Discovery of the genes in response to white spot syndrome virus (WSSV) infection in Fenneropenaeus chinensis through cDNA microarray

We used microarray technology to study differentially expressed genes in white spot syndrome virus (WSSV)-infected shrimp. A total of 3136 cDNA targets, including 1578 unique genes from a cephalothorax cDNA library and 1536 cDNA clones from reverse and forward suppression subtractive hybridization (SSH) libraries of Fenneropenaeus chinensis, plus 14 negative and 8 blank control clones, were spotted onto a 18 x 18 mm area of NH2-modified glass slides. Gene expression patterns in the cephalothorax of shrimp at 6 h after WSSV injection and moribund shrimp naturally infected by WSSV were analyzed. A total of 105 elements on the arrays showed a similar regulation pattern in artificially infected shrimp and naturally infected moribund shrimp; parts of the results were confirmed by semiquantitative reverse transcriptase-polymerase chain reaction (RT-PCR). The up-regulated expression of immune-related genes, including heat shock proteins (HSP70 and HSP90), trehalose-phosphate synthase (TPS), ubiquitin C, and so forth, were observed when shrimp were challenged with WSSV. Genes including myosin LC2, ATP synthase A chain, and arginine kinase were found to be down-regulated after WSSV infection. The expression of housekeeping genes such as actin, elongation factor, and tubulin is not stable, and so these genes are not suitable as internal standards for semiquantitative RT-PCR when shrimp are challenged by WSSV. As a substitute, we found that triosephosphate isomerase (TPI) was an ideal candidate of interstandards in this situation.

Computação da Seleção Genômica Ampla (GWS).

Métodos para GWS; Teoria dos métodos de regressão; Computação do método Random (Ridge) Regression BLUP (RR-BLUP/GWS); Fenótipos corrigidos; Frequências alélicas, variância dos marcadores e herdabilidade; Marcadores codominantes (SNP) ? Modelo genotípico; Marcadores dominantes (DArT) - Modelo genotípico; Marcadores codominantes (SNP) ? Modelo gamético ou alélico; Número de marcadores com efeitos significativos; Populações de estimação, validação e seleção; População de validação e Jacknife; Correlação e regressão entre valores genéticos preditos e fenótipos na população de validação; Análise de associação na GWAS; Software Selegen Genômica: Random (Ridge) Regression BLUP: RR-BLUP/GWS; Exemplo aplicado ao melhoramento do eucalipto.

Genômica e proteômica de Anaplasma marginale: contribuições para o controle da riquétsia.

A anaplasmose bovina é causada pela riquétsia intra-eritrocítica Anaplasma marginale, responsável por importantes prejuízos econômicos, por causa da alta morbidade e mortalidade em rebanhos bovinos suscetíveis. A vacinação tem sido uma forma econômica e eficiente de controlar a enfermidade. No entanto, os métodos de imunização tradicionais apresentam efeitos adversos em algumas categorias de animais. Nas últimas décadas, os estudos sobre imunização contra Anaplasma concentraram-se nas proteínas de superfície MSP1a, 1b, 2, 3, 4 e 5. No entanto, até o momento, os resultados foram pouco promissores, apontando a necessidade de ampliar o conhecimento sobre o rol das proteínas de membrana da riquétsia e das relações estruturais entre elas. Nesse contexto, os estudos do genoma e do proteoma da riquétsia têm contribuído com essa finalidade. Pela análise genômica, 14 genes para novas proteínas de membrana externa foram identificados (omp 1-14), dentre os quais, omp2, 3 e 6 não são transcritos. Esses genes ostraram-se altamente conservados entre isolados da riquétsia. As proteínas OMP4, 7, 10 e 14 foram reconhecidas por soros de bovinos imunizados com membrana de A. marginale, mostrando potencial para desenvolvimento de imunógenos. Além disso, mediante análise proteômica, foi possível detectar novas proteínas de membrana, negligenciadas pela anotação genômica. Dentre elas estão AM097 - conjugal transfer protein, AM956 - PepA citosol amino peptidase, AM254 - fator de elongação Tu e quatro proteínas de função desconhecida: AM127, 197, 387 e 854, as quais também foram reconhecidas por soros de bovinos imunizados com membrana de A. marginale.

