O005 Perturbations in gut microbial metabolites promotes a pro-inflammatory state increasing risk of acute rejection post renal transplantation
Bibliographic record
Abstract
Abstract Introduction Emerging evidence suggests that the gastrointestinal microbiota-immune axis impacts on extra-intestinal health. We aim to identify the influence of microbial metabolites on recipient's immunity; testing the hypothesis that reduced availability of bacterial-derived metabolites associated with immunoregulation e.g., short chain fatty acids (SCFAs) and tryptophan-derivatives promote a pro-inflammatory state increasing risk of acute rejection (AR). Methods Ninety recipients and 21 live-donors were recruited with urine, stool and blood samples collected at baseline and up to 12-months after surgery. Flow cytometry was used to assess for circulating subpopulations of CD19+ B-cells and CD4+ T cells (cTFH - CD3+CD4+CD45RA-CXCR5+ and cTFR -CD3+CD4+CXCR5+FoxP3+). Faecal SCFAs and indole derivatives were identified by mass spectroscopy and high-performance liquid chromatography. Results Patients with AR had an almost 10-fold reduction of FoxP3+ cTFR cells after transplantation compared to baseline (p=0.03). There were lower frequencies of cTFR cells at 3-months when compared to matched recipients without AR (0.009%±0.014% vs 0.10%±0.12%; p=0.01). In AR, there was a trend for higher frequencies of plasmablasts, resting memory B-cells and TFH cells at baseline, with fewer transitional B-cells at 3-months. Despite tryptophan availability increasing after transplantation, patients with AR displayed reduced levels of SCFAs at 1-month. Conclusion Complex changes in microbial metabolites exist after transplantation that may influence the balance of cTFH vs cTFR cells. Despite increase in dietary tryptophan, recipients with AR may harbour gut microbes that are unable to metabolise tryptophan into immune-regulatory metabolites, which predisposes a pro-inflammatory immune state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".