892 resultados para GENE-EXPRESSION SIGNATURES


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Plos Genetics, 5(7): ARTe1000566

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Dissertação para obtenção do Grau de Mestre em Biotecnologia

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Dissertation presented to obtain the Ph.D degree in Biology

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Behçet's disease (BD) is a complex disease with genetic and environmental risk factors implicated in its etiology; however, its pathophysiology is poorly understood. To decipher BD's genetic underpinnings, we combined gene expression profiling with pathway analysis and association studies. We compared the gene expression profiles in peripheral blood mononuclear cells (PBMCs) of 15 patients and 14 matched controls using Affymetrix microarrays and found that the neuregulin signaling pathway was over-represented among the differentially expressed genes. The Epiregulin (EREG), Amphiregulin (AREG), and Neuregulin-1 (NRG1) genes of this pathway stand out as they are also among the top differentially expressed genes. Twelve haplotype tagging SNPs at the EREG-AREG locus and 15 SNPs in NRG1 found associated in at least one published BD genome-wide association study were tested for association with BD in a dataset of 976 Iranian patients and 839 controls. We found a novel association with BD for the rs6845297 SNP located downstream of EREG, and replicated three associations at NRG1 (rs4489285, rs383632, and rs1462891). Multifactor dimensionality reduction analysis indicated the existence of epistatic interactions between EREG and NRG1 variants. EREG-AREG and NRG1, which are members of the epidermal growth factor (EGF) family, seem to modulate BD susceptibility through main effects and gene–gene interactions. These association findings support a role for the EGF/ErbB signaling pathway inBD pathogenesis that warrants further investigation and highlight the importance of combining genetic and genomic approaches to dissect the genetic architecture of complex diseases.

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Dissertation presented to obtain the Ph.D degree in Biology

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Dissertation presented to obtain the Ph.D degree in Biology

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Dissertation presented to obtain the Ph.D degree in Plant Physiology

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Dissertation presented to obtain the Master Degree in Molecular, Genetics and Biomedicine

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The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

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El objetivo de este proyecto, enmarcado en el área de metodología de análisis en bioingeniería-biotecnología aplicadas al estudio del cancer, es el análisis y caracterización a través modelos estadísticos con efectos mixtos y técnicas de aprendizaje automático, de perfiles de expresión de proteínas y genes de las vías metabolicas asociadas a progresión tumoral. Dicho estudio se llevará a cabo mediante la utilización de tecnologías de alto rendimiento. Las mismas permiten evaluar miles de genes/proteínas en forma simultánea, generando así una gran cantidad de datos de expresión. Se hipotetiza que para un análisis e interpretación de la información subyacente, caracterizada por su abundancia y complejidad, podría realizarse mediante técnicas estadístico-computacionales eficientes en el contexto de modelos mixtos y técnias de aprendizaje automático. Para que el análisis sea efectivo es necesario contemplar los efectos ocasionados por los diferentes factores experimentales ajenos al fenómeno biológico bajo estudio. Estos efectos pueden enmascarar la información subycente y así perder informacion relavante en el contexto de progresión tumoral. La identificación de estos efectos permitirá obtener, eficientemente, los perfiles de expresión molecular que podrían permitir el desarrollo de métodos de diagnóstico basados en ellos. Con este trabajo se espera poner a disposición de investigadores de nuestro medio, herramientas y procedimientos de análisis que maximicen la eficiencia en el uso de los recursos asignados a la masiva captura de datos genómicos/proteómicos que permitan extraer información biológica relevante pertinente al análisis, clasificación o predicción de cáncer, el diseño de tratamientos y terapias específicos y el mejoramiento de los métodos de detección como así tambien aportar al entendimieto de la progresión tumoral mediante análisis computacional intensivo.

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Abstract In species with social hierarchies, the death of dominant individuals typically upheaves the social hierarchy and provides an opportunity for subordinate individuals to become reproductives. Such a phenomenon occurs in the monogyne form of the fire ant, Solenopsis invicta, where colonies typically contain a single wingless reproductive queen, thousands of workers and hundreds of winged nonreproductive virgin queens. Upon the death of the mother queen, many virgin queens shed their wings and initiate reproductive development instead of departing on a mating flight. Workers progressively execute almost all of them over the following weeks. To identify the molecular changes that occur in virgin queens as they perceive the loss of their mother queen and begin to compete for reproductive dominance, we collected virgin queens before the loss of their mother queen, 6 h after orphaning and 24 h after orphaning. Their RNA was extracted and hybridized against microarrays to examine the expression levels of approximately 10 000 genes. We identified 297 genes that were consistently differentially expressed after orphaning. These include genes that are putatively involved in the signalling and onset of reproductive development, as well as genes underlying major physiological changes in the young queens.

