Metabolic Control of Two Dynamic Pools of Diacylglycerol in Budding Yeast
Bibliographic record
Abstract
The location of lipids and their distributions across cellular membranes have critical biological consequences, particularly for lipids involved in cellular signaling. In this study, diacylglycerol pools were monitored in budding yeast under conditions where lipid homeostasis was altered. Two predominant pools of diacylglycerol were visualized using the C1 domain of mammalian PKCδ fused to GFP. One pool was associated with vacuolar membranes and the other localized to sites of polarized growth. Upon growth resumption, diacylglycerol pools appeared more dynamic than those of phosphatidic acid and phosphatidylserine. During this period, diacylglycerol enriched puncta and vacuolar rings experienced constant morphological changes with clusters of lipid droplets closely attached. Lack of conversion of diacylglycerol to phosphatidate by Dgk1 led to the accumulation of diacylglycerol in a vacuolar associated compartment. Diacylglycerol distribution was strongly affected in cells lacking the phosphatidylserine synthase Cho1/Pss1. Supplementation of lysophosphatidylserine to cho1 cells did not correct mislocalization of DAG, pointing to a role for phosphatidylserine synthesis and traffic in the establishment of cellular diacylglycerol pools. Support or Funding Information This work was supported by a National Science and Engineering Research Council of Canada to M.R. Terebiznik and V. Zaremberg. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".