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649 results for “brain gene expression”
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Mate choice in the brain: Species differ in how male traits 'turn on' gene expression in female brains
<p>Mate choice plays a fundamental role in speciation, yet we know little about the molecular mechanisms that underpin this crucial decision-making process. Stickleback fish differentially adapted to limnetic and benthic habitats are reproductively isolated and females of each species use different male traits to evaluate prospective partners and reject heterospecific males. Here, we integrate behavioral data from a mate choice experiment with gene expression profiles from the brains of females actively deciding whether to mate. We find substantial gene expression variation between limnetic and benthic females, regardless of behavioral context, suggesting general divergence in constitutive gene expression patterns, corresponding to their genetic differentiation. Intriguingly, female gene co-expression modules covary with male display traits but in opposing directions for sympatric populations of the two species, suggesting male displays elicit a dynamic genomic response that reflects known differences in female preferences. Furthermore, we confirm the role of numerous candidate genes previously implicated in female mate choice in other species, suggesting that evolutionary tinkering with these conserved molecular processes underlies divergent mate preferences and sexual isolation. Taken together, our study adds important new insights to our understanding of the molecular processes underlying female decision-making critical for generating sexual isolation and speciation.</p>
Data from: Mate choice in the brain: Species differ in how male traits ‘turn on’ gene expression in female brains
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Exploratory analysis of sleep deprivation effects on gene expression and regional brain metabolism
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Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry: Source and Supplementary Data
<p><em><strong>Source and Supplementary data for AANRI manuscript</strong></em></p>
Dim artificial light at night alters immediate early gene expression throughout the avian brain
<p>Artificial light at night (ALAN) is a pervasive pollutant that alters physiology and behavior. However, the underlying mechanisms triggering these alterations are unknown, as previous work shows that dim levels of ALAN may have a masking effect, bypassing the central clock. Light stimulates neuronal activity in numerous brain regions which could in turn activate downstream effectors regulating physiological response. In the present study, taking advantage of immediate early gene (IEG) expression as a proxy for neuronal activity, we determined the brain regions activated in response to ALAN. We exposed zebra finches to dim ALAN (1.5 lux) and analyzed 24 regions throughout the brain. We found that the overall expression of two different IEGs, cFos and ZENK, in birds exposed to ALAN were significantly different from birds inactive at night. Additionally, we found that ALAN-exposed birds had significantly different IEG expression from birds inactive at night and active during the day in several brain areas associated with vision, movement, learning and memory, pain processing, and hormone regulation. These results give insight into the mechanistic pathways responding to ALAN that underlie downstream, well-documented behavioral and physiological changes.</p>
Large captivity effect based on gene expression comparisons between captive and wild shrew brains
<p class="MsoNormal">Compared to their free-ranging counterparts, wild animals in captivity are subject to different conditions with lasting effects on their physiology and behavior. Alterations in gene expression in response to environmental changes occur upstream of physiological and behavioral phenotypes, but there are no experiments analyzing differential gene expression in captive vs. free-ranging mammals. We assessed gene expression profiles of three brain regions (cortex, olfactory bulb, and hippocampus) of wild juvenile shrews (<em>Sorex araneus</em>) in comparison to shrews kept in captivity for two months. We found hundreds of differentially expressed genes in all three brain regions, suggesting a large and uniform captivity effect. Many of the downregulated genes in captive shrews significantly enrich pathways associated with neurodegenerative disease (p<0.001), oxidative phosphorylation (p<0.001), and genes encoding ribosomal proteins (p<0.001). Transcriptomic changes associated with captivity in the shrew resemble responses identified in several human pathologies, such as major depressive disorder and neurodegeneration. Thus, not only does captivity impact brain function and expression, but captivity effects may also confound analyses of natural physiological processes in wild individuals under captive conditions.</p>
Data from: Short-term sleep loss alters cytokine gene expression in brain and peripheral tissues and increases plasma corticosterone of zebra finch (Taeniopygia guttata)
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Cryptic genetic variation in brain gene expression precedes the evolution of cannibalism in spadefoot toad tadpoles
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Dim artificial light at night alters immediate early gene expression throughout the avian brain
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Gene expression comparisons between captive and wild shrew brains reveal captivity effects
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Differentially evolutionary pathways and their interactions in genes expressed in brain of human and macaque
As the key organ that separates human from other non-human primates, brain has continuously evolved to adapt to the changes of environments and climates. Although human shares most genetic, molecular and cellular features with primates like macaque, there are significant differences in the structure and function in brain of human and these species. Thus, exploring the differences between brains of human and non-human primates in the context of evolution will provide insights into the development, function and diseases of human nervous system. Since the genes involved in many aspects of human brain are under common pressures of natural selection, their evolutionary features can be analyzed collectively at pathway-level. In this study, we tried to explore the molecular mechanisms underlying human brain by comparing the evolutionary features of pathways enriched in genes expressed in human brain and macaque brain. We identified 31 differentially evolutionary pathways in the brain of human and macaque, among which included those related to neurological diseases, signaling transduction, immune and metabolism. By analyzing differentially expressed genes in brain regions and development stages between human and macaque, we also found that ten and four pathways with evolutionary differences, respectively. We further performed crosstalk analysis of pathways to obtain an intuitive correlation between the pathways, which is helpful to understand the mechanism of interaction between pathways. Our results shed light on a comprehensive view of the evolutionary pathways of the human nervous system and provide a reference for the study of human brain development.
