| NSF Org: |
DEB Division Of Environmental Biology |
| Recipient: |
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| Initial Amendment Date: | June 2, 2020 |
| Latest Amendment Date: | June 2, 2020 |
| Award Number: | 2034507 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Samuel Scheiner
DEB Division Of Environmental Biology BIO Directorate for Biological Sciences |
| Start Date: | June 1, 2020 |
| End Date: | May 31, 2021 (Estimated) |
| Total Intended Award Amount: | $17,361.00 |
| Total Awarded Amount to Date: | $17,361.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
910 GENESEE ST ROCHESTER NY US 14611-3847 (585)275-4031 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 14627-0140 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | COVID-19 Research |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.074 |
ABSTRACT
The goal of this project is to identify potential interactions between Angiotensin-converting enzyme 2 (ACE2) and other human proteins that have been implicated in human health problems related to covid-19 infection. ACE2 is the human cell receptor that the corona virus SARS-CoV-2 binds to and uses to enter and infect human cells. It also regulates blood pressure and is involved in digestion. Identifying previously undescribed ACE2 protein interactions will advance our understanding of its biological functions and its contribution to covid-19 pathology. A better understanding of the proteins with which ACE2 interacts can help identify therapeutic targets and lead to reducing the severity of covid-19 pathologies and complications, a key broader impact of this project.
The project will use a novel computational and evolutionary approach to identifying candidate ACE2 interacting proteins based on a set of mammalian proteins that ?coevolve? with ACE2. The approach, termed ?evolutionary rate correlation? (ERC) detects proteins that show highly correlated evolutionary rates during mammalian evolution. Such proteins are strong candidates for biological interactions with the ACE2 receptor. Preliminary results have identified candidate interacting proteins that are not currently known to be ACE2 interactors, but which are relevant to covid-19 pathologies. Among the top 20 coevolving proteins with highly significant correlations, four are involved in the blood coagulation cascade and six additional ones are implicated in blood cell related phenotypes. Three other proteins are implicated in inflammatory processes or endotoxic shock. These are striking findings, with clear potential implications to major covid-19 pathologies, including severe thrombosis (blood clotting), and Kawasaki-like syndromes in children. A strong association between ACE2 protein and these blood associated proteins has not previously been reported. Also detected are strong ERCs to lipid metabolism proteins. Based on these ERCs, the project will develop a protein interaction network, and expand the analysis to additional candidate coronavirus interacting proteins. This RAPID award is made by the Evolutionary Processes Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
COVID-199, caused by the coronavirus SARS-COV-2, involves a complex of symptoms far beyond that of a standard respiratory virus. Symptoms can include vascular disorders such as extensive blood-clotting and excessive inflammatory responses (cytokine storms), both of which damage tissues and organs. In addition, complications of COVID-19 often can last well after the infection is no longer detected.
The coronavirus uses a cell receptor, Angiotensin-converting enzyme 2 (ACE2), to enter and infect cells, and ACE2 also circulates in blood, although its functions there are largely unknown. Therefore, a more complete understanding of ACE2?s protein partners can have important implications to COVID-19 syndromes and possible treatments.
In this study, a new approach is taken to identify protein candidates that interact with ACE2, by identifying proteins that coevolve with ACE2 during mammalian evolution. The rationale of the approach, called evolutionary rate correlation (ERC), is that proteins that show correlated rates of evolution are more likely to be functionally connected. The goal of this study is to use ERC to identify ACE2?s evolutionary protein partners. These are candidates for normal ACE2 functional protein interactions which, when disrupted, could contribute to COVID-19 pathologies.
Consistent with the reasoning above, the ERC approach has detected novel candidates for ACE2 protein interactions with likely relevance to COVID-19 pathologies, such as systemic blood clotting, immunity and inflammatory response. These protein connections to ACE2 have not been described in previous studies. However, some of the proteins have been implicated in severe COVID-19 by other methods, lending some credence to the ERC approach. Among the novel findings is a strong ACE2 evolutionary association with Clusterin, a protein involved in housekeeping functions in blood, and unexpected ACE2 network connections to several blood coagulation proteins and to proteins involved in inflammation. These results will need to be validated, but if supported, could have important implications to COVID-19 syndromes.
A reciprocal rank approach was also developed to identify networks with strong evolutionary connections among proteins. Evolutionary approaches such as ERC can be a useful addition to the toolkit of genetic and biochemical methods used to study protein interactions. Toward this goal, the protein data set produced here, along with ongoing expansions, will be a resource for researchers wishing to utilize ERCs to identify candidate protein interactions for a wide range of biological processes, as well as to gain a better understanding of protein networks in disease.
Last Modified: 09/21/2021
Modified by: John H Werren
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