Identification of high mortality sub-phenotypes of pediatric multiple organ dysfunction syndrome (MODS) for management and treatment thereof

Abstract

Subphenotypes of MODS associated with poor prognosis have been identified using multiomics analysis of pediatric patients with and without sepsis. Compositions and methods for the diagnosis and management of severe MODS are disclosed.

Description

[0001] IDENTIFICATION OF HIGH MORTALITY SUB-PHENOTYPES OF PEDIATRIC MULTIPLE ORGAN DYSFUNCTION SYNDROME (MODS) FOR MANAGEMENT AND TREATMENT THEREOF

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003] This application claims priority to U.S. Provisional Application No. 63 / 650,523 filed May 22, 2024, the entire disclosure being incorporated herein by reference as though set forth in full.

[0004] STATEMENT OF FEDERALLY SPONSORED RESEARCH

[0005] This invention was made with government support under HD047349 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0006] FIELD OF THE INVENTION

[0007] The present invention relates to identification of signaling pathways which promote T cell immune-metabolic dysregulation. More specifically, through longitudinal multiomics analysis, we have identified three pediatric MODS subphenotypes exhibiting pathologic JAK / STAT hyperactivation leading to life threatening T cell dysregulation, causing the most severe forms of MODS.

[0008] BACKGROUND OF THE INVENTION

[0009] Several publications and patent documents are cited throughout the specification in order to describe the state of the art to which this invention pertains. Each of these citations is incorporated by reference herein as though set forth in full.

[0010] Sepsis is defined as life-threatening organ dysfunction that develops in the setting of immune dysregulation (J) and represents a leading cause of adult and pediatric mortality worldwide (2). Pediatric sepsis is associated with distinct epidemiology and outcomes compared to adult sepsis (3), with the majority of pediatric sepsis deaths occurring in immunocompromised children (4-6), typically in the setting of persistent multiple organ dysfunction syndrome (MODS) (7, 8). MODS represents a “final common pathway” by which severe, systemic inflammation arising from diverse clinical insults (e.g. sepsis, trauma, shock) leads to progressive organ failure due to a combination of endothelial, epithelial, mitochondrial, and immunologic dysfunction (9-11),' in children, sepsis is the most common cause of MODS (12- 14).

[0011] Identifying modifiable pathobiology is an essential first step to successful translation of core biologic insights into precision therapeutics for critical illness (15). In pediatric sepsis, both innate and adaptive immune dysfunction (16-18) are associated with secondary infection (19), persistent organ dysfunction (20) and mortality (21). Mitochondrial dysfunction, a hallmark of this sepsis-associated immune suppression (22, 23), is also associated with organ failure in pediatric sepsis (24-26). Yet despite the established link between immune dysregulation and both morbidity and mortality in pediatric sepsis, it remains unclear which host immune factors contribute causally to adverse sepsis outcomes (27).

[0012] SUMMARY OF THE INVENTION

[0013] In accordance with the present invention, a method for identifying a patient at increased risk for severe multiorgan dysfunction syndrome having a poor prognosis is provided. In one embodiment, the method comprises determining in a biological sample obtained from the patient, the expression levels of at least 24 MODS severity signature proteins at MODS onset, and identifying a MODS subphenotype in said patient selected from Group A, Group B or Group C shown in Figure 2C, patients having a MODS Group C severity protein signature being at increased risk for pathologic dysregulation of the IAK / STAT3 signaling pathway at the onset of MODS along with increased disease severity and poor prognosis. In another embodiment, the patient is a pediatric patient and the proteins comprising said MODS severity signature are listed Figure 2D. Protein expression levels of said 24 proteins listed in Figure 2D for example, can be determined in a patient sample selected from whole blood, PBMCs and plasma.

[0014] In yet another embodiment of the invention, the method includes treatment of a subject at risk for severe MODS. Such agents can include at least one agent that modulates JAK / STAT3 signaling is administered to said patient, thereby providing therapeutic benefit to the patient. The agent is preferably an inhibitor of JAK / STAT signaling. The treatment regimen can also include administration of effective amounts of at least one antibiotic, anti-inflammatory agent, anticoagulant, anti-thrombotic agent and / or steroid.

[0015] In one aspect of the invention, a method for identifying a patient at increased risk for severe multiorgan dysfunction syndrome (MODS) is provided. An exemplary method entails determining the expression levels of a panel of MODS severity signature proteins at MODS onset selected from SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, S100P, CTSH, LPO, VWF, SM0C2, DCTPP1, LDLR, VCAN, SIGLEC10, IL- 15, SEMA4C, GDF2, SUSD1, MPIG6B, and EGF; and identifying a patient as having a severe MODS subphenotype when expression levels of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SWOP, CTSH, LPO, VWF, SMOC2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 are elevated and expression levels of SUSD1, MPIG6B, EGF are reduced relative to levels observed in healthy pediatric participants. The identified patients are then diagnosed as being at increased risk for pathologic dysregulation of the JAK / STAT signaling pathway and hypercytokinemia at the onset of MODS, resulting in increased disease severity and increased risk of death. The patient can be an adult, an adolescent or a pediatric patient. In preferred embodiments, the patient is a pediatric patient. Expression levels of said severity signature proteins can be determined in a patient sample including but not limited to whole blood, peripheral blood mononuclear cells, plasma, and serum. In certain approaches, the protein expression levels are determined via a method selected from the group consisting of affinity-based immunoassay, proximity extension assay, aptamer-based proteomic assay, a mass-spectrometry-based assay, cytometry-based protein quantification, and immunohistochemistry.

[0016] Also provided is a method for treating a patient at increased risk for severe multiorgan dysfunction syndrome (MODS). An exemplary method comprises identifying said patient as being at increased risk of severe MODS when expression levels of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SM0C2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 are elevated and expression levels of SUSD1, MPIG6B, EGF at the onset of MODS are reduced relative to levels observed in healthy pediatric participants; and administering to the patient an effective amount of an agent that modulates JAK and / or STAT signaling selected from a small molecule JAK inhibitor, a small molecule JAK3 inhibitor, a biological inhibitor of IL-6 or IFN-y signaling pathway, a nucleic acid based inhibitor, an engineered cytokine trap, a decoy receptor for neutralization of one or more of IL-6, IFN-y for inhibition of STAT- activating cytokines. In certain aspects of the treatment method, the small-molecule JAK inhibitor is selected from ruxolitinib, baricitinib, tofacitinib, upadacitinib, abrocitinib, itacitinib, fedratinib and pacritinib. In other approaches, the the small-molecule STAT3 inhibitor is selected from napabucasin, stattic, Cl 88-9, SD-36, WP- 1066, and silibinin. The inhibitor can be a biological inhibitor of IL-6 or IFN-y pathways, including but not limited to tocilizumab, sarilumab, siltuximab, clazakizumab, olokizumab, sirukumab, emapalumab, fontolizumab, and AMG-811. In certain aspects, the inhibitor is a nucleic acid based inhibitor selected from a siRNA, an antisense oligonucleotide, or a CRISPR complex targeting JAK1, JAK2, TYK2, STAT1, or STAT3. Suitable agents also include an engineered cytokine trap or decoy receptor that neutralizes IL-6, IFN-y and, or downstream STAT- activating cytokines. Combination therapies are also useful in the methods described above. For example, the method can further comprise administering at least one antibiotic, antiinflammatory agent, anticoagulant, or steroid to the patient. Agents so administered can act additively or synergistically to reduce symptoms. Further useful combinations include (i) a JAK inhibitor in combination with an IL- 6 receptor antagonist; (ii) a JAK inhibitor in combination with an IFN-y neutralizing antibody; (iii) an IL-6 antagonist in combination with a corticosteroid; or (iv) a JAK inhibitor in combination with broad- spectrum antibiotics and low- molecular- weight heparin.

[0017] Also provided is a kit for practicing the methods described above. An exemplary kit comprising of (i) agents which bind said MODS severity signature proteins for quantifying expression levels thereof, (ii) a container, (iii) a label, and (iv) instructions for use.

[0018] In another embodiment of the invention, a method for identifying a patient at increased risk for severe multiorgan dysfunction syndrome (MODS) can comprise identifying elevated expression levels of at least two MODS severity signature proteins at MODS onset selected from SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SMOC2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 and decreased expression levels of one, two, or three of SUSD1, MPIG6B, EGF relative to levels observed in healthy pediatric participants, said identified patients being at increased risk for pathologic dysregulation of the JAK / STAT signaling pathway and hypercytokinemia at the onset of MODS, resulting in increased disease severity and increased risk of death. Also provided is a screening method for identifying agents which modulate proteins associated with severe multiorgan dysfunction syndrome (MODS) is disclosed. An exemplary method comprises contacting a first plasma sample obtained from a MODS patient having severe disease and a second plasma sample obtained from healthy patient with an agent, determining expression levels of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, S100P, CTSH, LPO, VWF, SM0C2, DCTPP1, LDLR, VCAN, SIGLEC10, IL- 15, SEMA4C, GDF2, SUSD1, MPIG6B, and EGF in the samples of step a) and c) identifying an agent which reduces levels of at least five of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, S100P, CTSH, LPO, VWF, SMOC2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 and elevate at least one of SUSD1, MPIG6B, and EGF as being effective to reduce MODS symptoms.

[0019] BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figures 1A -1H. Shared proteomic features in pediatric MODS patients with and without sepsis. (Fig. 1A) Schematic of participant cohorts and sample processing pipeline using plasma proteomics and spectral flow cytometry. (Fig. IB) Cohort demographics, exposures, and clinical outcomes, stratified by MODS etiology (i.e. sepsis vs non-sepsis). Comparisons between groups using chi-squared and Wilcoxon rank sum test, as appropriate. (Fig. 1C) Proinflammatory cytokines are increased in patients with MODS but do not differ between patients with and without sepsis, except for IFN-y which is increased in patients with sepsis compared to patients without sepsis. Comparisons between groups by Wilcoxon rank sum test. Box-and-whiskers plots include a box indicating median and interquartile range, and whiskers extending to the data point not further than the 1.5x interquartile range. (Fig. ID) Principal component analysis based on expression of 1,448 proteins analyzed by proximity extension assay in 186 samples from 88 patients with MODS and 25 age-matched healthy control (HC) participants reveals substantial heterogeneity and overlap between MODS patients with and without sepsis. (Fig. IE) Clustered heatmap showing row-normalized protein expression among 88 patients at MODS onset and 25 HC participants. Colored annotation bar indicates MODS subgroup. (Fig. IF) Supporting evidence for k-3 clusters in Fig. 2A. This cumulative distribution function (CDF) plot demonstrates increased cluster stability up to k=3 (as indicated by flatter line), with decreasing cluster stability with finer clustering. (Fig. 1G) Relative Cluster Stability Index (RCSI) is highest at k=3. (Fig. IF) M3C calculates empirical p- values from reference distributions to test the null hypothesis that k=l. In our analysis, at l<=3 the null hypothesis is rejected ( =0.038).

[0021] Figures 2A-2E. (Fig. 2A) Principal component analysis based on expression of 214 severity- associated proteins in 88 patients at MODS onset reveals separation among three MODS subphenotypes defined through Monte Carlo consensus clustering (42). (Fig. 2B) Cumulative incidence of survival to PICU discharge and PICU mortality by MODS day among three MODS subphenotypes. (Fig. 2C) MODS etiology, cohort demographics, exposures, and clinical outcomes, stratified by MODS subphenotype. Comparisons between groups by chi- squared and Wilcoxon rank sum test, as appropriate. (Fig. 2D) Forest plot showing estimated fold-change in normalized protein expression associated with a +1 standard deviation increase in PELOD-2 score for 24 proteins identified through ordinal elastic net regression (44) which differ across MODS subphenotypes. Forest plot includes a point estimate of fold-change and whiskers which represent the 95% confidence interval around this estimate. (Fig. 2E) Heatmap showing normalized protein expression of the 24 proteins identified by ordinal elastic net regression among 88 patients at MODS onset. Colored annotation bars indicate MODS subphenotype and sepsis status. (Fig 2F) PCA resolving three MODS subphenotypes based on expression of 24 elastic net derived severity-associated proteins. PCA dimensionality reduction demonstrates effective resolution of three MODS subphenotypes based on expression of 24 elastic net derived severity-associated proteins, with the majority of variance within the dataset defined by Dimension 1 as expected.

[0022] Figures 3A - 3G. PBMC effector cell abundance and activation state vary by plasma protein-derived MODS subphenotype. (Fig. 3A) PBMC immune phenotype tSNE-CUDA projection demonstrates marked differences in frequency of lymphoid and myeloid lineages between HC participants and patients with MODS, and among MODS subphenotypes. Differential abundance across groups by cell subtype was determined by Kruskal-Wallis test. (Fig. 3B) Stacked bar plots show differences in CD3+ and CD3- cell abundance by MODS subphenotype corresponding to the FlowSOM metaclusters in the tSNE-CUDA projections at left. (Fig. 3C) Compared to HC participants, the abundance of CD8+ T cell subsets are reduced in patients with MODS, with an ordinal trajectory across severity subgroups noted in CD8+ central memory cells, CD8+ effector memory cells, and CD8+ Temra cells. Effector CD8+ cells in Group C patients are significantly reduced in frequency compared to other MODS subgroups. Comparisons between groups by Wilcoxon rank sum test. (Fig. 3D) Shifts in other lymphoid and myeloid subsets across MODS subgroups demonstrate a loss of peripheral non-naive CD4+ and CD8+ T cells, y5-T cells, memory B cells, cytotoxic NK cells, and plasmacytoid dendritic cells, with the most marked differences in the Group C patients with the highest disease severity. Comparisons between groups by Wilcoxon rank sum test. (Fig. 3E) In non-naive CD4+, non- naive CD8+, and cytotoxic NK cells, Ki67 expression is increased Group B and Group C patients compared to Group A (all <0.05), indicative of increased proliferation of these effector cell subgroups. (Fig. 3F) In non-naive CD8+ T cells, CD38 and HLA-DR co-expression is markedly increased Group B and Group C patients compared to Group A (both p <0.01), indicative of CD8+ T cell activation in these groups. (Fig. 3G) Boxplots quantifying differences in proliferation and activation by MODS subphenotype. Comparisons between groups by Wilcoxon rank sum test. Fig. 3H. Representative gating strategy for spectral flow immune phenotyping panel.

[0023] Figures 4A - 4E. Group C patients have a hyperinflammatory plasma protein phenotype characterized by multiple concurrent mechanisms of immune dysregulation. (Fig. 4A) Volcano plot comparing proteomic profile of Group C patients to Group A / B patients, showing that 1061 / 1448 measured proteins were differentially expressed in Group C. (Fig. 4B) After correction for patient age, sex, severity of illness and days since MODS onset with a linear mixed-effects model, 1003 proteins remained differentially expressed in group C. Based on these differentially expressed proteins, Ingenuity Pathway Analysis was used to identify enrichment of 22 canonical pathways in the Group C proteome after B-H correction. (Fig. 4C) Individual patient module enrichment scores calculated using GSVA and MSigDB Hallmark pathways demonstrate markedly increased IL-6 / JAK / STAT3 and IL-2 / JAK / STAT5 signaling in Group B and Group C patients, modest increase in TNF / NFkB signaling in Group B and Group C patients, and an ordinal reduction in PI3K / AKT / MTOR signaling across MODS subphenotypes. Comparisons between groups by Wilcoxon rank sum test. (Fig. 4D) Spearman’s correlation between GSVA module enrichment score and PBMC abundance, activation, and proliferation within MODS patients. Correlations with Benjamini-Hochberg corrected - value <0.05 are shown. (Fig. 4E) Eongitudinal expression of canonical cytokine storm markers demonstrates persistence of immune dysregulation beyond MODS onset. For each panel, longitudinal normalized protein expression is presented by days from MODS onset through day 14. Loess regression lines with 95% confidence intervals are presented for each MODS subphenotype.

[0024] Figures 5A -5G. Cell-specific signatures of immuno-metabolic dysregulation in MODS.

[0025] (Fig. 5A) Study schematic for the transcriptomics experiment, which included 9 patients with MODS and 3 age-matched HC participants. Cells were sorted, sequenced, and analyzed using a bioinformatics pipeline to compare pathway enrichment scores by condition and cell type. (Fig. 5B) PBMC immune phenotype UMAP projection demonstrates differences in lymphoid and myeloid lineages between healthy and Group C patients. Differential abundance by cell subtype was determined by Wilcoxon rank sum test. (Fig. 5C) Stacked bar plots show differences in CD3+ and CD3- cell abundance by group corresponding to the metaclusters in the UMAP projections to the left. (Fig. 5D) Pseudobulk analysis of module enrichment scores across PBMC subsets showed that CD4+, CD8+, and TCRgd-i- T cells from patients with MODS had increased IL-6 / JAK / STAT3 signaling compared to controls while monocytes, NK cells, and B cell IL- 6 / JAK / STAT3 signaling did not differ between groups. (Fig. 5F) Heatmap depicting the correlation between SRSq and inflammatory pathway by cell type. Box color depicts the adjusted R2, and the R2value is displayed for all significant associations. Bivariate plots of single-cell glycolysis and oxidative phosphorylation enrichment scores for both Group C and HC PBMCs. Labeled boxes denote populations of increased and decreased immuno-metabolic pathway enrichment scores. Also shown, at right, ridge plot denotes IL-6 / JAK / STAT3 module enrichment by PBMC subset. (Fig. 5G) The estimated effect of inflammatory pathway signaling on immuno- metabolic activity of effector T and NK cells at the single cell level. Pathway beta coefficients and 95% confidence intervals are derived from mixed effects logistic regression model of the association between module enrichment score and immuno-metabolic profile of non-naive CD4+, non-naive CD8+, and cytotoxic NK cells. Positive coefficients are associated with increased glycolysis / oxidative phosphorylation activity while negative coefficients are associated with decreased glycolysis / oxidative phosphorylation activity among 26 MODS patients with >3 sample timepoints. Solid lines represent individual patient trajectories; dashed lines represent loess regression lines for each MODS subphenotype. Figures. 6A -6F. JAK / STAT3 signaling as a candidate target for precision immunomodulation in a subset of patients with MODS based on plasma proteomics. (Fig. 6A) Study schematic for this analysis, which includes 88 patients with MODS, 18 patients with lEIs, and 25 HC participants. Normalized protein expression was analyzed using GSEA and GSVA to determine similarities in protein expression between MODS subphenotypes and monogenic lEIs. (Fig. 6B) GSEA analysis of population-level enrichment of the Hallmark IL- 6 / JAK / STAT3 Signaling pathway in Group C patients at MODS onset compared to HC participants. (Fig. 6C) Heatmap showing normalized protein expression of 31 JAK / STAT target proteins among 88 patients at MODS onset and 18 patients with inborn errors of immunity (IEI) characterized by STAT1 and STAT3 gain-of-function (GOF) and loss-of-function (LOF) mutations. Colored annotation bars indicate MODS subphenotype and IEI subphenotype. (Fig. 6D) Clustered heatmap of patient-level enrichment of five inflammatory pathways in MODS patients and IEI patients using GSVA. Colored annotation bars indicate MODS subphenotype and IEI subphenotype. (Fig. 6E) Comparison of Hallmark IL-6 / JAK / STAT3 signaling pathway enrichment scores by MODS subphenotype and IEI subphenotype. Comparisons between groups by Wilcoxon rank sum test. (Fig. 6F) Longitudinal IL-6 / JAK / STAT3 module enrichment scores among 26 MODS patients with >3 sample timepoints. Solid lines represent individual patient trajectories; dashed lines represent loess regression lines for each MODS subphenotype.

[0026] Figures 7A-7I. STAT1 and STAT3 signaling promote T cell immunometabolic dysregulation. (Fig. 6A) Study schematic for the scRNA-seq experiment, including 9 patients with MODS and 3 HC participants. Cells were sorted, sequenced, and analyzed using a bioinformatics pipeline to compute module enrichment scores by condition and cell type, transcriptional responses to cytokines, and inferred ligand-receptor interactions. (Fig. 7B) UMAP projection demonstrates differences in lymphoid and myeloid lineages between healthy and Group C patients. (Fig. 7C) Stacked bar plots show differences in CD3+ and CD3- cell abundance by group corresponding to the metaclusters in the UMAP projections to the left. (Fig. 7D) Pseudobulk lymphoid protective module enrichment scores are lower in CD4+naive, CD4+non-naive, and CD8+non-naive T cell populations. Module scores were calculated with UCell, and comparisons were determined by Wilcoxon rank sum test (*** reflects p< 10-16). (Fig. 7) Lymphoid protective module scores across nine lymphoid subsets demonstrate widespread transcriptional dysregulation in Group C lymphocytes. Comparisons were determined by Wilcoxon rank sum test. (Fig. 7F) Transcriptional responses to cytokines inferred using the Immune Dictionary framework reveal increased signaling activity in Group C patients in response to multiple pro-inflammatory cytokines. Module scores were calculated with UCell. Median fold change represents Group C enrichment over HC enrichment. Comparisons between C and HC were determined by Wilcoxon rank sum test. (Fig. 7G) Compared to HC participants, CD8+T cells from Group C patients exhibit increased transcriptional activity in glycolysis and oxidative phosphorylation pathways and suppressed mTOR signaling across all CD8+differentiation states. Comparisons were determined by Wilcoxon rank sum test (*** reflects p< 10-16). (Fig. 7H) Bivariate enrichment scores for glycolysis and oxidative phosphorylation modules in CD8+T cells show a distinct population of metabolically activated cells in Group C. Ridge plots demonstrate that enrichment scores for IL-6, IFN-y, and IL- ip cytokine modules are significantly elevated in these cells. Comparisons between activated and non-activated cells were made using multivariable mixed-effects modeling with cytokine as fixed effects and patient identity incorporated as a random effect. (Fig. 71) Ligand-receptor interaction analysis identified increased interaction strength between MHC class I molecules on monocytes and CD8A on CD8+ T cells in Group C patients compared to healthy controls, suggesting increased CD8+ activation. Color and size of dot indicate the interaction strength and p-value of the inferred cellcell communication networks from between Group C and HC.

[0027] Figures 8A -8D. Polyclonal, antigen-independent T cell activation in Group C patients. (Fig. 8A) Analysis of clonal abundance showed no evidence of dominant clonotypes in Group C patients, with nearly all clones classified as rare (<0.01% abundance). (Fig. 8B) TCR diversity, assessed using the Shannon index, was significantly higher in Group C patients compared to healthy control participants (p<0.001), driven primarily by naive CD8+T cell. Comparisons were determined by Wilcoxon rank sum test. (Fig. 8C) Chord diagram illustrating limited TCR clonal overlap across T cell subsets in Group C patients, consistent with a lack of antigen-driven clonal expansion. (Fig. 8D) Immunometabolic pathway analysis in virus-specific CD8+T cells revealed no significant differences in transcriptional signatures of glycolysis, oxidative phosphorylation, and T cell exhaustion among cells with CMV-specific, non-CMV- specific, and unannotated TCRs, suggesting that immunometabolic dysfunction in Group C patients is not driven by antigen- specific T cells. Pathway enrichment scores were calculated using UCell and compared across groups by Kruskal-Wallis test. Figures 9A - 9E. IL-6 and IFN-y signaling drive aberrant STAT pathway activation and T cell dysfunction. (Fig. 9A) Baseline phosphorylation of STAT1 and STAT3 was elevated in non- naive CD8+T cells from Group C patients compared to healthy controls (HC), consistent with chronic cytokine stimulation (p=0.041 and p=0,026, respectively). Comparisons were determined by Wilcoxon rank sum test. (Fig. 9B) Schematic of the ex vivo TCR stimulation model. PBMCs were cultured with plate-bound anti-CD3 and soluble anti-CD28 for up to 24 hours. (Fig. 9C) Time-course of pSTAT3 expression following TCR stimulation. Group C CD8+T cells showed elevated early (0-4 hr) pSTAT3 expression, followed by a decline by 24 hours, consistent with cytokine-induced signaling desensitization. Conversely, HC cells increased pSTAT3 expression over time. (Fig. 9D) TCR stimulation (+ IL-6) induced robust pSTATl and pSTAT3 responses in HC samples (p=O.O33). In contrast, Group C CD8+T cells failed to upregulate pSTATs under the same conditions, indicating functional refractoriness. Comparisons were determined using the Kruskall- Wallis test. Representative histograms illustrate impaired pSTAT3 induction in Group C. (Fig. 9E) Plasma from Group C patients suppressed TCR-induced pSTATl and pSTAT3 phosphorylation in healthy donor PBMCs. In contrast, culture in complete media or HC plasma led to increased pSTAT3 expression with TCR stimulation (both <0.001 ). Comparisons were determined by Wilcoxon rank sum test. Representative histograms illustrate inhibition of pSTAT3 in healthy cells exposed to Group C plasma.

[0028] DETAILED DESCRIPTION OF THE INVENTION

[0029] As described herein, a prospective, longitudinal cohort study of 88 pediatric patients with multiple organ dysfunction syndrome (MODS), including patients with and without sepsis was conducted to define subphenotypes associated with targetable mechanisms of immune dysregulation. Proteomic profiles and identified shared features of immune dysregulation in MODS patients were first assessed in the presence and absence of sepsis. Unsupervised clustering of plasma cytokines identified three immunologic subgroups, including a high-severity group (“Group C”) characterized by marked hypercytokinemia, driven primarily by IL-6 and IFN-y. Group C exhibited distinct alterations in immune cell frequency and activation status, along with a strong association between hyperinflammatory signaling and lymphocyte dysfunction. Single-cell RNA sequencing revealed transcriptional signatures of T cell activation and metabolic stress, and identified widespread suppression of a “lymphoid protective” gene program across CD8+subsets. In the setting of increased expression of activation markers, T cell receptor repertoire analysis revealed no dominant clonotypes, consistent with a bystander mechanism of T cell activation. Phosphoflow cytometry demonstrated baseline hyperactivation of STAT1 and STAT3 in CD8+T cells from patients in Group C, and these cells failed to respond to T cell receptor stimulation. Together, these findings define IL-6 / IFN-y-drivcn T-cell dysfunction as a distinct endotype of immune dysregulation in pediatric sepsis, highlighting the JAK / STAT axis as a potential target for immunomodulatory therapy.

[0030] DEFINITIONS

[0031] The phrases “Multiple organ dysfunction syndrome (MODS)”, “multi-organ failure”, “multiple systems organ failure”, as used herein, refer to the development of potentially reversible physiologic derangement involving two or more organ systems not involved in the disorder presenting upon hospitalization, typically arising in the wake of a potentially lifethreatening physiologic insult. MODS is as poorly understood as it is prevalent. The syndrome involves the dysfunction of many organs, it also affects physiologic systems not classically thought of as organs, including the hematologic system, immune system, or the endocrine system. The clinical course and causes of MODS are highly variable.

[0032] The term “subphenotype” refers to a subset of patients having a proteomic expression profile associated with a particular phenotype that shares specific features, such as clinical variables, outcome severity, or responses to treatment or medical measures, that clearly differentiates this subgroup from others.

[0033] The term “antithrombotic” is also intended to mean a lower than normal ability of the platelets to interact in the clot building process secondary to administration of compounds and / or variants that inhibit and / or decreases the platelets ability to aggregate and inhibit the platelets ability to form clots (thrombus formation).

[0034] The terms “antiaggregatory” and “antithrombotic” is used interchangeably and refers to the effect of compound(s) that reduce ability of platelets to interact in the clot building process and hence form thrombi.

[0035] The term “modulating / preserving endothelial integrity” is intended to mean pharmacological treatment aiming at maintaining the endothelium in a quiescent inactivated anticoagulant state. Thus a “compound capable of modulating / preserving endothelial integrity” is intended to mean any compound that may assist in maintaining / inducing the endothelium in a quiescent inactivated anti-coagulant state.

[0036] The phrase “fibrinolytic activity” or fibrinolysis is intended to mean process wherein a fibrin clot, the product of coagulation, is broken down.

[0037] The term “augmenting fibrinolytic activity” is intended to mean pharmacological treatment aiming at augmenting the break down of fibrin clots.

[0038] The term “homeostasis” refers to the body's ability to regulate physiologically its inner environment to ensure its stability. An inability to maintain homeostasis may lead to death or a disease.

[0039] The term “critically ill”, herein also “acutely ill”, is meant to include any condition rendering the patient in need for intensive care therapy. Intensive care therapy may include but is not limited to induction of homeostasis, ventilation (eg. mechanical ventilation), hemodialysis, vasopressor support, fluid support, parenteral nutrition, administration of red blood cell concentrates, fresh frozen plasma, platelet concentrates, whole blood, systemic antibiotic and / or antiviral and / or antifungal and / or antiprotozoic therapy, granulocyte infusion, T cell infusion, stem cell infusion, anticoagulant therapy including but not limited to administration of activated protein C and / or antithrombin and / or TFPI and / or heparins, including low molecular weight heparins, and / or thrombin inhibitors, administration of corticosteroids, tight glycemic control.

