Unified transcriptomic signature associated with infection severity, their risk factors, and treatments

Abstract

This disclosure provides a method for determining subject's risk of a developing a morbid health outcome selected from response to steroid treatment, response to antibody treatment of asthma, bacterial or fungal infection, diabetes, burn, and obesity. In some cases, the method may involve analysis of gene expression in cells obtained from the subject. A kit for performing the method is also provided.

Description

Atty. Dckt.: STAN-2229WOUNIFIED TRANSCRIPTOMIC SIGNATURE ASSOCIATED WITH INFECTION SEVERITY, THEIR RISK FACTORS, AND TREATMENTSCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims benefit of provisional application 63 / 704,470, filed October 7, 2024, which application is hereby incorporated by reference in its entirety.GOVERNMENT SUPPORT

[0002] This invention was made with Government support under contract AH 67903 awarded by the National Institutes of Health. The Government has certain rights in the invention.BACKGROUND OF THE INVENTION

[0003] Older age, being male, obesity, smoking and co-morbidities such as asthma and diabetes mellitus have been identified as risk factors for severe outcomes in infectious diseases. More than 70% of US adults are estimated to have at least one risk factor for severe infection and over 40% have two or more risk factors. Notably, the risk for severe outcomes from infection increases with the number of risk factors, suggesting they are additive. Despite the overwhelming epidemiological evidence, the underlying mechanisms for these risk factors have been largely investigated independent of each other. Importantly, how these risk factors affect the underlying immune responses prior to infection, and whether they affect the same immunological pathways that increase the risk of severe outcomes, including mortality, remains unknown.SUMMARY OF THE INVENTION

[0004] Compositions and methods are provided for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity. Kits for performing the methods are also provided.

[0005] In one aspect, an in vitro method for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity is provided, the method comprising: obtaining a biological sample from the subject; isolating cells from the biological sample; extracting RNA transcripts from the cells; conducting transcriptomic analyses comprising measuring amounts of RNA transcripts encoded by at least two of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9,Atty. Dckt.: STAN-2229WOEX0C2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, 0LR1 , BUB3, SCAND1 , ITGB7, D0K3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA to obtain gene expression data; and based on the gene expression data, providing a report indicating the subject's risk of developing severe symptoms, wherein: a. increased expression of BCL6, NQO2, 0RM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L11 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and b. decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 , RAD23B, ARHGAP45, MAP3K4, USP11 , SMYD2, SSR2, IL7R, FBLN5, TRAF5, TRAF31 P3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk that the subject will develop severe symptoms.

[0006] In certain embodiments, the steroid treatment is for viral infection or burn.

[0007] In certain embodiments, the steroid is hydrocortisone.

[0008] In certain embodiments, the bacterial infection comprises sepsis, urinary tract infection.

[0009] In certain embodiments, the bacterial infection further comprises surgical site infection, bacteremia, abdominal or pelvic infection, pneumonia, nosocomial pneumonia, osteomyelitis, cellulitis, trauma infection, meningitis, peritonitis, cholangitis, soft tissue infection, purulent myositis, and septic arthritis.

[0010] In certain embodiments, the biological sample is blood.

[0011] In certain embodiments, the cells are peripheral blood mononuclear cells.

[0012] In certain embodiments, the transcriptomic analysis comprises a quantitative polymerase chain (qPCR) assay or an RNA-seq assay.

[0013] In certain embodiments, wherein step (b) comprises calculating a severe or mild (SoM) score based on the amounts of the RNA transcripts, he score indicates the probability that the subject will have a morbid health outcome.

[0014] In certain embodiments, the RNA transcripts analyzed in step (a) comprise at least one gene from each of the following modules: module 1 : NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; module 2: BCL2L1 1 , BCAT1 , BTBD7, CEP55,Atty. Dckt.: STAN-2229WOHMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; module 3: MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123 and CASP7; and module 4: DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1 .

[0015] In another aspect, a method for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity and treating the subject is provided, the method comprising: obtaining a biological sample from the subject; isolating cells from the biological sample; extracting RNA transcripts from the cells; conducting transcriptomic analyses comprising measuring amounts of RNA transcripts encoded by at least two of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP1 1 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA to obtain gene expression data; based on the gene expression data, providing a report indicating the subject's risk of developing severe symptoms, wherein: a. increased expression of BCL6, NQO2, ORM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1-0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L11 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and b. decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 , RAD23B, ARHGAP45, MAP3K4, USP1 1 , SMYD2, SSR2, IL7R, FBLN5, TRAF5, TRAF31 P3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk that the subject will develop severe symptoms; and treating the subject to reduce the subject’s risk of developing the severe symptoms.

[0016] In certain embodiments, treating the subject comprises treating the subject with a treatment not predicted to increase the subject’s risk of developing the severe symptoms.

[0017] In certain embodiments, treating the subject comprises administering an antibiotic, an anti-fungal agent, an asthma treatment, a diabetes treatment, a burn treatment, or an obesity treatment to the subject.Atty. Dckt.: STAN-2229WO

[0018] In certain embodiments, treating the subject comprises not administering the steroid treatment to the subject determined to be at risk of developing the severe symptoms from the steroid treatment, or switching to treatment with a different steroid.

[0019] In certain embodiments, treating the subject comprises not administering the antibody treatment for the asthma to the subject determined to be at risk of developing the severe symptoms from the antibody treatment, or switching to treatment with a different antibody.

[0020] In another aspect, a kit is provided for assessing a subject’s risk of developing morbid health outcomes selected from response to steroid treatment, response to antibody treatment of asthma, bacterial or fungal infection, diabetes, burn and obesity, comprising reagents for measuring the amount of RNA transcripts encoded by at least 2, at least 3, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40 or at least 50 or all of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP1 1 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA.

[0021] In certain embodiments, the reagents comprise, for each RNA transcript, a sequencespecific oligonucleotide that hybridizes to the transcript.

[0022] In certain embodiments, the sequence-specific oligonucleotide is biotinylated and / or labeled with an optically-detectable moiety.

[0023] In certain embodiments, the reagents comprise, for each RNA transcript, a pair of PCR primers that amplify a sequence from the RNA transcript, or cDNA made from the same.

[0024] In certain embodiments, the reagents comprise an array of oligonucleotide probes, wherein the array comprises, for each RNA transcript, at least one sequence-specific oligonucleotide that hybridizes to the transcript.

[0025] In certain embodiments, the kit is an RT-PCR kit, a competitive RT-PCR kit, a real-time RT-PCR kit, a DNA chip kit, a microarray kit, a SAGE (Serial Analysis of Gene Expression) kit, or a gene chip kit.

[0026] In another aspect, an RNA transcript of a gene selected from NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4, TYK2, BCL2L1 1 , BCAT1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM8, DEFA4, LCN2, CTSG, AZU1 , MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123, CASP7, DOK2, HLA-DPB1 ,Atty. Dckt.: STAN-2229WOBUB3, SMYD2, SIDT1 , EX0C2, TRIB2, and KLRB1 for use as a biomarker for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity is provided.

[0027] In another aspect, a composition is provided, the composition comprising RNA transcripts encoded by at least two genes selected from HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 - 0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA for use in determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity.

[0028] In certain embodiments, the composition comprises: one or more RNA transcripts from at least one gene selected from NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; one or more RNA transcripts from at least one gene selected from BCL2L1 1 , BCAT1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; one or more RNA transcripts from at least one gene selected from MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123 and CASP7; and one or more RNA transcripts from at least one gene selected from DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1 .BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The invention is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures:

[0030] FIGS. 1A-1 G: Immune dysregulation quantified by the SoM score is conserved in bacterial infections and is associated with severity and differential response to hydrocortisone treatment. (FIG. 1 A) Criteria for assigning bacterial infection severity categories to samples and the number of samples in each severity category. Mild and moderate categories were considered non-severe, and serious, critical, and fatal categories were considered severe.Atty. Dckt.: STAN-2229WO(FIG. 1 B) Variation of the z-scaled SoM score with severity of bacterial infection. R denotes Pearson correlation, and p its corresponding p value. (FIG. 1 C) Receiver operating characteristic curve for discriminating severe infection from non-severe bacterial infection using the SoM score. AUC denotes area under curve. 95% confidence interval estimates for AUG are shown alongside the AUC. (FIGS. 1 D and 1 E) Bar plot of mortality comparing placebo and hydrocortisone arms for subjects with baseline protective scores lower than and higher than median in the (FIG. 1 D) Burn and (FIG. 1 E) Vanish cohorts, p values were calculated using the Fisher test. (FIG. 1 F) Interaction plot showing the marginal dependence of mortality on the protective score for the placebo and hydrocortisone arms in the Vanish cohort. Logistic model p value for the interaction term between protective score and steroid arm is shown. (FIG. 1G) Forest plot depicting log OR for mortality among individuals with protective score greater than median in the Burn and VANISH cohorts. Summary effect size and p value were computed using a mixed effects model. Whiskers and width of the diamond indicate 95% Cl of effect size in the corresponding cohort and the summary effect size, respectively. See also FIG. 6 and Table S1 ..

[0031] FIGS. 2A-2E: Gene modules in the SoM signatures are preferentially expressed in distinct immune cell types. UMAP visualization of single cell whole blood transcriptome data colored by (FIG. 2A) dataset, (FIG. 2B) phenotype, (FIG. 2C) cell type, and (FIG. 2D) SoM, detrimental, protective, and modules 1 -4 scores. Each point is a cell and UMAP was projected based on the gene expression for each cell. (FIG. 2E) Heatmap of average score value for the 7 scores in 20 cell types. See also Figures S2-S3.

[0032] FIGS. 3A-3G: Immune dysregulation quantified by the SoM score is associated with severity and response to monoclonal antibody treatments in individuals with asthma. (FIGS. 3A-3C) Variation of the z-scaled (FIG. 3A) SoM, (FIG. 3B) detrimental, and (FIG. 3C) protective scores with asthma severity. P-values were calculated using the Jonckheere- Terpstra trend test. (FIGS. 3D-3F) Comparison of the z-scaled (FIG. 3D) SoM, (FIG. 3E) detrimental, and (FIG. 3F) protective scores at asthma exacerbation and after convalescence. P-values were calculated using the paired Wilcox test. (FIG. 3G) Forest plots of dataset-wise and summary effect sizes for the (left) SoM, (middle) detrimental, and (right) protective scores in non-responders compared to responders for monoclonal antibody treatment for asthma. P- values for the summary effect sizes are shown. NR denotes non-responders and R responders. See also FIG. 9.

[0033] FIGS. 4A-4F: Immune dysregulation quantified by the SoM score is higher in subjects with risk factors for severe infections. (FIG. 4A) Summary of case-control meta-analyses for risk factors of infection. Heatmap depicts summary effect size for a given score and risk factor. * denotes p<0.05, where p is the p-value for the summary effect size from the meta-analysis.Atty. Dckt.: STAN-2229WO(FIG. 4B) Summary of linear regression models for risk factors of infection in the Framingham cohort. Heatmap depicts the regression coefficient for a given score and risk factor. * denotes p<0.05, where p is the p-value for the regression coefficients. (FIG. 4C) Variation of the z- scaled SoM, detrimental, and protective scores with number of risk factors. P-values were computed using the Wilcox test. (FIG. 4D) Comparison of the z-scaled module 2 score at baseline and after 12 weeks for the control and calorie restriction groups in a randomized diet restriction study. P-values were computed using the paired Wilcox test. (FIGS. 4E-4F) Variation in the SoM, detrimental, and protective scores adjusted for age, sex, BMI and (FIG. 4E) disease or (FIG. 4F) smoking status among (FIG. 4E) current, recent (quit within the last 5 years), and past (quit more than 5 years ago) smokers and (FIG. 4F) subjects with controlled (A1C<7), moderately uncontrolled (7<A1 C<10), and highly uncontrolled (A1C>10) diabetes. P-values were computed using the Wilcox test. See also FIG. 10 and Table S2.

[0034] FIGS. 5A-5C: Immune dysregulation quantified by the SoM score is associated with all-cause mortality. (FIGS. 5A-5B) Summary of Cox proportional-hazards models in the Framingham cohort evaluating the association with 7-year mortality for (FIG. 5A) the SoM score, age, sex, BMI, disease and smoking statuses, and (FIG. 5B) the SoM score adjusted for age, sex, BMI, disease and smoking statuses. Hazard ratio, its 95% confidence estimates, and the corresponding p-values are shown. (FIG. 5C) Kaplan-Meier survival curve for the adjusted SoM score divided into high and low groups based on median value. Cox model hazard ratio and p-value are shown. See also FIG. 11 .

[0035] FIGS. 6A-6L: Immune dysregulation as quantified by the detrimental and protective scores in the SoM signature is associated with severity of bacterial infections and differential response to hydrocortisone treatment, related to FIG. 1. (FIGS. 6A, 6B) Variation of the z- scaled (FIG. 6A) detrimental, and (FIG. 6B) protective scores with severity of bacterial infection. R denotes Pearson correlation, and p its corresponding p-value. (FIG. 6C) Forest plot with effect size for the SoM score between non-survivors (NS) and survivors (S) in the Burn and VANISH cohorts. Summary effect size and p-value were computed using a random effects model. (D-l) Box plot of z-scaled (FIG. 6D) protective, (FIG. 6E) detrimental, and modules (FIG. 6F) 1 , (FIG. 6G) 2, (FIG. 6H) 3 and (FIG. 6I) 4 scores comparing placebo and hydrocortisone arms on days 0 and 7 in the Burn cohort. P-values were calculated using the Wilcox test. (FIG. 6J) Bar plot of mortality comparing the randomized placebo and hydrocortisone arms for subjects with baseline protective scores lower than and higher than median in the Vanish cohort. P-values were calculated using the Fisher test. (FIG. 6K) Bar plot of mortality comparing the placebo and hydrocortisone arms for subjects with baseline detrimental scores lower than and higher than median in the Vanish cohort. P-values were calculated using the Fisher test. (FIG. 6L) Interaction plot showing the marginal dependenceAtty. Dckt.: STAN-2229WO of mortality on the detrimental score for the placebo and hydrocortisone arms in the Vanish cohort. Logistic model p-value for the interaction term between detrimental score and steroid arm is shown.

[0036] FIGS. 7A-7B: Expression of the SoM modules in immune cells, related to FIG. 2. UMAP visualization of single cell transcriptome data split by phenotype for COVID-19 phenotypes (FIG. 7A) and bacterial sepsis, other infection, and healthy phenotypes (FIG. 7B). SoM, detrimental, protective, and modules 1 -4 scores are further split within each group. Each point is a cell and UMAP was projected based on the gene expression for each cell.

[0037] FIGS. 8A-8D: Comparison of expression of the SoM modules in whole blood and PBMCs, related to FIG. 2. (FIG. 8A) UMAP visualization of single cell transcriptome data split by tissue type (in Schrepping2020 dataset only). SoM, detrimental, protective, and modules 1 -4 scores are further split within each group. Each point is a cell and UMAP was projected based on the gene expression for each cell. (FIGS. 8B-8C) Box plot of (FIG. 8B) median SoM score and (FIG. 8C) cell proportion in immature and mature neutrophils in each phenotype. R denotes Pearson correlation, and p its corresponding p-value. (FIG. 8D) UMAP visualization of single cell whole blood transcriptome data split by phenotype (for individuals with severe or non-severe Covid-19 and healthy subjects only). Detrimental and module 1 scores are further split within each group. Each point is a cell and UMAP was projected based on the gene expression for each cell.

[0038] FIGS. 9A-9C: Immune dysregulation quantified by the detrimental and protective modules in the SoM signature is associated with severity of asthma and response to monoclonal antibody treatments, related to FIG. 3. (FIG. 9A) Variation of the z-scaled module 1 , 2, 3, and 4 scores with asthma severity. P-values were calculated using the Jonckheere- Terpstra trend test. (FIG. 9B) Scatter plot of detrimental and protective scores in subjects with moderate and severe asthma. Best fit linear line and correlation values depicted for each phenotype. R denotes Pearson correlation, and p its corresponding p-value. (FIG. 9C) Comparison of the z-scaled module 1 , 2, 3, and 4 scores at asthma exacerbation and after convalescence. P-values were calculated using the paired Wilcox test.

[0039] FIGS. 10A-10P: Detrimental and protective scores with the SoM signature are associated with the risk factors for severe infection and modified by changes in lifestyle, related to FIG. 4. (A-G) Scatter plot of (FIG. 10A) SoM, (FIG. 10B) detrimental, (FIG. 10C) protective, and modules (FIG. 10D) 1 , (FIG. 10E) 2, (FIG. 10F) 3 and (FIG. 10G) 4 scores vs. age, colored by sex. locally weighted scatterplot smoothing (LOESS) curves for each of the sexes are plotted. 95% confidence estimate for the LOESS curves are shown in grey. (FIG. 10H) Scatter plot of detrimental and protective scores facetted by the number of risk factors. Best fit linear line, its 95% confidence estimate, and correlation values depicted for each facet.Atty. Dckt.: STAN-2229WOR denotes Pearson correlation, and p its corresponding p-value. (FIGS. 101, 10J, 10M) Comparison of the z-scaled (FIG. 101) SoM, (FIG. 10J) module 1 , and (FIG. 10M) immune health metric (IHM) scores at baseline and after 12 weeks for the control and calorie restriction groups in a randomized diet restriction study. P-values were computed using the paired Wilcox test. (FIG. 10K) Summary of differential expression analysis of the genes in modules 1 and 2 due to diet restriction, smoking cessation, and diabetes treatment. Heatmap color depicts the effect size. * denotes FDR<0.05. (FIG. 10L) Scatter plot between the IHM and SoM scores for each subject in the Framingham cohort. R denotes Pearson correlation, and p its corresponding p-value. (FIG. 10N-10O) Variation in the IHM score adjusted for age, sex, BMI and (FIG. 10N) disease or (FIG. 100) smoking status among (FIG. 10N) current, recent (quit within the last 5 years), and past (quit more than 5 years ago) smokers and (FIG. 100) subjects with controlled (A1 C<7), moderately uncontrolled (7<A1 C<10), and highly uncontrolled (A1 C>10) diabetes. P-values were computed using the Wilcox test. (FIG. 10P) Summary of linear regression models for risk factors in the Arivale proteomic cohort. Heatmap color depicts the coefficient of regression and the circle size the corresponding p-value for a given protein abundance / module score and risk factor. * denotes p<0.05.

