Abstract
Genomic and proteomic studies of neurodegenerative disorders require complementary approaches to integrate the massive amount of data generated in high throughput experimental procedures. We propose a Bioinformatics pipeline in which expression studies guide the selection of candidate genes that should be screened for potential new genetic variations from a public expressed site tags (ESTs) database. Motivated by the former interest of our group in genetic polymorphisms involved with the immune system, we selected five genes from a previous expression microarrays study of hippocampal cornu ammonis (CA1) area of Alzheimer’s Disease subjects (AD). The CLCbio Workbench Combined® version 3.6.2. was initially used to build ESTs and mRNA files retrieved respectively from the Goldenpath of University of California Santa Cruz (UCSC) and National Center for Biotechnology Information (NCBI) databases and latter to perform multiple batches of Smith–Waterman alignments. A total of 116 ESTs sequences were selected after proper stringent parameters were applied to the first set of mismatches. The annotation revealed various classes of variations, most of them deletions (176). Amongst this specific group, some were frameshift deletions (35) and the virtual translation of a few others (5) were predicted to induce no change other than a single aminoacid removal, with no subsequent repercussions at the protein sequence. In addition, the analysis identified transitions (three), transversions (52), synonymous (41), non-synonymous (12), and deletions in 36 ESTs located in Untranslated Regions -UTRs (Supplementary data). Deletions are often associated to major genetics syndromes with dysmorphic features. However, various recent studies show that common microdeletions might be highly associated with common neuropsychiatric disorders such as schizophrenia, autism, mental retardation, or even in various ethnicities, detected in whole genome sequencing experiments. A virtual validation confirmed that some of the variations identified were previously reported and confirmed in DNA samples, showing that this method is a feasible way to detect genetic variations that merit further exploration in AD genetic risk factor association studies.
References
Akiyama, H., Barger, S., Barnum, S., et al. (2000). Inflammation and Alzheimer’s disease. Neurobiology of Aging, 21, 383–421. doi:10.1016/S0197-4580(00)00124-X.
Barker, G., Batley, J., O’Sullivan, H., Edwards, K. J., & Edwards, D. (2003). Redundancy based detection of sequence polymorphisms in expressed sequence tag data using auto SNP. Bioinformatics (Oxford, England), 19, 421–422. doi:10.1093/bioinformatics/btf881.
Blalock, E. M., Geddes, J. W., Chen, K. C., Porter, N. M., Markesbery, W. R., & Landfield, P. W. (2004). Incipient Alzheimer’s disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proceedings of the National Academy of Sciences of the United States of America, 101, 2173–2178. doi:10.1073/pnas.0308512100.
Bond, J., Scott, S., Hampshire, D. J., et al. (2003). Protein-truncating mutations in ASPM cause variable reduction in brain size. American Journal of Human Genetics, 73, 1170–1177. doi:10.1086/379085.
Breloer, M., & Fleischer, B. (2008). CD83 regulates lymphocyte maturation, activation and homeostasis. Trends in Immunology, 29, 186–194. doi:10.1016/j.it.2008.01.009.
Colangelo, V., Schurr, J., Ball, M. J., Pelaez, R. P., Bazan, N. G., & Lukiw, W. J. (2002). Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling. Journal of Neuroscience Research, 70, 462–473. doi:10.1002/jnr.10351.
Coppola, G., Karydas, A., Rademakers, R., et al. (2008). Gene expression study on peripheral blood identifies progranulin mutations. Annals of Neurology, 64, 92–96. doi:10.1002/ana.21397.
Fertuzinhos, S. M., Oliveira, J. R., Nishimura, A. L., et al. (2004). Analysis of IL-1alpha, IL-1beta, and IL-1RA [correction of IL-RA] polymorphisms in dysthymia. Journal of Molecular Neuroscience, 22, 251–256. doi:10.1385/JMN:22:3:251.
Forment, J., Gilbert, F., Robles, A., Conejero, V., Nuez, F., & Blanca, J. M. (2008). EST2uni: an open, parallel tool for automated EST analysis and database creation, with a data mining web interface and microarray expression data integration. BMC Bioinformatics, 9, 5–15. doi:10.1186/1471-2105-9-5.
Grimaldi, L. M. E., Casadei, C. M., Ferri, C., et al. (2000). Association of early-onset Alzheimer’s disease with an interleukin-1α gene polymorphism. Annals of Neurology, 47, 361–365. doi:10.1002/1531-8249(200003)47:3<361::AID-ANA12>3.0.CO;2-N.
Gul, A., Hassan, M. J., Hussain, S., Raza, S. I., Chishti, M. S., & Ahmad, W. (2006). A novel deletion mutation in CENPJ gene in a Pakistani family with autosomal recessive primary microcephaly. Journal of Human Genetics, 51, 760–764. doi:10.1007/s10038-006-0017-1.
Heneka, M. T., & O’ Banion, M. K. (2007). Inflammatory processes in Alzheimer’s disease. Journal of Neuroimmunology, 184, 69–91. doi:10.1016/j.jneuroim.2006.11.017.
Kidd, J. M., Cooper, G. M., Donahue, W. F., et al. (2008). Mapping and sequencing of structural variation from eight human genomes. Nature, 453, 56–64. doi:10.1038/nature06862.
