RESUMO
The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.
Assuntos
COVID-19 , Mutação de Sentido Incorreto , SARS-CoV-2 , Humanos , SARS-CoV-2/genética , SARS-CoV-2/imunologia , COVID-19/genética , COVID-19/virologia , COVID-19/imunologia , Reposicionamento de Medicamentos , Proteínas Virais/genética , Proteínas Virais/metabolismo , Ligação Proteica , Predisposição Genética para Doença , Suscetibilidade a Doenças , Tratamento Farmacológico da COVID-19RESUMO
OBJECTIVES: Cardiovascular disease through accelerated atherosclerosis is a leading cause of mortality for patients with systemic lupus erythematosus (SLE), likely due to increased chronic inflammation and cardiometabolic defects over age. We investigated age-associated changes in metabolomic profiles of SLE patients and healthy controls (HCs). METHODS: Serum NMR metabolomic profiles from female SLE patients (n = 164, age = 14-76) and HCs (n = 123, age = 13-72) were assessed across age by linear regression and by age group between patients/HCs (Group-1, age ≤ 25, n = 62/46; Group-2, age = 26-49, n = 50/46; Group-3, age ≥ 50, n = 52/31) using multiple t-tests. The impact of inflammation, disease activity and treatments were assessed, and UK Biobank disease-wide association analysis of metabolites was performed. RESULTS: Age-specific metabolomic profiles were identified in SLE patients vs HCs, including reduced amino acids (Group-1), increased very-low-density lipoproteins (Group-2), and increased low-density lipoproteins (Group-3). Twenty-five metabolites were significantly altered in all SLE age groups, dominated by decreased atheroprotective high-density lipoprotein (HDL) subsets, HDL-bound apolipoprotein(Apo)A1 and increased glycoprotein acetyls (GlycA). Furthermore, ApoA1 and GlycA were differentially associated with disease activity and serological measures, as well as atherosclerosis incidence and myocardial infarction mortality risk through disease-wide association. Separately, glycolysis pathway metabolites (acetone/citrate/creatinine/glycerol/lactate/pyruvate) uniquely increased with age in SLE, significantly influenced by prednisolone (increased pyruvate/lactate) and hydroxychloroquine (decreased citrate/creatinine) treatment and associated with type-1 and type-2 diabetes by disease-wide association. CONCLUSIONS: Increasing HDL (ApoA1) levels through therapeutic/nutritional intervention, whilst maintaining low disease activity, in SLE patients from a young age could improve cardiometabolic disease outcomes. Biomarkers from the glycolytic pathway could indicate adverse metabolic effects of current therapies.
RESUMO
CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.