Acetylcarnitine potentiates the anticarcinogenic effects of butyrate on SW480 colon cancer cells
Bibliographic record
Abstract
Butyrate is a potent anticarcinogenic compound against colon cancer cells in vitro. However, its rapid metabolism is hypothesized to limit its anticancer benefits in colonic epithelial cells. Carnitine, a potent antioxidant, is essential to fatty acid oxidation. The aims of this study were to identify a colon cancer cell line capable of transporting carnitine. We evaluated the effect of carnitine and acetylcarnitine (ALCAR) on the response of colon carcinoma cells to butyrate. We explored the mechanisms underlying the anticarcinogenic benefit. SW480 cells were incubated with butyrate ± carnitine or ALCAR. Carnitine uptake was assessed using [3H]-carnitine. Apoptosis and cell viability were assessed using an ELISA kit and flow cytometry, respectively. Modulation of proteins implicated in carnitine transport, cell death and proliferation were assessed by western blotting. SW480 cells were found to transport carnitine primarily via the OCTN2 transporter. Butyrate induced SW480 cell death occurred at concentrations of 2 mM and higher. Cells treated with the combination of butyrate (3 mM) with ALCAR exhibited increased mortality. The addition of carnitine or ALCAR also increased butyrate-induced apoptosis. Butyrate increased levels of cyclin D1, p21 and PARP p86, but decreased Bcl-XL and survivin levels. Butyrate also downregulated dephospho-β-catenin and increased acetylated histone H4 levels. Butyrate and carnitine decreased survivin levels by ≥25%. ALCAR independently induced a 20% decrease in p21. These results demonstrate that butyrate and ALCAR are potentially beneficial anticarcinogenic nutrients that inhibit colon cancer cell survival in vitro. The combination of both agents may have superior anticarcinogenic properties than butyrate alone.
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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 | 0.000 | 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) | 0.002 | 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".