Autophagy-enhancing strategies to promote intestinal viral resistance and mucosal barrier function in SARS-CoV-2 infection
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus disease 19 (COVID-19), continues to circulate globally despite the widespread vaccination and therapeutics like Paxlovid, remdesivir, and molnupiravir. COVID-19 is associated with both respiratory and gastrointestinal manifestations, with persistent intestinal pathology contributing to the post-COVID-19 condition. We have previously demonstrated the antiviral activity of autophagy-blocking drugs, such as Berbamine dihydrochloride, against intestinal SARS-CoV-2 acquisition. In addition, the autophagy blockers restored the barrier function of infected intestinal epithelium. In this addendum, using human intestinal organoids, we present evidence for a protective role of intrinsic higher levels of autophagy flux in limiting intestinal SARS-CoV-2 infection. Pharmacological treatment with Akt inhibitor MK-2206 hydrochloride suppressed viral entry into the intestinal epithelium. This antiviral effect of MK-2206 was shown to be dependent on Synaptosomal-associated protein 29-dependent (SNAP-29)-mediated autophagy flux. Furthermore, extrinsically enhanced autophagy with MK-2206 also prevented SARS-CoV-2-induced intestinal barrier damage. Our findings thus underscore the intricate role of autophagy pathways in the dissemination and pathogenesis of intestinal SARS-CoV-2, highlighting the therapeutic potential of host-directed therapies targeting autophagy to intervene in COVID-19-associated sequelae and improve intestinal health.
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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) | 0.001 | 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".