[0040] FIGS. 11A-11 D: Immune dysregulation quantified by the SoM score is significantly associated with all-cause mortality, related to FIG. 5. Summary of Cox proportional-hazards models in the Framingham cohort evaluating the association with (FIGS. 11 A, 11 C, 11 D) 10- year mortality and (FIG. 11 B) 7-year mortality among subjects older than 50 years for the (FIGS. 11A-11 B) SoM score, age, sex, BMI, disease and smoking statuses, and the SoM score adjusted for age, sex, BMI, disease and smoking statuses, (FIG. 11C) detrimental and protective scores, age, sex, BMI, disease and smoking statuses, and (FIG. 11 D) SoM score, C-reactive protein abundance, age, sex, disease and smoking statuses, BMI, and IHM score. Hazard ratio, its 95% confidence estimates, and the corresponding p-values are shown.DETAILED DESCRIPTION OF THE INVENTION

[0041] The present disclosure relates to compositions, methods, and kits for determining the risk of developing morbid health outcomes selected from response to steroid treatment, response to antibody treatment of asthma, bacterial or fungal infection, diabetes, burn, and obesity.

[0042] Before the present compositions, methods, and kits are described, it is to be understood that this invention is not limited to particular method or composition described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.Atty. Dckt.: STAN-2229WO

[0043] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0044] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. It is understood that the present disclosure supersedes any disclosure of an incorporated publication to the extent there is a contradiction.

[0045] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0046] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a cell" includes a plurality of such cells and reference to "the RNA transcript" includes reference to one or more RNA transcripts and equivalents thereof, known to those skilled in the art, and so forth.

[0047] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.DefinitionsAtty. Dckt.: STAN-2229WO

[0048] Biomarkers. The term “biomarker” as used herein refers to a compound, such as a mRNA, a pre-mRNA, a nascent RNA transcript, a mature RNA transcript, a protein, a polypeptide, a peptide, a metabolite, or a metabolic byproduct which is differentially expressed or present at different concentrations, levels or frequencies in one sample compared to another, such as a biological sample from a patient who is at risk of developing morbid health outcomes from a response to steroid treatment, a response to antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity compared to a biological sample from healthy control subjects (i.e. , subjects not having a high risk of developing morbid health outcomes). Biomarkers include, but are not limited to, HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 - 0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA.

[0049] In some embodiments, the concentration or level of a biomarker is determined before and after the administration of a treatment to a patient. The treatment may comprise, for example, without limitation, administering a steroid for treatment of an inflammatory or autoimmune condition, administering an antibody for treatment of asthma, administering an antibiotic for treatment of a bacterial infection, or administering an antifungal agent for treatment of a fungal infection. The degree of change in the concentration or level of a biomarker, or lack thereof, is interpreted as an indication of whether the treatment has the desired effect (e.g., reducing the risk of developing a morbid health outcome, reducing the risk of developing severe symptoms). In other words, the concentration or level of a biomarker is determined before and after the administration of the treatment to an individual, and the degree of change, or lack thereof, in the level is interpreted as an indication of whether the individual is “responsive” to the treatment and reducing the risk of developing severe symptoms or a morbid health outcome.

[0050] A "reference level" or "reference value" of a biomarker means a level of the biomarker that is indicative of a particular disease state, phenotype, or predisposition to developing a particular disease state or phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or predisposition to developing a particular disease state or phenotype, or lack thereof. A "positive" reference level of a biomarker means a level that is indicative of aAtty. Dckt.: STAN-2229WO particular disease state or phenotype. A "negative" reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype. A "reference level" of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and / or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and / or a median amount or concentration of the biomarker; and, in addition, "reference levels" of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other. Appropriate positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired biomarkers in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched or gender- matched so that comparisons may be made between biomarker levels in samples from subjects of a certain age or gender and reference levels for a particular disease state, phenotype, or lack thereof in a certain age or gender group). Such reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples (e.g., RT-PCR, RNA sequencing (RNA-seq), global run-on sequencing (GRO-seq), PCR, microarray analysis, immunoassays (e.g., ELISA), mass spectrometry (e.g., LC-MS, GC-MS), tandem mass spectrometry, NMR, biochemical or enzymatic assays, etc.), where the levels of biomarkers may differ based on the specific technique that is used.

[0051] The terms "quantity", "amount", and "level" are used interchangeably herein and may refer to an absolute quantification of a molecule or an analyte in a sample, or to a relative quantification of a molecule or analyte in a sample, i.e., relative to another value such as relative to a reference value as taught herein, or to a range of values for the biomarker. These values or ranges can be obtained from a single patient or from a group of patients.

[0052] Biological sample. The term “sample” with respect to an individual encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived or isolated therefrom and the progeny thereof. The definition also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations, such as blood cells, lymphocytes, etc. The definition also includes samples that have been enriched for particular types of molecules, e.g., nucleic acids, polypeptides, efc.The term “biological sample” encompasses a clinical sample. The types of “biological samples” include, but are not limited to: tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs,Atty. Dckt.: STAN-2229WO bone marrow, blood, plasma, serum, fine needle aspirate, lymph node aspirate, cystic aspirate, a paracentesis sample, a thoracentesis sample, and the like. A “biological sample” can include cells (e.g., target cells, normal cells, blood cells, tissue cells etc.) can be suspected of comprising such cells, or can be devoid of cells. A biological sample can include biological fluids derived from cells (e.g., a diseased cell, an infected cell, etc.), e.g., a sample comprising polynucleotides and / or polypeptides that is obtained from such cells (e.g., a cell lysate or other cell extract comprising polynucleotides and / or polypeptides). A biological sample comprising an inflicted cell from a patient can also include non-inflicted cells. In some embodiments the biological sample is blood or a derivative thereof, e.g. plasma, serum, etc.

[0053] Obtaining and assaying a sample. The term “assaying” is used herein to include the physical steps of manipulating a biological sample to generate data related to the sample. As will be readily understood by one of ordinary skill in the art, a biological sample must be “obtained” prior to assaying the sample. Thus, the term “assaying” implies that the sample has been obtained. The terms “obtained” or “obtaining” as used herein encompass the act of receiving an extracted or isolated biological sample. For example, a testing facility can “obtain” a biological sample in the mail (or via delivery, etc.) prior to assaying the sample. In some such cases, the biological sample was “extracted” or “isolated” from an individual by another party prior to mailing (i.e., delivery, transfer, etc.), and then “obtained” by the testing facility upon arrival of the sample. Thus, a testing facility can obtain the sample and then assay the sample, thereby producing data related to the sample.

[0054] The terms “obtained” or “obtaining” as used herein can also include the physical extraction or isolation of a biological sample from a subject. Accordingly, a biological sample can be isolated from a subject (and thus “obtained”) by the same person or same entity that subsequently assays the sample. When a biological sample is “extracted” or “isolated” from a first party or entity and then transferred (e.g., delivered, mailed, etc.) to a second party, the sample was “obtained” by the first party (and also “isolated” by the first party), and then subsequently “obtained” (but not “isolated”) by the second party. Accordingly, in some embodiments, the step of obtaining does not comprise the step of isolating a biological sample.

[0055] In some embodiments, the step of obtaining comprises the step of isolating a biological sample (e.g., a pre-treatment biological sample, a post-treatment biological sample, etc.). Methods and protocols for isolating various biological samples (e.g., a blood sample, a serum sample, a plasma sample, a biopsy sample, an aspirate, etc.) will be known to one of ordinary skill in the art and any convenient method may be used to isolate a biological sample.

[0056] It will be understood by one of ordinary skill in the art that in some cases, it is convenient to wait until multiple samples (e.g., a pre-treatment biological sample and a postAtty. Dckt.: STAN-2229WO treatment biological sample) have been obtained prior to assaying the samples. Accordingly, in some cases an isolated biological sample (e.g., a pre-treatment biological sample, a posttreatment biological sample, etc.) is stored until all appropriate samples have been obtained. One of ordinary skill in the art will understand how to appropriately store a variety of different types of biological samples and any convenient method of storage may be used (e.g., refrigeration) that is appropriate for the particular biological sample. In some embodiments, a pre-treatment biological sample is assayed prior to obtaining a post-treatment biological sample. In some cases, a pre-treatment biological sample and a post-treatment biological sample are assayed in parallel. In some cases, multiple different post-treatment biological samples and / or a pre-treatment biological sample are assayed in parallel. In some cases, biological samples are processed immediately or as soon as possible after they are obtained.

[0057] In subject methods, the concentration (i.e., “level”), or expression level of a gene product, which may be an RNA, a protein, etc., in a biological sample is measured (i.e., “determined”). By “expression level” (or “level”) it is meant the level of a gene product (e.g. the absolute and / or normalized value determined for the RNA expression level or for the expression level of the encoded polypeptide, or the concentration of the protein in a biological sample). The term “gene product” or “expression product” are used herein to refer to the RNA transcription products (RNA transcripts, e.g., nascent RNA, mRNA, pre-mRNA, unspliced RNA, splice variant mRNA, and / or fragmented RNA) of the gene, including mRNA, and the polypeptide translation products of such RNA transcripts. A gene product can be, for example, a nascent RNA, an unspliced RNA, an mRNA, a pre-mRNA, a splice variant mRNA, a fragmented RNA, a polypeptide, a post-translationally modified polypeptide, a splice variant polypeptide, etc. Measuring a level of transcription of a gene may comprise measuring the absolute level and / or normalized value determined for the level of a nascent RNA, an unspliced RNA, an mRNA, a pre-mRNA, a splice variant mRNA, or a fragmented RNA in a biological sample.

[0058] The terms “determining”, “measuring”, “evaluating”, “assessing,” “assaying,” and “analyzing” are used interchangeably herein to refer to any form of measurement, and include determining if an element is present or not. These terms include both quantitative and / or qualitative determinations. Assaying may be relative or absolute. For example, “assaying” can be determining whether the expression level or transcription level is less than or “greater than or equal to” a particular threshold, (the threshold can be pre-determined or can be determined by assaying a control sample). On the other hand, “assaying” to determine the expression level or transcription level can mean determining a quantitative value (using any convenient metric) that represents the level of expression (i.e., expression level, e.g., the amount of protein and / or RNA, e.g., nascent RNA or mature mRNA) of a particular biomarkerAtty. Dckt.: STAN-2229WO gene. The level of expression can be expressed in arbitrary units associated with a particular assay (e.g., fluorescence units, e.g., mean fluorescence intensity (MFI)), or can be expressed as an absolute value with defined units (e.g., number of nascent RNA or mature mRNA transcripts, number of RNA transcripts, concentration of RNA, etc.). Additionally, the level of expression of a biomarker gene can be compared to the expression level of one or more additional genes (e.g., nucleic acids and / or their encoded proteins) to derive a normalized value that represents a normalized expression level. The specific metric (or units) chosen is not crucial as long as the same units are used (or conversion to the same units is performed) when evaluating multiple biological samples from the same individual (e.g., biological samples taken at different points in time from the same individual). This is because the units cancel when calculating a fold-change (i.e., determining a ratio) in the expression level from one biological sample to the next (e.g., biological samples taken at different points in time from the same individual).

[0059] For measuring RNA levels, the amount or level of an RNA biomarker in the sample is determined. In some instances, the level of one or more additional RNAs may also be measured, and the level of an RNA biomarker compared to the level of the one or more additional RNAs to provide a normalized value for the RNA biomarker level. Any convenient protocol for evaluating RNA levels may be employed wherein the level of one or more RNAs in the assayed sample is determined.

[0060] A number of exemplary methods for measuring RNA (e.g., nascent RNA or mRNA) levels in a sample are known by one of ordinary skill in the art, and any convenient method can be used. Exemplary methods include, but are not limited to: GRO-seq to measure levels of nascent RNA transcripts (see, e.g., Gardini et al. (2017) Methods Mol. Biol. 1468:11 1 - 120, Tzerpos et al. (2021 ) Methods Mol. Biol. 2351 :25-39.Jordan-Pla et al. (2019) Methods 159-160:177-182, Cardiello et al. (2020) Transcription 11 (1 ):3-18; herein incorporated by reference in their entireties), RNA-seq to measure levels of RNA transcripts (see, e.g., Hrdlickova et al. (2017) Wiley Interdiscip Rev RNA 8(1 ):10.1002 / wrna.1364, Wang et al. (2009) Nat. Rev. Genet. 10(1 ):57-63; Withanage et al. (2022) Methods Mol. Biol. 2418:405- 424; Owens et al. (2019) Cold Spring Harb Protoc. 2019(6); herein incorporated by reference in their entireties), hybridization-based methods such as Northern blotting, array hybridization (e.g., microarray); in situ hybridization; and in situ hybridization followed by FACS, and the like (see, e.g., Parker & Barnes (1999) Methods in Molecular Biology 106:247-283); RNAse protection assays (Hod (1992) Biotechniques 13:852-854); PCR-based methods such as reverse transcription PCR (RT-PCR), quantitative RT-PCR (qRT-PCR), real-time RT-PCR, and the like (see, e.g., Weis et al. (1992) Trends in Genetics 8:263-264); and the like. See also, Robert E. Farrell Jr, RNA Methodologies: A Laboratory Guide for Isolation andAtty. Dckt.: STAN-2229WOCharacterization (6thEdition, Academic Press, November 22, 2022; herein incorporated by reference in its entirety.

[0061] In some embodiments, the biological sample can be assayed directly. In some embodiments, nucleic acids of the biological sample are amplified (e.g., by PCR) prior to assaying. For example, techniques such as PCR (Polymerase Chain Reaction), RT-PCR (reverse transcriptase PCR), qRT-PCR (quantitative RT-PCR, real time RT-PCR), etc. can be used prior to the hybridization methods and / or the sequencing methods discussed above.Additional terms.

[0062] The term "about," particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0063] The terms "individual”, “subject”, and “patient”, are used interchangeably herein and refer to any mammalian subject for whom diagnosis, treatment, or therapy is desired, particularly humans. Mammals include human and non-human mammals such as non-human primates, including chimpanzees and other apes and monkey species; laboratory animals such as mice, rats, rabbits, hamsters, guinea pigs, and chinchillas; domestic animals such as dogs and cats; farm animals such as sheep, goats, pigs, horses and cows. In some cases, the methods of the invention find use in experimental animals, in veterinary application, and in the development of animal models for disease, including, but not limited to, rodents including mice, rats, and hamsters; primates, and transgenic animals.

[0064] "Diagnosis" as used herein generally includes determination as to whether a subject is likely affected by a given disease, disorder or dysfunction. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, i.e., a biomarker, the presence, absence, or amount of which is indicative of the presence or absence of the disease, disorder or dysfunction.

[0065] "Prognosis" as used herein generally refers to a prediction of the probable course and outcome of a clinical condition or disease. A prognosis of a patient is usually made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease. It is understood that the term "prognosis" does not necessarily refer to the ability to predict the course or outcome of a condition with 100% accuracy. Instead, the skilled artisan will understand that the term "prognosis" refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a patient exhibiting a given condition, when compared to those individuals not exhibiting the condition.

[0066] The terms "treatment", "treating", "treat" and the like are used herein to generally refer to obtaining a desired pharmacologic and / or physiologic effect. The effect can be prophylacticAtty. Dckt.: STAN-2229WO in terms of completely or partially preventing a disease or symptom(s) thereof and / or may be therapeutic in terms of a partial or complete stabilization or cure for a disease and / or adverse effect attributable to the disease. The term “treatment" encompasses any treatment of a disease in a mammal, particularly a human, and includes: (a) preventing the disease and / or symptom(s) from occurring in a subject who may be predisposed to the disease or symptom but has not yet been diagnosed as having it; (b) inhibiting the disease and / or symptom(s), i.e., arresting their development; or (c) relieving the disease symptom(s), i.e., causing regression of the disease and / or symptom(s). Those in need of treatment include those already afflicted as well as those in which prevention is desired (e.g., those at risk of developing severe symptoms).

[0067] A therapeutic treatment is one in which the subject is afflicted prior to administration and a prophylactic treatment is one in which the subject is not afflicted prior to administration. In some embodiments, the subject has an increased likelihood of becoming afflicted or is suspected of being afflicted prior to treatment. In some embodiments, the subject is suspected of having an increased likelihood of becoming afflicted.

[0068] A "therapeutically effective dose" or “therapeutic dose” is an amount sufficient to effect desired clinical results (i.e., achieve therapeutic efficacy). A therapeutically effective dose can be administered in one or more administrations.

[0069] "Dosage unit" refers to physically discrete units suited as unitary dosages for the particular individual to be treated. Each unit can contain a predetermined quantity of active compound(s) calculated to produce the desired therapeutic effect(s) in association with the required pharmaceutical carrier. The specification for the dosage unit forms can be dictated by (a) the unique characteristics of the active compound(s) and the particular therapeutic effect(s) to be achieved, and (b) the limitations inherent in the art of compounding such active compound(s).

[0070] The term "survival" as used herein means the time from the start of treatment to the time of death.

[0071] “Isolated” refers to an entity of interest that is in an environment different from that in which it may naturally occur. “Isolated” is meant to include entities that are within samples that are substantially enriched for the entity of interest and / or in which the entity of interest is partially or substantially purified.

[0072] "Substantially purified" generally refers to isolation of a component such as a substance (compound, drug, inhibitor, metabolite, nucleic acid, polynucleotide, protein, or polypeptide) such that the substance comprises the majority percent of the sample in which it resides. Typically in a sample, a substantially purified component comprises 50%, preferably 80%-85%, more preferably 90-95% of the sample. Techniques for purifying polynucleotidesAtty. Dckt.: STAN-2229WO and polypeptides of interest are well-known in the art and include, for example, ion-exchange chromatography, affinity chromatography, gel filtration, and sedimentation according to density.

