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

Abstract

Previous investigations have combined transcriptional and genetic analyses in human cell lines1,2,3, but few have applied these techniques to human neural tissue4,5,6,7,8. 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).

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Figure 1: A global view of the PFC transcriptome.
Figure 2: Reversal of fetal expression changes in infancy and ageing.
Figure 3: Genetic control of PFC gene expression.
Figure 4: The genome produces a consistent molecular architecture in PFC.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

The entire data set has been deposited in Gene Expression Omnibus under accession number GSE30272 and dbGaP under accession number phs000417.v1.p1 and can also be interrogated at http://www.libd.org/braincloud.

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Acknowledgements

We thank the families who donated tissue to make this study possible. We also thank the Offices of the Chief Medical Examiner of the District of Columbia, and of the Commonwealth of Virginia, Northern District, and the National Institute of Child and Health Development Brain and Tissue Bank for their collaboration. We thank R. McKay, N. Schork, F. McMahon and S. Zeger for their consultations on many issues, L. Marchionni for his assistance in assembling functional gene groups, as well as A. Deep-Soboslay, L. B. Bigelow, L. Wang, R. Buerlein, H. Choxi, V. Imamovic, Y. Snitkovsky, J. D. Paltan-Ortiz, J. Sirovatka, K. Becker, E. Lehrman and R. Vakkalanka for their contributions to this work.

Author information

Authors and Affiliations

Authors

Contributions

C.C., design of the study, data exploration and analysis, writing of manuscript; B.K.L., design of the study, preparation of samples, data analysis, writing of the paper; T.Y., data analysis and web tool construction; T.M.H., brain collection, diagnosis, dissection (primary); writing/editing and commentary on analysis (secondary); planning experiment (primary); R.T., genotyping; J.T.L., surrogate variable analysis methods and code, statistical consultation; E.A.C., linear model methods, statistical consultation; A.G.E., microarray experiments; M.M.H., tissue characterization and micro/macro neuropathology; D.R.W., design and planning of the study, writing of manuscript; J.E.K., experimental design, characterization of specimens, data analysis and writing/editing.

Corresponding author

Correspondence to Joel E. Kleinman.

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The authors declare no competing financial interests.

Supplementary information

Supplemental Figure 1

Part 1 uses the dimension reducing MDS representation from Fig. 1C in order to visualize the possible effects of additional covariates on the expression data. Parts 2 and 3 are 3-dimensional extensions of the 2-dimensional analyses presented in Fig. 1 C and D (MDS and PCA, respectively). (PDF 390 kb)

Supplemental Figure 2

Part 1 shows an analysis related to that in Fig. 4. While all genetic polymorphisms are used to look for an association with global transcriptional distance in Fig. 4, here only SNPs involved in significant SNP:expression associations are included in the genetic distance. Again no association is found. Part 2 depicts one negative and two additional positive controls for the analytical framework used in Fig. 4. (PDF 835 kb)

Supplementary Tables

This file contains Supplementary Tables 1-8 comprising: Table 1 Correlations of expression trajectories (slope of expression change across age) across the different age stages analyzed here, p-values, and N’s are also included, as are different filtering criteria; Table 2 List of all microarray probes showing BOTH significant change with age during fetal development AND significant change with age during infancy; Table 3 Full listing of functional gene groups enriched in each of the four patterns of expression described in Fig. 2A; Table 4 Individual gene details for synaptic and axonal genes highlighted in Fig. 2A; Table 5 Listing of individual genes found in the fetal:aging and fetal:AD expression trajectory reversals; Table 6 Listing of individual SNP:expression associations which reached genome-wide significance in the analysis including all subjects as well as analyses including only African American and only Caucasian subjects; Table 7 Demographic and tissue resource details for all subjects in the collection and Table 8 Taqman qPCR verification of microarray expression profiles. (ZIP 3414 kb)

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Colantuoni, C., Lipska, B., Ye, T. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011). https://doi.org/10.1038/nature10524

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