Abstract 1860 Who let the DAG's out? A genome wide survey in yeast identifies novel proteins involved in cellular diacylglycerol distribution
Bibliographic record
Abstract
Diacylglycerol (DAG) was the first lipid second messenger to be discovered in the canonical activation pathway of protein kinase C (PKC). The informational outcomes of DAG seem to be intimately connected with its spatial distribution within the cell necessitating methods to track its different cellular pools. We have previously developed a fluorescent DAG probe based on the C1 domain of PKCδ to monitor cytoplasmic facing pools of DAG in yeast using fluorescence live microscopy. Two pools of DAG were identified in both the vacuole and at sites of polarized growth. To better understand how DAG distribution is regulated, a genome-wide, high-throughput imaging screen surveying single knockout and hypomorphic yeast collections expressing the DAG probe was conducted. From the ⁓6,000 strains imaged, we have identified a discrete group of mutant strains where DAG localized predominantly to the plasma membrane. The most extreme phenotype in this category was observed in a mutant producing a truncated version of an uncharacterized putative lipase, which we have named DAG related lipase 1 (DRL1). It was determined that the abnormal DAG phenotype depended on both the catalytic activity of DRL1 as well as its carboxy-end, as these were both requisites for reversion of the knock out phenotype. A comparative protein-protein interaction analysis using GFP-trap technology, as well as proximity dependent biotinylation (BioID), implicates Drl1 as an interactor with members of the Sec14 family of phosphatidylinositol transfer proteins (PITPs). This study aims to tackle one of the ∼1000 functionally uncharacterized genes in S. cerevisiae while also setting the groundwork for understanding DAG dynamics and regulation. This work has been financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant to VZ as well as the Alberta Graduate Excellence Scholarship to AH
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 0.003 |
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".