Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.
Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.
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结核分枝杆菌中基因调节和代谢网络的综合建模。
DOI：
10.1371/journal.pcbi.1004543
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发表时间：
2015-11
影响因子：
4.3
通讯作者：
Price ND
中科院分区：
文献类型：
--
作者：
Ma S;Minch KJ;Rustad TR;Hobbs S;Zhou SL;Sherman DR;Price ND
结核分枝杆菌（Mycobacterium tuberculosis，MTB）是结核病的致病菌，结核病是一种每年在全世界造成超过一百万人死亡的疾病，其中越来越多的菌株对抗生素具有耐药性。更好的治疗方法的开发将大大受益于对与MTB对不同遗传和环境扰动的反应相关的机制的更好理解。因此，我们扩展了一个基因组规模的调节代谢模型MTB使用的概率调节代谢（PROM）的框架。我们的模型，MTBPROM2.0，代表了大量的知识库更新和扩展的仿真能力。我们整合了一个最近的基于ChIP-seq的结合网络，该网络由2555个相互作用连接到104个转录因子（TF）（代表TF覆盖范围的3.5倍扩展）。我们将这个扩展的调控网络与一个精细的基因组规模代谢模型相结合，该模型可以正确预测69种源代谢物条件下的生长活力，并比原始模型更准确地预测代谢基因的必要性。我们使用MTBPROM2.0来模拟模型中敲除和过表达104个TF中的每一个的代谢结果。与原始PROM MTB模型相比，MTBPROM2.0提高了敲除生长缺陷预测的性能，并且它可以成功地预测与TF过表达相关的生长缺陷。此外，MTBPROM2.0的条件特异性模型成功地预测了在两种标准抗TB药物存在下过表达TF whiB 4的协同生长结果。MTBPROM2.0可以通过计算机模拟筛选条件特异性转录因子扰动，以产生推定的目标，这有助于优先考虑未来的治疗开发实验。结核病仍然是一个重大的全球健康挑战，需要加强药物开发工作。药物开发将有助于更多地了解导致这种疾病的细菌，结核分枝杆菌（MTB），以及它如何适应宿主体内的各种条件。为了帮助这项工作，我们扩展了一个计算模型，该模型使用我们对MTB转录调控网络（相互作用以控制靶基因丰度的基因）如何影响代谢网络（驱动生化反应的基因）的理解。使用这个模型，MTBPROM2.0，我们能够成功地预测是否破坏或促进调节基因的作用会导致MTB的生长缺陷。通过在许多环境条件下应用这些预测，该工具可以帮助找到潜在的新药物靶点，以获得更有效的MTB治疗。
Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts. Tuberculosis remains a major global health challenge with a need for enhanced drug development efforts. Drug development would be aided by understanding more about the bacteria that causes the disease, Mycobacterium tuberculosis (MTB), and how it adapts to survive the broad range of conditions within hosts. To help this effort, we extended a computational model that uses our understanding of how the MTB transcriptional regulatory network (genes that interact to control the abundance of target genes) influences the metabolic network (genes that drive biochemical reactions). Using this model, MTBPROM2.0, we were able to successfully predict whether disrupting or boosting the action of regulatory genes would cause a growth defect in MTB. By applying these predictions, across many environmental conditions, this tool can help find potential new drug targets for more effective MTB treatments.