Borna Mehrad, MD
Reinhard Laubenbacher, PhD
A key feature of the COVID-19 infection is the vast inter-individual heterogeneity in the severity of the infection. The complex biological mechanisms that underlie this variability remain mostly obscure. We propose to provide a mechanistic understanding of this susceptibility by leveraging 3 key innovations: First, we have developed a 3-dimensional lung culture system that allows for detailed interrogation of the early events in SARS-CoV-2 infection. Second, we have established an animal model of COVID-19 in mice transgenic for the human ACE2 receptor in our facility. Third, we have built a multi-scale mathematical model of lung infection in COVID-19, that we now seek to expand and personalize to individual hosts. We have two Aims in this project: In Aim 1, we will validate, expand, and personalize our existing multi-scale model, using an unbiased approach to identify and test hypotheses relating to susceptibility to severe COVID-19, and in Aim 2 we will test a specific hypothesis regarding the mechanism of the observed inter-individual heterogeneity in COVID-19 severity, namely that it is, in part, mediated by divergent activation of the mTOR pathway in type I alveolar epithelial cells. If successful, this project will identify the biological basis of the immune pathways that result in heterogeneous outcome of COVID-19, paving the way for personalized, host-specific interventions to improve the outcome of the infection.
COVID-19 is an ongoing global pandemic affecting many millions of people around the world. We currently have little understanding as to why some individuals develop a severe infection while most do not. This project seeks to achieve this understanding, and is relevant to public health because it can lead to better identification of at-risk individuals and more effective treatments for them.
Kadelka C, Wheeler M, Veliz-Cuba A, Murrugarra D, Laubenbacher R. Modularity of biological systems: a link between structure and function. J R Soc Interface. 2023 Oct;20(207):20230505. DOI: 10.1098/rsif.2023.0505 . Epub 2023 Oct 25. PMID: 37876275.