Stanley J. Watson’s research while affiliated with Concordia University Ann Arbor and other places

Sleep deprivation (SD) causes large disturbances in mood and cognition. The molecular basis for these effects can be explored using transcriptional profiling to quantify brain gene expression. In this report, we used a meta-analysis of public transcriptional profiling data to discover effects of SD on gene expression that are consistent across studies and paradigms. To conduct the meta-analysis, we used pre-specified search terms related to rodent SD paradigms to identify relevant studies within Gemma , a database containing >19,000 re-analyzed microarray and RNA-Seq datasets. Eight studies met our systematic inclusion/exclusion criteria, characterizing the effect of 18 SD interventions on gene expression in the mouse cerebral cortex (collective n =293). For each gene with sufficient data (n=16,290), we fit a random effects meta-analysis model to the SD effect sizes (log(2) fold changes). Our meta-analysis revealed 182 differentially expressed genes in response to SD (false discovery rate<0.05), 104 of which were upregulated and 78 downregulated. Gene-set enrichment analysis revealed down-regulation in pathways related to stress response (e.g., glucocorticoid receptor Nr3c1 ), cell death and cell differentiation, and upregulation related to hypoxia and inflammation. Exploratory analyses found that the duration of SD (range: 3-12 hrs) did not significantly influence the magnitude of differential expression. Recovery sleep, which was included in three studies (range: 2-18 hrs), reversed the impact of SD on four transcripts. Our meta-analysis illustrates the diverse molecular impact of SD on the rodent cerebral cortex, producing effects that occasionally parallel those seen in the periphery. Graphical Abstract

Background Major depressive disorder (MDD) is characterized by persistent depressed mood and loss of interest and pleasure in life, known as anhedonia. The first line of treatment for MDD is antidepressant medication that enhances signaling by monoamine neurotransmitters, such as serotonin and norepinephrine. Other treatments include non-pharmaceutical treatments, such as electroconvulsive therapy and transmagnetic stimulation, and non-traditional pharmaceutical antidepressants that function via alternative, often unknown, mechanisms. Methods To identify mechanisms of action shared across antidepressant categories, we examined changes in gene expression following treatment with both traditional and non-traditional antidepressants, using a meta-analysis of public transcriptional profiling data from laboratory rodents (rats, mice). We focused on the hippocampus, which is a brain region that is well-documented via neuro-imaging to show morphological changes in depression that reverse with antidepressant usage. We specifically focused on treatment during adulthood, and included both clinically-used antidepressants (both pharmaceutical and non-pharmaceutical) and treatments demonstrated to effectively treat depressive mood symptoms. The outcome variable was gene expression in bulk dissected hippocampus as measured by microarray or RNA-Seq. To conduct our project, we systematically reviewed available datasets in the Gemma database of curated, reprocessed transcriptional profiling data using pre-defined search terms and inclusion/exclusion criteria. We identified 15 relevant studies containing a total of 22 antidepressant vs. control group comparisons (collective n =352). For each gene, a random effects meta-analysis model was then fit to the antidepressant vs. control effect sizes (Log2 Fold Changes) extracted from each study. Results Our meta-analysis yielded stable estimates for 16,439 genes, identifying 58 genes that were consistently differentially expressed (False Discovery Rate<0.05) across antidepressant experiments and categories. Of these genes, 23 were upregulated and 35 were downregulated. The functions associated with the differentially expressed genes were diverse, including modulation of the stress response, immune regulation, neurodevelopment and neuroplasticity. Conclusion The genes that we identified as consistently differentially expressed across antidepressant categories may be worth investigating as potential linchpins for antidepressant efficacy or as targets for novel therapies. Graphical Abstract Key Points Depression can be treated with traditional antidepressants targeting monoaminergic function, as well as multiple other drug classes and non-pharmaceutical interventions. Understanding the congruent effects of different types of antidepressant treatments on sensitive brain regions, such as the hippocampus, can highlight essential mechanisms of action. A meta-analysis of fifteen public transcriptional profiling datasets identified 58 genes that are differentially expressed in the hippocampus across antidepressant categories. Plain Language Summary Major depressive disorder is characterized by persistent depressed mood and loss of interest and pleasure in life. Worldwide, an estimated 5% of adults suffer from depression, making it a leading cause of disability. The current standard of care for depressed individuals includes psychotherapy and antidepressant medications that enhance signaling by monoamine neurotransmitters, such as serotonin and norepinephrine. Other treatments include non-traditional antidepressants that function via alternative, often unknown, mechanisms. To identify mechanisms of action shared across different categories of antidepressants, we performed a meta-analysis using fifteen public datasets to characterize changes in gene expression (mRNA) following treatment with both traditional and non-traditional antidepressants. We focused on the hippocampus, which is a brain region that is sensitive to both depression and antidepressant usage. We found 23 genes that had consistently higher levels of expression (mRNA) and 35 genes that had consistently lower levels of expression (mRNA) across antidepressant categories. The functions associated with these genes were diverse, including regulation of stress response, the immune system, brain growth and adaptability. These genes are worth investigating further as potential linchpins for antidepressant efficacy or as targets for novel therapies.

