全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Systems Biology as a Comparative Approach to Understand Complex Gene Expression in Neurological Diseases

DOI: 10.3390/bs3020253

Keywords: systems biology, neurological diseases, gene expression, autism, autism spectrum disorders, network analysis, protein-protein interactions, translational bioinformatics, behavioral diagnosis

Full-Text   Cite this paper   Add to My Lib

Abstract:

Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.

References

[1]  Wheelock, C.E.; Wheelock, A.M.; Kawashima, S.; Diez, D.; Kanehisa, M.; van Erk, M.; Kleemann, R.; Haeggstr?m, J.Z.; Goto, S. Systems Biology approaches and pathway tools for investigating cardiovascular disease. Mol. BioSyst. 2009, 5, 588–602.
[2]  Mooney, S.D.; Krishnan, V.G.; Evani, U.S. Bioinformatic tools for identifying disease gene and SNP candidates. Meth. Mol. Biol. 2010, 628, 307–319, doi:10.1007/978-1-60327-367-1_17.
[3]  Human Genome Project. Available online: http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml (accessed on 8 May 2013).
[4]  Weston, A.D.; Hood, L. Systems Biology, Proteomics, and the Future of Health Care: Toward Predictive, Preventative and Personalized Medicine. J. Proteome Res. 2004, 3, 179–196, doi:10.1021/pr0499693.
[5]  Ingenuity Systems. Available online: http://www.ingenuity.com/products/ipa (accessed on 8 May 2013).
[6]  MetaCore GeneGo. Available online: http://thomsonreuters.com/products_services/science/science_products/a-z/metacore/ (accessed on 8 May 2013).
[7]  von Mering, C.; Jensen, L.J.; Snel, B.; Hooper, S.D.; Krupp, M.; Foglierini, M.; Jouffre, N.; Huynen, M.A.; Bork, P. STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucl. Acids Res. 2005, 33, D433–D437.
[8]  Cline, M.S.; Smoot, M.; Cerami, E.; Kuchinsky, A.; Landys, N.; Workman, C.; Christmas, R.; Avila-Campilo, I.; Creech, M.; Gross, B.; et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2007, 2, 2366–2382, doi:10.1038/nprot.2007.324.
[9]  Wang, L.; Khankhanian, P.; Baranzini, S.E.; Mousavi, P. iCTNet: A Cytoscape plugin to produce and analyze integrative complex traits networks. BMC Bioinformatics 2011, 12, 380.
[10]  Merico, D.; Gfeller, D.; Bader, G.D. How to visually interpret biological data using networks. Nat. Biotechnol. 2009, 10, 921–924.
[11]  Sauer, U.; Heinemann, M.; Zamboni, N. Genetics: Getting closer to the whole picture. Science 2007, 316, 550–551, doi:10.1126/science.1142502.
[12]  Arrell, D.K.; Terzik, A. Network systems biology for drug discovery. Nature 2010, 88, 120–125.
[13]  Barabási, A.L.; Oltvai, Z. Network biology: Understanding the cell’s functional organization. Nat. Rev. Genet. 2004, 5, 101–113, doi:10.1038/nrg1272.
[14]  Barabási, A.L.; Albert, R. Emergence of scaling in random networks. Science 1999, 286, 509–512, doi:10.1126/science.286.5439.509.
[15]  Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44–57.
[16]  Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucl. Acids Res. 2009, 37, 1–13.
[17]  The Gene Ontology Consortium. Gene ontology: Tool for the unification of biology. Nat. Genet. 2000, 25, 25–29.
[18]  Noorbakhsh, F.; Overall, C.M.; Power, C. Deciphering complex mechanisms in neurodegenerative diseases: The advent of systems biology. Trends Neurosci. 2009, 32, 88–100, doi:10.1016/j.tins.2008.10.003.
[19]  Villoslada, P.; Steinman, L.; Baranzini, S.E. Systems biology and its application to the understanding of neurological diseases. Ann. Neurol. 2009, 65, 124–139, doi:10.1002/ana.21634.
[20]  Villoslada, P.; Baranzini, S. Data integration and systems biology approaches for biomarker discovery: Challenges and opportunities for multiple sclerosis. J. Neuroimmunol. 2012, 248, 58–65, doi:10.1016/j.jneuroim.2012.01.001.
