Tripartite ER-Mitochondria-Lipid Droplets contact sites control adipocyte metabolic flexibility
Bibliographic record
Abstract
Abstract Adipocyte dysfunction is a major driver of obesity-associated cardiometabolic disease, underscoring the need to understand how lipid storage and mobilization are regulated and disrupted. The ER-anchored protein Seipin governs lipid droplet (LD) biogenesis and ER-LD and ER–mitochondria (MAM) contacts, and its loss impairs calcium transfer and causes lipodystrophy. Here, we investigated whether Seipin coordinates MAM and ER-LD remodeling during adipocyte lipid handling. In subcutaneous adipose tissue from inducible Seipin-knockout mice, electron microscopy and proximity ligation assays revealed that feeding reduces MAMs while increasing ER-LD and mitochondria-LD contacts, a remodeling abolished by Seipin deficiency. Lipid loading elevated tripartite MAM-LD contacts in controls but not knockouts. Fluorescence recovery after photobleaching showed that impaired triglyceride transfer to LDs in Seipin-deficient cells was rescued by the MAM-LD-stabilizing peptide ‘Linker-ER-Mi’, in a calcium-dependent manner. During adipogenesis and lipid loading, MAM-LD contacts increased, whereas MAM-cytosolic mitochondria contacts declined; however, obesity blunted this remodeling. Furthermore, disrupting membrane contact sites impaired lipid flux, lipolysis, and insulin signaling. Taken together, these findings identify MAM-LD as regulators of adipocyte metabolic flexibility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".