Activation of nicotinic acetylcholine receptor α7 subunit limits Zika viral infection via promoting autophagy and ferroptosis
Bibliographic record
Abstract
Vagus nerve regulates viral infection and inflammation via the alpha 7 nicotinic acetylcholine receptor (α7 nAChR); however, the role of α7 nAChR in ZIKA virus (ZIKV) infection, which can cause severe neurological diseases such as microcephaly and Guillain-Barré syndrome, remains unknown. Here, we first examined the role of α7 nAChR in ZIKV infection in vitro. A broad effect of α7 nAChR activation was identified in limiting ZIKV infection in multiple cell lines. Combined with transcriptomics analysis, we further demonstrated that α7 nAChR activation promoted autophagy and ferroptosis pathways to limit cellular ZIKV viral loads. Additionally, activation of α7 nAChR prevented ZIKV-induced p62 nucleus accumulation, which mediated an enhanced autophagy pathway. By regulating proteasome complex and an E3 ligase NEDD4, activation of α7 nAChR resulted in increased amount of cellular p62, which further enhanced the ferroptosis pathway to reduce ZIKV infection. Moreover, utilizing in vivo neonatal mouse models, we showed that α7 nAChR is essential in controlling the disease severity of ZIKV infection. Taken together, our findings identify an α7 nAChR-mediated effect that critically contributes to limiting ZIKV infection, and α7 nAChR activation offers a novel strategy for combating ZIKV infection and its complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".