CGI‐58 regulates triacylglycerol metabolism and lipid signaling pathways in plant cells
Bibliographic record
Abstract
CGI‐58 (Comparative Gene Identification‐58) encodes an α/β hydrolase‐type protein that is involved in multiple aspects of lipid metabolism in mammals, including the breakdown of triacylglycerols (TAGs) and formation of the hydrophobic barrier of the skin. Mutations in CGI‐58 result not only in aberrant skin formation, but also in accumulation of lipid droplets (LDs) in non‐lipid‐storing tissues such as skin, liver, and blood cells. The activity of CGI‐58 is determined at least in part by its interaction with other proteins including perilipin, which serves to anchor CGI‐58 to the surface of LDs. Phosphorylation of perilipin releases CGI‐58 and allows it to interact with Adipose Triglyceride Lipase (ATGL), thereby stimulating TAG breakdown. Interestingly, disruption of the CGI‐58 homolog in Arabidopsis results also in the accumulation of LDs in tissues that do not normally store lipids (e.g., leaves and stems), but plants lack any apparent homologs to perilipin or ATGL. In an effort to better understand the function of CGI‐58 in plants, we conducted a yeast 2‐hybrid assay using Arabidopsis CGI‐58 as bait. Although we did not identify any obvious LD‐associated proteins or lipases, we did identify several proteins known to be involved in distinct aspects of plant lipid metabolism. Evidence will be presented showing that CGI‐58 modulates the level of linolenic acid as well as lipid signaling pathways in plant cells.
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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) | 0.000 | 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 | 0.001 |
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".