Biology subjects

Knight, J. C.

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

24 records · Page 2Linked to original sources

bioRxiv2020

Genetic Analyses of Blood Cell Structure for Biological and Pharmacological Inference

Thousands of genetic associations with phenotypes of blood cells are known, but few are with phenotypes relevant to cell function. We performed GWAS of 63 flow-cytometry phenotypes, including measures of cell granularity, nucleic acid content, and reactivity, in 39,656 participants in the INTERVAL study, identifying 2,172 variant-trait associations. These include associations mediated by functional cellular structures such as secretory granules, implicated in vascular, thrombotic, inflammatory and neoplastic diseases. By integrating our results with epigenetic data and with signals from molecular abundance/disease GWAS, we infer the hematopoietic origins of population phenotypic variation and identify the transcription factor FOG2 as a regulator of platelet -granularity. We show how flow cytometry genetics can suggest cell types mediating complex disease risk and suggest efficacious drug targets, presenting Daclizumab/Vedolizumab in autoimmune disease as positive controls. Finally, we add to existing evidence supporting IL7/IL7-R as drug targets for multiple sclerosis.

genetics↗

bioRxiv2019

A RUNX3 enhancer polymorphism associated with ankylosing spondylitis influences recruitment of Interferon Regulatory Factor 5 and factors of the Nucleosome Remodelling Deacetylase Complex in CD8+ T-cells

ObjectivesTo investigate the functional consequences of the single nucleotide polymorphism rs4648889 in a putative enhancer upstream of the RUNX3 promoter strongly associated with ankylosing spondylitis (AS).\n\nMethodsThe effects of rs4648889 on transcription factor (TF) binding were tested by DNA pull-down and quantitative mass spectrometry. The results were validated by electrophoretic mobility gel shift assays (EMSA), Western blot (WB) analysis of the pulled-down eluates, and chromatin immuno-precipitation (ChIP)-qPCR.\n\nResultsSeveral TFs showed differential allelic binding to a 50bp DNA probe spanning rs4648889. Binding was increased to the AS-risk A allele for IKZF3 (aiolos) in nuclear extracts from CD8+ T-cells (3.7-fold, p<0.03) and several components of the NUcleosome Remodeling Deacetylase (NuRD) complex, including Chromodomain-Helicase-DNA-binding protein 4 (3.6-fold, p<0.05) and Retinoblastoma-Binding Protein 4 (4.1-fold, p<0.02). In contrast, binding of interferon regulatory factor (IRF) 5 was increased to the AS-protective G allele. These results were confirmed by EMSA, WB and ChIP-qPCR.\n\nConclusionsThe association of AS with rs4648889 most likely results from its influence on the binding of this enhancer-like region to TFs, including IRF5, IKZF3 and members of the NuRD complex. Further investigation of these factors and RUNX3-related pathways may reveal important new therapeutic possibilities in AS.

genetics↗

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↗

bioRxiv2018

Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis

Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, and identified variants in CCDC33 associated with susceptibility. Pneumococcal variation explained a large amount of invasive potential, but serotype explained only half of this variation. Newly developed methods identified pneumococcal genes involved in invasiveness including pspC and zmpD, and allowed a human-bacteria interaction analysis, finding associations between pneumococcal lineage and STK32C.

genomics↗

bioRxiv2018

The Autoimmune Disease Risk Allele rs6897932 Modulates Monocyte IL7R Surface and Soluble Receptor Levels in a Context-Specific Manner

IL-7 is a key factor in T-cell immunity and IL7R polymorphisms are implicated in autoimmune pathogenesis. IL7R mRNA is induced in stimulated monocytes in a genetically determined manner, yet a role for IL7R in monocyte biology remains unexplored. Here we characterize genetic regulation of IL7R at the protein level across multiple cell subsets and conditions in healthy individuals. We find monocyte surface and soluble IL7R (sIL7R) protein are markedly expressed in response to lipopolysaccharide (LPS). We further demonstrate alleles of rs6897932, a non-synonymous IL7R polymorphism associated with susceptibility to Multiple Sclerosis, Ankylosing Spondylitis and Primary Biliary Cirrhosis, form the key determinant of both surface IL7R and sIL7R in the context of inflammation. No effect of this allele was observed in unstimulated monocytes or across lymphoid subsets. Production of sIL7R by monocytes greatly exceeded that of CD4+ T-cells, and was strongly associated with both rs6897932 genotype and expression of the splicing factor gene DDX39A. Stimulated monocytes were sensitive to exogenous IL-7, which elicits a defined transcriptional signature. Flow cytometry and single-cell sequencing of synovial fluid derived monocytes from patients with spondyloarthritis showed an enlarged subset of IL7R+ monocytes with a unique transcriptional profile that markedly overlaps that induced by IL-7 in-vitro and shows similarity to the previously described Mono4 subset. These data demonstrate disease-associated genetic variants at IL7R specifically impact monocyte surface IL7R and sIL7R following innate immune stimulation, suggesting a previously unappreciated key role for monocytes in IL-7 pathway biology and IL7R-associated diseases.

immunology↗

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↗