Prenatal immune activation interacts with stress and corticosterone exposure later in life to modulate N-methyl-d-aspartate receptor synaptic function and plasticity
Bibliographic record
Abstract
Prenatal infection is an environmental risk factor for schizophrenia while later in life, stressful events have been associated with the onset and severity of psychosis. Recent findings on the impact of stress on the N-methyl-d-aspartate receptor (NMDAR), of which hypofunctioning is implicated in schizophrenia, suggest changes in stress-induced regulation of the glutamatergic system may be related to the pathogenesis of schizophrenia. Our study aimed to test whether prenatal immune activation could interact with stress at adolescence to alter NMDAR function. We used offspring from rat dams administered bacterial lipopolysaccharide (LPS) during pregnancy (gestational days 15 and 16), an animal model expressing schizophrenia-related behavioural phenotypes. Using electrophysiological techniques, we investigated effects of stress and the stress hormone corticosterone (Cort) on NMDAR-mediated synaptic function and long-term depression (LTD) in hippocampal CA1 slices from these adolescent (aged 28-39 d) male offspring. In prenatal LPS offspring, NMDAR-mediated synaptic function and LTD were reduced and abolished, respectively, compared to prenatal saline controls. Notably, in vivo stress and in vitro Cort treatment facilitated LTD in slices from prenatal LPS rats but not prenatal saline controls. Finally, Cort enhanced NMDAR-mediated synaptic function in slices from prenatal LPS rats only. We conclude that prenatal immune activation results in NMDAR hypofunction in the hippocampus of adolescent rats but also increases responsiveness of NMDAR-mediated synaptic function and LTD towards stress. Prenatal infection could confer susceptibility to schizophrenia through modification of hippocampal NMDAR function, with hypofunction in resting conditions and heightened responsiveness to stress, thus impacting the development of the disorder.
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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".