SARS-CoV-2 spike protein causes synaptic dysfunction and p-tau and α-synuclein aggregation leading cognitive impairment: The protective role of metformin
Bibliographic record
Abstract
Patients recovering from post-coronavirus disease 2019 (COVID-19) often experience cognitive dysfunction, including difficulties with focusing, conversation, or memory issues, which can persist for weeks or even months after infection. Although the persistence of the SARS-CoV-2 spike protein has been reported in both the brain and serum, and is thought to potentially contribute to the long-term effects of SARS-CoV-2 infection on the brain, the underlying mechanisms leading to cognitive dysfunction remain unclear. This study investigates the molecular mechanisms by which the SARS-CoV-2 S1 protein (hereafter S1) induces cognitive impairment and explores the therapeutic potential of metformin in mitigating these effects. We demonstrate that intranasally administered S1 quickly entered the hippocampus and was associated with cognitive impairment by 6 weeks post-injection. Transcriptomic analysis of hippocampal tissue revealed early alterations in gene expression associated with synaptic function. We observed that the expression of hypoxia-responsive genes was also altered, suggesting the involvement of HIF-1α signaling. Further analysis confirmed that S1 stabilized the HIF-1α protein in a hypoxia-independent manner, and siRNA-mediated knockdown of HIF-1α restored synaptic gene expression, including GRIN2A, SHANK1, and JPH3. By 6 weeks post-injection, hippocampal neuronal loss was accompanied by the accumulation of phosphorylated tau (p-tau) and aggregated α-synuclein. Notably, treatment with metformin rescued synaptic gene expression and attenuated p-tau and α-synuclein aggregation. These findings suggest that S1 disrupts synaptic homeostasis and promotes neurodegenerative processes, and that metformin may serve as a potential therapeutic strategy to mitigate long-term cognitive sequelae of COVID-19.
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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".