This 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.
