Low regulatory T-cells frequency is associated with graft rejection after small bowel transplantation: Clinical and experimental evidence
Bibliographic record
Abstract
BACKGROUND: Intestinal transplantation (ITx) represents the only curative option for patients with irreversible intestinal failure. Nevertheless, its rejection rate surpasses that of other solid organ transplants due to the heightened immunological load of the gut. Regulatory T-cells (Tregs) are key players in the induction and maintenance of peripheral tolerance, suggesting their potential involvement in modulating host vs. graft responses after ITx. Thus, we investigated the association of Tregs with allograft outcomes in pediatric patients and in an experimental model of small bowel transplantation. METHODS: Treg frequency in human samples was analyzed by Flow cytometry (CD4+CD25highCD127-, blood samples) and immunohistochemistry (FoxP3, graft samples). Experimental allogenic-heterotopic small bowel transplantation was performed in rats and animals divided into 3 groups: non-immunosuppressant treatment, rapamycin (2 mg/kg), and tacrolimus (0.6 mg/kg) treatment. Acute cellular rejection (ACR) was diagnosed based on clinical and histological findings, graft gene expression of pro- and anti-inflammatory mediators assessed by RT-qPCR, serum IL-6 and IL-10 levels by Luminex, and Treg frequency analyzed by flow cytometry (CD4+CD25highFoxP3+). RESULTS: Blood samples from patients undergoing ACR exhibited a significant reduction in the Treg number compared to those with normo-functional grafts. Similarly, a diminished number of FoxP3+ cells was observed in mucosa samples with ACR. In the experimental model, rapamycin-treated animals displayed clinical and histological findings resembling those not receiving immunosuppression treatment. Notably, ACR correlated with a high CD8/CD4 ratio, loss of T-cell chimerism, mRNA upregulation of pro-inflammatory genes and diminished graft Treg frequency. In contrast, tacrolimus treatment prevented ACR and facilitate blood and graft Treg expansion. Remarkably, recipients who achieved Treg expansion within the graft remained free of ACR even after discontinuation of the immunosuppressant treatment and this phenomenon was associated with increased levels of serum IL-10. CONCLUSION: Our clinical and experimental findings underscore the association between Treg frequency and graft rejection after ITx, advocating for strategies that promote their expansion within the gut mucosa to enhance long-term outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".