Biology subjects

Hagenauer, M. H.

Publications and source records attributed to Hagenauer, M. H..

15 recordsLinked to original sources

bioRxiv2026

A Meta-Analysis of the Converging Effects of Different Classes of Antipsychotics on the Frontal Cortex Transcriptome in Laboratory Rodents and Non-Human Primates

BackgroundPsychotic illnesses are among the most debilitating classes of psychiatric disorders, requiring targeted and effective treatment strategies. Although antipsychotics are the primary pharmacological therapy for psychosis, their full range of effects remain unclear, including effects within the frontal cortex, a brain region linked structurally and functionally to psychotic disorders. MethodsTo examine the effects of antipsychotic treatment on the frontal cortex, we conducted a meta-analysis of publicly available rodent (rat, mice) transcriptional profiling datasets (microarray, RNA-Seq). Five datasets (GSE45229, GSE93918, GSE2547, GSE4031.1, GSE66275) were identified within the Gemma database using pre-specified search terms and inclusion/exclusion criteria (date: 7/7/2024), yielding differential expression results for eight drug vs. control comparisons (collective n=68). A random-effects meta-analysis model was fit to the log2 fold changes for each gene, and p-values adjusted for false discovery rate (FDR), with follow-up analyses exploring robustness, heterogeneity, and publication bias. To increase the power and generalizability of our findings, an exploratory meta-analysis was also run incorporating antipsychotic effects from both rodents and nonhuman primates (collective n=101), and compared to findings from individuals with schizophrenia. ResultsOur meta-analysis yielded stable estimates for 12,190 genes, identifying 63 genes that were differentially expressed following antipsychotic treatment ("DEGs", FDR<0.05). Differential expression included genes important for serotonergic and cholinergic signalling, and was enriched within pathways linked to oligodendrocyte development and myelination, physiological and cellular stress responses, and cardiovascular function. An exploratory meta-analysis combining rodent and nonhuman primate results confirmed these observations and yielded additional findings (117 DEGs total). Comparisons with human post-mortem findings suggested that some schizophrenia-related gene expression may instead reflect antipsychotic treatment. ConclusionFurther validation is necessary, but our findings suggest that antipsychotics may assist in the regulation of specific structural and functional changes within the frontal cortex linked to psychotic disorders. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/734301v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1aaa2ecorg.highwire.dtl.DTLVardef@1ae6819org.highwire.dtl.DTLVardef@13454a2org.highwire.dtl.DTLVardef@a0877c_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIPsychosis is treated using two broad types of antipsychotic medication: first-generation (typical) and second-generation (atypical). C_LIO_LIUnderstanding the congruent effects of different types of antipsychotics on regions important for psychosis, such as the frontal cortex, can highlight essential mechanisms. C_LIO_LIA meta-analysis of public transcriptional profiling datasets identified genes and functional gene sets that are differentially expressed across antipsychotic categories. C_LI

neuroscience↗

bioRxiv2026

Significantly Improved Mouse and Rat Genome Annotation Using Sequence Read Archive RNA-seq Data

To detect currently unannotated genes with low expression levels with high sensitivity and accuracy, we developed a new exon->gene->transcript annotation pipeline that can identify previously undetected multi-exon transcripts using large volumes of RNA-Seq data. Our pipeline incorporates three new algorithms: 1) model-based spliced exon detection, 2) exon-to-gene assignment across multiple tissue/datasets through exon community discovery, and 3) ranking top transcripts by a stepwise minimum flow procedure. The design of our pipeline allowed us to leverage hundreds of Tbases of public RNA-seq data as input to improve mouse and rat genome annotation. Using this data, our pipeline identified close to 15K and 21K unannotated genes in GENCODE M37 and ENSEMBL 114 for mouse and rat, respectively. Each species also gained over 200K predicted transcripts containing at least one new exon, although most were transcripts from GENCODE/ENSEMBL annotated genes with newly assigned exons. To make our genome annotation available for common use, we have packaged this new annotation in standard file formats for the analysis of bulk and single cell RNA-seq data (GTF, 10X genome files). We have also provided two use examples which demonstrate the utility of our newly annotated genes in functional analyses, showing that their expression can be differentially regulated in relationship to cell type and selective breeding. Due to the efficiency provided by our pipeline, we expect that as new RNA-seq data become available in the coming years it will significantly benefit rat gene/transcript annotation, eventually enabling us to approach the target of complete gene and transcript annotation.

