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Genome mining reveals the prevalence and extensive diversity of toxin-antitoxin systems in Staphylococcus aureus


Yang, Haiyan (2022), Genome mining reveals the prevalence and extensive diversity of toxin-antitoxin systems in Staphylococcus aureus, Dryad, Dataset,


Staphylococcus aureus (S. aureus) is a ubiquitous Gram-positive bacterium that is highly pathogenic and adaptive, showing great persistence in the environment. The toxin-antitoxin (TA) system is considered a valuable strategy for bacterial pathogens to survive in stressed environments and plays a crucial role in the defense system. Numerous studies have wildly explored the distribution and function of TA systems in clinical pathogens. However, little is known about the diversity of the TA systems and their evolutionary dynamics in S. aureus. In this study, we performed a comprehensive in silico screening of 621 isolates recovered from public databases. We identified 44 TA groups belonging in S. aureus genomes using bioinformatic search and prediction tools, including SLING, TADB2.0 and TASmania. There was a median of 7 TA systems in each genome, and 3 type II TA groups which were found in more than 80% of the strains, including HD, HD_3, and YoeB, others were more sporadic. The majority of predicted TA genes are chromosomally encoded. The Staphylococcal cassette chromosome mec (SCCmec) genomic islands also harbored TA genes. Further functional characterization of the putative TA system could reveal how these widespread prevalent gene modules potentially influence the ecology, virulence, and disease management practices of S. aureus. Our study opens the prospect of characterizing these putative TA genes and the design of more targeted antimicrobial agents in the future.


S. aureus genome assemblies (FASTA), annotated genomes (GBK and GFF) and protein sequence (FAA) for each corresponding isolate record were gathered from the NCBI database ( as of August 31, 2021. Isolate background information was obtained by retrieving relevant literature and BioSample database ( with a self-written Python script.


National Natural Science Foundation of China