Transcriptomic profiles of susceptibility and resilience to stress in the amygdala and hippocampus of male rats
Bibliographic record
Abstract
Traumatic experiences elicit a wide range of cognitive responses in both humans and animals, leading to diverse outcomes such as enhanced performance, cognitive impairment, or the development of mood and anxiety disorders like posttraumatic stress disorder (PTSD). A key challenge in understanding these varied responses is to decipher the underlying biological mechanisms that contribute to individual variability in trauma resilience or susceptibility. The purpose of this study was to elucidate the molecular bases for these differences, focusing on the amygdala and hippocampus-brain regions integral to stress responses. We exposed adult, male rats to an acute, severe stressor and profiled persistent anxiety-like behavior outcomes 7 days later. We investigated the transcriptional signatures in the basolateral amygdala and hippocampal dentate gyrus via bulk RNA sequencing from animals with behavioral outcomes indicative of stress resilience or vulnerability. Our results suggest that the basolateral amygdala and dentate gyrus display distinct transcriptomic changes following acute, severe stress. Furthermore, we identified specific region-dependent genes related to insulin signaling, neural plasticity, and stress responses that correlate with resilient and vulnerable phenotypes. Notably, a larger number of genes separated stress-resilient animals from both control and stress-susceptible animals, underscoring that an active molecular response, particularly in the hippocampus, facilitates protection from the long-term consequences of severe stress. These findings provide novel insight into the mechanisms that engender individual variability in the behavioral responses to stress and offer new targets for the advancement of therapies for stress-induced neuropsychiatric disorders.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".