Impact of Immunosuppression on the Metagenomic Composition of the Intestinal Microbiome: a Systems Biology Approach to Post-Transplant Diabetes
Bibliographic record
Abstract
Solid organ transplantation (SOT) outcomes have continued to improve, although long-term use of immunosuppressants can lead to complications such as diabetes, compromising post-transplant outcomes. In this study, we have characterized the intestinal microbiome (IM) composition at the metagenomic level in the context of hyperglycemia induced by immunosuppressants. Sprague-Dawley rats were subjected to doses of tacrolimus and sirolimus that reliably induce hyperglycemia and an insulin-resistant state. Subsequent exposure to probiotics resulted in reversal of hyperglycemia. 16S rRNA and metagenomic sequencing of stool were done to identify the bacterial genes and pathways enriched in immunosuppression. Bacterial diversity was significantly decreased in sirolimus-treated rats, with 9 taxa significantly less present in both immunosuppression groups: Roseburia, Oscillospira, Mollicutes, Rothia, Micrococcaceae, Actinomycetales and Staphylococcus. Following probiotics, these changes were reversed to baseline. At the metagenomic level, the balance of metabolism was shifted towards the catabolic side with an increase of genes involved in sucrose degradation, similar to diabetes. Conversely, the control rats had greater abundance of anabolic processes and genes involved in starch degradation. Immunosuppression leads to a more catabolic microbial profile, which may influence development of diabetes after SOT. Modulation of the microbiome with probiotics may help in minimizing adverse long-term effects of immunosuppression.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".