genomics↗

bioRxiv2025

A Meta-Analysis of the Effects of Chronic Stress on the Prefrontal Transcriptome in Animal Models and Convergence with Existing Human Data

BackgroundChronic stress is a major risk factor for psychiatric disorders, including anxiety, depression, and post-traumatic stress disorder. Chronic stress can cause structural alterations like grey matter atrophy in key emotion-related areas such as the prefrontal cortex (PFC). To identify biological pathways affected by chronic stress in the PFC, researchers have performed transcriptional profiling (RNA-sequencing, microarray) to measure gene expression in rodent models. However, transcriptional signatures in the PFC that are shared across different chronic stress paradigms and laboratories remain relatively unexplored. MethodsWe performed a meta-analysis of publicly available transcriptional profiling datasets within the Gemma database. We identified six datasets that characterized the effects of either chronic social defeat stress (CSDS) or chronic unpredictable mild stress (CUMS) on gene expression in the PFC in mice (n=117). We fit a random effects meta-analysis model to the chronic stress effect sizes (log(2) fold changes) for each transcript (n=21,379) measured in most datasets. We then compared our results with two other published chronic stress meta-analyses, as well as transcriptional signatures associated with psychiatric disorders. ResultsWe identified 133 genes that were consistently differentially expressed across chronic stress studies and paradigms (false discovery rate (FDR)<0.05). Fast Gene Set Enrichment Analysis (fGSEA) revealed 53 gene sets enriched with differential expression (FDR<0.05), dominated by glial and neurovascular markers (e.g., oligodendrocyte, astrocyte, endothelial/vascular) and stress-related signatures (e.g., major depressive disorder, hormonal responses). Immediate-early gene markers of neuronal activity (Fos, Junb, Arc, Dusp1) were consistently suppressed. Many of the identified effects resembled those seen in previous meta-analyses characterizing stress effects (CSDS, early life stress), despite minimal overlap in included samples. Moreover, some effects resembled previous observations from psychiatric disorders, including alcohol abuse disorder, major depressive disorder, bipolar disorder, and schizophrenia. ConclusionOur study demonstrates that chronic stress induces a robust, cross-paradigm PFC signature characterized by down-regulation of glia/myelin and vascular pathways and suppression of immediate-early gene activity, highlighting cellular processes linking chronic stress exposure, PFC dysfunction, and psychiatric disorders. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/683091v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1062c5eorg.highwire.dtl.DTLVardef@4ae148org.highwire.dtl.DTLVardef@c747corg.highwire.dtl.DTLVardef@1b3a713_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIChronic stress is linked to many human psychiatric disorders involving the prefrontal cortex (PFC). C_LIO_LIMeta-analysis showed that stressed mice had less gene expression for glia and neural activity in the PFC. C_LIO_LIPFC gene expression in chronically stressed mice mirrors patterns seen in psychiatric patients. C_LI Plain Language SummaryChronic stress has been shown to have lasting effects on the brain, contributing to cognitive impairments and the risk of psychiatric disorders such as anxiety, depression, and post-traumatic stress disorder. The prefrontal cortex, a brain region that is important for behavioral and attentional control, is sensitive to chronic stress. We combined information from six public mouse datasets to identify consistent effects of chronic stress on gene expression (mRNA) in the prefrontal cortex. In these studies, mice that had experienced chronic stress showed a decreased amount of mRNA related to a variety of non-neuronal support cells (glia) and blood vessels, and general neural activity. The mouse chronic stress signature overlapped with gene-activity patterns reported in human psychiatric conditions, suggesting a biological bridge between chronic stress and the onset of psychopathology.

neuroscience↗

bioRxiv2025

Effect of Chronic Stress on Whole Blood Transcriptome: A Meta-Analysis of Publicly Available Datasets from Rodent Models

