Changes in macrophage transcriptome associate with systemic sclerosis and mediate GSDMA contribution to disease risk

Abstract

ObjectivesSeveral common and rare risk variants have been reported for systemic sclerosis (SSc), but the effector cell(s) mediating the function of these genetic variants remains to be elucidated. While innate immune cells have been proposed as the critical targets to interfere with the disease process underlying SSc, no studies have comprehensively established their effector role. Here we investigated the contribution of monocyte-derived macrophages (MDMs) in mediating genetic susceptibility to SSc.MethodsWe carried out RNA sequencing and genome-wide genotyping in MDMs from 57 patients with SSc and 15 controls. Our differential expression and expression quantitative trait locus (eQTL) analysis in SSc was further integrated with epigenetic, expression and eQTL data from skin, monocytes, neutrophils and lymphocytes.ResultsWe identified 602 genes upregulated and downregulated in SSc macrophages that were significantly enriched for genes previously implicated in SSc susceptibility (P=5×10−4), and 270 cis-regulated genes in MDMs. Among these, GSDMA was reported to carry an SSc risk variant (rs3894194) regulating expression of neighbouring genes in blood. We show that GSDMA is upregulated in SSc MDMs (P=8.4×10−4) but not in the skin, and is a significant eQTL in SSc macrophages and lipopolysaccharide/interferon gamma (IFNγ)-stimulated monocytes. Furthermore, we identify an SSc macrophage transcriptome signature characterised by upregulation of glycolysis, hypoxia and mTOR signalling and a downregulation of IFNγ response pathways.ConclusionsOur data further establish the link between macrophages and SSc, and suggest that the contribution of the rs3894194 risk variant to SSc susceptibility can be mediated by GSDMA expression in macrophages.

Quantifying the contribution of Neanderthal introgression to the heritability of complex traits

“…65 Although GRB10’ s role in immunity is still unclear, it has been associated with a subtype of systemic sclerosis (lcSSc); patients with systemic sclerosis have higher expression of GRB10 in monocytes. 66,67 Studies of Grb10 deficient mice demonstrated Grb10 ’s role in hematopoietic regeneration in vivo . 68 Additionally, in a transcriptome study of CD4+ Effector Memory T cells (CD4 + T EM ), GRB10 was the most significantly downregulated gene after T-cell receptor stimulation.…”

The use of Peripheral Blood-Mononuclear Cells in Scleroderma patients: an observational preliminary study

“…Notably, several papers showed that a main mechanism of action of MSCs from adipose tissue, is to promote tissue regeneration through M2 polarization [38][39][40], but hypoxia reduces their capability to polarize macrophages in the M2 phenotype [41] while for the PB-MNCs hypoxia is a physiological trigger for angiogenesis [42][43][44][45][46].…”

Design and power analysis for multi-sample single cell genomics experiments

“…This can be achieved by controlling the family-wise error rate (FWER: probability of at least one false positive) using the Bonferroni method [65]: = 0.05 / number of genes to guarantee FWER < 0.05. To obtain a range of typical effect sizes and mean expression distributions in specific immune cell types, we analysed several DEG studies based on FACS sorted bulk RNA-seq [54,55]. Combining our model for gene expression in scRNA-seq experiments with this power analysis of DE genes, we can calculate the overall detection power of DE genes in a scRNA-seq experiment as the product of the expression probability of the gene and the DE power for the gene (see Formula 1).…”

“…When published, we took directly the effect sizes, otherwise we recalculated the DE analysis with DEseq2. Differential gene expression: To get realistic estimates for effect sizes (fold changes), data sets from FACS sorted bulk RNA-seq studies were taken [54,55]. The data sets were used to rank the expression level of the DEGs among all other genes using the FPKM values.…”

“…We combine these with cell type specific priors for effect sizes based on DEGs and eQTL genes from bulk RNA-seq experiments on cells sorted by fluorescence activated cell sorting (FACS). The four DE studies [54][55][56][57] and one eQTL study [58] cover different biological applications to diseases such as asthma and cancer and to ageing. Together, this will enable researchers to design experiments across tissues with realistic effect size estimates.…”