The cognitive phenotype of Down syndrome: Insights from intracellular network analysis
Tóm tắt
Down syndrome (DS) is caused by trisomy of chromosome 21. All individuals with DS exhibit some level of cognitive dysfunction. It is generally accepted that these abnormalities are a result of the upregulation of genes encoded by chromosome 21. Many chromosome 21 proteins are known or predicted to function in critical neurological processes, but typically they function as modulators of these processes, not as key regulators. Thus, upregulation in DS is expected to cause only modest perturbations of normal processes. Systematic approaches such as intracellular network construction and analysis have not been generally applied in DS research. Networks can be assembled from high-throughput experiments or by text-mining of experimental literature. We survey some new developments in constructing such networks, focusing on newly developed network analysis methodologies. We propose how these methods could be integrated with creation and manipulation of mouse models of DS to advance our understanding of the perturbed cell signaling pathways in DS. This understanding could lead to potential therapeutics.
Tài liệu tham khảo
Hassold TJ, Jacobs PA. Trisomy in man.Annu Rev Genet 18: 69–97, 1984.
Epstein CJ. Down’s syndrome (trisomy 21). In: Metabolic and molecular basis of inherited disease (Scriver, CA, Beaudet AL, Sly WS, Valle D, eds), pp 749–794. New York: McGraw Hill. 1995.
Nadel L. Down’s syndrome: a genetic disorder in biobehavioral perspective.Genes Brain Behav 2: 156–166, 2003.
Benavides-Piccione R, Ballesteros-Yanez I, de Lagran MM, Elston G, Estivill X, Fillat C, et al. On dendrites in Down’s syndrome and DS murine models: a spiny way to learn.Prog Neurobiol 74: 111–126, 2004.
Chapman RS, Hesketh LJ. Behavioral phenotype of individuals with Down’s syndrome.Ment Retard Dev Disabil Res Rev 6: 84–95, 2000.
Tolmie JL. Down’s syndrome and other autosomal trisomies. In: Principles and practices of medical genetics (Rimoin D, O’Connor JM, Pyeritz RE, Emergy AEH, eds), Ed 3, Ch 47, pp 925–971. Scotland: WB Saunders, Livingstone, 1997.
Law G, Byrne A, Buckley S. Language and memory development in children with Down’s syndrome at mainstream and special schools: a comparison.Educ Psychol 20:447-445, 7.
Brock J, Jarrold C. Serial order reconstruction in Down’s syndrome: evidence for a selective deficit in verbal short-term memory.J Child Psychol Psychiatry 46: 304–316, 2005.
Pennington BF, Moon J, Edgin J, Stedron J, Nadel L. The neuropsychology of Down’s syndrome: evidence for hippocampal dysfunction.Child Dev 74: 75–93, 2003.
Lott IT, Head E. Alzheimer disease and Down’s syndrome: factors in pathogenesis.Neurobiol Aging 26: 383–389, 2005.
Roizen NJ, Patterson D. Down’s syndrome.Lancet 361: 1281–1289, 2003.
Gardiner K, Fortna A, Bechtel L, Davisson MT. Mouse models of Down’s syndrome: how useful can they be? Comparison of the gene content of human chromosome 21 with orthologous mouse genomic regions.Gene 318: 137–147, 2003.
Delabar JM, Creau N, Sinet PM, Ritter O, Antonarakis SE, Burmeister M, et al. Report of the Fourth International Workshop on Human Chromosome 21.Genomics 18: 735–745, 1993.
Rahmani Z, Blouin JL, Creau-Goldberg N, Watkins PC, Mattei JF, Poissonnier M, et al. Down’s syndrome critical region around D21S55 on proximal 21q22.3.Am J Med Genet Suppl 7: 98–103, 1990.
Korenberg JR, Kawashima H, Pulst SM, Ikeuchi T, Ogasawara N, Yamamoto K, et al. Molecular definition of a region of chromosome 21 that causes features of the Down’s syndrome phenotype.Am J Hum Genet 47: 236–246, 1990.
Korenberg JR, Chen XN, Schipper R, Sun Z, Gonsky R, Gerwehr S, et al. Down’s syndrome phenotypes: the consequences of chromosomal imbalance.Proc Natl Acad Sci USA 91: 4997–5001, 1994.
Dauphinot L, Lyle R, Rivals I, Dang MT, Moldrich RX, Golfier G, et al. The cerebellar transcriptome during postnatal development of the TslCje mouse, a segmental trisomy model for Down’s syndrome.Hum Mol Genet 14: 373–384, 2005.
Kahlem P, Sultan M, Herwig R, Steinfath M, Balzereit D, Eppens B, et al. Transcript level alterations reflect gene dosage effects across multiple tissues in a mouse model of Down’s syndrome.Genome Res 14: 1258–1267, 2004.
