Whole-Genome Sequencing for Resistance Level Prediction in Multidrug-Resistant Tuberculosis.
Whole-Genome Sequencing for Resistance Level Prediction in Multidrug-Resistant Tuberculosis.
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全基因组测序预测耐多药结核病的耐药水平
DOI：
10.1128/spectrum.02714-21
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发表时间：
2022-06-29
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：
明确耐药突变和定量表型药敏试验之间的精确关系将增加全基因组测序(WGS)在预测结核病耐药方面的价值。然而，目前大量的WGS数据集缺乏相应的定量表型数据-MICS。采用MYCOTBI纸片法测定深圳市慢性病控制中心中国分离的154株临床耐多药结核分枝杆菌对9种抗结核药物的最低抑菌浓度。将MIC与WGS预测的耐药谱进行比较，WGS可以预测对异烟肼、利福平、链霉素、氟喹诺酮类和氨基糖苷类药物的耐药性水平。我们还发现了一些可能与耐药性无关的突变，如GID基因的EmbB D328G突变和EIS启动子的C−12T突变。然而，一些携带相同突变的菌株对相应的药物表现出不同程度的抗药性。具有RpsL K88R、FabG1 C−15T突变的不同菌株，以及一些具有embB和rpoB突变的菌株，对相应药物的MIC相差8倍或更多。这种变异无法解释，但可能受到细菌遗传背景的影响。此外，我们还发现32.3%的利福平耐药株对利福平敏感，特别是rpoB突变为H445D、H445L、H445S、D435V、D435F、L452P、S441Q和S441V的菌株。研究细菌遗传背景对MIC的影响以及利福平耐药突变与利福平耐药水平的关系，将有助于提高WGS指导药物治疗方案选择的能力。重要的是，全基因组测序(WGS)在耐药预测方面具有很好的潜力。MICs是增加特定抗结核药物剂量或改变整个治疗方案的基本适应症。然而，许多已知的耐药突变与MIC之间的关系尚不清楚，特别是对较罕见的突变。结果表明，WGS可以预测对异烟肼、利福平、链霉素、氟喹诺酮类和氨基糖苷类药物的耐药性水平。然而，一些突变可能与耐药性无关，而另一些突变可能会使携带这些突变的菌株产生不同的MIC。此外，32.3%的利福平(RIF)耐药株被归类为对利福平(RFB)敏感，rpoB基因的一些突变可能与这种表型有关。我们关于具有一些较罕见突变的菌株的MIC分布的数据增加了与此类突变相关的耐药性水平的累积数据，以帮助指导进一步的研究并得出有意义的结论。
Defining the precise relationship between resistance mutations and quantitative phenotypic drug susceptibility testing will increase the value of whole-genome sequencing (WGS) for predicting tuberculosis drug resistance. However, a large number of WGS data sets currently lack corresponding quantitative phenotypic data—the MICs. Using MYCOTBI plates, we determined the MICs to nine antituberculosis drugs for 154 clinical multidrug-resistant tuberculosis isolates from the Shenzhen Center for Chronic Disease Control in Shenzhen, China. Comparing MICs with predicted drug-resistance profiles inferred by WGS showed that WGS could predict the levels of resistance to isoniazid, rifampicin, streptomycin, fluoroquinolones, and aminoglycosides. We also found some mutations that may not be associated with drug resistance, such as EmbB D328G, mutations in the gid gene, and C−12T in the eis promoter. However, some strains carrying the same mutations showed different levels of resistance to the corresponding drugs. The MICs of different strains with the RpsL K88R, fabG1 C−15T mutations and some with mutations in embB and rpoB, had MICs to the corresponding drugs that varied by 8-fold or more. This variation is unexplained but could be influenced by the bacterial genetic background. Additionally, we found that 32.3% of rifampicin-resistant isolates were rifabutin-susceptible, particularly those with rpoB mutations H445D, H445L, H445S, D435V, D435F, L452P, S441Q, and S441V. Studying the influence of bacterial genetic background on the MIC and the relationship between rifampicin-resistant mutations and rifabutin resistance levels should improve the ability of WGS to guide the selection of medical treatment regimens. IMPORTANCE Whole-genome sequencing (WGS) has excellent potential in drug-resistance prediction. The MICs are essential indications of adding a particular antituberculosis drug dosage or changing the entire treatment regimen. However, the relationship between many known drug-resistant mutations and MICs is unclear, especially for rarer ones. The results showed that WGS could predict resistance levels to isoniazid, rifampicin, streptomycin, fluoroquinolones, and aminoglycosides. However, some mutations may not be associated with drug resistance, and some others may confer various MICs to strains carrying them. Also, 32.3% of rifampicin (RIF)-resistant strains were classified as sensitive to rifabutin (RFB), and some mutations in the rpoB gene may be associated with this phenotype. Our data on the MIC distribution of strains with some rarer mutations add to the accumulated data on the resistance level associated with such mutations to help guide further research and draw meaningful conclusions.
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影响因子：
6.7
作者：
Gröschel MI;Walker TM;van der Werf TS;Lange C;Niemann S;Merker M
通讯作者：
Merker M
中国上海多重耐药结核分枝杆菌的传播：利用全基因组测序和流行病学调查的回顾性观察研究。
DOI：
10.1016/s1473-3099(16)30418-2
发表时间：
2017-03
期刊：
The Lancet. Infectious diseases
影响因子：
--
作者：
Yang C;Luo T;Shen X;Wu J;Gan M;Xu P;Wu Z;Lin S;Tian J;Liu Q;Yuan Z;Mei J;DeRiemer K;Gao Q
通讯作者：
Gao Q
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：
10.7
作者：
Castro, Rhastin A. D.;Ross, Amanda;Gagneux, Sebastien
通讯作者：
Gagneux, Sebastien
影响因子：
9.4
作者：
Jamieson, F. B.;Guthrie, J. L.;Mehaffy, C.
通讯作者：
Mehaffy, C.