GSE151889: RIPK1 gene variants associate with increased obesity in humans and can be therapeutically silenced to improve metabolic dysfunction in obese mice

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Info Studies were incorporated in a barcode, matrix, feature format. This format can be found on the 10x Genomics website for processing of single cell studies to obtain the gene expression matrices. For each study, the metadata incorporated in GEO were manually curated into profiles and the samples were separated based on applicable groups and conditions. The expression data from the cells for the samples within the same profile and condition were aggregated into an expression matrix with the cell barcodes having the sample name appended to it to ensure unique cell names.

Description (from GEO)

Submission Date: Jun 05, 2020

Summary: Obesity is a major public health burden worldwide, greatly increasing the risk of diabetes, cardiovascular diseases and cancer. Obesity and associated insulin resistance are characterized by chronic low-grade inflammation driven by the cooperation of the innate immune system and dysregulated metabolism in adipose tissue and other metabolic organs. RIPK1 (Receptor-Interacting serine/threonine Protein Kinase 1) is a central regulator of inflammatory cell function that coordinates inflammation, apoptosis and necroptosis in response to inflammatory stimuli. Here, we show that genetic polymorphisms near the human RIPK1 locus associate with increased RIPK1 gene expression in adipose tissue and are strongly linked with the risk of obesity in a human population. We show that one of these SNPs is within a binding site for E4BP4 and increases RIPK1 promoter activity and RIPK1 gene expression in adipose tissue. Therapeutic silencing of RIPK1 in vivo in a mouse model of diet-induced obesity dramatically reduces fat mass, total body weight and improves insulin sensitivity, while simultaneously reducing macrophage and promoting invariant natural killer T-cell accumulation in adipose tissue. These findings demonstrate RIPK1 is a genetic driver of obesity in humans, and that reducing RIPK1 expression is a potential novel therapeutic approach to target obesity and related diseases.

GEO Accession ID: GSE151889

PMID: 32989316

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Info Preprocessing and downstream analysis were computed using the scanpy Python library and the steps of processing followed the Seurat vignette. Cells and genes with no expression or very low expression were removed from the dataset based on a predefined threshold. The data was then normalized across the expression within the cells and log normalized. The top 2000 highly variable genes were extracted to be used for downstream analysis. For each of these aggregated data matrices, the clusters were computed using the leiden algorithm. Scanpy was then used to compute the PCA, t-SNE, and UMAPs. The points in the plots are labelled by their corresponding cell type labels. The cell type labels were computed using the wilcoxon method as the differential gene expression method. The top 250 genes were then used for enrichment analysis against the CellMarker library in order to determine the most appropriate cell type label with the lowest p-value.
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