[0040] “Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" refers to a biological analysis approach in which the data sets analyzed comprise multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome (i.e., a metagenome and / or meta-transcriptome, depending upon how it is sequenced). By combining these "omes", complex biological big data are analyzed to identify novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association.

[0041] A “subject” includes humans and other mammals, and thus the methods are applicable to both human therapy and veterinary applications, in particular to human therapy. The term “mammal” includes humans, non-human primates (e.g. baboons, orangutans, monkeys), mice, pigs, cows, goats, cats, dogs, rabbits, rats, guinea pigs, hamsters, horse, monkeys, sheep or other non-human mammal. The term “sample” or “test sample” as used herein denotes a sample taken or isolated from a biological organism, e.g., a plasma sample from a subject. Exemplary biological samples include, but are not limited to, a biofluid sample; blood, serum; plasma; urine; saliva; and / or tissue sample etc. The term also includes a mixture of the above-mentioned samples. The term “test sample” also includes untreated or pretreated (or pre-processed) biological samples. In some embodiments, a test sample can comprise cells from subject. In some embodiments, a test sample can be a PBMC test sample, e.g. the sample can comprise white blood cells.

[0042] “Treatment”, as used in this application, is intended to include both prevention of an expected development or treatment of an established organ failure, including MODS.

[0043] For purposes of the invention, "Nucleic acid", "nucleotide sequence" or a "nucleic acid molecule" as used herein refers to any DNA or RNA molecule, either single or double stranded and, if single stranded, the molecule of its complementary sequence in either linear or circular form. In discussing nucleic acid molecules, a sequence or structure of a particular nucleic acid molecule may be described herein according to the normal convention of providing the sequence in the 5' to 3' direction. With reference to nucleic acids of the invention, the term "isolated nucleic acid" is sometimes used. This term, when applied to DNA, refers to a DNA molecule that is separated from sequences with which it is immediately contiguous in the naturally occurring genome of the organism in which it originated. For example, an "isolated nucleic acid" may comprise a DNA molecule inserted into a vector, such as a plasmid or virus vector, or integrated into the genomic DNA of a prokaryotic or eukaryotic cell or host organism. Alternatively, this term may refer to a DNA that has been sufficiently separated from (e.g., substantially free of) other cellular components with which it would naturally be associated. "Isolated" is not meant to exclude artificial or synthetic mixtures with other compounds or materials, or the presence of impurities that do not interfere with the fundamental activity, and that may be present, for example, due to incomplete purification. When applied to RNA, the term "isolated nucleic acid" refers primarily to an RNA molecule encoded by an isolated DNA molecule as defined above. Alternatively, the term may refer to an RNA molecule that has been sufficiently separated from other nucleic acids with which it would be associated in its natural state (i.e., in cells or tissues). An isolated nucleic acid (either DNA or RNA) may further represent a molecule produced directly by biological or synthetic means and separated from other components present during its production. According to the present invention, an isolated or biologically pure molecule or cell is a compound that has been removed from its natural milieu. As such, "isolated" and "biologically pure" do not necessarily reflect the extent to which the compound has been purified. An isolated compound of the present invention can be obtained from its natural source, can be produced using laboratory synthetic techniques or can be produced by any such chemical synthetic route.

[0044] The terms "miRNA" and "microRNA" refer to about 10-35 nt, preferably about 15-30 nt, and more preferably about 19-26 nt, non-coding RNAs derived from endogenous genes encoded in the genomes of plants and animals. They are processed from longer hairpin-like precursors termed pre-miRNAs that are often hundreds of nucleotides in length. MicroRNAs assemble in complexes termed miRNPs and recognize their targets by antisense complementarity. These highly conserved, endogenously expressed RNAs are believed to regulate the expression of genes by binding to the 3'-untranslated regions (3'-UTR) of specific mRNAs as well as other regions on targeted mRNAs. Without being bound by theory, a possible mechanism of action assumes that if the microRNAs match 100% their target, i.e. the complementarity is complete, the target mRNA is cleaved, and the miRNA acts like a siRNA. However, if the match is incomplete, i.e. the complementarity is partial, then the translation of the target mRNA is blocked. The manner by which a miRNA base-pairs with its mRNA target correlates with its function: if the complementarity between a mRNA and its target is extensive, the RNA target is cleaved; if the complementarity is partial, the stability of the target mRNA in not affected but its translation is repressed.

[0045] The term "RNA interference" or "RNAi" refers generally to a process or system in which a RNA molecule changes the expression of a nucleic acid sequence with which RNA molecule shares substantial or total homology. The term "RNAi agent" refers to an RNA sequence that elicits RNAi.

[0046] Short hairpin RNAs (shRNA) comprise synthetic double-stranded RNA molecules with a hairpin structure, which are typically processed by the cell's RNA silencing machinery into short interfering RNAs (siRNAs). These siRNAs then interact with RISC (RNA-induced silencing complex) to target and degrade complementary mRNA, effectively reducing or eliminating protein expression. In certain apporaches, these shRNAs are composed of a short (e.g., 19-25 nucleotide) antisense strand, followed by a 5-9 nucleotide loop, and the analogous sense strand. Alternatively, the sense strand may precede the nucleotide loop structure and the antisense strand may follow. These shRNAs may be contained in plasmids, retroviruses, and lentiviruses and expressed from, for example, the pol III U6 promoter, or another promoter (see, e.g., Stewart, et al. (2003) RNA April; 9(4):493 -501 incorporated by reference herein).

[0047] An "siRNA" refers to a molecule involved in the RNA interference process for a sequence-specific post-transcriptional gene silencing or gene knockdown by providing small interfering RNAs (siRNAs) that has homology with the sequence of the targeted gene. Small interfering RNAs (siRNAs) can be synthesized in vitro or generated by ribonuclease III cleavage from longer dsRNA and are the mediators of sequence- specific mRNA degradation. Preferably, the shRNA or siRNA of the invention are chemically synthesized using appropriately protected ribonucleoside phosphoramidites and a conventional DNA / RNA synthesizer. The siRNA can be synthesized as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions. Commercial suppliers of synthetic RNA molecules or synthesis reagents include Applied Biosystems (Foster City, Calif., USA), Proligo (Hamburg, Germany), Dharmacon Research (Lafayette, Colo., USA), Pierce Chemical (part of Perbio Science, Rockford, Ill., USA), Glen Research (Sterling, Va., USA), ChemGenes (Ashland, Mass., USA) and Cruachem (Glasgow, UK).

[0048] A "small nucleic acid inhibitor" refers to any sequence based nucleic acid molecule which, when introduced into a cell expressing the target nucleic acid, is capable of modulating expression of that target. siRNA, antisense, miRNA, shRNA and the like may be utilized in the methods of the invention.

[0049] Small nucleic acids (e.g., miRNAs, pre-miRNAs, pri-miRNAs, miRNA*, anti-miRNA, or a miRNA binding site, or a variant thereof), antisense oligonucleotides, ribozymes, and triple helix molecules of the methods and compositions presented herein may be prepared by any method known in the art for the synthesis of DNA and RNA molecules. These include techniques for chemically synthesizing oligodeoxyribonucleotides and oligoribonucleotides well-known in the art such as for example solid phase phosphoramidite chemical synthesis. Alternatively, RNA molecules may be generated by in vitro and in vivo transcription of DNA sequences encoding the antisense RNA molecule. Such DNA sequences may be incorporated into a wide variety of vectors which incorporate suitable RNA polymerase promoters such as the T7 or SP6 polymerase promoters. Alternatively, antisense cDNA constructs that synthesize antisense RNA constitutively or inducibly, depending on the promoter used, can be introduced stably into cell lines.

[0050] The term "delivery" as used herein refers to the introduction of foreign molecule (i.e., a chemotherapeutic agent, a vector harboring an shRNA, siRNA or miRNA optionally comprising a nanoparticle) into cells. The term "administration" as used herein means the introduction of a foreign molecule into a cell. The term is intended to be synonymous with the term "delivery".

[0051] In some embodiments, the recombinant mammalian expression vector is capable of directing expression of the nucleic acid preferentially in a particular cell type (e.g., tissuespecific regulatory elements are used to express the nucleic acid). Tissue-specific regulatory elements are known in the art. Non-limiting examples of suitable tissue-specific promoters include the albumin promoter (liver-specific; Pinkert, et al., 1987. Genes Dev. 1: 268-277), lymphoid-specific promoters (Calame and Eaton, 1988. Adv. Immunol. 43: 235-275), in particular promoters of T cell receptors (Winoto and Baltimore, 1989. EMBO J. 8: 729-733) and immunoglobulins (Baneiji, et al., 1983. Cell 33: 729-740; Queen and Baltimore, 1983. Cell 33: 741-748), neuron- specific promoters (e.g., the neurofilament promoter; Byrne and Ruddle, 1989. Proc. Natl. Acad. Sci. USA 86: 5473-5477), pancreas- specific promoters (Edlund, et al., 1985. Science 230: 912-916), and mammary gland- specific promoters (e.g., milk whey promoter, U.S. Pat. No. 4,873,316 and European Application Publication No. 264,166). Developmentally- regulated promoters are also encompassed, e.g., the murine hox promoters (Kessel and Gruss, 1990. Science 249: 374-379) and the a-fetoprotein promoter (Campes and Tilghman, 1989. Genes Dev. 3: 537-546). In order to obtain high levels of expression, suitable circadian rhythm protein encoding nucleic acids can be codon-optimized.

[0052] In general, "CRISPR system" refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRIS PR-associated ("Cas") genes, including sequences encoding a Cas gene, a tracr (trans-activating CRISPR) sequence (e.g. tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a "direct repeat" and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a "spacer" in the context of an endogenous CRISPR system), or other sequences and transcripts from a CRISPR locus. In some embodiments, one or more elements of a CRISPR system is derived from a type I, type II, or type III CRISPR system. In some embodiments, one or more elements of a CRISPR system is derived from a particular organism comprising an endogenous CRISPR system, such as Streptococcus pyogenes. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). In the context of formation of a CRISPR complex, "target sequence" refers to a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. Full complementarity is not necessarily required, provided there is sufficient complementarity to cause hybridization and promote formation of a CRISPR complex. A target sequence may comprise any polynucleotide, such as DNA or RNA polynucleotides. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell. In some embodiments, the target sequence may be within an organelle of a eukaryotic cell, for example, mitochondrion or chloroplast. A sequence or template that may be used for recombination into the targeted locus comprising the target sequences is referred to as an "editing template" or "editing polynucleotide" or "editing sequence". In aspects of the invention, an exogenous template polynucleotide may be referred to as an editing template. In an aspect of the invention the recombination is homologous recombination.

[0053] In some embodiments, one or more vectors driving expression of one or more elements of a CRISPR system are introduced into a host cell such that expression of the elements of the CRISPR system direct formation of a CRISPR complex at one or more target sites. For example, a Cas enzyme, a guide sequence linked to a tracr-mate sequence, and a tracr sequence could each be operably linked to separate regulatory elements on separate vectors. Alternatively, two or more of the elements expressed from the same or different regulatory elements, may be combined in a single vector, with one or more additional vectors providing any components of the CRISPR system not included in the first vector. CRISPR system elements that are combined in a single vector may be arranged in any suitable orientation, such as one element located 5' with respect to ("upstream" of) or 3' with respect to ("downstream" of) a second element. The coding sequence of one element may be located on the same or opposite strand of the coding sequence of a second element, and oriented in the same or opposite direction. In some embodiments, a single promoter drives expression of a transcript encoding a CRISPR enzyme and one or more of the guide sequence, tracr mate sequence (optionally operably linked to the guide sequence), and a tracr sequence embedded within one or more intron sequences (e.g. each in a different intron, two or more in at least one intron, or all in a single intron). In some embodiments, the CRISPR enzyme, guide sequence, tr er mate sequence, and tracr sequence are operably linked to and expressed from the same promoter.

[0054] In alternate embodiments, the CRISPR-mediated base editor is base editor 4 (BE4) rather than BE3. Notably, base editing can be employed to alter any of the mutated nucleic acids listed in Table 3 or any of the molecules shown in Figure 2D.

[0055] In some embodiments, the method further comprises assessing C bases within the window for a change to another base. In embodiments, the gRNA is selected if the BE3 PAM sequence (NGG) is 13-17 nucleotides distal to the target cytosine base(s). In particular embodiments, the change is via a C to T on a sense strand, and the modified codon is changed to a nonsense codon. In additional embodiments, the change is via a G to A on an antisense strand, and the modified codon is changed to a nonsense codon. In further embodiments, the change is via a C to T on a sense strand, and the change is a missense variant. In additional embodiments, the change is via a G to A on an antisense strand, and the change is a mis sense variant. The base editing may occur prior to disease onset, wherein the disease is a phenotype resulting from a mutation in the therapeutic gene. In certain embodiments, the base editing decreases a risk of developing a disease.

[0056] The term "vector" relates to a single or double stranded circular nucleic acid molecule that can be infected, transfected or transformed into cells and replicate independently or within the host cell genome. A circular double stranded nucleic acid molecule can be cut and thereby linearized upon treatment with restriction enzymes. An assortment of vectors, restriction enzymes, and the knowledge of the nucleotide sequences that are targeted by restriction enzymes are readily available to those skilled in the art, and include any replicon, such as a plasmid, cosmid, bacmid, phage or virus, to which another genetic sequence or element (either DNA or RNA) may be attached so as to bring about the replication of the attached sequence or element. A nucleic acid molecule of the invention can be inserted into a vector by cutting the vector with restriction enzymes and ligating the two pieces together.

[0057] In some aspects, the invention provides methods comprising delivering one or more polynucleotides (e.g., a CRISPR system, an antisense oligonucleotide, an siRNA, an shRNA, a triplex nucleic acid, etc), such as or one or more vectors as described herein, one or more transcripts thereof, and / or one or proteins transcribed therefrom, to a host cell. In some aspects, the invention further provides cells produced by such methods, and organisms (such as animals, plants, or fungi) comprising or produced from such cells. In some embodiments, a CRISPR enzyme in combination with (and optionally complexed with) a guide sequence is delivered to a cell. Conventional viral and non-viral based gene transfer methods can be used to introduce nucleic acids in mammalian cells or target tissues. Such methods can be used to administer nucleic acids encoding components of a CRISPR system to cells in culture, or in a host organism. Non-viral vector delivery systems include DNA plasmids, RNA (e.g. a transcript of a vector described herein), naked nucleic acid, and nucleic acid complexed with a delivery vehicle, such as a liposome. Viral vector delivery systems include DNA and RNA viruses, which have either episomal or integrated genomes after delivery to the cell. For a review of gene therapy procedures, see Anderson, Science 256:808-813 (1992); Nabel & Feigner, TIBTECH 11 :211-217 (1993); Mitani & Caskey, TIBTECH 11: 162-166 (1993); Dillon, TIBTECH 11 : 167-175 (1993); Miller, Nature 357:455-460 (1992); Van Brunt, Biotechnology 6(10): 1149- 1154 (1988); Vigne, Restorative Neurology and Neuroscience 8:35-36 (1995); Kremer & Perricaudet, British Medical Bulletin 5 l(l):31-44 (1995); Haddada et al., in Current Topics in Microbiology and Immunology Doerfler and Bihm (eds) (1995); and Yu et al., Gene Therapy 1 :13-26 (1994).

[0058] Methods of non-viral delivery of nucleic acids include lipofection, nucleofection, microinjection, biolistics, virosomes, liposomes, immunoliposomes, polycation or lipidmucleic acid conjugates, naked DNA, artificial virions, and agent-enhanced uptake of DNA. Lipofection is described in e.g., U.S. Pat. Nos. 5,049,386, 4,946,787; and 4,897,355) and lipofection reagents are sold commercially (e.g., Transfectam™ and Lipofectin™). Cationic and neutral lipids that are suitable for efficient receptor-recognition lipofection of polynucleotides include those of Feigner, WO 91 / 17424; WO 91 / 16024. Delivery can be to cells (e.g. in vitro or ex vivo administration) or target tissues (e.g. in vivo administration).

[0059] The preparation of lipidmucleic acid complexes, including targeted liposomes such as immunolipid complexes, is well known to one of skill in the art (see, e.g., Crystal, Science 270:404-410 (1995); Blaese et al., Cancer Gene Ther. 2:291-297 (1995); Behr et al., Bioconjugate Chem. 5:382-389 (1994); Remy et al., Bioconjugate Chem. 5:647-654 (1994); Gao et al., Gene Therapy 2:710-722 (1995); Ahmad et al., Cancer Res. 52:4817-4820 (1992); U.S. Pat. Nos. 4,186,183, 4,217,344, 4,235,871, 4,261,975, 4,485,054, 4,501,728, 4,774,085, 4,837,028, and 4,946,787). The use of RNA or DNA viral based systems for the delivery of nucleic acids take advantage of highly evolved processes for targeting a virus to specific cells in the body and trafficking the viral pay load to the nucleus. Viral vectors can be administered directly to patients (in vivo) or they can be used to treat cells in vitro, and the modified cells may optionally be administered to patients (ex vivo). Conventional viral based systems could include retroviral, lentivirus, adenoviral, adeno-associated and herpes simplex virus vectors for gene transfer. Integration in the host genome is possible with the retrovirus, lentivirus, and adeno-associated virus gene transfer methods, often resulting in long term expression of the inserted transgene. Additionally, high transduction efficiencies have been observed in many different cell types and target tissues.

[0060] The tropism of a retrovirus can be altered by incorporating foreign envelope proteins, expanding the potential target population of target cells. Lentiviral vectors are retroviral vectors that are able to transduce or infect non-dividing cells and typically produce high viral titers. Selection of a retroviral gene transfer system would therefore depend on the target tissue. Retroviral vectors are comprised of cis-acting long terminal repeats with packaging capacity for up to 6-10 kb of foreign sequence. The minimum cis-acting LTRs are sufficient for replication and packaging of the vectors, which are then used to integrate the therapeutic gene into the target cell to provide permanent transgene expression. Widely used retroviral vectors include those based upon murine leukemia virus (MuLV), gibbon ape leukemia virus (GaLV), Simian Immuno deficiency virus (SIV), human immuno deficiency virus (HIV), and combinations thereof (see, e.g., Buchscher et al., J. Virol. 66:2731-2739 (1992); Johann et al., J. Virol. 66: 1635-1640 (1992); Sommnerfelt et al., Virol. 176:58-59 (1990); Wilson et al., J. Virol. 63:2374-2378 (1989); Miller et al., J. Virol. 65:2220-2224 (1991); PCT / US94 / 05700).

[0061] In applications where transient expression is preferred, adenoviral based systems may be used. Adenoviral based vectors are capable of very high transduction efficiency in many cell types and do not require cell division. With such vectors, high titer and levels of expression have been obtained. This vector can be produced in large quantities in a relatively simple system.

[0062] Adeno-associated virus ("AAV") vectors may also be used to transduce cells with target nucleic acids, e.g., in the in vitro production of nucleic acids and peptides, and for in vivo and ex vivo gene therapy procedures (see, e.g., West et al., Virology 160:38-47 (1987); U.S. Pat. No. 4,797,368; WO 93 / 24641; Kotin, Human Gene Therapy 5:793-801 (1994); Muzyczka, J. Clin. Invest. 94:1351 (1994). Construction of recombinant AAV vectors are described in a number of publications, including U.S. Pat. No. 5,173,414; Tratschin et al., Mol. Cell. Biol. 5:3251-3260 (1985); Tratschin, et al., Mol. Cell. Biol. 4:2072-2081 (1984); Hermonat & Muzyczka, PNAS 81:6466-6470 (1984); and Samulski et al., J. Virol. 63:03822-3828 (1989).

[0063] Packaging cells are typically used to form virus particles that are capable of infecting a host cell. Such cells include 293 cells, which package adenovirus, and \| / 2 cells or PA317 cells, which package retrovirus. Viral vectors used in gene therapy are usually generated by producing a cell line that packages a nucleic acid vector into a viral particle. The vectors typically contain the minimal viral sequences required for packaging and subsequent integration into a host, other viral sequences being replaced by an expression cassette for the polynucleotide(s) to be expressed. The missing viral functions are typically supplied in trans by the packaging cell line. For example, AAV vectors used in gene therapy typically only possess ITR sequences from the AAV genome which are required for packaging and integration into the host genome. Viral DNA is packaged in a cell line, which contains a helper plasmid encoding the other AAV genes, namely rep and cap, but lacking ITR sequences. The cell line may also be infected with adenovirus as a helper. The helper virus promotes replication of the AAV vector and expression of AAV genes from the helper plasmid. The helper plasmid is not packaged in significant amounts due to a lack of ITR sequences. Contamination with adenovirus can be reduced by, e.g., heat treatment to which adenovirus is more sensitive than AAV.

[0064] Moreover, various well-known modifications to nucleic acid molecules may be introduced as a means of increasing intracellular stability and half-life. Possible modifications include but are not limited to the addition of flanking sequences of ribonucleotides or deoxyribonucleotides to the 5' and / or 3' ends of the molecule or the use of phosphorothioate or 2' O-methyl rather than phosphodiesterase linkages within the oligodeoxyribonucleotide backbone. One of skill in the art will readily understand that polypeptides, small nucleic acids, and antisense oligonucleotides can be further linked to another peptide or polypeptide (e.g., a heterologous peptide), e.g., that serves as a means of protein detection. Non-limiting examples of label peptide or polypeptide moieties useful for detection in the invention include, without limitation, suitable enzymes such as horseradish peroxidase, alkaline phosphatase, betagalactosidase, or acetylcholinesterase; epitope tags, such as FLAG, MYC, HA, or HIS tags; fluorophores such as green fluorescent protein; dyes; radioisotopes; digoxygenin; biotin; antibodies; polymers; as well as others known in the art, for example, in Principles of Fluorescence Spectroscopy, Joseph R. Lakowicz (Editor), Plenum Pub Corp, 2nd edition (July 1999).

[0065] Preferably, as used herein, the term "pharmaceutically acceptable" means approved by a regulatory agency of the Federal or a state government or listed in the U.S. Pharmacopeia or other generally recognized pharmacopeia for use in animals, and more particularly in humans. The term "carrier" refers, for example to a diluent, adjuvant, excipient, auxilliary agent or vehicle with which an active agent of the present invention is administered. Such pharmaceutical carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, vegetable or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil and the like. Water or aqueous saline solutions and aqueous dextrose and glycerol solutions are preferably employed as carriers, particularly for injectable solutions. Suitable pharmaceutical carriers are described in "Remington's Pharmaceutical Sciences" by E. W. Martin.

[0066] A pharmaceutical composition of the present invention can be administered by any suitable route, for example, by injection, by oral, pulmonary, nasal or other forms of administration. In general, pharmaceutical compositions contemplated to be within the scope of the invention, comprise, inter alia, pharmaceutically acceptable diluents, preservatives, solubilizers, emulsifiers, adjuvants and / or carriers. Such compositions can include diluents of various buffer content (e.g., Tris HC1, acetate, phosphate), pH and ionic strength; additives such as detergents and solubilizing agents (e.g., Tween 80, Polysorbate 80), anti oxidants (e.g., ascorbic acid, sodium metabisulfite), preservatives (e.g., Thimersol, benzyl alcohol) and bulking substances (e.g., lactose, mannitol); incorporation of the material into particulate preparations of polymeric compounds such as polylactic acid, polyglycolic acid, etc., or into liposomes. Such compositions may influence the physical state, stability, rate of in vivo release, and rate of in vivo clearance of components of a pharmaceutical composition of the present invention. See, e.g., Remington's Pharmaceutical Sciences, 18th Ed. (1990, Mack Publishing Co., Easton, Pa. 18042) pages 1435 1712 which are herein incorporated by reference. A pharmaceutical composition of the present invention can be prepared, for example, in liquid form, or can be in dried powder, such as lyophilized form. Particular methods of administering such compositions are described infra. In yet another embodiment, a pharmaceutical composition of the present invention can be delivered in a controlled release system, such as using an intravenous infusion, an implantable osmotic pump, a transdermal patch, liposomes, or other modes of administration. In a particular embodiment, a pump may be used [see Langer, supra; Sefton, CRC Crit. Ref. Biomed. Eng. 14:201 (1987); Buchwald et al., Surgery 88:507 (1980); Saudek et al., N. Engl. I. Med. 321 :574 (1989)]. In another embodiment, polymeric materials can be used [see Medical Applications of Controlled Release, Langer and Wise (eds.), CRC Press: Boca Raton, Fla. (1974); Controlled Drug Bioavailability, Drug Product Design and Performance, Smolen and Ball (eds.), Wiley: New York (1984); Ranger and Peppas, J. Macromol. Sci. Rev. Macromol. Chem. 23:61 (1983); see also Levy et al., Science 228: 190 (1985); During et al., Ann. Neurol. 25:351 (1989); Howard et al., J. Neurosurg. 71:105 (1989)]. In yet another embodiment, a controlled release system can be placed in proximity of the target tissues of the animal, thus requiring only a fraction of the systemic dose [see, e.g., Goodson, in Medical Applications of Controlled Release, supra, vol. 2, pp. 115 138 (1984)]. In particular, a controlled release device can be introduced into an animal in proximity of the site of inappropriate immune activation or a tumor. Other controlled release systems are discussed in the review by Langer [Science 249:1527 1533 (1990)].

[0067] As used herein the term "biomarker" refers to a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.

[0068] “Reperfusion injury” as used herein refers to damage to tissue caused when blood supply returns to the tissue after a period of ischemia. The absence of oxygen and nutrients from blood creates a condition in which the restoration of circulation results in inflammation and oxidative damage through the induction of oxidative stress rather than restoration of normal function.

[0069] The term “systemic inflammation” is altered organ function in an acutely ill patient due to the nonspecific conserved response of the body (vasculature, immune system, tissues) to infections, non-infectious antigens, trauma, burn, organ / tissue destruction / degeneration / damage, ischemia, haemorrhage, intoxication, and / or malignancy.

[0070] “Sepsis” as used herein is intended to refer to whole-body inflammatory state (called a systemic inflammatory response syndrome or SIRS) and the presence of a known or suspected infection. Severe sepsis occurs when sepsis leads to organ dysfunction, low blood pressure (hypotension), or insufficient blood flow (hypoperfusion) to one or more organs (causing, for example, lactic acidosis, decreased urine production, or altered mental status). Sepsis can lead to septic shock, multiple organ dysfunction syndrome / multiple organ failure, and death. Organ dysfunction results from a dysregulated immune response to infection leading to a cascade of physiologic perturbations (e.g. inadequate tissue perfusion, endothelial dysfunction, complement activation, and disseminated intravascular coagulation).

[0071] Table 1

[0072] Modified Proulx criteria used for screening and enrollment in the MODS cohort.

[0073] Pediatr Crit Care Med 10, 12-22 (2009).

[0074] “SIRS” or systemic inflammatory response syndrome as used herein is intended to mean systemic inflammation in response to an insult without confirmed infectious process. When an infection is suspected or proven (by culture, stain, or polymerase chain reaction (PCR)), together with SIRS, this is per definition sepsis. Specific evidence for infection includes WBCs in normally sterile fluid (such as urine or cerebrospinal fluid (CSF), evidence of a perforated viscus (free air on abdominal x-ray or CT scan, signs of acute peritonitis), abnormal chest x-ray (CXR) consistent with pneumonia (with focal opacification), or petechiae, purpura, or purpura fulminans.

[0075] “Trauma” as used herein is intended to mean any body wound or shock produced by sudden physical injury, as from accident, injury, or impact.

[0076] As used herein, the term “activity” refers to a biological activity.

[0077] As used herein, the term “pharmacological activity” refers to the inherent physical properties of a peptide or polypeptide. These properties include but are not limited to half-life, solubility, and stability and other pharmacokinetic properties.

[0078] The term “hit” refers to a test compound that shows desired properties in an assay. The term “test compound” refers to a chemical to be tested by one or more screening method(s) as a putative modulator. A test compound can be any chemical, such as an inorganic chemical, an organic chemical, a protein, a peptide, a carbohydrate, a lipid, or a combination thereof. Usually, various predetermined concentrations of test compounds are used for screening, such as 0.01 micromolar, 1 micromolar and 10 micromolar. Test compound controls can include the measurement of a signal in the absence of the test compound or comparison to a compound known to modulate the target.

[0079] The terms “high,” “higher,” “increases,” “elevates,” or “elevation” refer to increases above basal levels, e.g., as compared to a control. The terms “low,” “lower,” “reduces,” or “reduction” refer to decreases below basal levels, e.g., as compared to a control.