[0073] The terms "pharmaceutically acceptable", "physiologically tolerable" and grammatical variations thereof, as they refer to compositions, carriers, diluents and reagents, are used interchangeably and represent that the materials are capable of administration to or upon a human without the production of undesirable physiological effects to a degree that would prohibit administration of the composition.

[0074] "Pharmaceutically acceptable excipient” means an excipient that is useful in preparing a pharmaceutical composition that is generally safe, non-toxic, and desirable, and includes excipients that are acceptable for veterinary use as well as for human pharmaceutical use. Such excipients can be solid, liquid, semisolid, or, in the case of an aerosol composition, gaseous.

[0075] "Pharmaceutically acceptable salts and esters" means salts and esters that are pharmaceutically acceptable and have the desired pharmacological properties. Such salts include salts that can be formed where acidic protons present in the compounds are capable of reacting with inorganic or organic bases. Suitable inorganic salts include those formed with the alkali metals, e.g. sodium and potassium, magnesium, calcium, and aluminum. Suitable organic salts include those formed with organic bases such as the amine bases, e.g., ethanolamine, diethanolamine, triethanolamine, tromethamine, N-methylglucamine, and the like. Such salts also include acid addition salts formed with inorganic acids (e.g., hydrochloric and hydrobromic acids) and organic acids (e.g., acetic acid, citric acid, maleic acid, and the alkane- and arene-sulfonic acids such as methanesulfonic acid and benzenesulfonic acid). Pharmaceutically acceptable esters include esters formed from carboxy, sulfonyloxy, and phosphonoxy groups present in the compounds, e.g., Ci6alkyl esters. When there are two acidic groups present, a pharmaceutically acceptable salt or ester can be a mono-acid-mono- salt or ester or a di-salt or ester; and similarly where there are more than two acidic groups present, some or all of such groups can be salified or esterified. Compounds named in this invention can be present in unsalified or unesterified form, or in salified and / or esterified form, and the naming of such compounds is intended to include both the original (unsalified and unesterified) compound and its pharmaceutically acceptable salts and esters. Also, certain compounds named in this invention may be present in more than one stereoisomeric form, and the naming of such compounds is intended to include all single stereoisomers and all mixtures (whether racemic or otherwise) of such stereoisomers.

[0076] The terms “polynucleotide,” “oligonucleotide,” “nucleic acid” and “nucleic acid molecule” are used herein to include a polymeric form of nucleotides of any length, eitherAtty. Dckt.: STAN-2229WO ribonucleotides or deoxyribonucleotides. This term refers only to the primary structure of the molecule. Thus, the term includes triple-, double- and single-stranded DNA, as well as triple-, double- and single-stranded RNA. It also includes modifications, such as by methylation and / or by capping, and unmodified forms of the polynucleotide. More particularly, the terms “polynucleotide,” “oligonucleotide,” “nucleic acid” and “nucleic acid molecule” include polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D- ribose), any other type of polynucleotide which is an N- or C-glycoside of a purine or pyrimidine base, and other polymers containing nonnucleotidic backbones, for example, peptide nucleic acids (PNAs), morpholino nucleic acids, locked nucleic acids (LNAs), glycol nucleic acids (GNAs), threose nucleic acids (TNAs) and hexitol nucleic acids (HNAs). and other synthetic sequence-specific nucleic acid polymers providing that the polymers contain nucleobases in a configuration which allows for base pairing and base stacking, such as is found in DNA and RNA. There is no intended distinction in length between the terms “polynucleotide,” “oligonucleotide,” “nucleic acid” and “nucleic acid molecule,” and these terms will be used interchangeably. Thus, these terms include, for example, 3'-deoxy-2',5'-DNA, oligodeoxyribonucleotide N3' P5' phosphoramidates, 2'-O-alkyl-substituted RNA, double- and single-stranded DNA, as well as double- and single-stranded RNA, DNA:RNA hybrids, and hybrids between PNAs and DNA or RNA, and also include known types of modifications, for example, labels which are known in the art, methylation, “caps,” substitution of one or more of the naturally occurring nucleotides with an analog, internucleotide modifications such as, for example, those with uncharged linkages (e.g., methyl phosphonates, phosphotriesters, phosphoramidates, carbamates, etc.), with negatively charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), and with positively charged linkages (e.g., aminoalklyphosphoramidates, aminoalkylphosphotriesters), those containing pendant moieties, such as, for example, proteins (including nucleases, toxins, antibodies, signal peptides, poly-L-lysine, etc.), those with intercalators (e.g., acridine, psoralen, etc.), those containing chelators (e.g., metals, radioactive metals, boron, oxidative metals, etc.), those containing alkylators, those with modified linkages (e.g., alpha anomeric nucleic acids, etc.), as well as unmodified forms of the polynucleotide or oligonucleotide.

[0077] “Providing an analysis” is used herein to refer to the delivery of an oral or written analysis (i.e., a document, a report, etc.). A written analysis can be a printed or electronic document. A suitable analysis (e.g., an oral or written report) provides any or all of the following information: identifying information of the subject (name, age, etc.), a description of what type of biological sample(s) was used and / or how it was used, the technique used to assay the sample (e.g., RT-PCR, microarray analysis, RNA-seq, or GRO-seq), the results of the assay, the assessment as to whether or not the individual is determined to be at risk of developingAtty. Dckt.: STAN-2229WO severe symptoms or a morbid health outcome, a recommendation to continue or alter therapy, a recommended strategy for additional therapy, etc. The report can be in any format including, but not limited to printed information on a suitable medium or substrate (e.g., paper); or electronic format. If in electronic format, the report can be in any computer readable medium, e.g., diskette, compact disk (CD), flash drive, and the like, on which the information has been recorded. In addition, the report may be present as a website address which may be used via the internet to access the information at a remote site.Diagnostic Methods

[0078] As noted above, a method is provided for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity. In some embodiments, the method may comprise: (a) measuring the amount of RNA transcripts encoded by at least two (e.g., at least 3, at least 4, at least 5, at least 10, at least 20, at least 30, at least 40 or all) of HLA-DPB1, BCL6, NQO2, ORM1, DEFA4, KLRB1, CTSG, LCN2, AZU1, TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1, KLRD1, EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1, TCEAL9, EXOC2, BCAT1, PRF1, PRSS23, TRIB2, FURIN, ACSL1, EZH1, HMMR, UBE2L6, CASP7, OLR1, BUB3, SCAND1, ITGB7, DOK3, SIDT1, RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11, SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1-0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1, OASL, TRAF3IP3, TMEM123, TLN1, CCR7, LTBP3, CHMP7, PITPNC1, NUCB1, RBM15B, FAM8A1, BTBD7, ATG3, BCL2A1, IFITM1, DDB1, BCL2L11, LAPTM4A, KIF23, TYK2, PIK3R1, BANF1, TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1, and NAPA in a sample of RNA obtained from the subject, to obtain gene expression data; and

[0079] (b) based on the gene expression data, providing a report indicating the risk of the subject developing morbid health outcomes from the response to the steroid treatment, the response to the antibody treatment of asthma, the bacterial or fungal infection, the diabetes, the burn or the obesity.

[0080] As noted above, in these embodiments: (i) increased expression of BCL6, NQO2, ORM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1-0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L1 1 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and (ii) decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 ,Atty. Dckt.: STAN-2229WORAD23B, ARHGAP45, MAP3K4, USP11 , SMYD2, SSR2, IL7R, FBLN5, TRAF5, TRAF3IP3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk of the subject will develop morbid health outcomes from the response to steroid treatment, response to antibody treatment of asthma, bacterial or fungal infection, diabetes, burn or obesity.

[0081] The method may be practiced by measuring the transcripts of any combination of the following genes.Atty. Dckt.: STAN-2229WOAtty. Dckt.: STAN-2229WO

[0082] The method can be practiced with a number of different gene combinations. In some embodiments, the RNA transcripts analyzed may include the transcripts of the top two, top 3, top 4, top 5, top 6 or top 7 of the genes from table shown above.

[0083] In some embodiments, the RNA transcripts analyzed may include the transcripts of any of the gene combinations shown below, although several combinations of genes that have an AUROC value of at least 0.8 could be used. For example, the assay may use any of the following combinations of genes: HLA-DPB1 and BCL6, HLA-DPB1 and NQO2, HLA-DPB1 and ORM1 , HLA-DPB1 and DEFA4, HLA-DPB1 and KLRB1 , HLA-DPB1 and CTSG, HLA- DPB1 and LCN2, HLA-DPB1 and AZU1 , HLA-DPB1 and TXN, BCL6 and HLA-DPB1 , BCL6 and NQO2, BCL6 and ORM1 , BCL6 and DEFA4, BCL6 and KLRB1 , BCL6 and CTSG, BCL6 and LCN2, BCL6 and AZU1 , BCL6 and TXN, NQO2 and HLA-DPB1 , NQO2 and BCL6, NQO2 and ORM1 , NQO2 and DEFA4, NQO2 and KLRB1 , NQO2 and CTSG, NQO2 and LCN2, NQO2 and AZU1 , NQO2 and TXN, ORM1 and HLA-DPB1 , ORM1 and BCL6, ORM1 and NQO2, ORM1 and DEFA4, ORM1 and KLRB1 , ORM1 and CTSG, ORM1 and LCN2, ORM1Atty. Dckt.: STAN-2229WOKLRB1 and 0RM1 , KLRB1 and DEFA4, KLRB1 and CTSG, KLRB1 and LCN2, KLRB1 and AZU1 , KLRB1 and TXN, CTSG and HLA-DPB1 , CTSG and BCL6, CTSG and NQ02, CTSG and 0RM1 , CTSG and DEFA4, CTSG and KLRB1 , CTSG and LCN2, CTSG and AZU1 , CTSG and TXN, LCN2 and HLA-DPB1 , LCN2 and BCL6, LCN2 and NQ02, LCN2 and 0RM1 , LCN2 and DEFA4, LCN2 and KLRB1 , LCN2 and CTSG, LCN2 and AZU1 , LCN2 and TXN, AZU1 and HLA-DPB1 , AZU1 and BCL6, AZU1 and NQ02, AZU1 and 0RM1 , AZU1 and DEFA4, AZU1 and KLRB1 , AZU1 and CTSG, AZU1 and LCN2, AZU1 and TXN, TXN and HLA-DPB1 , TXN and BCL6, TXN and NQ02, TXN and 0RM1 , TXN and DEFA4, TXN and KLRB1 , TXN and CTSG, TXN and LCN2 or TXN and AZU1 .

[0084] In any embodiment, the levels of the transcripts measured in the assay can be integrated to produce a score, referred to as a "sever or mild" (SoM) infection score, upon which a diagnosis and / or treatment decision may be based. The results can then be integrated to produce a SoM infection score, and the diagnosis / treatment decisions can be based on the score. In some embodiments, the higher the score, the more likely it is that the patient will develop severe symptoms.

[0085] In some embodiments, the difference between the geometric mean of the overexpressed genes and the geometric mean of the under-expressed genes can be calculated to provide a score.

[0086] In some embodiments, the genes can be placed into modules and the expression of one or at least two (e.g., 3, 4, or 5) or all genes from each module may be tested. Exemplary modules are shown below:

[0087] Module 1 : NQO2, SLPI, ORM1 , KLHL2, BCL2A1 , ANXA3, SRGN, TXN, ACSL1 , AQP9, ADM, BCL6, TLR2, TLN1 , NUCB1 , PFKFB4, DOK3, GRN and TYK2.

[0088] Module 2: ATP8B4, KIF23, TCEAL9, IGFBP2, BCAT1 , BCL2L1 1 , SOCS6, BTBD7, CEP55, HMMR, PRC1 , KIF15, TRIP13, CDT1 , ELL2, CAMP, OLR1 , DEFA4, CEACAM8, LCN2, CTSG and AZU1 .

[0089] Module 3: MAFB, ANXA2, SCAND1 , IFITM1 , IFITM3, IFITM2, OASL, UBE2L6, VAMP5, CCL2, CREG1 , H1 -0, NAPA, FURIN, LAPTM4A, SSR2, RAD23B, FAM8A1 , ATG3, VRK2, TMEM123, CASP7 and POMP.

[0090] Module 4: HLA-DPB1 , DOK2, BANF1 , RBM15B, DDB1 , LRBA, TRIM28, LTBP3, USP1 1 , ITGB7, EZH1 , ARHGAP45, TRAF5, BUB3, SMYD2, TRAF3IP3, MAP3K4, CHMP7, PITPNC1 , SIDT1 , EXOC2, PIK3R1 , CCR7, IL7R, EPHX2, TRIB2, FBLN5, KLRB1 , KLRG1 , PRF1 , KLRD1 and PRSS2.

[0091] If the genes are divided into modules, then a score can be calculated by summing the scores for module 1 and 2 and then divided by the sum of the scores for module 3 and 4. Other calculations that provide a similar result are envisioned. In some embodiments, theAtty. Dckt.: STAN-2229WO geometric means of the expression of genes from each module can be calculated. A SoM score can be calculated by taking the sum of the geometric means of modules 1 and 2 and dividing that by the sum of the geometric means of modules 3 and 4.

[0092] For example, the subject’s risk of developing morbid health outcomes from response to steroid treatment, response to antibody treatment of asthma, bacterial or fungal infection, diabetes, burn, or obesity may be reliably and accurately predicted using:

[0093] A 42-gene module signature composed of Module 1 : NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; Module 2: BCL2L11 , BCAT1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; Module 3: MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123, CASP7; Module 4: DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1.

[0094] A 10-gene module signature composed of Module 1 : BCL6, NQO2; Module 2: DEFA4, CEP55, HMMR; Module 3: ATG3, VAMP5; Module 4: KLRB1 , HLA-DPB1 , DOK2.

[0095] A 9-gene module signature composed of Module 1 : BCL6, NQO2; Module 2: DEFA4, CEP55, HMMR; Module 3: ATG3, VAMP5; Module 4: KLRB1 , HLA-DPB1 .

[0096] An 8-gene module signature composed of: Module 1 : BCL6, NQO2; Module 2: DEFA4, CEP55, HMMR; Module 3: VAMP5; Module 4: KLRB1 , HLA-DPB1 .

[0097] A 7-gene module signature composed of: Module 1 : NQO2; Module 2: DEFA4, CEP55, HMMR; Module 3: VAMP5; Module 4: KLRB1 , HLA-DPB1 .

[0098] A 6-gene module signature composed of: Module 1 : NQO2; Module 2: CEP55, HMMR; Module 3: VAMP5; Module 4: KLRB1 , HLA-DPB1 .

[0099] A 5-gene module signature composed of: Module 1 : NQO2; Module 2: HMMR; Module 3: VAMP5; Module 4: KLRB1 , HLA-DPB1 .

[0100] A 4-gene module signature: Module 1 : NQO2; Module 2: HMMR; Module 3: VAMP5; Module 4: HLA-DPB1 .

[0101] A 20-gene signature composed of: upregulated composed of: BCL6, NQO2, ORM1 , DEFA4, AQP9, GRN, CEP55, TRIP13, SCAN D1 , IFITM2, POMP, BTBD7, SOCS6; downregulated: HLA-DPB1 , KLRB1 , DOK2, ARHGAP45, SSR2, LAPTM4A, TYK2.

[0102] A 10-gene signature composed of: upregulated: NQO2, SCAND1 , BCL6, TCEAL9, TRIP13; downregulated: HLA-DPB1 , MAFB, ATG3, DOK2, EXOC2.

[0103] A 9-gene signature composed of: upregulated: TXN, NQO2, BCL6, LCN2, ORM1 ; downregulated: HLA-DPB1 , DOK2, KLRD1 .

[0104] A 6-gene signature composed of upregulated: BCL6, POMP, SCAND1 ; downregulated: HLA-DPB1 , MAFB, DOK2.

[0105] The measuring step can be done using any suitable method. For example, the amount of the RNA transcripts in the sample may be measured by RNA-seq (see, e.g., Morin et alAtty. Dckt.: STAN-2229WOBioTechniques 2008 45: 81-94; Wang et al 2009 Nature Reviews Genetics 10 : 57-63), RT- PCR (Freeman et al. BioTechniques 1999 26: 112-22, 124-5), or by labeling the RNA or cDNA made from the same and hybridizing the labeled RNA or cDNA to an array. An array may contain spatially- addressable or optically-addressable sequence-specific oligonucleotide probes that specifically hybridize to transcripts being measured, or cDNA made from the same. Spatially-addressable arrays (which are commonly referred to as “microarrays” in the art) are described in, e.g., Sealfon et al (see, e.g., Methods Mol Biol. 201 1 ;671 :3-34). Optically- addressable arrays (which are commonly referred to as “bead arrays” in the art) use beads that internally dyed with fluorophores of differing colors, intensities and / or ratios such that the beads can be distinguished from each other, where the beads are also attached to an oligonucleotide probe. Exemplary bead-based assays are described in Dupont et al. J. Reprod Immunol. (2005 66:175-91 ) and Khalifian et al. (J Invest Dermatol. 2015 135: 1 -5). The abundance of transcripts in a sample can also be analyzed by quantitative RT-PCR or isothermal amplification method such as those described in Gao et al. (J. Virol Methods. 2018 255: 71 -75), Pease et al (Biomed Microdevices (2018) 20: 56) or Nixon et (Biomol. Det. and Quant 2014 2: 4-10), for example. Many other methods for measuring the amount of an RNA transcript in a sample are known in the art.

[0106] As noted above, the method is practiced on RNA obtained from a biological sample of a patient. The biological sample of RNA obtained from the subject may comprise RNA isolated from whole blood, white blood cells, PBMCs, neutrophils or buffy coat, for example. In alternative embodiments, the RNA from a nasal swab, a throat swab, or nasal mucous may be analyzed. Methods for making total RNA, polyA+ RNA, RNA that has been depleted for abundant transcripts, and RNA that has been enriched for the transcripts being measured are well known (see, e.g., Hitchen et al. J Biomol Tech. 2013 24: S43-S44). If the method involves making cDNA from the RNA, then the cDNA may be made using an oligo(d)T primer, a random primer or a population of gene-specific primers that hybridize to the transcripts being analyzed.

