A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremia

被引：7

作者：

Giulieri, Stefano G. ^{[1
,2
,3
,4
]}

Guerillot, Romain ^{[1
]}

Holmes, Natasha E. ^{[3
,4
]}

Baines, Sarah L. ^{[1
,5
]}

Hachani, Abderrahman ^{[1
]}

Hayes, Ashleigh S. ^{[1
]}

Daniel, Diane S. ^{[1
]}

Seemann, Torsten ^{[5
]}

Davis, Joshua S. ^{[6
,7
]}

Van Hal, Sebastiaan ^{[8
,9
]}

Tong, Steven Y. C. ^{[2
,3
]}

Stinear, Timothy P. ^{[1
]}

Howden, Benjamin P. ^{[1
,4
,5
]}

机构：

[1] Univ Melbourne, Doherty Inst Infect & Immun, Dept Microbiol & Immunol, Melbourne, Vic 3000, Australia

[2] Royal Melbourne Hosp, Peter Doherty Inst Infect & Immun, Victorian Infect Dis Serv, Melbourne, Vic, Australia

[3] Univ Melbourne, Peter Doherty Inst Infect & Immun, Dept Infect Dis, Melbourne, Vic 3000, Australia

[4] Austin Hlth, Dept Infect Dis, Heidelberg, Vic 3084, Australia

[5] Univ Melbourne, Ctr Pathogen Genom, Melbourne, Vic 3000, Australia

[6] John Hunter Hosp, Dept Infect Dis, New Lambton Hts, NSW 2305, Australia

[7] Charles Darwin Univ, Menzies Sch Hlth Res, Casuarina, NT 0810, Australia

[8] Royal Prince Alfred Hosp, Dept Infect Dis & Microbiol, Camperdown, NSW 2050, Australia

[9] Univ Sydney, Cent Clin Sch, Camperdown, NSW 2050, Australia

来源：

CELL REPORTS | 2023年 / 42卷 / 09期

基金：

英国医学研究理事会;

关键词：

REDUCED SUSCEPTIBILITY; TREATMENT FAILURE; VANCOMYCIN; RESISTANCE; MORTALITY; INFECTIONS; ASSOCIATION; DAPTOMYCIN; METABOLISM; MUTATIONS;

D O I：

10.1016/j.celrep.2023.113069

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

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.

引用

页数：18