[0080] The term “modulate” as used herein refers to the ability of a compound to change an activity in some measurable way as compared to an appropriate control. As a result of the presence of compounds in the assays, activities can increase or decrease as compared to controls in the absence of these compounds. Preferably, an increase in activity is at least 25%, more preferably at least 50%, most preferably at least 100% compared to the level of activity in the absence of the compound. Similarly, a decrease in activity is preferably at least 25%, more preferably at least 50%, most preferably at least 100% compared to the level of activity in the absence of the compound. A compound that increases a known activity is an “agonist”. One that decreases, or prevents, a known activity is an “antagonist”.

[0081] The term “inhibit” means to reduce or decrease in activity or expression. This can be a complete inhibition or activity or expression, or a partial inhibition. Inhibition can be compared to a control or to a standard level. Inhibition can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,

[0082] 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,

[0083] 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,

[0084] 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,

[0085] 94, 95, 96, 97, 98, 99, or 100%.

[0086] The term “preventing” as used herein refers to administering a compound prior to the onset of clinical symptoms of a disease or conditions so as to prevent a physical manifestation of aberrations associated with the disease or condition.

[0087] The term “in need of treatment” as used herein refers to a judgment made by a caregiver (e.g. physician, nurse, nurse practitioner, or individual in the case of humans; veterinarian in the case of animals, including non-human mammals) that a subject requires or will benefit from treatment. This judgment is made based on a variety of factors that are in the realm of a care giver's expertise, but that includes the knowledge that the subject is ill, or will be ill, as the result of a condition that is treatable by the disclosed compounds.

[0088] By the term “effective amount” of a compound as provided herein is meant a nontoxic but sufficient amount of the compound to provide the desired result.

[0089] By “pharmaceutically acceptable” is meant a material that is not biologically or otherwise undesirable, i.e., the material can be administered to a subject along with the selected compound without causing any undesirable biological effects or interacting in a deleterious manner with any of the other components of the pharmaceutical composition in which it is contained.

[0090] The following materials and methods are provided to facilitate the practice of the invention.

[0091] Experimental design: After obtaining IRB approval (IRB #19-017032), we enrolled patients with multiple organ dysfunction syndrome (MODS) into a prospective observational cohort study in the Pediatric Intensive Care Unit and Cardiac Intensive Care Unit at Children’s Hospital of Philadelphia. Patients with new dysfunction of >2 organs defined by modified Proulx criteria

[0092] T1 94) (Table 1) within the last three calendar days and age >40 weeks post-conceptual age and <18 years were eligible for enrollment. Exclusion criteria included limitations of care orders at the time of eligibility, clinical suspicion for brain death, and prior enrollment. This study coenrolled with the multicenter PediAtric ReseArch of Drugs, Immunoparalysis and Genetics during MODS (PARADIGM) study, with shared inclusion / exclusion criteria and case report form but independent biospecimen collection, processing, and analysis. We enrolled June 2020 to December 2022, when we reached our prespecified enrollment target of 88 patients, which we expected to achieve 90% power to detect moderate differences (Cohen’s d = 0.5) between three MODS subgroups, based on pilot flow cytometry and proteomics data. Longitudinal whole blood samples were obtained twice weekly in sodium heparin tubes from onset of MODS through death or resolution of all organ failure.

[0093] Clinical metadata: Clinical metadata were abstracted by a nurse research coordinator using a standardized case report form into REDCap (95) for the parent PARADIGM study. Data quality was verified through manual and automated queries. MODS onset was defined as the calendar day that a patient developed new dysfunction of >2 organs defined by modified Proulx criteria (Table 1) (9). MODS inciting diagnosis was abstracted from attending physician daily progress notes and coded as “sepsis,” “trauma,” “cardiopulmonary bypass,” or “non-infected.” For sepsis patients, site and organism were recorded if available. Patients with “culture negative sepsis” were classified as sepsis if they developed organ dysfunction in the setting of a clinical suspicion for infection and received sepsis therapies, in concordance with pediatric sepsis guidelines (96, 97). Daily data were extracted from MODS onset through day +28 to allow for daily calculation of the PELOD-2 organ dysfunction score (13). We defined cumulative PELOD-2 score through day +28 as our primary outcome because it incorporates both degree and duration of organ failure into a composite outcome variable which is associated with meaningful differences in long-term mortality (98) and health related quality of life (99) in pediatric patients with sepsis.

[0094] Healthy control participant samples: Cryopreserved PBMC and heparin plasma samples from 25 age-matched participants without immunological disease were identified from an existing pediatric biorepository (IRB #18-015920) to serve as a healthy control group.

[0095] Inborn errors of immunity patient samples: Heparin plasma samples from patients with STAT1 gain-of-function (9), STAT1 loss-of-function (1), STAT3 gain-of-function (5), and STAT3 loss-of-function (3) mutation were identified from collaborators and used as comparators for MODS patients with dysregulated JAK / STAT signaling. These participants were consented in accordance with local IRB protocols and samples were shared through collaborative research agreements.

[0096] Sample processing: Biospecimens were collected starting within 3 calendar days of MODS onset and continued twice weekly through death, recovery from all acute organ failures, or until six samples were obtained. Blood samples were obtained in heparin plasma tubes by clinical research coordinators and processed on site within one hour of acquisition. Platelet poor plasma was separated by centrifugation and snap frozen on dry ice. Peripheral blood mononuclear cells (PBMCs) were isolated using SepMate (STEMCELL Technologies, Vancouver, Canada) density gradient centrifugation using Lymphoprep media. Whole blood was mixed 1:1 with PBS and layered onto Lymphoprep gradient. SepMate tubes were centrifuged and the buffy coat suspension was spun down for cell isolation. ACK lysis was completed and cells were resuspended in complete RPMI (cRPMI; RPMI 1640 supplemented with 10% FBS, 1% L- Glutamine, 1% Pen-Strep) for counting prior to cryopreservation in 500pl of freezing media (90% FBS, 10% DMSO).

[0097] Plasma proteomics by proximity extension assay: For each patient and timepoint, lOOpl of heparin plasma was submitted to Olink Proteomics (Uppsala, Sweden) for analysis on the Explore 1536 proximity extension assay (PEA) platform. PEA is a dual-recognition assay which uses paired antibodies labeled with DNA oligonucelotides to identify and quantify protein expression through Next Generation Sequencing (41). After normalization and quality control, protein concentrations are reported in log2-transformed Normalized Protein expression (NPX) units. In total, we analyzed 229 samples from 131 patients across three experiments using 8 shared bridging samples for between-plate-normalization. 1448 proteins met quality control thresholds in all three experiments and were included in downstream analyses.

[0098] Spectral flow cytometry staining and acquisition: Cryopreserved PBMCs were thawed in IQmL cRPMI and IxlO6cells per sample were plated in a 96-well round-bottom plate. Cell pellets were sequentially incubated with live / dead blue with Fc block, surface antibody stain with Brilliant Stain buffer, permeabilization reagent, and intracellular antibody stain with Brilliant Stain buffer. After staining, cell pellets were resuspended in 1.6% PFA and held at 4°C until acquisition on a Cytek Aurora spectral flow cytometer (Cytek Biosciences, Fremont, CA) the following morning. In total, we analyzed 303 samples from 113 patients across eight experiments. See Table 4 for details of flow cytometry antibodies and buffers and Supplemental Methods for complete staining protocol.

[0099] Flow cytometric cell sorting for scRNA-seq: Cryoprcscrvcd PBMCs were thawed in lOmL cRPMI and IxlO6cells were transferred to a new tube for staining. Cell pellets were incubated with I OOpI mixture of live / dead blue and CD45 antibody, then washed and passed through a 35pm nylon mesh cell strainer prior to acquisition. Using a Cytek Aurora CS cell sorter (Cytek Biosciences, Fremont, CA), live singlet CD45+cells were sorted into 1.5ml Eppendorf tubes prefilled with 500pl of PBS + 20% FBS. Sorted cells were washed with 500pl PBS + 10% FBS twice to remove sheath fluid EDTA prior to 10X processing.

[0100] Single cell RNA sequencing (scRNA-seq): We utilized the 10X Chromium Next GEM Single Cell 5' Kit v2 and Chromium Single Cell Human TCR Amplification Kit (10X Genomics, Pleasanton, CA) for single cell analyses of sorted PBMC samples (see above) from 9 patients in MODS Group C and 3 age-matched HC participants. Single-cell isolation and library preparation were performed in the Center for Applied Genomics at Children’s Hospital of Philadelphia. Sequencing was performed using the Illumina S2 flow cell (Illumina, San Diego, CA). Data were demultiplexed and processed using the Cell Ranger 8.0 analytical pipeline (10X Genomics, Pleasanton, CA), with reads aligned to the GRCh38 reference genome.

[0101] Identification of MODS subphenotypes: We constructed linear mixed effects models for each protein to determine the association between normalized protein expression and cumulative PELOD-2 score, with age and sex modeled as fixed effects and day from MODS onset modeled as a random effect. Proteins which were significantly associated with cumulative PELOD-2 score after Benjamini-Hochberg correction (FDR <0.05) were retained for further analysis. We used consensus clustering to define subphenotypes and identified optimal k via the Monte Carlo reference-based consensus clustering algorithm (42) which uses the proportion of ambiguous clustering (PAC) score to test the hypothesis that k=n clusters is more informative than k=l. Optimal number of clusters in the MODS data (k=3) was confirmed by Monte Carlo bootstrapping, elbow method, and gap statistic. Competing risk survival analysis: We fit a proportional subdistribution hazards regression model to assess the effect of covariates on the subdistribution of death and survival to PICU discharge in a competing risk setting and then estimated the cumulative incidence function from this model for each MODS subphenotype and outcome using the Fine and Gray model (43). We then tested the hypothesis that the subdistribution hazard differed between Group C and Group A / B for both death and survival to PICU discharge.

[0102] Model reduction via ordinal elastic net: We defined a parsimonious protein signature to classify MODS subphenotypes by training an ordinal elastic net model (44) for feature selection using the severity-associated proteins previously identified through linear mixed effects modeling. This approach fit a semi-parallel elementwise link multinomial-ordinal regression model with elastic net penalty which defines coefficients for each protein: subphenotype and then shrinks the model to a single coefficient for each protein via elastic net penalty to minimize Gini index. We assessed the performance of the parsimonious elastic net protein set to discriminate the three subphenotypes using principle component analysis, linear mixed effects postestimation, and polytomous discrimination index (100).

[0103] Identification of spectral flow cytometry metaclusters: After arcsinh scaling (45) and quality control with flowAI (46), we performed FlowSOM metaclustering (47) to identify 14 PBMC populations by surface and intracellular marker expression. PBMCs were first clustered into k=60 FlowSOM metaclusters and then combined in a stepwise fashion to generate the 14 canonical populations identified in the figure and used for downstream analysis. Metacluster similarity was determined by surface and intracellular marker expression and tSNE-CUDA (48) proximity and then confirmed with manual gating. Proliferation and activation markers were analyzed using bivariate plots.

[0104] Single cell transcrip tomics analytic pipeline: After sequencing and alignment to the GRCh38 reference genome, transcriptomics data were analyzed using the Seurat 5.0 preprocessing and integration pipeline (101). Briefly, after review of standard quality control metrics, we selected cells with 1200-20000 transcripts per cell, 750-5000 genes per cell, and <20% mitochondrial RNA content for downstream analysis. After standard normalization and scaling, we performed anchor-based RPCA integration across samples and conditions. After quality control and integration, cell identities were inferred using the Azimuth reference-based mapping pipeline (55) and used to define 14 PBMC populations by transcriptional profile (data not shown).

[0105] Pseudobulk analysis was performed by condition, sample, and cell type using Seurat’s AggregateExpression function. Differential expression analysis was performed using DESeq2 (102). The association of effector cell pathway enrichment and immuno-metabolic profile at the single cell level was determined using a generalized linear mixed-effects model incorporating pathway enrichment scores as fixed effects and patient identity as a random effect.

[0106] Pathway enrichment analysis: We first performed pathway enrichment analysis by subphenotype by analyzing estimated protein expression in Group C patients (adjusted for age, sex, and PELOD-2 score) with Ingenuity Pathway Analysis (Qiagen) (49). We then analyzed pathway expression in the protein dataset in bulk by GSEA (69) and at the individual patient level using GSVA (50), with a focus on enrichment of 5 canonical proinflammatory pathways using Human Molecular Signatures Database (MSigDB) Hallmark gene sets (51). We also applied GSVA to pseudobulk and single cell transcriptomics data in a similar approach using UCell (5(5) and extended our analysis to include relevant Hallmark immunometabolic pathways. A list of gene sets used in GSEA and GSVA analysis is provided below:

[0107] TABLE 2

[0108] SRS module concordance: To determine the concordance between Hallmark proinflammatory module enrichment scores and the extended sepsis response signature (SRS) gene set, we calculated an extended SRS gene set enrichment score for each cell in the transcriptomics dataset using UCell (56). We then generated a linear regression model to compare to enrichment of the extended SRS gene set to each Hallmark proinflammatory pathway. We calculated the adjusted R2for the correlation between extended SRS and inflammatory pathway enrichment by cell type.

[0109] Single cell transcriptomics analytic pipeline: After sequencing, the reads were aligned to the GRCh38 reference genome using Cell Ranger pipeline from lOx Genomics. The transcriptomic data were analyzed using the Seurat 5.0 preprocessing and integration pipeline ((59). After review of standard quality control metrics, cells with less than 200 genes, greater than 3 median absolute deviation above the median, and > 10% mitochondrial RNA content were removed from downstream analysis. After normalization and scaling using the scTransform function in Seurat, we performed anchor-based RPCA integration across samples and conditions using the top 3000 genes as anchors. Following linear dimension reduction using PC A, the data was clustered using the Louvain algorithm and projected into 2D coordinates using the RunUMAP function in Seurat. The cell identities were then inferred using the scType package (77) followed by curated manual annotation using canonical marker genes to define 14 immune cell populations by transcriptional profile. Results of each quality control and integration step were determined (data not shown). Pseudobulk analysis was performed by condition, sample, and cell type using Seurat’s AggregateExpression function. Differential expression analysis was performed using the FindMarker function in Seurat. Cell-cell communication analysis was performed using CellChat (79). To assess associations between effector cell pathways and T cell immunometabolic states, we employed generalized linear mixed-effects models, treating module enrichment scores as fixed effects and patient identity as a random effect. T cell receptor sequencing analytic pipeline: T-cell receptor (TCR) sequencing data were analyzed using the scRepertoire package (80). VDJ output from the Cell Ranger pipeline (lOx Genomics) were integrated with single-cell RNA sequencing gene expression data using the combineExpression function. Clonotypes were defined based on CDR3 amino acid sequences and visualized using clonotype homeostasis and proportional abundance plots. Diversity was quantified using Shannon diversity (111). To further characterize TCR sequence features, clonotype-level annotation was performed using the TCR Repertoire Explorer (TReX) package (112).

[0110] Immune Dictionary cytokine signature analysis: To assess cytokine-specific transcriptional responses in our scRNA-seq dataset, we applied the Immune Dictionary framework, which maps cytokine signaling to downstream gene expression profiles in specific immune cell types. Singlecell transcriptomic data were scored using curated cytokine-response gene signatures derived from the published experimental datasets (77). A list of the gene sets used in this analysis are included in Table 9. Module enrichment scores were computed using UCell for each cytokine response transcriptional signature, enabling cell-specific quantification of cytokine signaling activity.

[0111] Analysis software: All computational analysis was completed using R 4.3.2 and Bioconductor 3.18. Flow cytometry data was processed using FlowJo 10.9. Figures were compiled in Adobe Illustrator.

[0112] The following examples are provided to illustrate certain embodiments of the invention. They are not intended to limit the invention in any way.

[0113] EXAMPLE I

[0114] Identification of three severity-associated MODS subphenotypes

[0115] A successful precision medicine approach to pediatric sepsis requires an understanding of the molecular events which underlie the development of organ failure, a knowledge of which events are reversible, and an ability to identify these high-risk patients in real time (28). Existing approaches to identify pediatric sepsis subphenotypes based on clinical and laboratory features have identified several high-risk patient subgroups (29-32), but these subgroups lack defined mechanisms of immune dysregulation and thus cannot streamline precision treatment decisions. A wide range of pathogenic inborn errors of immunity (IEI) have been identified in cohorts of pediatric patients with sepsis (55), suggesting that diverse genetic factors may contribute to the dysregulated host immune phenotypes that develop in the setting of critical illness. Recent attempts to define this heterogeneous host response in adults using molecular approaches (34-40) have yielded new mechanistic insights into sepsis pathobiology, suggesting that deep immune phenotyping may be able to overcome the immunologic heterogeneity that has previously hampered precision sepsis efforts.

[0116] Given the role of immune dysregulation in sepsis pathobiology and the persistent lack of novel targeted therapies for these high-risk patients, we designed a longitudinal multiomics cohort study of pediatric MODS patients with and without sepsis with the goal of identifying severity-associated MODS subphenotypes. We hypothesized that severity-associated MODS subphenotypes would be associated with targetable mechanisms of immune dysregulation. Using longitudinal proteomics, cytometry, and transcriptional data, we defined three prognostic subphenotypes in critically ill children with and without sepsis and identified an association between pathologic JAK / STAT3 hyperactivation and T cell immunometabolic dysregulation in the subset of pediatric MODS patients with the highest severity of illness and worst prognosis.

[0117] To identify MODS subphenotypes in patients with and without sepsis, we collected peripheral whole blood samples in a prospective, observational cohort of 88 pediatric MODS patients. Blood was collected from onset through resolution of MODS, and PBMC and heparin plasma was cryopreserved for later analysis, as shown in Fig. 1A. We compared these patients to a separate cohort of 25 age-matched healthy control patients. Our initial analyses included a discovery -oriented 1536-marker proteomics panel from Olink Proteomics (Table 3) and a 35- marker spectral flow cytometry-based immune phenotyping panel (Table 4).

[0118] Demographics and clinical outcomes were similar between our 35 MODS patients with sepsis and 53 MODS patients without sepsis (Fig. IB), and the only organ dysfunction category that differed between groups was hematologic dysfunction, which was driven by increased thrombocytopenia among sepsis patients at MODS onset (platelet count median [IQR] : 119 [99- 187] vs 199 [131-278], p<0.001). Length of stay, cumulative organ dysfunction scores, and survival to PICU discharge did not differ between MODS subgroups. Sepsis is characterized by a surge in pro-inflammatory cytokines, and while we noted increased proinflammatory cytokines in sepsis patients compared to healthy control patients, we were surprised that mean IL- IB, TNF, and IL-6 levels did not differ between sepsis and non-sepsis patients with MODS (Fig. 1C). We then used principal component analysis to visualize the heterogeneity within the MODS cohort and overlap between sepsis and non-sepsis patients. We noted separation between healthy control patients and MODS patients but substantial overlap between MODS patients with and without sepsis (Fig. ID). MODS patients also exhibited substantial heterogeneity along component 1 and component 2, dimensions whose top loadings correspond to shock, growth regulation, and inflammatory signaling.

[0119] Finally, we constructed a heatmap of row-normalized protein expression of the full proteomics dataset and used Lmcans clustering to visualize heterogeneity within the cohort at MODS onset. As shown in Fig. IE, k-means clustering separates healthy control patients from MODS patients but does not discriminate MODS patients with sepsis and without sepsis. At k=3 clusters, there is a suggestion of a subgroup of proinflammatory MODS patients.

[0120] We used linear mixed-effects models to identify plasma proteins associated with severity of illness, defined by the PELOD-2 organ dysfunction score, after adjustment for age, sex, and day from MODS onset. This yielded a set of 214 plasma proteins which were significantly associated with illness severity (Table 6). We then employed consensus clustering - an unsupervised machine learning methodology for subclass discovery (42) - to identify distinct clusters of patients with MODS based on protein expression (Fig. 2A). Optimal cluster number (k=3) was identified via Monte Carlo bootstrapping and confirmed by elbow method and gap statistic (Fig. IF). To understand the association between cluster membership and clinical outcomes, we tested the effect of MODS subphenotype on the cumulative incidence of both mortality and survival to PICU discharge with the Fine-Gray subdistribution hazard model (43). In this competing risk survival analysis, patients in Group C have higher cumulative incidence of death (p=0.03) and lower cumulative incidence of survival to PICU discharge (p=0.04) compared to patients in Clusters A and B, with separation occurring in the first week and persisting to day +28 (Fig. 2B).

[0121] Though defined by protein expression alone, the identified MODS subphenotypes also differ by clinical features and outcomes (Fig. 2C). MODS etiology varies by Group, with most sepsis patients in Group B and C and all trauma patients in Group A. Group C patients have higher severity of illness, more non-cardiopulmonary organ failures, and increased cumulative organ dysfunction scores and mortality compared to Groups A / B. Etiology of MODS and computed subphenotype for each patient is detailed in Table 7.

[0122] Noting that these protein-derived MODS subphenotypes are associated with an ordinal increase in number of organ failures and mortality across subphenotypes, we hypothesized a reduced set of plasma proteins could successfully identify MODS subphenotypes and could thus be more suitable for translation to the clinical setting. To define this parsimonious protein signature, we trained an ordinal elastic net model (44) - which incorporates elastic net regularization into a penalized ordinal regression model - and generated a 24-protein signature which successfully discriminates the three subphenotypes (Fig. 2F).

[0123] We next sought to evaluate the performance of this parsimonious protein model through two complementary approaches. First, we tested the association between the elastic net proteins severity of illness. Using a linear mixed-effects model, we estimated the fold change in protein expression associated with one standard deviation change in PELOD-2 organ dysfunction score (Fig. 2D). This model demonstrates that modest changes in expression of these 24 proteins are associated with meaningful differences in illness severity. Second, we tested the discrimination of our 24-protein signature for MODS subphenotype. As shown in the heatmap of protein expression at MODS onset in Fig. 2E, hierarchical clustering of elastic net proteins effectively separates MODS subphenotypes but does not discriminate between MODS patients with and without sepsis.

[0124] To quantify the discrimination of the parsimonious elastic net protein set for MODS subphenotypes, we used linear discriminant analysis to calculate the polytomous discrimination index (PDI) for each MODS subphenotype. Category-specific PDI indicated excellent discrimination for each subphenotype (Group A 0.98, Group B 0.96, Group C 0.99), and overall PDI for the model was 0.98. Taken together, these results suggest that our reduced 24-protein signature reflects severity of illness and successfully discriminates subphenotypes at MODS onset.

[0125] Immune cell frequency and activation vary by MODS subphenotype

[0126] Having identified three MODS subphenotypes based on severity-associated protein expression and developed a parsimonious model to classify patients with MODS, we hypothesized that protein-derived subphenotypes would be associated with differences in cellular immunophenotype. For this analysis, we performed high dimensional spectral flow cytometry on cryopreserved PBMCs obtained at MODS onset using a custom-designed 35 marker panel (Table 4) which includes both phenotypic and functional markers. After arcsinh scaling (45) and quality control with flowAI (46), we performed FlowSOM metaclustering (47) identified 14 PBMC populations by surface and intracellular marker expression. Fig. 3H presents a representative example of our gating strategy for this immune phenotyping panel.

[0127] To visualize immunophenotypic differences between HC participants and the three MODS subphenotypes (Fig. 3A), we subsampled the data to 100,000 cells per MODS subphenotype and applied t-distributed Stochastic Neighbor Embedding with Compute Unified Device Architecture (tSNE-CUDA) dimensionality reduction (48). Differences in cell populations by MODS subphenotype are quantified in stacked bar plots (Fig. 3B), showing ordinal reduction of lymphoid and myeloid effector cells across MODS groups. The abundance of non-naive (central memory, effector memory, and terminally differentiated) CD8+ T cells were markedly reduced in Group C patients (Fig. 3C), and this loss of non-naive CD8+ T cells was strongly associated with severity of illness by linear regression (p<0.001 ). A similar ordinal trajectory was noted across many cell types (Fig. 3D), including T cells, B cells, NK cells, and dendritic cells (Cuzik test of trend <0.001 for each cell type shown). As seen with the reduced frequency of non-naive T cells, reduction in frequency of each of these cell types was strongly associated with severity of illness by linear regression (all p<0.001).

[0128] In addition to shifts in cellular frequency, we hypothesized that protein-derived subphenotypes would be associated with differences in immune cell activation, as measured by expression of markers of proliferation (Ki67) and activation (CD38 and HLA-DR), across MODS subphenotypes. Representative bivariate plots of Ki67 expression in non-naive CD4+ and CD8+ T cells and cytotoxic NK cells demonstrate increased proliferation in Group B and Group C patients compared to Group A patients (Fig 3E). Similarly, CD38 and HLA-DR coexpression in non-naive CD8+ T cells was markedly increased Group B and Group C patients compared to Group A, indicative of CD8+ T cell activation in these groups (Fig. 3F). Corresponding boxplots in Fig. 3G quantify these differences in proliferation and activation by MODS subphenotype. Taken together, these data suggest that CD8+ T cells have markedly reduced abundance but concurrently have increased markers of proliferation and activation in Group C patients, and that these cellular phenotypes are associated with worsening severity of illness. Multiple concurrent mechanisms of immune dysregulation define Group C patients

[0129] Because Group C patients have distinct clinical, proteomic profiles, and cellular features compared to other patients with MODS, we hypothesized that expression of key canonical inflammatory pathways would differ by MODS subphenotype. Using the proteomics dataset, we first examined differential protein expression after adjustment for patient age, sex, severity of illness, and days since MODS onset using a linear mixed-effects model. In unadjusted analysis, 1061 / 1448 measured proteins were differentially expressed in Group C (Fig. 4A), and in our adjusted model 1003 / 1061 proteins remained differentially expressed. For 98% (980 / 1003) of these differentially expressed proteins, expression was upregulated in Group C compared to Group A / B.

[0130] Using the adjusted protein expression dataset, we then performed pathway enrichment analysis using complementary group- and patient-based strategies. First, we used Ingenuity Pathway Analysis (Qiagen) (49) to identify enrichment of 22 canonical pathways in the Group C proteome in comparison to Group A / B (Fig. 4B). Noting that 8 of the top 10 differentially expressed pathways were related to hyperinflammatory signaling, we then applied Gene Set Variation Analysis (GSVA) (50) to study enrichment of 5 canonical proinflammatory pathways on the individual patient level using Human Molecular Signatures Database (MSigDB) hallmark gene sets (51). Enrichment scores for each pathway are visualized in Fig. 4C and demonstrate markedly increased IL-6 / JAK / STAT3 and IL-2 / JAK / STAT5 signaling in Group B and Group C patients (each / <0.001 vs Group A), modest increase in TNF / NFkB signaling in Group B and Group C patients (each p<0,05 vs Group A), and an ordinal reduction in PI3K / AKT / MTOR signaling across MODS subphenotypes (Cuzik test of trend p<0.001). Conversely, IFN-y response module scores were minimally increased in patients with MODS and did not vary by MODS subphenotype. We then assessed the correlation between pathway enrichment and PBMC immune cell subset abundance and activation in patients with MODS (Fig. 4D). We noted that IL-6 / JAK / STAT3 pathway plasma protein expression had the strongest correlation with proliferation and activation of PBMC subsets previously identified in Fig. 3E-3F, including non- naive CD4+ and CD8+ proliferation (Ki67 expression) and activation (HLA-DR / CD38 coexpression and PD-1 / CD39 coexpression). Classical and non-classical monocyte HLA-DR expression was inversely correlated with IL-6 / JAK / STAT3 module enrichment score, while immune regulatory cell populations (regulatory T cells, myeloid-derived suppressor cells) were positively correlated with IL-6 / JAK / STAT3 module enrichment score.

[0131] Finally, we studied the longitudinal expression of canonical cytokine storm markers (52) in patients with MODS to understand the duration of immune dysregulation in Group C patients. Normalized protein expression by day since MODS onset by subphenotype is shown in Fig. 4E. We noted that IL-6, MCP-1, and IL- 18 expression are significantly higher in Group C patients for the first 7 days after MODS onset (each <0.001 at day +7), while IL-1 and TNF expression remain different for only the first 4 days ( =0.05 and p=0.009 respectively at day +4) and IFN-y does not differentiate MODS subphenotypes.

[0132] Cell-specific signatures of immunometabolic dysregulation in patients with IL-6 / JAK / STAT3 hyperactivation

[0133] Having identified a link between IL-6 / JAK / STAT3 pathway signaling, severity of illness, relative decrease in frequency of effector cells populations, and increased T cell proliferation and activation by flow cytometry, we next hypothesized that IL-6 / JAK / STAT3 signaling would have cell-specific effects in Group C patients. To assess the impact of IL-6 / JAK / STAT3 hyperactivation on immune cell phenotype and function in patients with MODS, we conducted a single cell RNA sequencing experiment (10X Genomics, Pleasanton, CA) on cryopreserved PBMCs obtained at MODS onset for Group C patients (n=9) and age-matched HC participants (n=3). IL-6 / JAK / STAT3 pathway protein expression module enrichment scores were significantly different between the 9 patients with MODS and 3 HC participants (0.331 vs. -0.494, <0.0001 ) selected for the transcriptomics experiment.

