The dichotomous roles of microbial-modified bile acids 7-oxo-DCA and isoDCA in intestinal tumorigenesis
Bibliographic record
Abstract
The gut microbiota has a significant impact on the development and function of intestinal epithelial cells (IECs) by modifying bile acid (BA) metabolites. Recently, specific gut microbiome-derived BAs, such as 7-oxo-deoxycholic acid (7-oxo-DCA) and isodeoxycholic acid (isoDCA), have been identified to be shifted inversely in colitis and hepatic liver diseases. Although the responsible gut microbes have been identified, metabolites’ effects on IECs remain largely unclear. We found that although high-fat diet treatment in mice elevated both 7-oxo-DCA and isoDCA levels, during intestinal tumorigenesis, 7-oxo-DCA levels rise while isoDCA levels decrease. Interestingly, 7-oxo-DCA promotes cancer cell growth, while isoDCA suppresses it. Moreover, 7-oxo-DCA promotes whereas isoDCA inhibits the proliferation of intestinal stem cells in organoids derived from WT and APC Min/+ mice, as well as in patient-derived colon cancer organoids. The APC Min/+ mice administered with 7-oxo-DCA heightened gut permeability and increased tumor burden, whereas isoDCA protected gut barrier and reduced tumor loads. Both BAs reshape the BA pool and shifted gut microbiome. Mechanistically, we identified 7-oxo-DCA as a natural antagonist of Farnesoid X Receptor (FXR) to downregulate FXR signaling, as opposed to isoDCA, which is a potent FXR agonist to upregulate FXR signaling. In conclusion, we unveiled the opposing roles of 7-oxo-DCA and isoDCA to promote or inhibit intestinal tumorigenesis, respectively. Manipulating the BA–FXR axis during tumor initiation and progression holds great promise for developing innovative diagnostic and therapeutic approaches for the treatment of colorectal cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
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".