Lyle R, Gehrig C, Neergaard-Henrichsen C, Deutsch S, Antonarakis SE. Gene expression from the aneuploid chromosome in a trisomy mouse model of Down’s syndrome.Genome Res 14: 1268–1274, 2004.
Amano K, Sago H, Uchikawa C, Suzuki T, Kotliarova SE, Nukina N, et al. Dosage-dependent over-expression of genes in the trisomic region of TslCje mouse model for Down’s syndrome.Hum Mol Genet 13: 1333–1340, 2004.
Mao R, Zielke CL, Zielke HR, Pevsner J. Global up-regulation of chromosome 21 gene expression in the developing Down’s syndrome brain.Genomics 81: 457–467, 2003.
Gardiner K. Gene-dosage effects in Down’s syndrome and trisomic mouse models.Genome Biol 5: 244, 2004.
Deutsch S, Lyle R, Dermitzakis ET, Attar H, Subrahmanyan L, Gehrig C, et al. Gene expression variation and expression quantitative trait mapping of human chromosome 21 genes.Hum Mol Genet 14: 3741–3749, 2005.
O’Leary DA, Pritchard MA, Xu D, Kola I, Hertzog PJ, Ristevski S. Tissue-specific overexpression of the HSA21 gene GABPal-pha: implications for DS.Biochim Biophys Acta 1739: 81–87, 2004.
Gardiner K, Costa AC. The proteins of human chromosome 21.Am J Med Genet (in press).
Nikolaienko O, Nguyen C, Crinc LS, Cios KJ, Gardiner K. Human chromosome 21/Down’s syndrome gene function and pathway database.Gene 364: 90–98, 2005.
Gardiner K, Davisson MT, Crnic LS. Building protein interaction maps for Down’s syndrome.Brief Funct Genomic Proteomic 3: 142–156, 2004.
Rothermel BA, Vega RB, Williams RS. The role of modulatory calcineurin-interacting proteins in calcineurin signaling.Trends Cardiovasc Med 13: 15–21, 2003.
DasGupta R, Kaykas A, Moon RT, Perrimon N. Functional genomic analysis of the Wnt-wingless signaling pathway.Science 308: 826–833, 2005.
Ozawa S, Kamiya H, Tsuzuki K. Glutamate receptors in the mammalian central nervous system.Prog Neurobiol 54: 581–618, 1998.
Cooper JD, Salehi A, Delcroix JD, Howe CL, Belichenko PV, Chua-Couzens J, et al. Failed retrograde transport of NGF in a mouse model of Down’s syndrome: reversal of cholinergic neurodegenerative phenotypes following NGF infusion.Proc Natl Acad Sci USA 98: 10439–10444, 2001.
Roper RJ, Baxter LL, Saran NG, Klinedinst DK, Beachy PA, Reeves RH. Defective cerebellar response to mitogenic Hedgehog signaling in Down’s syndrome mice.Proc Natl Acad Sci USA 03: 1452–1456, 2006.
Granholm AC, Ford KA, Hyde LA, Bimonte HA, Hunter CL, Nelson M, et al. Estrogen restores cognition and cholinergic phenotype in an animal model of Down’s syndrome.Physiol Behav 77: 371–385, 2002.
Clark S, Schwalbe J, Stasko MR, Yarowsky PJ, Costa ACS. Fluoxetine rescues deficient neurogenesis in hippocampus of the mouse model for Down’s syndrome Ts65Dn.Exp Neurol (in press).
Li W, Kurata H. A grid layout algorithm for automatic drawing of biochemical networks.Bioinformatics 21: 2036–2042, 2003.
Gardner TS, di Bernardo D, Lorenz D, Collins JJ. Inferring genetic networks and identifying compound mode of action via expression profiling.Science 301: 102–105, 2003.
Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc Natl Acad Sci USA 98: 4569–4574, 2001.
Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, et al. A protein interaction map ofDrosophila melanogaster.Science 302: 1727–1736, 2003.
Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, et al. A map of the interactome network of the metazoanC. elegans.Science 303: 540–543, 2004.
Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, et al. Towards a proteome-scale map of the human protein-protein interaction network.Nature 437: 1173–1178, 2005.
Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, et al. A human protein-protein interaction network: a resource for annotating the proteome.Cell 122: 957–968, 2005.
Nuhse TS, Stensballe A, Jensen ON, Peck SC. Large-scale analysis of in vivo phosphorylated membrane proteins by immobilized metal ion affinity chromatography and mass spectrometry.Mol Cell Proteomics 2: 1234–1243, 2003.