[0134] Fig. 5A shows the study schematic for this analysis. We sorted live CD45+ PBMCs using a Cytek Aurora CS cytometer (Cytek Biosciences, Fremont, CA) and then profiled 10,000 cells per patient using a 5’ RNA tag single cell sequencing approach (scRNA-seq; 10X Genomics, Pleasanton, CA). Libraries were sequenced to a depth of -30,000 reads per cell on a Novaseq S2 (Illumina, San Diego, CA). Transcripts were aligned to the GRCh38 reference genome using Cell Ranger v8.0 (10X Genomics, Pleasanton, CA). After quality control and integration, cell identities were inferred using the Azimuth reference-based mapping pipeline (55) and used to define 14 PBMC populations by transcriptional profile.

[0135] To visualize immunophenotypic differences between HC participants and Group C patients in the scRNA-seq experiment (Fig. 5B), we subsampled the data to 30,000 cells per group and applied Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction (54). Differences in cell population abundance are quantified in stacked bar plots Leading edge proteins and other enriched Hallmark pathways from this analysis are shown in Table 9.

[0136] (Fig. 5C), and demonstrate shifts in CD4+ lymphocytes, CD8+ lymphocytes, B cells, NK cells, and monocytes abundance between HC participants and Group C (all p<0.001). Compared to the flow cytometry immune profiling assay results, our transcriptomics analysis revealed a higher proportion of non-naive CD4+ T cells, but other cell subset frequencies are similar between modalities. These shifts in differential abundance between flow cytometry and transcriptional data may reflect inherent differences in cell subset identification using transcriptional and cell surface markers (55). Consistent the flow cytometry data set, the transcriptomic data set reflects the loss of effector cell subsets in Group C patients compared to HC participants, including loss of non-naive CD8+ T cells and cytotoxic NK cells.

[0137] To begin to understand the cell-specific impact of IL-6 / JAK / STAT3 pathway expression in patients with MODS, we performed pseudobulk analysis of pathway expression data using UCell (56). Pseudobulk analysis aggregates transcriptional profiles by cell subtype prior to statistical testing and has been shown to outperform other methods of differential expression analysis in single cell datasets (57). By pseudobulk analysis, we noted that CD4+, CD8+, and TCRgd+ T cells from patients with MODS had increased IL-6 / JAK / STAT3 module enrichment scores compared to controls (all / <0.001 ) while monocytes, NK cells, and B cell IL- 6 / JAK / STAT3 pathway expression did not differ between groups, as shown in the split enrichment plots in Fig. 5D.

[0138] Transcriptomic immune dysfunction scores have recently been applied to adult and pediatric sepsis, critical influenza, and COVID- 19 for risk stratification (35-39, 58, 59~). Among these, the quantitative sepsis response signature score (SRSq) is a prognostic metric summarizing the host transcriptional response to sepsis based on expression of 19 genes by bulk RNA sequencing (59). Because higher SRSq is associated with proinflammatory signaling, lymphocyte dysfunction, severity of illness, and higher risk of poor outcomes, we hypothesized that SRS gene enrichment would be associated with IL-6 / JAK / STAT3 pathway expression in Group C PBMC subsets. For each PBMC subset of interest, we generated a linear regression model to compare proinflammatory module enrichment to enrichment of the extended SRS gene set. The heatmap in Fig. 5E depicts the correlation between SRS and inflammatory pathway enrichment scores by cell type, and the adjusted R2is displayed for all significant associations. While the IL-6 / JAK / STAT3 pathway had the highest degree of correlation to SRSq, the positive correlation was moderate and only applied to gene expression within the T cell compartment.

[0139] STAT3 pathway activation has been shown to downregulate innate and adaptive immune responses in the tumor microenvironment (60), attenuating Thl lymphocyte responses (61) and enhancing MDSC (62) and regulatory T cell function (63). STAT3 signaling also alters immune cell mitochondrial respiration, enhancing both oxidative phosphorylation (64) and glycolysis (65). Having identified loss of effector T cells and NK cells in our experimental data and increased IL-6 / JAK / STAT3 signaling in our scRNA-seq data, we next hypothesized that IL- 6 / JAK / STAT3 hyperactivation would be associated with altered PBMC immuno-metabolic state.

[0140] To investigate the link between IL-6 / JAK / STAT3 signaling and PBMC immunometabolism, we first calculated glycolysis and oxidative phosphorylation Hallmark pathway module enrichment scores using UCell (56). We then constructed bivariate plots of glycolysis and oxidative phosphorylation module enrichment scores for both Group C and HC PBMCs (Fig. 5F). Compared to HC PBMCs, we identified two distinct populations of PBMCs in Group C - one with high module enrichment scores for both glycolysis and oxidative phosphorylation, and another with low module enrichment scores for glycolysis and oxidative phosphorylation. These populations are denoted on the bivariate plot with labeled boxes. As shown in the ridge plot in Fig. 5F, among cells with increased metabolic activity, we noted that IL-6 / JAK / STAT3 enrichment scores were lower than median pseudobulk values for each PBMC subset, suggesting that increased JAK / STAT3 signaling may suppress immuno-metabolic function.

[0141] Based on this insight, we hypothesized that hyperactive IL-6 / JAK / STAT3 signaling would be associated with reduced effector cell metabolic activity. Because multiple inflammatory signaling pathways are concurrently active in Group C patients, we then constructed a mixed effects logistic regression model to determine the association between module enrichment score and immuno-metabolic profile of non-naive CD4+, non-naive CD8+, and cytotoxic NK cells in patients with MODS at the single cell level. As shown in Fig. 5G, IL 6 / JAK / STAT3 and IL-2 / JAK / STAT5 signaling at the single-cell level are associated with low glycolysis and oxidative phosphorylation module scores, while IFN-y and PI3K / AKT / MT0R signaling at the single-cell level are associated with high glycolysis and oxidative phosphorylation module scores (all p<0.01). These results suggest that STAT3 and STAT5 hyperactivation in T cells is associated with poor immuno-metabolic function, which may partially explain the link between hypercytokinemia and T cell dysfunction in sepsis. Bulk PBMC mitochondrial function has been previously associated with adverse outcomes in pediatric sepsis (24-26), and these results suggest a mechanism by which this immunometabolic dysregulation may occur.

[0142] JAK / STAT3 as a candidate target for precision immunomodulation

[0143] Having established increased plasma expression of IL-6 / JAK / STAT3 target proteins in Group C patients and that IL-6 / JAK / STAT3 module enrichment was associated with dysregulated cellular immunity and transcriptional evidence of immuno-metabolic dysregulation, we next sought to assess whether JAK / STAT3 pathway activation represented a physiologic or pathologic response in the setting of critical illness. The JAK / STAT3 signaling pathway is a canonical inflammatory pathway associated with capillary leak, endothelial dysfunction, emergency granulopoiesis, and lymphocyte dysregulation (66). Informed by two recent preclinical studies demonstrating a protective effect of selective STAT3 inhibition in mice with CLP-induced sepsis (67, 68), we hypothesized that the degree and duration of STAT3 activation in Group C patients is pathologic and therefore a candidate target for precision immunomodulation .

[0144] To test this hypothesis, we compared plasma protein expression between patients with MODS and patients with rare, monogenic inborn errors of immunity (IEI) involving constitutive activation or inactivation of the STAT1 and STAT3 signaling pathways. IEIS can provide context to help decode complex phenotypes by defining in vivo human immune signatures which correspond to signaling pathway perturbation. For this analysis, IEI patients were categorized as STAT1 gain-of-function (GOF) (n=9), STAT3 GOF (n=5), STAT1 loss-of-function (LOF) (n=l), or STAT3 LOF (n=3). Protein expression was measured using a 384-marker inflammatory proteomics panel from Olink Proteomics (Table 8), and we analyzed pathway expression in bulk by GSEA (69) and at the individual patient level using GSVA (50).

[0145] Fig. 6A shows the study schematic for this analysis, which includes 88 patients with MODS, 18 patients with IEIs, and 25 HC participants. Orthogonal to our Ingenuity Pathway Analysis findings, we first confirmed population-level enrichment (normalized enrichment score 1.53, - value 0.004) of the IL-6 / JAK / STAT3 signaling pathway in Group C patients at MODS onset compared to HC participants using GSEA (Fig. 6B). Leading edge proteins and other enriched Hallmark pathways from this analysis are shown in Table 9.

[0146] To assess expression of JAK / STAT target proteins across MODS and IEI patients, we identified 31 measured proteins from the KEGG JAK / STAT signaling pathway and performed hierarchical clustering based on normalized protein expression. As shown in Fig. 6C, we noted that STAT1 GOF and STAT3 GOF co-localize with Group C patients while STAT1 LOF and STAT3 LOF patients co-localize with Group A patients in hierarchical clustering. This proteinlevel analysis also highlights the residual biologic heterogeneity within our severity-defined subphenotypes.

[0147] Having demonstrated relative enrichment of JAK / STAT target proteins in MODS Group C patients, STAT1 GOF patients, and STAT3 GOF patients, we next used GSVA to assess patient-level enrichment of five inflammatory pathways in MODS and IEI patients. Fig. 6D shows a clustered heatmap of module enrichment scores for each patient in the dataset. While the cohort of STAT3 GOF and some STAT1 GOF patients show increased IL-6 / JAK / STAT3 signaling, only STAT3 GOF patients display concurrent decreased PI3K / AKT / MTOR signaling. This pattern of concurrent increased IL-6 / JAK / STAT3 signaling and decreased PI3K / AKT / MTOR signaling was associated with PBMC immuno-metabolic dysregulation in our scRNA-seq analysis (Fig. 5G).

[0148] Noting the similarities between Group C patients and STAT3 GOF patients, we next compared IL-6 / JAK / STAT3 module enrichment scores across MODS and IEI patients (Fig. 6E). Compared to Group A patients, IL-6 / JAK / STAT3 module enrichment scores were increased in Group C patients ( <0.001 ) and STAT3 GOF patients ( =0.001 ), and many patients with MODS had enrichment scores which exceeded the mean score of STAT3 GOF patients. Because patients with STAT3 GOF have constitutive pathway activation leading to immune dysregulation, this analysis suggests that Group C patients exhibit pathologic dysregulation of the JAK / STAT3 signaling pathway at the onset of MODS.

[0149] Finally, we sought to determine the trajectory of JAK / STAT3 dysregulation in Group C patients. For this analysis, we computed longitudinal IL-6 / JAK / STAT3 module enrichment scores among 26 patients with MODS in whom >3 longitudinal samples were obtained. We noted that Group C patients had the highest IL-6 / JAK / STAT3 module enrichment scores, and that they remained persistently elevated through the first two weeks of MODS (Fig. 6F). In contrast, IL-6 / JAK / STAT3 module enrichment scores gradually decreased in Group B patients and rapidly decreased in Group A patients. In this longitudinal analysis, it was uncommon for patient IL-6 / JAK / STAT3 module enrichment scores to increase over time, with two Group C patients increasing from a low IL-6 / JAK / STAT3 module enrichment scores to a higher score around one week after MODS onset.

[0150] STAT1 and STAT3 signaling are associated with T cell immunometabolic dysregulation in MODS

[0151] Having demonstrated STAT3 pathway signaling in Group C patients at levels comparable to or exceeding those seen in patients with known STAT3 GOF patients, we next hypothesized that plasma STAT3 hyperactivation would result in immunometabolic dysregulation within lymphocytes from Group C patients. To test this hypothesis, we performed single cell RNA sequencing using cryopreserved PBMCs obtained at MODS onset for Group C patients (n=9) and pediatric HC participants (n=3). Plasma IL-6 / JAK / STAT3 module enrichment scores were significantly different between the 9 patients with MODS and 3 HC participants (0.331 vs. -0.494, <0.0001 ) selected for the transcriptomics experiment.

[0152] A schematic of our experimental approach is shown in Fig. 7A. We sorted live CD45+PBMCs and then profiled 10,000 cells per patient using 5’ RNA tag single cell sequencing approach (scRNA-seq; 10X Genomics, Pleasanton, CA). Libraries were sequenced to a depth of ~30,000 reads per cell on a Novaseq S2 (Illumina, San Diego, CA). Transcripts were aligned to the GRCh38 reference genome using Cell Ranger v8.0. After quality control using SoupX (67) and DoubletFinder (6S) and integration using RPCA via Seurat v5 (69), cell identities were inferred using ScType (70) and refined using Azimuth (71) and T cell phenotypes from Giles et al (72).

[0153] Using this approach, we identified 15 immune cell populations by transcriptional profile for downstream analysis. We applied UMAP dimensionality reduction (73) and subsampled 30,000 cells from each group (HC and Group C) for visualization of immunophenotypic differences (Fig. 7B). Differences in cell populations between HC participants and Group C are quantified in stacked bar plots (Fig. 7C) and largely mirror the differences in lineages seen in our flow cytometry analysis, redemonstrating the altered immune cell composition in Group C patients.

[0154] To validate our finding of T cell dysregulation in Group C patients, we turned to a transcriptional framework developed by the SUBSPACE consortium (74), which identified a “lymphoid protective” gene set derived from adult patients with sepsis. A lower score on this module represents a “lymphoid detrimental” transcriptional program, which is associated with T cell dysfunction and poor outcomes. To quantify these transcriptional states in our pediatric cohort, we calculated “lymphoid protective” module enrichment scores at the single-cell level using UCell (75). To enable robust comparison by T cell subset, we first aggregated single-cell expression into pseudobulk profiles for statistical analysis. Pseudobulk analysis aggregates transcriptional profiles by cell subtype prior to statistical testing and has been shown to help avoid Type 1 error when analyzing differential expression in single cell datasets (7(5). Group C patients exhibited significantly lower lymphoid protective scores compared to HC participants in CD4+ naive, CD4+ non-naive, and CD8+ non-naive populations (Fig. 7D).

[0155] To broaden our assessment of transcriptional dysregulation, we extended our analysis to include per-cell lymphoid protective module enrichment scores across 9 major lymphoid subsets (Fig. 7E). Group C patients exhibited significantly lower module scores across all subsets, with the most pronounced deficits observed in the CD8+compartment. Together, these results suggest that lymphoid dysregulation in Group C is not restricted to numerical depletion but reflects widespread changes to immune regulatory gene programs necessary for effective immune surveillance.

[0156] We next sought to identify cytokines which may drive the immune dysregulation observed in lymphocytes from Group C patients. To identify transcriptional profiles associated with specific cytokine-cell type pairs, we leveraged the Immune Dictionary framework (77), which enables cell-type specific identification of cytokine activity based on downstream gene expression. As shown in Fig. 7F, Group C patients exhibited significantly increased transcriptional responses to multiple pro-inflammatory cytokines in comparison to HC participants, including IL-6 (which predominantly activates STAT3) and IFN-y (which predominantly activates STAT1). These findings suggest that sustained exposure to a proinflammatory cytokine milieu, particularly IL-6 and IFN-y, may drive the transcriptional reprogramming observed in lymphocytes from Group C patients. To understand the effects of sepsis on T cell immunometabolism, we calculated KEGG pathway module enrichment scores for glycolysis, oxidative phosphorylation, and mTOR signaling pathways using UCell (75) as detailed in Methods. As shown in Fig. 7G, CD8+T cells from Group C patients exhibited increased transcriptional activity in glycolysis and oxidative phosphorylation pathways compared to HC participants across all differentiation states. Conversely, mTOR signaling was suppressed across CD8+subsets from Group C, a pattern suggesting cytokine-driven mTOR repression that skews T cells toward short-lived effector fates (78).

[0157] Given that IL-6 and IFN-y can enhance glycolysis and oxidative phosphorylation pathways and inhibit mTOR through STAT-dependent feedback, we next assessed whether exposure to these signals explained the metabolic phenotype observed in Group C CD8+T cells. To test whether specific cytokines underpinned this bioenergetic shift, we modeled single -cell Hallmark glycolysis and oxidative phosphorylation module scores jointly (Fig. 7H). This revealed a distinct subset of Group C CD8+T cells with high glycolysis and oxidative phosphorylation activity (labeled as “metabolic activation”). We next hypothesized that CD8+T cell responses to proinflammatory cytokines would be associated with increased metabolic activation. In multivariable mixed-effects logistic regression analysis, enrichment scores for IL- 6, IFN-y, and IE- 1 P transcriptional responses are significantly increased in immunometabolically activated CD8+T cells as compared to cells within the HC baseline range (all p<0.001 ), as shown in the ridge plots in Fig. 7H.

[0158] Finally, to further validate the observed activation of CD8+T cells in Group C patients, we performed a ligand-receptor interaction analysis using CellChat (79). This approach integrates scRNA-seq data with curated ligand-receptor interaction databases to infer patterns of cellular communication based on co-expression of signaling components across cell types. As shown in Fig. 71, Group C patients exhibited significantly stronger inferred signaling interactions between monocyte-expressed HLA class I molecules and CD8A in CD8+T cells, compared to healthy controls (p < 0.01). This enhanced antigen presentation signature suggests increased activation of CD8+effector T cells, reinforcing our transcriptional and metabolic findings and highlighting the potential role of myeloid-lymphoid crosstalk in perpetuating immune dysregulation.

[0159] T cell activation in MODS occurs without antigen-specific clonal expansion Having demonstrated lymphoid dysregulation and links between inflammatory signaling and T cell immunometabolism in Group C patients and having shown previously that infection is not enriched in Group C patients, we next investigated whether these changes reflected antigenspecific immune responses or non-specific, bystander activation. To do so, we analyzed T cell receptor (TCR) repertoires captured alongside our scRNA-seq data using 5' tag-based single-cell V(D)J sequencing (10X Genomics, Pleasanton, CA). VDJ libraries were sequenced to a depth of -10,000 reads per cell on a Novaseq S2 (Illumina, San Diego, CA) and processed with Cell Ranger v8.0, aligning to the GRCh38 reference genome. TCR data were integrated with scRNA- seq analyses in Seurat using the scRepertoire package (80). We used this dataset to test the hypothesis that T cell activation in Group C patients represented antigen-independent “bystander” inactivation as opposed to antigen- specific activation in response to a pathogen.

[0160] A schematic of our experimental approach is shown in Fig. 8A. We sorted live CD45+PBMCs and then profiled 10,000 cells per patient using 5’ RNA tag single cell sequencing approach (scRNA-seq; 10X Genomics, Pleasanton, CA). Libraries were sequenced to a depth of -30,000 reads per cell on a Novaseq S2 (Illumina, San Diego, CA). Transcripts were aligned to the GRCh38 reference genome using Cell Ranger v8.0. After quality control using SoupX (67) and DoubletFinder (68) and integration using RPCA via Seurat v5 (69), cell identities were inferred using ScType (70) and refined using Azimuth (71) and T cell phenotypes from Giles et al (72).

[0161] Using this approach, we identified 15 immune cell populations by transcriptional profile for downstream analysis. We applied UMAP dimensionality reduction (73) and subsampled 30,000 cells from each group (HC and Group C) for visualization of immunophenotypic differences (Fig. 8B). Differences in cell populations between HC participants and Group C are quantified in stacked bar plots (Fig. 7C) and largely mirror the differences in lineages seen in our flow cytometry analysis, redemonstrating the altered immune cell composition in Group C patients.

[0162] To validate our finding of T cell dysregulation in Group C patients, we turned to a transcriptional framework developed by the SUBSPACE consortium (74), which identified a “lymphoid protective” gene set derived from adult patients with sepsis. A lower score on this module represents a “lymphoid detrimental” transcriptional program, which is associated with T cell dysfunction and poor outcomes. To quantify these transcriptional states in our pediatric cohort, we calculated “lymphoid protective” module enrichment scores at the single-cell level using UCell (75). To enable robust comparison by T cell subset, we first aggregated single-cell expression into pseudobulk profiles for statistical analysis. Pseudobulk analysis aggregates transcriptional profiles by cell subtype prior to statistical testing and has been shown to help avoid Type 1 error when analyzing differential expression in single cell datasets (76). Group C patients exhibited significantly lower lymphoid protective scores compared to HC participants in CD4+ naive, CD4+ non-naive, and CD8+ non-naive populations (Fig. 8C).

[0163] To broaden our assessment of transcriptional dysregulation, we extended our analysis to include per-cell lymphoid protective module enrichment scores across 9 major lymphoid subsets (Fig. 8D). Group C patients exhibited significantly lower module scores across all subsets, with the most pronounced deficits observed in the CD8+compartment. Together, these results suggest that lymphoid dysregulation in Group C is not restricted to numerical depletion but reflects widespread changes to immune regulatory gene programs necessary for effective immune surveillance.

[0164] IL-6 and IFN-y signaling drive aberrant STAT pathway activation and T cell dysfunction in MODS

[0165] Building on our transcriptional analyses, which revealed strong enrichment of IL-6 and IFN-y response modules in CD8+T cells from Group C patients, we next sought to confirm that these cytokines drive functional changes in signaling at the protein level. To do so, we developed a 13-marker T cell phosphoflow cytometry panel (Table 9) which includes a lineage backbone plus phospho STAT (pSTAT) antibodies that recognize pSTATl, pSTAT3, and pSTAT5 (81).

[0166] We first examined basal phosphorylation of STAT1 and STAT3, key transcription factors downstream of IFN-y and IL-6 signaling, respectively. Compared to HC, Group C patients at baseline exhibited higher expression of pSTATl (p=0.041) and pSTAT3 (p=0.026) in non-naive CD8+ T cells (Fig. 9A). This finding is consistent with elevated plasma cytokine levels, as well as our transcriptional cytokine response analysis, suggesting that IL-6 and IFN-y exert a combined effect on CD8+T cells in Group C patients.

[0167] Given the elevated baseline STAT signaling in Group C, we next assessed how this cytokine preconditioning affected CD8+T cell responsiveness to receptor-mediated stimulation. We hypothesized that Group C T cells would demonstrate increased pSTAT expression in response to TCR stimulation. As illustrated in Fig. 9B, PBMCs were thawed and cultured for up to 24 hours with plate-bound anti-CD3 and soluble anti-CD28 antibodies in AIM-V media.

[0168] We next studied the kinetics of TCR- induced pSTAT3 phosphorylation in CD8+T cells from Group C and HC samples. As shown in Fig. 9C, Group C CD8+T cells initially exhibited higher pSTAT3 expression compared to HC during the first 4 hours of stimulation. However, by 24 hours pSTAT3 levels sharply declined, suggesting a rapid onset of signaling desensitization in response to sustained TCR stimulation. In contrast, healthy control cells maintained or increased STAT3 phosphorylation over the same time course.

[0169] We next compared the ability of Group C CD8+T cells to respond to TCR stimulation alone or in combination with IL-6. As shown in Fig. 9D, HC cells upregulated both pSTATl and pSTAT3 following TCR stimulation, and IL-6 further augmented pSTAT3 levels. In contrast, Group C cells failed to increase pSTATl or pSTAT3 expression in response to TCR stimulation with and without IL-6. Representative pSTAT3 histograms highlight the lack of response to stimulation in Group C T cells. These results indicate that CD8+T cells from Group C patients are functionally refractory to JAK / STAT signaling downstream of TCR engagement, consistent with cytokine-induced signaling desensitization.

[0170] To determine whether soluble mediators in Group C plasma were sufficient to drive the observed pSTATl and pSTAT3 signaling abnormalities, we performed plasma culture experiments using healthy donor PBMCs. As shown in Fig. 9E, healthy donor cells have low pSTATl and pSTAT3 expression at baseline, and pSTAT3 expression increases significantly when exposed to TCR stimulation in the setting of complete media or media supplemented with healthy control patient plasma (both p<0.001). In contrast, co-culture of healthy PBMCs with media supplemented with plasma from Group C patients for 24 hours completely suppressed TCR- induced pSTATl and pSTAT3 phosphorylation, maintaining expression at baseline levels. Representative pSTAT3 histograms highlight the inhibitory effect of Group C plasma on pSTAT3 expression in healthy donor cells.

[0171] Together, these findings demonstrate that sustained exposure to IL-6 and IFN-y in Group C patients drives chronic activation of the STAT1 and STAT3 signaling pathways, leading to a state of functional desensitization in CD8+T cells. Despite elevated basal pSTAT levels, Group C T cells fail to respond appropriately to subsequent TCR-mediated stimulation, suggesting the presence of cytokine-induced exhaustion or signaling refractoriness. Plasma transfer experiments confirm that this dysfunction is extrinsically imposed by the inflammatory milieu, rather than due to intrinsic defects in the cells themselves. These data implicate IL-6 and IFN-y driven STAT1 and STAT3 signaling as central mediators of T cell dysregulation within this endotype and provide a mechanistic link between systemic inflammation, transcriptional rewiring, and impaired adaptive immunity in MODS.

[0172] Discussion

[0173] In a prospective cohort of children admitted to a quaternary care PICU with MODS from diverse etiologies, we have defined a distinct immunologic endotype marked by persistent STAT1 / STAT3 hyperactivation and T cell immunometabolic dysfunction in a subset of patients with the highest severity of illness and worst prognosis. These patients can be identified by a 24- protein signature at MODS onset via a 24-protein plasma biomarker signature that is agnostic to underlying etiology, suggesting a convergent immunopathologic trajectory across diverse infectious and non-infectious insults.

[0174] Multi-modal immune profiling identified that Group C is characterized by sustained exposure to IL-6 and IFN-y, leading to chronic activation of STAT1 and STAT3 signaling pathways. Single-cell RNA sequencing revealed broad suppression of lymphoid protective gene programs and transcriptional signatures of T cell exhaustion, metabolic stress, and cytokine- driven activation, particularly within CD8+subsets. Despite high expression of activation markers by flow cytometry and scRNA-seq, our TCR repertoire analysis revealed no evidence of clonal expansion, consistent with antigen-independent, bystander T cell activation. Functionally, CD8+T cells from Group C patients exhibited increased baseline STAT1 and STAT3 phosphorylation, yet failed to respond to TCR-mediated stimulation. These findings indicate a state of cytokine-induced desensitization or functional exhaustion, which was recapitulated in healthy PBMCs following exposure to Group C plasma — confirming an extrinsic, cytokine- driven mechanism of T cell dysfunction.

[0175] Pathologic STAT1 and STAT3 signaling abnormalities in this immune endotype persist for up to two weeks and are targetable through approved anti-cytokine antibodies, JAK inhibitors, and experimental STAT inhibitors (82), and thus represent viable future targets for precision medicine studies, and potential future therapeutics, in pediatric patients with sepsis. Supporting this hypothesis, during the CO VID- 19 pandemic some patients requiring supplemental oxygen were noted to benefit from a combination of dexamethasone and a JAK inhibitor (83). In randomized controlled trials, use of the JAK inhibitors baricitinib and tofacitinib were associated with reduced mortality (84-87), faster recovery (88), and lower rates of disease progression (84) in multiple adult trials focusing on varying levels of illness severity. Several available JAK inhibitor agents have good bioavailability, rapid onset of action, and short half-lives, features that are favorable for use in the critical care setting. If STAT1 / STAT3 hyperactivation is in fact a causal contributor to organ failure in pediatric sepsis, the safety and efficacy of baricitinib in critical COVID-19 suggests a strong rationale for targeting JAK signaling in future pediatric MODS clinical trials.

[0176] Our highly granular, longitudinal approach to immune profiling yields new insights into the pathobiology of pediatric sepsis and MODS and identifies a novel targetable mechanism of immune dysregulation in critically ill children with and without sepsis. Our findings redemonstrate the association we previously suggested (23) between T cell dysfunction, mitochondrial dysfunction, and clinical outcomes in pediatric sepsis, and implicate a causative molecular mechanism for sepsis-associated T cell immunometabolic dysregulation. As other investigators have shown, the highest severity patients in our cohort have innate (17, 21) and adaptive (18, 20) immune dysfunction and mitochondrial dysfunction (25, 26, 89).

[0177] Sepsis-3 criteria define sepsis as organ dysfunction that develops in the setting of a dysregulated response to infection (1), and nearly all patients with sepsis in this study had evidence of immune dysregulation. To our surprise, we identified features of immune dysregulation in many patients with MODS who did not have suspected infection, and our endotypes include patients with and without sepsis. These findings supplement the findings of previous studies (90, 91) which have focused on identifying clinical and transcriptional differences between patients with sepsis compared to sterile inflammation. Our findings demonstrating overlapping signatures of septic and sterile MODS may be explained by the high severity of illness in our cohort, as these patients may exhibit a final common pathway of critical illness which develops in the setting of overwhelming proinflammatory signals. Conversely, it is possible that populations of patients with MODS are enriched with children who have undiagnosed primary immune regulatory disorders. Several studies have identified enrichment of IEI in pediatric patients with sepsis (38-40),' increased prevalence of lEIs in pediatric MODS cohorts may therefore influence the association between immune dysregulation and clinical outcomes.

