Intronic regulation of SARS-CoV-2 receptor (ACE2) expression mediated by immune signaling and oxidative stress pathways
Bibliographic record
Abstract
Abstract The angiotensin-converting enzyme 2 (ACE2) protein has been highly studied as a key catalytic regulator of the renin-angiotensin system (RAS), involved in fluid homeostasis and blood pressure modulation. In addition to its important physiological role as a broadly-expressed membrane-bound protein, ACE2 serves as a cell-surface receptor for some viruses - most notably, coronaviruses such as SARS-CoV and SARS-CoV-2. Differing levels of ACE2 expression may impact viral susceptibility and subsequent changes to expression may be a pathogenic mechanism of disease risk and manifestation. Therefore, an improved understanding of how ACE2 expression is regulated at the genomic and transcriptional level may help us understand not only how the effects of pre-existing conditions (e.g., chronic obstructive pulmonary disease) may manifest with increased COVID-19 incidence, but also the mechanisms that regulate ACE2 levels following viral infection. Here, we initially perform bioinformatic analyses of several datasets to generate hypotheses about ACE2 gene-regulatory mechanisms in the context of immune signaling and chronic oxidative stress. We then identify putative non-coding regulatory elements within ACE2 intronic regions as potential determinants of ACE2 expression activity. We perform functional validation of our computational predictions in vitro via targeted CRISPR-Cas9 deletions of the identified ACE2 cis -regulatory elements in the context of both immunological stimulation and oxidative stress conditions. We demonstrate that intronic ACE2 regulatory elements are responsive to both immune signaling and oxidative-stress pathways, and this contributes to our understanding of how expression of this gene may be modulated at both baseline and during immune challenge. Our work supports the further pursuit of these putative mechanisms in our understanding, prevention, and treatment of infection and disease caused by ACE2-utilizing viruses such as SARS-CoV, SARS-CoV-2, and future emerging SARS-related viruses. Author Summary The recent emergence of the virus SARS-CoV-2 which has caused the COVID-19 pandemic has prompted scientists to intensively study how the virus enters human host cells. This work has revealed a key protein, ACE2, that acts as a receptor permitting the virus to infect cells. Much research has focused on how the virus physically interacts with ACE2, yet little is known on how ACE2 is turned on or off in human cells at the level of the DNA molecule. Understanding this level of regulation may offer additional ways to prevent or lower viral entry into human hosts. Here, we have examined the control of the ACE2 gene, the DNA sequence that instructs ACE2 protein receptor formation, and we have done so in the context of immune stimulation. We have indeed identified a number of DNA on/off switches for ACE2 that appear responsive to immuno-logical and oxidative stress. These switches may fine-tune how ACE2 is turned on or off before, during, and/or after infection by SARS-CoV-2 or other related coronaviruses. Our studies help pave the way for additional functional studies on these switches, and their potential therapeutic targeting in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".