Multi-scale model of microbial phenotype modulation by mucins

粘液对感染性病原体提供了一种关键的保护性屏障；它在清除微生物方面的作用 从肺中提取[1]一直受到人们的重视。我们团队最近关于微生物调节的研究[2，3] 粘蛋白的表型，糖蛋白是粘液的主要成分，正在创造一种全新的 对粘膜层相互作用的复杂性的认识。然而，到目前为止，我们的理解是有限的。 在这个复杂的环境中对组件进行分类，而对基础机制缺乏了解 微生物和粘蛋白相互作用产生的表型。了解粘蛋白的机制-- 微生物表型的驱动调节在包括囊性疾病在内的多种疾病中至关重要 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，并引起微生物基因调控的变化[1，5]。最近 我们铜绿假单胞菌(囊性纤维化的关键病原体)团队开发的代谢模型可以明确地 解释了基因调控的这些变化与细胞的代谢功能之间的联系 细菌在这些复杂的环境[6]。拟议工作的基本中心假设是 微生物表型是粘蛋白调节的运输和代谢相关特性的函数。一个 将构建综合的、多尺度的计算模型，以指导实验设计和促进 对紧急微生物-粘蛋白现象的理解。我们将开发一个整合代谢的框架 具有传输现象的连续介质模型的网络模型，该模型使用基于代理的模型 用于类似多尺度建模挑战的模板。具体来说，我们将解决以下问题：(1) 粘蛋白如何调节微生物的新陈代谢？(2)粘蛋白如何改变微生物的运输 代谢产物？(3)临床相关的代谢和转运相关的关键调节因子是什么？ 粘液中微生物的表型？对潜在复杂事物进行机械性理解的重要性 微生物、粘蛋白、代谢物和运输现象之间的相互作用怎么强调都不为过；例如， 由粘液和微生物相互作用引起的急性下呼吸道感染是一个关键因素。 全球健康问题比癌症、心脏病、疟疾和艾滋病毒带来更大的疾病负担[7]。我们的 计算机建模、微生物生理学和粘液生物学的专家团队已经做好了应对这一问题的准备。 复杂的问题。我们将建立代谢和运输的计算模型框架 并确定假单胞菌表型的关键调节因子。我们将能够预测和 通过调节粘膜环境来控制生物膜的分散，从而产生更多的 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很重要的。该框架将作为预测值的模板 这样的模型来询问复杂的微生物与人类宿主的相互作用，以用于许多其他应用。

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.