Data from: Early life exposure to low levels of AHR agonist PCB126 (3,3',4,4',5-pentachlorobiphenyl) reprograms gene expression in adult brain
Early life exposure to environmental chemicals can have long-term consequences that are not always apparent until later in life. We recently demonstrated that developmental exposure of zebrafish to low, non-embryotoxic levels of 3,3',4,4',5-pentachlorobiphenyl (PCB126) did not affect larval behavior, but caused changes in adult behavior. The objective of this study was to investigate the underlying molecular basis for adult behavioral phenotypes resulting from early life exposure to PCB126. We exposed zebrafish embryos to PCB126 during early development and measured transcriptional profiles in whole embryos, larvae and adult male brains using RNA-sequencing. Early life exposure to 0.3 nM PCB126 induced cyp1a transcript levels in 2-dpf embryos, but not in 5-dpf larvae, suggesting transient activation of aryl hydrocarbon receptor with this treatment. No significant induction of cyp1a was observed in the brains of adults exposed as embryos to PCB126. However, a total of 2209 and 1628 genes were differentially expressed in 0.3 nM and 1.2 nM PCB126-exposed groups, respectively. KEGG pathway analyses of upregulated genes in the brain suggest enrichment of calcium signaling, MAPK and notch signaling, and lysine degradation pathways. Calcium is an important signaling molecule in the brain and altered calcium homeostasis could affect neurobehavior. The downregulated genes in the brain were enriched with oxidative phosphorylation and various metabolic pathways, suggesting that the metabolic capacity of the brain is impaired. Overall, our results suggest that PCB exposure during sensitive periods of early development alters normal development of the brain by reprogramming gene expression patterns, which may result in alterations in adult behavior.
Inter-individual gene expression variability implies stable regulation of brain-biased genes across organs
<div> <div> <h2>Abstract</h2> <a href="https://github.com/christabel-bucao/fish-variability-across-organs/#abstract"></a></div> <p>Phenotypic variation among individuals plays a key role in evolution, since variation provides the material on which natural selection can act. One important link between genetic and phenotypic variation is gene expression. As for other phenotypes, the range of accessible expression variation is limited and biased by different evolutionary and developmental constraints. Gene expression variability broadly refers to the tendency of a gene to vary in expression (i.e., between individuals or cells) due to stochastic fluctuations or differences in genetic, epigenetic, or environmental factors, separately from the differences between e.g. organs. Variability due to biomolecular stochasticity (transcriptional ‘noise’) and cell-to-cell heterogeneity has been well-studied in isogenic populations of unicellular organisms such as bacteria and yeasts. However, for more complex organisms with multiple cells, tissues, and organs sharing the same genetic background, the interplay between inter-individual expression variability, gene and organ function, and gene regulation remains an open question. In this study, we used highly multiplexed 3’-end Bulk RNA Barcoding and sequencing (BRB-seq) to generate transcriptome profiles spanning at least nine organs in outbred individuals of three ray-finned fish species: zebrafish, Northern pike, and spotted gar. For each condition, we measured expression variation per gene independent of mean expression level. We observed that lowly variable genes are enriched in cellular housekeeping functions whereas highly variable genes are enriched in stimulus-response functions. Furthermore, genes with highly variable expression between individuals evolve under weaker purifying selection at the coding sequence level, indicating that intra-species gene expression variability predicts inter-species protein sequence divergence. Genes that are broadly expressed across organs tend to be both highly expressed and lowly variable between individuals, whereas organ-biased genes are typically highly variable within their top organ of expression. For genes with organ-biased expression profiles, we inferred differences in selective pressure on gene regulation depending on their top organ. We found that genes with peak expression in the brain have low inter-individual expression variability across non-nervous organs, suggesting stabilizing selection on regulatory evolution of brain-biased genes. Conversely, liver-biased genes have highly variable expression across organs, implying weaker regulatory constraints. These patterns show that gene regulatory mechanisms evolved differently based on constraints on the primary organ.