Externalizing and internalizing behavioral tendencies underlie many psychiatric and substance use disorders. These tendencies are associated with differences in temperament that emerge early in development via the interplay of genetic and environmental factors. To better understand the neurobiology of temperament, we have selectively bred rats for generations to produce two lines with highly divergent behavior: bred Low Responders (bLRs) are highly inhibited and anxious in novel environments, whereas bred High Responders (bHRs) are highly exploratory, sensation-seeking, and prone to drug-seeking behavior. Recently, we delineated these heritable differences by intercrossing bHRs and bLRs (F 0 -F 1 -F 2 ) to produce a heterogeneous F 2 sample with well-characterized lineage and behavior (exploratory locomotion, anxiety-like behavior, Pavlovian conditioning). The identified genetic loci encompassed variants that could influence behavior via many mechanisms, including proximal effects on gene expression. Here we measured gene expression in male and female F 0 s ( n = 12 bHRs, 12 bLRs) and in a large sample of heterogeneous F 2 s ( n = 250) using hippocampal RNA-Seq. This enabled triangulation of behavior with both genetic and functional genomic data to implicate specific genes and biological pathways. Our results show that bHR/bLR differential gene expression is robust, surpassing sex differences in expression, and predicts expression associated with F 2 behavior. In F 0 and F 2 samples, gene sets related to growth/proliferation are upregulated with bHR-like behavior, whereas gene sets related to mitochondrial function, oxidative stress, and microglial activation are upregulated with bLR-like behavior. Integrating our F 2 RNA-Seq data with previously-collected whole genome sequencing data identified genes with hippocampal expression correlated with proximal genetic variation ( cis -expression quantitative trait loci or cis -eQTLs). These cis -eQTLs successfully predict bHR/bLR differential gene expression based on F 0 genotype. Sixteen of these genes are associated with cis -eQTLs colocalized within loci we previously linked to behavior and are strong candidates for mediating the influence of genetic variation on behavioral temperament. Eight of these genes are related to bioenergetics. Convergence between our study and others targeting similar behavioral traits revealed five more genes consistently related to temperament. Overall, our results implicate hippocampal bioenergetic regulation of oxidative stress, microglial activation, and growth-related processes in shaping behavioral temperament, thereby modulating vulnerability to psychiatric and addictive disorders.

