Abnormal Food Timing Promotes Alcohol-Associated Dysbiosis and Colon Carcinogenesis Pathways
Bibliographic record
Abstract
Background: Alcohol consumption is an established risk factor for colon cancer (CRC). Identifying cofactor(s) that modulate the effect of alcohol on colon inflammation and carcinogenesis could help risk stratification for CRC. Disruption of circadian rhythm by light/dark shift, promotes alcohol-induced colonic inflammation and cancer. More recently, we found that abnormal food timing causes circadian rhythm disruption and promotes alcohol associated colon carcinogenesis. In this study, we examined the interaction of wrong time feeding (WTF) and alcohol on CRC-related pathways, in relation to changes in microbial community structure. Method: Polyposis mice (TS4Cre x cAPCΔ468) underwent four conditions: Alcohol or Water and feeding during the light (wrong-time fed/WTF) or during the dark (right-time fed/RTF). Colonic cecum mucosal gene expression was analysed by RNA-seq. Microbiota 16S ribosomal RNA sequencing analysis was used to examine colonic feces. Modelling was used to estimate the extent of the gene expression changes that could be related to the changes in the colonic microbial composition. Results: The circadian rhythm pathway was the most altered pathway by the WTF treatment, indicating that WTF is disruptive to the colonic circadian rhythm. Pathway analysis revealed interaction of WTF with alcohol in dysregulating pathways related to colon carcinogenesis. Similarly, the interaction of alcohol and WTF was detected at multiple parameters of the colonic microbiota including alpha- and beta-diversity, as well as the community structure. Our modelling revealed that almost a third of total gene alterations induced by our treatments could be related to alterations in the abundance of the microbial taxa. Conclusion: These data support the promoting effect of abnormal food timing alcohol-associated CRC related pathways in the colon, and suggests colon dysbiosis as a targetable mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".