[0178] Leveraging the proteomics dataset, we used penalized regression to identify a prognostic protein signature detectable at MODS onset and demonstrated excellent discrimination using linear discriminant analysis. Elastic net regularization is a common approach applied to feature selection in high-dimensional data which generates a high-performing sparse model with good prediction accuracy (52) and has been employed with similar results in adult sepsis studies (47). The novel parsimonious 24-protein MODS severity model can be further validated across multiple proteomics platforms, including without limitation, aptamer-based and antibody-based approaches (92).

[0179] The longitudinal analysis of protein and pathway expression in this study provides evidence that immune dysregulation present at MODS onset persists for more than a week in many patients. Persistent immune dysregulation is amenable to consideration of a trial of precision therapeutics in which patients could be identified at MODS onset and then assigned to a treatment arm based on biomarker-based prognostic and predictive enrichment strategies. Equally relevant, prognostic endotypes defined at MODS onset demonstrated relatively stable proinflammatory pathway enrichment scores through time, suggesting that endotype membership is patient / episode- specific and minimally impacted by time from MODS onset.

[0180] The cellular correlates of immune dysfunction observed in this study - defined by persistent STAT1 and STAT3 hyperactivation, altered metabolic programming, and impaired signal responsiveness - were reproducible across multiple platforms, including spectral flow cytometry and scRNA-seq. These phenotypic features offer new functional biomarkers to serve as surrogate endpoints for early -phase interventional studies. For example, ex vivo pSTAT signaling assays could be used to evaluate pharmacodynamic responses to JAK inhibition or cytokine blockade, while immune cell metabolic profiling (using Seahorse or SCENITH (93) assays) could track immunometabolic recovery following treatment. Ultimately, these findings define a path toward biomarker-guided, mechanism-informed interventions for critically ill children and establish a new translational roadmap for targeting immune dysregulation in pediatric sepsis.

[0181] Incorporating rare, monogenic lEIs to contextualize complex sepsis endotypes is a key strength of our study. Typically identified in children with severe presentations of specific infectious diseases (94), lEIs are known to be enriched in pediatric patients with CO VID- 19 (95- 98), influenza (99-101), and sepsis (38, 39, 102). Genotype-phenotype associations between monogenic lEIs and disease susceptibility may offer insights into causal pathobiology of illness which subsequently inform precision therapeutics (103). Our comparison between patients with MODS and patients with gain-of-function and dominant-negative STAT disorders allows us to further elucidate the impact of chronic amplification of these key pathways from human disease and suggests that the extent of STAT3 hyperactivation in the most severe endotype acutely exceeds that of patients with constitutive STAT3 activation.

[0182] Collectively, our longitudinal multiomics analysis defines a prognostic plasma proteomic profile or signature present at MODS onset which is shared between patients with and without sepsis. Within our highest severity immune endotype, we identify a pathologic and reversible role for STAT1 and STAT3 hyperactivation which is associated with T cell immunometabolic dysregulation and poor prognosis. Our data demonstrate strong concordance across proteomic, cellular, and transcriptomic platforms, providing robust support for a model in which sustained IL-6 and IFN-y exposure drives aberrant STAT1 and STAT3 signaling, culminating in immune dysregulation and impaired T cell responsiveness. Taken together, these findings provide a new approach for biomarker-guided, mechanism-informed precision therapies in critically ill children, thereby laying the foundation for clinical trials targeting the JAK / STAT axis and supporting the broader implementation of functional immune profiling as a tool for tailoring immunomodulation in pediatric critical care.

[0183] TABLES REFERRED TO IN THE SPECIFICATION

[0184] Table 3. 1472 proteins measured in proteomics panel used in MODS and healthy control cohorts.

[0185] UNIPROT ID PROTEIN SYMBOL EXPLORE 384 PANEL

[0186] A1 L4H1 SSC5D Cardiometabolic A6NI73 LILRA5 Cardiometabolic NT-proBNP NT-proBNP Cardiometabolic 000161 SNAP23 Cardiometabolic 000533 CHL1 Cardiometabolic 000584 RNASET2 Cardiometabolic 014786 NRP1 Cardiometabolic 014793 MSTN Cardiometabolic 014798 TNFRSF10C Cardiometabolic