Background: Early life stress (ELS) refers to exposure to negative childhood experiences, such as neglect, disaster, and physical, mental, or emotional abuse. ELS can permanently alter the brain, leading to cognitive impairment, increased sensitivity to future stressors, and mental health risks. The prefrontal cortex (PFC) is a key brain region implicated in the effects of ELS. Methods: To better understand the effects of ELS on the PFC, we ran a meta-analysis of publicly available transcriptional profiling datasets. We identified five datasets (GSE89692, GSE116416, GSE14720, GSE153043, GSE124387) that characterized the long-term effects of multi-day postnatal ELS paradigms (maternal separation, limited nesting/bedding) in male and female laboratory rodents (rats, mice). The outcome variable was gene expression in the PFC later in adulthood as measured by microarray or RNA-Seq. To conduct the meta-analysis, preprocessed gene expression data were extracted from the Gemma database. Following quality control, the final sample size was n=89: n=42 controls & n=47 ELS: GSE116416 n=23 (no outliers); GSE116416 n=44 (2 outliers); GSE14720 n=7 (no outliers); GSE153043 n=9 (1 outlier), and GSE124387 n=6 (no outliers). Differential expression was calculated using the limma pipeline followed by an empirical Bayes correction. For each gene, a random effects meta-analysis model was then fit to the ELS vs. Control effect sizes (Log2 Fold Changes) from each study. Results: Our meta-analysis yielded stable estimates for 11,885 genes, identifying five genes with differential expression following ELS (false discovery rate< 0.05): transforming growth factor alpha (Tgfa), IQ motif containing GTPase activating protein 3 (Iqgap3), collagen, type XI, alpha 1 (Col11a1), claudin 11 (Cldn11) and myelin associated glycoprotein (Mag), all of which were downregulated. Broadly, gene sets associated with oligodendrocyte differentiation, myelination, and brain development were downregulated following ELS. In contrast, genes previously shown to be upregulated in Major Depressive Disorder patients were upregulated following ELS. Conclusion: These findings suggest that ELS during critical periods of development may produce long-term effects on the efficiency of transmission in the PFC and drive changes in gene expression similar to those underlying depression.

Transcription Factors (TFs) regulate gene expression by facilitating or disrupting the formation of transcription initiation machinery at particular genomic loci. Since TF occupancy is driven in part by recognition of DNA sequence, genetic variation can influence TF-DNA associations and gene regulation. To identify variants that impact TF binding in human brain tissues, we assessed allele specific binding (ASB) at heterozygous variants for 94 TFs in 9 brain regions from two donors. Leveraging graph genomes constructed from phased genomic sequence data, we compared ChIP-seq signals between alleles at heterozygous variants within each brain region and identified thousands of variants exhibiting ASB for at least one TF. ASB reproducibility was measured by comparisons between independent experiments both within and between donors. We found that rarer alleles in the general population more frequently led to reduced TF binding, whereas common variation had an equal likelihood of increasing or decreasing binding. Motif analysis revealed TF-specific effects, with ASB variants for certain TFs displaying a greater incidence of motif alterations, as well as enrichments for variants under purifying selection. Notably, neuron-specific cis -regulatory elements (cCREs) showed depletion for ASB variants. We identified 2,670 ASB variants with prior evidence of allele-specific gene expression in the brain from GTEx data and observed increasing eQTL effect direction concordance as ASB significance increases. These results provide a valuable and unique resource for mechanistic analysis of cis -regulatory variation in human brain tissue.

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP–seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain “ignorome”. To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types (“Brain.GMT”) that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, “brain ignorome” genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types (“Brain.GMT”). •Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human). •Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Stress is a major influence on mental health status; the ways that individuals respond to or copes with stressors determine whether they are negatively affected in the future. Stress responses are established by an interplay between genetics, environment, and life experiences. Psychosocial stress is particularly impactful during adolescence, a critical period for the development of mood disorders. In this study we compared two established, selectively-bred Sprague Dawley rat lines, the “internalizing” bred Low Responder (bLR) line versus the “externalizing” bred High Responder (bHR) line, to investigate how genetic temperament and adolescent environment impact future responses to social interactions and psychosocial stress, and how these determinants of stress response interact. Male bLR and bHR rats were exposed to social and environmental enrichment in adolescence prior to experiencing social defeat and were then assessed for social interaction and anxiety-like behavior. Adolescent enrichment caused rats to display more social interaction, as well as nominally less social avoidance, less submission during defeat, and resilience to the effects of social stress on corticosterone, in a manner that seemed more notable in bLRs. For bHRs, enrichment also caused greater aggression during a neutral social encounter and nominally during defeat, and decreased anxiety-like behavior. To explore the neurobiology underlying the development of social resilience in the anxious phenotype bLRs, RNA-seq was conducted on the hippocampus and nucleus accumbens, two brain regions that mediate stress regulation and social behavior. Gene sets previously associated with stress, social behavior, aggression and exploratory activity were enriched with differential expression in both regions, with a particularly large effect on gene sets that regulate social behaviors. Our findings provide further evidence that adolescent enrichment can serve as an inoculating experience against future stressors. The ability to induce social resilience in a usually anxious line of animals by manipulating their environment has translational implications, as it underscores the feasibility of intervention strategies targeted at genetically vulnerable adolescent populations.