A NOVEL MULTIOMICS- AND RDOC-BASED FRAMEWORK FOR EVALUATION OF ANIMAL MODELS OF NEUROPSYCHIATRY DISORDERS

Animal models in neuropsychiatric research are limited by poor standardization of protocols and behavioral testing. This variability reduces reproducibility, leading to inconsistent biomolecular findings, and makes it difficult to synthesize evidence, weakening translational purposes. We used the Flinders Sensitive Line (FSL), a well-established genetic model of depression, to develop a standardized framework for whole-model characterization using multiomics and Research Domain Criteria (RDoC)-based analyses. We systematically identified all published original studies using FSL through PubMed, Scopus, Embase, and Web of Science. Data from included papers were extracted and classified into RDoC domains and dimensions of analysis. For each study, we extracted information on the experimental models, treatments, and strain characteristics. All datasets were processed in R (version 4.3.2). Molecular readouts were annotated using BioMart (Ensembl), Ensembl gene IDs were assigned, and pathway enrichment analysis was performed using KEGG. We included 343 studies (12,184 experimental groups). We found that FSL deficits across different domains depend on the control strain, with stronger deficits when compared with the Flinders Resistant Line than with Sprague–Dawley. The main domains affected were social processes, negative valence, and cognition. We also observed domain-specific effects of drugs and environmental manipulations. Regarding the molecular dimension, we identified alterations in multiple targets related to metabolic/autoimmune diseases. Therefore, an RDoC-based synthesis of the FSL literature shows that control-strain choice substantially influences the behavioral phenotype; drugs and environmental interventions differentially rescue deficits across domains; and many of the altered molecular pathways may explain the link between maladaptive behaviors and metabolic/immune disorders.