Alterations in the intestinal microbiota contribute to the gastrointestinal toxicity of the anti‐rejection drug mycophenolate mofetil
Bibliographic record
Abstract
Transplantation is curative for end‐stage organ failure but requires potent immunosuppressive medications to prevent rejection including the immunosuppressant mycophenolate mofetil (MMF). While MMF is effective as maintenance therapy, it is commonly associated with gastrointestinal (GI) side effects (e.g. diarrhea, rapid weight loss, colitis) which can necessitate its discontinuation, increasing the chances of graft failure and rejection. While the mechanism(s) contributing to MMF‐related GI toxicity are not understood, we recently reported a clinical case where MMF complications were associated with shifts in the composition of the intestinal microbiota. Thus, in the current study, we hypothesized that the GI toxicity associated with MMF was dependent on the intestinal microbiota. To test this, we fed C57BL/6 mice chow containing MMF and assessed a variety of outcomes. Mice consuming MMF exhibited significant weight loss (>20% after 9 days), which reflected a loss of body fat and lean muscle mass. This was accompanied by marked inflammation of the colon. MMF treatment also promoted changes in gut microbial composition, demonstrated by a loss of overall diversity, expansion of Proteobacteria and loss of beneficial bacterial genera. PICRUSt predicted gene counts identified several components of LPS biosynthesis that were significantly upregulated following MMF treatment. This was associated with an increase in fecal and serum levels of LPS in MMF‐treated mice. Treatment with broad‐spectrum antibiotics prevented and reversed MMFinduced weight loss and colonic inflammation. Importantly, MMF did not induce intestinal toxicity in germ free mice. Our results reveal that MMF triggers GI toxicity by altering the intestinal microbiota and highlights the microbiota as an important driver of the side‐effects associated with immunosuppressive anti‐rejection therapies. Support or Funding Information Alberta Innovates, Canadian Institutes of Health Research, Canadian Foundation for Innovation, Dr. Lloyd Sutherland Fund in IBD/GI Research, Cumming School of Medicine This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 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".