miR–9-5p regulates immunometabolic and epigenetic pathways in β-glucan–trained immunity via IDH3α
Bibliographic record
Abstract
Trained immunity, induced by β-glucan in monocytes, is mediated by activating metabolic pathways that result in epigenetic rewiring of cellular functional programs; however, molecular mechanisms underlying these changes remain unclear. Here, we report a key immunometabolic and epigenetic pathway mediated by the miR-9-5p-isocitrate dehydrogenase 3α (IDH3α) axis in trained immunity. We found that β-glucan-trained miR-9-5p-/- monocytes showed decreased IL-1β, IL-6, and TNF-α production after LPS stimulation. Trained miR-9-5p-/- mice produced decreased levels of proinflammatory cytokines upon rechallenge in vivo and had worse protection against Candida albicans infection. miR-9-5p targeted IDH3α and reduced α-ketoglutarate (α-KG) levels to stabilize HIF-1α, which promoted glycolysis. Accumulating succinate and fumarate via miR-9-5p action integrated immunometabolic circuits to induce histone modifications by inhibiting KDM5 demethylases. β-Glucan-trained monocytes exhibited low IDH3α levels, and IDH3α overexpression blocked the induction of trained immunity by monocytes. Monocytes with IDH3α variants from autosomal recessive retinitis pigmentosa patients showed a trained immunity phenotype at immunometabolic and epigenetic levels. These findings suggest that miR-9-5p and IDH3α act as critical metabolic and epigenetic switches in trained immunity.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".