BackgroundChronic stress increases risk for neuropsychiatric disorders in humans. By modeling stress-induced changes in animals, we may improve diagnosis or treatment of these disorders. Successful translation benefits from studies with sufficient statistical power and outcome measurements that can be directly compared across species. We performed a meta-analysis to examine the impact of chronic stress on the whole blood transcriptome. MethodsDatasets were systematically identified in Gemma, a database of reprocessed public transcriptional profiling studies; datasets GSE68076, GSE72262, and GSE84185 met inclusion/exclusion parameters. Each study exposed eight-week old mice to chronic stress (5-10 days social defeat stress or 6-8 weeks chronic mild stress). The final sample size was n=92 (n=45 Non-Stress/n=47 Stress). Stress-related differential expression in each dataset was quantified using the Limma pipeline followed by empirical Bayes moderation. For the 9,219 genes represented in all three datasets, we ran a meta-analysis of Log(2) Fold Changes using a random effects model and corrected for false discovery rate (FDR). Functional patterns were assessed with fast Gene Set Enrichment Analysis. Cell type specific enrichment for each of the differentially expressed genes was further explored using a public 10x genomics scRNA-Seq dataset from mouse peripheral blood mononuclear cells. ResultsFindings included 23 downregulated and 16 upregulated transcripts in stress-exposed mice (FDR<0.05). Results indicated a down-regulation in gene sets related to B cells, immune response, DNA and chromatin regulation, ribosomal activity, translation, and catabolic cellular processes. Upregulated gene sets related to erythrocytes and oxygen binding. ConclusionOur results provide molecular insight into stress-related immune dysregulation and add weight to the hypothesis that environmental stress escalates cellular aging, supporting the use of blood transcriptome as a bridge between human and rodent models. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/657043v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@161fc2eorg.highwire.dtl.DTLVardef@1f358b9org.highwire.dtl.DTLVardef@145ef33org.highwire.dtl.DTLVardef@5b1169_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsSuccessful translational research benefits from studies with outcome measurements that can be directly compared across species and sufficient statistical power. The present report is a meta-analysis of three mouse experiments examining the impact of chronic stress on the whole blood transcriptome. Our results provide insight into stress-related immune dysregulation and add weight to the hypothesis that environmental stress escalates cellular aging. These findings illustrate the utility of the blood transcriptome as a bridge between human and rodent models.

genomics↗

bioRxiv2025

Short Report: A Meta-Analysis of the Effects of Sleep Deprivation on the Cortical Transcriptome in Animal Models

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 SD effects 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. These studies characterized 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: FDR<0.05), most of which (115/182) showed similar effects (FDR<0.05) in an independent large dataset (GSE114845: n=86 RNA-Seq samples from n=222 mice). Gene-set enrichment analysis revealed down-regulation in pathways related to stress response (e.g., glucocorticoid receptor Nr3c1), vasculature, growth and development, and upregulation related to stress, inflammation, and neuropeptide signalling. Exploratory analyses suggested that recovery sleep (included in six contrasts: range: 1-18 hrs), could reverse the impact of SD on gene expression. Our meta-analysis provides a useful reference database illustrating the diverse molecular impact of SD on the rodent cerebral cortex. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/648791v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@52984dorg.highwire.dtl.DTLVardef@8d1174org.highwire.dtl.DTLVardef@174ed85org.highwire.dtl.DTLVardef@195e8bf_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

neuroscience↗

bioRxiv2025

The Effects of Antidepressants on the Hippocampus: A Meta-Analysis of Public Transcriptional Profiling Data

