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Vickers, A.

Publications and source records attributed to Vickers, A..

2 recordsLinked to original sources

Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or cardio-metabolic health, remains unclear.\n\nWe investigated smoking-associated DNA methylation and gene expression variation in adipose tissue from 542 healthy female twins with available well-characterized cardio-metabolic phenotype profiles. We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and up-regulated in smokers. We replicated and validated a proportion of the signals in blood, adipose, skin, and lung tissue datasets, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. We tested for associations between the observed smoking signals and several adiposity phenotypes that constitute cardio-metabolic disease risk. Visceral fat and android/gynoid ratio were associated with methylation at smoking-markers with functional impacts on expression, such as CYP1A1, and in signals shared across tissues, such as NOTCH1. At smoking-signals BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation.\n\nOur results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue, a subset of which link to human cardio-metabolic health and may give insights into the wide ranging risk effects of smoking across the body.\n\nAuthor SummaryTobacco smoking is the strongest environmental risk factor for human disease. Here, we investigate how smoking systemically changes methylome and transcriptome signatures in multiple tissues in the human body. We observe strong and coordinated epigenetic and gene expression changes in adipose tissue, some of which are mirrored in blood, skin, and lung tissue. Smoking leaves a strong short-lived impact on gene expression levels, while methylation changes are long-lasting after smoking cessation. We investigated if these changes observed in a metabolically-relevant (adipose) tissue had impacts on human disease, and observed strong associations with cardio-metabolic disease traits. Some of the smoking signals could predict future gain in obesity and cardio-metabolic disease risk in current smokers who subsequently go on to quit smoking. Our results provide novel insights into understanding the widespread health consequence of smoking outside the lung.

genomics

Identifying the genetic basis of variation in cell behaviour in human iPS cell lines from healthy donors

Large cohorts of human iPSCs from healthy donors are potentially a powerful tool for investigating the relationship between genetic variants and cellular phenotypes. Here we integrate high content imaging, gene expression and DNA sequence datasets for over 100 human iPSC lines to identify the genetic basis of inter-individual variability in cell behaviour. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we identified genes that correlated in expression with intrinsic (genetic) and extrinsic (ECM) factors. However, variation in mRNA levels could not account for outlier cell behaviour. Instead, we identified rare, deleterious SNVs in the coding sequence of genes involved in ECM adhesion that occurred in cell lines that were outliers for one or more phenotypes such as cell spreading. These also correlated with altered germ layer differentiation on micropatterned surfaces. Our study thus establishes a strategy for integrating genetic and cell biological measurements for high-throughput analysis.

cell biology