MFGE8 links absorption of dietary fatty acids with catabolism of enterocyte lipid stores through HNF4γ-dependent transcription of CES enzymes
Bibliographic record
Abstract
ABSTRACT Enterocytes modulate the extent of postprandial lipemia, a potent risk factor for developing atherosclerotic disease, by storing dietary fats in cytoplasmic lipid droplets (cLDs). We have previously demonstrated that the integrin ligand MFGE8 links absorption of dietary fats with activation of triglyceride (TG) hydrolases that catabolize cLDs for chylomicron production. The hydrolase(s) responsible for mobilization of TG from diet-derived cLDs is unknown though recent evidence indicates that this process is independent of the canonical pathway of TG hydrolysis mediated by ATGL. Here we identify CES1D as the key hydrolase downstream of the MFGE8-αvβ5 integrin pathway that regulates catabolism of diet-drive cLDs. Mfge8 KO enterocytes have reduced CES1D transcript and protein levels and reduced protein levels of the transcription factor HNF4γ. Mice KO for Ces1d or Hnf4γ have decreased enterocyte TG hydrolase activity coupled with retention of TG in cLDs. Mechanistically, MFGE8-dependent fatty acid uptake through CD36 leads to stabilization of HNF4γ protein levels; HNF4γ then increases Ces1d transcription. Our work identifies a regulatory network by which MFGE8 and αvβ5 regulate the severity of postprandial lipemia by linking dietary fat absorption with protein stabilization of a transcription factor that increases expression of enterocyte TG hydrolases that catabolize diet-derived cLDs.
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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 | 0.000 | 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".