ILRUN downregulates ACE2 expression and blocks infection of human cells by SARS-CoV-2
Bibliographic record
Abstract
ABSTRACT The human protein-coding gene ILRUN (inflammation and lipid regulator with UBA-like and NBR1-like domain, previously C6orf106) is a recently-characterised inhibitor of the transcription regulators p300 and CREB-binding protein (CBP). Here we have utilised RNA-seq to define cellular pathways regulated by ILRUN in the context of severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) infection. We find that inhibition of ILRUN expression increases cellular expression of several members of the renin-angiotensin aldosterone system (RAAS), including the SARS-CoV-2 entry receptor angiotensin converting enzyme 2 (ACE2). Furthermore, inhibition of ILRUN results in increased SARS-CoV-2 replication. These data identify ILRUN as a novel inhibitor of SARS-CoV-2 replication and represents, to our knowledge, the first report of ILRUN as a regulator of the RAAS. SIGNIFICANCE STATEMENT There is no doubt that the current rapid global spread of COVID-19 has had significant and far-reaching impacts on our health and economy and will continue to do so. Research in emerging infectious diseases, such as severe acute respiratory syndrome-associated coronavirus (SARS-CoV-2), is growing rapidly, with new breakthroughs in the understanding of host-virus interactions and the development of innovative and exciting therapeutic strategies and new knowledge and tools to better protect against the impacts of disease. The human protein-coding gene ILRUN is a recently-characterised inhibitor of the transcription regulators p300 and CREB-binding protein (CBP). Here we present the first evidence that ILRUN modulation has implications for SARS-CoV-2 infections. Virus infectivity assays confirmed that gene silencing of ILRUN had a proviral effect and increased SARS-CoV-2 replication, whilst over-expression of ILRUN inhibited SARS-CoV-2 production. Additionally, we observed that ILRUN also regulates the expression of key elements of the RAAS. These data have important implications for the development of antiviral strategies to deal with the current SARS-CoV-2 pandemic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".