Screening of human chromosome 21 genes in the dorsolateral prefrontal cortex of individuals with Down syndrome
- Authors:
- Published online on: November 4, 2014 https://doi.org/10.3892/mmr.2014.2855
- Pages: 1235-1239
Abstract
Introduction
Down syndrome (DS) is one of the principal causes of mental retardation and congenital heart malformations, and is a complex disorder with genetic and metabolic components. It is attributed to the presence of three copies of chromosome 21 (1), and presents various common clinical features. These include gastrointestinal disruptions, immune system defects and Alzheimer’s disease-associated pathological and neurochemical alterations (2). Bittles et al (3) established that DS affects 1/650-1,000 live births and is the most common genetic cause of intellectual disability, cognitive impairment and congenital heart defect in the human population (3). Previously, DS was identified to be associated with an increased frequency of infections, hematological malignancies and autoimmune problems, in addition to being the leading cause of a variety of birth defects and medical conditions (4). For these reasons, there is a requirement for identification of novel targets for this disease and investigation of the potential mechanism underlying DS.
Human chromosome 21 (HC21) is the smallest human autosome and an extra copy of it can lead to DS (5). Thus far, a number of studies have identified an association between HC21 and DS. The presence of three copies of HC21 has been demonstrated to induce the overexpression of its resident genes, which may be the pathogenesis underlying the abnormalities occurring in DS (2). Furthermore, several genes in HC21 have been detected to be associated with DS. For instance, as the product of an HC21 gene highly expressed in brain, heart and skeletal muscle, DSCR1 is overexpressed in fetal brains of individuals with DS (2). In addition, the contribution of microRNAs (miRNAs) to DS has been investigated, and studies indicate that HC21-derived miRNAs are overexpressed in the brains and hearts of patients with DS (6). Although several HC21-associated genes have been identified to be involved in the development of DS, the whole genome has not been studied.
In the current study, the HC21 genes associated with DS were screened based on the whole genome expression. The differentially expressed genes (DEGs) between DS and normal specimens were identified, and then their functions were analyzed by gene ontology (GO). The locations of these DEGs were identified and the HC21-associated genes that may be involved in the development of DS were screened.
Materials and methods
Affymetrix data
The Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information is the largest entirely public gene expression resource, and includes 214,268 samples and 4,500 platforms (7). The chip data GSE5390 (8) were downloaded from the GEO database, and included 7 DS and 8 normal samples. The platform was GPL96 [HG-U133A] Affymetrix Human Genome U133A Array (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL96).
Data preprocessing and identification of DEGs
The original microarray data were normalized using the mean method, which aimed to adjust the data for effects that were due to variation in the technology, rather than biological differences (9). Subsequently, the LIMMA package (http://www.bioconductor.org/packages/release/bioc/html/limma) in R language (10) was utilized to identify DEGs and the Bonferroni correction in multtest package (http://www.bioconductor.org/packages/release/bioc/html/multtest.html) (11) was applied, resulting in adjusted P-values, and identification of the false discovery rate (FDR) (12). FDR<0.05 and |logFC|>1 were selected as the thresholds.
Clustering analysis
Clustering algorithms are used for the task of class identification in spatial databases. Previously, a study identified that the same tissue can exhibit significantly different expression levels under varied conditions (13). Therefore, clustering analysis was conducted for the DEGs between DS and healthy normal samples (14). The results were then visualized using Treeview (http://www.jam-software.com/virtual-treeview/) (15).
DEGs between the two groups
Based on the above processes, the up- and downregulated genes between DS and normal samples were screened. The t-test (16) was used to detect significant differences between groups.
Functional analysis
The Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov) bioinformatics resource consists of an integrated biological knowledge base and analytic tools. These are aimed at systematically extracting biological meaning from large lists of genes or proteins (17). GO terms are significantly overrepresented in a set of genes from three aspects, including the cellular component, molecular function and biological process (18). In the present study, GO analysis was performed on the up- and downregulated genes based on a hypergeometric distribution algorithm. GO terms with P<0.05 were screened for further analysis.
Location of DEGs on chromosomes
The University of California, Santa Cruz Interaction Browser (UCSCIB) (http://sysbio.soe.ucsc.edu/nets) is an online tool for biologists to simultaneously view high-throughput data sets in order to analyze functional relationships between biological entities (19). In the current study, the locations of DEGs on chromosomes were detected based on human genome 19 in the UCSCIB, and all DEGs on HC21 were selected for further analysis.
Results
DEGs
Subsequent to preprocessing of the microarray data, the normalized data were presented as in Fig. 1A. Based on the LIMMA package, a total of 300 DEGs were identified with an FDR<0.05 and |logFC|>1, including 25 upregulated and 275 downregulated genes.
Clustering analysis
The expression data were extracted and underwent clustering analysis, the result of which is displayed in Fig. 1B. The expression of DEGs was significantly different between DS and normal samples (FDR<0.05). A clear difference between the colors in the two groups can be observed.
Analysis of the DEGs
The t-test was used to compare the gene expression in the DS and healthy groups (Fig. 1C). The results demonstrated that the differences between the control and DS groups were significant, with the smallest P-value in the upregulated genes (P=2.2×10−6).
