257 Young Scholar Presentation: Using an integrated systems biology platform to determine the mode of action of feed additives in nursery pig diets
Bibliographic record
Abstract
Abstract The mechanisms of action for antibiotic growth promotion are poorly understood, making it difficult to select effective alternatives that are capable of providing similar responses. In order to evaluate the complete impact of supplementing antibiotics and other feed additives to nursery pigs, an integrated systems biology platform encompasses the merging of traditional nutrition models with gut physiology, nutrition and omics technologies. The objective of this study was to evaluate the growth response, metabolic responses, and intestinal microbiome composition of nursery pigs fed antibiotics. Antibiotic diets containing chlortetracycline and sulfamethazine (AB), and diets containing no antibiotics (CON) were fed in 3 trials. Pigs were weighed at d 10, 21, and 42 post-weaning and ADG, ADFI, and G:F was calculated. On d 42, one pig/pen was selected for blood, cecal, and ileum content collection to assess metabolomic profiles via liquid chromatography-mass spectrometry. The composition of bacterial communities in those samples was determined by sequencing the V4 region of the 16s rRNA bacterial gene in the MiSeq platform. Metabolomics and microbiome data were analyzed using multivariate approaches and growth performance data were analyzed using a generalized mixed model. Pigs fed AB were heavier (P < 0.05) on day 42. There were also significant differences (P < 0.05) in the cecal and serum metabolite profile of pigs fed AB compared with CON. However, there were no changes in bile acid or short chain fatty acid concentrations in cecal samples when feeding AB. Although AB had no effect on alpha or beta microbiome diversity, the abundance of specific bacterial taxa shifted significantly (P < 0.05). These findings suggest that feeding antibiotics improve growth, alter metabolism, and affect the abundance of specific taxonomic groups of bacteria in the gut. In the future, this approach will be utilized to evaluate antibiotic alternatives for nursery pigs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.171 | 0.035 |
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".