Screening of human chromosome 21 genes in the dorsolateral prefrontal cortex of individuals with Down syndrome

Kong,Xiang‑Dong; Liu,Ning; Xu,Xue‑Ju; Zhao,Zhen‑Hua; Jiang,Miao

doi:10.3892/mmr.2014.2855

Screening of human chromosome 21 genes in the dorsolateral prefrontal cortex of individuals with Down syndrome

Authors:
- Xiang‑Dong Kong
- Ning Liu
- Xue‑Ju Xu
- Zhen‑Hua Zhao
- Miao Jiang
View Affiliations

Affiliations: Prenatal Diagnosis Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, P.R. China, Department of Paediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, P.R. China
Published online on: November 4, 2014 https://doi.org/10.3892/mmr.2014.2855
Pages: 1235-1239

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

The aim of the current study was to identify the genes on human chromosome 21 (HC21) that may serve important functions in the pathogenesis of Down syndrome (DS). The microarray data GSE5390 were obtained from the Gene Expression Omnibus database, which contained 7 DS and 8 healthy normal samples. The data were then normalized and the differentially expressed genes (DEGs) were identified using the LIMMA package and Bonferroni correction. Furthermore, the DEGs underwent clustering and gene ontology analysis. Additionally, the locations of the DEGs on HC21 were confirmed using human genome 19 in the University of California, Santa Cruz Interaction Browser. A total of 25 upregulated and 275 downregulated genes were screened between DS and healthy samples with a false discovery rate of <0.05 and |logFC|>1. The expression levels of these genes in the two samples were different. In addition, the up‑ and downregulated genes were markedly enriched in organic substance biological processes (P=4.48x10‑10) and cell‑cell signaling (P=0.000227). Furthermore, 17 overexpressed genes were identified on the 21q21‑22 area, including COL6A2, TTC3 and ABCG1. Together, these observations suggest that 17 upregulated genes on HC21 may be involved in the development of DS and provide the basis for understanding this disability.

