Neonatal Exposure to Amoxicillin Alters Long-Term Immune Response Despite Transient Effects on Gut-Microbiota in Piglets
Bibliographic record
Abstract
Antibiotic exposure during neonatal development may result in transient or persistent alterations of key microbes that are vital for normal development of local and systemic immunity, potentially impairing immune competence later in life. To further elucidate the relationship between antibiotic exposure and immune development, newborn pigs were exposed to a therapeutic pediatric dose (30mg/kg/day) of amoxicillin (AB) or placebo (PL) from post-natal day (PND) 0-14. Subsequently, immune cell phenotype, microbial composition, and immune response to an intraperitoneal (IP) challenge with Salmonella enterica serovar Typhimurium were evaluated. AB exposure caused significant changes in fecal microbial composition on PND 3 (P = 0.024). This stemmed from a two-fold increase in Enterobacteriaceae with live cecal coliforms on PND 7 indicating at 10-fold increase (P = 0.036). Alterations in microbial composition were transient, and successional patterns were normalizing by PND 14 (P = 0.693). Differences in PBMC (peripheral blood mononuclear cell) immune cell subtypes were detected, with the percentage of CD3+CD4+ T cells among the broader T cell population (CD3+CD4+/CD3+) being significantly higher (P = 0.031) in AB pigs and the numbers of CD4+CD45RA+ (naïve) T cells per litre of blood were lower on PND 21 in AB pigs (P = 0.036). Meanwhile, PBMCs from AB pigs produced significantly more IFN upon stimulation with a T-cell mitogen on PND 21 and 49 (P = 0.0206). When AB pigs were challenged with heat-killed Salmonella (IP) on PND 49, IFNγ gene expression in peripheral blood was upregulated compared to those treated with PL (P = 0.043). Additionally, AB pigs showed stronger activation among neutrophils infiltrating the peritoneal cavity after in vivo immune challenge, based on higher levels of NF-B nuclear translocation (P = 0.001). Overall, our results indicate that early life treatment with a therapeutically relevant dose of a commonly prescribed antibiotic has a programming effect on the immune system. Despite antibiotics only causing a transient disruption in gut-associated microbial communities, implications were long-term, with antibiotic treated pigs mounting an upregulated response to an immune challenge. This research adds to the growing body of evidence indicating adverse immune outcomes of early life antibiotic exposures.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".