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中国防痨杂志 ›› 2026, Vol. 48 ›› Issue (5): 603-615.doi: 10.19982/j.issn.1000-6621.20250467

• 论著 • 上一篇    下一篇

结核分枝杆菌对利奈唑胺耐药的相关基因突变特征分析

武娅宁, 黄咪孙, 郭溢, 薄相龙, 王瑞欢, 邢建亮, 范雪亭, 许达, 赵丽丽, 赵秀芹, 李桂莲(), 刘海灿(), 李马超()   

  1. 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心传染病预防控制所结核病控制室, 北京 102206
  • 收稿日期:2025-11-27 出版日期:2026-05-10 发布日期:2026-04-27
  • 通信作者: 李桂莲,刘海灿,李马超 E-mail:liguilian@icdc.cn;liuhaican@icdc.cn;limachao@icdc.cn
  • 基金资助:
    北京自然科学基金面上项目(7242189);国家科技重大专项(2025ZD01907103);中国疾病预防控制中心传染病预防控制所课题基金项目(33066);中国疾病预防控制中心传染病预防控制所课题基金项目(33077)

Analysis of genetic mutation characteristics associated with linezolid resistance in Mycobacterium tuberculosis

Wu Yaning, Huang Misun, Guo Yi, Bo Xianglong, Wang Ruihuan, Xing Jianliang, Fan Xueting, Xu Da, Zhao Lili, Zhao Xiuqin, Li Guilian(), Liu Haican(), Li Machao()   

  1. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Tuberculosis Control Research Laboratory, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
  • Received:2025-11-27 Online:2026-05-10 Published:2026-04-27
  • Contact: Li Guilian,Liu Haican,Li Machao E-mail:liguilian@icdc.cn;liuhaican@icdc.cn;limachao@icdc.cn
  • Supported by:
    Beijing Natural Science Foundation(7242189);National Key Science and Technology Projects(2025ZD01907103);National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention(33066);National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention(33077)

摘要:

目的: 分析结核分枝杆菌(Mycobacterium tuberculosis,MTB)临床分离株对利奈唑胺的耐药表型特征及其与利奈唑胺耐药相关基因的突变特征,探讨耐药表型与基因型的相关性。方法: 收集2017年6月至2020年12月新疆维吾尔自治区南疆地区的757株MTB临床分离株,采用微孔板微量肉汤稀释法测定利奈唑胺最小抑菌浓度(minimum inhibitory concentration,MIC),并通过全基因组测序技术分析3个已知的耐药相关基因(rrlrplCtsnR),以及与rplC和(或)tsnR具有交互作用的基因(交互作用评分≥0.70)的突变情况,结合统计学方法分析利奈唑胺的MIC值,表型和基因型耐药分布特征及相互关联性。结果: 757株菌株中北京基因型(L2谱系)占62.22%。利奈唑胺总耐药率(MIC≥1μg/ml)为6.34%(48/757),MIC50和MIC90均为0.5μg/ml,MIC99为2μg/ml。耐多药(multidrug-resistant,MDR)和准广泛耐药(pre-extensively drug-resistant,pre-XDR)菌株的利奈唑胺耐药率分别为6.09%(12/197)与17.57%(13/74),MIC50分别为0.25μg/ml和0.5μg/ml,MIC90分别为0.5μg/ml和1μg/ml,MIC99分别为2μg/ml和4μg/ml,且pre-XDR表型与利奈唑胺耐药显著相关(χ2=17.407,P<0.001)。rrlrplCtsnR基因总突变率分别为8.85%(67/757)、0.53%(4/757)和1.32%(10/757)。48株利奈唑胺耐药菌株中,43.75%(21/48)检出基因突变,rrl基因突变检出18株(37.50%),tsnR基因突变检出3株(6.25%),rplC基因未见突变。在利奈唑胺敏感菌株中,分别检出24种rrl基因、4种rplC基因和5种tsnR基因突变。以微孔板法为金标准,rrl基因单独预测利奈唑胺耐药的敏感度为37.50%(18/48),特异度为93.09%(660/709);rrltsnR基因联合可将敏感度提升至43.75%(21/48)。在152个交互作用基因中,发现10个基因(Rv1540dnaJ1fbiCfmufusA2infCnusGpurArpoCrpsF)与利奈唑胺耐药显著相关(其中,nusG采用Fisher’s精确检验,P<0.05;其余基因经卡方检验,χ2值分别为5.941、4.985、5.027、4.732、3.944、4.673、19.814、5.789、29.234,P值均<0.05)。结论: pre-XDR 菌株对利奈唑胺耐药风险显著升高;基于rrlrplCtsnR基因的分子检测方法预测表型耐药的敏感度较低,提示存在未被阐明的耐药机制;建议在临床实践中对pre-XDR等高危患者采用表型药物敏感性试验与基因检测联合诊断策略,加快利奈唑胺新耐药机制挖掘,以提高利奈唑胺耐药识别的准确性,为临床合理用药提供可靠依据。