GO analysis
In the present study, DAVID was used to conduct GO enrichment analysis of up- and downregulated genes. Up- and downregulated genes were observed to be significantly enriched in 17 and 11 biological processes, respectively (P<0.05; Table I). The downregulated genes, including PCSK1, FGF9, NPTX2, CRH, TAC1, HOMER1 and SST were significantly enriched in the cell-cell signaling term (P=0.000227). By contrast, upregulated genes, including ATP6V0E1, tetratricopeptide repeat domain 3 (TTC3), collagen type VI α2 (COL6A2) and ATP binding cassette transporter G1 (ABCG1) were significantly associated with organic substance biological processes (P=4.48×10−10).
Gene location and analysis
The chromosome locations of all DEGs were detected, and DEGs on HC21 were collected. It was observed that all the DEGs located on HC21 were upregulated genes in the 21q21–22 area. A total of 17 DEGs were located on HC21, including COL6A2, DSCAM, TTC3, ABCG1, SON, SLC5A3, ITSN1, PIGP and CSTB (Table II).
Discussion
As the principal genetic cause of mental retardation, DS is a genetic disorder resulting from full or partial trisomy of HC21 (20). In the current study, the genes on HC21 that were associated with DS were identified. Microarray data that included DS and healthy normal samples were collected and DEGs between these two groups were identified. The expression levels of these genes in the two samples were significantly different, particularly the overexpressed genes. The functions of these genes were analyzed using GO and the results suggested that there was a clear enrichment of DEGs in organic substance biological processes and cell-cell signaling. Furthermore, based on UCSCIB, 17 upregulated genes were identified in the 21q21–22 area, which may be involved in the development of DS.
Based on original data GSE5390, a total of 300 DEGs, including 25 up- and 275 downregulated genes, were identified between DS and normal samples. In addition, using clustering analysis and comparison of the expression data between the groups, it was observed that the expression of these DEGs was significantly different between the DS and normal samples, with a greater difference in upregulated genes. These results indicate that genetic variation may induce DS and that the overexpressed genes may be key in this process. Thus, further attention is required for upregulated genes in the pathogenesis of DS.
The function of these DEGs was analyzed by GO. Once the DEGs between DS and normal samples had been identified, the function of these genes in the development of DS were predicted. The results demonstrated that the upregulated genes were enriched in 17 biological processes, and the most significant was in response to organic substance biological processes (P=4.48×10−10). This term represents any process that results in a change in the state or activity of a cell or an organism (including movement, secretion, enzyme production and gene expression) as a result of a stimulus from an organic substance. A total of 39 upregulated genes, including ATP6V0E1, TTC3, COL6A2 and ABCG1, were enriched in this biological process, suggesting that by disturbing this term, the overexpressed genes may induce DS. Additionally, the downregulated genes, including PCSK1, FGF9, NPTX2, CRH, TAC1, HOMER1 and SST, were significantly enriched in cell-cell signaling (P=0.000227). This term denotes any process that mediates the transfer of information from one cell to another. This observation indicates that the downregulated genes affect signaling between cells, which may result in DS.
The locations of the identified DEGs were investigated. In the current study, the aim was to identify genes on HC21 that may induce DS, hence these were the genes that were focused on. The results indicated that the DEGs on HC21 were all upregulated and were mainly in the 21q21–22 area. In detail, 17 overexpressed genes were identified on HC21, including TTC3, COL6A2, ABCG1, SIK1 and PIGP. Notably, the overexpressed genes (TTC3, COL6A2 and ABCG1) were enriched in response to the organic substance biological process. This observation suggests that these genes on HC21 may induce DS by disturbing the response to the organic substance-associated biological process. COL6A2, on the 21q22.3 has previously been detected in several medical conditions, including Ullrich congenital muscular dystrophy (21), congenital heart defects (22) and progressive myoclonus epilepsy syndrome (23). ABCG1, on the 21q22.3 mediates the transport of cholesterol from cells to high density lipoprotein (24). In addition, this substance serves a function in the immune response and protects against oxidative stress-induced macrophage apoptosis during efferocytosis (25–26). TTC3, on the 21q22.3, is one of the supernumerary genes in the DS critical region (27), and is involved in neuronal cell differentiation (28). These genes are notable candidates for the learning disability and cerebral cortex dysplasia observed in DS. Furthermore, the DEGs of the same area, such as PIGP and SIK1, may also be involved in the development and progression of DS.
In the present study, candidates for involvement in the initiation and progression of DS were identified on HC21. Notably, the DEGs on HC21 were all overexpressed genes and were significantly enriched in response to organic substance biological process. These observations indicate that the overexpression of ABCG1, TTC3 and COL6A2, which are located on the 21q21–22 area, may induce DS by disturbing several biological processes. The work of the current study may provide therapeutic targets for DS and aid in the elucidation of its pathogenesis.
References
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
Smyth GK and Speed T: Normalization of cDNA microarray data. Methods. 31:265–273. 2003. View Article : Google Scholar : PubMed/NCBI | |
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 | |
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. | |
Benjamini Y: Discovering the false discovery rate. J Roy Stat Soc B. 72:405–416. 2010. View Article : Google Scholar | |
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 | |
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 | |
Saldanha AJ: Java Treeview - extensible visualization of microarray data. Bioinformatics. 20:3246–3248. 2004. View Article : Google Scholar : PubMed/NCBI | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
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 : | |
Toker A: TTC3 ubiquitination terminates Akt-ivation. Dev Cell. 17:752–754. 2009. View Article : Google Scholar | |
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 |