Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study

Abstract

Background: Effective targeted therapy for sepsis requires an understanding of the heterogeneity in the individual host response to infection. We investigated this heterogeneity by defining interindividual variation in the transcriptome of patients with sepsis and related this to outcome and genetic diversity.

Methods: We assayed peripheral blood leucocyte global gene expression for a prospective discovery cohort of 265 adult patients admitted to UK intensive care units with sepsis due to community-acquired pneumonia and evidence of organ dysfunction. We then validated our findings in a replication cohort consisting of a further 106 patients. We mapped genomic determinants of variation in gene transcription between patients as expression quantitative trait loci (eQTL).

Findings: We discovered that following admission to intensive care, transcriptomic analysis of peripheral blood leucocytes defines two distinct sepsis response signatures (SRS1 and SRS2). The presence of SRS1 (detected in 108 [41%] patients in discovery cohort) identifies individuals with an immunosuppressed phenotype that included features of endotoxin tolerance, T-cell exhaustion, and downregulation of human leucocyte antigen (HLA) class II. SRS1 was associated with higher 14 day mortality than was SRS2 (discovery cohort hazard ratio (HR) 2·4, 95% CI 1·3-4·5, p=0·005; validation cohort HR 2·8, 95% CI 1·5-5·1, p=0·0007). We found that a predictive set of seven genes enabled the classification of patients as SRS1 or SRS2. We identified cis-acting and trans-acting eQTL for key immune and metabolic response genes and sepsis response networks. Sepsis eQTL were enriched in endotoxin-induced epigenetic marks and modulated the individual host response to sepsis, including effects specific to SRS group. We identified regulatory genetic variants involving key mediators of gene networks implicated in the hypoxic response and the switch to glycolysis that occurs in sepsis, including HIF1α and mTOR, and mediators of endotoxin tolerance, T-cell activation, and viral defence.

Interpretation: Our integrated genomics approach advances understanding of heterogeneity in sepsis by defining subgroups of patients with different immune response states and prognoses, as well as revealing the role of underlying genetic variation. Our findings provide new insights into the pathogenesis of sepsis and create opportunities for a precision medicine approach to enable targeted therapeutic intervention to improve sepsis outcomes.

Funding: European Commission, Medical Research Council (UK), and the Wellcome Trust.

Figure 1

Transcriptomic sepsis response signatures Unsupervised hierarchical cluster analysis for the top 10% most variable probes (n=2619) for the 265 patients in the discovery cohort (A). First three PCs plotted with the proportion of variance explained by each component (B); individuals are coloured by group membership based on two groups assigned with k-means. Volcano plot (C) of differentially expressed probes for SRS1 versus SRS2 (red colouring shows fold change >1·5, false discovery rate <0·05). Most enriched functions, disease phenotypes, and predicted upstream regulators were derived from differentially expressed genes (D). Kaplan-Meier survival plot by SRS group (95% CIs shaded) for (E) discovery cohort and (F) validation cohort. SRS=sepsis response group. PC=principal component.

Figure 2

Overview of differentially expressed genes between SRS groups involving HLA and T-cell activation Red shading shows upregulation and green shading shows downregulation of genes (FC>1·5, FDR<0·05) for SRS1 versus SRS2. Solid lines show direct association, dashed lines show indirect association. SRS=sepsis response signature. FC=fold change. FDR=false discovery rate. MHC=major histocompatibility complex.

Figure 3

Differentially expressed networks between SRS The network with the lowest p value (p=1 × 10⁻³⁵) identified from differential expression analysis of SRS1 versus SRS2 in the discovery cohort included 35 genes, with HIF1A and EPAS1 as nodes (A). Log FC shown with FDR. Sepsis cis-eQTL affecting genes in hypoxia network (B). Presence of cis-eQTL shown by red molecules with p values. eQTL=expression quantitative trait loci. SRS=sepsis response signature. FC=fold change. FDR=false discovery rate.

Figure 4

Sepsis eQTL Circos plot (A) of, from outer rim inwards, Manhattan plot for cis-eQTL in patients with sepsis (FDR <0·05) with names of genes with large effect sizes (variance explained, r²>70%) or examples of pathophysiological relevance shown; chromosome number; trans-eQTL (FDR<0·05) involving expression-associated SNPs also showing cis-eQTL (associated trans genes [orange] and SNPs [blue]). Box plots and local association plots for TLR4 and TNF (B). eQTL=expression quantitative trait loci. SNP=single-nucleotide polymorphism. FDR=false discovery rate.

Figure 5

Sepsis eQTL and epigenetic marks Enrichment of expression-associated SNPs with the lowest p values for each gene (FDR<0·05) for sepsis cis-eQTL within DNase I hypersensitive sites (DNase I), and within peak intervals defined for different histone modifications (H3K27ac, H3K4me1, and H3K4me3), in monocytes tolerised by exposure to lipopolysaccharide for 24 h. p value shown for enrichment versus all local SNPs in terms of the proportion of expression-associated SNPs that overlapped with each mark (Fisher's exact test) and distance to the nearest mark (Mann-Whitney test). FDR=false discovery rate. SNP=single-nucleotide polymorphism. eQTL=expression quantitative trait loci. *p<2·2 × 10⁻¹⁶.

Figure 6

Sepsis eQTL and response state SRS1-specific eQTL for MTOR (A,B). There is reduced expression in SRS1 individuals (FC 0·85, FDR 4·9 × 10⁻⁸). SRS2-specific eQTL for LAX1 (C,D). LAX1 is differentially expressed between SRS groups (0·44 FC, FDR 7·8 × 10⁻²⁵). Box plots by allele for SRS1 and for SRS2 groups (A,C). Regional association plots (B,D). eQTL=expression quantitative trait loci. SRS=sepsis response signature. FC=fold change. FDR=false discovery rate.