Irish JM, Hovland R, Krutzik PO, Perez OD, Bruserud O, Gjertsen BT, et al. Single cell profiling of potentiated phospho-protein networks in cancer cells.Cell 118: 217–228, 2004.
Ptacek J, Devgan G, Michaud G, Zhu H, Zhu X, Fasolo J, et al. Global analysis of protein phosphorylation in yeast.Nature 438: 679–684, 2005.
Jones RB, Gordus A, Krall JA, MacBeath G. A quantitative protein interaction network for the ErbB receptors using protein microarrays.Nature 439: 168–174, 2006.
Friedman C, Kra P, Yu H, Krauthammer M, Rzhetsky A. GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles.Bioinformatics 17: S74-S82, 2002.
Marcotte EM, Xenarios I, Eisenberg D. Mining literature for protein-protein interactions.Bioinformatics 17: 359–363, 2001.
Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio: the analysis and navigation of molecular networks.Bioinformatics 19: 2155–2157, 2004.
Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I. Extracting human protein interactions from MEDLINE using a full-sentence parser.Bioinformatics 20: 604–611, 2004.
Mishra GR, Suresh M, Kumaran K, Kannabiran N, Suresh S, Bala P, et al. Human protein reference database: 2006 update.Nucleic Acids Res 34: D411-D414, 2006.
Gough NR. Science’s signal transduction knowledge environment: the connections maps database.Ann NY Acad Sci 971: 585–587, 2002.
Ma’ayan A, Jenkins SL, Neves S, Hasseldine A, Grace E, Dubin-Thaler B, et al. Formation of regulatory patterns during signal propagation in a mammalian cellular network.Science 309: 1078, 2005.
von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, et al. Comparative assessment of large-scale data sets of protein-protein interactions.Nature 417: 399–403, 2002.
Hucka M. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.Bioinformatics 19: 524–531, 2003.
Sivakumaran S, Hariharaputran S, Mishra J, Bhalla US. The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks.Bioinformatics 19: 408–415, 2003.
Bader GD, Betel D, Hogue CW. BIND: the Biomolecular Interaction Network Database.Nucleic Acids Res 31: 248–250, 2003.
Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G. MINT: a Molecular INTeraction database.FEBS Lett 513: 135–140, 2002.
Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, et al. IntAct: an open source molecular interaction database.Nucleic Acids Res 32: D452-D455, 2004.
Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: The Database of Interacting Proteins: 2001 update.Nucleic Acids Res 29: 239–241, 2001.
Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update.Nucleic Acids Res 32: D449-D451, 2004.
Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at GenomeNet.Nucleic Acids Res 30: 42–46, 2002.
Choudhary J, Grant SG. Proteomics in postgenomic neuroscience: the end of the beginning.Nat Neurosci 7: 440–445, 2004.
Hermjakob H, Montecchi-Palazzi L, Bader G, Wojcik J, Salwinski L, Ceol A, et al. The HUPO PSI’s molecular interaction format-a community standard for the representation of protein interaction data.Nat Biotechnol 22: 177–183, 2004.
Bader GD, Cary MP, Sander C. Pathguide: a pathway resource list.Nucleic Acids Res 34: D504-D506, 2006.
Gerdes AM, Harder M, Bonnevie-Nielsen V. Increased IFN-α-induced sensitivity but reduced reactivity of 2′,5′-oligoadenylate synthetase (2,5AS) in trisomy 21 blood lymphocytes.Clin Exp Immunol 93: 93–96, 1993.
Albert R. Scale-free networks in cell biology.J Cell Sci 118: 4947–4957, 2005.
Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks.Nature 393: 440–442, 1998.
Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks.Nature 411: 41–42, 2001.
Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network motifs: simple building blocks of complex networks.Science 298: 824–827, 2002.
Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network.Nature 430: 88–93, 2004.
Borneman AR, Leigh-Bell JA, Yu H, Bertone P, Gerstein M, Snyder M. Target hub proteins serve as master regulators of development in yeast.J Cataract Refract Surg 31: 2051–2054. 2005.
Ma’ayan A, Blitzer RD, Iyengar R. Toward predictive models of mammalian cells.Annu Rev Biophys Biomol Struct 34: 319–349. 2005.
Bornholdt S. Systems biology: less is more in modeling large genetic networks.Science 310: 449–451, 2005.
Tyson JJ, Chen K, Novak B. Network dynamics and cell physiology.Nat Rev Mol Cell Biol 2: 908–916, 2002.
Kuipers B. Qualitative reasoning: modeling and simulation with incomplete knowledge. Cambridge, MA: MIT Press, 1994.
King RD, Garrett SM, Coghill GM. On the use of qualitative reasoning to simulate and identify metabolic pathways.Bioinformatics 21: 2017–2026, 2005.
