Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: a systematic review and individual isolate meta-analysis.
Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: a systematic review and individual isolate meta-analysis.
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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：系统评价和个体分离荟萃分析。
DOI：
10.1016/s2666-5247(23)00317-8
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发表时间：
2024
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;Naidoo,Kogieleum;Wolf,Allison;Mathema,Barun;Larsen,MichelleH;O'Donnell,Max
临床贝达奎兰耐药主要涉及MMPR5(Rv0678)基因突变。然而，MMPR5耐药相关变异体(RAV)与表型结核分枝杆菌耐药之间存在可变的关系。我们使用传统的和基于机器的学习技术进行了系统的综述，以评估测序贝达奎兰耐药相关基因的最大敏感性，并评估RAV与表型耐药之间的关联。方法我们筛选公共数据库中从数据库建立到2022年10月31日发表的文章。符合条件的研究对临床来源的结核分支杆菌进行了至少MMPR5和DATP的测序，并测量了贝达奎兰的最低抑菌浓度(MICs)。使用偏向风险评分工具来识别偏向。聚合单个基因突变和相应的MIC，并计算优势比，以确定突变与耐药性的关联。基于机器的学习方法被用来定义简约的诊断RAV集的测试特征，并将MMPR5突变映射到蛋白质结构以突出耐药机制。本研究注册于PROSPERO数据库(CRD42022346547)，共发现18篇符合条件的研究，其中975M株结核分枝杆菌至少含有一种潜在的RAV(在mmpR5、ATPE、atpB、orPepQ突变)，其中201株(20.6%)表现出对贝达奎兰的表型耐药性。285株耐药株中84株(29.5%)无候选基因突变。采用任意突变方法的敏感度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显著相关(校正p<；0·05)。用于预测中间或抗性和抗性表型的梯度增强机器分类模型的接收器算子特征c统计量均为0·73(95%CI为0·70-0·76)。α1螺旋DNA结合区的移码突变，α2和α3螺旋铰链区以及α4螺旋结合区的替换。解释对候选基因进行测序不够敏感，不足以诊断临床贝达奎兰耐药，但一旦确定，一些突变应该被认为与耐药有关。基因组工具与快速表型诊断相结合最有可能是有效的。这项研究在贡献研究中受到选择性抽样的限制，只认为单个基因座是耐药性的原因。基金会弗朗西斯·克里克研究所和美国国立卫生研究院国家过敏和传染病研究所。
BackgroundClinical bedaquiline resistance predominantly involves mutations inmmpR5(Rv0678). However,mmpR5resistance-associated variants (RAVs) have a variable relationship with phenotypicMycobacterium tuberculosisresistance. We did a systematic review to assess the maximal sensitivity of sequencing bedaquiline resistance-associated genes and evaluate the association between RAVs and phenotypic resistance, using traditional and machine-based learning techniques.MethodsWe screened public databases for articles published from database inception until Oct 31, 2022. Eligible studies performed sequencing of at leastmmpR5andatpEon clinically sourcedM tuberculosisisolates and measured bedaquiline minimum inhibitory concentrations (MICs). A bias risk scoring tool was used to identify bias. Individual genetic mutations and corresponding MICs were aggregated, and odds ratios calculated to determine association of mutations with resistance. Machine-based learning methods were used to define test characteristics of parsimonious sets of diagnostic RAVs, andmmpR5mutations were mapped to the protein structure to highlight mechanisms of resistance. This study was registered in the PROSPERO database (CRD42022346547).Findings18 eligible studies were identified, comprising 975M tuberculosisisolates containing at least one potential RAV (mutation inmmpR5,atpE,atpB, orpepQ), with 201 (20·6%) showing phenotypic bedaquiline resistance. 84 (29·5%) of 285 resistant isolates had no candidate gene mutation. Sensitivity and positive predictive value of taking an any mutation approach was 69% and 14%, respectively. 13 mutations, all inmmpR5, had a significant association with a resistant MIC (adjusted p<0·05). Gradient-boosted machine classifier models for predicting intermediate or resistant and resistant phenotypes both had receiver operator characteristic c statistic of 0·73 (95% CI 0·70–0·76). Frameshift mutations clustered in the α1 helix DNA-binding domain, and substitutions in the α2 and α3 helix hinge region and in the α4 helix-binding domain.InterpretationSequencing candidate genes is insufficiently sensitive to diagnose clinical bedaquiline resistance, but where identified, some mutations should be assumed to be associated with resistance. Genomic tools are most likely to be effective in combination with rapid phenotypic diagnostics. This study was limited by selective sampling in contributing studies and only considering single genetic loci as causative of resistance.FundingFrancis Crick Institute and National Institute of Allergy and Infectious Diseases at the National Institutes of Health.