[0107] In measuring the transcript, the absolute amount of each transcript may be determined, or the amount of each transcript relative to one or more control transcripts, e.g., one or more constitutively expressed transcripts, may be determined. Whether the amount of a transcript is increased or decreased may be in relation to the amount of the transcript (e.g., the average amount of the transcript) in control samples (e.g., in equivalent samples collected from a population of at least 100, at least 200, or at least 500 subjects that do not have severe symptoms).

[0108] In some embodiments, the method may comprise providing a report indicating the risk of a subject developing severe symptoms or a morbid health outcome.. In some embodiments, this step may involve calculating one or more scores based on the weighted amounts of eachAtty. Dckt.: STAN-2229WO of the transcripts, where the one or more scores correlate with the phenotype and can be a number such as a probability, likelihood or score out of 10, for example. In these embodiments, the method may comprise inputting the amounts of each of the transcripts into one or more algorithms, executing the algorithms, and receiving a score for each phenotype based on the calculations. In these embodiments, other measurements from the subject, e.g., whether the subject is male, the age of the subject, white blood cell count, neutrophils count, band count, lymphocyte count, monocyte count, whether the subject is immunosuppressed, and / or whether there are Gram-negative bacteria present, etc., may be input into the algorithm.

[0109] In some embodiments, the method may involve creating a report that shows the risk score of the subject, e.g., in an electronic form, and forwarding the report to a doctor or other medical professional to help identify a suitable course of action, e.g., to identify a suitable therapy for the subject. The report may be used along with other metrics as a diagnostic to determine whether the subject has a disease or condition.

[0110] In any embodiment, report can be forwarded to a “remote location”, where “remote location,” means a location other than the location at which the image is examined. For example, a remote location could be another location (e.g., office, lab, etc.) in the same city, another location in a different city, another location in a different state, another location in a different country, etc. As such, when one item is indicated as being "remote" from another, what is meant is that the two items can be in the same room but separated, or at least in different rooms or different buildings, and can be at least one mile, ten miles, or at least one hundred miles apart. "Communicating" information references transmitting the data representing that information as electrical signals over a suitable communication channel (e.g., a private or public network). "Forwarding" an item refers to any means of getting that item from one location to the next, whether by physically transporting that item or otherwise (where that is possible) and includes, at least in the case of data, physically transporting a medium carrying the data or communicating the data. Examples of communicating media include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the internet or including email transmissions and information recorded on websites and the like. In certain embodiments, the report may be analyzed by an MD or other qualified medical professional, and a report based on the results of the analysis of the image may be forwarded to the subject from which the sample was obtained.[oom] In computer-related embodiments, a system may include a computer containing a processor, a storage component (i.e., memory), a display component, and other components typically present in general purpose computers. The storage component stores informationAtty. Dckt.: STAN-2229WO accessible by the processor, including instructions that may be executed by the processor and data that may be retrieved, manipulated or stored by the processor.

[0112] The storage component includes instructions for determining a risk score. The computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive patient data and analyze patient data according to one or more algorithms. The display component may display information regarding the diagnosis of the patient.

[0113] The storage component may be of any type capable of storing information accessible by the processor, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write-capable, and read-only memories. The processor may be any well-known processor, such as processors from Intel Corporation. Alternatively, the processor may be a dedicated controller such as an ASIC.

[0114] The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms "instructions," "steps" and "programs" may be used interchangeably herein. The instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.

[0115] Data may be retrieved, stored or modified by the processor in accordance with the instructions. For instance, although the diagnostic system is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having a plurality of different fields and records, XML documents, or flat files. The data may also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII or Unicode. Moreover, the data may comprise any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations) or information which is used by a function to calculate the relevant data.Therapeutic Methods

[0116] Therapeutic methods are also provided. In some embodiments, the method may involve identifying a patient as being at high risk of developing a morbid health outcome from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity and then treating the patient accordingly (e.g., to reduce the risk of the patient developing severe symptoms, increase survival).

[0117] For example, based on the risk, the method may comprise administering an antibody treatment of asthma, an antibiotic, an anti-fungal agent, a diabetes treatment, a burnAtty. Dckt.: STAN-2229WO treatment, an obesity treatment, etc. to the patient. In some embodiments, based on the risk the method may comprise not administering an antibody treatment of asthma, an antibiotic, an anti-fungal agent, a diabetes treatment, a burn treatment, an obesity treatment etc. to the patient.

[0118] As described above, the levels of these transcripts may be used to calculate a single score, e.g., a number, where the score indicates whether the subject will develop severe symptoms.

[0119] As would be apparent, this method may be practiced with any of the subsets of the genes listed above.

[0120] In some embodiments, the method may comprise comparing the risk to a threshold or curve, determining that the risk is above a threshold, and administering intensive care or a therapy to the patient. This care / therapy may be preemptive in some cases since the patient may not yet display severe symptoms at the point at which the test is done.

[0121] In some embodiments, the treatment may be administering intensive care to the patient, where the intensive care may comprises one or more of providing supplemental oxygen to the patient, putting the patient on mechanical ventilation, connecting the patient with a device to monitor a bodily function selected from one or more of heart and pulse rate, air flow to the lungs, blood pressure, blood flow, central venous pressure, amount of oxygen in the blood, and body temperature, and adding an intravenous line to the patient. The patient may be admitted to an ICU (intensive care unit).

[0122] In some embodiments, treatment of a bacterial infection may comprise administering one or more antibiotics, including broad spectrum, bactericidal, or bacteriostatic antibiotics such as penicillins including penicillin G, penicillin V, procaine penicillin, benzathine penicillin, veetids (Pen-Vee-K), piperacillin, pipracil, pfizerpen, temocillin, negaban, ticarcillin, and Ticar; penicillin combinations such as amoxicillin / clavulanate, augmentin, ampicillin / sulbactam, unasyn, piperacillin / tazobactam, zosyn, ticarcillin / clavulanate, and timentin; tetacyclines such as chlortetracycline, doxycycline, demeclocycline, eravacycline, lymecycline, meclocycline, methacycline, minocycline, omadacycline, oxytetracycline, rolitetracycline, sarecycline, tetracycline, and tigecycline; cephalosporins such as cefacetrile (cephacetrile), cefadroxil (cefadroxyl; duricef), cefalexin (cephalexin; keflex), cefaloglycin (cephaloglycin), cefalonium (cephalonium), cefaloridine (cephaloradine), cefalotin (cephalothin; keflin), cefapirin (cephapirin; cefadryl), cefatrizine, cefazaflur, cefazedone, cefazolin (cephazolin; ancef, kefzol), cefradine (cephradine; velosef), cefroxadine, ceftezole, cefaclor (ceclor, distaclor, keflor, raniclor), cefonicid (monocid), cefprozil (cefproxil; cefzil), cefuroxime (zefu, zinnat, zinacef, ceftin, biofuroksym, xorimax), cefuzonam, loracarbef (lorabid) cefbuperazone, cefmetazole (zefazone), cefminox, cefotetan (cefotan), cefoxitin (mefoxin), cefotiamAtty. Dckt.: STAN-2229WO(pansporin), cefcapene, cefdaloxime, cefdinir (sefdin, zinir, omnicef, kefnir), cefditoren, cefetamet, cefixime (fixx, zifi, suprax), cefmenoxime, cefodizime, cefotaxime (claforan), cefovecin (convenia), cefpimizole, cefpodoxime (vantin, pecef, simplicef), cefteram, ceftamere (enshort), ceftibuten (cedax), ceftiofur (naxcel, excenel), ceftiolene, ceftizoxime (cefizox), ceftriaxone (rocephin), cefoperazone (cefobid), ceftazidime (meezat, fortum, fortaz), latamoxef (moxalactam), cefclidine, cefepime (maxipime), cefluprenam, cefoselis, cefozopran, cefpirome (cefrom), cefquinome, flomoxef, ceftobiprole, ceftaroline, ceftolozane, cefaloram, cefaparole, cefcanel, cefedrolor, cefempidone, cefetrizole, cefivitril, cefmatilen, cefmepidium, cefoxazole, cefrotil, cefsumide, ceftioxide, cefuracetime, and nitrocefin; quinolonesZ / fluoroquinolones such as flumequine (Flubactin), oxolinic acid (Uroxin), rosoxacin (Eradacil), cinoxacin (Cinobac), nalidixic acid (NegGam, Wintomylon), piromidic acid (Panacid), pipemidic acid (Dolcol), ciprofloxacin (Zoxan, Ciprobay, Cipro, Ciproxin), fleroxacin (Megalone, Roquinol), lomefloxacin (Maxaquin), nadifloxacin (Acuatim, Nadoxin, Nadixa), norfloxacin (Lexinor, Noroxin, Quinabic, Janacin), ofloxacin (Floxin, Oxaldin, Tarivid), pefloxacin (Peflacine), rufloxacin (Uroflox), enoxacin (Enroxil, Penetrex), balofloxacin (Baloxin), grepafloxacin (Raxar), levofloxacin (Cravit, Levaquin), pazufloxacin (Pasil, Pazucross), sparfloxacin (Zagam), temafloxacin (Omniflox), tosufloxacin (Ozex, Tosacin), clinafloxacin, gatifloxacin (Zigat, Tequin, Zymar-ophthalmic), moxifloxacin (Avelox,Vigamox), sitafloxacin (Gracevit), prulifloxacin (Quisnon), besifloxacin (Besivance), delafloxacin (Baxdela), gemifloxacin (Factive) and trovafloxacin (Trovan), ozenoxacin, danofloxacin (Advocin, Advocid), difloxacin (Dicural, Vetequinon), enrofloxacin (Baytril), ibafloxacin (Ibaflin), marbofloxacin (Marbocyl, Zenequin), orbifloxacin (Orbax, Vietas), and sarafloxacin (Floxasol, Saraflox, Sarafin); macrolides such as azithromycin, clarithromycin, erythromycin, fidaxomicin, telithromycin, carbomycin A, josamycin, kitasamycin, midecamycin / midecamycin acetate, oleandomycin, solithromycin, spiramycin, troleandomycin, tylosin / tylocine, roxithromycin, telithromycin, cethromycin, solithromycin, tacrolimus, pimecrolimus, sirolimus, amphotericin B, nystatin, and cruentaren; sulfonamides such as sulfonamide, sulfacetamide, sulfadiazine, sulfadimidine, sulfafurazole (sulfisoxazole), sulfisomidine (sulfaisodimidine), sulfamethoxazole, sulfamoxole, sulfanitran, sulfadimethoxine, sulfamethoxypyridazine, sulfametoxydiazine, sulfadoxine, sulfametopyrazine, and terephtyl; aminoglycosides such as kanamycin A, amikacin, tobramycin, dibekacin, gentamicin, sisomicin, netilmicin, neomycins B, C, neomycin E (paromomycin), streptomycin, plazomicin, amikin, garamycin, kantrex, neo- fradin, netromycin, nebcin, humatin, spectinomycin(Bs), and trobicin; carbapenems such as imipenem, meropenem, ertapenem, doripenem, panipenem / betamipron, biapenem, tebipenem, razupenem (PZ-601 ), lenapenem, tomopenem, and thienamycin (thienpenem); ansamycins such as geldanamycin, herbimycin, rifaximin, and xifaxan; carbacephems suchAtty. Dckt.: STAN-2229WO as loracarbef and lorabid; carbapenems such as ertapenem, invanz, doripenem, doribax, imipenem / cilastatin, primaxin, meropenem, and merrem; glycopeptides such as teicoplanin, targocid, vancomycin, vancocin, telavancin, vibativ, dalbavancin, dalvance, oritavancin, and orbactiv; lincosamides such as clindamycin, cleocin, lincomycin, and lincocin; lipopeptides such as daptomycin and cubicin; macrolides such as azithromycin, zithromax, sumamed, xithrone, clarithromycin, biaxin, dirithromycin, dynabac, erythromycin, erythocin, erythroped, roxithromycin, troleandomycin, tao, telithromycin, ketek, spiramycin, and rovamycine; monobactams such as aztreonam and azactam; nitrofurans such as furazolidone, furoxone, nitrofurantoin, macrodantin, and macrobid; oxazolidinones such as linezolid, zyvox, vrsa, posizolid, radezolid, and torezolid; polypeptides such as bacitracin, colistin, coly-mycin-S, and polymyxin B; drugs against mycobacteria such as clofazimine, lamprene, dapsone, avlosulfon, capreomycin, capastat, cycloserine, seromycin, ethambutol, myambutol, ethionamide, trecator, isoniazid, I.N.H., pyrazinamide, aldinamide, rifampicin, rifadin, rimactane, rifabutin, mycobutin, rifapentine, priftin, and streptomycin; and other antibiotics such as arsphenamine, salvarsan, chloramphenicol, Chloromycetin, fosfomycin, monurol, monuril, fusidic acid, fucidin, metronidazole, flagyl, mupirocin, bactroban, platensimycin, quinupristin / dalfopristin, synercid, thiamphenicol, tigecycline, tigacyl, tinidazole, tindamax fasigyn, trimethoprim, proloprim, and trimpex; adjuvants, including aluminum salts (alum), such as aluminum hydroxide, aluminum phosphate, aluminum sulfate, etc.; oil-in-water emulsion formulations; (saponin adjuvants; Complete Freund's Adjuvant (CFA) and Incomplete Freund's Adjuvant (IFA) ; cytokines, such as interleukins (IL-1 , IL-2, IL-4, IL-5, IL-6, IL-7, IL-12, interferons, macrophage colony stimulating factor (M-CSF), tumor necrosis factor (TNF), etc.; detoxified mutants of a bacterial ADP-ribosylating toxin such as a cholera toxin (CT), pertussis toxin (PT), or an E. coli heat-labile toxin (LT); oligonucleotides comprising CpG motifs; as well as other immunostimulatory molecules; and vaccines against bacteria and infectious diseases, including any vaccine comprising bacterial antigenic proteins or attenuated or dead bacteria and, optionally, adjuvants for boosting an immune response against bacteria, such as vaccines against tuberculosis, diphtheria, tetanus, pertussis, Haemophilus influenzae t pe B, cholera, typhoid, Streptococcus pneumoniae, and the like.

[0123] In certain embodiments, treating the subject for a bacterial infection comprises not administering a particular type of antibiotic treatment determined to increase the subject’s risk of developing severe symptoms, or switching from treatment with one type of antibiotic to treatment with a different antibiotic. For example, if the subject is initially treated with penicillin, which is determined to increase the risk of developing severe symptoms, the treatment with penicillin is discontinued. Other types of antibiotics such as fluoroquinolones, macrolides, or sulfonamides may be administered to the subject. Alternatively, a non-antibiotic treatmentAtty. Dckt.: STAN-2229WO such as a vaccine or cytokine may be administered to the subject determined to be at risk of developing severe symptoms from antibiotic treatment.

[0124] In some embodiments, treatment of a fungal infection may comprise administering one or more antifungal agents such as amphotericin B, nystatin, voriconazole, fluconazole, itraconazole, clotrimazole, miconazole, caspofungin, micafungin, anidulafungin, rezafungin, terbinafine, flucytosine, and the like.

[0125] In certain embodiments, treating the subject for a fungal infection comprises not administering a particular type of antifungal treatment determined to increase the subject’s risk of developing severe symptoms, or switching from treatment with one type of antifungal agent to treatment with a different type of antifungal agent. For example, if the subject is initially treated with amphotericin B, which is determined to increase the risk of developing severe symptoms, the treatment with the amphotericin B is discontinued. Other types of antifungal agents may be administered to the subject such as nystatin, voriconazole, or fluconazole.

[0126] In some embodiments, treatment of asthma may comprise administering an inhaled corticosteroid such as fluticasone, budesonide, or mometasone, or a combination of an inhaled corticosteroid and a long-acting beta-agonist such as fluticasone-salmeterol or budesonide-formoterol; a leukotriene modifier such as montelukast, zafirlukast, or zileuton; monoclonal antibodies such as anti-lgE antibodies such as omalizumab, anti-interleukin-5 antibodies such as mepolizumab, benralizumab, or reslizumab, anti-interleukin-4 receptor alpha subunit antibodies such as dupilumab, and anti-thymic stromal lymphopoietin antibodies such as tezepelumab; a muscle relaxant such as theophylline to keep airways open by relaxing the muscles around them; and the like.

[0127] In certain embodiments, treating the subject for asthma comprises not administering a particular type of antibody treatment determined to increase the subject’s risk of developing severe symptoms, or switching from treatment with one type of antibody to a treatment with a different type of antibody. For example, if the subject is initially treated with an anti-lgE antibody such as omalizumab, which is determined to increase the risk of developing severe symptoms, the treatment with the anti-lgE antibody is discontinued. Other types of antibodies such as anti-interleukin-5 antibodies (e.g., mepolizumab, benralizumab, or reslizumab), antiinterleukin-4 receptor alpha subunit antibodies (e.g., dupilumab), or anti-thymic stromal lymphopoietin antibodies (e.g., tezepelumab) may be administered to the subject. Alternatively, a non-antibody treatment may be administered to the subject determined to be at risk of developing severe symptoms from antibody treatment.

[0128] In certain embodiments, a steroid treatment comprises treating the subject with a corticosteroid such as prednisone, methylprednisolone, dexamethasone, prednisolone, hydrocortisone, mometasone, betamethasone, cortisone, triamcinolone, fluticasone,Atty. Dckt.: STAN-2229WO budesonide, or clobetasol. In certain embodiments, treating the subject comprises not administering a particular type of steroid treatment determined to increase the subject’s risk of developing severe symptoms, or switching from treatment with one type of steroid to a treatment with a different type of steroid. For example, if the subject is initially treated with hydrocortisone, which is determined to increase the risk of developing severe symptoms, the treatment with the hydrocortisone is discontinued. Other types of steroids such as prednisone, methylprednisolone, dexamethasone, or prednisolone may be administered. Alternatively, a non-steroid treatment may be administered to the subject determined to be at risk of developing severe symptoms from steroid treatment.