Analise computacional para estudo de genômica funcional. I. Identificação do domínio de ligação de calmodulina em proteinas HSP70 e BIP.

2003

Estudo de modelagem molecular por homologia para análise genômica funcional. I - BIP (Binding Protein).

2005

Hibridização genômica in situ em triticeae: um enfoque metodológico.

2009

A citogenética na caracterização genômica do trigo.

2003

A tecnologia de microarranjos na identificação de genes de interesse na bovinocultura.

Como ferramenta de análise da expressão gênica, a tecnologia dos microarranjos de DNA permite a investigação de milhares de transcritos de um tecido, de maneira simultânea. Essa metodologia tem revolucionado diversas áreas do conhecimento, por meio do aumento substancial da capacidade analítica dos processos moleculares. Hoje, dentro da ciência animal, a disponibilidade desse método de investigação tem permitido aos pesquisadores identificar variações na expressão de determinados genes que possam ocorrer como respostas biológicas devido à condição experimental (idade, raça, estado fisiológico). Sob um aspecto prático, essa tecnologia tem seu papel na identificação de genes envolvidos na determinação de características fenotípicas de interesse econômico, os quais podem ser usados no desenvolvimento de métodos para a seleção de genótipos superiores dentro dos programas de Melhoramento Genético. Esse é um dos motivos de sua adoção em um dos projetos que compõe a carteira dos Macroprogramas da Embrapa, o MP1 Rede Genômica Animal. Assim, esta publicação tem como propósito posicionar o leitor a respeito da contribuição que a tecnologia de microarranjos de expressão tem trazido com respeito à identificação de genes e processos biológicos que atuam na manifestação de características de interesse econômico, em especial na bovinocultura. Além disso, pretende despertar o interesse da comunidade científica para uma área que ainda demanda muita pesquisa e à qual a Embrapa Informática Agropecuária tem dedicado um grande esforço: a análise dos dados gerados dos experimentos com microarranjos da Rede Genômica Animal.

How well do we understand the clusters found in microarray data?

Clare, A. and King R.D. (2002) How well do we understand the clusters found in microarray data? In In Silico Biol. 2, 0046

In situ synthesis of DNA microarray on functionalized cyclic olefin copolymer substrate.

Thermoplastic materials such as cyclic-olefin copolymers (COC) provide a versatile and cost-effective alternative to the traditional glass or silicon substrate for rapid prototyping and industrial scale fabrication of microdevices. To extend the utility of COC as an effective microarray substrate, we developed a new method that enabled for the first time in situ synthesis of DNA oligonucleotide microarrays on the COC substrate. To achieve high-quality DNA synthesis, a SiO(2) thin film array was prepatterned on the inert and hydrophobic COC surface using RF sputtering technique. The subsequent in situ DNA synthesis was confined to the surface of the prepatterned hydrophilic SiO(2) thin film features by precision delivery of the phosphoramidite chemistry using an inkjet DNA synthesizer. The in situ SiO(2)-COC DNA microarray demonstrated superior quality and stability in hybridization assays and thermal cycling reactions. Furthermore, we demonstrate that pools of high-quality mixed-oligos could be cleaved off the SiO(2)-COC microarrays and used directly for construction of DNA origami nanostructures. It is believed that this method will not only enable synthesis of high-quality and low-cost COC DNA microarrays but also provide a basis for further development of integrated microfluidics microarrays for a broad range of bioanalytical and biofabrication applications.

SplicerAV: a tool for mining microarray expression data for changes in RNA processing.

