JBM  Vol.3 No.9 , September 2015
A Microarray Analysis of Parkinson’s Disease: New Clues and Evaluation
ABSTRACT

Parkinson’s disease (PD) is complex and most likely results from an unknown combination of genetic and environmental factors. Here, we defined discrete genes (DGs) in a microarray analysis and found that the percentage of DGs versus all analyzable genes correlated with PD progression. Furthermore, this new parameter was also easily used to evaluate the therapeutic effect of high- frequency electro-acupuncture (EA), thus improving symptoms of PD model rats.


Cite this paper
Huo, L. , Liang, X. , He, Y. and Wang, X. (2015) A Microarray Analysis of Parkinson’s Disease: New Clues and Evaluation. Journal of Biosciences and Medicines, 3, 55-60. doi: 10.4236/jbm.2015.39009.
References
[1]   Zhu, Y., Shen, X. and Pan, W. (2009) Network-Based Support Vector Machine for Classification of Microarray Samples. BMC Bioinformatics, 10, S21. http://dx.doi.org/10.1186/1471-2105-10-S1-S21

[2]   Henney, A. and Superti-Furga, G. (2008) A Network Solution. Nature, 455, 730-731. http://dx.doi.org/10.1038/455730a

[3]   Yunger, S., Rosenfeld, L., Garini, Y. and Shav-Tal, Y. (2010) Single-Allele Analysis of Transcription Kinetics in Living Mammalian cells. Nature Methods, 7, 631-633. http://dx.doi.org/10.1038/nmeth.1482

[4]   Braak, H., Del Tredici, K., R?b, U., de Vos, R.A., Jansen Steur, E.N. and Braak, E. (2003) Staging of Brain Pathology Related to Sporadic Parkinson’s Disease. Neurobiology of Aging, 24, 197-211. http://dx.doi.org/10.1016/S0197-4580(02)00065-9

[5]   Dennis Jr., G., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., et al. (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Bi-ology, 4, 3. http://dx.doi.org/10.1186/gb-2003-4-5-p3

[6]   Huang, D.W., Sherman, B.T. and Lempicki, R.A. (2009) Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources. Nature Protocols, 4, 44-57. http://dx.doi.org/10.1038/nprot.2008.211

[7]   Moran, L.B., Croisier, E., Duke, D.C., Kalaitzakis, M.E., Ron-caroli, F., Deprez, M., et al. (2007) Analysis of Alpha- Synuclein, Dopamine and Parkin Pathways in Neuropathologi-cally Confirmed Parkinsonian Nigra. Acta Neuropathologica, 113, 253-263. http://dx.doi.org/10.1007/s00401-006-0181-6

[8]   Huo, L.R., Liang, X.B., Li, B., Liang, J.T., He, Y., Jia, Y.J., et al. (2012) The Cortical and Striatal Gene Expression Profile of 100?hz Electroacupuncture Treatment in 6-Hydroxydopamine-Induced Parkinson’s Disease Model. Evid Based Complement Alternat Med, 2012, 908439. http://dx.doi.org/10.1155/2012/908439

[9]   Abbott, A. (2002) The Society of Proteins. Nature, 417, 894-896. http://dx.doi.org/10.1038/417894a

[10]   Cristian, A., Katz, M., Cutrone, E. and Walker, R.H. (2005) Evaluation of Acupuncture in the Treatment of Parkinson’s Disease: A Double-Blind Pilot Study. Mov Disord, 20, 1185-1188. http://dx.doi.org/10.1002/mds.20503

[11]   Shulman, L.M., Wen, X., Weiner, W.J., Bateman, D., Minagar, A., Duncan, R., et al. (2002) Acupuncture Therapy for the Symptoms of Parkinson’s Disease. Mov Disord, 17, 799-802. http://dx.doi.org/10.1002/mds.10134

[12]   Jeon, S., Kim, Y.J, Kim, S.T., Moon, W., Chae, Y., Kang, M., et al. (2008) Proteomic Analysis of the Neuroprotective Mechanisms of Acupuncture Treatment in a Parkinson’s Disease Mouse Model. Proteomics, 8, 4822-4832. http://dx.doi.org/10.1002/pmic.200700955

[13]   Jia, J., Sun, Z., Li, B., Pan, Y., Wang, H., Wang, X., et al. (2009) Electro-Acupuncture Stimulation Improves Motor Disorders in Parkinsonian Rats. Behav Brain Res, 205, 214-218. http://dx.doi.org/10.1016/j.bbr.2009.06.024

[14]   Liu, X.Y., Zhou, H.F., Pan, Y.L., Liang, X.B., Niu, D.B., Xue, B., et al. (2004) Electro-Acupuncture Stimulation Protects Dopaminergic Neurons from Inflammation-Mediated Damage in Medial Forebrain Bundle-Transected Rats. Exp Neurol, 189, 189-196. http://dx.doi.org/10.1016/j.expneurol.2004.05.028

[15]   Whit?eld, C.W., Cziko, A.M. and Robinson, G.E. (2003) Gene Expression Profiles in the Brain Predict Behavior in Individual Honey Bees. Science, 302, 296-299. http://dx.doi.org/10.1126/science.1086807

 
 
Top