Early‐life antibiotic exposure aggravates hepatic steatosis through enhanced endotoxemia and lipotoxic effects driven by gut <i>Parabacteroides</i>
Bibliographic record
Abstract
Abstract Compelling evidence supports a link between early‐life gut microbiota and the metabolic outcomes in later life. Using an early‐life antibiotic exposure model in BALB/c mice, we investigated the life‐course impact of prenatal and/or postnatal antibiotic exposures on the gut microbiome of offspring and the development of metabolic dysfunction‐associated steatotic liver disease (MASLD). Compared to prenatal antibiotic exposure alone, postnatal antibiotic exposure more profoundly affected gut microbiota development and succession, which led to aggravated endotoxemia and metabolic dysfunctions. This was primarily resulted from the overblooming of gut Parabacteroides and hepatic accumulation of cytotoxic lysophosphatidyl cholines (LPCs), which acted in conjunction with LPS derived from Parabacteroides distasonis (LPS_PA) to induce cholesterol metabolic dysregulations, endoplasmic reticulum (ER) stress and apoptosis. Integrated serum metabolomics, hepatic lipidomics and transcriptomics revealed enhanced glycerophospholipid hydrolysis and LPC production in association with the upregulation of PLA2G10, the gene controlling the expression of the group X secretory Phospholipase A2s (sPLA2‐X). Taken together, our results show microbial modulations on the systemic MASLD pathogenesis and hepatocellular lipotoxicity pathways following early‐life antibiotic exposure, hence help inform refined clinical practices to avoid any prolonged maternal antibiotic administration in early life and potential gut microbiota‐targeted intervention strategies.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".