Multi-scale model of microbial phenotype modulation by mucins

粘液提供了一个关键的保护屏障，防止感染原;其在清除微生物中的作用 ”[11]《易经》云：“君子之道，焉可诬也？”我们的团队最近的研究[2，3]对微生物的调节 粘蛋白是粘液的主要成分， 理解粘膜层中相互作用的复杂性。然而，迄今为止，我们的理解是有限的 在这个复杂的环境中编目组件，对潜在的机制知之甚少， 从微生物和粘蛋白的相互作用中出现的表型。了解机械粘蛋白- 微生物表型的驱动调节在多种疾病中是至关重要的， 纤维化，一种以粘液清除缺陷为特征的疾病[4]。有新的证据表明粘蛋白 改变分泌因子的运输，并使微生物中的基因调控发生变化[1，5]。最近 我们的团队开发的铜绿假单胞菌（囊性纤维化的关键病原体）代谢模型可以明确地 解释了这些基因调控变化与代谢功能之间的联系， 在这些复杂的环境中的细菌[6]。这项工作的核心假设是， 微生物表型是粘蛋白调节的运输和代谢相关性质的函数。一个 将建立综合的、多尺度的计算模型，以指导实验设计， 了解微生物粘蛋白现象。我们将开发一个框架， 网络模型与连续模型的运输现象，使用基于代理的模型，可以作为一个 类似多尺度建模挑战的模板。具体而言，我们将解决以下问题：（1） 粘蛋白如何调节微生物的代谢？(2)粘蛋白如何改变微生物的运输 和代谢物吗(3)哪些是临床相关的代谢和转运相关的调节剂， 粘液中微生物的表型对潜在复杂性的机械理解的重要性 微生物、粘蛋白、代谢物和运输现象的相互作用不能被夸大;例如， 急性下呼吸道感染，由粘液和微生物之间的相互作用驱动，是一个关键的 这是一个全球性的健康问题，其疾病负担比癌症、心脏病、疟疾和艾滋病毒更大[7]。我们 一个在计算模型、微生物生理学和粘液生物学方面的专家小组已经做好了解决这个问题的准备 复杂的问题我们将建立一个代谢和运输的计算建模框架， 粘膜环境和鉴定假单胞菌表型的关键调节剂。我们将能够预测， 通过调节粘膜环境来控制生物膜分散，从而可能导致更多的 有效的抗生素靶向和最终治疗铜绿假单胞菌感染和其他疾病的策略， 其中被破坏的粘膜界面是重要的。该框架将作为预测值的模板 这些模型的许多其他应用询问复杂的微生物-人类宿主相互作用。

Mucus provides a critical protective barrier against infectious agents; its role in the clearance of microorganisms from the lung [1] is long-appreciated. Recent studies by our team [2,3] on the modulation of microbial phenotypes by mucins, glycoproteins that are a primary component of mucus, are creating a whole new appreciation for the complexity of interactions in the mucosal layer. However, to date, our understanding is limited to cataloging the components in this complex milieu with little understanding of the mechanisms underlying the phenotypes that emerge from the interactions of microbes and mucins. Understanding the mechanistic mucin- driven modulation of microbial phenotypes is of paramount importance in multiple diseases including cystic fibrosis, a disease characterized by defective clearance of mucus [4]. There is emerging evidence that mucin alters the transport of secreted factors and elicits changes in gene regulation in microbes [1,5]. Recently developed metabolic models by our team of P. aeruginosa (a key pathogen in cystic fibrosis) can explicitly account for the connection between these changes in gene regulation and the metabolic functionality of the bacterium in these complex environments [6]. The underlying central hypothesis to the proposed work is that microbial phenotypes are a function of mucin-modulated transport- and metabolism-related properties. An integrative, multi-scale computational model will be constructed to guide experimental design and facilitate understanding of emergent microbe-mucin phenomena. We will develop a framework for integrating metabolic network models with continuum models of transport phenomena using agent-based models that can serve as a template for similar multi-scale modeling challenges. Specifically, we will address the following questions: (1) How do mucins modulate the metabolism of microbes? (2) How do mucins alter transport of microbes and metabolites? (3) What are the key metabolic- and transport-related modulators of clinically-relevant phenotypes of a microbe in mucus? The importance of a mechanistic understanding of the underlying complex interactions of microbes, mucins, metabolites, and transport phenomena cannot be overstated; for example, acute lower respiratory tract infections, driven by the interaction between mucus and microbes, are a critical global health problem with a greater burden of disease than cancer, heart disease, malaria, and HIV [7]. Our team of experts in computational modeling, microbial physiology, and mucus biology is well poised to tackle this complex problem. We will establish a framework for computational modeling of metabolism and transport in mucosal environments and identify key modulators of Pseudomonas phenotypes. We will be able to predict and control biofilm dispersion through modulation of the mucosal environment, resulting in the potential for more effective antibiotic targeting and ultimately strategies to treat P. aeruginosa infections and other diseases in which a disrupted mucosal interface is important. This framework will serve as a template for the predictive value of such models to interrogate complex microbe-human host interactions for many other applications.