The success of fecal microbial transplantation in <i>Clostridium difficile</i> infection correlates with bacteriophage relative abundance in the donor: a retrospective cohort study
Bibliographic record
Abstract
Background Fecal microbial transplantation (FMT) is used in the treatment of relapsing Clostridium difficile infection (rCDI). Failure rate for FMT is as high as 10% but the mechanisms contributing to a failed FMT are not understood. We utilized metagenomic data to identify the role of bacteria and bacteriophages on FMT success.Results Subjects with rCDI (n = 19) received FMT from volunteer donors (n = 7) via colonoscopy. Twelve patients fully recovered after a single FMT, while seven patients required a subsequent FMT. DNA was extracted from patient and donor stool samples for shotgun metagenomic analysis. Metagenomics libraries were analyzed focusing on bacterial taxonomy and bacteriophage sequences. Gammaproteobacteria were dominant in rCDI patients prior to FMT largely due to elevated levels of Klebsiella and Escherichia. A successful FMT led to increased levels of Clostridia and Bacteroidia and a reduction in Gammaproteobacteria. In contrast, a failed FMT led to no significant changes in bacterial composition. Bacteriophages were classified during whole metagenomic analysis of each sample and were markedly different between rCDI patients, donors, and a healthy control cohort (n = 96). Bacteriophage sequence reads were increased in CDI patients compared with donors and healthy controls. Successful FMT donors had higher bacteriophage α-diversity and lower relative abundance compared to the donors of a failed initial FMT.Conclusions In this retrospective analysis, FMTs with increased bacteriophage α-diversity were more likely to successfully treat rCDI. In addition, the relative number of bacteriophage reads was lower in donations leading to a successful FMT. These results suggest that bacteriophage abundance may have some role in determining the relative success of FMT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".