Depression can be treated with traditional pharmaceuticals targeting monoaminergic function, non-traditional drug classes and neuromodulatory interventions. To identify mechanisms of action shared across clinically-effective antidepressant treatment categories, we performed two systematic meta-analyses of public transcriptional profiling data from adult laboratory rodents (rats, mice). The outcome variable was gene expression, measured by microarray or RNA-Seq from bulk-dissected tissue from two depression-related brain regions (hippocampus, cortex). Relevant datasets were identified in the Gemma database of curated, reprocessed transcriptional profiling data using predefined search terms and inclusion/exclusion criteria (hippocampus: 6-24-2024, cortex: 7-10-2024). Differential expression results were extracted for all genes, minimizing bias. For each gene, a random effects meta-analysis model was fit to antidepressant vs. control effect sizes (Log2 Fold Changes) from each study for each brain region, with follow-up analyses exploring sources of effect heterogeneity. For the hippocampus, 15 relevant studies were identified, containing 22 antidepressant vs. control group comparisons (collective n=313 samples), with approximately half representing traditional versus non-traditional antidepressants. Of 16,439 analyzed genes, 58 were consistently differentially expressed (False Discovery Rate (FDR)<0.05) following treatment. Antidepressant effects were enriched in the dentate gyrus and in gene sets related to stress regulation, brain growth and plasticity, vasculature and glia, and immune function. Comparisons with single nucleus RNA-Seq confirmed effects on specific hippocampal cell types, including potential rejuvenation of dentate granule neurons. For the cortex, 13 studies were identified, containing 16 antidepressant vs. control group comparisons (collective n=233 samples). Of 15,583 analyzed genes, only one was consistently differentially expressed (FDR<0.05: Atp6v1b2), but overall expression patterns moderately resembled the hippocampus. These genes and pathways showing consistent differential expression across treatment categories may be promising targets for novel therapies. Future work should explore relevance to human clinical populations and potential heterogeneity introduced by sex and subregion. Key PointsO_LIDepression can be treated with traditional antidepressants targeting monoaminergic function, as well as other drug classes and non-pharmaceutical interventions. C_LIO_LIUnderstanding the congruent effects of different types of antidepressant treatments on sensitive brain regions, such as the hippocampus and cortex, can highlight essential mechanisms of action. C_LIO_LIA meta-analysis of public transcriptional profiling datasets identified genes and functional gene sets that are differentially expressed across antidepressant categories. C_LI Plain Language SummaryMajor 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 public datasets to characterize changes in gene expression (mRNA) following treatment with both traditional and non-traditional antidepressants. We focused on the hippocampus and cortex, which are two brain regions that are sensitive to both depression and antidepressant usage. We found 59 genes that had consistently higher or 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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/648805v3_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@14538e3org.highwire.dtl.DTLVardef@199c5e3org.highwire.dtl.DTLVardef@8ed8a6org.highwire.dtl.DTLVardef@31b1f8_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

A meta-analysis of the effects of early life stress on the prefrontal cortex transcriptome suggests long-term effects on myelin

BackgroundEarly 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. MethodsTo 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. ResultsOur 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. ConclusionThese 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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/624315v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1619926org.highwire.dtl.DTLVardef@8da510org.highwire.dtl.DTLVardef@14feffcorg.highwire.dtl.DTLVardef@114aae3_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIEarly life stress (ELS) can have long-term effects on the prefrontal cortex (PFC) and its related cognitive and emotional functions. C_LIO_LITo elucidate these long-term effects, we conducted a meta-analysis of five publicly available PFC transcriptional profiling datasets from adult rodents that had previously experienced ELS. C_LIO_LIThis meta-analysis revealed a consistent downregulation of myelin-related genes in the PFC following ELS, and an upregulation of genes related to Major Depressive Disorder. C_LI Plain Language SummaryEarly life stress refers to exposure to negative childhood experiences, such as neglect, disaster, and physical, mental, or emotional abuse. Early life stress can permanently alter the brain, including the prefrontal cortex, which can lead to cognitive and emotional dysfunction that lasts into adulthood. We performed a meta-analysis using five public datasets to identify consistent long-term effects of early life stress on gene expression (mRNA) in the prefrontal cortex of adult rodents. In these studies, rodents that had experienced early life stress consistently showed a decreased amount of mRNA for genes related to myelin. Myelin is the fatty layer that insulates the axons of neurons, allowing them to transmit electrical signals more efficiently. These gene expression changes may suggest long-term effects of early life stress on the efficiency of prefrontal neurotransmission, disrupting cognitive and emotional processing.

neuroscience↗

bioRxiv2024

Resource: A Curated Database of Brain-Related Functional Gene Sets (Brain.GMT)

O_LITranscriptional 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". C_LIO_LITo 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. C_LIO_LIAlthough 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. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/588301v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@d8a5beorg.highwire.dtl.DTLVardef@1697a1aorg.highwire.dtl.DTLVardef@1cf4cf8org.highwire.dtl.DTLVardef@c634dd_HPS_FORMAT_FIGEXP M_FIG C_FIG Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@b8ac07org.highwire.dtl.DTLVardef@2628f8org.highwire.dtl.DTLVardef@14bb803org.highwire.dtl.DTLVardef@1ddbfborg.highwire.dtl.DTLVardef@5567e8_HPS_FORMAT_FIGEXP M_TBL C_TBL

neuroscience↗

bioRxiv2023

Adolescent environmental enrichment induces social resilience and alters neural gene expression in a selectively bred rodent model with anxious phenotype