关键词: 分枝杆菌,结核, 利奈唑胺, 药物耐受性, 突变, 最小抑菌浓度

Abstract:

Objective: To analyze the phenotypic characteristics of linezolid resistance and the mutation profiles of resistance-associated genes in clinical Mycobacterium tuberculosis (MTB) isolates, and to investigate the correlation between phenotypic and genotypic resistance. Methods: A total of 757 MTB clinical isolates collected from Southern Xinjiang Uygur Autonomous Region between June 2017 and December 2020 were included. The minimum inhibitory concentration (MIC) of linezolid was determined using the microplate broth microdilution method. Whole-genome sequencing was performed to analyze mutations in three known resistance-related genes (rrl, rplC, and tsnR) as well as in genes interacting with rplC and/or tsnR (interaction score ≥0.70). Statistical analyses were applied to evaluate the MIC distribution, phenotypic and genotypic resistance profiles, and their correlations. Results: Among the 757 isolates, the Beijing genotype (lineage 2) was predominant (62.22%). The overall resistance rate to linezolid (MIC≥1 μg/ml) was 6.34% (48/757), with both MIC50 and MIC90 values of 0.5 μg/ml and MIC99 of 2 μg/ml. Among multidrug-resistant (MDR) and pre-extensively drug-resistant (pre-XDR) strains, the linezolid resistance rates were 6.09% (12/197) and 17.57% (13/74) respectively. Their MIC50 values were 0.25 μg/ml and 0.5 μg/ml, MIC90 values were 0.5 μg/ml and 1 μg/ml, and MIC99 values were 2 μg/ml and 4 μg/ml, respectively. Pre-XDR phenotype was significantly associated with linezolid resistance (χ2=17.407, P<0.001). Mutation frequencies in rrl, rplC, and tsnR genes were 8.85% (67/757), 0.53% (4/757), and 1.32% (10/757), respectively. Among the 48 linezolid-resistant isolates, 43.75% (21/48) harbored mutations in these genes. Specifically, mutations in rrl and tsnR were detected in 18 (37.50%) and 3 (6.25%) isolates, respectively, while no mutations were found in rplC. Among the susceptible isolates, 24 types of rrl mutations, 4 types of rplC mutations, and 5 types of tsnR mutations were identified. Using the broth microdilution method as the gold standard, the sensitivity and specificity of rrl mutations alone for predicting linezolid resistance were 37.50% (18/48) and 93.09% (660/709), respectively. Combining rrl and tsnR mutations increased the sensitivity to 43.75% (21/48). Among the 152 interacting genes, ten genes (Rv1540, dnaJ1, fbiC, fmu, fusA2, infC, nusG, purA, rpoC, and rpsF) were significantly associated with linezolid resistance (for nusG, Fisher’s exact test was used, P<0.05; for the other genes, the chi-square test was used, with χ2 values were 5.941, 4.985, 5.027, 4.732, 3.944, 4.673, 19.814, 5.789, and 29.234 respectively, with all P<0.05). Conclusion: Pre-XDR strains have a significantly increased risk of linezolid resistance. Molecular detection methods based on rrl, rplC, or tsnR genes showed limited sensitivity for predicting phenotypic resistance, indicating the existence of unelucidated resistance mechanisms. It is recommended that in clinical practice, a combined diagnostic strategy incorporating phenotypic drug susceptibility testing and genomic analysis be adopted for high-risk patients such as those with pre-XDR, and research on novel linezolid resistance mechanisms should be accelerated to improve the accuracy of linezolid resistance identification and provide reliable evidence for rational clinical drug use.

Key words: Mycobacterium tuberculosis, Linezolid, Drug resistance, Mutation, Minimum inhibitory concentration

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