Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis.
Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis.
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DOI：
10.1016/j.crmeth.2021.100123
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发表时间：
2021-12-20
期刊：
影响因子：
--
通讯作者：
Baliga NS
中科院分区：
文献类型：
--
作者：
Srinivas V;Ruiz RA;Pan M;Immanuel SRC;Peterson EJR;Baliga NS
迫切需要新的药物治疗方案来迅速治愈肺结核。在这里，我们报告了药物反应测定仪（DRonA）和“MLSynergy”算法的发展，这些算法可以进行快速药物反应测定并预测结核分枝杆菌（Mtb）对药物组合的反应。利用细胞活力的转录组特征，DRonA可以在肉汤培养、巨噬细胞感染和患者痰中通过多种机制检测结核分枝杆菌的杀伤，为耗时和资源密集的细菌学检测提供了一种高效且更敏感的替代方法。此外，MLSynergy以DRonA为基础，利用单药治疗结核分枝杆菌的转录组预测增效和拮抗多药组合。DRonA和MLSynergy共同代表了一个可用于在宿主相关情况下快速监测药物作用的通用框架，并加速发现有效的高阶药物组合。DRonA通过多种机制检测结核分枝杆菌（Mtb）的杀伤，并在不同情况下预测休眠的不可培养的结核分枝杆菌细胞的生存能力DRonA与MLSynergy预测两种和三种药物联合治疗的结果每年导致180万人死亡的结核病（TB）的治疗仍然很困难，特别是随着耐多药结核分枝杆菌（Mtb）菌株的出现。虽然迫切需要新的药物方案来治疗结核病，但由于病原体的生长速度缓慢，在高度封闭的设施中进行细菌学分析的复杂性，以及病原体的药物敏感性随环境而变化，药物评估过程缓慢且效率低下。本文描述的算法“DRonA”和“MLSynergy”使用药物治疗结核分枝杆菌的转录组来预测不同情况下的药物反应和药物相互作用。为了开发新的、更短的结核病药物方案，迫切需要方便和快速地测量治疗效果。Srinivas等人报道了DRonA和MLSynergy算法的发展，这些算法利用转录组学特征对结核分枝杆菌药物反应和多药相互作用进行计算机预测。
There is an urgent need for new drug regimens to rapidly cure tuberculosis. Here, we report the development of drug response assayer (DRonA) and “MLSynergy,” algorithms to perform rapid drug response assays and predict response of Mycobacterium tuberculosis (Mtb) to drug combinations. Using a transcriptome signature for cell viability, DRonA detects Mtb killing by diverse mechanisms in broth culture, macrophage infection, and patient sputum, providing an efficient and more sensitive alternative to time- and resource-intensive bacteriologic assays. Further, MLSynergy builds on DRonA to predict synergistic and antagonistic multidrug combinations using transcriptomes of Mtb treated with single drugs. Together, DRonA and MLSynergy represent a generalizable framework for rapid monitoring of drug effects in host-relevant contexts and accelerate the discovery of efficacious high-order drug combinations. DRonA defines the transcriptome signature for M. tuberculosis (Mtb) viability DRonA detects killing of Mtb by diverse mechanisms and in diverse contexts DRonA predicts viability of dormant non-culturable Mtb cells DRonA with MLSynergy predicts the outcome of two- and three-drug combinations The treatment of tuberculosis (TB), which kills 1.8 million each year, remains difficult, especially with the emergence of multidrug resistant strains of Mycobacterium tuberculosis (Mtb). While there is an urgent need for new drug regimens to treat TB, the process of drug evaluation is slow and inefficient, owing to the slow growth rate of the pathogen, the complexity of performing bacteriologic assays in a high-containment facility, and the context-dependent variability in drug sensitivity of the pathogen. The algorithms “DRonA” and “MLSynergy” described here use transcriptomes of drug-treated Mtb to predict drug response and drug interaction in diverse contexts. To develop new, shorter tuberculosis drug regimens, there is a critical need to easily and rapidly measure treatment efficacy. Srinivas et al. report the development of DRonA and MLSynergy algorithms, which leverage transcriptomic signatures of viability for in silico prediction of Mtb drug response and multidrug interactions.
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影响因子：
13.6
作者：
Cokol M;Kuru N;Bicak E;Larkins-Ford J;Aldridge BB
通讯作者：
Aldridge BB
影响因子：
4.6
作者：
Odermatt NT;Sala C;Benjak A;Cole ST
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Cole ST
影响因子：
3.7
作者：
Biketov, Sergey;Potapov, Vasilii;Ganina, Elena;Downing, Katrina;Kana, Bavesh D.;Kaprelyants, Arseny
通讯作者：
Kaprelyants, Arseny
影响因子：
16.6
作者：
Koul, Anil;Vranckx, Luc;Dhar, Neeraj;Gohlmann, Hinrich W. H.;Oezdemir, Emre;Neefs, Jean-Marc;Schulz, Melanie;Lu, Ping;Mortz, Ejvind;McKinney, John D.;Andries, Koen;Bald, Dirk
通讯作者：
Bald, Dirk
DOI：
10.1093/cid/ciw473
发表时间：
2016-11-01
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Deshpande D;Srivastava S;Nuermberger E;Pasipanodya JG;Swaminathan S;Gumbo T
通讯作者：
Gumbo T