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.

neuroscience↗

bioRxiv2022

Transcriptional Profiling of the Hippocampus in an F2 Cross of a Genetic Rat Model of Internalizing vs. Externalizing Behavior and Addiction Liability

Selectively-bred High Responder (bHR) and Low Responder (bLR) rats model the extreme externalizing and internalizing behavior accompanying many psychiatric disorders. To elucidate gene expression underlying these heritable behavioral differences, bHRs and bLRs (generation 37) were used to produce a F0-F1-F2 cross. We measured exploratory locomotion, anxiety-like behavior, and reward cue sensitivity (Pavlovian Conditioned Approach), and performed hippocampal RNA-Seq in male and female F0s (n=24) and F2s (n=250). Behaviors that diverged during selective breeding remained correlated in F2s, implying a shared genetic basis. F0 bHR/bLR differential expression was robust, surpassing differences associated with sex, and predicted expression patterns associated with F2 behavior. With bHR-like behavior, gene sets related to growth/proliferation were upregulated, whereas with bLR-like behavior, gene sets related to mitochondrial function, oxidative stress, and microglial activation were upregulated. This differential expression could be successfully predicted based on F0 genotype using cis-expression quantitative trait loci (cis-eQTLs) identified in the F2s. Colocalization of these cis-eQTLs with behavioral Quantitative Trait Loci pinpointed 16 differentially expressed genes that were strong candidates for mediating the influence of genetic variation on behavioral temperament. Our findings implicate hippocampal bioenergetic regulation of oxidative stress, microglial activation, and growth-related processes in shaping behavioral temperament, modulating vulnerability to psychiatric disorders.

neuroscience↗

bioRxiv2019

A Meta-Analysis of Hippocampal Transcriptional Profiling Studies in a Selectively-Bred Rat Model Provides Converging Evidence with Genetic Sequencing to Implicate Specific Candidate Genes and Pathways in the Liability for Internalizing and Externalizing Psychiatric Disorders

BackgroundFor over 16 years, we have selectively bred rats to show either high or low levels of exploratory activity within a novel environment. These "bred High Responder" (bHR) and "bred Low Responder" (bLR) rats serve as a model for temperamental extremes, exhibiting large differences in many internalizing and externalizing behaviors relevant to mood and substance abuse disorders. MethodsOur study elucidated persistent differences in gene expression related to bHR/bLR phenotype across development and adulthood in the hippocampus, a region critical for emotional regulation. We meta-analyzed eight transcriptional profiling datasets (microarray, RNA-Seq) spanning 43 generations of selective breeding (adult: n=46, P7: n=22, P14: n=49, P21: n=21; all male). We cross-referenced these results with exome sequencing performed on our colony to pinpoint candidates likely to mediate the effect of selective breeding on behavioral phenotype. ResultsGenetic and transcriptional profiling results converged to implicate two genes with previous associations with metabolism and mood: Thyrotropin releasing hormone receptor and Uncoupling protein 2. Our results also highlighted bHR/bLR functional differences in the hippocampus, including a network essential for neurodevelopmental programming, proliferation, and differentiation, containing hub genes Bone morphogenetic protein 4 and Marker of proliferation ki-67. Finally, we observed differential expression related to microglial activation, which is important for synaptic pruning, including two genes within implicated chromosomal regions: Complement C1q A chain and Milk fat globule-EGF factor 8. ConclusionThese candidate genes and functional pathways have the capability to direct bHR/bLR rats along divergent developmental trajectories and promote a widely different reactivity to the environment.

neuroscience↗

bioRxiv2017

Investigating circadian rhythmicity in pain sensitivity using a neural circuit model for spinal cord processing of pain

