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.
There has been a dramatic and significant increase in nephrology advanced trainee numbers over the past decade at a more rapid rate than the growth in dialysis and transplant patient numbers. This study suggests that training experience has diminished over the past decade and supports a 3-year core clinical nephrology training programme in Australia.
As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high-performance computing (HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation, we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world’s fastest supercomputers, Summit and Titan. We also show iRF-LOOP’s ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.
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