[0129] Treatment of type I diabetes comprises administering insulin. The amount of insulin needed can be determined by using a continuous glucose monitor or by measuring levels of glucose in a blood sample (pricking a finger to collect blood for glucose testing). Insulin can be delivered to the subject by injection with a syringe or insulin pump.

[0130] Treatment of type II diabetes may initially comprise lifestyle changes to reduce weight and increase exercise. As the disease progresses, treatment of type II diabetes may comprise administering metformin, sulfonylureas such as glimepiride, glipizide, glyburide, and gliclazide; sodium-glucose cotransporter 2 (SGLT2) inhibitors such as canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, and bexagliflozin; dipeptidyl peptidase-4 (DPP-4) inhibitors such as sitagliptin, saxagliptin, linagliptin, alogliptin, and vildagliptin; glucagon-like peptide-1 (GLP- 1 ) receptor agonists such as dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide, and tirzepatide; insulin; bariatric surgery; and the like.

[0131] In certain embodiments, treating the subject for type II diabetes comprises switching to a more aggressive treatment if the subject is determined to be at risk of developing severe symptoms. For example, if the subject is initially treated by encouraging lifestyle changes to reduce weight and increase exercise, but the disease is progressing with increased risk of developing severe symptoms, treatment may be switched to treating the subject with insulin and bariatric surgery.

[0132] Treatment of obesity may comprise dietary changes to reduce calories, increasing exercise, administering GLP-1 receptor agonists such as semaglutide, liraglutide, and tirzepatide; orlistat; phentermine-topiramate; bupropion-naltrexone; bariatric surgery; and the like. In certain embodiments, treating the subject for obesity comprises switching to a more aggressive treatment if the subject is determined to be at risk of developing severe symptoms. For example, if the subject is initially treated by encouraging lifestyle changes to reduce weight and increase exercise, but the subject continues to gain weight with increased risk of developing severe symptoms, treatment may be switched to treating with a GLP-1 receptor agonist or bariatric surgery.Atty. Dckt.: STAN-2229WO

[0133] Treatment of burns depends on severity. A first-degree or second-degree burn can be treated by removing the heat source, cooling the burned area with cool running water, applying a moisturizing lotion or antibiotic ointment, and administering a pain reliever. For third-degree burns, treatment may comprise covering the burned area with a cloth or sterile gauze, elevating the burned area, if possible, above heart level to reduce swelling, administering intravenous fluids to prevent dehydration, administering analgesics (e.g., acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs) such as ibuprofen and naproxen, or opioids such as morphine, methadone, hydromorphone, and oxycodone), applying burn creams or ointments containing silver sulfadiazine, surgery, including skin grafts to cover large burn wounds, and rehabilitation, including physical therapy.

[0134] In certain embodiments, treating the subject for a burn comprises switching to a more aggressive treatment if the subject is determined to be at risk of developing severe symptoms. For example, the subject may be treated initially by covering burned areas with a cloth or sterile gauze and administering an analgesic. However, if the subject is determined to have an increased risk of developing severe symptoms, treatment may be switched to a more aggressive treatment including surgery.

[0135] Methods of treatment and dosages for administering the therapeutics listed above are known in the art or can be derived from the art.Kits

[0136] Also provided by this disclosure are kits for practicing the subject methods, as described above. In some embodiments, the kit comprises reagents for measuring the amounts of RNA transcripts encoded by at least 2, at least 3, at least 5, at least 10, at least 15, at least 20, at least 30 or all of HLA-DPB1, BCL6, NQO2, ORM1, DEFA4, KLRB1, CTSG, LCN2, AZU1, TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1, KLRD1, EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1, TCEAL9, EXOC2, BCAT1, PRF1, PRSS23, TRIB2, FURIN, ACSL1, EZH1, HMMR, UBE2L6, CASP7, OLR1, BUB3, SCAND1, ITGB7, DOK3, SIDT1, RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11, SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1-0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1, OASL, TRAF3IP3, TMEM123, TLN1, CCR7, LTBP3, CHMP7, PITPNC1, NUCB1, RBM15B, FAM8A1, BTBD7, ATG3, BCL2A 1, IFITM1, DDB1, BCL2L11, LAPTM4A, KIF23, TYK2, PIK3R1, BANF1, TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1, and NAPA. In some embodiments, the kit may comprise, for each RNA transcript, a sequencespecific oligonucleotide that hybridizes to the transcript. In some embodiments, the sequencespecific oligonucleotide may be biotinylated and / or labeled with an optically-detectable moiety. In some embodiments, the kit may comprise, for each RNA transcript, a pair of PCR primersAtty. Dckt.: STAN-2229WO that amplify a sequence from the RNA transcript, or cDNA made from the same. In some embodiments, the kit may comprise an array of oligonucleotide probes, wherein the array comprises, for each RNA transcript, at least one sequence-specific oligonucleotide that hybridizes to the transcript. The oligonucleotide probes may be spatially addressable on the surface of a planar support, or tethered to optically addressable beads, for example.

[0137] The various components of the kit may be present in separate containers or certain compatible components may be precombined into a single container, as desired.

[0138] In addition to the above-mentioned components, the subject kit may further include instructions for using the components of the kit to practice the subject method.Examples of Non-Limiting Aspects of the Disclosure

[0139] Aspects, including embodiments, of the present subject matter described above may be beneficial alone or in combination, with one or more other aspects or embodiments. Without limiting the foregoing description, certain non-limiting aspects of the disclosure numbered 1 - 25 are provided below. As will be apparent to those of skill in the art upon reading this disclosure, each of the individually numbered aspects may be used or combined with any of the preceding or following individually numbered aspects. This is intended to provide support for all such combinations of aspects and is not limited to combinations of aspects explicitly provided below.1 . An in vitro method for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity, the method comprising: obtaining a biological sample from the subject; isolating cells from the biological sample; extracting RNA transcripts from the cells; conducting transcriptomic analyses comprising measuring amounts of RNA transcripts encoded by at least two of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP1 1 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L1 1 ,Atty. Dckt.: STAN-2229WOLAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, S0CS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA to obtain gene expression data; and based on the gene expression data, providing a report indicating the subject's risk of developing severe symptoms, wherein: a. increased expression of BCL6, NQ02, 0RM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L11 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and b. decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 , RAD23B, ARHGAP45, MAP3K4, USP1 1 , SMYD2, SSR2, IL7R, FBLN5, TRAF5, TRAF31 P3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk that the subject will develop severe symptoms.2. The method of aspect 1 , wherein the steroid treatment is for viral infection or burn.3. The method of aspect 1 , wherein the steroid is hydrocortisone.4. The method of aspect 1 , wherein the bacterial infection comprises sepsis, urinary tract infection.5. The method of aspect 4, wherein the bacterial infection further comprises surgical site infection, bacteremia, abdominal or pelvic infection, pneumonia, nosocomial pneumonia, osteomyelitis, cellulitis, trauma infection, meningitis, peritonitis, cholangitis, soft tissue infection, purulent myositis, and septic arthritis.6. The method of any one of aspects 1 -5, wherein the biological sample is blood.7. The method of one of aspects 1 -6, wherein the cells are peripheral blood mononuclear cells.8. The method of one of aspects 1 -7, wherein the transcriptomic analysis comprises a qPCR assay.Atty. Dckt.: STAN-2229WO9. The method of one of aspects 1 -8, wherein the transcriptomic analysis comprises an RNA-seq assay.10. The method of one of aspects 1 -9, wherein step (b) comprises calculating a severe or mild (SoM) score based on the amounts of the RNA transcripts, wherein the score indicates the probability that the subject will have a morbid health outcome.1 1 . The method of one of aspects 1 -19, wherein the RNA transcripts analyzed in step (a) comprise at least one gene from each of the following modules: module 1 : NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; module 2: BCL2L1 1 , BCAT1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; module 3: MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123 and CASP7; and module 4: DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1 .12. A kit for assessing a subject’s risk of developing morbid health outcomes selected from response to steroid treatment, response to antibody treatment of asthma, bacterial or fungal infection, diabetes, burn and obesity, comprising reagents for measuring the amount of RNA transcripts encoded by at least 2, at least 3, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40 or at least 50 or all of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP1 1 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L1 1 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA.13. The kit of aspect 12, wherein the reagents comprise, for each RNA transcript, a sequencespecific oligonucleotide that hybridizes to the transcript.Atty. Dckt.: STAN-2229WO14. The kit of aspect 13, wherein sequence-specific oligonucleotide is biotinylated and / or labeled with an optically-detectable moiety.15. The kit of aspect 12, wherein the reagents comprise, for each RNA transcript, a pair of PCR primers that amplify a sequence from the RNA transcript, or cDNA made from the same.16. The kit of aspect 12, wherein the reagents comprise an array of oligonucleotide probes, wherein the array comprises, for each RNA transcript, at least one sequence-specific oligonucleotide that hybridizes to the transcript.17. The kit of aspect 12, wherein the kit is an RT-PCR kit, a competitive RT-PCR kit, a realtime RT-PCR kit, a DNA chip kit, a microarray kit, a SAGE (Serial Analysis of Gene Expression) kit, or a gene chip kit.18. An RNA transcript of a gene selected from NQ02, SLPI, ORM1, KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4, TYK2, BCL2L11, BCAT1, BTBD7, CEP55, HMMR, PRC1, KIF15, CAMP, CEACAM8, DEFA4, LCN2, CTSG, AZU1, MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123, CASP7, DOK2, HLA-DPB1, BUB3, SMYD2, SIDT1, EXOC2, TRIB2, and KLRB1 for use as a biomarker for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity.19. A composition comprising RNA transcripts encoded by at least two genes selected from HLA-DPB1 , BCL6, NQ02, 0RM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA for use in determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity.20. The composition of aspect 19, wherein the composition comprises:Atty. Dckt.: STAN-2229WO one or more RNA transcripts from at least one gene selected from NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; one or more RNA transcripts from at least one gene selected from BCL2L1 1 , BCAT 1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; one or more RNA transcripts from at least one gene selected from MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123 and CASP7; and one or more RNA transcripts from at least one gene selected from DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1.21 . A method for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity and treating the subject, the method comprising: obtaining a biological sample from the subject; isolating cells from the biological sample; extracting RNA transcripts from the cells; conducting transcriptomic analyses comprising measuring amounts of RNA transcripts encoded by at least two of HLA-DPB1 , BCL6, NQO2, 0RM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP1 1 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L1 1 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA to obtain gene expression data; based on the gene expression data, providing a report indicating the subject's risk of developing severe symptoms, wherein: a. increased expression of BCL6, NQ02, 0RM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L11 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and b. decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 , RAD23B, ARHGAP45,Atty. Dckt.: STAN-2229WOMAP3K4, USP1 1 , SMYD2, SSR2, I L7R, FBLN5, TRAF5, TRAF31 P3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk that the subject will develop severe symptoms; and treating the subject to reduce the subject’s risk of developing the severe symptoms.22. The method of aspect 21 , wherein said treating comprises treating the subject with a treatment not predicted to increase the subject’s risk of developing the severe symptoms.23. The method of aspect 21 or 22, wherein said treating comprises administering an antibiotic, an anti-fungal agent, an asthma treatment, a diabetes treatment, a burn treatment, or an obesity treatment to the subject.24. The method of aspect 22, wherein said treating comprises not administering the steroid treatment to the subject determined to be at risk of developing the severe symptoms from the steroid treatment or switching to treatment with a different steroid.25. The method of aspect 22, wherein said treating comprises not administering the antibody treatment for the asthma to the subject determined to be at risk of developing the severe symptoms from the antibody treatment or switching to treatment with a different antibody.

[0140] The invention now being fully described, it will be apparent to one of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.EXPERIMENTAL

[0141] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.Atty. Dckt.: STAN-2229WO

[0142] All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.

[0143] The present invention has been described in terms of particular embodiments found or proposed by the present inventor to comprise preferred modes for the practice of the invention. It will be appreciated by those of skill in the art that, in light of the present disclosure, numerous modifications and changes can be made in the particular embodiments exemplified without departing from the intended scope of the invention. For example, due to codon redundancy, changes can be made in the underlying DNA sequence without affecting the protein sequence. Moreover, due to biological functional equivalency considerations, changes can be made in protein structure without affecting the biological action in kind or amount. All such modifications are intended to be included within the scope of the appended claims.Example 1A Conserved Immune Dysregulation Signature is Associated with Infection Severity, Risk Factors Prior to Infection, and Treatment Response

[0144] Older age1 3. being male2-4 7, obesity8 10, smoking11 12and co-morbidities such as asthma1314and diabetes mellitus8’15 16have been identified as risk factors for severe outcomes in infectious diseases. More than 70% of US adults are estimated to have at least one risk factor for severe infection and over 40% have two or more risk factors.15 16Notably, the risk for severe outcomes from infection increases with the number of risk factors, suggesting they are additive.17Despite the overwhelming epidemiological evidence, the underlying mechanisms for these risk factors have been largely investigated independent of each other. Importantly, how these risk factors affect the underlying immune responses prior to infection, and whether they affect the same immunological pathways that increase the risk of severe outcomes, including mortality, remains unknown.

[0145] Previously, we have described a 42-gene signature score, called Severe-or-Mild (SoM) score, which predicts at presentation (i.e., prior to outcome is known) which individuals with viral infection will progress to have severe outcome with high accuracy.18Furthermore, we found that the SoM signature is evolutionarily conserved across at least 13 viruses in humans18and macaques.19Finally, we found that 42 genes in the SoM score are derived from four modules, which are divided into two detrimental and two protective modules, where higher expression of detrimental modules and lower expression of protective modules at presentation is associated with increased and decreased risk of severe outcomes, respectively. Since the 42 genes and the associated pathways are not specific to response to viral infections, weAtty. Dckt.: STAN-2229WO hypothesized that subjects with bacterial infections would also have a higher SoM score at presentation compared to healthy subjects. Given the strong association of the SoM score at presentation with the severity of infection, we hypothesized that the SoM score will be higher prior to infection in subjects with one or more risk factors for severe outcome compared to those without any risk factor.

[0146] To test these hypotheses, we carried out a large multi-omics, multi-cohort analyses by integrating gene expression data from (1 ) individuals with bacterial infection at presentation (17 studies, 1 ,295 samples), (2) individuals with burn / trauma (30 subjects) or sepsis (206 subjects), (3) single-cell RNAseq (scRNA-seq) from individuals with infection (bacterial or viral) (584,260 cells, 223 samples, 4 studies), (4) individuals with asthma (4 studies, 603 subjects), (5) healthy subjects with a risk factor for severe infection (38 studies, 1 ,821 samples), (6) the Framingham Heart Study (5,321 samples), and (7) healthy obese subjects randomly assigned to calorie restricted diet (70 subjects). Further, we integrated proteomics profiles of 2,487 subjects with these gene expression data. Across these 12,026 individuals from 68 independent studies, our analyses found that the SoM score was associated not only with the severity of bacterial infection, asthma, and risk factors at presentation, but also their treatments, and was associated with all-cause mortality.ResultsThe SoM host response signature score is conserved in bacterial infections and associated with severity

[0147] We first investigated whether the SoM score, which we previously identified using only respiratory viral infections, was also correlated with severity in individuals with bacterial infection.1820We identified, curated, and processed 17 datasets21 37that profiled 1 ,295 blood samples from 467 healthy controls and 828 individuals with bacterial infections with varying levels of severity (FIG. 1 A and Table S1 ; STAR Methods). Next, we calculated the SoM score for each sample as described before.18Briefly, for each module in each sample, we computed a module score as the geometric mean of the expression of genes in the module. Next, we computed the detrimental score as the sum of scores of modules 1 and 2, and protective score as the sum of scores of modules 3 and 4 score. Finally, we computed the SoM score for a sample as the difference between the detrimental and protective scores, which represents the dysregulation within the immune system such that higher SoM score corresponds to higher immune dysregulation.

[0148] The SoM score was positively correlated with severity of bacterial infections (r=0.55, p<2.2e-16; FIG. 1 B). Similar to viral infections, the detrimental score was positively (r=0.61 ,Atty. Dckt.: STAN-2229WO p<2.2e-16; FIG. 6A), and the protective score was negatively correlated with severity (r=-0.16, p=1.1 e-08; FIG. 6B). Finally, the SoM score distinguished individuals with severe bacterial infection from those with non-severe bacterial infection with an area under the receiver operating characteristic (AUROC) curve of 0.68 (95% Cl: 0.64-0.73; FIG. 1 C). Collectively, these results strongly suggest that in individuals with severe infection, irrespective of bacterial or viral etiology, there is an overlap in dysregulation of host response.Protective host response score is associated with differential response to hydrocortisone

[0149] Over the last 5 decades, more than 100 large clinical trials in sepsis have failed due to heterogeneity in treatment response.38Among the multiple reasons for these failures, one of the reasons is the variability in immune responses, such that potential treatment benefits in some subgroups are negated by harms in others. We hypothesized that quantifying detrimental and protective responses in individuals with sepsis will reduce the heterogeneity to allow identification of individuals who will benefit or be harmed by immunomodulatory treatments.

