Evidence map›Paper›PMID 40025456›Full record

ArticleBMC microbiology2025

The implications of mutations in multiple genes associated with ethambutol resistance among multidrug-resistant tuberculosis isolates from China.

Ma-Chao Li, Wei Wang, Tong-Yang Xiao, Hai-Can Liu, Shi-Qiang Lin, Hao Hang, Xiang-Long Bo, Xiao-Tian Nan, Cheng Qian, Xue-Ting Fan and 4 more

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Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Ma-Chao Li *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Wei Wang *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Tong-Yang Xiao *Department of Clinical Laboratory, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong, China.
Hai-Can Liu *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Shi-Qiang LinCollege of Life Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
Hao HangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Xiang-Long BoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Xiao-Tian NanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Cheng QianBeijing Center for Disease Control and Prevention, Beijing, 100013, China.
Xue-Ting FanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Xiu-Qin ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Gui-Lian LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Kang-Lin WanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Li-Li ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China. zhaolili@icdc.cn.

Funding

National Key Research and Development Program of China No. 2023YFC2307204
6 · The paper itself

Abstract

objectivesTo assess the mutation effects of ethambutol (EMB) resistance-associated genes, including embCAB operon, ubiA, embR, and aftA, on the EMB resistance levels among multidrug-resistant tuberculosis (MDR-TB) isolates from China.

methodsA total of 159 MDR-TB from China had their EMB MICs quantified, and the sequences of the four ethambutol resistance-associated regions were analyzed. A multivariate regression model was established to evaluate the effects of mutations on EMB resistance.

resultsOur results showed that overall 95.6% (109/114 isolates) of EMB-resistant isolates harbored at least one mutation within the regions associated with EMB resistance. Most mutations were in embB, particularly in the embB300-500, and the embC-embA intergenic regions. Mutations in other genes were seldom seen and mainly occurred along with mutations in the embB or the embC-embA among the EMB-resistant isolates. DNA sequencing of the embB300-500 and the embC-embA was the most effective approach for detecting EMB resistance, with an accuracy of 91.2%. Nevertheless, some EMB-susceptible isolates still had a single mutation in the gene related to EMB resistance. Moreover, there was a significant correlation between EMB high-level resistance and multiple mutations.

conclusionDistinct individual mutations, as well as multiple concurrent mutations, within EMB resistance-associated genes, contributed to variable levels of EMB resistance. These results have broadened our understanding of the molecular characteristics of EMB resistance in China.

Indexed as

Antitubercular AgentsDrug Resistance, Multiple, BacterialEthambutolMutationMycobacterium tuberculosisTuberculosis, Multidrug-ResistantChinaGenes, BacterialHumansMicrobial Sensitivity TestsSequence Analysis, DNAAntitubercular AgentsEthambutolEthambutolMultidrug-resistant tuberculosisMutationResistance

Identifiers

PMID40025456
PMCPMC11871839

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.