Neuroscience in the era of functional genomics and systems biology (original) (raw)
Coppola, G. & Geschwind, D. H. Microarrays and the microscope: balancing throughput with resolution. J. Physiol. (Lond.)575, 353–359 (2006). ArticleCAS Google Scholar
Nelson, S. B., Hempel, C. & Sugino, K. Probing the transcriptome of neuronal cell types. Curr. Opin. Neurobiol.16, 571–576 (2006). ArticleCAS Google Scholar
Mirnics, K. & Pevsner, J. Progress in the use of microarray technology to study the neurobiology of disease. Nature Neurosci.7, 434–439 (2004). ArticleCAS Google Scholar
Geschwind, D. H. Mice, microarrays, and the genetic diversity of the brain. Proc. Natl Acad. Sci. USA97, 10676–10678 (2000). ArticleADSCAS Google Scholar
Hood, L., Heath, J. R., Phelps, M. E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science306, 640–643 (2004). ArticleADSCAS Google Scholar
Arlotta, P. et al. Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo. Neuron45, 207–221 (2005). ArticleCAS Google Scholar
Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci.28, 264–278 (2008). ArticleCAS Google Scholar
Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell135, 738–748 (2008). ArticleCAS Google Scholar
Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H. & Yang, X. W. FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains. Nature Neurosci.9, 443–452 (2006). ArticleCAS Google Scholar
Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nature Neurosci.9, 99–107 (2006). ArticleCAS Google Scholar
Emes, R. D. et al. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nature Neurosci.11, 799–806 (2008). In this paper, a combination of genomics and proteomics is used to identify synaptic proteins that have changed with evolution and study how they might relate to brain anatomy and function. ArticleCAS Google Scholar
Nagasaka, Y. et al. A unique gene expression signature discriminates familial Alzheimer's disease mutation carriers from their wild-type siblings. Proc. Natl Acad. Sci. USA102, 14854–14859 (2005). ArticleADSCAS Google Scholar
Nishimura, Y. et al. Genome-wide expression profiling of lymphoblastoid cell lines distinguishes different forms of autism and reveals shared pathways. Hum. Mol. Genet.16, 1682–1698 (2007). ArticleCAS Google Scholar
Karsten, S. L. et al. A genomic screen for modifiers of tauopathy identifies puromycin-sensitive aminopeptidase as an inhibitor of tau-induced neurodegeneration. Neuron51, 549–560 (2006). ArticleCAS Google Scholar
Lim, J. et al. A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell125, 801–814 (2006). ArticleCAS Google Scholar
Mirnics, K., Middleton, F. A., Marquez, A., Lewis, D. A. & Levitt, P. Molecular characterization of schizophrenia viewed by microarray analysis of gene expression in prefrontal cortex. Neuron28, 53–67 (2000). This paper was the first to demonstrate the utility of microarray analysis to uncover new genes and properties associated with neuropsychiatric disease. ArticleCAS Google Scholar
Wang, J., Williams, R. W. & Manly, K. F. WebQTL: web-based complex trait analysis. Neuroinformatics1, 299–308 (2003). Article Google Scholar
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature445, 168–176 (2007). ArticleADSCAS Google Scholar
Cirelli, C., Gutierrez, C. M. & Tononi, G. Extensive and divergent effects of sleep and wakefulness on brain gene expression. Neuron41, 35–43 (2004). ArticleCAS Google Scholar
Sandberg, R. et al. Regional and strain-specific gene expression mapping in the adult mouse brain. Proc. Natl Acad. Sci. USA97, 11038–11043 (2000). ArticleADSCAS Google Scholar
Geschwind, D. H. Sharing gene expression data: an array of options. Nature Rev. Neurosci.2, 435–438 (2001). ArticleCAS Google Scholar
Miller, J. A., Oldham, M. C. & Geschwind, D. H. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J. Neurosci.28, 1410–1420 (2008). ArticleCAS Google Scholar
Oldham, M. C., Horvath, S. & Geschwind, D. H. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc. Natl Acad. Sci. USA103, 17973–17978 (2006). ArticleADSCAS Google Scholar
Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nature Neurosci.11, 1271–1282 (2008). This paper demonstrates that the brain transcriptome in its normal state has a reproducible structure that can be used to guide discovery. ArticleCAS Google Scholar
Winden, K. et al. The organization of the transcriptional network in specific neuronal classes. Mol. Syst. Biol.5, 291 (2009). Article Google Scholar
Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science302, 249–255 (2003). ArticleADSCAS Google Scholar
Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res.14, 1085–1094 (2004). ArticleCAS Google Scholar
Thompson, C. L. et al. Genomic anatomy of the hippocampus. Neuron60, 1010–1021 (2008). This paper is an example of the power of using tools such as the ABA as a reference together with other wet-lab tools to uncover new neuroanatomical connections, in this case new hippocampal subdivisions. ArticleCAS Google Scholar
Gong, S. et al. A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature425, 917–925 (2003). ArticleADSCAS Google Scholar
Okaty, B. W., Miller, M. N., Sugino, K., Hempel, C. M. & Nelson, S. B. Transcriptional and electrophysiological maturation of neocortical fast-spiking GABAergic interneurons. J. Neurosci.29, 7040–7052 (2009). ArticleCAS Google Scholar
Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell135, 749–762 (2008). ArticleCAS Google Scholar
Cowley, M. J. et al. Intra- and inter-individual genetic differences in gene expression. Mamm. Genome20, 281–295 (2009). ArticleCAS Google Scholar
Nadler, J. J. et al. Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics174, 1229–1236 (2006). ArticleCAS Google Scholar
Johnson, M. B. et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron62, 494–509 (2009). ArticleCAS Google Scholar
Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell125, 173–186 (2006). ArticleCAS Google Scholar
Brunner, E. et al. A high-quality catalog of the Drosophila melanogaster proteome. Nature Biotechnol.25, 576–583 (2007). ArticleCAS Google Scholar
Fernandez, E. et al. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins. Mol. Syst. Biol.5, 269 (2009). Article Google Scholar
Anderson, C. N. & Grant, S. G. High throughput protein expression screening in the nervous system — needs and limitations. J. Physiol. (Lond.)575, 367–372 (2006). ArticleCAS Google Scholar
Husi, H., Ward, M. A., Choudhary, J. S., Blackstock, W. P. & Grant, S. G. Proteomic analysis of NMDA receptor–adhesion protein signaling complexes. Nature Neurosci.3, 661–669 (2000). ArticleCAS Google Scholar
Takamori, S. et al. Molecular anatomy of a trafficking organelle. Cell127, 831–846 (2006). ArticleCAS Google Scholar
Trinidad, J. C. et al. Quantitative analysis of synaptic phosphorylation and protein expression. Mol. Cell. Proteomics7, 684–696 (2008). ArticleCAS Google Scholar
Croning, M. D., Marshall, M. C., McLaren, P., Armstrong, J. D. & Grant, S. G. G2Cdb: the Genes to Cognition database. Nucleic Acids Res.37, D846–D851 (2009). ArticleCAS Google Scholar
Magdaleno, S. et al. BGEM: an in situ hybridization database of gene expression in the embryonic and adult mouse nervous system. PLoS Biol.4, e86 (2006). Article Google Scholar
Zapala, M. A. et al. Adult mouse brain gene expression patterns bear an embryologic imprint. Proc. Natl Acad. Sci. USA102, 10357–10362 (2005). ArticleADSCAS Google Scholar
Valor, L. M., Charlesworth, P., Humphreys, L., Anderson, C. N. & Grant, S. G. Network activity-independent coordinated gene expression program for synapse assembly. Proc. Natl Acad. Sci. USA104, 4658–4663 (2007). This paper exemplifies the combination of multiple layers of functional data — in this case neuronal activity recordings and morphological measurements — with gene expression data to directly uncover how changes in function and gene expression relate to each other over time. ArticleADSCAS Google Scholar
Cheung, V. G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature437, 1365–1369 (2005). ArticleADSCAS Google Scholar
Chesler, E. J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genet.37, 233–242 (2005). This paper provides an early example of combining data across multiple levels of function, factoring genotypes, phenotypes and gene expression in mouse to identify systems-level interactions. ArticleCAS Google Scholar
Hovatta, I. et al. DNA variation and brain region-specific expression profiles exhibit different relationships between inbred mouse strains: implications for eQTL mapping studies. Genome Biol.8, R25 (2007). Article Google Scholar
Ghazalpour, A. et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet.2, e130 (2006). Article Google Scholar
Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature452, 429–435 (2008). ArticleADSCAS Google Scholar
van der Zwaag, B. et al. Gene-network analysis identifies susceptibility genes related to glycobiology in autism. PLoS ONE4, e5324 (2009). ArticleADS Google Scholar
Webster, J. A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am. J. Hum. Genet.84, 445–458 (2009). ArticleCAS Google Scholar
Myers, A. J. et al. A survey of genetic human cortical gene expression. Nature Genet.39, 1494–1499 (2007). ArticleCAS Google Scholar
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res.18, 1509–1517 (2008). ArticleCAS Google Scholar
Liu, F. et al. Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates. BMC Genomics8, 153 (2007). ArticleCAS Google Scholar
Preuss, T. M., Caceres, M., Oldham, M. C. & Geschwind, D. H. Human brain evolution: insights from microarrays. Nature Rev. Genet.5, 850–860 (2004). ArticleCAS Google Scholar
Barabási, A. L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nature Rev. Genet.5, 101–113 (2004). Article Google Scholar
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A.-L. The large-scale organization of metabolic networks. Nature407, 651–654 (2000). This paper is a seminal demonstration of the higher-order organization of metabolism across phylogeny. ArticleADSCAS Google Scholar
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol.4, 17 (2005). ArticleMathSciNet Google Scholar
Khaitovich, P. et al. A neutral model of transcriptome evolution. PLoS Biol.2, e132 (2004). Article Google Scholar
Lam, Y. C. et al. ATAXIN-1 interacts with the repressor Capicua in its native complex to cause SCA1 neuropathology. Cell127, 1335–1347 (2006). ArticleCAS Google Scholar
Canterini, S., Bosco, A., De Matteis, V., Mangia, F. & Fiorenza, M. T. THG-1pit moves to nucleus at the onset of cerebellar granule neurons apoptosis. Mol. Cell. Neurosci.40, 249–257 (2009). ArticleCAS Google Scholar
Bilder, R. M. et al. Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience doi:10.1016/j.neuroscience.2009.01.027 (20 January 2009).
