Subunit 1 (ANAPC1), N-Myristoyltransferase 2 (NMT2) and Zinc Finger Protein 337 (ZNF337) biomarkers, or any combination thereof.

15. The method according to any one of claims 12 to 14, wherein the expression level of a subset of biomarkers in at least one sample of the subject is determined at the nucleic acid level, wherein said method comprises the step of contacting at least one detecting molecule or any combination or mixture of plurality of detecting molecules with a biological sample of said subject, or with any nucleic acid product obtained therefrom, and wherein each of said detecting molecules is specific for one of said biomarkers.

16. The method of any one of claims 1 to 11 , wherein the subset of biomarkers for R calculation comprise at least two of: Interferon y (IFNy), C-X-C motif chemokine ligand 10 (CXCL10), Monocyte chemotactic protein- 2 (MCP-2), C-X-C motif chemokine ligand 11 (CXCL11) and C- X-C motif chemokine ligand 9 (CXCL9) biomarkers.

17. The method of any of claims 1 to 11, wherein the subset of biomarkers for SI calculation comprises at least two of: Interleukin 6 (IL6), Interleukin 8 (IL8), Chemokine (C-C motif) ligand 3 (CCL3), Chemokine (C-C motif) ligand 20 (CCL20) and Chemokine (C-C motif) ligand 4 (CCL4) biomarkers.

18. The method according to any one of claims 16 to 17, wherein the expression level of a subset of biomarkers in at least one sample of the subject is determined at the protein level, andwherein said method comprises the step of contacting at least one detecting molecule or any combination or mixture of plurality of detecting molecules with a biological sample of said subject, or with any protein product obtained therefrom, wherein each of said detecting molecules is specific for one of said biomarkers.

19. The method according to any one of claims 1 to 18, wherein said biological sample is at least one of a body fluid sample and a cell sample.

20. The method according to claim 19, wherein said sample is at least one of blood sample, plasma sample, tissue sample, cell sample and tissue biopsy.

21. The method according to any one of claims 1 to 19, wherein said sepsis-associated conditions comprise poor long-term and short-term clinical outcomes, said conditions comprise at least one of: any immune dysregulation, hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure, acute renal failure (ARF), cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis.

22. The method according to any one of claims 1 to 21, wherein the method further comprises administering to a subject diagnosed with sepsis and / or associated conditions, an effective amount of at least one therapeutic compound that elevates the R / SI balance in said subject.

23. A prognostic method for determining the susceptibility of a subject to sepsis and / or associated conditions, and / or predicting the outcome of the sepsis and / or associated conditions in said subject, the method comprising the steps of:(a) calculating the R / SI balance of said subject; and(b) classifying said subject as a subject susceptible to sepsis and / or to develop a negative outcome of sepsis, if the R / SI balance of said subject is smaller than 0.

24. The prognostic method according to claim 23, wherein the R / SI balance score is calculated in said step (a), by the steps of:(i) determining in at least one biological sample of said subject the expression level of a subset of biomarkers to obtain an expression value for each of said biomarkers;(ii) calculating the R level and the SI level of said subject from the expression values obtained in step (i); and(iii) calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii).

25. The prognostic method according to any one of claims 23 and 24, wherein a negative R / SI balance score is indicative of an impaired balance score, said impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level; and wherein the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

26. The prognostic method according to any one of claims 23 to 25, wherein the R / SI balance score is calculated as defined for the diagnostic method of any one of claims 4 to 20.

27. The method according to any one of claims 23 to 26, wherein said method further comprises the step of administering to said subject an effective amount of at least one therapeutic compound that elevates the R / SI balance in said subject, wherein said compound is any one of:(i) a therapeutic compound that elevates the R level in a subject, if the subject is classified as having reduced R level; or(ii) a therapeutic compound that reduces the SI levels in a subject, if the subject is classified as having increased SI level; or(iii) a therapeutic compound that elevates the R level and reduces the SI level in a subject, if the subject is classified as having reduced R level and increased SI level.

28. A prognostic method for predicting and assessing responsiveness of a subject suffering from sepsis and / or associated conditions, to at least one compound or a treatment regimen comprising said compound, and optionally for monitoring disease progression, the method comprising the steps of:(a) determining the R / SI balance score in at least one sample of said subject; and(b) classifying said subject as:(i) a responder to said at least one compound or a treatment regimen comprising said compound, if at least one sample obtained after the initiation of said treatment regimen and / or a sample of said subject contacted with said compound displays elevation in theR / SI balance score, as compared with the R / SI balance score determined for a sample obtained prior to said treatment, or a sample not contacted with said compound; or(ii) a non-responder, if at least one sample obtained after the initiation of said treatment regimen and / or a sample of said subject contacted with said compound displays reduction, or no change in the R / SI balance score, as compared with the R / SI balance score determined for a sample obtained prior to said treatment, or a sample not contacted with said compound.

