Evolutionary conservation of drug action on lipoprotein metabolism-related targets
Bibliographic record
Abstract
Genetic analysis has shown that the slower than normal rhythmic defecation behavior of the clk-1 mutants of Caenorhabditis elegans is the result of altered lipoprotein metabolism. We show here that this phenotype can be suppressed by drugs that affect lipoprotein metabolism, including drugs that affect HMG-CoA reductase activity, reverse cholesterol transport, or HDL levels. These pharmacological effects are highly specific, as these drugs affect defecation only in clk-1 mutants and not in the wild-type and do not affect other behaviors of the mutants. Furthermore, drugs that affect processes not directly related to lipid metabolism show no or minimal activity. Based on these findings, we carried out a compound screen that identified 190 novel molecules that are active on clk-1 mutants, 15 of which also specifically decrease the secretion of apolipoprotein B (apoB) from HepG2 hepatoma cells. The other 175 compounds are potentially active on lipid-related processes that cannot be targeted in cell culture. One compound, CHGN005, was tested and found to be active at reducing apoB secretion in intestinal Caco-2 cells as well as in HepG2 cells. This compound was also tested in a mouse model of dyslipidemia and found to decrease plasma cholesterol and triglyceride levels. Thus, target processes for pharmacological intervention on lipoprotein synthesis, transport, and metabolism are conserved between nematodes and vertebrates, which allows the use of C. elegans for drug discovery.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".