Sex-Dependent Cross-Resilience to Social Defeat and Learned Helplessness: The Role of BDNF-ERK Signaling and Norepinephrine
Bibliographic record
Abstract
Abstract Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are two of the most prevalent and disabling psychiatric conditions, both closely tied to stress exposure. While they have distinct diagnostic criteria, MDD and PTSD share considerable comorbidity, overlapping behavioral symptoms, and common neurobiological pathways. However, not all individuals exposed to chronic or traumatic stress develop these disorders, highlighting the importance of resilience, the capacity to maintain psychological and physiological stability under stress. An emerging yet understudied concept is cross-resilience: the idea that resilience to one form of stress may confer protection against another. While animal models have effectively captured individual differences in stress responses, few have explored whether resilience generalizes across distinct stress modalities or differs by sex. We examined cross-resilience using two validated rodent models: chronic social defeat stress (CSDS) and learned helplessness (LH). These paradigms model complementary aspects of stress vulnerability. We assessed how prior CSDS experience influenced subsequent responses to LH. We also evaluated the role of the noradrenergic system using wild-type and norepinephrine (NE)-deficient (VMAT2 loxDBHcre KO) mice. Behavioral phenotyping was combined with molecular analyses in key brain regions involved in emotion, motivation, and fear processing: the ventral tegmental area (VTA), nucleus accumbens (NAc), and amygdala (AMY). We focused on BDNF and ERK/MAPK signaling pathways, known to mediate neuroplasticity and stress resilience. Our findings reveal sex-specific patterns of cross-resilience, with prior CSDS resilience predicting LH resilience in males but not females. Molecular results indicate distinct adaptations across brain regions and sexes, underscoring the biological complexity of resilience. These insights may inform personalized strategies for preventing and treating stress-related disorders.
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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".