Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam.
Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam.
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DOI：
10.1128/spectrum.02562-23
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发表时间：
2023-12-12
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
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作者：
先前的调查已经阐明了越南胡志明市结核分枝杆菌基因组多样性和传播动力学的景观。在这里，我们通过增加大量额外的基因组（总样本量：2,542）和大多数分离株的表型药物敏感性数据来扩大这项调查的范围。我们的目标是探索耐药性的流行和进化动力学以及我们从测序数据预测耐药性的能力。在对一线药物进行表型检测的分离株中，我们观察到链霉素的高比率[STR，37.7%]。（N = 573/1，520）]和异烟肼耐药[INH，25.7%利福平耐药率较低[RIF，4.9%（N = 87/1，786）]，乙胺丁醇耐药率较低[EMB，4.2%（N = 75/1，785）]。相对于全球基准，当将TB-Profiler算法应用于全基因组测序数据时，对STR和INH的耐药性预测准确（灵敏度分别为0.81和0.87），而对RIF和EMB的耐药性预测相对较差（灵敏度分别为0.70和0.44）。探索耐药性的演变揭示了主要的系统发育谱系通过某些基因的突变显示出不同的动力学和演变耐药性的趋势。发现北京亚系L2.2.1比来自其他谱系的分离物更频繁地获得从头抗性突变，并且没有明显的健身成本阻碍抗性的传播。平均而言，赋予INH和STR抗性的突变比赋予RIF抗性的突变出现得更早，并且现在在整个遗传学中更普遍。“背景”INH耐药的高流行率，结合RIF单一耐药的高比率（20.7%，N = 18/87），表明INH耐药的快速测定在这种情况下将是有价值的。这些试验将允许检测INH单一耐药，并允许将多重耐药分离株与RIF单一耐药分离株区分开来。耐药结核病（TB）感染是一个日益严重的问题，为了实现世卫组织到2035年将结核病死亡人数减少95%的目标，必须与之作斗争。虽然先前的研究已经探索了耐药性的演变和传播，但我们仍然缺乏对耐药突变所带来的适应性成本（如果有的话）以及结核分枝杆菌遗传谱系在决定耐药性演变可能性方面所起的作用的明确理解。本研究通过评估具有不同谱系组成的高负担东南亚环境中的抗性演变动态，深入了解这些问题。它表明，在抗性获得和传递的动态中存在明显的谱系特异性差异，并表明不同的谱系通过特征突变途径进化抗性。
A previous investigation has elucidated the landscape of Mtb genomic diversity and transmission dynamics in Ho Chi Minh City, Vietnam. Here, we expand the scope of this survey by adding a substantial number of additional genomes (total sample size: 2,542) and phenotypic drug susceptibility data for the majority of isolates. We aim to explore the prevalence and evolutionary dynamics of drug resistance and our ability to predict drug resistance from sequencing data. Among isolates tested phenotypically against first-line drugs, we observed high rates of streptomycin [STR, 37.7% (N = 573/1,520)] and isoniazid resistance [INH, 25.7% (N = 459/1,786)] and lower rates of resistance to rifampicin [RIF, 4.9% (N = 87/1,786)] and ethambutol [EMB, 4.2% (N = 75/1,785)]. Relative to global benchmarks, resistance to STR and INH was predicted accurately when applying the TB-Profiler algorithm to whole genome sequencing data (sensitivities of 0.81 and 0.87, respectively), while resistance to RIF and EMB was predicted relatively poorly (sensitivities of 0.70 and 0.44, respectively). Exploring the evolution of drug resistance revealed the main phylogenetic lineages to display differing dynamics and tendencies to evolve resistance via mutations in certain genes. The Beijing sublineage L2.2.1 was found to acquire de novo resistance mutations more frequently than isolates from other lineages and to suffer no apparent fitness cost acting to impede the transmission of resistance. Mutations conferring resistance to INH and STR arose earlier, on average, than those conferring resistance to RIF and are now more widespread across the phylogeny. The high prevalence of “background” INH resistance, combined with high rates of RIF mono-resistance (20.7%, N = 18/87), suggests that rapid assays for INH resistance will be valuable in this setting. These tests will allow the detection of INH mono-resistance and will allow multi-drug-resistant isolates to be distinguished from isolates with RIF mono-resistance. Drug-resistant tuberculosis (TB) infection is a growing and potent concern, and combating it will be necessary to achieve the WHO’s goal of a 95% reduction in TB deaths by 2035. While prior studies have explored the evolution and spread of drug resistance, we still lack a clear understanding of the fitness costs (if any) imposed by resistance-conferring mutations and the role that Mtb genetic lineage plays in determining the likelihood of resistance evolution. This study offers insight into these questions by assessing the dynamics of resistance evolution in a high-burden Southeast Asian setting with a diverse lineage composition. It demonstrates that there are clear lineage-specific differences in the dynamics of resistance acquisition and transmission and shows that different lineages evolve resistance via characteristic mutational pathways.
