Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities.

Obesity is a major risk factor for cardiometabolic diseases. Nevertheless, a substantial proportion of individuals with obesity do not suffer cardiometabolic comorbidities. The mechanisms that uncouple adiposity from its cardiometabolic complications are not fully understood. Here, we identify 62 loci of which the same allele is significantly associated with both higher adiposity and lower cardiometabolic risk. Functional analyses show that the 62 loci are enriched for genes expressed in adipose tissue, and for regulatory variants that influence nearby genes that affect adipocyte differentiation. Genes prioritized in each locus support a key role of fat distribution (FAM13A, IRS1 and PPARG) and adipocyte function (ALDH2, CCDC92, DNAH10, ESR1, FAM13A, MTOR, PIK3R1 and VEGFB). Several additional mechanisms are involved as well, such as insulin-glucose signalling (ADCY5, ARAP1, CREBBP, FAM13A, MTOR, PEPD, RAC1 and SH2B3), energy expenditure and fatty acid oxidation (IGF2BP2), browning of white adipose tissue (CSK, VEGFA, VEGFB and SLC22A3) and inflammation (SH2B3, DAGLB and ADCY9). Some of these genes may represent therapeutic targets to reduce cardiometabolic risk linked to excess adiposity.

Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs.

RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning. Dual graphs represent RNA helices as vertices and loops as edges. Unlike tree graphs, dual graphs can represent RNA pseudoknots (intertwined base pairs). For a representative set of RNA structures, we construct dual graphs from their secondary structures, and apply our partitioning algorithm to identify non-separable subgraphs (or blocks) without breaking pseudoknots. We report 56 subgraph blocks up to nine vertices; among them, 22 are frequently occurring, 15 of which contain pseudoknots. We then catalog atomic fragments corresponding to the subgraph blocks to define a library of building blocks that can be used for RNA design, which we call RAG-3Dual, as we have done for tree graphs. As an application, we analyze the distribution of these subgraph blocks within ribosomal RNAs of various prokaryotic and eukaryotic species to identify common subgraphs and possible ancestry relationships. Other applications of dual graph partitioning and motif library can be envisioned for RNA structure analysis and design.