Influenza A virus modulates ACE2 expression and SARS-CoV-2 infectivity in human cardiomyocytes
Bibliographic record
Abstract
Abstract Influenza A virus (IAV) and SARS-CoV-2 are both acute respiratory viruses currently circulating in the human population. Prior IAV infection enhances SARS-CoV-2 infectivity and lung pathogenesis in mice; however, underlying mechanisms and the extent that co-infection leads to involvement of other organs in disease severity remains unknown. Herein, we investigated the impact of prior IAV infection on SARS-CoV-2 pathogenesis and cardiomyocyte function. IAV infection induces the expression of ACE2 in human lung epithelial cells, lung fibroblasts, macrophages, cardiac fibroblasts (HCFs), and hiPSC-Cardiomyocytes (CMs). Interestingly, we detected poorly glycosylated ACE2 in lung epithelial cells and cardiac fibroblasts. In contrast, expression of a heavily glycosylated form of ACE2 is induced by IAV in CMs. In all cell types, IAV infection enhances SARS-CoV-2 viral entry. However, efficient SARS-CoV-2 replication was uniquely inhibited in CMs. Glycosylation of ACE2 correlated with enzymatic conversion of its substrate Angiotension II, induction of eNOS and nitric oxide production, providing a mechanistic underpinning for restricted viral replication in CMs. Our results indicate that IAV-mediated induction of ACE2 is double-edged, providing increased risk for SARS-CoV-2 coinfection in epithelial cells and HCFs, while limiting SARS-CoV-2 replication in CMs. We conclude that differential glycosylation of ACE2 may be a molecular determinant, not of SARS-CoV-2 infection, but of replication. Funding: NIH grants R01-AI 146252, R21-AI 146690 Funding: NIH grants R01-AI 146252, R21-AI 146690
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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 | 0.000 |
| 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".