29. The prognostic method according to claim 28, wherein the R / SI balance score is determined / calculated in said step (a), by the steps of:(i) determining in at least one biological sample of said subject the expression level of a subset of biomarkers to obtain an expression value for each of said biomarkers; and(ii) calculating the R level and the SI level of said subject from the expression values obtained in step (i); and(iii) calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii).

30. The prognostic method according to any one of claims 28 and 29, wherein a negative R / SI balance score is indicative of an impaired balance score, said impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level; and wherein the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

31. The prognostic method according to any one of claims 28 to 30, wherein the R / SI balance score is calculated as defined for the diagnostic method of any one of claims 4 to 21.

32. The prognostic method according to any one of claims 28 to 31, wherein said monitoring disease progression comprises at least one of: predicting, determining and / or assessing disease relapse and / or long-term sepsis, and wherein said method further comprises the steps of:(c) repeating step (a) to determine the R / SI balance score in at least one more temporally- separated sample of said subject; and(d) predicting and / or determining disease relapse and / or long-term sepsis in said subject, if at least one temporally separated sample obtained after the initiation of said treatment regimen displays reduction in the R / SI balance score.

33. The method according to any one of claims 28 to 32, wherein said sepsis-associated conditions comprise poor long-term and short-term clinical outcome, said conditions comprise at least one of: any immune dysregulation, hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure, ARF, cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis.

34. The method according to any one of claims 28 to 32, wherein said method further comprises:(e) one of:(i) maintaining the treatment regimen for a subject classified as a responder subject; or(ii) ceasing the treatment regimen for subject classified as a non-responder; and optionally, administering to said non-responder subject an alternative treatment regimen that elevates the R / SI balance score.

35. A method for determining a personalized treatment regimen for a subject suffering from sepsis and / or associated conditions, the method comprising the steps of:(a) calculating the R / SI balance score of said subject;(b) classifying said subject as one of:(i) a subject displaying reduced R level;(ii) a subject displaying increased SI level; and(iii) a subject displaying reduced R level and increased SI level;(c) selecting for said subject a treatment regimen and / or at least one compound that elevates the levels of R / SI balance score.

36. The method according to claim 35, wherein said treatment regimen and / or compound of (c) comprises any one of:(i) a treatment regimen and / or compound that elevates the R level in a subject, if the subject is classified as having reduced R level; or(ii) a treatment regimen and / or compound that reduces the SI levels in a subject, if the subject is classified as having increased SI level; or(iii) a treatment regimen and / or compound that elevates the R level and reduces the SI levels in a subject, if the subject is classified as having reduced R level and increased SI level.

37. The method according to any one of claims 35 and 36, wherein the method further comprises:(d) determining the responsiveness of said subject to said treatment regimen and / or compound, by the method as defined by any one of claims 28 to 33; and(e) one of:(i) maintaining the treatment regimen for a subject classified as a responder subject; or(ii) ceasing the treatment regimen for subject classified as a non-responder; and optionally, administering to said non-responder subject an alternative treatment regimen that elevates the R / SI balance score.

38. The method according to any one of claims 35 to 37, wherein the R / SI balance score is calculated in said step (a), by the steps of:(i) determining in at least one biological sample of said subject the expression level of a subset of biomarkers to obtain an expression value for each of said biomarkers; and(ii) calculating the R level and the SI level of said subject from the expression values obtained in step (i); and(iii) calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii).

39. The method according to any one of claims 35 to 38, wherein a negative R / SI balance score is indicative of an impaired balance score, said impaired R / SI balance score comprises at least one of: (i) reduced R level; (ii) increased SI level; and (iii) reduced R level and increased SI level; and wherein the severity of sepsis and / or associated conditions is negatively correlated with the R / SI balance score.

40. The method according to any one of claims 35 to 39, wherein the R / SI balance score is calculated as defined for the diagnostic method of any one of claims 4 to 21.

41. A method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of sepsis and / or associated conditions in a subject in need thereof, the method comprising the steps of:(a) determining the R / SI balance score of said subject by the steps of:(i) determining in at least one biological sample of said subject the expression level of a subset of biomarkers to obtain an expression value for each of said biomarkers; and(ii) calculating the R level and the SI level of said subject from the expression values obtained in step (i); and(iii) calculating the R / SI balance score by subtracting the SI level from the R level obtained in step (ii);(b) classifying said subject as one of:(i) a subject displaying reduced R level;(ii) a subject displaying increased SI level; or(iii) a subject displaying reduced R level and increased SI level; and(c) administering to said subject a therapeutic compound or subjecting said subject to a treatment regime that elevate the R / SI balance in said subject.