[21]  Broderick, G.; Craddock, T.J.A. Systems biology of complex symptom profile: Capturing interactivity across behavior, brain and immune regulation. Brain Behav. Immun. 2013, 29, 1–8, doi:10.1016/j.bbi.2012.09.008.
[22]  Palacios, R.; Goni, J.; Martinez-Forero, I.; Iranzo, J.; Sepulcre, J.; Melero, I.; Villoslada, P. A network analysis of the human T-cell activation gene network identifies JAGGED1 as a therapeutic target for autoimmune diseases. PLoS One 2007, 2, e1222.
[23]  Saez-Rodriguez, J.; Alexopoulos, L.G.; Epperlein, J.; Samaga, R.; Lauffenburger, D.A.; Klamt, S.; Sorger, P.K. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 2009, 5, 331.
[24]  Morris, M.K.; Saez-Rodriguez, J.; Sorger, P.K.; Lauffenburger, D.A. Logic-based models for the analysis of cell signaling networks. Biochemistry 2010, 49, 3216–3224.
[25]  Broome, T.M.; Coleman, R.A. A mathematical model of cell death in multiple sclerosis. J. Neurosci. Methods 2011, 201, 420–425, doi:10.1016/j.jneumeth.2011.08.008.
[26]  Velez de Mendizabal, N.; Carneiro, J.; Sole, R.V.; Goni, J.; Bragard, J.; Martinez-Forero, I.; Martinez-Pasamar, S.; Sepulcre, J.; Torrealdea, J.; Bagnato, F.; et al. Modelling the efector-regulatory T-cell cross-regulation reveals the intrinsic character of relapses in Multiple Sclerosis. BMC Syst. Biol. 2011, 5, 114.
[27]  Go?i, J.; Esteban, F.J.; Vélez de Mendizábal, N.; Sepulcre, J.; Ardanza-Trevijano, S.; Agirrezabal, I.; Villoslada, P. A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Syst. Biol. 2008, 2, 52.
[28]  Sebat, J.; Lakshmi, B.; Malhotra, D.; Troge, J.; Lese-Martin, C.; Walsh, T.; Yamrom, B.; Yoon, S.; Krasnitz, A.; Kendall, J.; et al. Strong association of De Novo copy number mutations with autism. Science 2007, 316, 445–449.
[29]  Ecker, C.; Spooren, W.; Murphy, D.G.M. Translational approaches to the biology of Autism: False dawn or a new era? Mol. Psychiatry 2013, 18, 435–442, doi:10.1038/mp.2012.102.
[30]  Wall, D.P.; Esteban, F.J.; DeLuca, T.F.; Huyck, M.; Monaghan, T.; Velez de Mendizabal, N.; Go?í, J.; Kohane, I.S. Comparative analysis of neurological disorders focuses genome-wide search for autism genes. Genomics 2009, 93, 120–129, doi:10.1016/j.ygeno.2008.09.015.
[31]  GeneCards ?. Available online: http://genecards.org/index.shtml (accessed on 8 May 2013).
[32]  OMIM ?. Available online: http://www.ncbi.nlm.nih.gov/omim (accessed on 8 May 2013).
[33]  Gregg, J.P.; Lit, L.; Baron, C.A.; Hertz-Picciotto, I.; Walker, W.; Davis, R.A.; Croen, L.A.; Ozonoff, S.; Hansen, R.; Pessah, I.N.; et al. Gene expressionchanges in children with autism. Genomics 2008, 91, 22–29.
[34]  Gene Expression Omnibus. Available online: http://www.ncbi.nlm.nih.gov/geo/query/acc=GSE6575 (accessed on 8 May 2013).
[35]  Nelson, T.H.; Jung, J.Y.; Deluca, T.F.; Hinebaugh, B.K.; St Gabriel, K.C.; Wall, D.P. Autworks: A cross-disease network biology application for Autism and related disorders. BMC Med. Genomics 2012, 5, 56, doi:10.1186/1755-8794-5-56.
[36]  Wall, D.P.; Pivovarov, R.; Tong, M.; Yung, J.Y.; Fusaro, V.A.; DeLuca, T.F.; Tonellato, P.J. Genotator: A disease-agnostic tool for genetic annotation of disease. BMC Med. Genomics 2010, 3, 50, doi:10.1186/1755-8794-3-50.
[37]  Esteban, F.J.; Wall, D.P. Using game theory to detect genes involved in Autism Spectrum Disorders. TOP 2011, 19, 121–129, doi:10.1007/s11750-009-0111-6.
[38]  Wall, D.P.; Kosmicki, J.; Deluca, T.F.; Harstad, E.; Fusaro, V.A. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl. Psychiatry 2012, 2, e100, doi:10.1038/tp.2012.10.
[39]  Lord, C.; Risi, S.; Lambrecht, L.; Cook, E.H., Jr; Leventhal, B.L.; DiLavore, P.C.; Pickles, A.; Rutter, M. The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 2000, 30, 205–223, doi:10.1023/A:1005592401947.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413