Leal, G. F., Roberts, E., Silva, E. O., Costa, S. M. R., Hampshire, D. J., & Woods, C. G. (2003). A novel locus for autosomal recessive primary microcephaly maps to 13q12.2. Journal of Medical Genetics, 40, 540–542. doi:10.1136/jmg.40.7.540.
Mao, G., Pan, X., Zhu, B.-B., et al. (2007). Identification and characterization of OGG1 mutation in patients with Alzheimer’s disease. Nucleic Acids Research, 35, 2759–2766. doi:10.1093/nar/gkm189.
Minghetti, L. (2004). Cyclooxygenase-2 (COX-2) in inflammatory and degenerative brain diseases. Journal of Neuropathology and Experimental Neurology, 63, 901–910.
Miller, J. A., Oldham, M. C., & Geschwind, D. H. (2008). A systems level analysis of transcriptional changes in Alzheimer’s Disease and normal aging. The Journal of Neuroscience, 28, 1410–1420. doi:10.1523/JNEUROSCI.4098-07.2008.
Navratil, V., Penel, S., Delmotte, S., Mouchiroud, D., Gautier, C., & Aouacheria, A. (2008). DigiPINS: a database for vertebrate exonic single nucleotide polymorphisms and its application to cancer association studies. Biochimie, 90, 563–569. doi:10.1016/j.biochi.2007.09.017.
Nicoll, J. A. R., Mrak, R. E., Graham, D. I., et al. (2000). Association of interleukin-1 gene polymorphism with Alzheimer’s disease. Annals of Neurology, 47, 365–368. doi:10.1002/1531-8249(200003) 47:3<365::AID-ANA13>3.0.CO;2-G.
Oliveira, J. R. M., Nishimura, A. L., Lemos, R. R., & Zatz, M. (2009). The genetics of disease in Brazil: 10 years of analysis in a unique population. Journal of Molecular Neuroscience, 37, 74–79. doi:10.1007/s12031-008-9124-0.
Picoult-Newberg, L., Ideker, T. E., Pohl, M. G., et al. (1999). Mining SNPs from EST databases. Genome Research, 9, 167–174.
Ray, S., Britschgi, M., Hebert, C., et al. (2007). Classification and prediction of clinical Alzheimer’s diagnosis based on plasma sinaling. Nature Medicine, 13, 1359–1362. doi:10.1038/nm1653.
Sakasi, A., Horikawa, Y., Suwa, T., et al. (2008). Case report of familial carney complex due to novel frameshift mutation c.597delC (p.phe200LeufsX6) in PRKAR1A. Molecular Genetics and Metabolism, 95, 182–187. doi:10.1016/j.ymgme.2008.07.009.
Stefansson, H., Rujescu, D., Cichon, S., et al. (2008). Large recurrent microdeletions associated with schizophrenia. Nature, 455, 232–236. doi:10.1038/nature07229.
Stein, L. D. (2008). Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nature Reviews Genetics, 9, 678–688. doi:10.1038/nrg2414.
Stephenson, D. T., Lemere, C. A., Selkoe, D. J., & Clemens, J. A. (1996). Cytosolic phospholipase A2 (cPLA2) immunoreactivity is elevated in Alzheimer’s disease brain. Neurobiology of Disease, 3, 51–63. doi:10.1006/nbdi.1996.0005.
Tang, J., Vosman, B., Voorrips, R. E., Gerard van der Linden, G., & Leunissen, J. A. M. (2006). QualitySNP: a pipeline for detecting single nucleotide polymorphims and insertions/deletions in EST data from diploid and polyploid species. BMC Bioinformatics, 7, 438–452. doi:10.1186/1471-2105-7-438.
Tang, J., Leunissen, J. A. M., Voorrips, R. E., Gerard van der Linden, G., & Vosman, B. (2008). HaploSNPer: a web-based allele and SNP detection tool. BMC Genetics, 9, 23–29. doi:10.1186/1471-2156-9-23.
Tuppo, E. E., & Arias, H. R. (2005). The role of inflammation in Alzheimer’s disease. The International Journal of Biochemistry & Cell Biology, 37, 289–305. doi:10.1016/j.biocel.2004.07.009.
Useche, F. J., Gao, G., Hanafey, M., & Rafalski, A. (2001). High-throughput identification, database storage and analysis of SNPs in EST sequences. Genome Informatics, 12, 194–203.
Walsh, T., Mcclellan, J. M., Mccarthy, S. E., et al. (2008). Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science, 320, 539–543. doi:10.1126/science.1155174.
Wisniewski, T., & Konietzko, U. (2008). Amyloid-beta immunisation for Alzheimer’s disease. The Lancet Neurology, 9, 805–811. doi:10.1016/S1474-4422(08)70170-4.
Yu, C. E., Dawson, G., Munson, J., et al. (2002). Presence of large deletions in kindreds with autism. American Journal of Human Genetics, 71, 100–115. doi:10.1086/341291.
Acknowledgments
We are greatly indebted with Aaron Rowe for reviewing this manuscript. This study was support by the following Brazilian funding agencies and academic bureaus: PROPESQ-UFPE, CAPES, CNPq, and FACEPE.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lemos, R.R., Castelletti, C., Lima Filho, J.L. et al. In Silico Identification of New Genetic Variations as Potential Risk Factors for Alzheimer’s Disease in a Microarray-oriented Simulation. J Mol Neurosci 39, 242–247 (2009). https://doi.org/10.1007/s12031-009-9191-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12031-009-9191-x