Biology subjects

Hinds, C. J.

Publications and source records attributed to Hinds, C. J..

3 recordsLinked to original sources

bioRxiv2023

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene expression-based patient subgroups (Sepsis Response Signatures: SRS) informative for outcome and underlying pathophysiology. Here we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-seq data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs, and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. These included HIF1A and CEBPB, which were associated with progenitor and immature neutrophil subsets respectively, further implicating glycolysis and emergency granulopoiesis in SRS1. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection. Understanding the regulatory networks underlying patient heterogeneity provides additional information for developing immunomodulatory treatments and a personalised medicine approach to treating sepsis.

genomics↗

bioRxiv2019

Targeted metagenomic sequencing enhances the identification of pathogens associated with acute infection.

The routine identification of pathogens during infection remains challenging because it relies on multiple modalities such as culture and nucleic acid amplification and tests that tend to be specific for very few of an enormous number of possible infectious agents. Metagenomics promises single-test identification, but shotgun sequencing remains unwieldy and expensive or in many cases insufficiently sensitive to detect the amount of pathogen material in a clinical sample. Here we present the validation and application of Castanet, a method for metagenomic sequencing with enrichment that exploits clinical knowledge to construct a broad panel of relevant organisms for detection at low cost with sensitivity comparable to PCR. Castanet targets both DNA and RNA, works with small sample volumes, and can be implemented in a high-throughput diagnostic setting. We used Castanet to analyse plasma samples from 573 patients from the GAinS sepsis cohort and CSF samples from 243 patients from the ChiMES meningitis cohort that had been evaluated using standard clinical microbiology methods, identifying relevant pathogens in many cases where no pathogen had previously been detected. Castanet is intended for use in defining the distribution of pathogens in samples, diseases and populations, for large-scale clinical studies and for verifying the performance of routine testing regimens. By providing sequence as output, Castanet combines pathogen identification directly with subtyping and phylo-epidemiology.

microbiology↗

bioRxiv2016

Mortality prediction in sepsis via gene expression analysis: a community approach

Improved risk stratification and prognosis 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 three scientific groups were invited to independently generate prognostic models for 30-day mortality using 12 discovery cohorts (N=650) containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance was validated in 5 cohorts of community-onset sepsis patients (N=189) in which the models showed summary AUROCs ranging from 0.765-0.89. Similar performance was observed in 4 cohorts of hospital-acquired sepsis (N=282). Combining the new gene-expression-based prognostic models with prior clinical severity scores led to significant improvement in prediction of 30-day mortality (p<0.01). These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis, improving both resource allocation and prognostic enrichment in clinical trials.

bioinformatics↗