View Figures

View References

1	Hobbs CA, Sherman SL, Yi P, et al: Polymorphisms in genes involved in folate metabolism as maternal risk factors for Down syndrome. Am J Hum Genet. 67:623–630. 2000. View Article : Google Scholar : PubMed/NCBI
2	Fuentes JJ, Genescà L, Kingsbury TJ, et al: DSCR1, overexpressed in Down syndrome, is an inhibitor of calcineurin-mediated signaling pathways. Hum Mol Genet. 9:1681–1690. 2000. View Article : Google Scholar : PubMed/NCBI
3	Bittles AH, Bower C, Hussain R and Glasson EJ: The four ages of Down syndrome. Eur J Public Health. 17:221–225. 2007. View Article : Google Scholar
4	de Hingh YC, van der Vossen PW, Gemen EF, et al: Intrinsic abnormalities of lymphocyte counts in children with down syndrome. J Pediatr. 147:744–747. 2005. View Article : Google Scholar : PubMed/NCBI
5	Hattori M, Fujiyama A, Taylor TD, et al: Chromosome 21 mapping and sequency consortium: The DNA sequence of human chromosome 21. Nature. 405:311–319. 2000. View Article : Google Scholar : PubMed/NCBI
6	Kuhn DE, Nuovo GJ, Terry AV Jr, Martin MM, et al: Human chromosome 21-derived miRNAs are overexpressed in down syndrome brains and hearts. Biochem Biophys Res Commun. 370:473–477. 2008. View Article : Google Scholar : PubMed/NCBI
7	Barrett T, Troup DB, Wilhite SE, et al: NCBI GEO: mining tens of millions of expression profiles - database and tools update. Nucleic Acids Res. 35:D760–D765. 2007. View Article : Google Scholar
8	Lockstone HE, Harris LW, Swatton JE, Wayland MT, Holland AJ and Bahn S: Gene expression profiling in the adult Down syndrome brain. Genomics. 90:647–660. 2007. View Article : Google Scholar : PubMed/NCBI
9	Smyth GK and Speed T: Normalization of cDNA microarray data. Methods. 31:265–273. 2003. View Article : Google Scholar : PubMed/NCBI
10	Smyth GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Gentleman R, Carey V, Huber W, Irizarry R and Dudoit S: Springer; New York: pp. 397–420. 2005, View Article : Google Scholar
11	Benjamini Y and Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 57:289–300. 1995.
12	Benjamini Y: Discovering the false discovery rate. J Roy Stat Soc B. 72:405–416. 2010. View Article : Google Scholar
13	Ester M, Kriegel HP, Sander J and Xu X: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International conference on Knowledge Discovery and Data Mining (KDD-96); Association for the Advancement of Artificial Intelligence; Palo Alto, CA: pp. 226–231. 1996
14	Eisen MB, Spellman PT, Brown PO and Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95:14863–14868. 1998. View Article : Google Scholar : PubMed/NCBI
15	Saldanha AJ: Java Treeview - extensible visualization of microarray data. Bioinformatics. 20:3246–3248. 2004. View Article : Google Scholar : PubMed/NCBI
16	Ruxton GD: The unequal variance t-test is an underused alternative to Student’s t-test and the Mann-Whitney U test. Behav Ecol. 17:688–690. 2006. View Article : Google Scholar
17	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
18	Ashburner M, Ball CA, Blake JA, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
19	Wong CK, Vaske CJ, Ng S, Sanborn JZ, Benz SC, Haussler D and Stuart JM: The UCSC Interaction Browser: multidimensional data views in pathway context. Nucleic Acids Res. 41:W218–W224. 2013. View Article : Google Scholar : PubMed/NCBI
20	Reynolds LE, Watson AR, Baker M, et al: Tumour angiogenesis is reduced in the Tc1 mouse model of Down’s syndrome. Nature. 465:813–817. 2010. View Article : Google Scholar : PubMed/NCBI
21	Zhang RZ, Sabatelli P, Pan TC, et al: Effects on collagen VI mRNA stability and microfibrillar assembly of three COL6A2 mutations in two families with Ullrich congenital muscular dystrophy. J Biol Chem. 277:43557–43564. 2002. View Article : Google Scholar : PubMed/NCBI
22	Grossman TR, Gamliel A, Wessells RJ, et al: Over-expression of DSCAM and COL6A2 cooperatively generates congenital heart defects. PLoS Genet. 7:e10023442011. View Article : Google Scholar : PubMed/NCBI
23	Karkheiran S, Krebs CE, Makarov V, et al: Identification of COL6A2 mutations in progressive myoclonus epilepsy syndrome. Hum Genet. 132:275–283. 2013. View Article : Google Scholar
24	Vaughan AM and Oram JF: ABCG1 redistributes cell cholesterol to domains removable by high density lipoprotein but not by lipid-depleted apolipoproteins. J Biol Chem. 280:30150–30157. 2005. View Article : Google Scholar : PubMed/NCBI
25	Yvan-Charvet L, Pagler TA, Seimon TA, et al: ABCA1 and ABCG1 protect against oxidative stress-induced macrophage apoptosis during efferocytosis. Circ Res. 106:1861–1869. 2010. View Article : Google Scholar : PubMed/NCBI
26	Yvan-Charvet L, Wang N and Tall AR: Role of HDL, ABCA1, and ABCG1 transporters in cholesterol efflux and immune responses. Arterioscler Thromb Vasc Biol. 30:139–143. 2010. View Article : Google Scholar :
27	Toker A: TTC3 ubiquitination terminates Akt-ivation. Dev Cell. 17:752–754. 2009. View Article : Google Scholar
28	Oegema R, de Klein A, Verkerk AJ, et al: Distinctive phenotypic abnormalities associated with submicroscopic 21q22 deletion including DYRK1A. Mol Syndromol. 1:113–120. 2010.PubMed/NCBI