The omega-3 hydroxy fatty acid 7( <i>S</i> )-HDHA is a high-affinity PPARα ligand that regulates brain neuronal morphology
Bibliographic record
Abstract
The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARα) is emerging as an important target in the brain for the treatment or prevention of cognitive disorders. The identification of high-affinity ligands for brain PPARα may reveal the mechanisms underlying the synaptic effects of this receptor and facilitate drug development. Here, using an affinity purification–untargeted mass spectrometry (AP-UMS) approach, we identified an endogenous, selective PPARα ligand, 7( S )-hydroxy-docosahexaenoic acid [7( S )-HDHA]. Results from mass spectrometric detection of 7( S )-HDHA in mouse and rat brain tissues, time-resolved FRET analyses, and thermal shift assays collectively revealed that 7( S )-HDHA potently activated PPARα with an affinity greater than that of other ligands identified to date. We also found that 7( S )-HDHA activation of PPARα in cultured mouse cortical neurons stimulated neuronal growth and arborization, as well as the expression of genes associated with synaptic plasticity. The findings suggest that this DHA derivative supports and enhances neuronal synaptic capacity in the brain.
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 | 0.000 |
| 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".