24 research outputs found
Using functional genomics to decipher the complexity of microbial pathogenicity
From the first identification of bacteria as a causative agent of disease, researchers have been developing methods and techniques to understand their pathogenic processes. For decades, this work has been limited to looking at a small number of genetically manipulatable isolates in in vitro assays or animal models of infection. Despite these limitations such work has facilitated the development of successful therapeutic strategies, most notably vaccines that target specific virulence-related features. There are however many antimicrobial resistant pathogens for which vaccination strategies have not worked, as we simply do not know enough about how they cause disease. We are however at the dawn of a new era in the study of microbial pathogenicity, where large collections of bacteria isolated directly from human infections can be sequenced and assayed to identify the bacterial features that affect disease severity in humans. Here, we describe our attempt to perform such a study focussed on the major human pathogen Staphylococcus aureus, which demonstrates the step changes such approaches can make to understanding microbial pathogenicity
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Mapping the human genetic architecture of COVID-19
Matters Arising to this article was published on 03 August 2022, available online at: https://doi.org/10.1038/s41586-022-04826-7 . A second Matters Arising to this article was published on 06 September 2023, available online at: https://doi.org/10.1038/s41586-023-06355-3 .Data availability:
Summary statistics generated by the COVID-19 HGI are available at https://www.covid19hg.org/results/r5/ and are available in the GWAS Catalog (study code GCST011074). The analyses described here include the freeze-5 data. COVID-19 HGI continues to regularly release new data freezes. Summary statistics for non-European ancestry samples are not currently available due to the small individual sample sizes of these groups, but results for lead variants of 13 loci are reported in Supplementary Table 3. Individual level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale lab (https://www.nealelab.is/uk-biobank/), Finucane lab (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results) and the eQTL catalogue release 3 (https://www.ebi.ac.uk/eqtl/).Code availability:
The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses are available on GitHub (https://github.com/covid19-hg/) and the code for the Mendelian randomization and genetic correlation pipeline is available on GitHub at https://github.com/marcoralab/MRcovid.Reporting summary:
Further information on research design is available in the Nature Research Reporting Summary linked to this paper online at: https://www.nature.com/articles/s41586-021-03767-x#MOESM2 .Supplementary information is available onlne at: https://www.nature.com/articles/s41586-021-03767-x#Sec24 .Extended data figures and tables are available online at: https://www.nature.com/articles/s41586-021-03767-x#Sec23 .Copyright © The Author(s) 2021. The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease