Emma E. Davenport

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

Summary 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.

Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study.

Summary Background Sepsis continues to be a major cause of death, disability, and health-care expenditure worldwide. Despite evidence suggesting that host genetics can influence sepsis outcomes, no specific loci have yet been convincingly replicated. The aim of this study was to identify genetic variants that influence sepsis survival. Methods We did a genome-wide association study in three independent cohorts of white adult patients admitted to intensive care units with sepsis, severe sepsis, or septic shock (as defined by the International Consensus Criteria) due to pneumonia or intra-abdominal infection (cohorts 1–3, n=2534 patients). The primary outcome was 28 day survival. Results for the cohort of patients with sepsis due to pneumonia were combined in a meta-analysis of 1553 patients from all three cohorts, of whom 359 died within 28 days of admission to the intensive-care unit. The most significantly associated single nucleotide polymorphisms (SNPs) were genotyped in a further 538 white patients with sepsis due to pneumonia (cohort 4), of whom 106 died. Findings In the genome-wide meta-analysis of three independent pneumonia cohorts (cohorts 1–3), common variants in the FER gene were strongly associated with survival (p=9·7 × 10−8). Further genotyping of the top associated SNP (rs4957796) in the additional cohort (cohort 4) resulted in a combined p value of 5·6 × 10−8 (odds ratio 0·56, 95% CI 0·45–0·69). In a time-to-event analysis, each allele reduced the mortality over 28 days by 44% (hazard ratio for death 0·56, 95% CI 0·45–0·69; likelihood ratio test p=3·4 × 10−9, after adjustment for age and stratification by cohort). Mortality was 9·5% in patients carrying the CC genotype, 15·2% in those carrying the TC genotype, and 25·3% in those carrying the TT genotype. No significant genetic associations were identified when patients with sepsis due to pneumonia and intra-abdominal infection were combined. Interpretation We have identified common variants in the FER gene that associate with a reduced risk of death from sepsis due to pneumonia. The FER gene and associated molecular pathways are potential novel targets for therapy or prevention and candidates for the development of biomarkers for risk stratification. Funding European Commission and the Wellcome Trust.

Application of whole genome and RNA sequencing to investigate the genomic landscape of common variable immunodeficiency disorders

Common Variable Immunodeficiency Disorders (CVIDs) are the most prevalent cause of primary antibody failure. CVIDs are highly variable and a genetic causes have been identified in <5% of patients. Here, we performed whole genome sequencing (WGS) of 34 CVID patients (94% sporadic) and combined them with transcriptomic profiling (RNA-sequencing of B cells) from three patients and three healthy controls. We identified variants in CVID disease genes TNFRSF13B, TNFRSF13C, LRBA and NLRP12 and enrichment of variants in known and novel disease pathways. The pathways identified include B-cell receptor signalling, non-homologous end-joining, regulation of apoptosis, T cell regulation and ICOS signalling. Our data confirm the polygenic nature of CVID and suggest individual-specific aetiologies in many cases. Together our data show that WGS in combination with RNA-sequencing allows for a better understanding of CVIDs and the identification of novel disease associated pathways.

Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study

BACKGROUND Host responses during sepsis are highly heterogeneous, which hampers the identification of patients at high risk of mortality and their selection for targeted therapies. In this study, we aimed to identify biologically relevant molecular endotypes in patients with sepsis. METHODS This was a prospective observational cohort study that included consecutive patients admitted for sepsis to two intensive care units (ICUs) in the Netherlands between Jan 1, 2011, and July 20, 2012 (discovery and first validation cohorts) and patients admitted with sepsis due to community-acquired pneumonia to 29 ICUs in the UK (second validation cohort). We generated genome-wide blood gene expression profiles from admission samples and analysed them by unsupervised consensus clustering and machine learning. The primary objective of this study was to establish endotypes for patients with sepsis, and assess the association of these endotypes with clinical traits and survival outcomes. We also established candidate biomarkers for the endotypes to allow identification of patient endotypes in clinical practice. FINDINGS The discovery cohort had 306 patients, the first validation cohort had 216, and the second validation cohort had 265 patients. Four molecular endotypes for sepsis, designated Mars1-4, were identified in the discovery cohort, and were associated with 28-day mortality (log-rank p=0·022). In the discovery cohort, the worst outcome was found for patients classified as having a Mars1 endotype, and at 28 days, 35 (39%) of 90 people with a Mars1 endotype had died (hazard ratio [HR] vs all other endotypes 1·86 [95% CI 1·21-2·86]; p=0·0045), compared with 23 (22%) of 105 people with a Mars2 endotype (HR 0·64 [0·40-1·04]; p=0·061), 16 (23%) of 71 people with a Mars3 endotype (HR 0·71 [0·41-1·22]; p=0·19), and 13 (33%) of 40 patients with a Mars4 endotype (HR 1·13 [0·63-2·04]; p=0·69). Analysis of the net reclassification improvement using a combined clinical and endotype model significantly improved risk prediction to 0·33 (0·09-0·58; p=0·008). A 140-gene expression signature reliably stratified patients with sepsis to the four endotypes in both the first and second validation cohorts. Only Mars1 was consistently significantly associated with 28-day mortality across the cohorts. To facilitate possible clinical use, a biomarker was derived for each endotype; BPGM and TAP2 reliably identified patients with a Mars1 endotype. INTERPRETATION This study provides a method for the molecular classification of patients with sepsis to four different endotypes upon ICU admission. Detection of sepsis endotypes might assist in providing personalised patient management and in selection for trials. FUNDING Center for Translational Molecular Medicine, Netherlands.

