Detecting microbial engraftment after FMT using placebo sequencing and culture enriched metagenomics to sort signals from noise
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) has shown efficacy for the treatment of ulcerative colitis but with variable response between patients and trials. The mechanisms underlying FMT’s therapeutic effects remains poorly understood but is generally assumed to involve engraftment of donor microbiota into the recipient’s microbiome. Reports of microbial engraftment following FMT have been inconsistent between studies. Here, we investigate microbial engraftment in a previous randomized controlled trial (NCT01545908), in which FMT was sourced from a single donor, using amplicon-based profiling, shotgun metagenomics, and culture-enriched metagenomics. Placebo samples were included to estimate engraftment noise, and a significant level of false-positive engraftment was observed which confounds the prediction of true engraftment. We show that analyzing engraftment across multiple patients from a single donor enhances the accuracy of detection. We identified a unique set of genes engrafted in responders to FMT which supports strain displacement as the primary mechanism of engraftment in our cohort. Here, the authors profile bacterial engraftment in UC patients receiving FMT from a single donor and placebo samples to estimate noise, showing a significant level of false-positive engraftment, which confounds the prediction of true engraftment, while analyzing engraftment across multiple patients from a single donor enhances the accuracy of detection.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".