42. The method according to claim 41, wherein said treatment regimen and / or compound administered in step (c), comprises any one of:(i) a treatment regimen and / or compound that elevates the R level in a subject, if the subject is classified as having reduced R level; or(ii) a treatment regimen and / or compound that reduces the SI levels in a subject, if the subject is classified as having increased SI level; or(iii) a treatment regimen and / or compound that elevates the R level and reduces SI level, reduces the SI levels in a subject, if the subject is classified as having reduced R level and increased SI level.

43. The method according to any one of claims 41 and 42, wherein the method further comprising:(d) determining the responsiveness of said subject to said treatment regimen and / or compound, by the method as defined by any one of claims 28 to 33; and(e) one of:(i) maintaining the treatment regimen for a subject classified as a responder subject; or(ii) ceasing the treatment regimen for subject classified as a non-responder; and optionally, administering to said non-responder subject an alternative treatment regimen that elevates the R / SI balance score.

44. The method according to any one of claims 41 to 43, wherein the R / SI balance score is calculated as defined for the diagnostic method of any one of claims 4 to 21.

45. The method according to any one of claims 41 to 44, wherein said sepsis-associated conditions comprise poor long-term and short-term clinical outcome, said conditions comprise at least one of: any immune dysfunction, hypotension, septic shock, organ failure, disseminated intravascular coagulation, morbidity, renal failure, ARF, cognitive impairment, stress disorders, depression, dementia, cardiovascular events, recurrent infections and sepsis.

46. A screening method for identifying and / or evaluating at least one therapeutic compound for the treatment of sepsis and / or associated conditions, the method comprising the steps of:(a) calculating the R / SI balance score of at least one biological sample contacted with a candidate compound, said sample is of a subject suffering from sepsis and / or associated conditions;(b) determining that said candidate compound is a therapeutic compound for sepsis and / or associated conditions if said candidate compound elevates the R / SI balance score, as compared with the R / SI balance score determined for a control sample.

47. The screening method according to claim 46, wherein said candidate compound is at least one of a small molecule, aptamer, a peptide, a nucleic acid molecule and an immunological agent, and any combinations thereof.

48. A diagnostic composition comprising at least one detecting molecule or any combination or mixture of plurality of detecting molecules, and / or means, specific for determining the level of expression of at least one of:(i) at least one biomarker of R / SI balance score, said at least one biomarker comprise at least one of IQCB1, SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3,BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNCI 3D and WDTC1 or any combination thereof; and / or(ii) at least one biomarker of resistance (R), said at least one biomarker comprise at least one of IFNy, CXCL10, MCP-2, CXCL11, CXCL9, , G0LGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ or any combination thereof; and / or(iii) at least one biomarker of said systemic inflammation (SI), said at least one biomarker comprise at least one of CCL4, IL6, CCL3, CCL20, IL8, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP 1, AN APC1, NMT2 andZNF337 or any combination thereof; wherein each of said detecting molecules is specific for one of said biomarker / s.

49. The diagnostic composition according to claim 48, wherein said detecting molecule / s comprise amino acid detecting molecules and / or nucleic acid detecting molecules.

50. A kit comprising: a. at least one detecting molecule specific for determining the level of expression of at least one of:(i) at least one biomarker of R / SI balance, said at least one biomarker is at least one of IQCB 1 , SET, WDR89, ZNF559, NDC1, SLC25A32, NCBP2, ABCE1, NOP58, ZZZ3, BRIX1, DDX18, PRPF39, PDCL3, ARL5A, UBA2, HSPA9, HLA-DRA, KIF3C, FURIN, UNC13D and WDTC1 or any combination thereof; and(ii) at least one biomarker of resistance, said at least one biomarker is at least one of IFNy, CXCL10, MCP-2, CXCL11, CXCL9, GOLGA5, HNRNPA2B1, ARL8B, SCFD1, COPB1, CDK7, PPP2CA, VTA1, UGP2, SRP54, COPB2, PACS2, GIGYF1, KRBA1 and PIGQ or any combination thereof; and(iii) at least one biomarker of said systemic inflammation, said at least one biomarker is at least one of CCL4, IL6, CCL3, CCL20, IL8, DACH1, DYSF, GYG1, CYSTM1, ALPL, FLOT1, CD82, GBA, LRG1, TARBP1, ANAPC1, NMT2 and ZNF337 or any combination thereof; wherein each of said detecting molecules is specific for one of said biomarker / s; said kit optionally further comprises at least one of: b. pre-determined calibration curve / s or predetermined standard / s providing standard expression values of said at least one biomarker; andc. at least one control sample.

51. The diagnostic composition according to any one of claims 48 and 49, or the kit according to claim 50, for use in a method for diagnosing, prognosing and / or classifying sepsis and associated conditions in a subject by determining the R / SI balance of said subject.

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