348 The initial gut microbial composition is a key factor driving host responses to antibiotic treatment.
Bibliographic record
Abstract
Antibiotics are important for treating bacterial infections in veterinary medicine, but the efficacy and side effects of antibiotics as well as host health outcomes vary in medicine and experimental models. To investigate the causality of specific variations in pre-existing microbiome on host responses to antibiotics, C57Bl/6 female mice (n = 32, 6 weeks of age) harboring a normal microbiota without Escherichia coli were allocated to 4 treatments in a 2 × 2 factorial arrangement with or without addition of commensal E. coli; and with or without metronidazole in drinking water for 4 days. The gut microbiota was characterized by sequencing 16S rRNA gene amplicons on a MiSeq platform and resulting compositional data were analyzed using QIIME, and changes in beta-diversity were assessed by PERMANOVA analysis. Host innate defense responses were measured by gene expression and ELISA and analyzed using the GLM procedure of SAS. E. coli colonized readily without causing a notable shift in microbiota (adonis, P = 0.26) or host response (P > 0.1). However, the presence of E. coli strongly affected metronidazole-induced microbiota shifts, including reduced richness (Chao1, P < 0.05) and alpha-diversity (Shannon index, P < 0.01), and significantly increased the serum endotoxin levels (P < 0.05) which has previously been correlated with host proinflammatory response and protein synthesis repression. Remarkably, E. coli led to variations in host response to metronidazole treatment, indicated by increased colonic expression of antimicrobial peptides, regenerating islet-derived protein 3β (Reg3β ) and Reg3γ (P < 0.05), and tumor necrosis factor-α (P < 0.05). This study indicates that even minor variations in initial commensal microbiota can drive shifts in microbial composition and host immune response as well as metabolic outcomes in response to antibiotic administration.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".