Initial Gut Microbial Composition as a Key Factor Driving Host Response to Antibiotic Treatment, as Exemplified by the Presence or Absence of Commensal Escherichia coli
Bibliographic record
Abstract
ABSTRACT Antibiotics are important for treating bacterial infection; however, efficacies and side effects of antibiotics vary in medicine and experimental models. A few studies have correlated microbiota composition variations with health outcomes in response to antibiotics; however, no study has demonstrated causality. We had noted variation in colonic expression of C-type lectins, regenerating islet-derived protein 3β (Reg3β) and Reg3γ, after metronidazole treatment in a mouse model. To investigate the effects of specific variations in the preexisting microbiome on host response to antibiotics, mice harboring a normal microbiota were allocated to 4 treatments in a 2-by-2 factorial arrangement with or without commensal Escherichia coli and with or without metronidazole in drinking water. E. coli colonized readily without causing a notable shift in the microbiota or host response. Metronidazole administration reduced microbiota biodiversity, indicated by decreased Chao1 and Shannon index values, and altered microbiota composition. However, the presence of E. coli strongly affected metronidazole-induced microbiota shifts. Remarkably, this single commensal bacterium in the context of a complex population led to variations in host responses to metronidazole treatment, including increased expression of antimicrobial peptides Reg3β and Reg3γ and intestinal inflammation indicated by tumor necrosis factor alpha levels. Similar results were obtained from 2-week antibiotic exposure and with additional E. coli isolates. The results of this proof-of-concept study indicate that even minor variations in initial commensal microbiota can drive shifts in microbial composition and host response after antibiotic administration. As well as providing an explanation for variability in animal models using antibiotics, the findings encourage the development of personalized medication in antibiotic therapies. IMPORTANCE This work provides an understanding of variability in studies where antibiotics are used to alter the gut microbiota to generate a host response. Furthermore, although providing evidence only for the one antibiotic, the study demonstrated that initial gut microbial composition is a key factor driving host response to antibiotic administration, creating a compelling argument for considering personalized medication based on individual variations in gut microbiota.
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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) | 0.001 | 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) | 0.002 | 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".