Investigating milk-derived extracellular vesicles as mediators of maternal stress and environmental intervention
Bibliographic record
Abstract
Parental communication signals are transmitted through nursing and critically shape neurodevelopmental trajectories. Mirroring some well characterized effects of gestational challenges in rodents, maternal immune activation (MIA) during the lactational period disrupts maternal physiology, decreases lipid content, and is associated with adverse neurobehavioral outcomes in offspring. This occurs without MIA significantly affecting maternal care. While gestational MIA models are responsive to environmental interventions, which beneficially alter maternal milk composition and associated offspring outcomes, the bioactive mediators in milk underlying resilience remain poorly understood. Milk-derived extracellular vesicles (MEVs) transport and deposit biologically active cargo, including microRNAs (miRNAs) that induce post-translational regulation of candidate mRNA in the nursing offspring's tissues and cells. Using a rat model, we show that lactational MIA alters MEV-miRNA cargo and the expression of hippocampal miRNAs in offspring. Several miRNAs in MEVs were also found in the hippocampus of matching offspring. Remarkably, the miRNA changes in MEVs and the neonatal hippocampus were rescued when dams were raised in an enriched environment, suggesting environmental enrichment protected from the effects of MIA. This was supported by the behavioral phenotype. RNA-seq of adult offspring hippocampus showed long-term transcriptional changes associated with the gene targets of early-life regulated miRNAs. Our results position MEV-miRNA as dynamic programming signals by which maternal experience is communicated to offspring, encoding both stress-induced and protective cues that influence development. This suggests that breastfeeding interventions can regulate the genetic cargo of the milk, programming the life of developing infants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".