Using polymorphisms in FKBP5 to define biologically distinct subtypes of posttraumatic stress disorder: Evidence from endocrine and gene expression studies

Context: Polymorphisms in the gene encoding the glucocorticoid receptor (GR) regulating co-chaperone FKBP5 have been shown to alter GR sensitivity and are associated with an increased risk to develop posttraumatic stress disorder (PTSD). Objective: To investigate interactions of the FKBP5 single-nucleotide polymorphism rs9296158 and PTSD symptoms on baseline cortisol level, low-dose dexamethasone suppression, and whole-blood gene expression. Design: Association of FKBP5 genotypes and PTSD symptoms with endocrine measures and genome-wide expression profiles. Setting: Waiting rooms of general medical and gynecological clinics of an urban hospital at Emory University. Participants: The 211 participants were primarily African American (90.05%) and of low socioeconomic status and had high rates of trauma and PTSD. Main Outcome Measures: Baseline and post-dexamethasone suppression cortisol measures and gene expression levels. Results: In our endocrine study, we found that only risk allele A carriers of rs9296158 showed GR supersensitivity with PTSD; in contrast, baseline cortisol levels were decreased in PTSD only in patients with the GG genotype. Expression of 183 transcripts was significantly correlated with PTSD symptoms after multiple testing corrections. When adding FKBP5 genotype and its interaction with PTSD symptoms, expression levels of an additional 32 genes were significantly regulated by the interaction term. Within these 32 genes, previously reported PTSD candidates were identified, including FKBP5 and the IL18 and STAT pathways. Significant overrepresentation of steroid hormone transcription factor binding sites within these 32 transcripts was observed, highlighting the fact that the earlier-described genotype and PTSDdependent differences in GR sensitivity could drive the observed gene expression pattern. Results were validated by reverse transcriptase-polymerase chain reaction and replicated in an independent sample (N=98). Conclusions: These data suggest that the inheritance of GR sensitivity-moderating FKBP5 polymorphisms can determine specific types of hypothalamic-pituitaryadrenal axis dysfunction within PTSD, which are also reflected in gene-expression changes of a subset of GRresponsive genes. Thus, these findings indicate that functional variants in FKBP5 are associated with biologically distinct subtypes of PTSD

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response

Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus

Emerging evidence emphasizes the strong impact of regulatory genomic elements in neurodevelopmental processes and the complex pathways of brain disorders. The present genome-wide quantitative trait loci analyses explore the cis-regulatory effects of single-nucleotide polymorphisms (SNPs) on DNA methylation (meQTL) and gene expression (eQTL) in 110 human hippocampal biopsies. We identify cis-meQTLs at 14,118 CpG methylation sites and cis-eQTLs for 302 3'-mRNA transcripts of 288 genes. Hippocampal cis-meQTL-CpGs are enriched in flanking regions of active promoters, CpG island shores, binding sites of the transcription factor CTCF and brain eQTLs. Cis-acting SNPs of hippocampal meQTLs and eQTLs significantly overlap schizophrenia-associated SNPs. Correlations of CpG methylation and RNA expression are found for 34 genes. Our comprehensive maps of cis-acting hippocampal meQTLs and eQTLs provide a link between disease-associated SNPs and the regulatory genome that will improve the functional interpretation of non-coding genetic variants in the molecular genetic dissection of brain disorders

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future

Gene Expression in Spontaneous Experimental Autoimmune Encephalomyelitis Is Linked to Human Multiple Sclerosis Risk Genes

Recent genome-wide association studies have identified over 230 genetic risk loci for multiple sclerosis. Current experimental autoimmune encephalomyelitis (EAE) models requiring active induction of disease may not be optimally suited for the characterization of the function of these genes. We have thus used gene expression profiling to study whether spontaneous opticospinal EAE (OSE) or MOG-induced EAE mirrors the genetic contribution to the pathogenesis of multiple sclerosis more faithfully. To this end, we compared gene expression in OSE and MOG EAE models and analyzed the relationship of both models to human multiple sclerosis risk genes and T helper cell biology. We observed stronger gene expression changes and an involvement of more pathways of the adaptive immune system in OSE than MOG EAE. Furthermore, we demonstrated a more extensive enrichment of human MS risk genes among transcripts differentially expressed in OSE than was the case for MOG EAE. Transcripts differentially expressed only in diseased OSE mice but not in MOG EAE were significantly enriched for T helper cell-specific transcripts. These transcripts are part of immune-regulatory pathways. The activation of the adaptive immune system and the enrichment of both human multiple sclerosis risk genes and T helper cell-specific transcripts were also observed in OSE mice showing only mild disease signs. These expression changes may, therefore, be indicative of processes at disease onset. In summary, more human multiple sclerosis risk genes were differentially expressed in OSE than was observed for MOG EAE, especially in T(H)1 cells. When studying the functional role of multiple sclerosis risk genes and pathways during disease onset and their interactions with the environment, spontaneous OSE may thus show advantages over MOG-induced EAE.Data Availability Statement The datasets presented in this study can be found in online repositories. The names of the repository and accession number(s) can be found at: https://www.ebi.ac.uk/arrayexpress/, E-MTAB-9132; https://www.ebi.ac.uk/arrayexpress/, E-MTAB-9133

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