講演情報
[PE2-3]The Comprehensive Analysis of Structural Variant (SV) Profile in Mycobacterium tuberculosis (MTB) Genome and Their Association with Patient/Pathogen Phenotypes
○Worakitchanon Wittawin1, Hideki Yanai2, Piboonsiri Pundharika1,3, Chaiyasirinroje Boonchai4, Wichukchinda Nuanjan3, Yosuke Omae5, Palittapongarnpim Prasit6,7, Katsushi Tokunaga5, Mahasirimongkol Surakameth3, Akihiro Fujimoto1 (1.Department of Human Genetics, School of International Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, 2.Fukujuji Hospital and Research Institute of Tuberculosis (RIT), Tokyo, Japan, 3.Medical Genetics Center, Medical Life Sciences Institute, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand, 4.TB/HIV Research Foundation (THRF), Chaingrai, Thailand, 5.Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, Japan, 6.Center for Microbial Genomics, Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand, 7.National Science and Technology Development Agency, Pathumthani, Thailand<br/>)
Tuberculosis, a global obstacle to human health, is caused by intracellular bacteria named Mycobacterium tuberculosis (MTB). Recently nine human-adapted MTB lineages have been identified according to their genetic differences. Notably, these lineages exhibit strong differences in their phenotypes, such as virulence, progression to disease, and transmission potential. Previous studies have reported that genetic variations in MTB are associated with patient and bacterial phenotypes, and the analysis of genetic variations should provide us with important information on MTB treatment. However, among various types of genetic variations, structural variations (SVs) are controversial. In this study, we aim to identify SVs with high accuracy and investigate their associations with patients and bacterial phenotypes. To explore a SV profile, we analyzed a large dataset of short-read whole-genome sequencing (WGS) from 1,960 clinical MTB isolates. SV lists across the MTB genome were obtained using four SV-callers. To remove false positives and false negatives of the SV-callers, we developed a new method, resulting in the detection of 1,227 SV positions. Of these, 849 SVs were novel. Remarkably, 244 of these novel SVs were in PE/PPE regions, which have been known to be highly repetitive. We also obtained that 242 SVs were significantly associated with patient and pathogen phenotypes. Our study provides a comprehensive picture of SVs across the MTB genome and indicates their contributions to patients and bacterial phenotypes.