Medeiros Filho Fernando, do Nascimento Ana Paula Barbosa, Costa Maiana de Oliveira Cerqueira E, Merigueti Thiago Castanheira, de Menezes Marcio Argollo, Nicolás Marisa Fabiana, Dos Santos Marcelo Trindade, Carvalho-Assef Ana Paula D'Alincourt, da Silva Fabrício Alves Barbosa
Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Laboratório Nacional de Computação Científica, Petrópolis, Brazil.
Front Mol Biosci. 2021 Sep 20;8:728129. doi: 10.3389/fmolb.2021.728129. eCollection 2021.
is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of . We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.
是一种机会性人类病原体，由于其能够在身体不同部位引起感染以及对广泛的临床可用抗生素具有抗性，一直是全球持续存在的健康问题。世界卫生组织将多重耐药菌列为需要紧急研发有效治疗方案的顶级生物体之一。为实现这些目标已经采取了几种方法，但它们都依赖于发现潜在的药物靶点。从测序技术获得的大量数据已被用于创建生物体的计算模型，这为更好地理解其生物学行为提供了强大的工具。在本工作中，我们应用了一种方法将转录组数据与的基因组规模代谢网络整合。我们将代谢模型和整合模型都提交进行动态模拟，并将它们的性能与已发表的生长曲线进行比较。此外，我们使用这些模型来识别潜在的治疗靶点，并比较结果以分析这样一种假设，即富含生物学测量数据的计算模型可以提供更具选择性和（或）特异性的预测。我们的结果表明，来自整合模型的动态模拟产生更准确的生长曲线，通量分布与生物学观察更一致。此外，从整合模型中识别药物靶点更具选择性，因为预测的基因是代谢模型中发现的基因的一个子集。我们的分析导致识别出26个非宿主同源靶点。其中，我们根据与人类微生物组的较低保守性突出了五个排名靠前的基因。总体而言，本工作中识别出的一些基因已经通过不同方法被提出和（或）已经作为抗微生物化合物的靶点进行了研究，这强化了使用整合模型作为选择生物学相关治疗靶点起点的益处。
