RAPID: Integrative analysis of multi-omics data to understand ACE2 regulation and cytokine storm
Division of Chemical, Bioengineering, Environmental, and Transport Systems
2029319
Grant
Steven Peretti
$200,000.00
Christina Chan
[email protected] (Principal Investigator)
Michigan State University
426 AUDITORIUM RD RM 2
EAST LANSING
MI
488242600
Many drugs are being tested for efficacy against COVID-19. The side effects of these drugs are poorly understood. The issue is complicated because a number of organ systems (lungs, heart, liver) can be affected by the infection. In addition, underlying conditions such as hypertension, diabetes, or cardiovascular disease increase the likelihood of serious complications or death. This project is designed to identify the effects of these drugs on the organ systems of vulnerable populations. This information would inform the selection and application of effective drugs that also cause minimal negative consequences. This project will also advance the education and research experience of under-represented groups in the STEM disciplines.
This project combines “horizontal” and “vertical” analyses of global genomic datasets. The “horizontal” perspective will map the landscape of gene expression under various conditions that will enable broader consideration of potential changes that drug treatments could have on Covid-19. The “vertical” perspective will identify regulatory mechanisms that suggest possible treatments to target specific responses (e.g., increases in the different types and levels of cytokines or decreases in the ACE2 levels) for the different phenotypes. The integrative approach of this proposal will capitalize on the timely results from the latest studies and incorporate these results into the gene regulatory network analysis to provide phenotypic-specific guidance on potential anti-inflammatory treatments and insight into the host response as a function of the phenotype. The scientific and engineering contribution of this project is the development and application of an integrative, multi-scale, and multi-faceted approach that models cellular interactions (signaling and regulatory) to enable prediction of the phenotypic responses to external stimuli, including drugs and pathogens. This integrative modeling framework will be applicable to other pathogens and patient populations.
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.