Antibiotic-Induced Perturbations of the Intestinal Microbiota Alter Host Susceptibility to Enteric Infection
Bibliographic record
Abstract
Intestinal microbiota comprises microbial communities that reside in the gastrointestinal tract and are critical to normal host physiology. Understanding the microbiota's role in host response to invading pathogens will further advance our knowledge of host-microbe interactions. Salmonella enterica serovar Typhimurium was used as a model enteric pathogen to investigate the effect of intestinal microbiota perturbation on host susceptibility to infection. Antibiotics were used to perturb the intestinal microbiota. C57BL/6 mice were treated with clinically relevant doses of streptomycin and vancomycin in drinking water for 2 days, followed by oral infection with Salmonella enterica serovar Typhimurium. Alterations in microbiota composition and numbers were evaluated by fluorescent in situ hybridization, differential plating, and Sybr green staining. Antibiotics had a dose-dependent effect on intestinal microbiota composition. The chosen antibiotic regimen did not significantly alter the total numbers of intestinal bacteria but altered the microbiota composition. Greater preinfection perturbations in the microbiota resulted in increased mouse susceptibility to Salmonella serovar Typhimurium intestinal colonization, greater postinfection alterations in the microbiota, and more severe intestinal pathology. These results suggest that antibiotic treatment alters the balance of the microbial community, which predisposes the host to Salmonella serovar Typhimurium infection, demonstrating the importance of a healthy microbiota in host response to enteric pathogens.
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 | 0.000 | 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 | 0.000 | 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".