Prolonged effect of antibiotic therapy on the gut microbiota composition, functionality, and antibiotic resistance genes’ profiles in healthy stool donors
Bibliographic record
Abstract
Introduction Fecal microbiota transplantation (FMT) is highly effective in preventing Clostridioides difficile recurrence by restoring gut microbiota composition and function. However, the impact of recent antibiotic use, a key exclusion criterion for stool donors, on gut microbiota recovery is poorly understood. Methods We investigated microbial recovery dynamics following antibiotic use in three long-term stool donors from Canada and Finland. Using longitudinal stool sampling, metagenomic sequencing, and qPCR, we assessed changes in bacterial diversity, community composition, microbial functions, the gut phageome, and the risk of transmitting antibiotic-resistant genes (ARGs). Results Antibiotics caused lasting disruption to bacterial communities, significantly reducing important taxa like Bifidobacterium bifidum , Blautia wexlerae , Akkermansia muciniphila , Eubacterium sp. CAG 180 , and Eubacterium hallii , with effects persisting for months. Functional analyses revealed alterations in housekeeping genes critical for energy production and biosynthesis, with no direct links to key health-related pathways. Antibiotics also disrupted viral populations, decreasing diversity and increasing crAssphage abundance, reflecting disrupted host-bacteriophage dynamics. No significant increase in clinically important ARGs was detected. Discussion These findings highlight the unpredictable and complex recovery of gut microbiota post-antibiotics. Individualized suspension periods in donor programs, guided by metagenomic analyses, are recommended to optimize FMT outcomes in various indications by considering antibiotic spectrum, duration, and host-specific factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".