Insulin modulates mPFC gene expression and emotional behavior in a sex-specific manner following fetal growth restriction
Bibliographic record
Abstract
Prenatal adversity, such as exposure to stress and malnutrition, increases lifelong vulnerability to neuropsychiatric and metabolic disorders, in part through long-lasting alterations in neuroendocrine and immune signaling that shape brain development and behavior. Insulin, beyond its peripheral metabolic actions, acts as an immunometabolic signal in the brain, yet its contribution to the neurobehavioral consequences of fetal growth restriction (FR) remains poorly understood. Here, using a validated rat model of FR, we investigated how insulin modulates medial prefrontal cortex (mPFC) gene expression across development (P0, P21, P90) and whether mPFC insulin signaling influences adult emotional behavior. RNA sequencing revealed that insulin regulates distinct, sex-specific transcriptional programs in the mPFC, with reduced insulin responsiveness following FR and enrichment of microglia-associated genes among insulin-regulated transcripts in control males. Across conditions, insulin partially recapitulated FR-associated transcriptional signatures, and altered pathways involved in immune responses, metabolism, and neurodevelopment, including the Wnt/β-catenin signaling. Targeted insulin infusion into the mPFC of adult animals reversed behavioral phenotypes induced by FR in a sex- and context-dependent manner, decreasing emotional reactivity to environmental cues. Together, these findings identify insulin signaling as a developmentally dynamic, sex-specific modulator of neuroimmune gene networks in the mPFC following prenatal adversity, and highlight immunometabolic pathways as potential targets to mitigate the long-term behavioral consequences of poor fetal growth.
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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".