RIPK1 inhibition mitigates neuroinflammation and rescues depressive-like behaviors in a mouse model of LPS-induced depression
Bibliographic record
Abstract
BACKGROUND: Depression is often linked to inflammation in the brain. Researchers have been exploring ways to reduce this inflammation to improve depression symptoms. One potential target is a protein called RIPK1, which is known to contribute to brain inflammation. However, it's unclear how RIPK1 influences depression. Our study aims to determine whether RIPK1 inhibition could alleviate neuroinflammation-associated depression and elucidate its underlying mechanisms. METHODS: To investigate our research objectives, we established a neuroinflammation mouse model by administering LPS. Behavioral and biochemical assessments were conducted on these mice. The findings were subsequently validated through in vitro experiments. RESULTS: . Remarkably, Necrostatin (Nec-1 S), a RIPK1 inhibitor, mitigated these changes. We further found altered expression and phosphorylation of eIF4E, PI3K/AKT/mTOR, and synaptic proteins in hippocampal tissues, BV2, and N2a cells post-LPS treatment, which Nec-1 S also ameliorated. Importantly, eIF4E inhibition reversed some of the beneficial effects of Nec-1 S, suggesting a complex interaction between RIPK1 and eIF4E in LPS-induced neuroinflammation. Moreover, citronellol, a RIPK1 agonist, significantly altered eIF4E phosphorylation, indicating RIPK1's potential upstream regulatory role in eIF4E and its contribution to neuroinflammation-associated depression. CONCLUSION: These findings propose RIPK1 as a pivotal mediator in regulating neuroinflammation and neural plasticity, highlighting its significance as a potential therapeutic target for depression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".