Combination of deep behavioral phenotyping with brain region and cell type specific manipulations of FKBP51

The increasing prevalence of stress-related disorders, such as major depressive disorder (MDD) has become a significant global concern, with devastating effects on individuals' personal lives and societal well-being. The exposure to severe and chronic stressors is a major risk factor for the development of such disorders, and recent traumatic events have further exacerbated this mental health crisis. The susceptibility to MDD is determined by a complex interplay of genetic, epigenetic, and environmental factors. One specific gene of significance in this context is FKBP5 (Fkbp5 in rodents), encoding the co-chaperone FK506 binding protein 51 (FKBP51). The interplay between severe stress exposure and genetic risk variants of FKBP5 has been associated with an increased vulnerability to psychopathology. A significant symptom observed in individuals with MDD is social dysfunction, characterized by the avoidance of social interactions and the display of maladaptive behaviors, such as aggression or irritability. However, traditional preclinical assessment methods for stress-induced behavioral symptoms, such as social aversion, have faced criticism due to their reductionistic nature, often failing to capture ethologically relevant behavioral constructs. Advancements in high-throughput pose estimation tools have provided opportunities for comprehensive behavioral analysis through automatically annotated behavioral assessments. This thesis explores various tools for automatically annotated behavioral assessment in preclinical psychiatry research, employing both supervised classification and unsupervised clustering strategies. Applying the newly established ad validated deep phenotyping methods, the thesis further investigates the brain region and cell type specific role of FKBP51 across different stress models and uncovers the underlying neurobiological mechanisms and behavioral profiles using automatically annotated behavioral assessment. The effectiveness of both supervised classification and unsupervised clustering strategies is demonstrated in characterizing individual and social behavioral profiles in mice subjected to various stress conditions. Moreover, the thesis highlights the distinct sex-specific effects of different stress paradigms on the regulation of the hypothalamic-pituitary-adrenal (HPA) axis, including the expression of Fkbp5 in several stress-related brain regions, in particular the Locus Coeruleus (LC). Taken together, the current thesis emphasizes the importance of brain region and cell type specific regulation of Fkbp5 and underscores the benefits of automatically annotated behavioral assessment tools. This is put into perspective with future research prospects, advocating for the integration of diverse data modalities, such as in vivo measurements of stress mediators and neuronal activity recordings. This integrated approach aims to enhance our understanding of complex behaviors and the underlying molecular mechanisms. Ultimately, this can contribute to a better comprehension of the behavioral phenotypes and associated neurobiological alterations in stress-related disorders. These insights hold potential to facilitate the development of novel treatments for psychiatric disorders

Metabolic effects of early life stress and pre-pregnancy obesity are long lasting and sex specific in mice

Early life stress (ELS) is associated with metabolic, cognitive, and psychiatric diseases and has a very high prevalence, highlighting the urgent need for a better understanding of the versatile physiological changes and identification of predictive biomarkers. In addition to programming the hypothalamic-pituitary-adrenal (HPA) axis, ELS may also affect the gut microbiota and metabolome, opening up a promising research direction for identifying early biomarkers of ELS-induced (mal)adaptation. Other factors affecting these parameters include maternal metabolic status and diet, with maternal obesity shown to predispose offspring to later metabolic disease. The aim of the present study was to investigate the long-term effects of ELS and maternal obesity on the metabolic and stress phenotype of rodent offspring. To this end, offspring of both sexes were subjected to an adverse early-life experience, and their metabolic and stress phenotypes were examined. In addition, we assessed whether a prenatal maternal and an adult high-fat diet (HFD) stressor further shape observed ELS-induced phenotypes. We show that ELS has long-term effects on male body weight (BW) across the lifespan, whereas females more successfully counteract ELS-induced weight loss, possibly by adapting their microbiota, thereby stabilizing a balanced metabolome. Furthermore, the metabolic effects of a maternal HFD on BW are exclusively triggered by a dietary challenge in adult offspring and are more pronounced in males than in females. Overall, our study suggests that the female microbiota protects against an ELS challenge, rendering them more resilient to additional maternal- and adult nutritional stressors than males.This work was supported by the “GUTMOM” grant of the ERA-Net Cofund HDHL-INTIMIC (INtesTInal MIcrobiomics) under the JPI HDHL (Joint Programming Initiative – A healthy diet for a healthy life) umbrella (01EA1805; MVS), the SCHM2360-5-1 grant (MVS) from the German Research Foundation (DFG), the ZonMw grant from the Netherlands Organisation for Health Research and Development (project number 529051019), the DIM-ELI-2 grant of La Fundación La Marató-TV3 (ref. 2018-27/30-31), the PID2019-108973RB-C22 and PCIN2017-117 grants from the Ministerio de Ciencia e Innovación of Spain and the grants GV/2020/048 and IDIFEDER/2021/072 from the Generalitat Valenciana of Spain. Open Access funding enabled and organized by Projekt DEAL.Peer reviewe

Advancing social behavioral neuroscience by integrating ethology and comparative psychology methods through machine learning

Social behavior is naturally occurring in vertebrate species, which holds a strong evolutionary component and is crucial for the normal development and survival of individuals throughout life. Behavioral neuroscience has seen different influential methods for social behavioral phenotyping. The ethological research approach has extensively investigated social behavior in natural habitats, while the comparative psychology approach was developed utilizing standardized and univariate social behavioral tests. The development of advanced and precise tracking tools, together with post-tracking analysis packages, has recently enabled a novel behavioral phenotyping method, that includes the strengths of both approaches. The implementation of such methods will be beneficial for fundamental social behavioral research but will also enable an increased understanding of the influences of many different factors that can influence social behavior, such as stress exposure. Furthermore, future research will increase the number of data modalities, such as sensory, physiological, and neuronal activity data, and will thereby significantly enhance our understanding of the biological basis of social behavior and guide intervention strategies for behavioral abnormalities in psychiatric disorders

DeepOF: a Python package for supervised and unsupervised pattern recognition in mice motion tracking data

DeepOF (Deep Open Field) is a Python package that provides a suite of tools for analysing behaviour in freely-moving rodents. Specifically, it focuses on post-processing time-series data extracted from videos using DeepLabCut (Mathis et al., 2018). This paper included code and functionality peer-review, which we think is an important milestone for our package moving forward

Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress

Severe stress exposure increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and behavioral changes. Current markerless pose estimation tools allow for more complex and naturalistic behavioral tests. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. Applying this tool to chronic social defeat in male mice, the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter and fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. Moreover, DeepOF aims to facilitate reproducibility and unification of behavioral classification by providing an open-source tool, which can advance the study of rodent individual and social behavior, thereby enabling biological insights and, for example, subsequent drug development for psychiatric disorders