Post-mortem molecular profiling of three psychiatric disorders

Abstract

BackgroundPsychiatric disorders are multigenic diseases with complex etiology that contribute significantly to human morbidity and mortality. Although clinically distinct, several disorders share many symptoms, suggesting common underlying molecular changes exist that may implicate important regulators of pathogenesis and provide new therapeutic targets.MethodsWe performed RNA sequencing on tissue from the anterior cingulate cortex, dorsolateral prefrontal cortex, and nucleus accumbens from three groups of 24 patients each diagnosed with schizophrenia, bipolar disorder, or major depressive disorder, and from 24 control subjects. We identified differentially expressed genes and validated the results in an independent cohort. Anterior cingulate cortex samples were also subjected to metabolomic analysis. ChIP-seq data were used to characterize binding of the transcription factor EGR1.ResultsWe compared molecular signatures across the three brain regions and disorders in the transcriptomes of post-mortem human brain samples. The most significant disease-related differences were in the anterior cingulate cortex of schizophrenia samples compared to controls. Transcriptional changes were assessed in an independent cohort, revealing the transcription factor EGR1 as significantly down-regulated in both cohorts and as a potential regulator of broader transcription changes observed in schizophrenia patients. Additionally, broad down-regulation of genes specific to neurons and concordant up-regulation of genes specific to astrocytes was observed in schizophrenia and bipolar disorder patients relative to controls. Metabolomic profiling identified disruption of GABA levels in schizophrenia patients.ConclusionsWe provide a comprehensive post-mortem transcriptome profile of three psychiatric disorders across three brain regions. We highlight a high-confidence set of independently validated genes differentially expressed between schizophrenia and control patients in the anterior cingulate cortex and integrate transcriptional changes with untargeted metabolite profiling.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0458-5) contains supplementary material, which is available to authorized users.

Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data

“…Marker genes of four major classes of brain cell types (namely neurons, astrocytes, oligodendrocytes, and microglia) were previously used to gain cell type-specific information from brain bulk tissue data (Sibille et al, 2008; Kuhn et al, 2011; Tan et al, 2013b; Hagenauer et al, 2016; Skene and Grant, 2016; Ramaker et al, 2017), and infer changes in cellular abundance. Following the practice of others, we applied similar approach to our marker genes, summarizing their expression profiles as the first principal component of their expression (see Materials and Methods; Xu et al, 2013; Chikina et al, 2015; Westra et al, 2015).…”

Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression

“…The cases in each dataset were diagnosed with MDD through psychological autopsies that included interviews with family or individuals best-acquainted with the deceased. More information is outlined in the respective studies 13,15,20,21 .…”

“…Samples from the anterior cingulate cortex, dorsolateral prefrontal cortex and the nucleus accumbens were profiled by Ramaker et al using RNA sequencing. We used data from the controls and those with MDD for a total of 48 subjects 21 . We re-processed the metadata and raw count files obtained from GSE73721 using R's BioJupies package 22 .…”

Integrative transcriptomics reveals sexually dimorphic microRNA control of the cholinergic/neurokine interface in schizophrenia and bipolar disorder

“…We used previously published data sets for several analyses. These comprise several cortical data sets in raw format (Affymetrix) from NCBI GEO: GSE35978 (Chen et al, 2013) (SCZ & BD), GSE53987 (Iwamoto et al, 2005) (SCZ & BD), GSE12649 (Lanz et al, 2015) (SCZ & BD), GSE17612 (Maycox et al, 2009) (SCZ), GSE21138 (Narayan et al, 2008) (SCZ), GSE5392 (Ryan et al, 2006) (BD); next-generation sequencing from NCBI GEO: GSE80655 (Ramaker et al, 2017), GSE106589 (Hoffman et al, 2017), GSE68559 (Webb et al, 2015), GSE96659 (Fontenot et al, 2017), GSE45642 (Li et al, 2013); data of DLPFC sequencing of 600 SCZ patients and controls was obtained from the Common Mind Consortium (http:// www.synapse.org/CMC).…”