The initial state of the human gut microbiome determines its reshaping by antibiotics
Bibliographic record
Abstract
Microbiome studies have demonstrated the high inter-individual diversity of the gut microbiota. However, how the initial composition of the microbiome affects the impact of antibiotics on microbial communities is relatively unexplored. To specifically address this question, we administered a second-generation cephalosporin, cefprozil, to healthy volunteers. Stool samples gathered before antibiotic exposure, at the end of the treatment and 3 months later were analysed using shotgun metagenomic sequencing. On average, 15 billion nucleotides were sequenced for each sample. We show that standard antibiotic treatment can alter the gut microbiome in a specific, reproducible and predictable manner. The most consistent effect of the antibiotic was the increase of Lachnoclostridium bolteae in 16 out of the 18 cefprozil-exposed participants. Strikingly, we identified a subgroup of participants who were enriched in the opportunistic pathogen Enterobacter cloacae after exposure to the antibiotic, an effect linked to lower initial microbiome diversity and to a Bacteroides enterotype. Although the resistance gene content of participants' microbiomes was altered by the antibiotic, the impact of cefprozil remained specific to individual participants. Resistance genes that were not detectable prior to treatment were observed after a 7-day course of antibiotic administration. Specifically, point mutations in beta-lactamase blaCfxA-6 were enriched after antibiotic treatment in several participants. This suggests that monitoring the initial composition of the microbiome before treatment could assist in the prevention of some of the adverse effects associated with antibiotics or other treatments.
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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 | 0.002 |
| 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 | 0.001 | 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".