</p> <h2>Directory Structure</h2> </div> <ul> <li> <p><code>config/</code>: Contains YAML file indicating package versions for conda environment</p> </li> <li> <p><code>data/</code>: Contains input data</p> <ul> <li><code>counts/</code>: Contains counts and UMI-deduplicated counts. Currently under embargo and will be made available upon acceptance for publication.</li> <li><code>gene_metadata/</code>: Contains gene biotype information from Ensembl</li> <li><code>sample_metadata/</code>: Contains sample metadata files for each species</li> <li><code>selectome/</code>: Contains selection statistics from the <a href="https://selectome.org/" rel="nofollow">Selectome</a> database<br><br></li> </ul> </li> <li> <p><code>results/</code>: Contains output files sorted by subfolders labeled after each step of the analysis pipeline. Only R notebook HTML files are available on the Git repository, please check Zenodo for R data files.</p> <ul> <li><code>run_pipeline.Rdata</code>: Contains all parameters used for each step of the analysis pipeline<br><br></li> </ul> </li> <li> <p><code>workflow/</code>: Contains scripts used for the analysis pipeline</p> <ul> <li><code>analysis/</code>: Contains all steps of the analysis pipeline, available as .Rmd files</li> <li><code>functions/</code>: Contains all functions used for analysis/</li> <li><code>renv/</code>: Used for package management in R</li> <li><code>run_pipeline.R</code>: Runs all the steps under analysis/</li> <li><code>run_go_figure.sh</code>: Runs <a href="https://gitlab.com/evogenlab/GO-Figure" rel="nofollow">GO-Figure!</a> 1.0.0 (downloaded separately)</li> <li><code>demultiplex_brbseq_fastq.sh</code>: Used for demultiplexing BRB-seq fastq files using <a href="https://github.com/DeplanckeLab/BRB-seqTools">BRB-seqTools</a> 1.6.1 (downloaded separately) for uploading to NCBI SRA</li> <li><code>rename_fastq_files.sh</code>: Used for renaming demultiplexed fastq files by mapping each barcode to their corresponding sample name</li> <li><code>renv.lock</code>: Lockfile for managing R package versions. Run <code>renv::restore()</code> to set up the R environment based on packages specified in the lockfile. All package versions used are also specified in the output HTML files under results/.</li> </ul> </li> </ul> <div> <h2>Species Codes</h2> </div> <ul> <li><strong>LOC</strong>: <em>Lepisosteus oculatus</em> (spotted gar)</li> <li><strong>ELU</strong>: <em>Esox lucius</em> (Northern pike)</li> <li><strong>DRE</strong>: <em>Danio rerio</em> (zebrafish)</li> </ul>
Systematic Gene Expression Mapping Clusters Nuclear Receptors According to Their Function in the Brain - Website save
<p>This is a copy of the website that was related to mousepat.ics-mci.fr</p>
Adipose Tissue Gene Expression and Metabolomics Links to the Gut Microbiome-brain Axis
ClinicalTrials.gov study NCT06869941. IPD Sharing: Not stated. Countries: 1. Publications: 31.
Investigation of Lithium on Signal Transduction, Gene Expression and Brain Myo-Inositol Levels in Manic Patients
ClinicalTrials.gov study NCT00870311. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Data from: Species-specific patterns of nonapeptide brain gene expression relative to pair-bonding behaviour in grouping and non-grouping cichlids
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Differentially evolutionary pathways and their interactions in genes expressed in brain of human and macaque
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Data from: Evolution of sex-biased gene expression and dosage compensation in the eye and brain of Heliconius butterflies
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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