[0187] 014917 PCDH17 Cardiometabolic

[0188] 015031 PLXNB2 Cardiometabolic

[0189] 015354 GPR37 Cardiometabolic

[0190] 015467 CCL16 Cardiometabolic

[0191] 043186 CRX Cardiometabolic

[0192] 043854 EDIL3 Cardiometabolic

[0193] 060496 D0K2 Cardiometabolic

[0194] 060635 TSPAN1 Cardiometabolic

[0195] 060664 PLIN3 Cardiometabolic

[0196] 075015 FCGR3B Cardiometabolic

[0197] 075023 LILRB5 Cardiometabolic

[0198] 075326 SEMA7A Cardiometabolic

[0199] 075340 PDCD6 Cardiometabolic

[0200] 075354 ENTPD6 Cardiometabolic

[0201] 075356 ENTPD5 Cardiometabolic

[0202] 075594 PGLYRP1 Cardiometabolic

[0203] 075791 GRAP2 Cardiometabolic

[0204] 094903 PLPBP Cardiometabolic

[0205] 095183 VAMP5 Cardiometabolic

[0206] 095445 APOM Cardiometabolic

[0207] 095502 NPTXR Cardiometabolic

[0208] 095544 NADK Cardiometabolic

[0209] 095684 CEP43 Cardiometabolic

[0210] 095841 ANGPTL1 Cardiometabolic

[0211] 095988 TCL1 B Cardiometabolic

[0212] 095998 IL18BP Cardiometabolic

[0213] 096017 CHEK2 Cardiometabolic

[0214] P00441 SOD1 Cardiometabolic

[0215] P00533 EGFR Cardiometabolic

[0216] P00568 AK1 Cardiometabolic

[0217] P00740 F9 Cardiometabolic

[0218] P00750 PLAT Cardiometabolic

[0219] P00797 REN Cardiometabolic

[0220] P00915 CA1 Cardiometabolic

[0221] P01033 TIMP1 Cardiometabolic

[0222] P01034 CST3 Cardiometabolic

[0223] P01 130 LDLR Cardiometabolic

[0224] P01222 TSHB Cardiometabolic

[0225] P01241 GH1 Cardiometabolic

[0226] P01375 TNF Cardiometabolic

[0227] P01589 IL2RA Cardiometabolic P02144 MB Cardiometabolic

[0228] P02452 C0L1A1 Cardiometabolic

[0229] P02462 C0L4A1 Cardiometabolic

[0230] P02786 TFRC Cardiometabolic

[0231] P03950 ANG Cardiometabolic

[0232] P04054 PLA2G1 B Cardiometabolic

[0233] P04066 FUCA1 Cardiometabolic

[0234] P04070 PROC Cardiometabolic

[0235] P04080 CSTB Cardiometabolic

[0236] P04085 PDGFA Cardiometabolic

[0237] P04275 VWF Cardiometabolic

[0238] P04746 AMY2A Cardiometabolic

[0239] P04792 HSPB1 Cardiometabolic

[0240] P05107 ITGB2 Cardiometabolic

[0241] P05121 SERPINE1 Cardiometabolic

[0242] P05231 IL6 Cardiometabolic

[0243] P05362 ICAM1 Cardiometabolic

[0244] P05451 REG1 A Cardiometabolic

[0245] P05556 ITGB1 Cardiometabolic

[0246] P06681 C2 Cardiometabolic

[0247] P06858 LPL Cardiometabolic

[0248] P07204 THBD Cardiometabolic

[0249] P07339 CTSD Cardiometabolic

[0250] P07359 GP1 BA Cardiometabolic

[0251] P07451 CA3 Cardiometabolic

[0252] P07478 PRSS2 Cardiometabolic

[0253] P07585 DCN Cardiometabolic

[0254] P07711 CTSL Cardiometabolic

[0255] P07858 CTSB Cardiometabolic

[0256] P07911 UMOD Cardiometabolic

[0257] P081 18 MSMB Cardiometabolic

[0258] P08174 CD55 Cardiometabolic

[0259] P08236 GUSB Cardiometabolic

[0260] P08263 GSTA1 Cardiometabolic

[0261] P08319 ADH4 Cardiometabolic

[0262] P08571 CD14 Cardiometabolic

[0263] P08581 MET Cardiometabolic

[0264] P08670 VIM Cardiometabolic

[0265] P08709 F7 Cardiometabolic

[0266] P08833 IGFBP1 Cardiometabolic

[0267] P08887 IL6R Cardiometabolic

[0268] P09093 CELA3A Cardiometabolic

[0269] P09237 MMP7 Cardiometabolic P09382 LGALS1 Cardiometabolic

[0270] P09417 QDPR Cardiometabolic

[0271] P09467 FBP1 Cardiometabolic

[0272] P09496 CLTA Cardiometabolic

[0273] P09525 ANXA4 Cardiometabolic

[0274] P09601 HM0X1 Cardiometabolic

[0275] P09619 PDGFRB Cardiometabolic

[0276] P09668 CTSH Cardiometabolic

[0277] P10145 CXCL8 Cardiometabolic

[0278] P10451 SPP1 Cardiometabolic

[0279] P10586 PTPRF Cardiometabolic

[0280] P10644 PRKAR1 A Cardiometabolic

[0281] P10646 TFPI Cardiometabolic

[0282] P10721 KIT Cardiometabolic

[0283] P12104 FABP2 Cardiometabolic

[0284] P121 11 COL6A3 Cardiometabolic

[0285] P12318 FCGR2A Cardiometabolic

[0286] P12724 RNASE3 Cardiometabolic

[0287] P12830 CDH1 Cardiometabolic

[0288] P13501 CCL5 Cardiometabolic

[0289] P13591 NCAM1 Cardiometabolic

[0290] P13598 ICAM2 Cardiometabolic

[0291] P13686 ACP5 Cardiometabolic

[0292] P13807 GYS1 Cardiometabolic

[0293] P13987 CD59 Cardiometabolic

[0294] P14543 NID1 Cardiometabolic

[0295] P14555 PLA2G2A Cardiometabolic

[0296] P15085 CPA1 Cardiometabolic

[0297] P15086 CPB1 Cardiometabolic

[0298] P15090 FABP4 Cardiometabolic

[0299] P15144 ANPEP Cardiometabolic

[0300] P15529 CD46 Cardiometabolic

[0301] P15907 ST6GAL1 Cardiometabolic

[0302] P16109 SELP Cardiometabolic

[0303] P161 12 ACAN Cardiometabolic

[0304] P16234 PDGFRA Cardiometabolic

[0305] P16581 SELE Cardiometabolic

[0306] P16860 NPPB Cardiometabolic

[0307] P17516 AKR1 C4 Cardiometabolic

[0308] P17676 CEBPB Cardiometabolic

[0309] P17813 ENG Cardiometabolic

[0310] P17931 LGALS3 Cardiometabolic

[0311] P17936 IGFBP3 Cardiometabolic P18065 IGFBP2 Cardiometabolic

[0312] P18428 LBP Cardiometabolic

[0313] P18827 SDC1 Cardiometabolic

[0314] P19021 PAM Cardiometabolic

[0315] P19022 CDH2 Cardiometabolic

[0316] P19320 VCAM1 Cardiometabolic

[0317] P19429 TNNI3 Cardiometabolic

[0318] P19957 PI3 Cardiometabolic

[0319] P19961 AMY2B Cardiometabolic

[0320] P19971 TYMP Cardiometabolic

[0321] P20023 CR2 Cardiometabolic

[0322] P20062 TCN2 Cardiometabolic

[0323] P20160 AZU1 Cardiometabolic

[0324] P20711 DDC Cardiometabolic

[0325] P20718 GZMH Cardiometabolic

[0326] P21246 PTN Cardiometabolic

[0327] P21549 AGXT Cardiometabolic

[0328] P21583 KITLG Cardiometabolic

[0329] P21964 COMT Cardiometabolic

[0330] P21980 TGM2 Cardiometabolic

[0331] P22004 BMP6 Cardiometabolic

[0332] P22748 CA4 Cardiometabolic

[0333] P23141 CES1 Cardiometabolic

[0334] P23284 PPIB Cardiometabolic

[0335] P23526 AHCY Cardiometabolic

[0336] P24158 PRTN3 Cardiometabolic

[0337] P24592 IGFBP6 Cardiometabolic

[0338] P24821 TNC Cardiometabolic

[0339] P25445 FAS Cardiometabolic

[0340] P25815 SWOP Cardiometabolic

[0341] P27352 CBLIF Cardiometabolic

[0342] P27487 DPP4 Cardiometabolic

[0343] P30530 AXL Cardiometabolic

[0344] P31 146 CORO1 A Cardiometabolic

[0345] P31431 SDC4 Cardiometabolic

[0346] P31483 TIA1 Cardiometabolic

[0347] P31949 SW0A1 1 Cardiometabolic

[0348] P31997 CEACAM8 Cardiometabolic

[0349] P32942 ICAM3 Cardiometabolic

[0350] P33151 CDH5 Cardiometabolic

[0351] P34913 EPHX2 Cardiometabolic

[0352] P34947 GRK5 Cardiometabolic

[0353] P34998 CRHR1 Cardiometabolic P35218 CA5A Cardiometabolic

[0354] P35247 SFTPD Cardiometabolic

[0355] P35443 THBS4 Cardiometabolic

[0356] P35590 TIE1 Cardiometabolic

[0357] P35754 GLRX Cardiometabolic

[0358] P36222 CHI3L1 Cardiometabolic

[0359] P36952 SERPINB5 Cardiometabolic

[0360] P39060 C0L18A1 Cardiometabolic

[0361] P40189 IL6ST Cardiometabolic

[0362] P40225 THPO Cardiometabolic

[0363] P40818 USP8 Cardiometabolic

[0364] P41 159 LEP Cardiometabolic

[0365] P41218 MNDA Cardiometabolic

[0366] P41222 PTGDS Cardiometabolic

[0367] P41236 PPP1 R2 Cardiometabolic

[0368] P42574 CASP3 Cardiometabolic

[0369] P42785 PROP Cardiometabolic

[0370] P42830 CXCL5 Cardiometabolic

[0371] P43121 MCAM Cardiometabolic

[0372] P46379 BAG6 Cardiometabolic

[0373] P46531 NOTCH 1 Cardiometabolic

[0374] P48304 REG1 B Cardiometabolic

[0375] P48357 LEPR Cardiometabolic

[0376] P48745 CCN3 Cardiometabolic

[0377] P48960 ADGRE5 Cardiometabolic

[0378] P49747 COMP Cardiometabolic

[0379] P51 161 FABP6 Cardiometabolic

[0380] P51693 APLP1 Cardiometabolic

[0381] P52789 HK2 Cardiometabolic

[0382] P52888 THOP1 Cardiometabolic

[0383] P54760 EPHB4 Cardiometabolic

[0384] P55058 PLTP Cardiometabolic

[0385] P55082 MFAP3 Cardiometabolic

[0386] P55259 GP2 Cardiometabolic

[0387] P55285 CDH6 Cardiometabolic

[0388] P55774 CCL18 Cardiometabolic

[0389] P55808 XG Cardiometabolic

[0390] P58546 MTPN Cardiometabolic

[0391] P59665 DEFA1_DEFA1 B Cardiometabolic

[0392] P61978 HNRNPK Cardiometabolic

[0393] P62736 ACTA2 Cardiometabolic

[0394] P78324 SIRPA Cardiometabolic

[0395] P78380 OLR1 Cardiometabolic P80188 LCN2 Cardiometabolic

[0396] P80370 DLK1 Cardiometabolic

[0397] P98160 HSPG2 Cardiometabolic

[0398] Q01638 IL1 RL1 Cardiometabolic

[0399] Q01973 R0R1 Cardiometabolic

[0400] Q03154 ACY1 Cardiometabolic

[0401] Q03167 TGFBR3 Cardiometabolic

[0402] Q04760 GL01 Cardiometabolic

[0403] Q05315 CLC Cardiometabolic

[0404] Q06033 ITIH3 Cardiometabolic

[0405] Q06141 REG3A Cardiometabolic

[0406] Q06418 TYR03 Cardiometabolic

[0407] Q07108 CD69 Cardiometabolic

[0408] Q07507 DPT Cardiometabolic

[0409] Q07654 TFF3 Cardiometabolic

[0410] Q12794 HYAL1 Cardiometabolic

[0411] Q12805 EFEMP1 Cardiometabolic

[0412] Q12860 CNTN1 Cardiometabolic

[0413] Q12864 CDH17 Cardiometabolic

[0414] Q12884 FAP Cardiometabolic

[0415] Q12912 IRAG2 Cardiometabolic

[0416] Q13043 STK4 Cardiometabolic

[0417] Q13105 ZBTB17 Cardiometabolic

[0418] Q13158 FADD Cardiometabolic

[0419] Q13231 CHIT1 Cardiometabolic

[0420] Q13275 SEMA3F Cardiometabolic

[0421] Q13332 PTPRS Cardiometabolic

[0422] Q13361 MFAP5 Cardiometabolic

[0423] Q13444 ADAM15 Cardiometabolic

[0424] Q13508 ART3 Cardiometabolic

[0425] Q13541 EIF4EBP1 Cardiometabolic

[0426] Q13740 ALCAM Cardiometabolic

[0427] Q13822 ENPP2 Cardiometabolic

[0428] Q13867 BLMH Cardiometabolic

[0429] Q14162 SCARF1 Cardiometabolic

[0430] Q14393 GAS6 Cardiometabolic

[0431] Q14515 SPARCL1 Cardiometabolic

[0432] Q14767 LTBP2 Cardiometabolic

[0433] Q14956 GPNMB Cardiometabolic

[0434] Q15067 AC0X1 Cardiometabolic

[0435] Q151 13 PCOLCE Cardiometabolic

[0436] Q15165 PON2 Cardiometabolic

[0437] Q15485 FCN2 Cardiometabolic Q15582 TGFBI Cardiometabolic

[0438] Q15828 CST6 Cardiometabolic

[0439] Q15831 STK1 1 Cardiometabolic

[0440] Q15846 CLUL1 Cardiometabolic

[0441] Q16270 IGFBP7 Cardiometabolic

[0442] Q16619 CTF1 Cardiometabolic

[0443] Q16620 NTRK2 Cardiometabolic

[0444] Q16627 CCL14 Cardiometabolic

[0445] Q16663 CCL15 Cardiometabolic

[0446] Q16769 QPCT Cardiometabolic

[0447] Q16773 KYAT1 Cardiometabolic

[0448] Q16820 MEP1 B Cardiometabolic

[0449] Q16853 A0C3 Cardiometabolic

[0450] Q53H82 LACTB2 Cardiometabolic

[0451] Q5VY43 PEAR1 Cardiometabolic

[0452] Q6EMK4 VASN Cardiometabolic

[0453] Q6GTS8 PM20D1 Cardiometabolic

[0454] Q6PJW8 CNST Cardiometabolic

[0455] Q6UWL2 SUSD1 Cardiometabolic

[0456] Q6WN34 CHRDL2 Cardiometabolic

[0457] Q76LX8 ADAMTS13 Cardiometabolic

[0458] Q76M96 CCDC80 Cardiometabolic

[0459] Q86U17 SERPINA1 1 Cardiometabolic

[0460] Q86VB7 CD163 Cardiometabolic

[0461] Q86VZ4 LRP1 1 Cardiometabolic

[0462] Q8IW75 SERPINA12 Cardiometabolic

[0463] Q8IZP9 ADGRG2 Cardiometabolic

[0464] Q8N1 Q1 CA13 Cardiometabolic

[0465] Q8N423 LILRB2 Cardiometabolic

[0466] Q8NBP7 PCSK9 Cardiometabolic

[0467] Q8NC01 CLEC1 A Cardiometabolic

[0468] Q8NHL6 LILRB1 Cardiometabolic

[0469] Q8NHS0 DNAJB8 Cardiometabolic

[0470] Q8NI22 MCFD2 Cardiometabolic

[0471] Q8TDL5 BPIFB1 Cardiometabolic

[0472] Q8TE57 ADAMTS16 Cardiometabolic

[0473] Q8WTU2 SSC4D Cardiometabolic

[0474] Q8WVQ1 CANT1 Cardiometabolic

[0475] Q8WX77 IGFBPL1 Cardiometabolic

[0476] Q92520 FAM3C Cardiometabolic

[0477] Q92558 WASF1 Cardiometabolic

[0478] Q92692 NECTIN2 Cardiometabolic

[0479] Q92820 GGH Cardiometabolic Q92823 NRCAM Cardiometabolic

[0480] Q969D9 TSLP Cardiometabolic

[0481] Q969P0 IGSF8 Cardiometabolic

[0482] Q96A56 TP53INP1 Cardiometabolic

[0483] Q96AP7 ESAM Cardiometabolic

[0484] Q96H15 TIMD4 Cardiometabolic

[0485] Q96KN2 CNDP1 Cardiometabolic

[0486] Q96LA6 FCRL1 Cardiometabolic

[0487] Q96N03 VSTM2L Cardiometabolic

[0488] Q99523 S0RT1 Cardiometabolic

[0489] Q99549 MPH0SPH8 Cardiometabolic

[0490] Q99650 OSMR Cardiometabolic

[0491] Q99674 CGREF1 Cardiometabolic

[0492] Q99969 RARRES2 Cardiometabolic

[0493] Q99988 GDF15 Cardiometabolic

[0494] Q9BQB4 SOST Cardiometabolic

[0495] Q9BQR3 PRSS27 Cardiometabolic

[0496] Q9BUD6 SPON2 Cardiometabolic

[0497] Q9BWV1 BOG Cardiometabolic

[0498] Q9BXJ1 C1 QTNF1 Cardiometabolic

[0499] Q9BYF1 ACE2 Cardiometabolic

[0500] Q9GZM7 TINAGL1 Cardiometabolic

[0501] Q9H1 U4 MEGF9 Cardiometabolic

[0502] Q9H2A7 CXCL16 Cardiometabolic

[0503] Q9H5Y7 SLITRK6 Cardiometabolic

[0504] Q9H773 DCTPP1 Cardiometabolic

[0505] Q9H7M9 VSIR Cardiometabolic

[0506] Q9HBB8 CDHR5 Cardiometabolic

[0507] Q9HD89 RETN Cardiometabolic

[0508] Q9NNX6 CD209 Cardiometabolic

[0509] Q9NPY3 CD93 Cardiometabolic

[0510] Q9NQ79 CRTAC1 Cardiometabolic

[0511] Q9NQX5 NPDC1 Cardiometabolic

[0512] Q9NR28 DIABLO Cardiometabolic

[0513] Q9NRD8 DUOX2 Cardiometabolic

[0514] Q9NRV9 HEBP1 Cardiometabolic

[0515] Q9NWQ8 PAG1 Cardiometabolic

[0516] Q9NY25 CLEC5A Cardiometabolic

[0517] Q9NZK5 ADA2 Cardiometabolic

[0518] Q9UBP4 DKK3 Cardiometabolic

[0519] Q9UBR2 CTSZ Cardiometabolic

[0520] Q9UBU3 GHRL Cardiometabolic

[0521] Q9UEW3 MARCO Cardiometabolic Q9UGM5 FETUB Cardiometabolic

[0522] Q9UHD0 IL19 Cardiometabolic

[0523] Q9UHL4 DPP7 Cardiometabolic

[0524] Q9UK05 GDF2 Cardiometabolic

[0525] Q9UKJ0 PILRB Cardiometabolic

[0526] Q9UKL0 RCOR1 Cardiometabolic

[0527] Q9UKP3 ITGB1 BP2 Cardiometabolic

[0528] Q9ULL4 PLXNB3 Cardiometabolic

[0529] Q9UM47 NOTCH3 Cardiometabolic

[0530] Q9UMF0 ICAM5 Cardiometabolic

[0531] Q9Y275 TNFSF13B Cardiometabolic

[0532] Q9Y286 SIGLEC7 Cardiometabolic

[0533] Q9Y2B0 CNPY2 Cardiometabolic

[0534] Q9Y4L1 HYOU1 Cardiometabolic

[0535] Q9Y4X3 CCL27 Cardiometabolic

[0536] Q9Y5C1 ANGPTL3 Cardiometabolic

[0537] Q9Y5K6 CD2AP Cardiometabolic

[0538] Q9Y5X1 SNX9 Cardiometabolic

[0539] B1AKI9 ISM1 Inflammation

[0540] 000175 CCL24 Inflammation

[0541] 000182 LGALS9 Inflammation

[0542] 000241 SIRPB1 Inflammation

[0543] 000253 AGRP Inflammation

[0544] 000273 DFFA Inflammation

[0545] 000300 TNFRSF1 1 B Inflammation

[0546] 000339 MATN2 Inflammation

[0547] 000468 AGRN Inflammation

[0548] 000585 CCL21 Inflammation

[0549] 000626 CCL22 Inflammation

[0550] 014773 TPP1 Inflammation

[0551] 014788 TNFSF1 1 Inflammation

[0552] 014836 TNFRSF13B Inflammation

[0553] 014867 BACH1 Inflammation

[0554] 014904 WNT9A Inflammation

[0555] 015169 AXIN1 Inflammation

[0556] 015444 CCL25 Inflammation

[0557] 015455 TLR3 Inflammation

[0558] 043291 SPINT2 Inflammation

[0559] 043508 TNFSF12 Inflammation

[0560] 043521 -2 BCL2L1 1 Inflammation

[0561] 043561 LAT Inflammation

[0562] 043597 SPRY2 Inflammation

[0563] 043598 DNPH1 Inflammation 043639 NCK2 Inflammation

[0564] 043707 ACTN4 Inflammation

[0565] 043736 ITM2A Inflammation

[0566] 043915 VEGFD Inflammation

[0567] 060449 LY75 Inflammation

[0568] 060542 PSPN Inflammation

[0569] 060575 SPINK4 Inflammation

[0570] 060880 SH2D1 A Inflammation

[0571] 060884 DNAJA2 Inflammation

[0572] 060934 NBN Inflammation

[0573] 075077 ADAM23 Inflammation

[0574] 075462 CRLF1 Inflammation

[0575] 075475 PSIP1 Inflammation

[0576] 075563 SKAP2 Inflammation

[0577] 075888 TNFSF13 Inflammation

[0578] 076036 NCR1 Inflammation

[0579] 076038 SCGN Inflammation

[0580] 076096 CST7 Inflammation

[0581] 094856 NFASC Inflammation

[0582] 094992 HEXIM1 Inflammation

[0583] 095379 TNFAIP8 Inflammation

[0584] 095633 FSTL3 Inflammation

[0585] 095644 NFATC1 Inflammation

[0586] 095715 CXCL14 Inflammation

[0587] 095750 FGF19 Inflammation

[0588] 095760 IL33 Inflammation

[0589] 095866 MPIG6B Inflammation

[0590] 095971 CD160 Inflammation

[0591] P00813 ADA Inflammation

[0592] P01 127 PDGFB Inflammation

[0593] P01 133 EGF Inflammation

[0594] P01 135 TGFA Inflammation

[0595] P01 137 TGFB1 Inflammation

[0596] P01374 LTA Inflammation

[0597] P01375 TNF Inflammation

[0598] P01579 IFNG Inflammation

[0599] P01583 IL1 A Inflammation

[0600] P01584 IL1 B Inflammation

[0601] P01588 EPO Inflammation

[0602] P01591 JCHAIN Inflammation

[0603] P01730 CD4 Inflammation

[0604] P01903 HLA-DRA Inflammation

[0605] P02745 C1 QA Inflammation P02778 CXCL10 Inflammation

[0606] P03956 MMP1 Inflammation

[0607] P051 12 IL4 Inflammation

[0608] P051 13 IL5 Inflammation

[0609] P05231 IL6 Inflammation

[0610] P05412 JUN Inflammation

[0611] P07148 FABP1 Inflammation

[0612] P08727 KRT19 Inflammation

[0613] P09038 FGF2 Inflammation

[0614] P09238 MMP10 Inflammation

[0615] P09326 CD48 Inflammation

[0616] P09341 CXCL1 Inflammation

[0617] P09603 CSF1 Inflammation

[0618] P09874 PARP1 Inflammation

[0619] P09919 CSF3 Inflammation

[0620] P0DMV8 HSPA1A Inflammation

[0621] P10144 GZMB Inflammation

[0622] P10145 CXCL8 Inflammation

[0623] P10147 CCL3 Inflammation

[0624] P11274 BCR Inflammation

[0625] P11684 SCGB1A1 Inflammation

[0626] P12034 FGF5 Inflammation

[0627] P12532 CKMT1 A_CKMT1 B Inflammation

[0628] P12544 GZMA Inflammation

[0629] P12872 MLN Inflammation

[0630] P13232 IL7 Inflammation

[0631] P13236 CCL4 Inflammation

[0632] P13693 TPT1 Inflammation

[0633] P13725 OSM Inflammation

[0634] P13747 HLA-E Inflammation

[0635] P14210 HGF Inflammation

[0636] P14317 HCLS1 Inflammation

[0637] P14784 IL2RB Inflammation

[0638] P15260 IFNGR1 Inflammation

[0639] P15291 B4GALT1 Inflammation

[0640] P15692 VEGFA Inflammation

[0641] P16422 EPCAM Inflammation

[0642] P16455 MGMT Inflammation

[0643] P18510 IL1 RN Inflammation

[0644] P18564 ITGB6 Inflammation

[0645] P19256 CD58 Inflammation

[0646] P19474 TRIM21 Inflammation

[0647] P19801 AOC1 Inflammation P19876 CXCL3 Inflammation

[0648] P19878 NCF2 Inflammation

[0649] P19883 FST Inflammation

[0650] P20273 CD22 Inflammation

[0651] P20340 RAB6A Inflammation

[0652] P20783 NTF3 Inflammation

[0653] P20809 IL1 1 Inflammation

[0654] P20849 C0L9A1 Inflammation

[0655] P21709 EPHA1 Inflammation

[0656] P21860 ERBB3 Inflammation

[0657] P22301 IL10 Inflammation

[0658] P22304 IDS Inflammation

[0659] P22466 GAL Inflammation

[0660] P23229 ITGA6 Inflammation

[0661] P23582 NPPC Inflammation

[0662] P24001 IL32 Inflammation

[0663] P24071 FCAR Inflammation

[0664] P24387 CRHBP Inflammation

[0665] P24394 IL4R Inflammation

[0666] P251 16 F2R Inflammation

[0667] P25942 CD40 Inflammation

[0668] P26022 PTX3 Inflammation

[0669] P26951 IL3RA Inflammation

[0670] P27540 ARNT Inflammation

[0671] P27930 IL1 R2 Inflammation

[0672] P28827 PTPRM Inflammation

[0673] P28838 LAP3 Inflammation

[0674] P28845 HSD1 1 B1 Inflammation

[0675] P29279 CCN2 Inflammation

[0676] P29350 PTPN6 Inflammation

[0677] P29460 IL12B Inflammation

[0678] P29965 CD40LG Inflammation

[0679] P30044 PRDX5 Inflammation

[0680] P30048 PRDX3 Inflammation

[0681] P30203 CD6 Inflammation

[0682] P30613 PKLR Inflammation

[0683] P30838 ALDH3A1 Inflammation

[0684] P32456 GBP2 Inflammation

[0685] P32970 CD70 Inflammation

[0686] P33241 LSP1 Inflammation

[0687] P34896 SHMT1 Inflammation

[0688] P35225 IL13 Inflammation

[0689] P35613 BSG Inflammation P35625 TIMP3 Inflammation

[0690] P36941 LTBR Inflammation

[0691] P36959 GMPR Inflammation

[0692] P37235 HPCAL1 Inflammation

[0693] P40259 CD79B Inflammation

[0694] P40933 IL15 Inflammation

[0695] P41217 CD200 Inflammation

[0696] P42575 CASP2 Inflammation

[0697] P42701 IL12RB1 Inflammation

[0698] P42702 LIFR Inflammation

[0699] P42768 WAS Inflammation

[0700] P43234 CTSO Inflammation

[0701] P43489 TNFRSF4 Inflammation

[0702] P45984 MAPK9 Inflammation

[0703] P46109 CRKL Inflammation

[0704] P47712 PLA2G4A Inflammation

[0705] P48023 FASLG Inflammation

[0706] P48061 CXCL12 Inflammation

[0707] P49763 PGF Inflammation

[0708] P49771 FLT3LG Inflammation

[0709] P50452 SERPINB8 Inflammation

[0710] P50591 TNFSF10 Inflammation

[0711] P50995 ANXA11 Inflammation

[0712] P51617 IRAKI Inflammation

[0713] P51671 CCL11 Inflammation

[0714] P51888 PRELP Inflammation

[0715] P52564 MAP2K6 Inflammation

[0716] P53634 CTSC Inflammation

[0717] P54317 PNLIPRP2 Inflammation

[0718] P55145 MANF Inflammation

[0719] P55773 CCL23 Inflammation

[0720] P55957 BID Inflammation

[0721] P56470 LGALS4 Inflammation

[0722] P57771 RGS8 Inflammation

[0723] P58294 PROK1 Inflammation

[0724] P60568 IL2 Inflammation

[0725] P63241 EIF5A Inflammation

[0726] P68106 FKBP1 B Inflammation

[0727] P78310 CXADR Inflammation

[0728] P78362 SRPK2 Inflammation

[0729] P78410 BTN3A2 Inflammation

[0730] P78556 CCL20 Inflammation

[0731] P80098 CCL7 Inflammation P80162 CXCL6 Inflammation

[0732] Q01151 CD83 Inflammation

[0733] Q01344 IL5RA Inflammation

[0734] Q03403 TFF2 Inflammation

[0735] Q03405 PLAUR Inflammation

[0736] Q03426 MVK Inflammation

[0737] Q03431 PTH1 R Inflammation

[0738] Q04637 EIF4G1 Inflammation

[0739] Q04759 PRKCQ Inflammation

[0740] Q05084 ICA1 Inflammation

[0741] Q06520 SULT2A1 Inflammation

[0742] Q07065 CKAP4 Inflammation

[0743] Q07325 CXCL9 Inflammation

[0744] Q08174 PCDH1 Inflammation

[0745] Q08334 IL10RB Inflammation

[0746] Q0Z7S8 FABP9 Inflammation

[0747] Q12765 SCRN1 Inflammation

[0748] Q12778 F0X01 Inflammation

[0749] Q12866 MERTK Inflammation

[0750] Q12918 KLRB1 Inflammation

[0751] Q12933 TRAF2 Inflammation

[0752] Q12968 NFATC3 Inflammation

[0753] Q13007 IL24 Inflammation

[0754] Q13219 PAPPA Inflammation

[0755] Q13232 NME3 Inflammation

[0756] Q13241 KLRD1 Inflammation

[0757] Q13261 IL15RA Inflammation

[0758] Q13291 SLAMF1 Inflammation

[0759] Q13459 MY09B Inflammation

[0760] Q13478 IL18R1 Inflammation

[0761] Q13574 DGKZ Inflammation

[0762] Q13651 IL10RA Inflammation

[0763] Q14005 IL16 Inflammation

[0764] Q141 16 IL18 Inflammation

[0765] Q141 18 DAG1 Inflammation

[0766] Q14210 LY6D Inflammation

[0767] Q14242 SELPLG Inflammation

[0768] Q14435 GALNT3 Inflammation

[0769] Q14773 ICAM4 Inflammation

[0770] Q15109 AGER Inflammation

[0771] Q15166 P0N3 Inflammation

[0772] Q15389 ANGPT1 Inflammation

[0773] Q15517 CDSN Inflammation Q15661 TPSAB1 Inflammation

[0774] Q16363 LAMA4 Inflammation

[0775] Q16552 IL17A Inflammation

[0776] Q16651 PRSS8 Inflammation

[0777] Q16698 DECR1 Inflammation

[0778] Q16719 KYNU Inflammation

[0779] Q29980 Q29983 MICB MICA Inflammation

[0780] Q3KPI0 CEACAM21 Inflammation

[0781] Q4KMG0 CDON Inflammation

[0782] Q5KU26 COLEC12 Inflammation

[0783] Q5R372 RABGAP1 L Inflammation

[0784] Q5T4W7 ARTN Inflammation

[0785] Q5ZPR3 CD276 Inflammation

[0786] Q6DN72 FCRL6 Inflammation

[0787] Q6GTX8 LAIR1 Inflammation

[0788] Q6UB28 METAP1 D Inflammation

[0789] Q6UWV6 ENPP7 Inflammation

[0790] Q6UXB2 CXCL17 Inflammation

[0791] Q6UXB4 CLEC4G Inflammation

[0792] Q6UXH1 CRELD2 Inflammation

[0793] Q6UXK5 LRRN1 Inflammation

[0794] Q6ZMH5 SLC39A5 Inflammation

[0795] Q6ZUJ8 PIK3AP1 Inflammation

[0796] Q7KYR7 BTN2A1 Inflammation

[0797] Q7L8A9 VASH1 Inflammation

[0798] Q7Z6M3 MILR1 Inflammation

[0799] Q7Z739 YTHDF3 Inflammation

[0800] Q8IU57 IFNLR1 Inflammation

[0801] Q8IVG5 SAMD9L Inflammation

[0802] Q8IYS5 OSCAR Inflammation

[0803] Q8N608 DPP10 Inflammation

[0804] Q8N6P7 IL22RA1 Inflammation

[0805] Q8N8S7 ENAH Inflammation

[0806] Q8NDB2 BANK1 Inflammation

[0807] Q8NFT8 DNER Inflammation

[0808] Q8NHJ6 LILRB4 Inflammation

[0809] Q8TAD2 IL17D Inflammation

[0810] Q8TCS8 PNPT1 Inflammation

[0811] Q8TD46 CD200R1 Inflammation

[0812] Q8TEU8 WFIKKN2 Inflammation

[0813] Q8WTT0 CLEC4C Inflammation

[0814] Q8WU39 MZB1 Inflammation

[0815] Q8WV07 LTO1 Inflammation Q8WXD2 SCG3 Inflammation

[0816] Q8WXI8 CLEC4D Inflammation

[0817] Q92484 SMPDL3A Inflammation

[0818] Q92583 CCL17 Inflammation

[0819] Q92609 TBC1 D5 Inflammation

[0820] Q92844 TANK Inflammation

[0821] Q92956 TNFRSF14 Inflammation

[0822] Q969V3 NCLN Inflammation

[0823] Q96AX2 RAB37 Inflammation

[0824] Q96DB9 FXYD5 Inflammation

[0825] Q96KG7 MEGF10 Inflammation

[0826] Q96LA5 FCRL2 Inflammation

[0827] Q96LC7 SIGLEC10 Inflammation

[0828] Q96P31 FCRL3 Inflammation

[0829] Q96PD4 IL17F Inflammation

[0830] Q96PL1 SCGB3A2 Inflammation

[0831] Q96RJ3 TNFRSF13C Inflammation

[0832] Q96SB3 PPP1 R9B Inflammation

[0833] Q99435 NELL2 Inflammation

[0834] Q99538 LGMN Inflammation

[0835] Q99616 CCL13 Inflammation

[0836] Q99685 MGLL Inflammation

[0837] Q99748 NRTN Inflammation

[0838] Q99895 CTRC Inflammation

[0839] Q99983 OMD Inflammation

[0840] Q9BT73 PSMG3 Inflammation

[0841] Q9BU40 CHRDL1 Inflammation

[0842] Q9BXJ7 AMN Inflammation

[0843] Q9BXN2 CLEC7A Inflammation

[0844] Q9BY76 ANGPTL4 Inflammation

[0845] Q9BYZ8 REG4 Inflammation

[0846] Q9BZW8 CD244 Inflammation

[0847] Q9BZZ2 SIGLEC1 Inflammation

[0848] Q9C035 TRIM5 Inflammation

[0849] Q9GZT9 EGLN1 Inflammation

[0850] Q9H008 LHPP Inflammation

[0851] Q9H0P0 NT5C3A Inflammation

[0852] Q9H3U7 SMOC2 Inflammation

[0853] Q9H4D0 CLSTN2 Inflammation

[0854] Q9HB29 IL1 RL2 Inflammation

[0855] Q9HBG7 LY9 Inflammation

[0856] Q9HC38 GLOD4 Inflammation

[0857] Q9HCB6 SPON1 Inflammation Q9HCM2 PLXNA4 Inflammation

[0858] Q9HCU5 PREB Inflammation

[0859] Q9HD26 GOPC Inflammation

[0860] Q9NP70 AMBN Inflammation

[0861] Q9NQ25 SLAMF7 Inflammation

[0862] Q9NQ30 ESM1 Inflammation

[0863] Q9NQ76 MEPE Inflammation

[0864] Q9NR12 PDLIM7 Inflammation

[0865] Q9NRJ3 CCL28 Inflammation

[0866] Q9NRM6 IL17RB Inflammation

[0867] Q9NWZ3 IRAK4 Inflammation

[0868] Q9NYY1 IL20 Inflammation

[0869] Q9NZC2 TREM2 Inflammation

[0870] Q9NZN5 ARHGEF12 Inflammation

[0871] Q9NZV1 CRIM1 Inflammation

[0872] Q9P0M4 IL17C Inflammation

[0873] Q9UDT6 CLIP2 Inflammation

[0874] Q9UHC6 CNTNAP2 Inflammation

[0875] Q9UHF4 IL20RA Inflammation

[0876] Q9UHX3 ADGRE2 Inflammation

[0877] Q9UIB8 CD84 Inflammation

[0878] Q9UII2 ATP5IF1 Inflammation

[0879] Q9UJA9 ENPP5 Inflammation

[0880] Q9UJU6 DBNL Inflammation

[0881] Q9UKU9 ANGPTL2 Inflammation

[0882] Q9UKX5 ITGA1 1 Inflammation

[0883] Q9UMR7 CLEC4A Inflammation

[0884] Q9UN19 DAPP1 Inflammation

[0885] Q9UNE0 EDAR Inflammation

[0886] Q9UNK0 STX8 Inflammation

[0887] Q9UPV0 CEP164 Inflammation

[0888] Q9UQV4 LAMP3 Inflammation

[0889] Q9Y258 CCL26 Inflammation

[0890] Q9Y266 NUDC Inflammation

[0891] Q9Y2J8 PADI2 Inflammation

[0892] Q9Y3D6 FIS1 Inflammation

[0893] Q9Y3P8 SIT1 Inflammation

[0894] Q9Y478 PRKAB1 Inflammation

[0895] Q9Y5A7 NUB1 Inflammation

[0896] Q9Y6K9 IKBKG Inflammation

[0897] Q9Y6N7 ROBO1 Inflammation

[0898] Q9Y6Q6 TNFRSF1 1A Inflammation

[0899] A1 E959 ODAM Neurology 000214 LGALS8 Neurology

[0900] 000220 TNFRSF10A Neurology

[0901] 000308 WWP2 Neurology

[0902] 000399 DCTN6 Neurology

[0903] 000559 EBAG9 Neurology

[0904] 014523 C2CD2L Neurology

[0905] 014579 COPE Neurology

[0906] 014594 NCAN Neurology

[0907] 014618 CCS Neurology

[0908] 014625 CXCL11 Neurology

[0909] 014737 PDCD5 Neurology

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[0913] 015197 EPHB6 Neurology

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[0916] 015394 NCAM2 Neurology

[0917] 015496 PLA2G10 Neurology

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[0920] 043505 B4GAT1 Neurology

[0921] 043557 TNFSF14 Neurology

[0922] 043927 CXCL13 Neurology

[0923] 060240 PLIN1 Neurology

[0924] 060242 ADGRB3 Neurology

[0925] 060462 NRP2 Neurology

[0926] 060609 GFRA3 Neurology

[0927] 075509 TNFRSF21 Neurology

[0928] 076061 STC2 Neurology

[0929] 076070 SNCG Neurology

[0930] 076076 CCN5 Neurology

[0931] 094779 CNTN5 Neurology

[0932] 094813 SLIT2 Neurology

[0933] 094907 DKK1 Neurology

[0934] 094985 CLSTN1 Neurology

[0935] 095256 IL18RAP Neurology

[0936] 095407 TNFRSF6B Neurology

[0937] 095466 FMNL1 Neurology

[0938] 095630 STAMBP Neurology

[0939] 095727 CRTAM Neurology

[0940] 095817 BAG3 Neurology

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[0946] P01 138 NGF Neurology

[0947] P01 178 OXT Neurology

[0948] P01215 CGA Neurology

[0949] P01236 PRL Neurology

[0950] P01258 CALCA Neurology

[0951] P01375 TNF Neurology

[0952] P01732 CD8A Neurology

[0953] P01833 PIGR Neurology

[0954] P02533 KRT14 Neurology

[0955] P02749 APOH Neurology

[0956] P02771 AFP Neurology

[0957] P041 18 CLPS Neurology

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[0959] P04179 SOD2 Neurology

[0960] P04216 THY1 Neurology

[0961] P04233 CD74 Neurology

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[0973] P07741 APRT Neurology

[0974] P08134 RHOC Neurology

[0975] P08254 MMP3 Neurology

[0976] P08648 ITGA5 Neurology

[0977] P08758 ANXA5 Neurology

[0978] P08962 CD63 Neurology

[0979] P09104 EN02 Neurology

[0980] P09211 GSTP1 Neurology

[0981] P09466 PAEP Neurology

[0982] P09769 FGR Neurology

[0983] P10145 CXCL8 Neurology P10636 MAPT Neurology

[0984] P11215 ITGAM Neurology

[0985] P11464 PSG1 Neurology

[0986] P11717 IGF2R Neurology

[0987] P12081 HARS1 Neurology

[0988] P12429 ANXA3 Neurology

[0989] P12644 BMP4 Neurology

[0990] P13385 TDGF1 Neurology

[0991] P13473 LAMP2 Neurology

[0992] P13500 CCL2 Neurology

[0993] P13611 VCAN Neurology

[0994] P13647 KRT5 Neurology

[0995] P14174 MIF Neurology

[0996] P14207 FOLR2 Neurology

[0997] P14209 CD99 Neurology

[0998] P14384 CPM Neurology

[0999] P14625 HSP90B1 Neurology

[1000] P14778 IL1 R1 Neurology

[1001] P14780 MMP9 Neurology

[1002] P14868 DARS1 Neurology

[1003] P15018 LIF Neurology

[1004] P15151 PVR Neurology

[1005] P15289 ARSA Neurology

[1006] P15311 EZR Neurology

[1007] P15336 ATF2 Neurology

[1008] P15509 CSF2RA Neurology

[1009] P16278 GLB1 Neurology

[1010] P16284 PECAM1 Neurology

[1011] P16444 DPEP1 Neurology

[1012] P16871 IL7R Neurology

[1013] P17405 SMPD1 Neurology

[1014] P17538 CTRB1 Neurology

[1015] P18031 PTPN1 Neurology

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[1019] P20774 OGN Neurology

[1020] P20827 EFNA1 Neurology

[1021] P20936 RASA1 Neurology

[1022] P21217 Q11 128 FUT3_FUT5 Neurology

[1023] P21757 MSR1 Neurology

[1024] P22079 LPO Neurology

[1025] P22105 TNXB Neurology P22223 CDH3 Neurology

[1026] P22676 CALB2 Neurology

[1027] P22692 IGFBP4 Neurology

[1028] P22749 GNLY Neurology

[1029] P22894 MMP8 Neurology

[1030] P23276 KEL Neurology

[1031] P23280 CA6 Neurology

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[1034] P25774 CTSS Neurology

[1035] P26440 IVD Neurology

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[1037] P28799 GRN Neurology

[1038] P28906 CD34 Neurology

[1039] P28908 TNFRSF8 Neurology

[1040] P29218 IMPA1 Neurology

[1041] P29466 CASP1 Neurology

[1042] P29474 N0S3 Neurology

[1043] P29475 N0S1 Neurology

[1044] P30039 PBLD Neurology

[1045] P30043 BLVRB Neurology

[1046] P30086 PEBP1 Neurology

[1047] P30519 HM0X2 Neurology

[1048] P30533 LRPAP1 Neurology

[1049] P30740 SERPINB1 Neurology

[1050] P31948 STIP1 Neurology

[1051] P35237 SERPINB6 Neurology

[1052] P35442 THBS2 Neurology

[1053] P36269 GGT5 Neurology

[1054] P37023 ACVRL1 Neurology

[1055] P38484 IFNGR2 Neurology

[1056] P39905 GDNF Neurology

[1057] P40222 TXLNA Neurology

[1058] P41208 CETN2 Neurology

[1059] P42892 ECE1 Neurology

[1060] P45452 MMP13 Neurology

[1061] P48047 ATP5P0 Neurology

[1062] P48052 CPA2 Neurology

[1063] P48740 MASP1 Neurology

[1064] P49023 PXN Neurology

[1065] P49789 FHIT Neurology

[1066] P50135 HNMT Neurology

[1067] P50225 SULT1 A1 Neurology P50453 SERPINB9 Neurology

[1068] P50895 BCAM Neurology

[1069] P51452 DUSP3 Neurology

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[1073] P52943 CRIP2 Neurology

[1074] P53539 FOSB Neurology

[1075] P53582 METAP1 Neurology

[1076] P53985 SLC16A1 Neurology

[1077] P55103 INHBC Neurology

[1078] P55291 CDH15 Neurology

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[1081] P58417 NXPH1 Neurology

[1082] P60484 PTEN Neurology

[1083] P61244 MAX Neurology

[1084] P63098 PPP3R1 Neurology

[1085] P63313 TMSB10 Neurology

[1086] P78325 ADAM8 Neurology

[1087] P78333 GPC5 Neurology

[1088] P78423 CX3CL1 Neurology

[1089] P78560 CRADD Neurology

[1090] Q01469 FABP5 Neurology

[1091] Q02083 NAAA Neurology

[1092] Q02487 DSC2 Neurology

[1093] Q02643 GHRHR Neurology

[1094] Q02747 GUCA2A Neurology

[1095] Q02790 FKBP4 Neurology

[1096] Q03393 PTS Neurology

[1097] Q04900 CD164 Neurology

[1098] Q06323 PSME1 Neurology

[1099] Q06830 PRDX1 Neurology

[1100] Q0701 1 TNFRSF9 Neurology

[1101] Q07812 BAX Neurology

[1102] Q08345 DDR1 Neurology

[1103] Q08431 MFGE8 Neurology

[1104] Q08629 SPOCK1 Neurology

[1105] Q08708 CD300C Neurology

[1106] Q10588 BST1 Neurology

[1107] Q10589 BST2 Neurology

[1108] Q13093 PLA2G7 Neurology

[1109] Q13308 PTK7 Neurology Q13426 XRCC4 Neurology

[1110] Q13451 FKBP5 Neurology

[1111] Q14108 SCARB2 Neurology

[1112] Q14112 NID2 Neurology

[1113] Q14126 DSG2 Neurology

[1114] Q14696 MESD Neurology

[1115] Q14739 LBR Neurology

[1116] Q15043 SLC39A14 Neurology

[1117] Q15126 PMVK Neurology

[1118] Q15155 N0M01 Neurology

[1119] Q15633 TARBP2 Neurology

[1120] Q15814 TBCC Neurology

[1121] Q15818 NPTX1 Neurology

[1122] Q16288 NTRK3 Neurology

[1123] Q16740 CLPP Neurology

[1124] Q16762 TST Neurology

[1125] Q16864 ATP6V1 F Neurology

[1126] Q16881 TXNRD1 Neurology

[1127] Q2MKA7 RSP01 Neurology

[1128] Q2TAL6 VWC2 Neurology

[1129] Q4LE39 ARID4B Neurology

[1130] Q53H47 SETMAR Neurology

[1131] Q5JZY3 EPHA10 Neurology

[1132] Q5T2D2 TREML2 Neurology

[1133] Q6ISS4 LAIR2 Neurology

[1134] Q6NW40 RGMB Neurology

[1135] Q6P1 M0 SLC27A4 Neurology

[1136] Q6P1 N0 CC2D1A Neurology

[1137] Q6P4E1 G0LM2 Neurology

[1138] Q6PIL6 KCNIP4 Neurology

[1139] Q6UWL6 KIRREL2 Neurology

[1140] Q6UX15 LAYN Neurology

[1141] Q6UX27 VSTM1 Neurology

[1142] Q6UXG3 CD300LG Neurology

[1143] Q6UXH9 PAMR1 Neurology

[1144] Q6UXK2 ISLR2 Neurology

[1145] Q6XZF7 DNMBP Neurology

[1146] Q6YHK3 CD109 Neurology

[1147] Q6ZMC9 SIGLEC15 Neurology

[1148] Q6ZMJ2 SCARA5 Neurology

[1149] Q6ZMJ4 IL34 Neurology

[1150] Q86T13 CLEC14A Neurology

[1151] Q86VW0 SESTD1 Neurology Q86WV1 SKAP1 Neurology

[1152] Q86Z14 KLB Neurology

[1153] Q8IU54 IFNL1 Neurology

[1154] Q8IUN9 CLEC10A Neurology

[1155] Q8IWV2 CNTN4 Neurology

[1156] Q8N149 LILRA2 Neurology

[1157] Q8N2G4 LYPD1 Neurology

[1158] Q8N474 SFRP1 Neurology

[1159] Q8N6Q3 CD177 Neurology

[1160] Q8NBI3 DRAXIN Neurology

[1161] Q8NBJ7 SUMF2 Neurology

[1162] Q8NBS9 TXNDC5 Neurology

[1163] Q8NFP4 MDGA1 Neurology

[1164] Q8TCT1 PHOSPHO1 Neurology

[1165] Q8TCZ2 CD99L2 Neurology

[1166] Q8TDQ0 HAVCR2 Neurology

[1167] Q8WTV0 SCARB1 Neurology

[1168] Q8WUW1 BRK1 Neurology

[1169] Q8WV92 MITD1 Neurology

[1170] Q8WWN9 IPCEF1 Neurology

[1171] Q92597 NDRG1 Neurology

[1172] Q92752 TNR Neurology

[1173] Q92765 FRZB Neurology

[1174] Q92851 CASP10 Neurology

[1175] Q92854 SEMA4D Neurology

[1176] Q92917 GPKOW Neurology

[1177] Q92932 PTPRN2 Neurology

[1178] Q969Z4 RELT Neurology

[1179] Q96B36 AKT1 S1 Neurology

[1180] Q96B86 RGMA Neurology

[1181] Q96CD2 PPCDC Neurology

[1182] Q96F46 IL17RA Neurology

[1183] Q96FE7 PIK3IP1 Neurology

[1184] Q96FQ6 S100A16 Neurology

[1185] Q96GP6 SCARF2 Neurology

[1186] Q96GW7 BCAN Neurology

[1187] Q96IU4 ABHD14B Neurology

[1188] Q96JP9 CDHR1 Neurology

[1189] Q96NZ8 WFIKKN1 Neurology

[1190] Q96PP9 GBP4 Neurology

[1191] Q96RD9 FCRL5 Neurology

[1192] Q99426 TBCB Neurology

[1193] Q99497 PARK7 Neurology Q99727 TIMP4 Neurology

[1194] Q99731 CCL19 Neurology

[1195] Q99972 MYOC Neurology

[1196] Q9BRF8 CPPED1 Neurology

[1197] Q9BS40 LXN Neurology

[1198] Q9BW30 TPPP3 Neurology

[1199] Q9BXS1 IDI2 Neurology

[1200] Q9BYC5 FUT8 Neurology

[1201] Q9BZM5 ULBP2 Neurology

[1202] Q9H0C8 ILKAP Neurology

[1203] Q9H1 C3 GLT8D2 Neurology

[1204] Q9H2W6 MRPL46 Neurology

[1205] Q9H3R2 MUC13 Neurology

[1206] Q9H3S3 TMPRSS5 Neurology

[1207] Q9H446 RWDD1 Neurology

[1208] Q9H477 RBKS Neurology

[1209] Q9H5V8 CDCP1 Neurology

[1210] Q9HA65 TBC1 D17 Neurology

[1211] Q9HAN9 NMNAT1 Neurology

[1212] Q9HAW4 CLSPN Neurology

[1213] Q9HCK4 ROBO2 Neurology

[1214] Q9HCN6 GP6 Neurology

[1215] Q9HD42 CHMP1 A Neurology

[1216] Q9NP79 VTA1 Neurology

[1217] Q9NPH3 IL1 RAP Neurology

[1218] Q9NPH6 OBP2B Neurology

[1219] Q9NQ38 SPINK5 Neurology

[1220] Q9NQ88 TIGAR Neurology

[1221] Q9NR71 ASAH2 Neurology

[1222] Q9NRG1 PRTFDC1 Neurology

[1223] Q9NRW1 RAB6B Neurology

[1224] Q9NS71 GKN1 Neurology

[1225] Q9NSK7 C19orf12 Neurology

[1226] Q9NXA8 SIRT5 Neurology

[1227] Q9NZD4 AHSP Neurology

[1228] Q9NZQ7 CD274 Neurology

[1229] Q9P0K1 ADAM22 Neurology

[1230] Q9P126 CLEC1 B Neurology

[1231] Q9P232 CNTN3 Neurology

[1232] Q9UBB4 ATXN10 Neurology

[1233] Q9UBT3 DKK4 Neurology

[1234] Q9UBW5 BIN2 Neurology

[1235] Q9UGT4 SUSD2 Neurology Q9UHV9 PFDN2 Neurology

[1236] Q9UJ72 ANXA10 Neurology

[1237] Q9UJY5 GGA1 Neurology

[1238] Q9UK53 ING1 Neurology

[1239] Q9UKJ1 PILRA Neurology

[1240] Q9UKK9 NUDT5 Neurology

[1241] Q9UKV5 AMFR Neurology

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[1244] Q9UNZ2 NSFL1 C Neurology

