Search bioRxiv⌕ Search

Biology subjects

Dekkers, K. F.

Publications and source records attributed to Dekkers, K. F..

3 recordsLinked to original sources

Oleic acid triggers CD4+ T cells to be metabolically rewired and poised to differentiate into proinflammatory T cell subsets upon activation

T cells are the most common immune cells in atherosclerotic plaques and the function of T cells can be altered by fatty acids. Here, we show that pre-exposure of CD4+ T cells to oleic acid, an abundant fatty acid linked to cardiovascular events, results in a preferential differentiation into pro-inflammatory subsets upon activation by upregulating core metabolic pathways. RNA-sequencing of non-activated CD4+ T cells revealed that oleic acid upregulates genes encoding enzymes responsible for cholesterol and fatty acid biosynthesis. Transcription footprint analysis linked this rewiring to the differentiation of pro-inflammatory subsets. Indeed, spectral flow cytometry showed that pre-exposure to oleic acid results in a skew toward IL-9, IL-17A, IL-5 and IL-13 producing T cells upon activation. Importantly, inhibition of either cholesterol or fatty acid biosynthesis abolishes this effect, suggesting a beneficial role for statins beyond cholesterol lowering. Taken together, fatty acids may affect inflammatory diseases by influencing T cell metabolism.

genomics↗

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identified 818 genes that influence DNA methylation patterns in blood using large-scale population genomics data. By employing genetic instruments as causal anchors, we identified directed associations between gene expression and distant DNA methylation levels, whilst ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. We found that DNA methylation patterns are commonly shaped by transcription factors that consistently increase or decrease DNA methylation levels. However, we also observed genes encoding proteins without DNA binding activity with widespread effects on DNA methylation (e.g. NFKBIE, CDCA7(L) and NLRC5) and we suggest plausible mechanisms underlying these findings. Many of the reported genes were unknown to influence DNA methylation, resulting in a comprehensive resource providing insights in the principles underlying epigenetic regulation.

genomics↗

Genome-wide identification of directed gene networks using large-scale population genomics data

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 x 10-10), among which transcription factors were overrepresented (P = 3.3 x 10-7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

genomics↗