Increased D-Neopterin biosynthesis in gut microbiome promoted colonic carcinogenesis after ampicillin or amoxicillin treatment
Bibliographic record
Abstract
Abstract Background:Emerging data have manifested that antibiotic-induced perturbations can potentially increase susceptibility to colorectal cancer (CRC), while the underlying mechanism is still unclear.Results:Herein we established the humanized microbiome mice transplanted with feces and hematopoietic stem cells from CRC patients to explore how ampicillin or amoxicillin contributed to colorectal cancer progression. Metagenomics and untargeted metabolomics analyses have suggested the effect of ampicillin or amoxicillin on colonic tumorigenesis is correlated with an alteration of gut microbiota and increased levels of the microbiota-derived metabolite D-Neopterin (P< 0.05). Then D-Neopterin is evidenced to activate the expressions of IGF2BP1 and ARG1 in myeloid-derived suppressor cells (MDSCs) by scRNA-seq. Moreover, IGF2BP1 was determined to maintain the stability of ARG1 transcript by binding to three m6A sites of ARG1‐3’UTR in the sorted MDSCs.Conclusion:In essence, this study has identified the important role of ampicillin or amoxicillin-induced increase of D-Neopterin in enhancing colorectal cancer proliferation. It thus implicates that therapeutic regimen of CRC patients should eliminate the application of ampicillin or amoxicillin.
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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 | 0.000 | 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 | 0.000 |
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".