Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?

Copyright © 2021 The Author(s). Type 2 diabetes rates vary significantly across geographic regions. These differences are sometimes assumed to be entirely driven by differential distribution of environmental triggers, including obesity and insufficient physical activity (IPA). In this review, we discuss data which conflicts with this supposition. We carried out a secondary analysis of publicly available data to unravel the relative contribution of obesity and IPA towards diabetes risk across different populations. We used sex-specific, age-standardized estimates from Non-Communicable Disease Risk Factor Collaboration (NCD-RisC) on diabetes (1980–2014) and obesity (1975–2016) rates, in 200 countries, and from WHO on IPA rates in 168 countries in the year 2016. NCD-RisC and WHO organized countries into nine super-regions. All analyses were region- and sex-specific. Although obesity has been increasing since 1975 in every part of the world, this was not reflected in a proportional increase in diabetes rates in several regions, including Central and Eastern Europe, and High-income western countries region. Similarly, the association of physical inactivity with diabetes is not homogeneous across regions. Countries from different regions across the world could have very similar rates of diabetes, despite falling on opposite ends of IPA rate spectrum. The combined effect of obesity and IPA on diabetes risk was analyzed at the worldwide and country level. The overall findings highlighted the larger impact of obesity on disease risk; low IPA rates do not seem to be protective of diabetes, when obesity rates are high. Despite that, some countries deviate from this overall observation. Sex differences were observed across all our analyses. Overall, data presented in this review indicate that different populations, while experiencing similar environmental shifts, are apparently differentially subject to diabetes risk. Sex-related differences observed suggest that males and females are either subject to different risk factor exposures or have different responses to them.The work presented in this paper is part of a PhD project by the first author (Budour Alkaf); internal funding by Imperial College London Diabetes Centre is gratefully acknowledged. The authors are also grateful for The Medical Research Council, UK (grant numbers: MR/M013138/1, MRC/BBSRC MR/S03658X/1 (JPI HDHL H2020)) for their financial support during the authors time when conducting this piece of work and writing this paper

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe

Unconventional RNA-binding proteins step into the virus-host battlefront

The crucial participation of cellular RNA‐binding proteins (RBPs) in virtually all steps of virus infection has been known for decades. However, most of the studies characterizing this phenomenon have focused on well‐established RBPs harboring classical RNA‐binding domains (RBDs). Recent proteome‐wide approaches have greatly expanded the census of RBPs, discovering hundreds of proteins that interact with RNA through unconventional RBDs. These domains include protein–protein interaction platforms, enzymatic cores, and intrinsically disordered regions. Here, we compared the experimentally determined census of RBPs to gene ontology terms and literature, finding that 472 proteins have previous links with viruses. We discuss what these proteins are and what their roles in infection might be. We also review some of the pioneering examples of unorthodox RBPs whose RNA‐binding activity has been shown to be critical for virus infection. Finally, we highlight the potential of these proteins for host‐based therapies against viruses

Identification of RNA-binding domains of RNA-binding proteins in cultured cells on a system-wide scale with RBDmap

RBDmap identifies, in a proteome-wide manner, the regions of RNA-binding proteins (RBPs) engaged in native interactions with RNA. In brief, cells are irradiated with UV light to induce protein-RNA crosslinks. Resulting covalently linked protein-RNA complexes are purified with oligo(dT) magnetic beads, following stringent denaturing washes. After elution, RBPs are subjected to partial proteolysis, where the protein regions still bound to the RNA and those released to the supernatant are separated by a second oligo (dT) selection. After sample preparation and mass spectrometric analysis, peptide intensity ratios between the RNA-bound and released fractions are used to determine the RNA-binding regions. As a Protocol Extension article, this article describes an adaptation of an existing Protocol, and offers additional applications. The earlier protocol (for the Interactome Capture method) describes how to identify the active RBPs of cultured cells, whilst this Protocol Extension enables the identification of the RNA-binding domains of RBPs in cultured cells. The experimental workflow takes one week, plus two additional weeks for proteomics and data analysis. Notably, RBDmap presents numerous advantages over classical methods to determine RNA-binding domains: it produces proteome-wide, high resolution maps of the protein regions contacting the RNA in a physiological context and can be adapted to different biological systems and conditions. Because RBDmap relies on the isolation of polyadenylated RNA via oligo (dT), it will not provide RNA-binding information on proteins interacting exclusively with non-polyadenylated transcripts. Applied to HeLa cells, RBDmap uncovered 1,174 RNA-binding sites in 529 proteins, many of which were previously unknown.

Absolute quantitation of individual SARS-CoV-2 RNA molecules provides a new paradigm for infection dynamics and variant differences

Despite an unprecedented global research effort on SARS-CoV-2, early replication events remain poorly understood. Given the clinical importance of emergent viral variants with increased transmission, there is an urgent need to understand the early stages of viral replication and transcription. We used single-molecule fluorescence in situ hybridisation (smFISH) to quantify positive sense RNA genomes with 95% detection efficiency, while simultaneously visualising negative sense genomes, subgenomic RNAs, and viral proteins. Our absolute quantification of viral RNAs and replication factories revealed that SARS-CoV-2 genomic RNA is long-lived after entry, suggesting that it avoids degradation by cellular nucleases. Moreover, we observed that SARS-CoV-2 replication is highly variable between cells, with only a small cell population displaying high burden of viral RNA. Unexpectedly, the B.1.1.7 variant, first identified in the UK, exhibits significantly slower replication kinetics than the Victoria strain, suggesting a novel mechanism contributing to its higher transmissibility with important clinical implications