Shared and Distinct Aspects of the Sepsis Transcriptomic Response to Fecal Peritonitis and Pneumonia

&NA; Rationale: Heterogeneity in the septic response has hindered efforts to understand pathophysiology and develop targeted therapies. Source of infection, with different causative organisms and temporal changes, might influence this heterogeneity. Objectives: To investigate individual and temporal variations in the transcriptomic response to sepsis due to fecal peritonitis, and to compare these with the same parameters in community‐acquired pneumonia. Methods: We performed genome‐wide gene expression profiling in peripheral blood leukocytes of adult patients admitted to intensive care with sepsis due to fecal peritonitis (n = 117) or community‐acquired pneumonia (n = 126), and of control subjects without sepsis (n = 10). Measurements and Main Results: A substantial portion of the transcribed genome (18%) was differentially expressed compared with that of control subjects, independent of source of infection, with eukaryotic initiation factor 2 signaling being the most enriched canonical pathway. We identified two sepsis response signature (SRS) subgroups in fecal peritonitis associated with early mortality (P = 0.01; hazard ratio, 4.78). We defined gene sets predictive of SRS group, and serial sampling demonstrated that subgroup membership is dynamic during intensive care unit admission. We found that SRS is the major predictor of transcriptomic variation; a small number of genes (n = 263) were differentially regulated according to the source of infection, enriched for IFN signaling and antigen presentation. We define temporal changes in gene expression from disease onset involving phagosome formation as well as natural killer cell and IL‐3 signaling. Conclusions: The majority of the sepsis transcriptomic response is independent of the source of infection and includes signatures reflecting immune response state and prognosis. A modest number of genes show evidence of specificity. Our findings highlight opportunities for patient stratification and precision medicine in sepsis.

Chronic mucocutaneous candidiasis: characterization of a family with STAT‐1 gain‐of‐function and development of an ex‐vivo assay for Th17 deficiency of diagnostic utility

Chronic mucocutaneous candidiasis (CMC) is characterized by recurrent and persistent superficial infections, with Candida albicans affecting the mucous membranes, skin and nails. It can be acquired or caused by primary immune deficiencies, particularly those that impair interleukin (IL)−17 and IL‐22 immunity. We describe a single kindred with CMC and the identification of a STAT1 GOF mutation by whole exome sequencing (WES). We show how detailed clinical and immunological phenotyping of this family in the context of WES has enabled revision of disease status and clinical management. Together with analysis of other CMC cases within our cohort of patients, we used knowledge arising from the characterization of this family to develop a rapid ex‐vivo screening assay for the detection of T helper type 17 (Th17) deficiency better suited to the routine diagnostic setting than established in‐vitro techniques, such as intracellular cytokine staining and enzyme‐linked immunosorbent assay (ELISA) using cell culture supernatants. We demonstrate that cell surface staining of unstimulated whole blood for CCR6+CXCR3–CCR4+CD161+ T helper cells generates results that correlate with intracellular cytokine staining for IL‐17A, and is able to discriminate between patients with molecularly defined CMC and healthy controls with 100% sensitivity and specificity within the cohort tested. Furthermore, removal of CCR4 and CD161 from the antibody staining panel did not affect assay performance, suggesting that the enumeration of CCR6+CXCR3–CD4+ T cells is sufficient for screening for Th17 deficiency in patients with CMC and could be used to guide further investigation aimed at identifying the underlying molecular cause.

A Common Haplotype of the TNF Receptor 2 Gene Modulates Endotoxin Tolerance

Endotoxin tolerance is characterized by the suppression of further TNF release upon recurrent exposure to LPS. This phenomenon is proposed to act as a homeostatic mechanism preventing uncontrolled cytokine release such as that observed in bacterial sepsis. The regulatory mechanisms and interindividual variation of endotoxin tolerance induction in man remain poorly characterized. In this paper, we describe a genetic association study of variation in endotoxin tolerance among healthy individuals. We identify a common promoter haplotype in TNFRSF1B (encoding TNFR2) to be strongly associated with reduced tolerance to LPS (p = 5.82 × 10−6). This identified haplotype is associated with increased expression of TNFR2 (p = 4.9 × 10−5), and we find basal expression of TNFR2, irrespective of genotype and unlike TNFR1, is associated with secondary TNF release (p < 0.0001). Functional studies demonstrate a positive-feedback loop via TNFR2 of LPS-induced TNF release, confirming this previously unrecognized role for TNFR2 in the modulation of LPS response.

