Centre for Mathematics & the Environment, University of Exeter, Penryn, TR10 9EZ, UK.
Department of Biology and Biochemistry and the Milner Centre for Evolution, University of Bath, Bath, BA2 7AY, UK.
Nat Microbiol. 2017 Oct;2(10):1381-1388. doi: 10.1038/s41564-017-0001-x. Epub 2017 Aug 7.
The bacterium Staphylococcus aureus is a major human pathogen for which the emergence of antibiotic resistance is a global public health concern. Infection severity, and in particular bacteraemia-associated mortality, has been attributed to several host-related factors, such as age and the presence of comorbidities. The role of the bacterium in infection severity is less well understood, as it is complicated by the multifaceted nature of bacterial virulence, which has so far prevented a robust mapping between genotype, phenotype and infection outcome. To investigate the role of bacterial factors in contributing to bacteraemia-associated mortality, we phenotyped a collection of sequenced clinical S. aureus isolates from patients with bloodstream infections, representing two globally important clonal types, CC22 and CC30. By adopting a genome-wide association study approach we identified and functionally verified several genetic loci that affect the expression of cytolytic toxicity and biofilm formation. By analysing the pooled data comprising bacterial genotype and phenotype together with clinical metadata within a machine-learning framework, we found significant clonal differences in the determinants most predictive of poor infection outcome. Whereas elevated cytolytic toxicity in combination with low levels of biofilm formation was predictive of an increased risk of mortality in infections by strains of a CC22 background, these virulence-specific factors had little influence on mortality rates associated with CC30 infections. Our results therefore suggest that different clones may have adopted different strategies to overcome host responses and cause severe pathology. Our study further demonstrates the use of a combined genomics and data analytic approach to enhance our understanding of bacterial pathogenesis at the individual level, which will be an important step towards personalized medicine and infectious disease management.
金黄色葡萄球菌是一种主要的人类病原体,其抗生素耐药性的出现是一个全球性的公共卫生关注问题。感染的严重程度,特别是与菌血症相关的死亡率,归因于几个与宿主相关的因素,如年龄和合并症的存在。细菌在感染严重程度中的作用还不太清楚,因为细菌毒力的多方面性质使基因型、表型和感染结果之间的关系难以确定。为了研究细菌因素在导致菌血症相关死亡率中的作用,我们对来自血流感染患者的一系列已测序的临床金黄色葡萄球菌分离株进行了表型分析,这些分离株代表了两种在全球范围内非常重要的克隆类型,CC22 和 CC30。通过采用全基因组关联研究方法,我们鉴定并功能验证了几个影响细胞溶解毒性和生物膜形成表达的遗传基因座。通过在机器学习框架中分析包含细菌基因型和表型以及临床元数据的合并数据,我们发现了对不良感染结果最具预测性的决定因素在克隆间存在显著差异。在 CC22 背景的菌株感染中,细胞溶解毒性升高加上生物膜形成水平低与死亡率增加相关,而这些特定于毒力的因素对 CC30 感染相关的死亡率影响不大。因此,我们的研究结果表明,不同的克隆可能已经采用了不同的策略来克服宿主的反应并导致严重的病理。我们的研究进一步证明了将基因组学和数据分析方法相结合的方法可用于增强我们对个体水平细菌发病机制的理解,这将是迈向个体化医疗和传染病管理的重要一步。