Whole genome sequencing-based drug resistance predictions of multidrug-resistant Mycobacterium tuberculosis isolates from Tanzania.
Whole genome sequencing-based drug resistance predictions of multidrug-resistant Mycobacterium tuberculosis isolates from Tanzania.
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DOI：
10.1093/jacamr/dlac042
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发表时间：
2022-04
影响因子：
3.4
通讯作者：
中科院分区：
文献类型：
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作者：
利福平或多药耐药（RR/MDR）结核分枝杆菌复合群（MTBC）菌株在全球范围内造成了相当大的发病率和死亡率。基于WGS的耐药性预测可以指导临床决策，特别是对于RR/MDR-TB疗法的设计。我们比较了来自坦桑尼亚MDR-TB患者的42株MTBC分离株的基于WGS的耐药性预测突变与Sensititre™ MycoTB检测中测量的14种抗生素的MIC。如果分离株的MIC高于流行病学临界值（ECOFF），则将其表型归类为耐药，如果其MIC低于或等于ECOFF，则将其表型归类为敏感。总体而言，具有高水平耐药突变（gNWT-R）的基因型非野生型MTBC分离株与MIC值高于ECOFF的分离株相关。例如，MIC中值利福平-gNWT-R菌株的浓度（mg/L）>4.0（IQR 4.0-4.0）与0.5相比（IQR 0.38-0.50）基因型野生型（gWT-S，P <0.001）;异烟肼-gNWT-R >4.0（IQR 2.0-4.0）与0.25相比（IQR 0.12-1.00）在gWT-S中（P = 0.001）;乙硫异烟胺-gNWT-R 15.0（IQR 10.0-20.0）与gWT-S组的2.50（IQR; 2.50-5.00）相比（P <0.001）。 WGS分别正确预测了95%（36/38）和100%（38/38）的利福平耐药菌株，ECOFF>0.5和>0.125 mg/L。 与氟喹诺酮类、氨基糖苷类、卷曲霉素、贝达喹啉、德拉马尼、利奈唑胺、氯法齐明、环丝氨酸或对氨基水杨酸耐药相关的基因中不存在已知的耐药突变。基于WGS的耐药预测在排除表型耐药方面效果良好，并且二线耐药介导突变的缺失有可能指导未来RR/MDR-TB方案的设计。
Rifampicin- or multidrug-resistant (RR/MDR) Mycobacterium tuberculosis complex (MTBC) strains account for considerable morbidity and mortality globally. WGS-based prediction of drug resistance may guide clinical decisions, especially for the design of RR/MDR-TB therapies. We compared WGS-based drug resistance-predictive mutations for 42 MTBC isolates from MDR-TB patients in Tanzania with the MICs of 14 antibiotics measured in the Sensititre™ MycoTB assay. An isolate was phenotypically categorized as resistant if it had an MIC above the epidemiological-cut-off (ECOFF) value, or as susceptible if it had an MIC below or equal to the ECOFF. Overall, genotypically non-wild-type MTBC isolates with high-level resistance mutations (gNWT-R) correlated with isolates with MIC values above the ECOFF. For instance, the median MIC value (mg/L) for rifampicin-gNWT-R strains was >4.0 (IQR 4.0–4.0) compared with 0.5 (IQR 0.38–0.50) in genotypically wild-type (gWT-S, P < 0.001); isoniazid-gNWT-R >4.0 (IQR 2.0–4.0) compared with 0.25 (IQR 0.12–1.00) among gWT-S (P = 0.001); ethionamide-gNWT-R 15.0 (IQR 10.0–20.0) compared with 2.50 (IQR; 2.50–5.00) among gWT-S (P < 0.001). WGS correctly predicted resistance in 95% (36/38) and 100% (38/38) of the rifampicin-resistant isolates with ECOFFs >0.5 and >0.125 mg/L, respectively. No known resistance-conferring mutations were present in genes associated with resistance to fluoroquinolones, aminoglycosides, capreomycin, bedaquiline, delamanid, linezolid, clofazimine, cycloserine, or p-amino salicylic acid. WGS-based drug resistance prediction worked well to rule-in phenotypic drug resistance and the absence of second-line drug resistance-mediating mutations has the potential to guide the design of RR/MDR-TB regimens in the future.
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影响因子：
3.2
作者：
Kidenya, Benson R.;Mshana, Stephen E.;Ocheretina, Oksana
通讯作者：
Ocheretina, Oksana
影响因子：
3.7
作者：
Homolka S;Projahn M;Feuerriegel S;Ubben T;Diel R;Nübel U;Niemann S
通讯作者：
Niemann S
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：
0.6
作者：
Lyakurwa, Dennis;Lyimo, Johnson;Mpondo, Bonaventura C. T.
通讯作者：
Mpondo, Bonaventura C. T.
影响因子：
3.7
作者：
Kendall EA;Azman AS;Cobelens FG;Dowdy DW
通讯作者：
Dowdy DW