A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765–0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.Sepsis is characterized by deregulated host response to infection. Efficient therapies are still needed but a limitation for sepsis treatment is the heterogeneity in patients. Here Sweeney et al. generate prognostic models based on gene expression to improve risk stratification classification and prediction for 30-day mortality of patients.

Identification of drug eQTL interactions from repeat transcriptional and environmental measurements in a lupus clinical trial

Background: Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms. Results: In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus we measured interferon (IFN) status, anti-IL-6 drug exposure and genome-wide gene expression at three time points (379 samples from 157 individuals). First, we show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point by 64%. Then, after identifying 4,818 cis-eQTLs, we observed a statistically significant enrichment of in vivo eQTL interactions with IFN status (p<0.001 by permutation) and anti-IL-6 drug exposure (p<0.001). We observed 210 and 72 interactions for IFN and anti-IL-6 respectively (FDR<20%). Anti-IL-6 interactions have not yet been described while 99 of the IFN interactions are novel. Finally, we found transcription factor binding motifs interrupted by eQTL interaction SNPs, pointing to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs. Conclusion: This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.We hypothesize that regulatory mechanisms influenced by an environmental perturbagen may be identified with eQTL (expression quantitative trait locus) interactions, which alter the relationship between a genetic variant and transcript levels. In an anti-IL-6 clinical trial of 157 patients with systemic lupus erythematosus (SLE) we measured cell counts, interferon (IFN) signature, and drug exposure at three time points alongside genome-wide transcription. Repeated transcriptomic measurements detected 4,976 cis eQTLs, 63% more than detectable from single measurements. We identified 154, 185 and 126 nominal eQTL interactions with T cell count, IFN status and anti-IL-6 drug exposure respectively (more than expected by chance, p<0.001). IFN interactions are enriched for IRF1 motifs, and 91/126 drug-eQTL interactions are consistent with interactions using free IL-6 protein levels. This same approach can be easily used to define informative drug exposure scores, and can be applied to larger drug trials to further our understanding of the drug mechanisms.If an expression quantitative trait locus (eQTL) effect is modulated by an environmental stimulus, such as drug exposure or disease status, it can point to key regulatory mediators. In a clinical trial for anti-IL-6 in 157 patients with systemic lupus erythematosus we measured cell counts, interferon (IFN) status, drug exposure and genome-wide gene expression at three time points. First, we confirmed an increase in power using repeat transcriptomic measurements. Then, after detecting 4,976 cis eQTLs, we discovered that 154, 185 and 126 had evidence of significant eQTL interactions with T cell proportion, IFN status and anti-IL-6 drug exposure respectively. Next, we found an enrichment of transcription factor binding motifs interrupted by eQTL interaction SNPs, pointing to regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, IFN interactions are enriched for IRF1 binding site motifs, while anti-IL-6 interactions are enriched for IRF4 motifs. Finally, we used the drug-eQTL interactions to define an informative drug exposure score, reflecting a drug9s effect in an individual patient, thus highlighting the potential for utilizing drug-eQTL interactions in a pharmacogenetic framework.

Rheumatoid arthritis heritability is concentrated in regulatory elements with CD4+ T cell-state-specific transcription factor binding profiles

Active regulatory elements within CD4+ T cells harbor disproportionate heritability (h2) for rheumatoid arthritis (RA). We hypothesized that regulatory elements specific to pathogenic CD4+ T cell-states better capture RA h2; however, defining these elements is challenging. To this end, we introduce IMPACT, a genome annotation strategy to identify cell-state-specific regulatory elements defined by key transcription factor binding profiles, learned from 398 chromatin and sequence annotations. Integrating IMPACT annotations of four CD4+ T cell-states with RA summary statistics of 38,242 Europeans and 22,515 East Asians, we observe that on average the top 5% of Treg predicted regulatory elements explain 85.7% (s.e. 19.4%, enrichment p<1.6e-05) of RA h2, and other cell-states explain a similar proportion. IMPACT captures RA h2 better than active CD4+ T cell regulatory elements, including super enhancers and specifically expressed genes (all p<0.05). IMPACT is generalizable to non-immune cell types and can identify other complex trait associated regulatory elements.Despite significant progress in annotating the genome with experimental methods, much of the regulatory noncoding genome remains poorly defined. Here we assert that regulatory elements may be characterized by leveraging local epigenomic signatures at sites where specific transcription factors (TFs) are bound. To link these two identifying features, we introduce IMPACT, a genome annotation strategy which identifies regulatory elements defined by cell-state-specific TF binding profiles, learned from 515 chromatin and sequence annotations. We validate IMPACT using multiple compelling applications. First, IMPACT predicts TF motif binding with high accuracy (average AUC 0.92, s.e. 0.03; across 8 TFs), a significant improvement (all p