Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: an individual isolate metaanalysis.
Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: an individual isolate metaanalysis.
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基因组学诊断耐贝达喹啉结核病的机会和局限性：个体分离荟萃分析。
DOI：
10.1101/2023.05.04.23289023
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
O'Donnell,Max
中科院分区：
文献类型：
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作者：
Nimmo,Camus;Bionghi,Neda;Cummings,MatthewJ;Perumal,Rubeshan;Hopson,Madeleine;AlJubaer,Shamim;Wolf,Allison;Mathema,Barun;Larsen,MichelleH;O'Donnell,Max
背景 临床贝达喹啉耐药主要涉及 mmpR5 (Rv0678) 突变。然而，mmpR5 耐药相关变异 (RAV) 与表型结核分枝杆菌耐药性存在可变关系。我们进行了系统评价，以 (1) 评估贝达喹啉耐药相关基因测序的最大灵敏度，以及 (2) 使用传统和基于机器的学习技术评估 RAV 与表型耐药之间的关联。方法 我们筛选了公共数据库中截至 2022 年 10 月发表的文章。符合条件的研究对临床来源的结核分枝杆菌分离株至少进行了 mmpR5 和 atpE 测序，并测量了贝达喹啉最低抑制浓度 (MIC)。我们进行了遗传分析来鉴定表型抗性，并确定了 RAV 与抗性的关联。采用基于机器的学习方法来定义优化的 RAV 组的测试特征，并将 mmpR5 突变映射到蛋白质结构以突出耐药机制。结果 确定了 18 项合格研究，包括 975 株结核分枝杆菌分离株，其中含有 ≥1 个潜在 RAV（mmpR5、atpE、atpB 或 pepQ 突变），其中 201 株（20.6%）表现出贝达喹啉表型耐药。 84/285 (29.5%) 耐药菌株没有候选基因突变。采用“任何突变”方法的敏感性和阳性预测值分别为 69% 和 14%。 13 个突变均位于 mmpR5 中，与耐药 MIC 显着相关（调整后 p<0.05）。用于预测中间/耐药和耐药表型的梯度增强机器分类器模型的接收者操作员特征 c 统计量均为 0.73。移码突变聚集在 α 1 螺旋 DNA 结合域中，而替换则集中在 α 2 和 3 螺旋铰链区以及 α 4 螺旋结合域中。讨论 测序候选基因对于诊断临床贝达喹啉耐药性不够敏感，但如果鉴定出有限数量的突变，则应假定与耐药性相关。基因组工具与快速表型诊断相结合最有可能有效。
Background Clinical bedaquiline resistance predominantly involves mutations in mmpR5 (Rv0678). However, mmpR5 resistance-associated variants (RAVs) have a variable relationship with phenotypic M. tuberculosis resistance. We performed a systematic review to (1) assess the maximal sensitivity of sequencing bedaquiline resistance-associated genes and (2) evaluate the association between RAVs and phenotypic resistance, using traditional and machine-based learning techniques. Methods We screened public databases for articles published until October 2022. Eligible studies performed sequencing of at least mmpR5 and atpE on clinically-sourced M. tuberculosis isolates and measured bedaquiline minimum inhibitory concentrations (MICs). We performed genetic analysis for identification of phenotypic resistance and determined the association of RAVs with resistance. Machine-based learning methods were employed to define test characteristics of optimised sets of RAVs, and mmpR5 mutations were mapped to the protein structure to highlight mechanisms of resistance. Results Eighteen eligible studies were identified, comprising 975 M. tuberculosis isolates containing ≥1 potential RAV (mutation in mmpR5, atpE, atpB or pepQ), with 201 (20.6%) demonstrating phenotypic bedaquiline resistance. 84/285 (29.5%) resistant isolates had no candidate gene mutation. Sensitivity and positive predictive value of taking an ‘any mutation’ approach was 69% and 14% respectively. Thirteen mutations, all in mmpR5, had a significant association with a resistant MIC (adjusted p<0.05). Gradient-boosted machine classifier models for predicting intermediate/resistant and resistant phenotypes both had receiver operator characteristic c-statistics of 0.73. Frameshift mutations clustered in the alpha 1 helix DNA binding domain, and substitutions in the alpha 2 and 3 helix hinge region and in the alpha 4 helix binding domain. Discussion Sequencing candidate genes is insufficiently sensitive to diagnose clinical bedaquiline resistance, but where identified a limited number of mutations should be assumed to be associated with resistance. Genomic tools are most likely to be effective in combination with rapid phenotypic diagnostics.