Biology subjects

Burnham, K. L.

Publications and source records attributed to Burnham, K. L..

6 recordsLinked to original sources

bioRxiv2026

Single-cell multi-ancestry regulatory map of systemic lupus erythematosus

Integrating genomic and single-cell transcriptomic profiles from patient cohorts can uncover regulatory effects underlying genetic associations with disease. Here we present SLEmap, a multi-ancestry single-cell expression quantitative trait locus (sc-eQTL) map generated from peripheral blood mononuclear cells from 281 patients with systemic lupus erythematosus (SLE). We identified 18,608 independent eQTLs in 5,656 genes, of which 149 signals in 66 genes colocalized with SLE GWAS loci. Nearly half of these colocalizations were detected exclusively through cell type-level analyses, highlighting the importance of cellular context for interpreting disease-associated genetic variation. Multi-ancestry data identified regulatory signals robust across diverse ancestral backgrounds and improved signal resolution. Most colocalizations were undetectable in sc-eQTLs from a healthy cohort (OneK1K), with novel SLE colocalizations highlighting disease-relevant regulatory mechanisms across distinct cell types, including the NF-{kappa}B pathway. These findings demonstrate the value of disease-specific, multi-ancestry single-cell regulatory maps for resolving the genes and cellular mechanisms underlying disease-associated genetic variation.

genetics↗

bioRxiv2024

Variant-to-function dissection of rare non-coding GWAS loci with high impact on blood traits

Understanding the function of genetic variants associated with human traits and diseases remains a significant challenge. Here we combined analyses based on natural genetic variation and genetic engineering to dissect the function of 94 non-coding variants associated with haematological traits. We describe 22 genetic variants impacting haematological variation through gene expression. Further, through in-depth functional analysis, we illustrate how a rare, non-coding variant near the CUX1 transcription factor impacts megakaryopoiesis through modulation of the CUX1 transcriptional cascade. Collectively, our findings enhance the functional interpretation of genetic association studies and advance understanding of how non-coding variants contribute to blood and immune system variation.

genetics↗

bioRxiv2023

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and over half of inactive genes. Using 2,821 paired whole genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions and gene inversion. Overall, we develop misexpression as a novel type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

genetics↗

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↗

bioRxiv2022

Transcriptional reprogramming from innate immune functions to a pro-thrombotic signature upon SARS-CoV-2 sensing by monocytes in COVID-19

Alterations in the myeloid immune compartment have been observed in COVID-19, but the specific mechanisms underlying these impairments are not completely understood. Here we examined the functionality of classical CD14+ monocytes as a main myeloid cell component in well-defined cohorts of patients with mild and moderate COVID-19 during the acute phase of infection and compared them to that of healthy individuals. We found that ex vivo isolated CD14+ monocytes from mild and moderate COVID-19 patients display specific patterns of costimulatory and inhibitory receptors that clearly distinguish them from healthy monocytes, as well as altered expression of histone marks and a dysfunctional metabolic profile. Decreased NF{kappa}B activation in COVID-19 monocytes ex vivo is accompanied by an intact type I IFN antiviral response. Subsequent pathogen sensing ex vivo led to a state of functional unresponsiveness characterized by a defect in pro-inflammatory cytokine expression, NF{kappa}B-driven cytokine responses and defective type I IFN response in moderate COVID-19 monocytes. Transcriptionally, COVID-19 monocytes switched their gene expression signature from canonical innate immune functions to a pro-thrombotic phenotype characterized by increased expression of pathways involved in hemostasis and immunothrombosis. In response to SARS-CoV-2 or other viral or bacterial components, monocytes displayed defects in the epigenetic remodelling and metabolic reprogramming that usually occurs upon pathogen sensing in innate immune cells. These results provide a potential mechanism by which innate immune dysfunction in COVID-19 may contribute to disease pathology.

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↗