Profiling the Fungal Microbiome after Fecal Microbiota Transplantation for Graft-versus-Host Disease: Insights from a Phase 1 Interventional Study
Bibliographic record
Abstract
Disruption of the intestinal bacterial microbiota is frequently observed in the context of allogeneic hematopoietic cell transplantation (HCT) and is particularly pronounced in patients who develop graft-versus-host disease (GVHD). Donor fecal microbiota transplantation (FMT) restores gut microbial diversity and reduces GVHD in HCT recipients. The composition of the intestinal fungal community in patients with GVHD, and whether fungal taxa are transferred during FMT are currently unknown. We performed a secondary analysis of our clinical trial of FMT in patients with steroid-refractory GVHD with a focus on the mycobiota. We characterized the fecal mycobiota of 17 patients and healthy FMT donors using internal transcribed spacer amplicon sequencing. The donor who provided the majority of FMT material in our study represents an n-of-one study of the intestinal flora over time. In this donor, mycobiota composition fluctuated over time while the bacterial microbiota remained stable over 16 months. Fungal DNA was detected more frequently in baseline stool samples from patients with steroid-refractory GVHD than in patients with steroid-dependent GVHD. We could detect fungal taxa in the majority of samples but did not see evidence of mycobiota transfer from donor to recipient. Our study demonstrates the feasibility of profiling the mycobiota alongside the more traditional bacterial microbiota, establishes the methodology, and provides a first insight into the mycobiota composition of patients with GVHD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".