Effect of postnatal low-dose exposure to environmental chemicals on the gut microbiome in a rodent model
Bibliographic record
Abstract
BACKGROUND: This proof-of-principle study examines whether postnatal, low-dose exposure to environmental chemicals modifies the composition of gut microbiome. Three chemicals that are widely used in personal care products-diethyl phthalate (DEP), methylparaben (MPB), triclosan (TCS)-and their mixture (MIX) were administered at doses comparable to human exposure to Sprague-Dawley rats from birth through adulthood. Fecal samples were collected at two time points: postnatal day (PND) 62 (adolescence) and PND 181 (adulthood). The gut microbiome was profiled by 16S ribosomal RNA gene sequencing, taxonomically assigned and assessed for diversity. RESULTS: Metagenomic profiling revealed that the low-dose chemical exposure resulted in significant changes in the overall bacterial composition, but in adolescent rats only. Specifically, the individual taxon relative abundance for Bacteroidetes (Prevotella) was increased while the relative abundance of Firmicutes (Bacilli) was reduced in all treated rats compared to controls. Increased abundance was observed for Elusimicrobia in DEP and MPB groups, Betaproteobacteria in MPB and MIX groups, and Deltaproteobacteria in TCS group. Surprisingly, these differences diminished by adulthood (PND 181) despite continuous exposure, suggesting that exposure to the environmental chemicals produced a more profound effect on the gut microbiome in adolescents. We also observed a small but consistent reduction in the bodyweight of exposed rats in adolescence, especially with DEP and MPB treatment (p < 0.05), which is consistent with our findings of a reduced Firmicutes/Bacteroidetes ratio at PND 62 in exposed rats. CONCLUSIONS: This study provides initial evidence that postnatal exposure to commonly used environmental chemicals at doses comparable to human exposure is capable of modifying the gut microbiota in adolescent rats; whether these changes lead to downstream health effects requires further investigation.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".