Freqüência Alélica e Ausência de Distúrbio de Segregação no Locus da Distrofia Miotônica em Indivíduos Normais

A Distrofia Miotônica de Steinert (DM) é uma doença autossômica dominante neuromuscular progressiva, causada por uma expansão da repetição CTG no locus DMPK do cromossomo humano 19q13.3. O número de repetições nos alelos normais varia de 5 a 37, enquanto que nos pacientes com DM, este número é sempre superior a 50. O tamanho da expansão está correlacionado à gravidade clínica e à idade de apresentação dos sintomas da doença. Pacientes com 50 a 150 cópias da repetição apresentam a forma mínima da doença, podendo ter apenas catarata como único sintoma. Os pacientes com 100 a 1000 repetições apresentam a forma clássica e pacientes que apresentam mais de 2000 cópias possuem a forma congênita. Baseados em 182 trios (mãefilho-suposto pai) com probabilidade de paternidade superior a 99,99%, foram estimadas freqüências gênicas e genotípicas, parâmetros de interesse forense e verificada a possibilidade de ocorrencia de desvio meiótico. Fenótipos estão em Equilíbrio de Hardy-Weinberg (P = 0,2653); o perfil de freqüências alélicas mostrou-se semelhante ao de populações européias e africanas, mas diferentes da população Asiática. Nossos achados não confirmam a existência de desvio meiótico

Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

Abstract\ud \ud \ud \ud Background\ud Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics.\ud \ud \ud \ud Results\ud The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis.\ud \ud \ud \ud Conclusions\ud These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.This study was funded by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP - (FAPESP #2008/56594-8, FAPESP #2010/05622-1, FAPESP #210/00932-2, FAPESP #2010/12069-7), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq # 563731/2010-9), and NAP-DIN (Núcleo de Apoio à Pesquisa em Doenças Inflamatórias)

Transcriptional profiles of the human pathogenic fungus paracoccidioides brasiliensis in mycelium and yeast cells

This work was supported by MCT, CNPq, CAPES, FUB, UFG, and FUNDECT-MS. PbGenome Network: Alda Maria T. Ferreira, Alessandra Dantas, Alessandra J. Baptista, Alexandre M. Bailão, Ana Lídia Bonato, André C. Amaral, Bruno S. Daher, Camila M. Silva, Christiane S. Costa, Clayton L. Borges, Cléber O. Soares, Cristina M. Junta, Daniel A. S. Anjos, Edans F. O. Sandes, Eduardo A. Donadi, Elza T. Sakamoto-Hojo, Flábio R. Araújo, Flávia C. Albuquerque, Gina C. Oliveira, João Ricardo M. Almeida, Juliana C. Oliveira, Kláudia G. Jorge, Larissa Fernandes, Lorena S. Derengowski, Luís Artur M. Bataus, Marcus A. M. Araújo, Marcus K. Inoue, Marlene T. De-Souza, Mauro F. Almeida, Nádia S. Parachin, Nadya S. Castro, Odair P. Martins, Patrícia L. N. Costa, Paula Sandrin-Garcia, Renata B. A. Soares, Stephano S. Mello, and Viviane C. B. ReisParacoccidioides brasiliensis is the causative agent of paracoccidioidomycosis, a disease that affects 10 million individuals in Latin America. This report depicts the results of the analysis of 6,022 assembled groups from mycelium and yeast phase expressed sequence tags, covering about 80% of the estimated genome of this dimorphic, thermo-regulated fungus. The data provide a comprehensive view of the fungal metabolism, including overexpressed transcripts, stage-specific genes, and also those that are up- or down-regulated as assessed by in silico electronic subtraction and cDNA microarrays. Also, a significant differential expression pattern in mycelium and yeast cells was detected, which was confirmed by Northern blot analysis, providing insights into differential metabolic adaptations. The overall transcriptome analysis provided information about sequences related to the cell cycle, stress response, drug resistance, and signal transduction pathways of the pathogen. Novel P. brasiliensis genes have been identified, probably corresponding to proteins that should be addressed as virulence factor candidates and potential new drug targets

Alterations in gene expression profiles correlated with cisplatin cytotoxicity in the glioma U343 cell line

Gliomas are the most common tumors in the central nervous system, the average survival time of patients with glioblastoma multiforme being about 1 year from diagnosis, in spite of harsh therapy. Aiming to study the transcriptional profiles displayed by glioma cells undergoing cisplatin treatment, gene expression analysis was performed by the cDNA microarray method. Cell survival and apoptosis induction following treatment were also evaluated. Drug concentrations of 12.5 to 300 μM caused a pronounced reduction in cell survival rates five days after treatment, whereas concentrations higher than 25 μM were effective in reducing the survival rates to ~1%. However, the maximum apoptosis frequency was 20.4% for 25 μM cisplatin in cells analyzed at 72 h, indicating that apoptosis is not the only kind of cell death induced by cisplatin. An analysis of gene expression revealed 67 significantly (FDR < 0.05) modulated genes: 29 of which down- and 38 up-regulated. These genes belong to several classes (metabolism, protein localization, cell proliferation, apoptosis, adhesion, stress response, cell cycle and DNA repair) that may represent several affected cell processes under the influence of cisplatin treatment. The expression pattern of three genes (RHOA, LIMK2 and TIMP2) was confirmed by the real time PCR method

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Do monumento comemorativo à arte pública

In this paper we tried to define and discuss the concept of public art in light of the theoretical work that justified the creation of this subject, with reference to the main arguments that have characterized the discussion on the topic. To that end, we present a model based on a set of aesthetic and conceptual transformation that occurred in art from the second half of the twentieth century onwards, as far as the plastic formulation, and the perspective of a new understanding of the Spectator place are concerned. This also implies the recognition of old values rejection that characterized the traditional public monument and the failure of its own compositional structure. Thus it is argued that public art is inseparable from the traditional monument crisis, in that it proposes a rupture with this historical context, promoting a new awareness of its form and function. For that purpose we present several artistic projects that illustrate the theoretical and practical implications of this concept.info:eu-repo/semantics/publishedVersio