Controlling Lipid Fluxes at Glycerol-3-phosphate Acyltransferase Step in Yeast
Bibliographic record
Abstract
The ability to channel excess fatty acids into neutral lipids like triacylglycerol (TAG) is a critical strategy used by cells to maintain lipid homeostasis. Upon activation to acyl-CoA, fatty acids become readily available as substrates for acyltransferases involved in neutral lipid synthesis. Neutral lipids are then packed into organelles derived from the endoplasmic reticulum called lipid particles (LPs). The first acylation step in the de novo pathway for TAG synthesis is catalyzed by glycerol-3-phosphate acyltransferases (GPATs). Two isoforms, Gat1p/Gpt2p and Gat2p/Sct1p, are present in the yeast Saccharomyces cerevisiae. Previous evidence indicated that these enzymes contribute differentially to the synthesis of TAG in actively growing cells. In this work we studied the role of the yeast GPATs in the formation of LPs induced by a surplus of oleic acid. Yeast lacking Gat1p (but not Gat2p) were sensitive to oleate and failed to accumulate LPs induced by this unsaturated fatty acid. It is shown that oleate induces dephosphorylation of Gat1p as well as an increment in its levels. Most importantly, we identified novel Gat1p crescent structures that are formed in the presence of oleate. These structures are connected with the endoplasmic reticulum and are intimately associated with LPs. No such structures were observed for Gat2p. A crucial point of control of lipid fluxes at the GPAT step is proposed.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".