BACKGROUND: Over the past two decades more than fifty thousand unique clinical and biological samples have been assayed using the Affymetrix HG-U133 and HG-U95 GeneChip microarray platforms. This substantial repository has been used extensively to characterize changes in gene expression between biological samples, but has not been previously mined en masse for changes in mRNA processing. We explored the possibility of using HG-U133 microarray data to identify changes in alternative mRNA processing in several available archival datasets. RESULTS: Data from these and other gene expression microarrays can now be mined for changes in transcript isoform abundance using a program described here, SplicerAV. Using in vivo and in vitro breast cancer microarray datasets, SplicerAV was able to perform both gene and isoform specific expression profiling within the same microarray dataset. Our reanalysis of Affymetrix U133 plus 2.0 data generated by in vitro over-expression of HRAS, E2F3, beta-catenin (CTNNB1), SRC, and MYC identified several hundred oncogene-induced mRNA isoform changes, one of which recognized a previously unknown mechanism of EGFR family activation. Using clinical data, SplicerAV predicted 241 isoform changes between low and high grade breast tumors; with changes enriched among genes coding for guanyl-nucleotide exchange factors, metalloprotease inhibitors, and mRNA processing factors. Isoform changes in 15 genes were associated with aggressive cancer across the three breast cancer datasets. CONCLUSIONS: Using SplicerAV, we identified several hundred previously uncharacterized isoform changes induced by in vitro oncogene over-expression and revealed a previously unknown mechanism of EGFR activation in human mammary epithelial cells. We analyzed Affymetrix GeneChip data from over 400 human breast tumors in three independent studies, making this the largest clinical dataset analyzed for en masse changes in alternative mRNA processing. The capacity to detect RNA isoform changes in archival microarray data using SplicerAV allowed us to carry out the first analysis of isoform specific mRNA changes directly associated with cancer survival.

permGPU: Using graphics processing units in RNA microarray association studies.

BACKGROUND: Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. RESULTS: We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. CONCLUSIONS: permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.

Robust test method for time-course microarray experiments.

BACKGROUND: In a time-course microarray experiment, the expression level for each gene is observed across a number of time-points in order to characterize the temporal trajectories of the gene-expression profiles. For many of these experiments, the scientific aim is the identification of genes for which the trajectories depend on an experimental or phenotypic factor. There is an extensive recent body of literature on statistical methodology for addressing this analytical problem. Most of the existing methods are based on estimating the time-course trajectories using parametric or non-parametric mean regression methods. The sensitivity of these regression methods to outliers, an issue that is well documented in the statistical literature, should be of concern when analyzing microarray data. RESULTS: In this paper, we propose a robust testing method for identifying genes whose expression time profiles depend on a factor. Furthermore, we propose a multiple testing procedure to adjust for multiplicity. CONCLUSIONS: Through an extensive simulation study, we will illustrate the performance of our method. Finally, we will report the results from applying our method to a case study and discussing potential extensions.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Two genes on A/J chromosome 18 are associated with susceptibility to Staphylococcus aureus infection by combined microarray and QTL analyses.

Although it has recently been shown that A/J mice are highly susceptible to Staphylococcus aureus sepsis as compared to C57BL/6J, the specific genes responsible for this differential phenotype are unknown. Using chromosome substitution strains (CSS), we found that loci on chromosomes 8, 11, and 18 influence susceptibility to S. aureus sepsis in A/J mice. We then used two candidate gene selection strategies to identify genes on these three chromosomes associated with S. aureus susceptibility, and targeted genes identified by both gene selection strategies. First, we used whole genome transcription profiling to identify 191 (56 on chr. 8, 100 on chr. 11, and 35 on chr. 18) genes on our three chromosomes of interest that are differentially expressed between S. aureus-infected A/J and C57BL/6J. Second, we identified two significant quantitative trait loci (QTL) for survival post-infection on chr. 18 using N(2) backcross mice (F(1) [C18A]xC57BL/6J). Ten genes on chr. 18 (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTL regions and were also identified by the expression array selection strategy. Using real-time PCR, 6 of these 10 genes (Chmp1b, Dtwd2, Isoc1, Lman1, Tnfaip8, and Seh1l) showed significantly different expression levels between S. aureus-infected A/J and C57BL/6J. For two (Tnfaip8 and Seh1l) of these 6 genes, siRNA-mediated knockdown of gene expression in S. aureus-challenged RAW264.7 macrophages induced significant changes in the cytokine response (IL-1 beta and GM-CSF) compared to negative controls. These cytokine response changes were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18 mice (which contain A/J chromosome 18 but are otherwise C57BL/6J), but not C57BL/6J mice. These findings suggest that two genes, Tnfaip8 and Seh1l, may contribute to susceptibility to S. aureus in A/J mice, and represent promising candidates for human genetic susceptibility studies.