Whole-Genome Sequencing and Machine Learning Analysis of Staphylococcus aureus from Multiple Heterogeneous Sources in China Reveals Common Genetic Traits of Antimicrobial Resistance
Wei Wang, Michelle Baker, Yue Hu, Jin Xu, Dajin Yang, Alexandre Maciel-Guerra, Ning Xue, Hui Li, Shaofei Yan, Menghan Li, Yao Bai, Yinping Dong, Zixin Peng, Jinjing Ma, Fengqin Li, Tania Dottorini
Abstract
Little information is available on the epidemiology and characterization of Staphylococcus aureus in China. The role of food is a cause of major concern: staphylococcal foodborne diseases affect thousands every year, and the presence of resistant Staphylococcus strains on raw retail meat products is well documented. We studied a large heterogeneous data set of S. aureus isolates from many provinces of China, isolated from food as well as from individuals. Our large whole-genome collection represents a unique catalogue that can be easily meta-analyzed and integrated with further studies and adds to the library of S. aureus sequences in the public domain in a currently underrepresented geographical region. The new Bayesian dating of the split times of major drug-resistant enriched clones is relevant in showing that Chinese and European methicillin-resistant S. aureus (MRSA) have evolved differently. Our machine learning approach, across a large number of antibiotics, shows novel determinants underlying resistance and reveals frequent resistant traits in specific clonal complexes, highlighting the importance of particular clonal complexes in China. Our findings substantially expand what is known of the evolution and genetic determinants of resistance in food-associated S. aureus in China and add crucial information for whole-genome sequencing (WGS)-based surveillance of S. aureus.