[1245] Q9UPY6 WASF3 Neurology

[1246] Q9Y240 CLEC11 A Neurology

[1247] Q9Y279 VSIG4 Neurology

[1248] Q9Y2V2 CARHSP1 Neurology

[1249] Q9Y2W6 TDRKH Neurology

[1250] Q9Y4K4 MAP4K5 Neurology

[1251] Q9Y5P4 CERT1 Neurology

[1252] Q9Y624 F1 1 R Neurology

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[1254] Q9Y6D9 MAD1 L1 Neurology

[1255] Q9Y6E0 STK24 Neurology

[1256] Q9Y6Y9 LY96 Neurology

[1257] A4D1 B5 GSAP Oncology

[1258] 000186 STXBP3 Oncology

[1259] 000221 NFKBIE Oncology

[1260] 000233 PSMD9 Oncology

[1261] 000244 ATOX1 Oncology

[1262] 000292 LEFTY2 Oncology

[1263] 000451 GFRA2 Oncology

[1264] 000548 DLL1 Oncology

[1265] 000592 PODXL Oncology

[1266] 000622 CCN1 Oncology

[1267] 000748 CES2 Oncology

[1268] 014558 HSPB6 Oncology

[1269] 014662 STX16 Oncology

[1270] 014713 ITGB1 BP1 Oncology

[1271] 014828 SCAMP3 Oncology

[1272] 014964 HGS Oncology

[1273] 014974 PPP1 R12A Oncology

[1274] 0151 16 LSM1 Oncology

[1275] 015123 ANGPT2 Oncology

[1276] 015263 DEFB4A DEFB4B Oncology

[1277] 015357 INPPL1 Oncology 043240 KLK10 Oncology

[1278] 043464 HTRA2 Oncology

[1279] 043524 FOXO3 Oncology

[1280] 043570 CA12 Oncology

[1281] 043699 SIGLEC6 Oncology

[1282] 043715 TRIAP1 Oncology

[1283] 043752 STX6 Oncology

[1284] 043827 ANGPTL7 Oncology

[1285] 043895 XPNPEP2 Oncology

[1286] 060243 HS6ST1 Oncology

[1287] 060259 KLK8 Oncology

[1288] 060760 HPGDS Oncology

[1289] 060763 USO1 Oncology

[1290] 060825 PFKFB2 Oncology

[1291] 060828 PQBP1 Oncology

[1292] 060907 TBL1X Oncology

[1293] 06091 1 CTSV Oncology

[1294] 075054 IGSF3 Oncology

[1295] 075144 ICOSLG Oncology

[1296] 075380 NDUFS6 Oncology

[1297] 075493 CA1 1 Oncology

[1298] 075569 PRKRA Oncology

[1299] 075629 CREG1 Oncology

[1300] 075695 RP2 Oncology

[1301] 075787 ATP6AP2 Oncology

[1302] 094760 DDAH1 Oncology

[1303] 095274 LYPD3 Oncology

[1304] 095388 CCN4 Oncology

[1305] 095498 VNN2 Oncology

[1306] 095721 SNAP29 Oncology

[1307] 095786 DDX58 Oncology

[1308] 095831 AIFM1 Oncology

[1309] P00519 ABL1 Oncology

[1310] P01229 LHB Oncology

[1311] P01242 GH2 Oncology

[1312] P01275 GCG Oncology

[1313] P01298 PPY Oncology

[1314] P01303 NPY Oncology

[1315] P01375 TNF Oncology

[1316] P02760 AM BP Oncology

[1317] P04626 ERBB2 Oncology

[1318] P04637 TP53 Oncology

[1319] P05089 ARG1 Oncology P05187 ALPP Oncology

[1320] P05231 IL6 Oncology

[1321] P05783 KRT18 Oncology

[1322] P05937 CALB1 Oncology

[1323] P06127 CD5 Oncology

[1324] P06731 CEACAM5 Oncology

[1325] P06756 ITGAV Oncology

[1326] P06850 CRH Oncology

[1327] P06870 KLK1 Oncology

[1328] P07237 P4HB Oncology

[1329] P07332 FES Oncology

[1330] P07947 YES1 Oncology

[1331] P07948 LYN Oncology

[1332] P07949 RET Oncology

[1333] P08069 IGF1 R Oncology

[1334] P08397 HMBS Oncology

[1335] P08473 MME Oncology

[1336] P09105 HBQ1 Oncology

[1337] P091 10 ACAA1 Oncology

[1338] P09486 SPARC Oncology

[1339] P09758 TACSTD2 Oncology

[1340] P09958 FURIN Oncology

[1341] P09960 LTA4H Oncology

[1342] P0CG37 CFC1 Oncology

[1343] P10145 CXCL8 Oncology

[1344] P10606 COX5B Oncology

[1345] P10747 CD28 Oncology

[1346] P12931 SRC Oncology

[1347] P13521 SCG2 Oncology

[1348] P13688 CEACAM1 Oncology

[1349] P13726 F3 Oncology

[1350] P14136 GFAP Oncology

[1351] P15121 AKR1 B1 Oncology

[1352] P15328 FOLR1 Oncology

[1353] P15514 AREG Oncology

[1354] P15848 ARSB Oncology

[1355] P16562 CRISP2 Oncology

[1356] P16870 CPE Oncology

[1357] P17948 FLT1 Oncology

[1358] P18084 ITGB5 Oncology

[1359] P18627 LAG3 Oncology

[1360] P20138 CD33 Oncology

[1361] P20472 PVALB Oncology P20851 C4BPB Oncology

[1362] P21589 NT5E Oncology

[1363] P21741 MDK Oncology

[1364] P21802 FGFR2 Oncology

[1365] P21810 BGN Oncology

[1366] P22307 SCP2 Oncology

[1367] P23515 OMG Oncology

[1368] P25685 DNAJB1 Oncology

[1369] P25786 PSMA1 Oncology

[1370] P26010 ITGB7 Oncology

[1371] P26447 S100A4 Oncology

[1372] P26842 CD27 Oncology

[1373] P27695 APEX1 Oncology

[1374] P28907 CD38 Oncology

[1375] P29017 CD1 C Oncology

[1376] P29317 EPHA2 Oncology

[1377] P29459_P29460 IL12AJL12B Oncology

[1378] P30041 PRDX6 Oncology

[1379] P30260 CDC27 Oncology

[1380] P31350 RRM2 Oncology

[1381] P31994 FCGR2B Oncology

[1382] P32004 L1 CAM Oncology

[1383] P32926 DSG3 Oncology

[1384] P34130 NTF4 Oncology

[1385] P34949 MPI Oncology

[1386] P35052 GPC1 Oncology

[1387] P35070 BTC Oncology

[1388] P35318 ADM Oncology

[1389] P35475 IDUA Oncology

[1390] P35637 FUS Oncology

[1391] P35813 PPM1 A Oncology

[1392] P35916 FLT4 Oncology

[1393] P35968 KDR Oncology

[1394] P36888 FLT3 Oncology

[1395] P37108 SRP14 Oncology

[1396] P37173 TGFBR2 Oncology

[1397] P38936 CDKN1 A Oncology

[1398] P39748 FEN1 Oncology

[1399] P39900 MMP12 Oncology

[1400] P40121 CAPG Oncology

[1401] P40198 CEACAM3 Oncology

[1402] P41271 NBL1 Oncology

[1403] P41439 FOLR3 Oncology P41586 ADCYAP1 R1 Oncology

[1404] P42331 ARHGAP25 Oncology

[1405] P42658 DPP6 Oncology

[1406] P43490 NAMPT Oncology

[1407] P43628 KIR2DL3 Oncology

[1408] P43629 KIR3DL1 Oncology

[1409] P46060 RANGAP1 Oncology

[1410] P47929 LGALS7_LGALS7B Oncology

[1411] P47992 XCL1 Oncology

[1412] P48307 TFPI2 Oncology

[1413] P48643 CCT5 Oncology

[1414] P49441 INPP1 Oncology

[1415] P49767 VEGFC Oncology

[1416] P49788 RARRES1 Oncology

[1417] P50120 RBP2 Oncology

[1418] P50579 METAP2 Oncology

[1419] P50583 NUDT2 Oncology

[1420] P50749 RASSF2 Oncology

[1421] P51580 TPMT Oncology

[1422] P51692 STAT5B Oncology

[1423] P51858 HDGF Oncology

[1424] P54727 RAD23B Oncology

[1425] P55008 AIF1 Oncology

[1426] P55039 DRG2 Oncology

[1427] P55273 CDKN2D Oncology

[1428] P55789 GFER Oncology

[1429] P56159 GFRA1 Oncology

[1430] P58499 FAM3B Oncology

[1431] P61218 POLR2F Oncology

[1432] P62166 NCS1 Oncology

[1433] P78552 IL13RA1 Oncology

[1434] P80075 CCL8 Oncology

[1435] P80303 NUCB2 Oncology

[1436] P80511 S100A12 Oncology

[1437] P82980 RBP5 Oncology

[1438] P98073 TMPRSS15 Oncology

[1439] P98082 DAB2 Oncology

[1440] Q00796 SORD Oncology

[1441] Q01543 FLI1 Oncology

[1442] Q02246 CNTN2 Oncology

[1443] Q02742 GCNT1 Oncology

[1444] Q02763 TEK Oncology

[1445] Q05516 ZBTB16 Oncology Q06787 FMR1 Oncology

[1446] Q07954 LRP1 Oncology

[1447] Q07960 ARHGAP1 Oncology

[1448] Q08AG7 MZT1 Oncology

[1449] Q10471 GALNT2 Oncology

[1450] Q1 1201 ST3GAL1 Oncology

[1451] Q12846 STX4 Oncology

[1452] Q13145 BAMBI Oncology

[1453] 013421 MSLN Oncology

[1454] 013490 BIRC2 Oncology

[1455] Q13561 DCTN2 Oncology

[1456] Q13576 IQGAP2 Oncology

[1457] Q14203 DCTN1 Oncology

[1458] Q14213_Q8NEV9 EBI3JL27 Oncology

[1459] Q14241 ELOA Oncology

[1460] Q14508 WFDC2 Oncology

[1461] Q14512 FGFBP1 Oncology

[1462] 014790 CASP8 Oncology

[1463] 0151 16 PDCD1 Oncology

[1464] 015303 ERBB4 Oncology

[1465] Q15427 SF3B4 Oncology

[1466] 015797 SMAD1 Oncology

[1467] 016543 CDC37 Oncology

[1468] Q16595 FXN Oncology

[1469] Q16653 MOG Oncology

[1470] Q16674 MIA Oncology

[1471] Q16772 GSTA3 Oncology

[1472] Q16775 HAGH Oncology

[1473] Q16790 CA9 Oncology

[1474] Q2VWP7 PRTG Oncology

[1475] Q3B7J2 GFOD2 Oncology

[1476] Q496F6 CD300E Oncology

[1477] Q49AH0 CDNF Oncology

[1478] Q5JTD0 TJAP1 Oncology

[1479] Q5VIR6 VPS53 Oncology

[1480] Q6BAA4 FCRLB Oncology

[1481] Q6EIG7 CLEC6A Oncology

[1482] Q6FI81 CIAPIN1 Oncology

[1483] Q6NXT1 ANKRD54 Oncology

[1484] Q6P2H3 CEP85 Oncology

[1485] Q6PCB0 VWA1 Oncology

[1486] Q6PGN9 PSRC1 Oncology

[1487] Q6UWN8 SPINK6 Oncology Q6UWW8 CES3 Oncology

[1488] Q6UX82 LYPD8 Oncology

[1489] Q6UXD5 SEZ6L2 Oncology

[1490] Q7L5N7 LPCAT2 Oncology

[1491] Q7L5Y9 MAEA Oncology

[1492] Q7LG56 RRM2B Oncology

[1493] Q7Z434 MAVS Oncology

[1494] Q7Z4W1 DCXR Oncology

[1495] Q7Z5A7 TAFA5 Oncology

[1496] Q7Z5L0 VM01 Oncology

[1497] Q7Z5R6 APBB1 IP Oncology

[1498] Q7Z6M1 RABEPK Oncology

[1499] Q7Z7D3 VTCN1 Oncology

[1500] Q86SF2 GALNT7 Oncology

[1501] Q86SJ2 AMIG02 Oncology

[1502] Q86SJ6 DSG4 Oncology

[1503] Q86SR1 GALNT10 Oncology

[1504] Q86WD7 SERPINA9 Oncology

[1505] Q8IUK5 PLXDC1 Oncology

[1506] Q8IWL1 SFTPA2 Oncology

[1507] Q8IWL2 SFTPA1 Oncology

[1508] Q8IX05 CD302 Oncology

[1509] Q8IXJ6 SIRT2 Oncology

[1510] Q8N129 CNPY4 Oncology

[1511] Q8N386 LRRC25 Oncology

[1512] Q8N5S9 CAMKK1 Oncology

[1513] Q8N9I9 DTX3 Oncology

[1514] Q8NBZ7 UXS1 Oncology

[1515] Q8NCC3 PLA2G15 Oncology

[1516] Q8NEZ2 VPS37A Oncology

[1517] Q8TD06 AGR3 Oncology

[1518] Q8TDQ1 CD300LF Oncology

[1519] Q8TE58 ADAMTS15 Oncology

[1520] Q8WUX2 CHAC2 Oncology

[1521] Q8WWY7 WFDC12 Oncology

[1522] Q8WXI7 MUC16 Oncology

[1523] Q8WYN0 ATG4A Oncology

[1524] Q92832 NELL1 Oncology

[1525] Q92876 KLK6 Oncology

[1526] Q92982 NINJ1 Oncology

[1527] Q96D42 HAVCR1 Oncology

[1528] Q96DU3 SLAMF6 Oncology

[1529] Q96EK5 KIFBP Oncology Q96I15 SCLY Oncology

[1530] Q96I82 KAZALD1 Oncology

[1531] Q96J42 TXNDC15 Oncology

[1532] Q96JA1 LRIG1 Oncology

[1533] Q96NA2 RILP Oncology

[1534] Q96NB1 CEP20 Oncology

[1535] Q96NY8 NECTIN4 Oncology

[1536] Q96PD2 DCBLD2 Oncology

[1537] Q96PQ0 S0RCS2 Oncology

[1538] Q96RT1 ERBIN Oncology

[1539] Q96SM3 CPXM1 Oncology

[1540] Q99075 HBEGF Oncology

[1541] Q99536 VAT1 Oncology

[1542] Q99683 MAP3K5 Oncology

[1543] Q99717 SMAD5 Oncology

[1544] Q99795 GPA33 Oncology

[1545] Q9BQ51 PDCD1 LG2 Oncology

[1546] Q9BS26 ERP44 Oncology

[1547] Q9BSG5 RTBDN Oncology

[1548] Q9BSL1 UBAC1 Oncology

[1549] Q9BSW2 CRACR2A Oncology

[1550] Q9BTE6 AARSD1 Oncology

[1551] Q9BUE0 MED18 Oncology

[1552] Q9BXY4 RSPO3 Oncology

[1553] Q9BYE9 CDHR2 Oncology

[1554] Q9BYH1 SEZ6L Oncology

[1555] Q9BZR6 RTN4R Oncology

[1556] Q9C005 DPY30 Oncology

[1557] Q9C0C4 SEMA4C Oncology

[1558] Q9GZV9 FGF23 Oncology

[1559] Q9GZY6 LAT2 Oncology

[1560] Q9H156 SLITRK2 Oncology

[1561] Q9H3G5 CPVL Oncology

[1562] Q9H4A9 DPEP2 Oncology

[1563] Q9H4F8 SMOC1 Oncology

[1564] Q9H4P4 RNF41 Oncology

[1565] Q9H6B4 CLMP Oncology

[1566] Q9H6S3 EPS8L2 Oncology

[1567] Q9H8J5 MANSC1 Oncology

[1568] Q9HAT2 SIAE Oncology

[1569] Q9HAV5 EDA2R Oncology

[1570] Q9HAV7 GRPEL1 Oncology

[1571] Q9NP84 TNFRSF12A Oncology Q9NPH0 ACP6 Oncology

[1572] Q9NRA1 PDGFC Oncology

[1573] Q9NS15 LTBP3 Oncology

[1574] Q9NS68 TNFRSF19 Oncology

[1575] Q9NSA1 FGF21 Oncology

[1576] Q9NTU7 CBLN4 Oncology

[1577] Q9NUY8 TBC1 D23 Oncology

[1578] Q9NX58 LYAR Oncology

[1579] Q9NZ53 P0DXL2 Oncology

[1580] Q9NZT2 OGFR Oncology

[1581] Q9P0G3 KLK14 Oncology

[1582] Q9P0J1 PDP1 Oncology

[1583] Q9P0V8 SLAMF8 Oncology

[1584] Q9P1 Z2 CALCOCO1 Oncology

[1585] Q9UBG3 CRNN Oncology

[1586] Q9UBM4 OPTC Oncology

[1587] Q9UBX1 CTSF Oncology

[1588] Q9UBX7 KLK11 Oncology

[1589] Q9UHD8 SEPTIN9 Oncology

[1590] Q9UHF1 EGFL7 Oncology

[1591] Q9UJ68 MSRA Oncology

[1592] Q9UJ71 CD207 Oncology

[1593] Q9UJM8 HAO1 Oncology

[1594] Q9UK85 DKKL1 Oncology

[1595] Q9UKR0 KLK12 Oncology

[1596] Q9UKR3 KLK13 Oncology

[1597] Q9UKS7 IKZF2 Oncology

[1598] Q9ULX7 CA14 Oncology

[1599] Q9UP79 ADAMTS8 Oncology

[1600] Q9UQB8 BAIAP2 Oncology

[1601] Q9UQQ2 SH2B3 Oncology

[1602] Q9Y223 GNE Oncology

[1603] Q9Y243 AKT3 Oncology

[1604] Q9Y265 RUVBL1 Oncology

[1605] Q9Y2Z0 SUGT1 Oncology

[1606] Q9Y336 SIGLEC9 Oncology

[1607] Q9Y570 PPME1 Oncology

[1608] Q9Y5K2 KLK4 Oncology

[1609] Q9Y5K8 ATP6V1 D Oncology

[1610] Q9Y5L3 ENTPD2 Oncology

[1611] Q9Y5V3 MAGED1 Oncology

[1612] Q9Y5W5 WIF1 Oncology

[1613] Q9Y639 NPTN Oncology Q9Y653 ADGRG1 Oncology

[1614] Q9Y662 HS3ST3B1 Oncology

[1615] Q9Y6A5 TACC3 Oncology

[1616] The first column provides UniPROT accession numbers providing sequence information for each of the genes listed. Each of these accession numbers and the related sequence information is incorporated herein by reference as though set forth in full. Members of MODS severity protein profile are underlined.

[1617] Table 4. Antibodies used in 35-marker spectral flow immune phenotyping panel

[1618] Table 5. PCA loadings in Fig. ID, using the full proteomics dataset.

[1619] Table 6. Severity-associated proteins identified through linear mixed-effects model after adjustment for age, gender, and day from MODS onset.

[1620] PROTEIN UNIPROT ID NOMINAL P- FDR ADJUSTED P- SYMBOL VALUE VALUE

[1621] HS3ST3B1 Q9Y662 9.78E-16 1.42E-12

[1622] FGFBP1 Q14512 1.01 E-12 6.71 E-10

[1623] HGF P14210 1.39E-12 6.71 E-10

[1624] RSPO1 Q2MKA7 2.86E-12 1.03E-09

[1625] ARTN Q5T4W7 8.08E-12 2.34E-09

[1626] FLT1 P17948 1.06E-10 2.57E-08

[1627] TFPI P10646 5.37E-10 1.11 E-07 TFPI2 P48307 2.86E-09 5.17E-07

[1628] MDK P21741 3.96E-09 5.96E-07

[1629] GALNT2 Q10471 4.12E-09 5.96E-07

[1630] TINAGL1 Q9GZM7 1.51 E-08 1.98E-06

[1631] ESM1 Q9NQ30 3.93E-08 4.75E-06

[1632] SMOC1 Q9H4F8 5.79E-08 6.34E-06

[1633] SOST Q9BQB4 6.13E-08 6.34E-06

[1634] TNFRSF1 1 B 000300 2.08E-07 1.76E-05

[1635] RSPO3 Q9BXY4 2.13E-07 1.76E-05

[1636] NID1 P14543 2.18E-07 1.76E-05

[1637] SDC1 P18827 2.19E-07 1.76E-05

[1638] SPP1 P10451 2.67E-07 2.04E-05

[1639] SFRP1 Q8N474 3.50E-07 2.53E-05

[1640] CCL5 P13501 1.59E-06 0.00010965

[1641] VWF P04275 2.69E-06 0.00017254

[1642] SPON1 Q9HCB6 2.74E-06 0.00017254

[1643] BGN P21810 3.18E-06 0.00018689

[1644] CCL28 Q9NRJ3 3.23E-06 0.00018689

[1645] EGF P01 133 3.42E-06 0.00019021

[1646] DKK3 Q9UBP4 5.48E-06 0.00029374

[1647] ANGPT1 Q15389 9.39E-06 0.00048507

[1648] IL1 RN P18510 9.71 E-06 0.00048507

[1649] CXCL9 Q07325 1.93E-05 0.00090306

[1650] BIN2 Q9UBW5 1.93E-05 0.00090306

[1651] MB P02144 2.35E-05 0.00106301

[1652] CEACAM21 Q3KPI0 2.47E-05 0.00108242

[1653] TNFRSF12A Q9NP84 2.65E-05 0.00112887

[1654] PDGFB P01 127 2.96E-05 0.00122666

[1655] VEGFC P49767 3.34E-05 0.00134263

[1656] NOS3 P29474 3.51 E-05 0.00137489

[1657] TNNI3 P19429 3.89E-05 0.00146878

[1658] HMOX1 P09601 3.96E-05 0.00146878

[1659] PDGFA P04085 5.71 E-05 0.00206624

[1660] PLAT P00750 8.06E-05 0.00284495

[1661] APBB1 IP Q7Z5R6 8.36E-05 0.0028812

[1662] IL16 Q14005 8.91 E-05 0.00300106

[1663] MEPE Q9NQ76 9.45E-05 0.0031 1057

[1664] IL10 P22301 0.0001 1972 0.0038524

[1665] CD33 P20138 0.00012755 0.004015

[1666] S100A16 Q96FQ6 0.00013185 0.00402445 GPC1 P35052 0.00013341 0.00402445 TNFSF13 075888 0.00014854 0.00438955 MMP10 P09238 0.00015158 0.0043897 EPHB4 P54760 0.00016373 0.00464875 CRLF1 075462 0.00017348 0.00483087

[1667] VCAN P1361 1 0.00018441 0.00483759 SERPINA12 Q8IW75 0.00018618 0.00483759 S100A1 1 P31949 0.00018661 0.00483759 GDF15 Q99988 0.00018709 0.00483759

[1668] PDGFRA P16234 0.00020495 0.00520657

[1669] B4GALT1 P15291 0.00021353 0.00532201 CTSD P07339 0.00021685 0.00532201 HAVCR1 Q96D42 0.00023537 0.00568035 TFF2 003403 0.00024155 0.00573388 AMN Q9BXJ7 0.00024836 0.00580035 ADGRE2 Q9UHX3 0.00025887 0.00594985

[1670] MAP3K5 Q99683 0.0002835 0.00641423

[1671] NTproBNP NTproBNP 0.00028916 0.00644165 FGR P09769 0.0003129 0.00679259 FCN2 Q15485 0.0003143 0.00679259 STC1 P52823 0.000335 0.00710924

[1672] ST6GAL1 P15907 0.00033877 0.00710924

[1673] EZR P1531 1 0.00034413 0.0071 1856

[1674] DCN P07585 0.00039365 0.00802816

[1675] CTSH P09668 0.00040018 0.00804802

[1676] RRM2 P31350 0.00046536 0.00914852

[1677] NOS1 P29475 0.00046879 0.00914852 CXCL13 043927 0.00047385 0.00914852 WFDC2 Q14508 0.0004906 0.00934724 PLXNA4 Q9HCM2 0.00050296 0.00944962

[1678] RETN Q9HD89 0.00050903 0.00944962 SPON2 Q9BUD6 0.00052581 0.00963761 REN P00797 0.00054345 0.00983643 LYN P07948 0.00055579 0.00993552 DLL1 000548 0.00056857 0.01004008 LDLR P01 130 0.00057583 0.01004588 IL6 P05231 0.00060132 0.01036567

[1679] HSPB6 014558 0.00065664 0.01 118602

[1680] PILRB Q9UKJ0 0.00072517 0.01220988 SMOC2 Q9H3U7 0.00075484 0.0125633 STC2 076061 0.00080228 0.01306763 LPO P22079 0.00080319 0.01306763

[1681] FRZB Q92765 0.000848 0.01364338

[1682] MPIG6B 095866 0.00094041 0.01474893

[1683] CD69 007108 0.00094445 0.01474893

[1684] TIMP1 P01033 0.00094727 0.01474893

[1685] FAS P25445 0.00095804 0.01475791

[1686] CCL21 000585 0.00099535 0.0151712

[1687] CTSB P07858 0.00102498 0.01546009

[1688] GPKOW Q92917 0.00104935 0.01554193

[1689] IGFBP1 P08833 0.00106045 0.01554193

[1690] SIRPB1 000241 0.00107062 0.01554193

[1691] IL1 RL2 Q9HB29 0.00107334 0.01554193

[1692] CTSL P0771 1 0.001 1079 0.01588349

[1693] CPE P16870 0.001 16593 0.01655167

[1694] SWOP P25815 0.00121713 0.0171107

[1695] CXCL17 Q6UXB2 0.00133171 0.01854148

[1696] SEMA4C Q9C0C4 0.00138819 0.0188901

[1697] FMNL1 095466 0.00141 182 0.0188901

[1698] C1 QTNF1 Q9BXJ1 0.00141639 0.0188901

[1699] ACP5 P13686 0.00141957 0.0188901

[1700] SNOG 076070 0.00142198 0.0188901

[1701] SIGLEC7 Q9Y286 0.00145966 0.01921437

[1702] FGF23 Q9GZV9 0.00150884 0.01953418

[1703] CD300E Q496F6 0.00151093 0.01953418

[1704] CD40LG P29965 0.00160008 0.02050369

[1705] TNFRSF10A 000220 0.00169329 0.02134872

[1706] TNFSF12 043508 0.00171232 0.02134872

[1707] GDNF P39905 0.0017197 0.02134872

[1708] TBC1 D23 Q9NUY8 0.001725 0.02134872

[1709] FUS P35637 0.0017979 0.02206241

[1710] IGSF3 075054 0.00185003 0.02251 127

[1711] LSP1 P33241 0.00189035 0.02281022

[1712] NPM1 P06748 0.00193614 0.02316964

[1713] IL1 RL1 001638 0.00195317 0.02318189

[1714] TNFRSF10B 014763 0.00199132 0.02344253

[1715] TNFRSF1 B P20333 0.00212602 0.02482646

[1716] CCL16 015467 0.0021721 1 0.0249033

[1717] VWC2 Q2TAL6 0.00218761 0.0249033

[1718] ARHGEF12 Q9NZN5 0.002231 12 0.0249033

[1719] TBCB Q99426 0.00224537 0.0249033

[1720] NADK 095544 0.00225418 0.0249033 CALCA P01258 0.00226121 0.0249033

[1721] CHI3L1 P36222 0.00226322 0.0249033

[1722] LTA P01374 0.00227019 0.0249033

[1723] CDKN1 A P38936 0.00230012 0.02504189

[1724] IL15 P40933 0.00241286 0.02607324

[1725] NAMPT P43490 0.00251583 0.02672163

[1726] CD74 P04233 0.00252515 0.02672163

[1727] NBN 060934 0.00253091 0.02672163

[1728] LRP1 Q07954 0.00254667 0.02672163

[1729] AGRN 000468 0.00260721 0.02716001

[1730] APEX1 P27695 0.00276131 0.02824187

[1731] TFF1 P04155 0.00276631 0.02824187

[1732] CFC1 P0CG37 0.00277532 0.02824187

[1733] SUSD1 Q6UWL2 0.00279348 0.02824187

[1734] SPARCL1 014515 0.00280858 0.02824187

[1735] SKAP2 075563 0.00296403 0.0295994

[1736] GAS6 Q14393 0.00301525 0.02990465

[1737] FABP4 P15090 0.00305374 0.0300804

[1738] SEMA3F Q13275 0.00307753 0.03010986

[1739] CLEC4C Q8WTT0 0.00312301 0.03034981

[1740] PCDH17 014917 0.00320416 0.03093086

[1741] CXCL5 P42830 0.00327345 0.03128217

[1742] GFRA3 060609 0.00328376 0.03128217

[1743] NMNAT1 Q9HAN9 0.00335931 0.03176033

[1744] CLIP2 Q9UDT6 0.00337822 0.03176033

[1745] GDF2 Q9UK05 0.00339976 0.03176033

[1746] POLR2F P61218 0.00342847 0.03182321

[1747] APOH P02749 0.00345854 0.03189508

[1748] PLIN1 060240 0.00348026 0.03189508

[1749] GGH Q92820 0.00351319 0.03198759

[1750] DBNL Q9UJU6 0.00353454 0.03198759

[1751] CCDC80 Q76M96 0.00356323 0.03204691

[1752] IGFBPL1 Q8WX77 0.00366758 0.03278185

[1753] PRTN3 P24158 0.00373993 0.03309129

[1754] MDGA1 Q8NFP4 0.00374791 0.03309129

[1755] ST3GAL1 Q11201 0.00381241 0.03345676

[1756] PHOSPHO1 Q8TCT1 0.00396184 0.03455869

[1757] CALB1 P05937 0.00402674 0.03491452

[1758] AOC1 P19801 0.00413462 0.0356365

[1759] LRIG1 Q96JA1 0.00419963 0.03598264

[1760] LPCAT2 Q7L5N7 0.00432535 0.03665853 DCTPP1 Q9H773 0.00434627 0.03665853 CA12 043570 0.00435447 0.03665853 EGLN1 Q9GZT9 0.00444154 0.03704733 CXCL16 Q9H2A7 0.00446448 0.03704733 SNAP23 000161 0.00447741 0.03704733 PXN P49023 0.00456971 0.03759628 PILRA Q9UKJ1 0.00465616 0.03803004 CLEC1 B Q9P126 0.00467496 0.03803004

