Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.

Frontiers in tuberculosis Pub Date : 2025-01-01 Epub Date: 2025-04-02 DOI:10.3389/ftubr.2025.1500899
Ethan Bustad, Edson Petry, Oliver Gu, Braden T Griebel, Tige R Rustad, David R Sherman, Jason H Yang, Shuyi Ma
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Abstract

Introduction: Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses, such as antibiotics, by inducing transcriptional stress response regulatory programs. Understanding how and when mycobacterial regulatory programs are activated could inform novel treatment strategies that hinder stress adaptation and potentiate the efficacy of new and existing drugs. Here, we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness under stress.

Methods: We assembled a large Mtb RNA expression compendium and applied this to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity (TFA) profiles. Using transcriptomic and functional genomics data, we trained an interpretable machine learning model that predicts Mtb fitness from TFA profiles.

Results: We demonstrated that a TFA-based model can predict Mtb growth arrest and growth resumption under hypoxia and reaeration using gene expression data alone. This model also directly elucidates the transcriptional programs driving these growth phenotypes.

Discussion: These integrative network modeling and machine learning analyses enable the prediction of mycobacterial fitness across different environmental and genetic contexts with mechanistic detail. We envision these models can inform the future design of prognostic assays and therapeutic interventions that can cripple Mtb growth and survival to cure tuberculosis disease.

利用转录调控网络可解释机器学习预测结核分枝杆菌的适应度。
简介:结核分枝杆菌(Mtb)是结核病的病原体,是全球最大的细菌病原体死亡来源。结核分枝杆菌通过诱导转录应激反应调控程序来适应和响应各种应激,如抗生素。了解分枝杆菌调控程序如何以及何时被激活,可以为阻碍应激适应的新治疗策略提供信息,并增强新药物和现有药物的疗效。在这里,我们试图定义和分析结核分枝杆菌在压力下调节细菌适应性的调控程序。方法:我们组装了一个大型的结核分枝杆菌RNA表达纲要,并应用它来推断一个全面的结核分枝杆菌转录调控网络和计算条件特异性转录因子活性(TFA)谱。利用转录组学和功能基因组学数据,我们训练了一个可解释的机器学习模型,该模型可以从TFA档案中预测Mtb适合度。结果:我们证明了基于tfa的模型可以仅使用基因表达数据预测缺氧和再生条件下Mtb的生长停滞和恢复。该模型还直接阐明了驱动这些生长表型的转录程序。讨论:这些整合的网络建模和机器学习分析能够预测分枝杆菌在不同环境和遗传背景下的适应性,并提供机制细节。我们设想这些模型可以为未来设计预测分析和治疗干预措施提供信息,从而削弱结核分枝杆菌的生长和存活,从而治愈结核病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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