A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm
- Oak Ridge National Laboratory, United States
- University of Tennessee Knoxville, United States
- University of Kentucky, United States
- Medical College of Wisconsin, United States
- Yale University, United States
- Cincinnati Children's Hospital Research Foundation, United States
- Cited 1
- Views 120,889
- Annotations
Abstract
Neither the disease mechanism nor treatments for COVID-19 are currently known. Here we present a novel molecular mechanism for COVID-19 that provides therapeutic intervention points that can be addressed with existing FDA-approved pharmaceuticals. The entry point for the virus is ACE2, which is a component of the counteracting hypotensive axis of RAS, that produces the nonapeptide angiotensin1-9 from angiotensin I. Bradykinin is a potent, but often forgotten, part of the vasopressor system that induces hypotension and vasodilation 1, and is regulated by ACE and enhanced by angiotensin1-9 2. Here we perform a completely new analysis on gene expression data from cells of bronchoalveolar lavage samples from COVID-19 patients that were used to sequence the virus, but the host information was discarded 3. Comparison with lavage samples from controls identify a critical imbalance in RAS represented by decreased expression of ACE in combination with increases in ACE2, renin (REN) , angiotensin (AGT), key RAS receptors (AGTR2, AGTR1), kinogen (KNG) and the kallikrein enzymes (KLKB1, many of KLK-1-15) that activate it, and both bradykinin receptors (BDKRB1, BDKRB2). This very atypical pattern of the RAS is predicted to elevate bradykinin levels in multiple tissues and systems that will likely cause increases in vascular dilation, vascular permeability and hypotension. These bradykinin-driven outcomes explain many of the symptoms being observed in COVID-19.
Article and author information
Author details
Funding
Oak Ridge National Laboratory (LOIS:10074)
- Mike R Garvin
- J Izaak Miller
- Erica T Prates
- Daniel Jacobson
U.S. Department of Energy (National Virtual Biotechnology Laboratory)
- Mike R Garvin
- Christiane Alvarez
- J Izaak Miller
- Erica T Prates
- Angelica M Walker
- Daniel Jacobson
National Institutes of Health (U24 HL148865)
- Bruce Aronow
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Frank L van de Veerdonk, Radboud University Medical Center, Netherlands
Publication history
- Received: May 21, 2020
- Accepted: July 6, 2020
- Accepted Manuscript published: July 7, 2020 (version 1)
- Accepted Manuscript updated: July 8, 2020 (version 2)
- Version of Record published: August 6, 2020 (version 3)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Metrics
-
- 120,889
- Page views
-
- 6,366
- Downloads
-
- 1
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
Downloads (link to download the article as PDF)
Download citations (links to download the citations from this article in formats compatible with various reference manager tools)
Open citations (links to open the citations from this article in various online reference manager services)
Further reading
-
- Computational and Systems Biology
Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.
-
- Cell Biology
- Computational and Systems Biology
Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.
