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      Diurnal rhythms in gene expression in the prefrontal cortex in schizophrenia

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          Abstract

          Schizophrenia is associated with disrupted cognitive control and sleep-wake cycles. Here we identify diurnal rhythms in gene expression in the human dorsolateral prefrontal cortex (dlPFC), in schizophrenia and control subjects. We find significant diurnal (24 h) rhythms in control subjects, however, most of these transcripts are not rhythmic in subjects with schizophrenia. Instead, subjects with schizophrenia have a different set of rhythmic transcripts. The top pathways identified in transcripts rhythmic only in subjects with schizophrenia are associated with mitochondrial function. Importantly, these rhythms drive differential expression patterns of these and several other genes that have long been implicated in schizophrenia (including BDNF and GABAergic-related transcripts). Indeed, differential expression of these transcripts is only seen in subjects that died during the night, with no change in subjects that died during the day. These data provide insights into a potential mechanism that underlies changes in gene expression in the dlPFC with schizophrenia.

          Abstract

          Sleep disturbance is common in psychiatric disease, and this may contribute to altered circadian rhythm in gene expression. Here the authors show that rhythms in gene expression in the dorsolateral prefrontal cortex in schizophrenia are different to that seen in healthy controls.

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          Gene expression deficits in a subclass of GABA neurons in the prefrontal cortex of subjects with schizophrenia.

          Markers of inhibitory neurotransmission are altered in the prefrontal cortex (PFC) of subjects with schizophrenia, and several lines of evidence suggest that these alterations may be most prominent in the subset of GABA-containing neurons that express the calcium-binding protein, parvalbumin (PV). To test this hypothesis, we evaluated the expression of mRNAs for PV, another calcium-binding protein, calretinin (CR), and glutamic acid decarboxylase (GAD67) in postmortem brain specimens from 15 pairs of subjects with schizophrenia and matched control subjects using single- and dual-label in situ hybridization. Signal intensity for PV mRNA expression in PFC area 9 was significantly decreased in the subjects with schizophrenia, predominantly in layers III and IV. Analysis at the cellular level revealed that this decrease was attributable principally to a reduction in PV mRNA expression per neuron rather than by a decreased density of PV mRNA-positive neurons. In contrast, the same measures of CR mRNA expression were not altered in schizophrenia. These findings were confirmed by findings from cDNA microarray studies using different probes. Across the subjects with schizophrenia, the decrease in neuronal PV mRNA expression was highly associated (r = 0.84) with the decrease in the density of neurons containing detectable levels of GAD67 mRNA. Furthermore, simultaneous detection of PV and GAD67 mRNAs revealed that in subjects with schizophrenia only 55% of PV mRNA-positive neurons had detectable levels of GAD67 mRNA. Given the critical role that PV-containing GABA neurons appear to play in regulating the cognitive functions mediated by the PFC, the selective alterations in gene expression in these neurons may contribute to the cognitive deficits characteristic of schizophrenia.
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            Circadian patterns of gene expression in the human brain and disruption in major depressive disorder.

            A cardinal symptom of major depressive disorder (MDD) is the disruption of circadian patterns. However, to date, there is no direct evidence of circadian clock dysregulation in the brains of patients who have MDD. Circadian rhythmicity of gene expression has been observed in animals and peripheral human tissues, but its presence and variability in the human brain were difficult to characterize. Here, we applied time-of-death analysis to gene expression data from high-quality postmortem brains, examining 24-h cyclic patterns in six cortical and limbic regions of 55 subjects with no history of psychiatric or neurological illnesses ("controls") and 34 patients with MDD. Our dataset covered ~12,000 transcripts in the dorsolateral prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, nucleus accumbens, and cerebellum. Several hundred transcripts in each region showed 24-h cyclic patterns in controls, and >100 transcripts exhibited consistent rhythmicity and phase synchrony across regions. Among the top-ranked rhythmic genes were the canonical clock genes BMAL1(ARNTL), PER1-2-3, NR1D1(REV-ERBa), DBP, BHLHE40 (DEC1), and BHLHE41(DEC2). The phasing of known circadian genes was consistent with data derived from other diurnal mammals. Cyclic patterns were much weaker in the brains of patients with MDD due to shifted peak timing and potentially disrupted phase relationships between individual circadian genes. This transcriptome-wide analysis of the human brain demonstrates a rhythmic rise and fall of gene expression in regions outside of the suprachiasmatic nucleus in control subjects. The description of its breakdown in MDD suggests potentially important molecular targets for treatment of mood disorders.
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              Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures

              Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/ .
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                Author and article information

                Contributors
                mcclungca@upmc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 August 2019
                9 August 2019
                2019
                : 10
                : 3355
                Affiliations
                [1 ]ISNI 0000 0004 1936 9000, GRID grid.21925.3d, Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, , University of Pittsburgh, ; Pittsburgh, 15213 PA USA
                [2 ]ISNI 0000 0004 1936 9000, GRID grid.21925.3d, Department of Biostatistics, , University of Pittsburgh, ; Pittsburgh, 15261 PA USA
                [3 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, Department of Biostatistics, , University of Florida, ; Gainesville, 32611 FL USA
                Author information
                http://orcid.org/0000-0002-6401-9494
                Article
                11335
                10.1038/s41467-019-11335-1
                6689017
                31399567
                d1ebed54-e18c-4dae-995f-d7be10cbe482
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 January 2019
                : 25 June 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: MH111601
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000874, Brain and Behavior Research Foundation (Brain & Behavior Research Foundation);
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                circadian regulation,schizophrenia,gene expression
                Uncategorized
                circadian regulation, schizophrenia, gene expression

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