[0150] To test this hypothesis, we first investigated whether the SoM score was associated with increased mortality in two independent cohorts, which included (1 ) individuals with burn- related vasodilatory shock (n=30; Burn cohort; GSE77791 )39’40that were randomized to receive hydrocortisone versus placebo and (2) the VANISH41trial, which included 176 individuals with septic shock, of which 1 17 were randomized to either hydrocortisone or placebo. Across both cohorts, high SoM score was significantly (p=0.035) associated with increased mortality at baseline (FIG. 6C). Next, we investigated whether steroid treatment affected protective or detrimental scores. In the Burn cohort, steroid treatment significantly reduced the protective scores (p=0.004; FIG. 6D) on day 7 compared to placebo, but not the detrimental scores, which increased in both groups (p>0.05; FIGS. 6E-6G). Within the protective scores, module 3 was not different following hydrocortisone treatment than the placebo arm (FIG. 6H). The reduction following hydrocortisone treatment was primarily driven by the significant reduction only in module 4 (p=0.02; FIG. 6I). In other words, steroid treatment significantly reduced T and NK cell-driven protective response (p=0.02; FIG. 6I), driving the significant reduction in the overall protective immune responses (p=0.004; FIG. 6D), which in turn increased the immune dysregulation within an individual. Therefore, we hypothesized that steroid treatment would harm individuals with higher protective scores at baseline as it would increase the immune dysregulation. To test this hypothesis, we divided the Burn cohort individuals in “high protective” and “low protective” score groups using the median of the protective scores as a threshold. In the high protective score group, the 28-day mortality was 43% in individuals, who were treated with hydrocortisone compared to 0% in individuals whoAtty. Dckt.: STAN-2229WO received placebo (FIG. 1 D), although this difference was not statistically significant (p>0.05), possibly due to small sample size.

[0151] Next, we used the VANISH41trial to replicate this result. We evaluated the effect of hydrocortisone on 28-day mortality in these individuals with septic shock. Similar to the Burn cohort, we divided these individuals into “high protective” and “low protective” score groups using the median of the protective scores. Similar to the Burn cohort, there was no difference in mortality in the low protective score group (p=1 ; FIG. 1 E), validating the results in the Burn cohort. However, in the high protective score group, the 28-day mortality was significantly higher for those treated with steroid than the placebo (45.5% vs 14.5%, odds ratio (OR)=4.79, 95% Cl: 1 .59-15.55, p=0.002; FIG. 1 E). The results remained the same when considering only 1 17 individuals that were randomized (50% vs 20%, odds ratio=3.74, 95% Cl: 1 .1 -14.6, p=0.03; FIG. 6J). Logistic regression analysis, controlling for sex and the interaction with APACHE II score, to evaluate the interaction between steroid treatment and the protective score found a significant interaction indicating that individuals with high protective scores (i.e. , low-risk individuals), and treated with hydrocortisone, experienced a higher rate of 28-day mortality (p=0.005; FIG. 1 F). Similar analysis using only the detrimental scores found no such interaction and difference in mortality by treatment (FIGS. 6K-6L). Across both datasets, hydrocortisone treatment in the high protective score group was associated with significantly increased risk of mortality (summary log OR=1.68, 95% Cl : 0.71 -2.65, p=6.6e-04; FIG. 1G). Together, these results demonstrated that the SoM score, along with its constituent detrimental and protective scores, is also conserved in individuals with bacterial infections, associated with severity of their outcomes, is differentially modulated by steroid treatment, and could be used to identify individuals who may be harmed by steroid treatment.Detrimental host response modules are primarily driven by neutrophils

[0152] Next, we explored the immune cell types associated with each of the four gene modules in the SoM signature. We collected, curated, and integrated four publicly available whole blood single-cell transcriptomic datasets (584,260 cells, 223 samples)4?45from healthy subjects and individuals with bacterial or viral infection of varying degrees of severity (FIGS. 2A-2C, FIG. 7, FIG. 8A, STAR Methods). We chose these datasets as they included scRNA- seq data from neutrophils. Among the detrimental modules, module 1 score was the highest in mature neutrophils, whereas module 2 score was almost exclusively expressed in immature neutrophils (FIG. 2D). Among the protective modules, module 3 score was the highest in monocytes but also detected in T and NK cells, whereas module 4 score was primarily expressed in T and NK cells (FIGS. 2D-2E). These results were in line with our previous observations, where we found that several genes in the detrimental modules were expressedAtty. Dckt.: STAN-2229WO in mature and developing neutrophils, whereas the genes in the protective modules were expressed in monocytes, T, and NK cells.46Together, these results suggest the detrimental host responses during acute infections are primarily driven by different neutrophil subsets, whereas protective responses are driven by monocytes, T, and NK cells. These results demonstrate that the SoM score quantifies immune dysregulation at a system-level by integrating gene modules from both myeloid and lymphoid immune cells.

[0153] Arguably, neutrophil-driven detrimental response and T and NK cells-driven protective responses could be representative of neutrophil-lymphocyte ratio (NLR) that has been repeatedly associated with the severity of infection. Therefore, we further analyzed the scRNA- seq data. The SoM score was significantly correlated with disease severity in neutrophils (p=0.014), but not in immature neutrophils (FIG. 8B), whereas the proportions of immature neutrophils were significantly correlated with severity (p=0.017) but not of neutrophils (FIG. 8C). These results suggest that both change in the transcriptome in neutrophils and proportions of immature neutrophils are associated with severity.

[0154] However, it is possible that these results could be affected by difficulties in profiling neutrophils at a single-cell resolution. Therefore, we further analyzed the subsets of neutrophils expressing the detrimental and module 1 scores in healthy controls and individuals with non-severe or severe COVID-19 (FIG. 8D). Although proportions of neutrophils were similar (FIG. 8C), the neutrophil subsets expressing the detrimental score and module 1 score were distinct in individuals with non-severe and severe infections (FIG. 8D). These results further suggest that even if NLR is associated with severity, distinct subsets of neutrophils are associated with severity. The current practice of considering all neutrophils similar in the NLR does not account for this heterogeneity. Our study provides a strong rationale for further exploring the heterogeneity in neutrophils.Immune dysregulation is higher in individuals with severe asthma and associated with poor response to monoclonal antibody treatment

[0155] Respiratory viral and bacterial infections have been repeatedly linked to airway remodeling and associated with severe asthma.4748Given that the SoM score was associated with severity in both viral and bacterial infections, we investigated whether individuals with asthma, who tend to have severe exacerbations, also had a higher SoM score compared to healthy subjects before the exacerbations. To this end, we used 498 blood transcriptome profiles (87 healthy, 77 moderate asthma, 334 severe asthma) from U-BIOPRED (GSE69683),49a multicenter prospective cohort study across 16 clinical centers in 11 European countries. The SoM score was significantly higher in individuals with severe asthma compared to healthy controls and those with moderate asthma (JT trend test p-value<2.2e-Atty. Dckt.: STAN-2229WO16; FIG. 3A), which was driven by significant increase in the detrimental response (JT trend test p-value<2.2e-16; FIG. 3B) and decrease in the protective response (JT trend test p- value=3.7e-07; FIG. 3C). While the increase in detrimental response was driven by increase in both detrimental modules 1 and 2 scores (JT trend p-values of 1.7e-14 and 7.7e-12, respectively; FIG. 9A), the decrease in protective response was solely due to reduced module 4 scores with increased severity (JT trend test p-value=2.1 e-09; FIG. 9A) with no significant change in module 3 scores (JT trend test p-value>0.1 ; FIG. 9A). The protective score was significantly inversely correlated with detrimental score in individuals with severe asthma (R=- 0.5, p<2.2e-16) but not in individuals with moderate asthma (R=-0.08, p=0.5; FIG. 9B).

[0156] Next, we asked whether there was further increase in the SoM scores during asthma exacerbation. In a cohort of 19 individuals with asthma, profiled during and after asthma exacerbation (GSE96530)50, the SoM score was significantly higher in peripheral blood during exacerbation than convalescence (paired Wilcox test p-value=0.011 ; FIG. 3D), which was driven by a significantly higher detrimental score (paired Wilcox test p-value=0.0012; FIG. 3E) without significant change in the protective score (paired Wilcox test p-value>0.2; FIG. 3F) during exacerbation. Similar to U-BIOPRED, higher SoM score during exacerbation was driven by significantly higher detrimental modules 1 and 2 during exacerbation (paired Wilcox test p-value<0.011 ; FIG. 9C) and significantly lower protective module 4 (paired Wilcox test p- value=0.02; FIG. 9C).

[0157] Individuals with neutrophilic asthma are known to respond poorly to monoclonal antibody (mAb)-based treatments.51 52Because the detrimental modules were primarily expressed in neutrophils, which were higher in those who tend to have severe asthma, we investigated whether the detrimental scores are higher in individuals with asthma that do not respond to mAb treatment. To test this hypothesis, we used transcriptome data from two cohorts that profiled asthma individuals treated with either omalizumab (GSE134544)53or benralizumab (GSE148725).54GSE134544 performed transcriptome profiling of whole blood from 45 individuals with moderate-to-severe asthma treated with omalizumab (34 responders, 1 1 non-responders).53GSE148725 performed transcriptome profiling of whole blood from 41 individuals with severe eosinophilic asthma treated with benralizumab (29 responders, 12 non- responders).54Our analysis only included samples prior to treatment initiation. Across both cohorts, non-responders had significantly higher SoM score (ES=0.55, p=0.03; FIG. 3G) that was driven by significantly higher detrimental score (ES=0.56, p=0.02; FIG. 3H), prior to start of mAb treatment. Although non-responders had lower protective score, it was only marginally statistically significant (ES= -0.51 , p=0.07; FIG. 31).

[0158] Collectively, our results suggest that the immune dysregulation, as quantified by the SoM score, is higher in individuals with severe asthma than those with mild / moderate asthmaAtty. Dckt.: STAN-2229WO prior to exacerbation, increases further during asthma exacerbation, and is higher in individuals with asthma that do not response to mAb treatment prior to treatment initiation.Detrimental host response modules are associated with risk factors for severe infections prior to infection

[0159] Because higher SoM score was associated with severe asthma and with severe outcomes in infectious diseases, irrespective of bacterial or viral infections, we next asked whether it was also associated with one or more risk factors for severe infections prior to infection. To answer this question, we identified, curated, and preprocessed 38 case-control studies comprising 1 ,821 blood samples (FIG. 4A and Table S2; STAR Methods). These studies included (1 ) eight datasets comparing healthy 60 years or older subjects (n=249) with those 40 years or younger (n=126)55 59, (2) sixteen datasets comparing healthy males (n=443) with healthy females (n=510)60 73, (3) four datasets comparing otherwise healthy subjects with BMI>30 (n=95) with those with BMI<25 (n=85)74 77, and (4) 10 datasets comparing individuals with type 1 or type 2 diabetes (n=185) with healthy controls (n=128).78-85None of these subjects had any known infections.

[0160] SoM score was significantly higher (p<0.05) in older subjects compared to those younger than 40, in males compared to females, in those with BMI>30 compared to those with BMI<25, and in individuals with diabetes compared to healthy people (FIG. 4A). The higher SoM score was primarily driven by an increase in the detrimental score that was significantly higher (p<0.0032) in subjects with the risk factors, whereas the protective score was not different (FIG. 4A).

[0161] We could not analyze the impact of presence of more than one risk factors (e.g., an older man with BMI>30) in these case-control studies. However, otherwise healthy people can have more than one risk factors (e.g., older male with BMI>30 who smokes). Furthermore, the studies included in our analysis only compared tails of a given risk factor (e.g., <40 years old vs. >60 years old, BMI<25 vs. BMI>30, etc.). In other words, these studies were not representative of the heterogeneity in the real-world population. To address these limitations, we used whole blood transcriptome profiles of 5,321 subjects in the Framingham Heart Study (FIG. 4B).86From clinical questionnaires and summaries of medical records, we collected age, sex, BMI, smoking status, and disease status for each subject (FIG. 4B; STAR Methods). The Framingham Heart Study had two advantages over the case-control studies: (1 ) it allowed analysis over the entire range of values for a risk factor, and (2) allowed analysis when individuals had more than one risk factors.

[0162] We used linear regression to investigate whether any of the risk factors were associated with the SoM, detrimental, and protective scores. We used age and BMI asAtty. Dckt.: STAN-2229WO continuous, and sex, smoking status, and disease status as binary independent variables. All risk factors (age, sex, BMI, presence of disease, smoking) were significantly positively associated with the SoM score in the Framingham cohort (p<0.034, FIG. 4B). Similar to the case-control studies, the positive association of the SoM score with the risk factors was driven by significant positive association of the detrimental score with age, sex, BMI, and smoking status (p<1.27e-10, FIG. 4B). The protective score was not associated with any risk factors, except it was significantly lower in those who smoked (p=0.01 1 , FIG. 4B). Notably, although neither detrimental nor protective score were associated with a presence of disease, the SoM score was significantly (p=0.0336) associated with the presence of a disease. Further, using 823 healthy adult subjects (390 males, 433 females) from the Framingham cohort, we found that in those younger than 53 years old, the SoM score was not different between males and females, but was higher in males than females in those older than 53 years (FIG. 10A), which was driven by increase in the detrimental immune score (FIG. 10B) as the protective immune score was not different between males and females across all ages (FIG. 10C). Further analysis of each of the four modules in the SoM score found that while both protective module (module 3 and 4) scores were similar in males and females across all ages, module 2 score was consistently higher in males, irrespective of age, whereas module 1 score was agedependent (FIGS. 10D-10G). In subjects 53 years old or younger, module 1 score was higher in females than males, but lower in females in those older than 53 years. Our results suggest that mature neutrophil-driven part of the detrimental immune response (module 1 ) is agedependent between sexes, whereas immature neutrophil-driven part (module 2) is ageindependent and higher in males than females.

[0163] Overall, these results were consistent with the results from the case-control metaanalyses. Together, these results suggests that the immune dysregulation, represented by the SoM score, is common across several known risk factors for severe outcomes in infectious diseases prior to infection. Furthermore, these results show that the increased immune dysregulation is primarily driven by increase in the detrimental scores without the corresponding increase in the protective scores. More importantly, our results demonstrate the presence of a conserved common immune dysregulation across multiple risk factors for severe outcome in infectious diseases.Detrimental host response increases with the number of risk factors

[0164] Epidemiological data have shown that the risk for severe outcomes from infection increases with the number of risk factors in an individual.17Therefore, we investigated whether the SoM, detrimental, and protective scores are correlated with the number of risk factors present in an individual. We compared each of the three scores against the total number ofAtty. Dckt.: STAN-2229WO risk factors present in each subject — BMI>30, >60 years old, male, current or former smoker, and each disease were counted as one risk factor. Subjects ranged from having no risk factors to as many as 8 risk factors. The SoM and detrimental scores significantly correlated with the number of risk factors in an individual such that they were highest in subjects with 4 or more risk factors, and lowest in subjects with no risk factors (Wilcox p<0.05, FIG. 4C). In contrast, the protective score was not correlated with the number of risk factors in an individual (FIG. 4C). There was moderate but significant positive correlation between detrimental and protective scores in subjects with no risk factors (R=0.32, p<0.01 ) that decreased as the number of risk factors increased (FIG. 10H). The two scores were not correlated in subjects with four or more risk factors (R=-0.01 , p=0.79; FIG. 10H). These results further support our analysis that the SoM score represents a conserved common immune dysregulation independently associated with risk factors, and is additive as the number of risk factors in an individual increases.Immune dysregulation decreases with the management of risk factors

[0165] Our analysis of individuals treated with hydrocortisone demonstrated that the detrimental and protective scores are modifiable. Because our analysis found that BMI at the time of gene expression was associated with the extent of immune dysregulation, we hypothesized that immune dysregulation (i.e., the SoM score) would decrease with lifestyle changes such as reduced BMI, quitting smoking, or controlling A1 C in individuals with diabetes.

[0166] To test this hypothesis, we used peripheral blood mononuclear cell (PBMC) transcriptome data from 70 healthy, overweight subjects (GSE88794),87of which 38 subjects were randomly assigned to a 20% calorie restriction diet for 12 weeks (calorie restriction group) and 32 were not (control group). The calorie restriction group had a mean weight loss of 5.6 kgs over 12 weeks.87In this randomized study, module 2 decreased significantly in the calorie restriction group (paired Wilcox test p-value=0.02; FIG. 4D), but remained unchanged in the control group (paired Wilcox test p-value>0.05; FIG. 4D). There was no difference in the SoM and module 1 scores in both the control and calorie restriction groups (paired Wilcox test p-value>0.05; FIGS. 10I-10J). However, majority of the genes in module 2, including a marker of myeloid derived suppressor cells (CEACAM8), epigenetic regulator of monocyte-to- macrophage differentiation (CTSG), and a neutrophil granule (DEFA4) were significantly down-regulated (FDR<5%) following calorie restriction (FIG. 10K).

[0167] Next, we investigated if the SoM score was lower among subjects that quit smoking. In the Framingham Heart Study, we identified 490 current smokers, 169 recent smokers who quit smoking within the last 5 years, and 23 who quit smoking at least 5 years prior (pastAtty. Dckt.: STAN-2229WO smokers) to gene expression measurement in the Framingham cohort. The SoM score, adjusted for age, sex, BMI, and disease status, was the highest in current smokers and lowest in past smokers (p<0.05; FIG. 4E). The detrimental and protective scores were not different between current and recent smokers (p>0.1 ; FIG. 4E), suggesting it took more than 5 years since quitting smoking for desired changes in immune dysregulation. Differential expression analysis of the genes in modules 1 and 2 found that 18 out of 24 genes were significantly lower (FDR<5%) following smoking cessation (FIG. 10K).

[0168] Finally, we investigated whether the SoM score was lower in individuals with diabetes with lower A1 C values. Among 474 individuals with diabetes in the Framingham cohort, we identified 317 with controlled diabetes (A1 C<7), 144 with moderately uncontrolled diabetes (7<A1 C<10), and 13 with uncontrolled diabetes (A1 C>10). After adjusting for age, sex, BMI, and smoking status, there was no difference in the SoM score between individuals with controlled or moderately uncontrolled diabetes (p=0.57; FIG. 4F), but was significantly higher in individuals with uncontrolled diabetes compared to moderately uncontrolled diabetes (p=0.05; FIG. 4F). This increase in the SoM score in individuals with uncontrolled diabetes was driven by a significant increase in their detrimental score (p=0.009), without significant change in the protective score (FIG. 4F). Differential expression analysis of the genes in modules 1 and 2 found that 22 out of 24 genes were significantly lower (FDR<5%) in those with controlled diabetes (A1 C<10) than those with uncontrolled diabetes (A1 C>10) (FIG. 10K). These results suggest individuals with diabetes with lower A1C values have reduced immune dysregulation, driven by changes in mature and immature neutrophil expression profiles, as represented by the SoM score.

