Baker Center Seminar Series on SARS-CoV2/COVID-19 - Dan Jacobson
Presenter: Dan Jacobson
https://www.ornl.gov/staff-profile/daniel-jacobson
Title: A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm
Abstract: Roughly 15% of individuals hospitalized for SARS-CoV-2 develop pneumonia-like symptoms and a subset deteriorates to acute respiratory distress requiring significant medical care 1. The underlying mechanism of COVID-19 is currently unknown, but the most common comorbidity is hypertension 2,3, which affects nearly one third of the global population 4 and is often treated with pharmaceutical intervention of the Renin-Angiotensin-System (RAS) by reducing the production of the hypertension-inducing angiotensin II octapeptide from the decapeptide angiotensin I with ACE inhibitors 5. The counteracting hypotensive axis of RAS includes ACE2, which produces the nonapeptide angiotensin1-9 from angiotensin I and also serves as an entry point for the virus. Bradykinin is another important part of the vasopressor system. Hypotension from vasodilation occurs as a result of bradykinin signaling 4, which is enhanced by angiotensin1-9 6. Here we analyze gene expression patterns from cells of bronchoalveolar lavage samples of COVID-19 patients and controls that indicate a severe imbalance in this system represented by decreased RNA expression of ACE and increased expression of ACE2, renin (REN) , angiotensin (AGT), key RAS receptors (AGTR2, AGTR1), kinogen (KNG) and the kallikrein enzymes (KLKB1, KLK-1-15) that activate it, and both bradykinin receptors (BDKRB1, BDKRB2). The resulting elevated bradykinin levels at multiple points of infection around the body (and perhaps system-wide in severe illness) 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.
Bio: We are happy to be the first group to break the Exascale barrier and to have done it for biology. At present, this (2.36 Exaops) calculation is the fastest scientific calculation ever done anywhere in the world. This project led to us winning the 2018 Gordon Bell Prize (the first ever for Systems Biology).
https://www.olcf.ornl.gov/2018/06/08/genomics-code-exceeds-exaops-on-su…
https://www.energy.gov/articles/doe-laboratories-win-gordon-bell-prize
My team focuses on the development and subsequent application of mathematical, statistical and computational methods to biological datasets in order to yield new insights into complex biological systems. Our approaches include the use of Network Theory and Topology Discovery/Clustering, Wavelet Theory, Machine & Deep Learning (amongst others: iterative Random Forests, Deep Neural Networks, etc.) and Linear Algebra (primarily as applied to large-scale multivariate modeling), together with traditional and more advanced computing architectures, such MPI parallelization and Apache Spark. We make use of various programming languages including C, Python, Perl, Scala and R. Areas of Statistics of particular interest to my lab include the use of both frequentist (parametric and non-parametric) and Bayesian methods as well as the development of new methods for Genome-Wide Association Studies (GWAS) and Phenome-Wide Associations Studies (PheWAS). These mathematical and statistical methods are applied to various population and (meta)multiomics data sets (Genomics, Phylogenomics, Transcriptomics, Proteomics, Metabolomics, Microbiomics, Viriomics, Phytobiomics, Chemiomics, etc.) individually as well as in combination in an attempt to better understand the functional relationships as well as biosynthesis, signaling, transcriptional, translational, degradation and kinetic regulatory networks at play in biological organisms and communities.
Many of our projects center around studying systems in involved the Center for Bioenergy Innovation (CBI), Plant-Microbial Interfaces (PMI) and Crassulacean Acid Metabolism (CAM) Biodesign programs at ORNL. However, we have a broad view of biological complexity and evolution that stretches from viruses to microbes to plants to humans (including cancer and neuroscience).
ORNL is home to some of the world’s largest supercomputers. My team uses petascale computing to analyze and model complex biological systems and are actively involved in the development of exascale applications for biology. Thus, there are excellent opportunities to be involved in the cutting edge of computational biology and supercomputing.
As interdisciplinary and multidisciplinary efforts are more and more critical for scientific discovery, we do maintain a wide network of collaborations from all around the world.