4 resultados para EXPRESSION DATA

em Universidade Federal do Rio Grande do Norte(UFRN)


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Human mesenchymal stem cells (MSC) are powerful sources for cell therapy in regenerative medicine. The long time cultivation can result in replicative senescence or can be related to the emergence of chromosomal alterations responsible for the acquisition of tumorigenesis features in vitro. In this study, for the first time, the expression profile of MSC with a paracentric chromosomal inversion (MSC/inv) was compared to normal karyotype (MSC/n) in early and late passages. Furthermore, we compared the transcriptome of each MSC in early passages with late passages. MSC used in this study were obtained from the umbilical vein of three donors, two MSC/n and one MSC/inv. After their cryopreservation, they have been expanded in vitro until reached senescence. Total RNA was extracted using the RNeasy mini kit (Qiagen) and marked with the GeneChip ® 3 IVT Express Kit (Affymetrix Inc.). Subsequently, the fragmented aRNA was hybridized on the microarranjo Affymetrix Human Genome U133 Plus 2.0 arrays (Affymetrix Inc.). The statistical analysis of differential gene expression was performed between groups MSC by the Partek Genomic Suite software, version 6.4 (Partek Inc.). Was considered statistically significant differences in expression to p-value Bonferroni correction ˂.01. Only signals with fold change ˃ 3.0 were included in the list of differentially expressed. Differences in gene expression data obtained from microarrays were confirmed by Real Time RT-PCR. For the interpretation of biological expression data were used: IPA (Ingenuity Systems) for analysis enrichment functions, the STRING 9.0 for construction of network interactions; Cytoscape 2.8 to the network visualization and analysis bottlenecks with the aid of the GraphPad Prism 5.0 software. BiNGO Cytoscape pluggin was used to access overrepresentation of Gene Ontology categories in Biological Networks. The comparison between senescent and young at each group of MSC has shown that there is a difference in the expression parttern, being higher in the senescent MSC/inv group. The results also showed difference in expression profiles between the MSC/inv versus MSC/n, being greater when they are senescent. New networks were identified for genes related to the response of two of MSC over cultivation time. Were also identified genes that can coordinate functional categories over represented at networks, such as CXCL12, SFRP1, xvi EGF, SPP1, MMP1 e THBS1. The biological interpretation of these data suggests that the population of MSC/inv has different constitutional characteristics, related to their potential for differentiation, proliferation and response to stimuli, responsible for a distinct process of replicative senescence in MSC/inv compared to MSC/n. The genes identified in this study are candidates for biomarkers of cellular senescence in MSC, but their functional relevance in this process should be evaluated in additional in vitro and/or in vivo assays

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The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using classic clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. This work presents the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods

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The main goal of this work is to investigate the suitability of applying cluster ensemble techniques (ensembles or committees) to gene expression data. More specifically, we will develop experiments with three diferent cluster ensembles methods, which have been used in many works in literature: coassociation matrix, relabeling and voting, and ensembles based on graph partitioning. The inputs for these methods will be the partitions generated by three clustering algorithms, representing diferent paradigms: kmeans, ExpectationMaximization (EM), and hierarchical method with average linkage. These algorithms have been widely applied to gene expression data. In general, the results obtained with our experiments indicate that the cluster ensemble methods present a better performance when compared to the individual techniques. This happens mainly for the heterogeneous ensembles, that is, ensembles built with base partitions generated with diferent clustering algorithms

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Human mesenchymal stem cells (MSC) are powerful sources for cell therapy in regenerative medicine. The long time cultivation can result in replicative senescence or can be related to the emergence of chromosomal alterations responsible for the acquisition of tumorigenesis features in vitro. In this study, for the first time, the expression profile of MSC with a paracentric chromosomal inversion (MSC/inv) was compared to normal karyotype (MSC/n) in early and late passages. Furthermore, we compared the transcriptome of each MSC in early passages with late passages. MSC used in this study were obtained from the umbilical vein of three donors, two MSC/n and one MSC/inv. After their cryopreservation, they have been expanded in vitro until reached senescence. Total RNA was extracted using the RNeasy mini kit (Qiagen) and marked with the GeneChip ® 3 IVT Express Kit (Affymetrix Inc.). Subsequently, the fragmented aRNA was hybridized on the microarranjo Affymetrix Human Genome U133 Plus 2.0 arrays (Affymetrix Inc.). The statistical analysis of differential gene expression was performed between groups MSC by the Partek Genomic Suite software, version 6.4 (Partek Inc.). Was considered statistically significant differences in expression to p-value Bonferroni correction ˂.01. Only signals with fold change ˃ 3.0 were included in the list of differentially expressed. Differences in gene expression data obtained from microarrays were confirmed by Real Time RT-PCR. For the interpretation of biological expression data were used: IPA (Ingenuity Systems) for analysis enrichment functions, the STRING 9.0 for construction of network interactions; Cytoscape 2.8 to the network visualization and analysis bottlenecks with the aid of the GraphPad Prism 5.0 software. BiNGO Cytoscape pluggin was used to access overrepresentation of Gene Ontology categories in Biological Networks. The comparison between senescent and young at each group of MSC has shown that there is a difference in the expression parttern, being higher in the senescent MSC/inv group. The results also showed difference in expression profiles between the MSC/inv versus MSC/n, being greater when they are senescent. New networks were identified for genes related to the response of two of MSC over cultivation time. Were also identified genes that can coordinate functional categories over represented at networks, such as CXCL12, SFRP1, xvi EGF, SPP1, MMP1 e THBS1. The biological interpretation of these data suggests that the population of MSC/inv has different constitutional characteristics, related to their potential for differentiation, proliferation and response to stimuli, responsible for a distinct process of replicative senescence in MSC/inv compared to MSC/n. The genes identified in this study are candidates for biomarkers of cellular senescence in MSC, but their functional relevance in this process should be evaluated in additional in vitro and/or in vivo assays