[0169] Next, we compared the SoM score to the immune health metric (IHM) score88, a 150- gene signature that delineated healthy individuals from those with monogenic diseases and correlated with aging in healthy individuals. None of the 42 genes defining the SoM score overlapped with the 150-gene IHM signature. Despite this, the SoM score and IHM score were inversely correlated in the Framingham cohort (R=-0.43, p<0.01 ; FIG. 10L), suggesting a partial overlap of pathways between these two scores. This is expected since both were associated with age. However, there was a significant difference between the two signatures. Unlike the SoM score that is modifiable with intervention, the IHM score did not change following 12-week calorie restriction (FIG. 10M). Similarly, there was no difference in the IHM score between current, recent, and past smokers (FIG. 10N), and in those with controlled or uncontrolled diabetes (FIG. 10O). Together, these results show that immune dysregulation quantified by the SoM score is modifiable with changes in diet and smoking.Abundance of proteins in modules 1 and 2 is higher in subjects with risk factorsAtty. Dckt.: STAN-2229WO

[0170] Next, we investigated whether these transcriptomics changes were also observed at the protein level using data from a cohort of 2,487 subjects from the Institute for Systems Biology (Arivale cohort).89This data contained protein measurements in plasma corresponding to 5 of the 42 genes defining the SoM score (ADM and GRN from module 1 ; CEACAM8, AZU1 , and OLR1 from module 2). Proteins from module 2 were positively associated with age, sex, BMI, and disease (FIG. 10P). GRN from module 1 was positively associated with age, BMI, and disease. Each of the 5 proteins were significantly and positively associated with BMI (p<0.05; STAR Methods; FIG. 10P). The effect of sex was mixed, with men having higher abundance of proteins in module 2 compared to women but lower abundance of proteins in module 1 (p<0.05; FIG. 10P). Overall, the results from analysis of proteomic data were mostly consistent with our observations from transcriptomic data.Individuals with increased dysregulation of host response have higher risk of mortality

[0171] Finally, we investigated whether immune dysregulation, as quantified by the SoM score, is associated with the higher risk of all-cause mortality. In the Framingham cohort, 262 of the 5321 subjects died within 7 years after gene expression was profiled. We found age, sex, disease status, and smoking status were significantly associated with all-cause mortality in the Framingham cohort (p<7e-4; FIG. 5A). Importantly, the SoM score was independently associated with increased risk of mortality (HR=1.64, p=4.6e-05; FIG. 5A). Therefore, we adjusted the SoM score by regressing out the effects of age, sex, BMI, disease and smoking status. A Cox regression model using the adjusted SoM score showed that it was significantly associated with the increased risk of mortality (HR=1.8, p=7.2e-06; FIG. 5B). Next, when we divided the Framingham cohort into two groups using the median adjusted SoM score as “high SoM” and “low SoM” groups, those in the high SoM group had a significantly higher risk of allcause mortality than those in the low SoM group (HR=1 .36, p=0.013; FIG. 5C). The adjusted SoM remained an independent risk factor of increased mortality even when considering mortality over a 10-year period (HR=1.44, p=3.3e-04; FIG. 11 A) or only those over 50 years of age (HR=1.73, p=2.3e-05; FIG. 11 B). Increase in detrimental and protective scores were independently associated with increase in mortality (HR=1 .58, p=0.0025) and reduction in mortality (HR=0.56, p=0.01 ), respectively (FIG. 11 C). Furthermore, when including C-reactive protein (CRP) and the IHM as independent predictors for mortality, the SoM score remained independently associated with mortality (HR=1 .44, p=0.0052; FIG. 11 D) with substantially higher HR than CRP, while the IHM was not associated with mortality. These results strongly suggest that those with higher immune dysregulation than expected for a given age, sex, BMI, disease status, and smoking status are at a higher risk of mortality.Atty. Dckt.: STAN-2229WOMethod detailsSoM score calculation

[0172] The SoM score, which we defined previously,18is based on the expression of 42 genes split across 4 modules. Module 1 is defined by NQO2, SLPI, ORM1, KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4, and TYK2, module 2 by BCL2L11, BCAT1, BTBD7, CEP55, HMMR, PRC1, KIF15, CAMP, CEACAM8, DEFA4, LCN2, CTSG, and AZU1, module 3 by MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123, and CASP7, and module 4 by DOK2, HLA-DPB1, BUB3, SMYD2, SIDT1, EXOC2, TRIB2, and KLRB1. \Ne calculated the score for each module as the geometric mean of the expression of the genes in that module. We calculated the detrimental score as the sum of module 1 and 2 scores, and the protective score as sum of module 3 and 4 scores. Finally, we calculated the SoM score as the difference between the detrimental and protective scores. Although we described the SoM score as the ratio of the detrimental and protective scores previously,18we used the difference here since the data was log-normalized and ratio becomes difference with logtransformation.Collection, curation, and preprocessing of transcriptomic data for bacterial infection at presentation

[0173] We searched the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and ArrayExpress for datasets comprised of samples from humans with bacterial infections, using search terms “sepsis,” “bacterial sepsis,” “bacterial infection,” terms for most specific organ system infections (e.g., urinary tract infection), and common infectious bacterial pathogens. We excluded datasets if they were missing healthy controls (required for COCONUT co-normalization), clinical severity information, confirmatory microbiological testing, or gene expression data for the genes in the SoM signature. We identified 17 datasets consisting of 828 blood samples from individuals with bacterial infections (Table S1 ).21 37

[0174] We assigned one of seven clinical severity levels (healthy control, asymptomatic / convalescent, mild, moderate, serious, critical, or fatal) based on the individual’s symptoms, level of care, and dissemination of disease as previously described18(FIG. 1A). Briefly, mild infections included individuals with a minor focal infection that did not require hospitalization. Moderate infections included individuals requiring admission to the hospital with a focal infection, or any systemic infection that did not require ICU-level care. Individuals with serious infections were those admitted to the ICU without mechanical ventilation or inotropes, individuals described as having “severe” infection without furtherAtty. Dckt.: STAN-2229WO specification, or individuals requiring hospitalization and supplemental respiratory support outside of the ICU. Individuals assigned to critical were those admitted to the ICU and required mechanical ventilation, inotrope support, or with a diagnosis of shock or central nervous system (CNS) involvement. Individuals with detectable infection but resolution of symptoms were classified as convalescent and included with asymptomatic individuals. Finally, we grouped individuals with mild or moderate severity as “non-severe” and those with serious, critical, or fatal severity as “severe.”

[0175] We co-normalized gene expression data across datasets using COCONUT" (COmbat CONormalization Using conTrols) to eliminate batch effects. COCONUT co-normalization pools microarray data by correcting for batch effect using healthy controls and then applying this correction to non-control samples.Association of transcriptomic scores with response to treatment in the VANISH trial

[0176] It is known that steroids improve outcomes in severe COVID-19 infection, however studies have suggested that steroids may be harmful in subgroups of individuals with sepsis. To evaluate whether detrimental and protective host response modules were associated with heterogeneity of treatment response to steroids, we performed a secondary analysis of the VANISH cohort. The VANISH trial was a multicenter, factorial-design, randomized, controlled clinical trial that evaluated vasopressin vs norepinephrine and hydrocortisone vs placebo in septic shock. A subset of these individuals had genome-wide gene expression analysis performed that is publicly available (E-MTAB-7581 ). We hypothesized that individuals with low SoM scores and high protective host response scores (i.e., lower risk individuals) would have worse outcomes with steroid treatment. To evaluate this, we calculated the SoM module scores for each individual in VANISH. We then performed logistic regression evaluating the interaction term between scores and their component module scores (SoM, protective, detrimental) and steroid assignment with mortality, adjusting for age, sex, and the interaction between gene scores and APACHE score.Analysis of steroid effect on host-response at the bulk level

[0177] To evaluate whether differences in protective score could be detected at the bulk transcriptomic level, we used publicly available bulk transcriptome data from samples collected as part of the Low-dose Hydrocortisone in Acutely Burned Individuals randomized clinical trial (GSE77791 ).39’40In short, this was a randomized control study that randomized individuals with acute burn-related vasodilatory shock to hydrocortisone 200mg / day versus placebo. Whole blood was collected from individuals in the trial at baseline and 24-, 120-, and 168-hours post-randomization, and whole genome expression was measured. GeneAtty. Dckt.: STAN-2229WO signatures were calculated for all individuals at all time points, and we evaluated the difference in gene scores between steroid and placebo-randomized individuals at baseline and day seven using Wilcoxon rank sum test.Integration and visualization of single cell data

[0178] We collected and curated the single-cell blood transcriptome datasets from healthy controls and individuals with COVID-19 or bacterial sepsis from four studies.42-45Datasets were processed with Seurat (v4.0.5).100We removed cells with fewer than 50 detected genes, or fewer than 200 mRNA reads, or more than 20% mitochondrial reads. Datasets were integrated with reciprocal PCA algorithm in the Seurat integration workflow. Clusters were identified with Seurat SNN graph construction on PCA embeddings after integration, followed by a Louvain community detection algorithm. The cell types were annotated using canonical cell type markers. Cells were visualized in a low dimensional space using uniform manifold approximation and projection (UMAP).Collection and analyses of case control studies for analysis of risk factors

[0179] To identify studies profiling healthy subjects younger than 40 or older than 60, we searched NCBI GEO using search terms “vaccine response”, “healthy”, “adult”, and “old or elderly, young”. In total, we identified 8 datasets containing 375 blood samples55-59(Table S2). For sex, we used 16 datasets60-73we curated previously,2containing 953 samples (Table S2). For obesity, we searched NCBI GEO using search terms “BMI”, “lifestyle”, “obese”, “lean”, “healthy”, and “adult”. We excluded datasets where the definition of lean and obese were different from BMI < 25 and BMI > 30 respectively. In total, we identified 4 datasets containing 180 samples74-77(Table S2). For diabetes, we searched NCBI GEO using search terms “diabetes”, “adult”, “blood”. In total, we identified 10 datasets containing 313 samples78-85(Table S2). For all risk factors, we excluded datasets where there were fewer than 5 cases or controls. We used Metaintegrator,101'102our multi-cohort meta-analysis framework, to compute summary effect sizes and p-values for each risk factor.Processing of data from Framingham Heart Study

[0180] We used data from the Offspring and Generation III cohorts in the Framingham Heart Study,86since only those two cohorts contained gene expression measurement. We only used the subjects in the consent group 1 denoted c1 .HMB-IRB-MDS.

[0181] For gene expression measurement, we used data as deposited in v18 in phe000002. The gene expression for the Offspring and Generation III cohorts, measured from whole blood samples taken during exam 8 and 2 respectively, were taken fromAtty. Dckt.: STAN-2229WOFinalFile_Gene_OFF_2446_Adjusted_c1 .txt and Final_Exon_GENIII_3180_c1 .txt respectively. We used the Affymetrix platform table GPL5188.txt to process the expression data. We conormalized the expression from the two cohorts using COCONUT," using the healthy subjects from both the cohorts as the reference. We computed the scores for SoM, detrimental, protective, and modules 1 -4 on the conormalized data.

[0182] We used the phenotypic and clinical measurements from data deposited in v31 , corresponding to exams 8 for Offspring and 2 for Generation III cohorts. We used age at the time of gene expression measurement and sex from the table pht003099. Using data from pht000309, we identified individuals with cardiovascular events as those having an EVENT corresponding to {1 , 2, 3, 4, 5, 6, 7, 10, 1 1 , 12, 13, 16, 17, 18, 21 , 22, 23, 24, 25, 26, 30, 39, 40, 41}. We denoted individuals with cancer as those having an entry in pht000039. We identified individuals with current diabetes from pht000041 for the Offspring cohort and pht007775 for the Generation III cohort. Using data from pht000691 , we denoted individuals with dementia as those with mri352 values different than 0. Using data from pht000747 for the Offspring cohort and pht003094 for the Generation III cohort, which contained questionnaires, doctor impressions, and clinic exam taken at the time of gene expression measurement, we identified height, weight, and whether a subject had any fever or illness in the past two weeks. Using these tables, we also identified if a subject had any potential illnesses or diseases based on doctor impression or questionnaires if the values of any of {H339, H340, H341 , H342, H344, H345, H347, H348, H349, H354, H357, H358, H359, H360, H363, H364, H367, H368, H369, H370, H374, H375, H378, H379} in the Offspring cohort or {g3b0404, g3b0405, g3b0406, g3b0408, g3b0407, g3b0410, g3b0411 , g3b0414, g3b0415, g3b0416, g3b0418, g3b0420, g3b0422, g3b0424, g3b0427, g3b0429, g3b0430, g3b0431 , g3b0432, g3b0434, g3b0435, g3b0436, g3b0439, g3b0441 , g3b0442, g3b0444, g3b0445, g3b0452, g3b0453, g3b0454, g3b0448, g3b0449, g3b0426, g3b0446} in the Generation III cohort was 1 . We identified current or former smokers as those who had either of {H060, H066} in the Offspring cohort or {g3b0091 , g3b0097} in the Generation III cohort to be different than 0. Using the same pair of measurements, we identified those that quit smoking as having the first measurement to be 1 and the second measurement to be a positive integer. We identified healthy subjects as those that did not have any cardiovascular event, cancer, diabetes, dementia, fever or illness in the past two weeks, potential illnesses or diseases based on doctor impression or questionnaires. Lastly, we identified mortality events using pht003317.Linear regression analysis of risk factors in Framingham cohort

[0183] We performed linear regression analysis to identify the risk factors associated with the SoM, detrimental, and protective scores. We used age and BMI as continuous, and sex,Atty. Dckt.: STAN-2229WO smoking status, and disease status as binary independent variables. For each score, we fitted a linear model and used the p-value of the regression coefficient to determine if an independent variable was associated with the score being predicted.Calculation of the IHM score

[0184] The immune health metric (IHM) score88is computed as the difference in the geometric means of the expression between {SLC16A10, NDRG2, AK5, CD7, RNFT2, PHC1, MAN1C1, FAM102A, RCAN3, EIF3H, RPS17, RPS5, PLXDC1, TESPA 1, SGK223, EIF3L, ANAPC16, CD27, NPAT, ID3, RACK1, APEX1, GCNT4, FCMR, TCF7, KLHL3, AXIN2, LY9, RPS25, LDHB, PKIA, RPL3, N6AMT1, GAL3ST4, SSBP2, CD1C, LEF1, RPL7, PIK3IP1, GPRASP1, ABI2, APBB1, SPTBN1, GPA33, CCR9, BCKDHB, SCAI, RPL4, NOG, TCEA3, ETS1, LDLRAP1, GPR183, ZNF548, ZNF91, NPM1, MSANTD2, KAT6B, SLC7A6, DCHS1, OXNAD1, RPS2, RPL7A, GRAP, RPL23A, RPL10A, RPS3, FAM175A, RPL29, EEF2, EDAR, ABLIM1, MBLAC2, CCR7, ZNF573, CAMK4, LRRN3, MAGI3, RPLP2, ZIK1, NT5E, FUT8, ZNF101, RPL34, RPS20, FOXP1, ZNF550, TSPYL2, ATP6V0E2-AS1 , GRPEL2, MGC57346, SEPT1, PRKACB, AGMAT, RPL11, MYC, ZZZ3, RPL5} and {CTRL, NTNG2, AP5B1, PDCD1LG2, DOCK4, S100A9, BACH1, FAM8A1, SECTM1, S100A8, TYMP, HK3, IL1RN, MYD88, REC8, ALPK1, SAT1, PRKCD, SLC26A8, PARP9, LMNB1, RELT, TAP1, JAK2, BRI3, GBP2, PLEKHO2, ETV7, ODF3B, SIGLEC5, CEACAM1, CARD 16, ZBP1, DDX60L, APOL2, CD63, TNFAIP6, KCNJ2, ANKRD22, SCARF1, SEMA4A, DNAJC5, SQRDL, HELZ2, GADD45B, FAS, HSPA6, PIK3AP1, CLEC7A, SERPING1, OR52K2, ITPRIP}. We note that the IHM and SoM scores are expected to be inversely correlated because the IHM score is designed to be higher in healthy individuals whereas the SoM is designed to be lower in healthy individuals.Description of the Arivale cohort

[0185] The Arivale cohort was derived from 6,223 individuals who participated in a wellness program offered by a currently closed commercial company (Arivale Inc., Washington, USA) between 2015-2019. An individual was eligible for enrollment if the individual was over 18 years old, not pregnant, and a resident of any U.S. state except New York; participants were primarily recruited from Washington, California, and Oregon. The participants were not screened for any particular disease. Plasma concentrations of proteins in the Arivale cohort were measured using the ProSeek Cardiovascular II, Cardiovascular III, and Inflammation panels (Olink Biosciences, Uppsala, Sweden) at Olink facilities in Boston, MA. The ProSeek method is based on the highly sensitive and specific proximity extension assay, which involves the binding of distinct polyclonal oligonucleotide-labelled antibodies to the target proteinAtty. Dckt.: STAN-2229WO followed by quantification with real-time quantitative polymerase chain reaction (rt-PCR).103Samples were processed in several batches; potential batch effects were adjusted using pooled control samples included with each batch. In this study we limited the original cohort to the participants whose datasets contained baseline proteomics. This study was conducted with de-identified data of the participants who had consented to the use of their anonymized data in research. All procedures were approved by the Western Institutional Review Board (WIRB) with Institutional Review Board (IRB) (Study Number: 20170658 at Institute for Systems Biology and 1178906 at Arivale).Quantification and statistical analysis

[0186] All statistical analyses were performed using R and corresponding details can be found on the figure legends.Discussion

[0187] Several risk factors, including age, sex, BMI, smoking, and comorbidities such as diabetes and asthma, are associated with poor outcomes in individuals with infectious diseases. Although these risk factors are known to alter immune status of an individual, it is unclear whether these risk factors lead to the same conversed immune responses. We hypothesized that these distinct risk factors associated with severe outcomes lead to a conserved immune status that would be associated with the increased risk of severe outcomes. Using more than 12,000 blood transcriptome and proteomic profiles across 68 independent studies, we tested this hypothesis using our previously described 42-gene signature in blood, referred to as the SoM signature18that predicts the risk of severe outcome at presentation. Although the SoM signature was identified and validated in only viral infections, we found it is also associated with the severe outcome in individuals with bacterial infections. Furthermore, we found the SoM signature score was higher in those with any of the risk factors for severe outcomes, was positively correlated with the number of risk factors in an individual, and predicted all-cause mortality, even when they were not infected and despite the real-world biological, clinical, and technical heterogeneity across 68 independent studies. Finally, we found that this common dysregulated immune response, represented and quantified by the SoM score, could predict response to steroids in individuals with a critical illness such sepsis or burn, or to monoclonal antibody treatment in individuals with asthma.

