Abstract

Objectives

We investigated the genetic diversities and lineage-specific transmission dynamics of multidrug-resistant tuberculosis (MDR-TB), with the goal of determining the potential factors driving the MDR epidemics in China.

Methods

We curated a large nationwide Mycobacterium tuberculosis (M. tuberculosis) whole genome sequence data set, including 1313 MDR strains. We reconstructed the phylogeny and mapped the transmission networks of MDR-TB across China using Bayesian inference. To identify drug-resistance variants linked to enhanced transmissibility, we employed ordinary least-squares (OLS) regression analysis.

Result

The majority of MDR-TB strains in China belong to lineage 2.2.1. Transmission chain analysis has indicated that the repeated and frequent transmission of L2.2.1 plays a central role in the establishment of MDR epidemic in China, but no occurrence of a large predominant MDR outbreak was detected. Using OLS regression, the most common single nucleotide polymorphisms (SNPs) associated with resistance to isoniazid (katG_p.Ser315Thr and katG_p.Ser315Asn) and rifampicin (rpoB_p.Ser450Leu, rpoB_p.His445Tyr, rpoB_p.His445Arg, rpoB_p.His445Asp, and rpoB_p.His445Asn) were more likely to be found in L2 clustered strains. Several putative compensatory mutations in rpoA, rpoC, and katG were significantly associated with clustering. The eastern, central, and southern regions of China had a high level of connectivity for the migration of L2 MDR strains throughout the country. The skyline plot showed distinct population size expansion dynamics for MDR-TB lineages in China.

Conclusion

MDR-TB epidemic in China is predominantly driven by the spread of highly transmissible Beijing strains. A range of drug-resistance mutations of L2 MDR-TB strains displayed minimal fitness costs and may facilitate their transmission.

Details

Title
Genomic analysis of lineage-specific transmission of multidrug resistance tuberculosis in China
Author
Yi-fan, Li 1 ; Xiang-long, Kong 2 ; Wan-mei, Song 3   VIAFID ORCID Logo  ; Ya-meng, Li 4 ; Ying-Ying, Li 4 ; Wei-wei, Fang 5 ; Jie-yu, Yang 5 ; Chun-Bao, Yu 6 ; Huai-chen, Li 5 ; Liu, Yao 5 

1  Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Shandong First Medical University, Jinan, People’s Republic of China 
2  Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences), Jinan, People’s Republic of China 
3  Department of Respiratory Medicine, Ruijin Hospital Affiliated to Shanghai Jiaotong University, Shanghai, People’s Republic of China 
4  Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of China; Department of Respiratory and Critical Care Medicine, Shandong University of Traditional Chinese Medicine, Jinan, People’s Republic of China 
5  Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of China 
6  Center for Integrative and Translational Medicine, Shandong Public Health Clinical Center, Jinan, People’s Republic of China 
Publication year
2024
Publication date
Dec 2024
Publisher
Taylor & Francis Ltd.
e-ISSN
22221751
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1080/22221751.2023.2294858
ProQuest document ID
3142112360
Copyright
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd. This work is licensed under the Creative Commons  Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.