School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China.
Department of Prevention and Health Care, The Sixth People's Hospital of Dongguan, Dongguan, China.
Commun Biol. 2024 Sep 18;7(1):1171. doi: 10.1038/s42003-024-06883-2.
Staphylococcus aureus (S. aureus) can cause various infections in humans and animals, contributing to high morbidity and mortality. To prevent and control cross-species transmission of S. aureus, it is necessary to understand the host-associated genetic variants. We performed a two-stage genome-wide association study (GWAS) including initial screening and further validation to compare genomic differences between human and pig S. aureus, aiming to identify host-associated determinants. Our multiple GWAS analyses found six consensus significant k-mers associated with host species, providing novel genetic evidence for distinguishing human from pig S. aureus. The best k-mer predictor achieved a high classification accuracy of 98.12% on its own and had extremely high resolution similar to the SNPs-based phylogeny, offering a very simple target for predicting the cross-species transmission risk of S. aureus. The final k-mer model revealed that 90% of S. aureus isolates from farm workers were predicted as livestock origin, suggesting a high risk of cross-species transmission. Bayesian inference revealed different cross-species transmission directions, with the human-to-pig transmission for ST5 and the pig-to-human transmission for ST398. Our findings provide a simple and accurate k-mer model for identifying and predicting the cross-species transmission risk of S. aureus.
金黄色葡萄球菌（S. aureus）可引起人类和动物的各种感染，导致高发病率和死亡率。为了预防和控制金黄色葡萄球菌的跨物种传播，有必要了解与宿主相关的遗传变异。我们进行了两阶段全基因组关联研究（GWAS），包括初步筛选和进一步验证，以比较人类和猪金黄色葡萄球菌的基因组差异，旨在确定与宿主相关的决定因素。我们的多项 GWAS 分析发现了六个与宿主物种相关的共识显著 k-mer，为区分人源和猪源金黄色葡萄球菌提供了新的遗传证据。最佳 k-mer 预测器在其自身的分类准确性达到了 98.12%，并且具有极高的分辨率，类似于基于 SNP 的系统发育，为预测金黄色葡萄球菌的跨物种传播风险提供了一个非常简单的目标。最终的 k-mer 模型表明，90%的农场工人分离株被预测为源自牲畜，表明存在高的跨物种传播风险。贝叶斯推断揭示了不同的跨物种传播方向，ST5 为人传人，ST398 为猪传人。我们的研究结果提供了一种简单而准确的 k-mer 模型，可用于识别和预测金黄色葡萄球菌的跨物种传播风险。
