Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/33763
Title: A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremia.
Austin Authors: Giulieri, Stefano G ;Guérillot, Romain;Holmes, Natasha E ;Baines, Sarah L;Hachani, Abderrahman;Hayes, Ashleigh S;Daniel, Diane S;Seemann, Torsten;Davis, Joshua S;Van Hal, Sebastiaan;Tong, Steven Y C;Stinear, Timothy P;Howden, Benjamin P 
Affiliation: Department of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia; Victorian Infectious Disease Service, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia; Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
Infectious Diseases
The Peter Doherty Institute
Centre for Pathogen Genomics, The University of Melbourne, Melbourne, VIC 3000, Australia.
Centre for Pathogen Genomics, The University of Melbourne, Melbourne, VIC 3000, Australia.
Department of Infectious Diseases, John Hunter Hospital, New Lambton Heights, NSW 2305, Australia; Menzies School of Health Research, Charles Darwin University, Casuarina, NT 0810, Australia.
Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia; Central Clinical School, University of Sydney, Camperdown, NSW 2050, Australia.
Victorian Infectious Disease Service, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia; Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia.
Issue Date: 26-Sep-2023
Date: 2023
Publication information: Cell Reports 2023-09-26; 42(9)
Abstract: Outcomes of severe bacterial infections are determined by the interplay between host, pathogen, and treatments. While human genomics has provided insights into host factors impacting Staphylococcus aureus infections, comparatively little is known about S. aureus genotypes and disease severity. Building on the hypothesis that bacterial pathoadaptation is a key outcome driver, we developed a genome-wide association study (GWAS) framework to identify adaptive mutations associated with treatment failure and mortality in S. aureus bacteremia (1,358 episodes). Our research highlights the potential of vancomycin-selected mutations and vancomycin minimum inhibitory concentration (MIC) as key explanatory variables to predict infection severity. The contribution of bacterial variation was much lower for clinical outcomes (heritability <5%); however, GWASs allowed us to identify additional, MIC-independent candidate pathogenesis loci. Using supervised machine learning, we were able to quantify the predictive potential of these adaptive signatures. Our statistical genomics framework provides a powerful means to capture adaptive mutations impacting severe bacterial infections.
URI: https://ahro.austin.org.au/austinjspui/handle/1/33763
DOI: 10.1016/j.celrep.2023.113069
ORCID: 
Journal: Cell Reports
Start page: 113069
PubMed URL: 37703880
ISSN: 2211-1247
Type: Journal Article
Subjects: CP: Genomics
CP: Microbiology
Staphylococcus aureus
bacteraemia
bacterial GWAS
machine learning
vancomycin resistance
Appears in Collections:Journal articles

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