Intronic regulation of SARS-CoV-2 receptor (ACE2) expression mediated by immune signaling and oxidative stress pathways
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».