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Background: Therapy of chronic hepatitis C (CHC) with pegIFNa/ribavirin achieves sustained virologic response (SVR) in ~55%. Pre-activation of the endogenous interferon system in the liver is associated non-response (NR). Recently, genome-wide association studies described associations of allelic variants near the IL28B (IFNλ3) gene with treatment response and with spontaneous clearance of the virus. We investigated if the IL28B genotype determines the constitutive expression of IFN stimulated genes (ISGs) in the liver of patients with CHC. Methods: We genotyped 93 patients with CHC for 3 IL28B single nucleotide polymorphisms (SNPs, rs12979860, rs8099917, rs12980275), extracted RNA from their liver biopsies and quantified the expression of IL28B and of 8 previously identified classifier genes which discriminate between SVR and NR (IFI44L, RSAD2, ISG15, IFI22, LAMP3, OAS3, LGALS3BP and HTATIP2). Decision tree ensembles in the form of a random forest classifier were used to calculate the relative predictive power of these different variables in a multivariate analysis. Results: The minor IL28B allele (bad risk for treatment response) was significantly associated with increased expression of ISGs, and, unexpectedly, with decreased expression of IL28B. Stratification of the patients into SVR and NR revealed that ISG expression was conditionally independent from the IL28B genotype, i.e. there was an increased expression of ISGs in NR compared to SVR irrespective of the IL28B genotype. The random forest feature score (RFFS) identified IFI27 (RFFS = 2.93), RSAD2 (1.88) and HTATIP2 (1.50) expression and the HCV genotype (1.62) as the strongest predictors of treatment response. ROC curves of the IL28B SNPs showed an AUC of 0.66 with an error rate (ERR) of 0.38. A classifier with the 3 best classifying genes showed an excellent test performance with an AUC of 0.94 and ERR of 0.15. The addition of IL28B genotype information did not improve the predictive power of the 3-gene classifier. Conclusions: IL28B genotype and hepatic ISG expression are conditionally independent predictors of treatment response in CHC. There is no direct link between altered IFNλ3 expression and pre-activation of the endogenous system in the liver. Hepatic ISG expression is by far the better predictor for treatment response than IL28B genotype.

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A remarkable social polymorphism is controlled by a single Mendelian factor in the fire ant Solenopsis invicta. A genomic element marked by the gene Gp-9 determines whether workers tolerate one or many fertile queens in their colony. Gp-9 was recently shown to be part of a supergene with two nonrecombining variants, SB and Sb. SB/SB and SB/Sb queens differ in how they initiate new colonies, and in many physiological traits, for example odour and maturation rate. To understand how a single genetic element can affect all these traits, we used a microarray to compare gene expression patterns between SB/SB and SB/Sb queens of three different age classes: 1-day-old unmated queens, 11-day-old unmated queens and mated, fully reproductive queens collected from mature field colonies. The number of genes that were differentially expressed between SB/SB and SB/Sb queens of the same age class was smallest in 1-day-old queens, maximal in 11-day-old queens and intermediate in reproductive queens. Gene ontology analysis showed that SB/SB queens upregulate reproductive genes faster than SB/Sb queens. For all age classes, genes inside the supergene were overrepresented among the differentially expressed genes. Consistent with the hypothesized greater number of transposons in the Sb supergene, 13 transposon genes were upregulated in SB/Sb queens. Viral genes were also upregulated in SB/Sb mature queens, consistent with the known greater parasite load in colonies headed by SB/Sb queens compared with colonies headed by SB/SB queens. Eighteen differentially expressed genes between reproductive queens were involved in chemical signalling. Our results suggest that many genes in the supergene are involved in regulating social organization and queen phenotypes in fire ants.

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We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com.

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CD8 T cells play a key role in mediating protective immunity against selected pathogens after vaccination. Understanding the mechanism of this protection is dependent upon definition of the heterogeneity and complexity of cellular immune responses generated by different vaccines. Here, we identify previously unrecognized subsets of CD8 T cells based upon analysis of gene-expression patterns within single cells and show that they are differentially induced by different vaccines. Three prime-boost vector combinations encoding HIV Env stimulated antigen-specific CD8 T-cell populations of similar magnitude, phenotype, and functionality. Remarkably, however, analysis of single-cell gene-expression profiles enabled discrimination of a majority of central memory (CM) and effector memory (EM) CD8 T cells elicited by the three vaccines. Subsets of T cells could be defined based on their expression of Eomes, Cxcr3, and Ccr7, or Klrk1, Klrg1, and Ccr5 in CM and EM cells, respectively. Of CM cells elicited by DNA prime-recombinant adenoviral (rAd) boost vectors, 67% were Eomes(-) Ccr7(+) Cxcr3(-), in contrast to only 7% and 2% stimulated by rAd5-rAd5 or rAd-LCMV, respectively. Of EM cells elicited by DNA-rAd, 74% were Klrk1(-) Klrg1(-)Ccr5(-) compared with only 26% and 20% for rAd5-rAd5 or rAd5-LCMV. Definition by single-cell gene profiling of specific CM and EM CD8 T-cell subsets that are differentially induced by different gene-based vaccines will facilitate the design and evaluation of vaccines, as well as enable our understanding of mechanisms of protective immunity.