ABSTRACT
Angiotensin-converting enzyme 2 (ACE2) is the cell receptor that the coronavirus SARS-CoV-2 binds to and uses to enter and infect human cells. COVID-19, the pandemic disease caused by the coronavirus, involves diverse pathologies beyond those of a respiratory disease, including micro-thrombosis (micro-clotting), cytokine storms, and inflammatory responses affecting many organ systems. Longer-term chronic illness can persist for many months, often well after the pathogen is no longer detected. A better understanding of the proteins that ACE2 interacts with can reveal information relevant to these disease manifestations and possible avenues for treatment. We have undertaken an approach to predict candidate ACE2 interacting proteins which uses evolutionary inference to identify a set of mammalian proteins that "coevolve" with ACE2. The approach, called evolutionary rate correlation (ERC), detects proteins that show highly correlated evolutionary rates during mammalian evolution. Such proteins are candidates for biological interactions with the ACE2 receptor. The approach has uncovered a number of key ACE2 protein interactions of potential relevance to COVID-19 pathologies. Some proteins have previously been reported to be associated with severe COVID-19, but are not currently known to interact with ACE2, while additional predicted novel ACE2 interactors are of potential relevance to the disease. Using reciprocal rankings of protein ERCs, we have identified strongly interconnected ACE2 associated protein networks relevant to COVID-19 pathologies. ACE2 has clear connections to coagulation pathway proteins, such as Coagulation Factor V and fibrinogen components FGA, FGB, and FGG, the latter possibly mediated through ACE2 connections to Clusterin (which clears misfolded extracellular proteins) and GPR141 (whose functions are relatively unknown). ACE2 also connects to proteins involved in cytokine signaling and immune response (e.g. XCR1, IFNAR2 and TLR8), and to Androgen Receptor (AR). The ERC prescreening approach has elucidated possible functions for relatively uncharacterized proteins and possible new functions for well-characterized ones. Suggestions are made for the validation of ERC-predicted ACE2 protein interactions. We propose that ACE2 has novel protein interactions that are disrupted during SARS-CoV-2 infection, contributing to the spectrum of COVID-19 pathologies.
ABSTRACT
PURPOSE OF REVIEW: This review a highlights that to use artificial intelligence (AI) tools effectively for hypertension research, a new foundation to further understand the biology of hypertension needs to occur by leveraging genome and RNA sequencing technology and derived tools on a broad scale in hypertension. RECENT FINDINGS: For the last few years, progress in research and management of essential hypertension has been stagnating while at the same time, the sequencing of the human genome has been generating many new research tools and opportunities to investigate the biology of hypertension. Cancer research has applied modern tools derived from DNA and RNA sequencing on a large scale, enabling the improved understanding of cancer biology and leading to many clinical applications. Compared with cancer, studies in hypertension, using whole genome, exome, or RNA sequencing tools, total less than 2% of the number cancer studies. While true, sequencing the genome of cancer tissue has provided cancer research an advantage, DNA and RNA sequencing derived tools can also be used in hypertension to generate new understanding how complex protein network, in non-cancer tissue, adapts and learns to be effective when for example, somatic mutations or environmental inputs change the gene expression profiles at different network nodes. The amount of data and differences in clinical condition classification at the individual sample level might be of such magnitude to overwhelm and stretch comprehension. Here is the opportunity to use AI tools for the analysis of data streams derived from DNA and RNA sequencing tools combined with clinical data to generate new hypotheses leading to the discovery of mechanisms and potential target molecules from which drugs or treatments can be developed and tested. Basic and clinical research taking advantage of new gene sequencing-based tools, to uncover mechanisms how complex protein networks regulate blood pressure in health and disease, will be critical to lift hypertension research and management from its stagnation. The use of AI analytic tools will help leverage such insights. However, applying AI tools to vast amounts of data that certainly exist in hypertension, without taking advantage of new gene sequencing-based research tools, will generate questionable results and will miss many new potential molecular targets and possibly treatments. Without such approaches, the vision of precision medicine for hypertension will be hard to accomplish and most likely not occur in the near future.