Primary processing of painful stimulation occurs in the dorsal horn of the spinal cord. In this article, we introduce mathematical models of the neural circuitry in the dorsal horn responsible for processing nerve fiber inputs from noxious stimulation of peripheral tissues and generating the resultant pain signal. The differential equation models describe the average firing rates of excitatory and inhibitory interneuron populations, as well as the wide dynamic range (WDR) neurons whose output correlates with the pain signal. The temporal profile of inputs on the different afferent nerve fibers that signal noxious and innocuous stimulation and the excitability properties of the included neuronal populations are constrained by experimental results. We consider models for the spinal cord circuit in isolation and when top-down inputs from higher brain areas that modulate pain processing are included. We validate the models by replicating experimentally observed phenomena of A fiber inhibition of pain and wind-up. We then use the models to investigate mechanisms for the observed phase shift in circadian rhythmicity of pain that occurs with neuropathic pain conditions. Our results suggest that changes in neuropathic pain rhythmicity can occur through dysregulation of inhibition within the dorsal horn circuit.

synthetic biology↗

bioRxiv2017

The Modulation of Pain by Circadian and Sleep-Dependent Processes: A Review of the Experimental Evidence

This proceedings paper is the first in a series of three papers developing mathematical models for the complex relationship between pain and the sleep-wake cycle. Here, we briefly review what is known about the relationship between pain and the sleep-wake cycle in humans and laboratory rodents in an effort to identify constraints for the models. While it is well accepted that sleep behavior is regulated by a daily (circadian) timekeeping system and homeostatic sleep drive, the joint modulation of these two primary biological processes on pain sensitivity has not been considered. Under experimental conditions, pain sensitivity varies across the 24 h day, with highest sensitivity occurring during the evening in humans. Pain sensitivity is also modulated by sleep behavior, with pain sensitivity increasing in response to the build up of homeostatic sleep pressure following sleep deprivation or sleep disruption. To explore the interaction between these two biological processes using modeling, we first compare the magnitude of their effects across a variety of experimental pain studies in humans. To do this comparison, we normalize the results from experimental pain studies relative to the range of physiologicallymeaningful stimulation levels. Following this normalization, we find that the estimated impact of the daily rhythm and of sleep deprivation on experimental pain measurements is surprisingly consistent across different pain modalities. We also review evidence documenting the impact of circadian rhythms and sleep deprivation on the neural circuitry in the spinal cord underlying pain sensation. The characterization of sleep-dependent and circadian influences on pain sensitivity in this review paper is used to develop and constrain the mathematical models introduced in the two companion articles.

neuroscience↗

bioRxiv2017

A Two-Process Model for Circadian and Sleep-dependent Modulation of Pain Sensitivity

Pain sensitivity is strongly modulated by time of day and by prior sleep behavior. These two factors, governed by the circadian rhythm and homeostatic sleep drive, respectively, likewise dictate the timing and duration of sleep. The fields of sleep and circadian research have identified much of the physiology underlying the circadian rhythm and homeostatic sleep drive with mathematical modeling playing an important role in understanding how these two processes interact to affect sleep behavior. We hypothesize that the daily rhythm of pain sensitivity and its sleep-dependent modulation reflect an interaction of the circadian rhythm and homeostatic sleep drive. To investigate this hypothesis, we adapt the formalism of a classic mathematical model for the regulation of sleep behavior by the circadian rhythm and homeostatic sleep drive, called the Two Process model, to simulate the interaction of these two processes on pain sensitivity. To construct the model, we utilize data from experimental reports on the daily rhythmicity of pain sensitivity in humans to define a \"daily pain sensitivity\" function. We decompose this function into two processes: a sleep-dependent process S(t) that follows the homeostatic sleep drive and a circadian process C(t) that is dictated by the circadian rhythm. By simulating different sleep schedules with the original Two Process model, we compute changes in the sleep-dependent process S(t) that modulates pain sensitivity. By combining S(t) with the circadian process C(t), our model predicts resultant changes in the daily pain sensitivity rhythm. We illustrate model predictions for changes in pain sensitivity due to sleep deprivation, sleep restriction and shift work schedules. We believe that this model may be a useful tool for pain management by providing predictions of the variations in pain sensitivity due to changing sleep schedules.

neuroscience↗

bioRxiv2016

INFERENCE OF CELL TYPE COMPOSITION FROM HUMAN BRAIN TRANSCRIPTOMIC DATASETS ILLUMINATES THE EFFECTS OF AGE, MANNER OF DEATH, DISSECTION, AND PSYCHIATRIC DIAGNOSIS

Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released: https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.\n\nThis variability was not simply due to dissection - the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

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