Immunosuppressive Treatment Alters Secretion of Ileal Antimicrobial Peptides and Gut Microbiota, and Favors Subsequent Colonization by Uropathogenic Escherichia coli
Bibliographic record
Abstract
BACKGROUND: Transplant recipients are treated with immunosuppressive (IS) therapies, which impact host-microbial interactions. We examined the impact of IS drugs on gut microbiota and on the expression of ileal antimicrobial peptides. METHODS: Mice were treated for 14 days with prednisolone, mycophenolate mofetil, tacrolimus, a combination of these 3 drugs, everolimus, or water. Feces were collected before and after treatment initiation. Ileal samples were collected after sacrifice. Fecal and ileal microbiota were analyzed by pyrosequencing of 16S rRNA genes and enumeration of selected bacteria by culture, and C-type lectins were assessed in ileal tissues by reverse transcriptase-quantitative polymerase chain reaction. RESULTS: Prednisolone disrupted fecal microbiota community structure, decreased Bacteroidetes, and increased Firmicutes in the feces. Prednisolone, tacrolimus, and mycophenolate mofetil modified fecal microbiota at the family level in each experimental replicate, but changes were not consistent between the replicates. In ileal samples, the genus Clostridium sensu stricto was dramatically reduced in the prednisolone and combined IS drug groups. These modifications corresponded to an altered ileal expression of C-type lectins Reg3γ and Reg3β, and of interleukin 22. Interestingly, the combined IS treatment enabled a commensal Escherichia coli to flourish, and dramatically increased colonization by uropathogenic E. coli strain 536. CONCLUSIONS: IS treatment alters innate antimicrobial defenses and disrupts the gut microbiota, which leads to overgrowth of indigenous E. coli and facilitates colonization by opportunistic pathogens.
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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 | 0.000 |
| 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".