Temporal dynamics and genetic control of transcription in the human prefrontal cortex

By C. Colantuoni, B. Lipska, Tianzhang Ye, T. Hyde, R. Tao, Jeffrey T. Leek, E. Colantuoni, A. Elkahloun, M. Herman, Daniel R. Weinberger and 1 more. Nature, 2011

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Previous investigations have combined transcriptional and genetic analyses in human cell lines, but few have applied these techniques to human neural tissue. To gain a global molecular perspective on the role of the human genome in cortical development, function and ageing, we explore the temporal dynamics and genetic control of transcription in human prefrontal cortex in an extensive series of post-mortem brains from fetal development through ageing. We discover a wave of gene expression changes occurring during fetal development which are reversed in early postnatal life. One half-century later in life, this pattern of reversals is mirrored in ageing and in neurodegeneration. Although we identify thousands of robust associations of individual genetic polymorphisms with gene expression, we also demonstrate that there is no association between the total extent of genetic differences between subjects and the global similarity of their transcriptional profiles. Hence, the human genome produces a consistent molecular architecture in the prefrontal cortex, despite millions of genetic differences across individuals and races. To enable further discovery, this entire data set is freely available (from Gene Expression Omnibus: accession GSE30272; and dbGaP: accession phs000417.v1.p1) and can also be interrogated via a biologist-friendly stand-alone application (http://www.libd.org/braincloud).

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/85e254c690926511d796a19f3b79e67f1e592411

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Citations in the corpus

Comprehensive functional genomic resource and integrative model for the human brain (2018, background): “Initial work focusing on brain-specific functional genomics has provided greater insight but could be enhanced with larger sample sizes (11, 12).”
Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia (2016, methodology): “Considering microarray-based eQTLs from the Harvard Brain Bank (27), BrainCloud (28), NIH (29), and the UK Brain Expression Consortium (UKBEC) (30), we estimated TT1 to be 0.75, 0.70, 0.79, and 0.93, respectively, indicating that our results captured most eQTLs found in other independent samples.”
Transcriptional Landscape of the Prenatal Human Brain (2014, methodology): “Specifically, gene expression was correlated with a template of frontal cortex samples (1) versus samples from other cortical areas (0), such that positive correlations correspond to rostral enrichment.”
The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016, neutral): “…of the Human Brain Transcriptome Recent genome-wide profiling studies of the developmental transcriptome of the human brain by several groups (Colantuoni et al., 2011; Ip et al., 2010; Jaffe et al., 2015; Johnson et al., 2009; Kang et al., 2011; Lambert et al., 2011; Mazin et al., 2013;…”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, methodology): “Development and ageing data are from BrainCloud 17 , regional data are from Braineac 16 and cell type expression data are from fluorescent-activated cell sorted transcriptomes from mouse cortex 162”
Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014, positive): “The transcriptome of the human brain changes dramatically across development and aging, with the largest gene expression changes occurring during fetal life, tapering into infancy 1,2 .”
A practical guide to linking brain-wide gene expression and neuroimaging data (2018, background): “The further advancement of developmental atlases of gene expression (Johnson et al., 2009; Colantuoni et al., 2011; Kang et al., 2011; Fertuzinhos et al., 2014; Bakken et al., 2016) will help to shed light on these dynamic processes.”
Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences (2016, background): “The transcriptome is cell-specific [72] Use of RNA data from cells/tissues not specific for the aims of a study may lead to misleading results Many RNA data sets are based on tissue homogenates Use RNA data obtained from source material relevant to the planned study Be aware of the possibility of co”