Supplementation of Choline-Deficient Diet With Pterostilbene Attenuates Cancer Development and Epigenetic Dysregulation of Gene Expression in Rat Livers
Bibliographic record
Abstract
Nearly 40% of humans have polymorphisms in genes involved in choline metabolism which makes them prone to developing choline deficiency and increased risk for liver damage and liver cancer. Choline is a source of methyl groups needed for many steps in metabolism and epigenetic regulation of gene expression. Although epigenetic aberrations are known to be induced by choline deficiency, it remains unknown how to reverse the changes and attenuate symptoms. Interestingly, certain dietary compounds such as polyphenols have been demonstrated to reverse aberrant epigenetic patterns and exert anti-cancer action. In the present study, we investigate the effects of pterostilbene (PTS) on liver cancer development in rats fed choline-deficient diet and explore mechanisms underlying these effects. Fischer 344 rats were fed a choline-sufficient (CSAA, healthy control group), a choline-deficient (CDAA, cancer group) L-amino acid-defined diet or a CDAA diet supplemented with PTS (134 mg/kg BW/day) (n = 6 per group). At the end of 52 weeks, analyses of liver nodules and histopathological features were performed followed by genome-wide investigation of gene expression in livers using RNA sequencing. DNA methylation was assessed by pyrosequencing. A total of 708 genes were significantly differentially expressed in CDAA + PTS group as compared with CDAA group. Among 351 upregulated genes were Bhmt (4.5-fold), G6pc (3.1-fold), and Aldh1l1 (2.6-fold). These metabolism-related genes were significantly downregulated in CDAA vs. CSAA group and their suppression was associated with liver cancer in previous reports. Among 357 genes found to be significantly downregulated by PTS were strong oncogenes such as Mmp12 (2-fold), Myc (1.9-fold) and Mmp27 (1.8-fold). We found PTS-mediated downregulation of Mmp12, that was a top gene upregulated in CDAA vs. CSAA, coincided with 43% hypermethylation of Mmp12 promoter. Our findings demonstrate that PTS-mediated changes in gene expression could correspond to changes in DNA methylation of gene regulatory regions and could at least partially explain the observed attenuation of cancer development due to choline deficiency. UBC VP Academic Award, CFI John. R. Evans Leaders Fund, and BC Knowledge Development Fund granted to BS.
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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 | 0.000 |
| 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 | 0.000 |
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".