Brain-derived neurotrophic factor prevents LPS-induced dysregulation of GABAergic interneuron markers in mouse hippocampus
Bibliographic record
Abstract
Background Inflammation causes reduced markers of GABAergic interneurons and brain-derived neurotrophic factor (BDNF) in the hippocampus, features often associated with neuropsychiatric disease pathophysiology. However, the mechanism connecting inflammation to GABAergic markers remains unclear. We hypothesized that reduced BDNF mediates the effects of LPS on GABAergic markers and that hippocampal BDNF infusion would prevent LPS-induced reduction in somatostatin (SST), and coexpressed markers, including cortistatin (CORT), and neuropeptide Y (NPY). Method C57BL/6 mice (n = 14; 12–14 weeks old; 50% female) received intracerebral administration of BDNF (250 ng) or vehicle control in the hippocampus via stereotaxic surgery (unilateral). Thirty minutes after BDNF administration, intraperitoneal injection of LPS (2 mg/kg) or phosphate buffered saline (PBS) was performed and mice were euthanized 18 h post LPS-injection. The hippocampus was collected for investigation of cellular markers using quantitative PCR and enzyme-linked immunosorbent assay (ELISA). Results LPS administration in mice that did not receive pre-treatment with BDNF led to a significant reduction in mRNA levels of Bdnf (p = 0.0049), Sst (p = 0.0416), Npy (p = 0.0088), and Cort (p = 0.0055). BDNF infusion into the hippocampus prior to LPS injection prevented the reduction in Bdnf, Sst, and Cort mRNA expression. BDNF also prevented the LPS-induced effect on protein levels of BDNF, SST and NPY. BDNF prevention of LPS effects occurred in the context of sustained elevation of inflammatory markers (interleukin 1-beta and glial fibrillary acidic protein). Conclusion BDNF may protect SST GABAergic interneurons from LPS-induced inflammation, providing novel insights into the molecular mechanisms linking inflammation and GABAergic dysfunction in neuropsychiatric diseases.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".