Rzhetsky, A. et al. GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J. Biomed. Inform.37, 43–53 (2004). ArticleCAS Google Scholar
Rodriguez-Esteban, R., Iossifov, I. & Rzhetsky, A. Imitating manual curation of text-mined facts in biomedicine. PLoS Comput. Biol.2, e118 (2006). ArticleADS Google Scholar
Iossifov, I., Zheng, T., Baron, M., Gilliam, T. C. & Rzhetsky, A. Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network. Genome Res.18, 1150–1162 (2008). ArticleCAS Google Scholar
Rzhetsky, A., Wajngurt, D., Park, N. & Zheng, T. Probing genetic overlap among complex human phenotypes. Proc. Natl Acad. Sci. USA104, 11694–11699 (2007). This paper demonstrates that with enough phenotypic information it is possible to build modelling networks that predict the underlying genetic overlap among neuropsychiatric diseases with previously distinct aetiologies. ArticleADSCAS Google Scholar
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci.10, 186–198 (2009). ArticleCAS Google Scholar
Honey, C. J., Kotter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA104, 10240–10245 (2007). ArticleADSCAS Google Scholar
Rilling, J. K. et al. The evolution of the arcuate fasciculus revealed with comparative DTI. Nature Neurosci.11, 426–428 (2008). ArticleCAS Google Scholar
Mischel, P. S., Cloughesy, T. F. & Nelson, S. F. DNA-microarray analysis of brain cancer: molecular classification for therapy. Nature Rev. Neurosci.5, 782–792 (2004). ArticleCAS Google Scholar
Tang, Y., Lu, A., Aronow, B. J. & Sharp, F. R. Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of disease. Ann. Neurol.50, 699–707 (2001). ArticleCAS Google Scholar
Thomas, E. A. et al. The HDAC inhibitor 4b ameliorates the disease phenotype and transcriptional abnormalities in Huntington's disease transgenic mice. Proc. Natl Acad. Sci. USA105, 15564–15569 (2008). ArticleADSCAS Google Scholar
Day, A., Carlson, M. R., Dong, J., O'Connor, B. D. & Nelson, S. F. Celsius: a community resource for Affymetrix microarray data. Genome Biol.8, R112 (2007).
McDowall, M. D., Scott, M. S. & Barton, G. J. PIPs: human protein–protein interaction prediction database. Nucleic Acids Res.37, D651–D656 (2009). ArticleCAS Google Scholar
Kamburov, A., Wierling, C., Lehrach, H. & Herwig, R. ConsensusPathDB — a database for integrating human functional interaction networks. Nucleic Acids Res.37, D623–D628 (2009). ArticleCAS Google Scholar
Chatr-Aryamontri, A., Zanzoni, A., Ceol, A. & Cesareni, G. Searching the protein interaction space through the MINT database. Methods Mol. Biol.484, 305–317 (2008). ArticleCAS Google Scholar
Mathivanan, S. et al. An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics7 (suppl. 5), S19 (2006). Article Google Scholar
Foster, L. J. et al. A mammalian organelle map by protein correlation profiling. Cell125, 187–199 (2006). ArticleCAS Google Scholar
Mathivanan, S. et al. Human Proteinpedia enables sharing of human protein data. Nature Biotechnol.26, 164–167 (2008). ArticleCAS Google Scholar
Linsen, S. E. et al. Limitations and possibilities of small RNA digital gene expression profiling. Nature Methods6, 474–476 (2009). ArticleCAS Google Scholar
Passalacqua, K. D. et al. Structure and complexity of a bacterial transcriptome. J. Bacteriol.191, 3203–3211 (2009). ArticleCAS Google Scholar