Multilocus sequence typing schemes for the emerging swine pathogen Mycoplasma hyosynoviae

VETERINARY MICROBIOLOGY(2024)

引用 0|浏览0
暂无评分
摘要
Mycoplasma (M.) hyosynoviae is a commensal of the upper respiratory tract in swine, which has the potential to spread systemically, usually resulting in arthritis in fattening pigs and gilts. To date, very little is known about the epidemiology of M. hyosynoviae, mainly due to a lack of suitable typing methods. Therefore, this study aimed to develop both a conventional multi locus sequence typing (MLST) and a core genome (cg) MLST scheme. The development of the cgMLST was based on whole genome sequences of 64 strains isolated from pigs and wild boars during routine diagnostics as well as nine publicly available genomes. A cgMLST scheme containing 390 target genes was established using the Ridom (c) SeqSphere+ software. Using this scheme as a foundation, seven housekeeping genes were selected for conventional MLST based on their capability to reflect genome wide relatedness and subsequently, all 73 strains were typed by applying both methods. Core genome MLST results revealed a high diversity of the studied strain population and less than 100 allele differences between epidemiologically unrelated strains were only detected for four isolates from the US. On the other hand, seven clonal clusters (<= 12 allele differences) comprising 20 isolates were identified. Comparison of the two typing methods resulted in highly congruent phylogenetic trees and an Adjusted Rand Coefficient of 0.893, while cgMLST showed marginally higher resolution when comparing closely related isolates, indicated by a slightly higher Simpson's ID (0.992) than conventional MLST (Simpson's ID = 0.990). Overall, both methods seem well suited for epidemiological analyses for scientific as well as diagnostic purposes. While MLST is faster and cheaper, cgMLST can be used to further differentiate closely related isolates.
更多
查看译文
关键词
CgMLST,MLST,Epidemiology,Swine,Mycoplasma hyosynoviae
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要