Antimicrobial growth promoters modulate host responses in mice with a defined intestinal microbiota
Bibliographic record
Abstract
Antibiotics can promote growth in livestock (antimicrobial growth promoters, AGPs), however lack of knowledge regarding mechanisms has hampered the development of effective non-antibiotic alternatives. Antibiotics affect eukaryotic cells at therapeutic concentrations, yet effects of AGPs on host physiology are relatively understudied, partially due to the complexity of host-microorganism interactions within the gastrointestinal tract. To determine the direct effects of AGPs on the host, we generated Altered Schaedler Flora (ASF) mice, and administered chlortetracycline (CTC) and tylosin phosphate (TYL) in feed. Mice were challenged with Citrobacter rodentium to determine how AGPs alter host responses to physiological stress. Although CTC and TYL had inconsistent effects on the ASF taxa, AGPs protected mice from weight loss following C. rodentium inoculation. Mice treated with either CTC or TYL had lower expression of βd1 and Il17a in the intestine and had a robust induction of Il17a and Il10. Furthermore, AGP administration resulted in a lower hepatic expression of acute phase proteins (Saa1, Hp, and Cp) in liver tissue, and ameliorated C. rodentium-induced reductions in the expression of genes involved in lipogenesis (Hmgcl and Fabp1). Collectively, this indicates that AGPs directly affect host physiology, and highlights important considerations in the development of non-antibiotic alternatives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".