[0188] We found this conserved immune dysregulation is modifiable as diet restrictions and smoking cessation would reduce this dysregulated immune response. Our results are in line with those by Saint-Andre et al.,90who found that smoking has a short-term impact on innate immune responses and a long-term impact on adaptive immune responses compared to neverAtty. Dckt.: STAN-2229WO smokers. However, when comparing current and past smokers, Saint-Andre et al. found that current smokers only had a higher innate response, and no difference in adaptive response.90Here, we only compared the effect of smoking cessation, i.e., current vs. past smokers. Consistent with Saint-Andre et al., we observed that after adjusting for age, sex, and BMI, the SoM score was higher in current smokers compared to past smokers, which was driven by higher detrimental scores, which are driven by myeloid cells, in current smokers than past smokers (FIG. 4E). We found no difference in protective scores, which are in part driven by T and NK cells, between current and past smokers (FIG. 4E).

[0189] Collectively, our results strongly support our hypothesis and demonstrate a presence of a dysregulated immune response that is conserved prior to and during infectious diseases, associated with severe outcomes, including all-cause mortality, and is modifiable through drug treatment and lifestyle changes.

[0190] Correlation analysis between the detrimental and protective scores in the Framingham cohort (FIG. 10H) and the individuals with asthma in U-BIOPRED (FIG. 9B) found that detrimental and protective immune responses are usually balanced (positively correlated) in otherwise healthy subjects such that impact of an increase in detrimental response is counteracted by a corresponding increase in protective response. As the number of risk factors increases, the imbalance between detrimental and protective responses also increases (i.e., reduced correlation), and both scores become inversely correlated with increased severity of asthma. Furthermore, both scores are independently associated with the risk of mortality in the expected direction in the Framingham Cohort (FIG. 11 C). These results are in line with correlation analysis because the correlation between the two scores ranges from moderately positive to moderately negative depending on the number of risk factors, disease status and severity. Collectively, these results further demonstrate the need for considering both scores together in an individual as neither the detrimental nor protective immune responses occur in isolation; that at any given time, both the detrimental and protective immune responses are present in an individual and the imbalance between them contributes to an outcome (e.g., risk of severe outcome or mortality).

[0191] We found moderate correlation between the 42-gene SoM score and the 150-gene IHM score, although none of the genes overlapped, suggesting some of the constituents of both signatures may be similar. For instance, a key constituent of the IHM are NK cells. In line with this, one of the protective response modules, module 4, is preferentially expressed in T and NK cells. Despite this correlation and common immune constituents, our results also suggest that these two signatures represent distinct immune responses. Specifically, the IHM signature was identified using 22 monogenic immune diseases, whereas the SoM signature was identified from response to respiratory viral infections. This difference in how bothAtty. Dckt.: STAN-2229WO signatures were identified suggest that the IHM signature may be rooted in genetics, whereas the SoM signature is triggered by epigenetics and lifestyle choices. This is further supported by the fact that the SoM signature was modifiable through drug treatment and changes in lifestyle (reduced calorie intakes, smoking cessation), but the IHM signature was not as drug treatments or lifestyle changes will not change genetics of an individual. Future studies that integrate both the IHM and the SoM signatures have the potential to disentangle and quantify immune health status of an individual with which they are born and how it changes over their lifetime due to environmental exposures and lifestyle choices.

[0192] Our scRNA-seq analysis found that the four gene modules in the SoM signature are preferentially expressed in different immune cell types, suggesting that the SoM score quantifies immune dysregulation at a system level. Specifically, each of the four modules in the SoM signature are preferentially expressed in different immune cell types, which include both myeloid and lymphoid cells such that mature and immature neutrophils expressed the detrimental response modules, whereas monocytes, T and NK cells expressed the protective response modules. It is likely that these four modules are further interacting with each other. For example, the detrimental modules include BCL2L11, CEACAM8, and ORM1, which are associated with lymphocyte apoptosis and immunosuppression.91 93Indeed, we found that the overall protective immune response in non-infected subjects with one or more risk factors was not different compared to those without any risk factors. However, in individuals with asthma, bacterial infection, and trauma, module 4, one of the protective modules that is preferentially expressed in T and NK cells, was lower compared to healthy subjects. These suggest the immunosuppressive functions of these neutrophils, particularly the downregulation of lymphocytes, as a potential mechanism explaining the observed detrimental effect. We further found that immunomodulation in the form of corticosteroid therapy in individuals with a high protective immune response (i.e., appropriate T / NK cell responses) were associated with increased mortality in both infectious and non-infectious critical illness. This is in line with prior analyses that have shown the impact that corticosteroids have on T / NK cells and how interference with this important immune function can result in worse outcomes.41’94’95Further studies are required to understand if and how the neutrophil-driven detrimental host responses are associated with lymphocyte-driven protective responses as well as how these changes are affected by immunomodulatory therapies.

[0193] While the association between neutrophils and asthma severity is known,51our results expand our understanding. Our analysis suggests this association is driven by specific subsets of neutrophils that express detrimental immune response modules. Our analysis also suggests that these neutrophil subsets are unlikely to be only associated with neutrophilic asthma as we found individuals with moderate-to-severe asthma who do not respond to omalizumab andAtty. Dckt.: STAN-2229WO those with severe eosinophilic asthma who do not respond to benralizumab had higher detrimental scores, which are driven primarily by a subset of neutrophils. Future studies should focus on elucidating the role of the module 2 expressing immature neutrophils in asthma.96

[0194] Heterogeneity of treatment effect in individuals with sepsis, burn, or asthma has been a significant roadblock in advancing novel treatments for these diseases. Our results in using the SoM score to identify individuals who may be harmed from hydrocortisone treatment or individuals with asthma that do not respond to monoclonal antibody treatment suggests its potential utility for clinical trials for enrollment through predictive enrichment. Across three inflammatory diseases, using baseline SoM score (i.e., prior to treatment initiation), we showed that individuals have differential response to immune-modulating therapies. These findings are in line with prior studies that have shown the association of immune endotypes with treatment responsiveness,41 94,95but now provide insights into underlying conserved immune dysregulation across biological, clinical, and technical heterogeneity. In other words, by identifying a set of individuals that may be harmed by or benefit from a potential treatment prior to treatment initiation, the SoM score has the potential to reduce the heterogeneity of treatment effect in future studies of critical illnesses.

[0195] The SoM score had lower accuracy for predicting severity in individuals with bacterial infection than those with viral infection. The lower accuracy is likely due to interferon stimulated genes in module 3 that are more responsive to viral infection than bacterial infection. Nevertheless, its ability to identify which individuals with burn and bacterial sepsis will be harmed by steroids suggest that it is important in identifying which individuals should or should not be treated with steroids.

[0196] Our results demonstrate that the SoM represents a common dysregulated immune response that is higher in individuals with risk factors prior to infection, predicts severity of infection at presentation, and predicts all-cause mortality. Furthermore, positive correlation of the SoM and the detrimental scores with the number of risk factors in an individual suggest that each risk factor contributes to increased baseline dysregulation. Even after adjusting for the effects of age, sex, BMI, disease and smoking statuses, the SoM score was associated with all-cause mortality. We also note that the hazard ratio of the adjusted SoM score (HR=1 .8) is substantially higher than a previously reported 54-gene signature, IMM-AGE, that captured immune aging (HR=1.05).97This breadth of association despite the broad heterogeneity across thousands of samples from tens of independent studies strongly suggests the SoM signature has broader implications and has potential to enable assessing baseline immune health.

[0197] In summary, we performed a multi-cohort transcriptome analysis using data spanning single-cell and bulk transcriptomics and bulk proteomics from 12,026 samples across 68Atty. Dckt.: STAN-2229WO studies and found the dysregulation between the detrimental and protective immune responses, as quantified by the SoM score, to be associated with multiple risk factors for severe bacterial and viral infections, and the differential treatment response to sepsis, burn, trauma, and chronic asthma. Our results point to the presence of a neutrophil subset in these conditions, which is characterized by increased expression of genes associated with detrimental host response. Our results provide testable hypotheses for future studies to uncover the mechanisms driving this increase in risk and design appropriate intervention strategies to alleviate this risk.References

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Claims

Atty. Dckt.: STAN-2229WOWhat is claimed is:1 . An in vitro method for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity, the method comprising: obtaining a biological sample from the subject; isolating cells from the biological sample; extracting RNA transcripts from the cells; conducting transcriptom ic analyses comprising measuring amounts of RNA transcripts encoded by at least two of HLA-DPB1 , BCL6, NQO2, 0RM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L1 1 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA to obtain gene expression data; and based on the gene expression data, providing a report indicating the subject's risk of developing severe symptoms, wherein: a. increased expression of BCL6, NQ02, 0RM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L11 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and b. decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 , RAD23B, ARHGAP45, MAP3K4, USP1 1 , SMYD2, SSR2, IL7R, FBLN5, TRAF5, TRAF31 P3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk that the subject will develop severe symptoms.

2. The method of claim 1 , wherein the steroid treatment is for viral infection or burn.

3. The method of claim 1 , wherein the steroid is hydrocortisone.Atty. Dckt.: STAN-2229WO4. The method of claim 1 , wherein the bacterial infection comprises sepsis, urinary tract infection.

5. The method of claim 4, wherein the bacterial infection further comprises surgical site infection, bacteremia, abdominal or pelvic infection, pneumonia, nosocomial pneumonia, osteomyelitis, cellulitis, trauma infection, meningitis, peritonitis, cholangitis, soft tissue infection, purulent myositis, and septic arthritis.

6. The method of any one of claims 1-5, wherein the biological sample is blood.

7. The method of one of claims 1-6, wherein the cells are peripheral blood mononuclear cells.

8. The method of one of claims 1-7, wherein the transcriptomic analysis comprises a qPCR assay.

9. The method of one of claims 1 -8, wherein the transcriptomic analysis comprises an RNA-seq assay.

10. The method of one of claims 1-9, wherein step (b) comprises calculating a severe or mild (SoM) score based on the amounts of the RNA transcripts, wherein the score indicates the probability that the subject will have a morbid health outcome.11 . The method of one of claims 1-10, wherein the RNA transcripts analyzed in step (a) comprise at least one gene from each of the following modules: module 1 : NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; module 2: BCL2L11 , BCAT1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; module 3: MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123 and CASP7; and module 4: DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1 .

12. A kit for assessing a subject’s risk of developing morbid health outcomes selected from response to steroid treatment, response to antibody treatment of asthma, bacterial or fungalAtty. Dckt.: STAN-2229WO infection, diabetes, burn and obesity, comprising reagents for measuring the amount of RNA transcripts encoded by at least 2, at least 3, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40 or at least 50 or all of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP1 1 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L1 1 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA.

13. The kit of claim 12, wherein the reagents comprise, for each RNA transcript, a sequencespecific oligonucleotide that hybridizes to the transcript.

14. The kit of claim 13, wherein sequence-specific oligonucleotide is biotinylated and / or labeled with an optically-detectable moiety.

15. The kit of claim 12, wherein the reagents comprise, for each RNA transcript, a pair of PCR primers that amplify a sequence from the RNA transcript, or cDNA made from the same.

16. The kit of claim 12, wherein the reagents comprise an array of oligonucleotide probes, wherein the array comprises, for each RNA transcript, at least one sequence-specific oligonucleotide that hybridizes to the transcript.

17. The kit of claim 12, wherein the kit is an RT-PCR kit, a competitive RT-PCR kit, a real-time RT-PCR kit, a DNA chip kit, a microarray kit, a SAGE (Serial Analysis of Gene Expression) kit, or a gene chip kit.

18. An RNA transcript of a gene selected from NQO2, SLPI, ORM1, KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4, TYK2, BCL2L11, BCAT1, BTBD7, CEP55, HMMR, PRC1, KIF15, CAMP, CEACAM8, DEFA4, LCN2, CTSG, AZU1, MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123, CASP7, DOK2, HLA-DPB1, BUB3, SMYD2, SIDT1, EXOC2, TRIB2, and KLRB1 for use as a biomarker for determining a subject’s risk ofAtty. Dckt.: STAN-2229WO developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity.

19. A composition comprising RNA transcripts encoded by at least two genes selected from HLA-DPB1 , BCL6, NQO2, 0RM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 , PRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, OLR1 , BUB3, SCAND1 , ITGB7, DOK3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L11 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA for use in determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity.

20. The composition of claim 19, wherein the composition comprises: one or more RNA transcripts from at least one gene selected from NQO2, SLPI, ORM1 , KLHL2, ANXA3, TXN, AQP9, BCL6, DOK3, PFKFB4 and TYK2; one or more RNA transcripts from at least one gene selected from BCL2L1 1 , BCAT 1 , BTBD7, CEP55, HMMR, PRC1 , KIF15, CAMP, CEACAM 8, DEFA4, LCN2, CTSG and AZU1 ; one or more RNA transcripts from at least one gene selected from MAFB, OASL, UBE2L6, VAMP5, CCL2, NAPA, ATG3, VRK2, TMEM123 and CASP7; and one or more RNA transcripts from at least one gene selected from DOK2, HLA-DPB1 , BUB3, SMYD2, SIDT1 , EXOC2, TRIB2 and KLRB1.21 . A method for determining a subject’s risk of developing morbid health outcomes from a steroid treatment, an antibody treatment of asthma, a bacterial or fungal infection, diabetes, a burn, or obesity and treating the subject, the method comprising: obtaining a biological sample from the subject; isolating cells from the biological sample; extracting RNA transcripts from the cells; conducting transcriptom ic analyses comprising measuring amounts of RNA transcripts encoded by at least two of HLA-DPB1 , BCL6, NQO2, ORM1 , DEFA4, KLRB1 , CTSG, LCN2, AZU1 , TXN, DOK2, CCL2, CEACAM8, AQP9, KLRG1 , KLRD1 , EPHX2, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, EXOC2, BCAT1 , PRF1 ,Atty. Dckt.: STAN-2229WOPRSS23, TRIB2, FURIN, ACSL1 , EZH1 , HMMR, UBE2L6, CASP7, 0LR1 , BUB3, SCAND1 , ITGB7, D0K3, SIDT1 , RAD23B, KIF15, ARHGAP45, MAP3K4, ATP8B4, IGFBP2, IFITM2, USP11 , SMYD2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, SSR2, VRK2, IL7R, FBLN5, MAFB, TRAF5, CDT1 , OASL, TRAF31 P3, TMEM123, TLN1 , CCR7, LTBP3, CHMP7, PITPNC1 , NUCB1 , RBM15B, FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , DDB1 , BCL2L1 1 , LAPTM4A, KIF23, TYK2, PIK3R1 , BANF1 , TRIM28, SOCS6, LRBA, ANXA2, IFITM3, CREG1 , and NAPA to obtain gene expression data; based on the gene expression data, providing a report indicating the subject's risk of developing severe symptoms, wherein: a. increased expression of BCL6, NQ02, 0RM1 , DEFA4, CTSG, LCN2, AZU1 , TXN, CCL2, CEACAM8, AQP9, GRN, CAMP, TLR2, ANXA3, SLPI, KLHL2, CEP55, SRGN, TRIP13, PRC1 , TCEAL9, BCAT1 , FURIN, ACSL1 , HMMR, UBE2L6, CASP7, OLR1 , SCAND1 , DOK3, KIF15, ATP8B4, IGFBP2, IFITM2, PFKFB4, VAMP5, ELL2, POMP, H1 -0, ADM, VRK2, MAFB, CDT1 , OASL, TMEM123, TLN1 , NUCB1 , FAM8A1 , BTBD7, ATG3, BCL2A1 , IFITM1 , BCL2L11 , KIF23, SOCS6, ANXA2, IFITM3, CREG1 and NAPA; and b. decreased expression of HLA-DPB1 , KLRB1 , DOK2, KLRG1 , KLRD1 , EPHX2, EXOC2, PRF1 , PRSS23, TRIB2, EZH1 , BUB3, ITGB7, SIDT1 , RAD23B, ARHGAP45, MAP3K4, USP1 1 , SMYD2, SSR2, IL7R, FBLN5, TRAF5, TRAF31 P3, CCR7, LTBP3, CHMP7, PITPNC1 , RBM15B, DDB1 , LAPTM4A, TYK2, PIK3R1 , BANF1 , TRIM28 and LRBA increases the risk that the subject will develop severe symptoms; and treating the subject to reduce the subject’s risk of developing the severe symptoms.

22. The method of claim 21 , wherein said treating comprises treating the subject with a treatment not predicted to increase the subject’s risk of developing the severe symptoms.

23. The method of claim 21 or 22, wherein said treating comprises administering an antibiotic, an anti-fungal agent, an asthma treatment, a diabetes treatment, a burn treatment, or an obesity treatment to the subject.

24. The method of claim 22, wherein said treating comprises not administering the steroid treatment to the subject determined to be at risk of developing the severe symptoms from the steroid treatment or switching to treatment with a different steroid.

25. The method of claim 22, wherein said treating comprises not administering the antibody treatment for the asthma to the subject determined to be at risk of developing theAtty. Dckt.: STAN-2229WO severe symptoms from the antibody treatment or switching to treatment with a different antibody.

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