Groot, H. E. | Search bioRxiv

bioRxiv2023

The gut microbiome across the cardiovascular risk spectrum

RationaleDespite significant progress in treatment strategies, cardiovascular disease remains a leading cause of death worldwide. Identifying new potential targets is crucial for enhancing preventive and therapeutic strategies. The gut microbiome has been associated with the development of coronary artery disease (CAD), however our understanding of the precise changes in the gut microbiome occurring during CAD development remains limited. ObjectiveTo investigate microbiome changes in participants without clinically manifest CAD with different cardiovascular risk levels and in patients with ST-elevation myocardial infarction (STEMI). Methods and ResultsIn this cross-sectional study we characterized the gut microbiome using metagenomics of 411 fecal samples from individuals with low (n=130), intermediate (n=130) and high (n=125) cardiovascular risk based on the Framingham score, and STEMI patients (n=26). We analyzed alpha and beta diversity of the gut microbiome and differential abundance of species and functional pathways among the different groups while accounting for confounders including medication and technical covariates. Abundances of Collinsella stercoris, Flavonifractor plautii and Ruthenibacterium lactatiformans showed a positive trend with cardiovascular risk, while Streptococcus thermophilus was negatively associated. Furthermore, in the differential abundance analysis we identified eight species and 49 predicted metabolic pathways that were differently abundant among the groups. These species included species linked to inflammation. Starch biosynthesis and phenolic compound degradation pathways were enriched in the gut microbiome of STEMI patients, while pathways associated with vitamin, lipid and amino-acid biosynthesis were depleted. ConclusionsWe identified four microbial species that demonstrated a gradual trend in their abundance from low risk individuals to those with STEMI, and species and pathways that were differently abundant in STEMI patients compared to groups without clinically manifest CAD. Further investigation is warranted to gain deeper understanding of their precise role in CAD progression and potential implications, with the ultimate goal of identifying novel therapeutic targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=142 HEIGHT=200 SRC="FIGDIR/small/546971v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@f7f0b1org.highwire.dtl.DTLVardef@1db0c8eorg.highwire.dtl.DTLVardef@154adeorg.highwire.dtl.DTLVardef@1e64c74_HPS_FORMAT_FIGEXP M_FIG C_FIG Created with BioRender.com

genomics↗

bioRxiv2022

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

BackgroundExpression quantitative trait loci (eQTL) studies have shown how genetic variants affect downstream gene expression. To identify the upstream regulatory processes, single-cell data can be used. Single-cell data also offers the unique opportunity to reconstruct personalized co-expression networks--by exploiting the large number of cells per individual, we can identify SNPs that alter co-expression patterns (co-expression QTLs, co-eQTLs) using a limited number of individuals. ResultsTo tackle the large multiple testing burden associated with a genome-wide analysis (i.e. the need to assess all combinations of SNPs and gene pairs), we conducted a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets from three studies (reflecting 173 unique participants and 1 million cells) using a novel filtering strategy followed by a permutation-based approach. Before analysis, we evaluated the co-expression patterns to be used for co-eQTL identification using different external resources. The subsequent analysis identified a robust set of cell-type-specific co-eQTLs for 72 independent SNPs that affect 946 gene pairs, which we then replicated in a large bulk cohort. These co-eQTLs provide novel insights into how disease-associated variants alter regulatory networks. For instance, one co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell-activation and autoimmune disease. Among these genes, we identified enrichment for targets of five T-cell-activation-related transcriptional factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. ConclusionOur co-eQTL results highlight the importance of studying gene regulation at the context-specific level to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy--combined with our technical guidelines--will soon identify many more co-eQTLs, further helping to elucidate unknown disease mechanisms.

genetics↗