Cholestatic liver disease leads to significant adaptative changes in neural circuits regulating social behavior in mice to enhance sociability
Bibliographic record
Abstract
BACKGROUND & AIMS: Cholestatic liver diseases (CLD) are commonly associated with behavioral changes, including social isolation, that negatively affects patient quality of life and remains unaltered by current therapies. It remains unclear whether CLD-associated social dysfunction stems from a direct effect on the brain, or from the psychological impact of CLD. The psychological component of disease is absent in animals, so we investigated the impact of CLD on social behavior and gene expression profiles in key social behavior-regulating brain regions in a mouse model. METHODS: CLD due to bile duct ligation was used with the three-chamber sociability test for behavioral phenotyping. Differentially expressed gene (DEG) signatures were delineated in 3 key brain regions regulating social behavior using RNA-seq. Ingenuity Pathway Analysis (IPA®) was applied to streamline DEG data interpretation and integrate findings with social behavior-regulating pathways to identify important brain molecular networks and regulatory mechanisms disrupted in CLD. RESULTS: CLD mice exhibited enhanced social interactive behavior and significantly altered gene expression in each of the three social behavior-regulating brain regions examined. DEG signatures in BDL mice were associated with key IPA®-identified social behavior-regulating pathways including Oxytocin in Brain Signaling, GABA Receptor Signaling, Dopamine Receptor Signaling, and Glutamate Receptor Signaling. CONCLUSIONS: CLD causes complex alterations in gene expression profiles in key social behavior-regulating brain areas/pathways linked to enhanced social interactive behavior. These findings, if paralleled in CLD patients, suggest that CLD-associated reductions in social interactions predominantly relate to psychological impacts of disease and may inform new approaches to improve management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".