Lentinula edodes cultured extract intake alleviates long-term immune deregulation induced by early-life gut microbiota dysbiosis
Bibliographic record
Abstract
The establishment of gut microbiota during early life is crucial for immune system development and its disturbance within this critical period exerts enduring adverse effects on health. Perinatal antibiotic exposure perturbs early-life microbiota and leads to long-term immune dysregulation. However, the underlying mechanisms remain inadequately explored. We investigated the persistent consequences of perinatal exposure to low-dose penicillin on gut immunity and the potential protective role of a prebiotic compound, Lentinula edodes cultured extract referred to as AHCC, against antibiotic-induced dysbiosis and immune dysregulation. Pregnant mice were subjected to penicillin and AHCC treatment from the third week of gestation until weaning of pups. Subsequently, the offspring were evaluated for gut microbiota at weaning as well as immune function, and microRNA (miRNA) changes at eight weeks of age. Microbiome analysis revealed substantial alterations in gut microbiota composition, characterized by an increase in Proteobacteria and a decrease in Firmicutes following antibiotic exposure. Lactobacillus, and some short-chain fatty acid (SCFA)-producing species were diminished by the antibiotic. AHCC intake prevented antibiotic effects on Proteobacteria in dams and offspring and some SCFA-producing bacteria in male offspring. In adult offspring, AHCC exhibited immunomodulatory activity by decreasing pro-inflammatory cytokines, including IL-2, IL-6, IL-15, and IL-21. In addition, antibiotic-induced increase in NF-κB was mitigated by AHCC. Early-life antibiotic exposure altered gut miRNA expression, increasing pro-inflammatory miR-221 and decreasing anti-inflammatory miR-145 in males while AHCC intake prevented antibiotic-mediated dysregulation of miRNA-145. These results highlight the potential of prebiotic intake as a promising strategy to prevent and mitigate persistent health issues arising from early-life dysbiosis.
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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) | 0.001 | 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".