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: 2006190
• 负责人: Ranjan Srivastava
• 金额: $ 38.2万
• 依托单位: University of Connecticut
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006190&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：使用多组学分析和动态建模来了解真菌细胞壁的应激反应这个项目将更好地理解模型丝状真菌，尼杜拉曲霉，如何在应激期间修复其细胞壁。这一认识对社会很重要，因为有益的丝状真菌物种每年被用来生产数十亿美元的商业产品，而致病物种则对作物造成数十亿美元的损害，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成分。在涉及的三个不同的实验室中，工作将包括研究生和本科生。该计划还包括通过密歇根大学迈耶霍夫学者项目向代表不足的少数族裔提供服务。这些强有力的跨学科培训机会将有助于发展一支多样化的STEM工作队伍。虽然对细胞壁压力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被很好地理解。我们试图确定三个特定的调控模块如何相互作用来调节对细胞壁压力的反应。为了做到这一点，我们使用磷酸蛋白质组分析来揭示激酶介导的调节行为，并使用转录分析来揭示与压力相关的基因表达的变化。这项拟议工作的一个重要方面是收集动态数据，以了解亚磷酸盐的占有率和基因表达如何随时间变化。除了实验之外，我们还将使用一种新的数学建模方法来建立描述壁应力响应的稳健动力学模型(耦合常微分方程组)。该模型的一个关键特征是，除了基因表达值外，一个关键变量将是亚磷酸盐的占有率。我们注意到，在一种蛋白质上通常有多个亚磷酸盐，这些亚磷酸盐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，然后进行实验测试。然后，这些实验的数据将被用来改进模型。这将使我们能够发展模型，以准确地代表系统行为。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。