[1761] VWA1 Q6PCB0 0.00483783 0.03899328 ECE1 P42892 0.00487365 0.03899328 PROP P42785 0.00487416 0.03899328 CA3 P07451 0.00493247 0.03924295 CD300LG Q6UXG3 0.00496042 0.03924964 THBS2 P35442 0.00502861 0.03954462 CLEC5A Q9NY25 0.00505232 0.03954462 TPSAB1 015661 0.00515863 0.0401597

[1762] MSTN 014793 0.00523403 0.04052877 SH2B3 Q9UQQ2 0.00541883 0.04160317 TNG P24821 0.00543025 0.04160317 CAPG P40121 0.00576071 0.04390268 IL5RA Q01344 0.00585835 0.04441308 VAT1 099536 0.00594437 0.04474683 EREG 014944 0.00596418 0.04474683 LAT 043561 0.0059973 0.04476337 CSF1 P09603 0.00606418 0.04503046

[1763] MYO9B Q13459 0.0061451 1 0.0452654 CASP2 P42575 0.00615835 0.0452654 TACSTD2 P09758 0.00632755 0.0461185 PSIP1 075475 0.0063381 1 0.0461185 IDS P22304 0.00647126 0.04678976 CXCL3 P19876 0.00650067 0.04678976 TIGAR Q9NQ88 0.0065273 0.04678976 TNFRSF6B 095407 0.00661772 0.04720426

[1764] SEMA7A 075326 0.0067107 0.04763283 CCL20 P78556 0.0068095 0.04791754 PAG1 Q9NWQ8 0.006817 0.04791754 RELT Q969Z4 0.00688022 0.04803883 BACH1 014867 0.00690061 0.04803883 IDI2 Q9BXS1 0.00704529 0.04841844 CLEC4A Q9UMR7 0.00705408 0.04841844 ADGRE5 P48960 0.00705545 0.04841844 SIGLEC10 Q96LC7 0.0071736 0.0488098

[1765] ALPP P05187 0.00717989 0.0488098

[1766] HS6ST1 060243 0.007363 0.04982069

[1767] Table 7. Etiology of MODS and computed subphenotype for each MODS patient.

[1768] Table 8. 368 proteins measured in proteomics panel used in IEI cohort.

[1769] UNIPROT ID PROTEIN SYMBOL EXPLORE 384 PANEL

[1770] B1AKI9 ISM1 Inflammation

[1771] 000175 CCL24 Inflammation

[1772] 000182 LGALS9 Inflammation

[1773] 000241 SIRPB1 Inflammation

[1774] 000253 AGRP Inflammation

[1775] 000273 DFFA Inflammation

[1776] 000300 TNFRSF1 1 B Inflammation

[1777] 000339 MATN2 Inflammation

[1778] 000468 AGRN Inflammation

[1779] 000585 CCL21 Inflammation

[1780] 000626 CCL22 Inflammation

[1781] 014773 TPP1 Inflammation

[1782] 014788 TNFSF1 1 Inflammation

[1783] 014836 TNFRSF13B Inflammation

[1784] 014867 BACH1 Inflammation

[1785] 014904 WNT9A Inflammation

[1786] 015169 AXIN1 Inflammation

[1787] 015444 CCL25 Inflammation

[1788] 015455 TLR3 Inflammation

[1789] 043291 SPINT2 Inflammation

[1790] 043508 TNFSF12 Inflammation

[1791] 043521 -2 BCL2L1 1 Inflammation

[1792] 043561 LAT Inflammation

[1793] 043597 SPRY2 Inflammation

[1794] 043598 DNPH1 Inflammation

[1795] 043639 NCK2 Inflammation

[1796] 043707 ACTN4 Inflammation

[1797] 043736 ITM2A Inflammation

[1798] 043915 VEGFD Inflammation

[1799] 060449 LY75 Inflammation

[1800] 060542 PSPN Inflammation

[1801] 060575 SPINK4 Inflammation

[1802] 060880 SH2D1A Inflammation

[1803] 060884 DNAJA2 Inflammation

[1804] 060934 NBN Inflammation

[1805] 075077 ADAM23 Inflammation

[1806] 075462 CRLF1 Inflammation

[1807] 075475 PSIP1 Inflammation 075563 SKAP2 Inflammation

[1808] 075888 TNFSF13 Inflammation

[1809] 076036 NCR1 Inflammation

[1810] 076038 SCGN Inflammation

[1811] 076096 CST7 Inflammation

[1812] 094856 NFASC Inflammation

[1813] 094992 HEXIM1 Inflammation

[1814] 095379 TNFAIP8 Inflammation

[1815] 095633 FSTL3 Inflammation

[1816] 095644 NFATC1 Inflammation

[1817] 095715 CXCL14 Inflammation

[1818] 095750 FGF19 Inflammation

[1819] 095760 IL33 Inflammation

[1820] 095866 MPIG6B Inflammation

[1821] 095971 CD160 Inflammation

[1822] P00813 ADA Inflammation

[1823] P01 127 PDGFB Inflammation

[1824] P01 133 EGF Inflammation

[1825] P01 135 TGFA Inflammation

[1826] P01 137 TGFB1 Inflammation

[1827] P01374 LTA Inflammation

[1828] P01375 TNF Inflammation

[1829] P01579 IFNG Inflammation

[1830] P01583 IL1A Inflammation

[1831] P01584 IL1 B Inflammation

[1832] P01588 EPO Inflammation

[1833] P01591 JCHAIN Inflammation

[1834] P01730 CD4 Inflammation

[1835] P01903 HLA-DRA Inflammation

[1836] P02745 C1 QA Inflammation

[1837] P02778 CXCL10 Inflammation

[1838] P03956 MMP1 Inflammation

[1839] P051 12 IL4 Inflammation

[1840] P051 13 IL5 Inflammation

[1841] P05231 IL6 Inflammation

[1842] P05412 JUN Inflammation

[1843] P07148 FABP1 Inflammation

[1844] P08727 KRT19 Inflammation

[1845] P09038 FGF2 Inflammation

[1846] P09238 MMP10 Inflammation

[1847] P09326 CD48 Inflammation

[1848] P09341 CXCL1 Inflammation

[1849] P09603 CSF1 Inflammation P09874 PARP1 Inflammation

[1850] P09919 CSF3 Inflammation

[1851] P0DMV8 HSPA1 A Inflammation

[1852] P10144 GZMB Inflammation

[1853] P10145 CXCL8 Inflammation

[1854] P10147 CCL3 Inflammation

[1855] P11274 BCR Inflammation

[1856] P11684 SCGB1 A1 Inflammation

[1857] P12034 FGF5 Inflammation

[1858] P12532 CKMT1 A_CKMT1 B Inflammation

[1859] P12544 GZMA Inflammation

[1860] P12872 MLN Inflammation

[1861] P13232 IL7 Inflammation

[1862] P13236 CCL4 Inflammation

[1863] P13693 TPT1 Inflammation

[1864] P13725 OSM Inflammation

[1865] P13747 HLA-E Inflammation

[1866] P14210 HGF Inflammation

[1867] P14317 HCLS1 Inflammation

[1868] P14784 IL2RB Inflammation

[1869] P15260 IFNGR1 Inflammation

[1870] P15291 B4GALT1 Inflammation

[1871] P15692 VEG FA Inflammation

[1872] P16422 EPCAM Inflammation

[1873] P16455 MGMT Inflammation

[1874] P18510 IL1 RN Inflammation

[1875] P18564 ITGB6 Inflammation

[1876] P19256 CD58 Inflammation

[1877] P19474 TRIM21 Inflammation

[1878] P19801 AOC1 Inflammation

[1879] P19876 CXCL3 Inflammation

[1880] P19878 NCF2 Inflammation

[1881] P19883 FST Inflammation

[1882] P20273 CD22 Inflammation

[1883] P20340 RAB6A Inflammation

[1884] P20783 NTF3 Inflammation

[1885] P20809 IL11 Inflammation

[1886] P20849 COL9A1 Inflammation

[1887] P21709 EPHA1 Inflammation

[1888] P21860 ERBB3 Inflammation

[1889] P22301 IL10 Inflammation

[1890] P22304 IDS Inflammation

[1891] P22466 GAL Inflammation P23229 ITGA6 Inflammation

[1892] P23582 NPPC Inflammation

[1893] P24001 IL32 Inflammation

[1894] P24071 FCAR Inflammation

[1895] P24387 CRHBP Inflammation

[1896] P24394 IL4R Inflammation

[1897] P251 16 F2R Inflammation

[1898] P25942 CD40 Inflammation

[1899] P26022 PTX3 Inflammation

[1900] P26951 IL3RA Inflammation

[1901] P27540 ARNT Inflammation

[1902] P27930 IL1 R2 Inflammation

[1903] P28827 PTPRM Inflammation

[1904] P28838 LAP3 Inflammation

[1905] P28845 HSD11 B1 Inflammation

[1906] P29279 CCN2 Inflammation

[1907] P29350 PTPN6 Inflammation

[1908] P29460 IL12B Inflammation

[1909] P29965 CD40LG Inflammation

[1910] P30044 PRDX5 Inflammation

[1911] P30048 PRDX3 Inflammation

[1912] P30203 CD6 Inflammation

[1913] P30613 PKLR Inflammation

[1914] P30838 ALDH3A1 Inflammation

[1915] P32456 GBP2 Inflammation

[1916] P32970 CD70 Inflammation

[1917] P33241 LSP1 Inflammation

[1918] P34896 SHMT1 Inflammation

[1919] P35225 IL13 Inflammation

[1920] P35613 BSG Inflammation

[1921] P35625 TIMP3 Inflammation

[1922] P36941 LTBR Inflammation

[1923] P36959 GMPR Inflammation

[1924] P37235 HPCAL1 Inflammation

[1925] P40259 CD79B Inflammation

[1926] P40933 IL15 Inflammation

[1927] P41217 CD200 Inflammation

[1928] P42575 CASP2 Inflammation

[1929] P42701 IL12RB1 Inflammation

[1930] P42702 LIFR Inflammation

[1931] P42768 WAS Inflammation

[1932] P43234 CTSO Inflammation

[1933] P43489 TNFRSF4 Inflammation P45984 MAPK9 Inflammation

[1934] P46109 CRKL Inflammation

[1935] P47712 PLA2G4A Inflammation

[1936] P48023 FASLG Inflammation

[1937] P48061 CXCL12 Inflammation

[1938] P49763 PGF Inflammation

[1939] P49771 FLT3LG Inflammation

[1940] P50452 SERPINB8 Inflammation

[1941] P50591 TNFSF10 Inflammation

[1942] P50995 ANXA1 1 Inflammation

[1943] P51617 IRAKI Inflammation

[1944] P51671 CCL1 1 Inflammation

[1945] P51888 PRELP Inflammation

[1946] P52564 MAP2K6 Inflammation

[1947] P53634 CTSC Inflammation

[1948] P54317 PNLIPRP2 Inflammation

[1949] P55145 MANF Inflammation

[1950] P55773 CCL23 Inflammation

[1951] P55957 BID Inflammation

[1952] P56470 LGALS4 Inflammation

[1953] P57771 RGS8 Inflammation

[1954] P58294 PR0K1 Inflammation

[1955] P60568 IL2 Inflammation

[1956] P63241 EIF5A Inflammation

[1957] P68106 FKBP1 B Inflammation

[1958] P78310 CXADR Inflammation

[1959] P78362 SRPK2 Inflammation

[1960] P78410 BTN3A2 Inflammation

[1961] P78556 CCL20 Inflammation

[1962] P80098 CCL7 Inflammation

[1963] P80162 CXCL6 Inflammation

[1964] Q01151 CD83 Inflammation

[1965] Q01344 IL5RA Inflammation

[1966] Q03403 TFF2 Inflammation

[1967] Q03405 PLAUR Inflammation

[1968] Q03426 MVK Inflammation

[1969] Q03431 PTH1 R Inflammation

[1970] Q04637 EIF4G1 Inflammation

[1971] Q04759 PRKCQ Inflammation

[1972] Q05084 ICA1 Inflammation

[1973] Q06520 SULT2A1 Inflammation

[1974] Q07065 CKAP4 Inflammation

[1975] Q07325 CXCL9 Inflammation Q08174 PCDH1 Inflammation

[1976] Q08334 IL10RB Inflammation

[1977] Q0Z7S8 FABP9 Inflammation

[1978] Q12765 SCRN1 Inflammation

[1979] Q12778 F0X01 Inflammation

[1980] Q12866 MERTK Inflammation

[1981] Q12918 KLRB1 Inflammation

[1982] Q12933 TRAF2 Inflammation

[1983] Q12968 NFATC3 Inflammation

[1984] Q13007 IL24 Inflammation

[1985] Q13219 PAPPA Inflammation

[1986] Q13232 NME3 Inflammation

[1987] Q13241 KLRD1 Inflammation

[1988] Q13261 IL15RA Inflammation

[1989] Q13291 SLAMF1 Inflammation

[1990] Q13459 MY09B Inflammation

[1991] Q13478 IL18R1 Inflammation

[1992] Q13574 DGKZ Inflammation

[1993] Q13651 IL10RA Inflammation

[1994] Q14005 IL16 Inflammation

[1995] Q141 16 IL18 Inflammation

[1996] Q141 18 DAG1 Inflammation

[1997] Q14210 LY6D Inflammation

[1998] Q14242 SELPLG Inflammation

[1999] Q14435 GALNT3 Inflammation

[2000] Q14773 ICAM4 Inflammation

[2001] Q15109 AGER Inflammation

[2002] Q15166 P0N3 Inflammation

[2003] Q15389 ANGPT1 Inflammation

[2004] Q15517 CDSN Inflammation

[2005] Q15661 TPSAB1 Inflammation

[2006] Q16363 LAMA4 Inflammation

[2007] Q16552 IL17A Inflammation

[2008] Q16651 PRSS8 Inflammation

[2009] Q16698 DECR1 Inflammation

[2010] Q16719 KYNU Inflammation

[2011] Q29980_Q29983 MICB MICA Inflammation

[2012] Q3KPI0 CEACAM21 Inflammation

[2013] Q4KMG0 ODON Inflammation

[2014] Q5KU26 COLEC12 Inflammation

[2015] Q5R372 RABGAP1 L Inflammation

[2016] Q5T4W7 ARTN Inflammation

[2017] Q5ZPR3 CD276 Inflammation Q6DN72 FCRL6 Inflammation

[2018] Q6GTX8 LAIR1 Inflammation

[2019] Q6UB28 METAP1 D Inflammation

[2020] Q6UWV6 ENPP7 Inflammation

[2021] Q6UXB2 CXCL17 Inflammation

[2022] Q6UXB4 CLEC4G Inflammation

[2023] Q6UXH1 CRELD2 Inflammation

[2024] Q6UXK5 LRRN1 Inflammation

[2025] Q6ZMH5 SLC39A5 Inflammation

[2026] Q6ZUJ8 PIK3AP1 Inflammation

[2027] Q7KYR7 BTN2A1 Inflammation

[2028] Q7L8A9 VASH1 Inflammation

[2029] Q7Z6M3 MILR1 Inflammation

[2030] Q7Z739 YTHDF3 Inflammation

[2031] Q8IU57 IFNLR1 Inflammation

[2032] Q8IVG5 SAMD9L Inflammation

[2033] Q8IYS5 OSCAR Inflammation

[2034] Q8N608 DPP10 Inflammation

[2035] Q8N6P7 IL22RA1 Inflammation

[2036] Q8N8S7 ENAH Inflammation

[2037] Q8NDB2 BANK1 Inflammation

[2038] Q8NFT8 DNER Inflammation

[2039] Q8NHJ6 LILRB4 Inflammation

[2040] Q8TAD2 IL17D Inflammation

[2041] Q8TCS8 PNPT1 Inflammation

[2042] Q8TD46 CD200R1 Inflammation

[2043] Q8TEU8 WFIKKN2 Inflammation

[2044] Q8WTT0 CLEC4C Inflammation

[2045] Q8WU39 MZB1 Inflammation

[2046] Q8WV07 LTO1 Inflammation

[2047] Q8WXD2 SCG3 Inflammation

[2048] Q8WXI8 CLEC4D Inflammation

[2049] Q92484 SMPDL3A Inflammation

[2050] Q92583 CCL17 Inflammation

[2051] Q92609 TBC1 D5 Inflammation

[2052] Q92844 TANK Inflammation

[2053] Q92956 TNFRSF14 Inflammation

[2054] Q969V3 NCLN Inflammation

[2055] Q96AX2 RAB37 Inflammation

[2056] Q96DB9 FXYD5 Inflammation

[2057] Q96KG7 MEGF10 Inflammation

[2058] Q96LA5 FCRL2 Inflammation

[2059] Q96LC7 SIGLEC10 Inflammation Q96P31 FCRL3 Inflammation

[2060] Q96PD4 IL17F Inflammation

[2061] Q96PL1 SCGB3A2 Inflammation

[2062] Q96RJ3 TNFRSF13C Inflammation

[2063] Q96SB3 PPP1 R9B Inflammation

[2064] Q99435 NELL2 Inflammation

[2065] Q99538 LGMN Inflammation

[2066] Q99616 CCL13 Inflammation

[2067] Q99685 MGLL Inflammation

[2068] Q99748 NRTN Inflammation

[2069] Q99895 CTRC Inflammation

[2070] Q99983 OMD Inflammation

[2071] Q9BT73 PSMG3 Inflammation

[2072] Q9BU40 CHRDL1 Inflammation

[2073] Q9BXJ7 AMN Inflammation

[2074] Q9BXN2 CLEC7A Inflammation

[2075] Q9BY76 ANGPTL4 Inflammation

[2076] Q9BYZ8 REG4 Inflammation

[2077] Q9BZW8 CD244 Inflammation

[2078] Q9BZZ2 SIGLEC1 Inflammation

[2079] Q9C035 TRIM5 Inflammation

[2080] Q9GZT9 EGLN1 Inflammation

[2081] Q9H008 LHPP Inflammation

[2082] Q9H0P0 NT5C3A Inflammation

[2083] Q9H3U7 SMOC2 Inflammation

[2084] Q9H4D0 CLSTN2 Inflammation

[2085] Q9HB29 IL1 RL2 Inflammation

[2086] Q9HBG7 LY9 Inflammation

[2087] Q9HC38 GLOD4 Inflammation

[2088] Q9HCB6 SPON1 Inflammation

[2089] Q9HCM2 PLXNA4 Inflammation

[2090] Q9HCU5 PREB Inflammation

[2091] Q9HD26 GOPC Inflammation

[2092] Q9NP70 AMBN Inflammation

[2093] Q9NQ25 SLAMF7 Inflammation

[2094] Q9NQ30 ESM1 Inflammation

[2095] Q9NQ76 MEPE Inflammation

[2096] Q9NR12 PDLIM7 Inflammation

[2097] Q9NRJ3 CCL28 Inflammation

[2098] Q9NRM6 IL17RB Inflammation

[2099] Q9NWZ3 IRAK4 Inflammation

[2100] Q9NYY1 IL20 Inflammation

[2101] Q9NZC2 TREM2 Inflammation Q9NZN5 ARHGEF12 Inflammation

[2102] Q9NZV1 CRIM1 Inflammation

[2103] Q9P0M4 IL17C Inflammation

[2104] Q9UDT6 CLIP2 Inflammation

[2105] Q9UHC6 CNTNAP2 Inflammation

[2106] Q9UHF4 IL20RA Inflammation

[2107] Q9UHX3 ADGRE2 Inflammation

[2108] Q9UIB8 CD84 Inflammation

[2109] Q9UII2 ATP5IF1 Inflammation

[2110] Q9UJA9 ENPP5 Inflammation

[2111] Q9UJU6 DBNL Inflammation

[2112] Q9UKU9 ANGPTL2 Inflammation

[2113] Q9UKX5 ITGA1 1 Inflammation

[2114] Q9UMR7 CLEC4A Inflammation

[2115] Q9UN19 DAPP1 Inflammation

[2116] Q9UNE0 ED AR Inflammation

[2117] Q9UNK0 STX8 Inflammation

[2118] Q9UPV0 CEP164 Inflammation

[2119] Q9UQV4 LAMP3 Inflammation

[2120] Q9Y258 CCL26 Inflammation

[2121] Q9Y266 NUDC Inflammation

[2122] Q9Y2J8 PADI2 Inflammation

[2123] Q9Y3D6 FIS1 Inflammation

[2124] Q9Y3P8 SIT1 Inflammation

[2125] Q9Y478 PRKAB1 Inflammation

[2126] Q9Y5A7 NUB1 Inflammation

[2127] Q9Y6K9 IKBKG Inflammation

[2128] Q9Y6N7 ROBO1 Inflammation

[2129] Q9Y6Q6 TNFRSF1 1 A Inflammation

[2130] Table 9. Leading edge analysis of protein expression in Group C patients compared to HC participants.

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[2230] While certain of the preferred embodiments of the present invention have been described and specifically exemplified above, it is not intended that the invention be limited to such embodiments. It will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the scope of the present invention, as set forth in the following claims.

Claims

What is claimed is:

1. A method for identifying a patient at increased risk for severe multiorgan dysfunction syndrome (MODS) comprising: a) determining the expression levels of a panel of MODS severity signature proteins at MODS onset selected from SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SMOC2, DCTPP1, LDLR, VCAN, SIGLEC10, IL- 15, SEMA4C, GDF2, SUSD1, MPIG6B, and EGF; and b) identifying a patient as having a severe MODS subphenotype when expression levels of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SMOC2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 are elevated and expression levels of SUSD1, MPIG6B, EGF are reduced relative to levels observed in healthy pediatric participants, said identified patients being at increased risk for pathologic dysregulation of the JAK / STAT signaling pathway and hypercytokinemia at the onset of MODS, resulting in increased disease severity and increased risk of death.

2. The method of claim 1, wherein said patient is a pediatric patient.

3. The method of claim 1 or claim 2, wherein protein expression levels of said severity signature proteins are determined in a patient sample selected from whole blood, peripheral blood mononuclear cells, plasma, and serum.

4. The method of claim 1, wherein protein expression levels are determined via a method selected from the group consisting of affinity-based immunoassay, proximity extension assay, aptamer-based proteomic assay, a mass-spectrometry-based assay, cytometry-based protein quantification, and immunohistochemistry.

5. A method for treating a patient at increased risk for severe multiorgan dysfunction syndrome (MODS) comprising; a) identifying said patient as being at increased risk of severe MODS when expression levels of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SMOC2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 areelevated and expression levels of SUSD1, MPIG6B, EGF at the onset of MODS are reduced relative to levels observed in healthy pediatric participants; and b) administering to the patient an effective amount of an agent that modulates JAK and / or STAT signaling, said agent being selected from a small molecule JAK inhibitor, a small molecule JAK3 inhibitor, a biological inhibitor of IL-6 or IFN-y signaling pathway, a nucleic acid based inhibitor, an engineered cytokine trap, a decoy receptor for neutralization of one or more of IL-6, IFN-y for inhibition of STAT- activating cytokines.

6. The method of claim 5, wherein said small-molecule JAK inhibitor is selected from ruxolitinib, baricitinib, tofacitinib, upadacitinib, abrocitinib, itacitinib, fedratinib and pacritinib.

7. The method of claim 5, wherein said small-molecule STAT3 inhibitor is napabucasin, stattic, C188-9, SD-36, WP-1066, and silibinin.

8. The method of claim 5, wherein said biologic inhibitor of IL-6 or ILN-y pathways is selected from tocilizumab, sarilumab, siltuximab, clazakizumab, olokizumab, sirukumab, emapalumab, fontolizumab, and AMG-811.

9. The method of claims 5 wherein said nucleic-acid-based inhibitor is a siRNA, an antisense oligonucleotide, or a CRISPR complex targeting JAK1, JAK2, TYK2, STAT1, or STAT3.

10. The method of claim 5 wherein the agent is an engineered cytokine trap or decoy receptor that neutralizes IL-6, ILN-y and, or downstream STAT-activating cytokines.

11. The method of claim 5, further comprising administering at least one antibiotic, antiinflammatory agent, anticoagulant, or steroid is administered to said patient.

12. The method of any one of claim 5 to 11, wherein a plurality of agents are administered which act additively or synergistically to reduce symptoms.

13. The method of claim 12, wherein the plurality of agents comprises:(i) a JAK inhibitor in combination with an IL-6 receptor antagonist;(ii) a JAK inhibitor in combination with an ILN-y neutralizing antibody;(iii) an IL-6 antagonist in combination with a corticosteroid; or(iv) a IAK inhibitor in combination with broad- spectrum antibiotics and low-molecular-weight heparin.

14. A kit comprising of (i) agents which bind said MODS severity signature proteins for quantifying expression levels thereof, (ii) a container, (iii) a label, and (iv) instructions for use.

15. A method for identifying a patient at increased risk for severe multiorgan dysfunction syndrome (MODS) comprising; identifying elevated expression levels of at least two MODS severity signature proteins at MODS onset selected from SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SM0C2, DCTPP1, LDLR, VCAN, SIGLEC10, IL- 15, SEMA4C, GDF2 and decreased expression levels of one, two, or three of SUSD1, MPIG6B, EGF relative to levels observed in healthy pediatric participants, said identified patients being at increased risk for pathologic dysregulation of the JAK / STAT signaling pathway and hypercytokinemia at the onset of MODS, resulting in increased disease severity and increased risk of death.

16. The method of claim 15, wherein said patient is a pediatric patient.

17. The method of claim 15, wherein protein expression levels of said severity signature proteins are determined in a patient sample selected from whole blood, peripheral blood mononuclear cells, plasma, and serum.

18. A method for identifying agents which modulate severe multiorgan dysfunction syndrome (MODS), comprising, a) contacting a first plasma sample obtained from a MODS patient having severe disease and a second plasma sample obtained from healthy patient with said agent, b) determining expression levels of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SM0C2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2, SUSD1, MPIG6B, and EGF in the samples of step a) and c) identifying an agent which reduces levels of at least five of SERPINA12, SDC1, IL-6, HAVCR1, REN, NOS3, AMN, WFDC2, NAMPT, SIOOP, CTSH, LPO, VWF, SM0C2, DCTPP1, LDLR, VCAN, SIGLEC10, IL-15, SEMA4C, GDF2 and elevate at least one of SUSD1, MPIG6B, and EGF as being effective to reduce MODS symptoms.

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