Pan- and core genome analysis of Mycobacterium tuberculosis in high-resolution transmission and genetic diversity studies

基因组 生物 谱系(遗传) 结核分枝杆菌复合物 遗传学 结核分枝杆菌 基因组学 比较基因组学 计算生物学 传输(电信) 肺结核 基因 进化生物学 医学 病理 工程类 电气工程
作者
Zhengwei Liu,Shaojun Pei,Xichao Ou,Xiangchen Li,Yelei Zhu,Yewei Lu,Mingwu Zhang,Yang Che,Kunyang Wu,Xiaomeng Wang,Yanlin Zhao
出处
期刊:Microbial genomics [Microbiology Society]
卷期号:11 (10)
标识
DOI:10.1099/mgen.0.001460
摘要

The application of pan-genomics in understanding Mycobacterium tuberculosis complex (MTBC) transmission remains understudied, particularly in high-burden settings such as China. We constructed an M. tuberculosis complex pan-genome (MTB_pan) using 307 complete genomes representing all eight lineages from the National Center for Biotechnology Information (NCBI). Core and accessory genomes were analysed, lineage-associated genes were identified and functional annotations were assessed using Pfam domains. Transmission dynamics were evaluated by comparing pan-genome- and H37Rv-based approaches for isolates collected from China, focusing on alignment rates, clustering efficiency and SNP distances. The MTB_pan (5.75 Mb) consisted of 3,893 core and 958 accessory genes, with 176 accessory genes significantly associated with specific lineages. These genes were enriched in PE/PPE/PGRS families, mobile genetic elements (e.g. IS6110) and pentapeptide repeats. The median alignment rate based on MTB_pan reached 99.8%, which was significantly higher than that based on H37Rv. The clustering rate of isolates based on MTB_pan (19.93%) was higher than that based on H37Rv (18.90%). The pairwise SNP distances below 50 SNPs within lineage 2 decreased significantly, while those within lineage 4 showed no significant differences. Compared to a single reference genome, clustering using the pan-genome improved the identification of same-province transmission events. Therefore, the pan-genomic analysis is a more powerful analytical tool that enables the establishment of a high-resolution picture of tuberculosis transmission in different epidemiological settings, which will enable more precise outbreak mapping and support data-driven tuberculosis control strategies.
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