Differential evolution of MAGE genes based on expression pattern and selection pressure.
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ABSTRACT:
SUBMITTER: Zhao Q
PROVIDER: S-EPMC3484994 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Melanoma antigen gene (MAGE) families are cancer-testis genes that normally show expression in the testes. However, their expressions have been linked with various types of human cancers, including BC. Therefore, the primary purposes of the present research were to assess the expression of MAGE-A, -B, and -C genes in Saudi female patients with BC and determine their regulation via the epigenetic mechanism. Ten BC samples were analyzed for the expression levels of nine MAGE-A genes, six MAGE-B genes, and three MAGE-C genes using the RT-PCR technique. All 18 evaluated genes except for MAGE-A1, -A3, -A4, and -B5 showed weak band expressions in some BC specimens. MAGE-A6 and -B2 were expressed in 40 % of the BC tissue samples, and MAGE-A9, -A10, and -B6 were expressed in 30 %. The lowest expression levels were found for MAGE-A11, -B1, -B3, -B4, -C1, and -C2 in 10 % of the BC specimens and for MAGE-A9,--B2, and --C3 in 20 % of the samples. The most frequently expressed gene was MAGE-A8 (found in 70 % of the BC samples), which suggests that it may serve as - a marker for screening of BC. In vitro treatment, the 5-aza-2'-deoxycytidine agent led to a significant rise in mRNA expressions for all tested genes related to the MAGE-A family, except for MAGE-A10. By contrast, among the genes in the MAGE-B and -C families, only MAGE-B1 and -C2 exhibited detectable mRNA expression levels after treatment.
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Project description:PurposeWe investigated sequences of the feline coronaviruses (FCoV), which include feline enteric coronavirus (FECV) and feline infectious peritonitis virus (FIPV), from China and other countries to gain insight into the adaptive evolution of this virus.MethodsAscites samples from 31 cats with suspected FIP and feces samples from 8 healthy cats were screened for the presence of FCoV. Partial viral genome sequences, including parts of the nsp12-nsp14, S, N, and 7b genes, were obtained and aligned with additional sequences obtained from the GenBank database. Bayesian phylogenetic analysis was conducted, and the possibility of recombination within these sequences was assessed. Analysis of the levels of selection pressure experienced by these sequences was assessed using methods on both the PAML and Datamonkey platforms.ResultsOf the 31 cats investigated, two suspected FIP cats and one healthy cat tested positive for FCoV. Phylogenetic analysis showed that all of the sequences from mainland China cluster together with a few sequences from the Netherlands as a distinct clade when analyzed with FCoV sequences from other countries. Fewer than 3 recombination breakpoints were detected in the nsp12-nsp14, S, N, and 7b genes, suggesting that analyses for positive selection could be conducted. A total of 4, 12, 4, and 4 positively selected sites were detected in the nsp12-nsp14, S, N, and 7b genes, respectively, with the previously described site 245 of the S gene, which distinguishes FIPV from FECV, being a positive selection site. Conversely, 106, 168, 25, and 17 negative selection sites in the nsp12-14, S, N, and 7b genes, respectively, were identified.ConclusionOur study provides evidence that the FCoV genes encoding replicative, entry, and virulence proteins potentially experienced adaptive evolution. A greater number of sites in each gene experienced negative rather than positive selection, which suggests that most of the protein sequence must be conservatively maintained for virus survival. A few of the sites showing evidence of positive selection might be associated with the more severe pathology of FIPV or help these viruses survive other harmful conditions.
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Project description:BACKGROUND:Detecting single nucleotide polymorphism (SNP) interactions is an important and challenging task in genome-wide association studies (GWAS). Various efforts have been devoted to detect SNP interactions. However, the large volume of SNP datasets results in such a big number of high-order SNP combinations that restrict the power of detecting interactions. METHODS:In this paper, to combat with this challenge, we propose a two-stage approach (called HiSSI) to detect high-order SNP-SNP interactions. In the screening stage, HiSSI employs a statistically significant pattern that takes into account family wise error rate, to control false positives and to effectively screen two-locus combinations candidate set. In the searching stage, HiSSI applies two different search strategies (exhaustive search and heuristic search based on differential evolution along with ?2-test) on candidate pairwise SNP combinations to detect high-order SNP interactions. RESULTS:Extensive experiments on simulated datasets are conducted to evaluate HiSSI and recently proposed and related approaches on both two-locus and three-locus disease models. A real genome-wide dataset: breast cancer dataset collected from the Wellcome Trust Case Control Consortium (WTCCC) is also used to test HiSSI. CONCLUSIONS:Simulated experiments on both two-locus and three-locus disease models show that HiSSI is more powerful than other related approaches. Real experiment on breast cancer dataset, in which HiSSI detects some significantly two-locus and three-locus interactions associated with breast cancer, again corroborate the effectiveness of HiSSI in high-order SNP-SNP interaction identification.
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Project description:BackgroundTo utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.ResultsWe combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2, Gem, and CXCL11 that have not been shown to associate with, but may play roles in, metastasis.ConclusionsCDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments.AvailabilityCDEP is implemented in R and freely available at: http://genomebioinfo.musc.edu/CDEP/.Contactzhengw@musc.edu.
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