Collaborative Research: Using multi-omic analyses and dynamic modeling to understand fungal cell-wall stress responses

合作研究：使用多组学分析和动态建模来了解真菌细胞壁应激反应

• 批准号: 2006189
• 负责人: Mark Marten
• 金额: $ 85.19万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006189&HistoricalAwards=false
• 关键词: Collaborative; Research; Using; multi; omic

Collaborative Research: Using multi-omic analyses and dynamic modeling to understand fungal cell-wall stress responsesThis project will develop a better understanding of how the model filamentous fungus, Aspergillus nidulans, repairs its cell wall during periods of stress. This understanding is important for society as beneficial species of filamentous fungi are used to produce billions of dollars of commercial products annually, while pathogenic species are responsible for billions of dollars in crop damage and are a significant threat to human health. In both beneficial and pathogenic species of fungi, the cell wall plays a critical role enabling the fungus to grow and survive in diverse environments. To help understand the fungal response to stress, sophisticated mathematical models will be developed to describe system behavior and these models have the potential to extend to other biological systems. This research will also have a broad impact on human capital, as it has a significant educational component. In the three different laboratories involved, work will include both graduate and undergraduate students. The plan also includes outreach to underrepresented minorities through the UMBC Meyerhof Scholars program. These robust and interdisciplinary training opportunities will contribute to the development of a diverse STEM workforce.While the ability to respond to cell wall stress is a critical feature of growth and morphogenesis in filamentous fungi, the different regulatory modules that underlie this response are not well understood. We seek to determine how three specific regulatory modules interact to mediate the response to cell wall stress. To do this, we use phosphoproteomic analysis to reveal kinase-mediated regulatory behavior and transcriptomic analysis to reveal stress-associated changes in gene expression. An important aspect of the proposed work is that dynamic data will be gathered to understand how phosphosite occupancy and gene expression change with time. In addition to experiments, we will use a novel mathematical modeling approach to build a robust kinetic model (set of coupled ordinary differential equations) describing wall stress response. A key feature of the modeling is that in addition to gene expression values, a critical variable will be phosphosite occupancy. We note there are often multiple phosphosites on a single protein and that these are often the substrates of different protein kinases. Our model will help us make connections in this complex network. To develop the model, we will use a Design-Build-Test-Learn approach, wherein the model is used to make network predictions which are then tested experimentally. The data from these experiments will then be used to improve the model. This will allow us to evolve the model to accurately represent system behavior.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

合作研究：利用多组学分析和动态建模来了解真菌细胞壁的应力响应本项目将更好地了解模型丝状真菌构巢曲霉在应力期间如何修复其细胞壁。这种理解对社会很重要，因为有益的丝状真菌物种每年用于生产数十亿美元的商业产品，而致病物种则造成数十亿美元的作物损失，并对人类健康构成重大威胁。在有益和致病的真菌物种中，细胞壁起着关键作用，使真菌能够在不同的环境中生长和生存。为了帮助理解真菌对压力的反应，将开发复杂的数学模型来描述系统行为，这些模型有可能扩展到其他生物系统。这项研究还将对人力资本产生广泛影响，因为它具有重要的教育组成部分。在涉及的三个不同的实验室，工作将包括研究生和本科生。该计划还包括通过UMBC Meyerhof学者计划向代表性不足的少数民族进行宣传。这些强大的跨学科培训机会将有助于培养多样化的STEM人才队伍。虽然对细胞壁应激的反应能力是丝状真菌生长和形态发生的关键特征，但这种反应背后的不同调控模块尚未得到很好的理解。 我们试图确定三个特定的调控模块如何相互作用，以介导细胞壁应激反应。为此，我们使用磷酸化蛋白质组学分析来揭示激酶介导的调节行为，并使用转录组学分析来揭示基因表达中与应激相关的变化。拟议工作的一个重要方面是，将收集动态数据，以了解磷酸化位点占有率和基因表达如何随时间变化。除了实验，我们将使用一种新的数学建模方法来建立一个强大的动力学模型（耦合常微分方程组）描述壁应力响应。建模的一个关键特征是，除了基因表达值之外，关键变量将是磷酸位点占有率。我们注意到在一个蛋白质上通常有多个磷酸化位点，这些磷酸化位点通常是不同蛋白激酶的底物。我们的模型将帮助我们在这个复杂的网络中建立联系。为了开发该模型，我们将使用设计-构建-测试-学习方法，其中该模型用于进行网络预测，然后通过实验进行测试。这些实验的数据将用于改进模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。