Albert R, Othmer HG. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes inDrosophila melanogaster, J Theor Biol 223: 1–18, 2003.
Grefenstette J, Kim S, Kauffman S. An analysis of the class of gene regulatory functions implied by a biochemical model.Biosystems (10.1016/j.biosystems. 2005.09.009), 26 December 2005.
Oliveira JS, Jones-Oliveira JB, Dixon DA, Bailey CG, Gull DW. Hyperdigraph-theoretic analysis of the EGFR signaling network: initial steps leading to GTP:Ras complex formation.J Comput Biol 11: 812–842, 2005.
Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways.Science 283: 381–387, 1999.
Goss PJ, Peccoud J. Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets.Proc Natl Acad Sci USA 95: 6750–6755, 1998.
Holland JH. Exploring the evolution of complexity in signaling networks.Complexity 7: 34, 2002.
Yang D, Zakharkin SO, Page GP, Brand JP, Edwards JW, Bartolucci AA, et al. Applications of Bayesian statistical methods in microarray data analysis.Am J Pharmacogenomics 4: 53–62, 2004.
Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data.J Comput Biol 7: 601–620. 2001.
Sachs K, Gifford D, Jaakkola T, Sorger P, Lauffenburger DA. Bayesian network approach to cell signaling pathway modeling.Sci STKE 2002: PE38, 2002.
Woolf PJ, Prudhomme W, Daheron L, Daley GQ, Lauffenburger DA. Bayesian analysis of signaling networks governing embryonic stem cell fate decisions.Bioinformatics 21: 741–753, 2005.
Prudhomme W, Daley GQ, Zandstra P, Lauffenburger DA. Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling.Proc Natl Acad Sci USA 101: 2900–2905, 2004.
Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data.Science 308: 523–529, 2005.
Dean T, Kanazawa K. A model for reasoning about persistence and causation.Comput Intell 5: 142–150, 1989.
Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.Bioinformatics 21: 71–79, 2005.
Murphy K, Mian S. Modeling gene expression data using dynamic Bayesian networks. Technical report, Computer Science Division, University of California, Berkeley, 1999.
Segal E, Pe’er D, Regev A, Koller D, Friedman N. Learning Module Networks.J Machine Learn Res 6: 557–588, 2005.
Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis.Science 310: 1646–1653, 2005.
Ma’ayan A, Iyengar R. From components to regulatory motifs in signalling networks.Brief Funct Genomic Proteomic 5: 57–61, 2006.
Kurata H, Masaki K, Sumida Y, Iwasaki R. CADLIVE dynamic simulator: direct link of biochemical networks to dynamic models.Genome Res 15: 590–600, 2005.
D’haeseleer P, Liang S, Somogyi R. Genetic network inference: from co-expression clustering to reverse engineering.Bioinformatics 16: 707–726, 2000.
Gat-Viks I, Tanay A, Shamir R. Modeling and analysis of heterogeneous regulation in biological networks.J Comput Biol 11: 1034–1049, 2005.
Imoto S, Iguchi T, Goto T, Tashiro K, Kuhara S, Miyano S. Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.J Bioinform Comput Biol 2: 77–98, 2003.
Costa AC, Walsh K, Davisson MT. Motor dysfunction in a mouse model for Down’s syndrome.Physiol Behav 68: 211–220, 1999.
Cmic LS, Pennington BF. Down’s syndrome: neuropsychology and animal models.Progr Infancy Res 1: 69–111, 2000.
O’Doherty A, Ruf S, Mulligan C, Hildreth V, Ellington ML, Cooke S, et al. An aneuploid mouse strain carrying human chromosome 21 with Down’s syndrome phenotypes.Science 309: 2033–2037, 2005.
Sago H, Carlson EJ, Smith DJ, Kilbridge J, Rubin EM, Mobley WC, et al. TslCje, a partial trisomy 16 mouse model for Down’s syndrome, exhibits learning and behavioral abnormalities.Proc Natl Acad Sci USA 95: 6256–6261, 1998.
Olson LE, Richtsmeier JT, Leszl J, Reeves RH. A chromosome 21 critical region does not cause specific Down’s syndrome phenotypes.Science 306: 687–690, 2004.
Ginsberg SD, Che S, Counts SE, Mufson, EJ. Single cell gene expression profiling in Alzheimer’s disease.NeuroRx, 3: 302–317, 2006.
Miller RM, Federoff HJ. Microarrays in Parkinson’s disease: a systematic approach.NeuroRx 3: 318–325, 2006.
Uhl GR. Molecular genetics of addiction vulnerability.NeuroRx. 3: 295–301, 2006.