TMOD-38. PRODUCTION OF D-2-HYDROXYGLUTARATE IN SACCHAROMYCES CEREVISIAE LEADS TO GROWTH IMPAIRMENT AND DECREASED SURVIVAL
Bibliographic record
Abstract
Abstract High levels of D-2-hydroxyglutarate (D2HG) are found in several types of cancers, most notably low grade gliomas (LGGs). The accumulation of D-2HG contributes to tumorigenesis through a variety of mechanisms including decreased utilization of oxidative phosphorylation and histone hypermethylation. The use of the budding yeast Saccharomyces cerevisiae as a model system to study cancer allows for faster, more efficient elucidation of various molecular mechanisms, including functional genomics via genomic array screening. S. cerevisiae encodes two homologs of the human D-2HG dehydrogenase: the mitochondrial Dld2 and cytosolic Dld3. We detected an increase in the production of D-2HG in the dld3∆ knockout strain by LC-MS. In addition, the dld3∆ knockout strain shows decreased survival and a growth impairment in glucose-containing liquid media. However, this strain did not show a significant growth impairment on glucose or glycerol-containing solid media. Using publicly available Synthetic Genomic Array (SGA) analysis data from TheCellMap.org, we investigated the top negative gene interactions for our dld3 knockout strain. GO analysis of these negative gene interactions showed enrichment of targets locating to the mitochondria, suggesting that the increase of 2-HG leads to mitochondrial impairment, consistent with previous observations in other models of LGGs. The top two targets of the SGA screen were mdm35, a mitochondrial interspace membrane protein involved in assembly of the mitochondrial respiratory chain complex and cdc8, a component of the de novo pyrimidine biosynthesis pathway. Taken together, these results suggest that the dld3∆ knockout strain is an appropriate model in which to study the D-2HG-driven changes that occur during tumorigenesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".