Berbamine suppresses intestinal SARS-CoV-2 infection via a BNIP3-dependent autophagy blockade
Bibliographic record
Abstract
SARS-CoV-2, the causative virus of COVID-19, continues to threaten global public health. COVID-19 is a multi-organ disease, causing not only respiratory distress, but also extrapulmonary manifestations, including gastrointestinal symptoms with SARS-CoV-2 RNA shedding in stool long after respiratory clearance. Despite global vaccination and existing antiviral treatments, variants of concern are still emerging and circulating. Of note, new Omicron BA.5 sublineages both increasingly evade neutralizing antibodies and demonstrate an increased preference for entry via the endocytic entry route. Alternative to direct-acting antivirals, host-directed therapies interfere with host mechanisms hijacked by viruses, and enhance cell-mediated resistance with a reduced likelihood of drug resistance development. Here, we demonstrate that the autophagy-blocking therapeutic berbamine dihydrochloride robustly prevents SARS-CoV-2 acquisition by human intestinal epithelial cells via an autophagy-mediated BNIP3 mechanism. Strikingly, berbamine dihydrochloride exhibited pan-antiviral activity against Omicron subvariants BA.2 and BA.5 at nanomolar potency, providing a proof of concept for the potential for targeting autophagy machinery to thwart infection of current circulating SARS-CoV-2 subvariants. Furthermore, we show that autophagy-blocking therapies limited virus-induced damage to intestinal barrier function, affirming the therapeutic relevance of autophagy manipulation to avert the intestinal permeability associated with acute COVID-19 and post-COVID-19 syndrome. Our findings underscore that SARS-CoV-2 exploits host autophagy machinery for intestinal dissemination and indicate that repurposed autophagy-based antivirals represent a pertinent therapeutic option to boost protection and ameliorate disease pathogenesis against current and future SARS-CoV-2 variants of concern.
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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".