The relevant data from this Data Report are hosted at the Human Genome Variation Database at: https://doi.org/10.6084/m9.figshare.hgv.3418, https://doi.org/10.6084/m9.figshare.hgv.3421.
Plant adaptation to terrestrial life started 450 million years ago and has played a major role in the evolution of life on Earth. The genetic mechanisms allowing this adaptation to a diversity of terrestrial constraints have been mostly studied by focusing on flowering plants. Here, we gathered a collection of 133 accessions of the model bryophyte Marchantia polymorpha and studied its intraspecific diversity using selection signature analyses, a genome–environment association study and a pangenome. We identified adaptive features, such as peroxidases or nucleotide-binding and leucine-rich repeats (NLRs), also observed in flowering plants, likely inherited from the first land plants. The M. polymorpha pangenome also harbors lineage-specific accessory genes absent from seed plants. We conclude that different land plant lineages still share many elements from the genetic toolkit evolved by their most recent common ancestor to adapt to the terrestrial habitat, refined by lineage-specific polymorphisms and gene family evolution.
The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants. We identified 604 independent rare noncoding single-variant associations with circulating protein levels. Unlike protein-coding variation, rare noncoding genetic variation was almost as likely to increase or decrease protein levels. Rare noncoding aggregate testing identified 357 conditionally independent associated regions. Of these, 74 (21%) were not detectable by single-variant testing alone. Our findings have important implications for the identification, and role, of rare noncoding genetic variation associated with common human phenotypes, including the importance of testing aggregates of noncoding variants.
The continuous evolution of SARS-CoV-2 has led to the emergence of several variants of concern (VOCs) that significantly affect global health. This study aims to investigate how these VOCs affect host cells at proteome level to better understand the mechanisms of disease. To achieve this, we first analyzed the (phospho)proteome changes of host cells infected with Alpha, Beta, Delta, and Omicron BA.1 and BA.5 variants over time frames extending from 1 to 36 h post infection. Our results revealed distinct temporal patterns of protein expression across the VOCs, with notable differences in the (phospho)proteome dynamics that suggest variant-specific adaptations. Specifically, we observed enhanced expression and activation of key components within crucial cellular pathways such as the RHO GTPase cycle, RNA splicing, and endoplasmic reticulum-associated degradation (ERAD)-related processes. We further utilized proximity biotinylation mass spectrometry (BioID-MS) to investigate how specific mutation of these VOCs influence viral–host protein interactions. Our comprehensive interactomics dataset uncovers distinct interaction profiles for each variant, illustrating how specific mutations can change viral protein functionality. Overall, our extensive analysis provides a detailed proteomic profile of host cells for each variant, offering valuable insights into how specific mutations may influence viral protein functionality and impact therapeutic target identification. These insights are crucial for the potential use and design of new antiviral substances, aiming to enhance the efficacy of treatments against evolving SARS-CoV-2 variants.
The impact of genetic ancestry on the development of clonal hematopoiesis (CH) remains largely unexplored. Here, we compared CH in 136,401 participants from the Mexico City Prospective Study (MCPS) to 416,118 individuals from the UK Biobank (UKB) and observed CH to be significantly less common in MCPS compared to UKB (adjusted odds ratio = 0.59, 95% confidence interval (CI) = [0.57, 0.61], P = 7.31 × 10−185). Among MCPS participants, CH frequency was positively correlated with the percentage of European ancestry (adjusted beta = 0.84, 95% CI = [0.66, 1.03], P = 7.35 × 10−19). Genome-wide and exome-wide association analyses in MCPS identified ancestry-specific variants in the TCL1B locus with opposing effects on DNMT3A-CH versus non-DNMT3A-CH. Meta-analysis of MCPS and UKB identified five novel loci associated with CH, including polymorphisms at PARP11/CCND2, MEIS1 and MYCN. Our CH study, the largest in a non-European population to date, demonstrates the power of cross-ancestry comparisons to derive novel insights into CH pathogenesis.
Type 2 diabetes (T2D) etiology is highly complex due to its multiple roots of origin. Polygenic risk scores (PRS) based on genome-wide association studies (GWAS) can partially explain T2D risk. Asian Indian people have up to six times higher risk of developing T2D than European people, and underlying causes of this disparity are unknown.
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