Prevention of LPS-Induced Microglia Activation, Cytokine Production and Sickness Behavior with TLR4 Receptor Interfering Peptides
Bibliographic record
Abstract
The innate immune receptor Toll-like 4 (TLR4) is the receptor activated by lipopolysaccharide (LPS), and TLR4-LPS interaction is well known to induce an innate immune response, triggering sickness behavior. Within the brain, TLR4 is highly expressed in brain microglia, and excessive inflammation resulting from activation of this pathway in the brain has been implicated in depressive disorders and neurodegenerative pathologies. We hypothesized that blocking LPS-induced activation of TLR4 would prevent downstream immune signaling in the brain and suppress the induction of sickness behavior. We used interfering peptides to block TLR4 activation and confirmed their efficacy in preventing second messenger activation and cytokine production normally induced by LPS treatment. Further, these peptides blocked morphological changes in microglia that are typically induced by LPS. We also demonstrated that intraperitoneal (i.p.) injection of Tat-TLR4 interfering peptides prevented LPS-induced sickness behavior, as assessed in home cage behavior and with the intracranial self-stimulation paradigm. These newly synthesised peptides inhibit TLR4 signaling thereby preventing changes in behavior and motivation caused by inflammatory stimuli. These peptides highlight the roll of TLR4 and microglia morphology changes in sickness behavior, and thus may be of therapeutic value in limiting the deleterious impact of excessive inflammation in specific CNS pathologies.
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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 | 0.000 |
| 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".