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影响因子：
30.8
作者：
Holt KE;McAdam P;Thai PVK;Thuong NTT;Ha DTM;Lan NN;Lan NH;Nhu NTQ;Hai HT;Ha VTN;Thwaites G;Edwards DJ;Nath AP;Pham K;Ascher DB;Farrar J;Khor CC;Teo YY;Inouye M;Caws M;Dunstan SJ
通讯作者：
Dunstan SJ
影响因子：
16.6
作者：
Eldholm, Vegard;Monteserin, Johana;Rieux, Adrien;Lopez, Beatriz;Sobkowiak, Benjamin;Ritacco, Viviana;Balloux, Francois
通讯作者：
Balloux, Francois
DOI：
10.1056/nejmoa1800474
发表时间：
2018-10-11
期刊：
The New England journal of medicine
影响因子：
--
作者：
CRyPTIC Consortium and the 100,000 Genomes Project;Allix-Béguec C;Arandjelovic I;Bi L;Beckert P;Bonnet M;Bradley P;Cabibbe AM;Cancino-Muñoz I;Caulfield MJ;Chaiprasert A;Cirillo DM;Clifton DA;Comas I;Crook DW;De Filippo MR;de Neeling H;Diel R;Drobniewski FA;Faksri K;Farhat MR;Fleming J;Fowler P;Fowler TA;Gao Q;Gardy J;Gascoyne-Binzi D;Gibertoni-Cruz AL;Gil-Brusola A;Golubchik T;Gonzalo X;Grandjean L;He G;Guthrie JL;Hoosdally S;Hunt M;Iqbal Z;Ismail N;Johnston J;Khanzada FM;Khor CC;Kohl TA;Kong C;Lipworth S;Liu Q;Maphalala G;Martinez E;Mathys V;Merker M;Miotto P;Mistry N;Moore DAJ;Murray M;Niemann S;Omar SV;Ong RT;Peto TEA;Posey JE;Prammananan T;Pym A;Rodrigues C;Rodrigues M;Rodwell T;Rossolini GM;Sánchez Padilla E;Schito M;Shen X;Shendure J;Sintchenko V;Sloutsky A;Smith EG;Snyder M;Soetaert K;Starks AM;Supply P;Suriyapol P;Tahseen S;Tang P;Teo YY;Thuong TNT;Thwaites G;Tortoli E;van Soolingen D;Walker AS;Walker TM;Wilcox M;Wilson DJ;Wyllie D;Yang Y;Zhang H;Zhao Y;Zhu B
通讯作者：
Zhu B
影响因子：
--
作者：
Iketleng, Thato;Lessells, Richard;de Oliveira, Tulio
通讯作者：
de Oliveira, Tulio
DOI：
10.1016/s2666-5247(20)30195-6
发表时间：
2021-03
期刊：
The Lancet. Microbe
影响因子：
--
作者：
Ektefaie Y;Dixit A;Freschi L;Farhat MR
通讯作者：
Farhat MR