Biology subjects

Burnett, J. E.

Publications and source records attributed to Burnett, J. E..

3 recordsLinked to original sources

bioRxiv2026

Genetic Determinants of Cell-Type-Specific Responses to Cardiotoxic Chemotherapeutic Agents

Doxorubicin (DOX) and 5-fluorouracil (5-FU) are effective for treating a variety of cancers, yet many patients taking these drugs experience adverse cardiovascular events that are severe enough to pause treatment, often prolonging the course of chemotherapy and reducing its efficacy. Individual risk for drug-induced cardiotoxicity is likely mediated, at least in part, by genetic variants that control how the heart and vasculature respond to drug-induced perturbations. To identify loci associated with drug-induced cardiotoxicity, we developed an in vitro model that allows us to characterize the effects of chemotherapeutic drugs on a wide range of cardiac cell types, including cardiomyocytes, endothelial cells, and fibroblasts. Using single-cell RNA-seq, we profiled responses to DOX and 5-FU across 46 individuals and 14 cell types, identifying hundreds of expression quantitative trait loci that modify cardiovascular transcription in response to each drug (response eQTLs). Drug-responsive eQTL genes include genes that were previously linked to drug-induced cardiotoxicity and cardiovascular disease, such as ABCB4, HAS3, TNFRSF1A, and OGFR. Over a third of drug-responsive genes are shared between the DOX and 5-FU treatments, suggesting that genes predictive of DOX-induced cardiotoxicity may extend to 5-FU. Among the credible colocalizations we identified between GWAS signals and drug-response eQTLs, we discovered a locus associated with ischemic heart disease that is also a DOX-responsive eQTL for IL6R. IL6R was previously nominated as a druggable target for heart disease, and its binding partner IL6 is a candidate biomarker for anthracycline-induced cardiotoxicity. These findings advance the discovery of genetic variants underlying cardiotoxicity and support development of genotype-based predictors for treating cancer.

genomics↗

bioRxiv2018

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 241 mean expression QTLs (eQTLs) at 10% FDR, of which 82% replicate in bulk RNA-seq data from the same individuals. We further identified 14 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs) which could alter the variance of expression independently of the mean can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 424 individuals as a lower bound to achieve 80% power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.\n\nAuthor summaryCommon genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

genomics↗

bioRxiv2016

Impact of regulatory variation across human iPSCs and differentiated cells

Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation across different cell types and as models for studies of complex disease. We established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression, chromatin accessibility and DNA methylation. Regulatory variation between individuals is lower in iPSCs than in the differentiated cell types, consistent with the intuition that developmental processes are generally canalized. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific QTLs are in shared open chromatin. Finally, we developed a deep neural network to predict open chromatin regions from DNA sequence alone and were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.

genomics↗