In situ FTIR spectroelectrochemistry of a microarray electrode

A thin-layer microdisk array electrode (TLMDAE) was designed for in situ reflectance FTIR spectroelectrochemical studies. A theoretical estimation, cyclic voltammetry, chronoamperometry and in situ IR measurements demonstrate that this novel design of array electrode results in advantages such as reduced ohmic potential drop, small cell constant and facility for diffusional exchange between thin layer and bulk solution. It has been suggested that the enhanced edge diffusion on the TLMDAE leads to a reduced accumulation of species in the thin layer. (C) 1997 Elsevier Science S.A.

COBALT PORPHYRIN NAFION FILM ON CARBON MICROARRAY ELECTRODE TO MONITOR OXYGEN FOR ENZYME ANALYSIS OF GLUCOSE

A microcarbon array electrode was modified by the placement of a Nafion film containing cobalt tetramethylpyridyl phorphyrin on its surface. This electrode was applied to the analysis of solution glucose when it was further modified by the immobilization of glucose oxidase on the outermost surface of the Nafion by the cross-linking of serum albumin with glutaraldehyde. The concomitant decrease in the concentration of oxygen, as it was consumed in the enzymatic reaction of glucose with glucose oxidase, was determined by either cyclic voltammetry or a double potential step method at the porphyrin-Nafion catalytic electrode. Glucose could be determined in the range of 0.01-4 mM rapidly, without interference from substances such as ascorbate or other saccharides.

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.

长牡蛎SNP标记的开发及多态性检测

长牡蛎是重要的经济养殖贝类，良种化、抗逆性状及快速生长个体的培育是长牡蛎养殖业得以持续发展的基础。目前飞速发展的分子标记辅助育种技术为优良品种的快速培育提供了理论基础和实践经验。本研究以长牡蛎为主要研究材料，探讨了长牡蛎SNP标记的筛选和多态性评价。 本研究利用已有长牡蛎EST库中的序列进行单核苷酸多态（SNP）标记开发。通过对长牡蛎（Crassostrea gigas）已有的EST序列数据库检索，经过序列聚类和拼接得到EST簇4548个，含有不少于4条EST序列的簇共1079个，经过进一步设置筛选条件，整理出可供利用的EST簇313个，得到候选SNP位点共计1140个。目前根据候选SNP位点共设计引物82组，通过片段长度差异等位基因特异性PCR（fragment length discrepant allele specific PCR，FLDAS-PCR）的分型方法，在一野生群体中进行检测和验证，结果共有17个SNP候选位点显示多态性，期望杂合度分布区间为0.088至0.506，观测杂合度分布区间为0.091至0.667；通过哈代-温伯格(HW) 平衡、连锁不平衡检验，结果显示除3个SNP位点的差异显著（P值<0.05）,不符合HW平衡之外，其他14个位点没有明显的连锁不平衡。对含有17个SNP的EST的共同序列进行BlastX分析，推测其功能并确定开放阅读框，从而预测17个SNP的性质。 本研究表明对于目前基因组学研究尚处在初级阶段的海洋生物物种，通过基于EST数据库的SNP开发是一条重要途径，可以有效弥补海洋生物基因组学滞后影响SNP标记开发的现状。

Estrutura básica do MicroArray Gene Expression Markup Language - MAGE-ML.

Um dos objetivos da Rede Genômica Animal é a identificação de genes que contribuam para o melhoramento de características de interesse econômico em animais de produção. Uma das ferramentas para prospecção e análise desses genes é o Microarranjo de DNA, uma técnica que permite avaliar a expressão gênica em condições específicas. Apesar de seu uso amplamente difundido na comunidade científica, os procedimentos e as informações de experimentos nem sempre são padronizados, a despeito dos esforços na criação de uma linguagem padrão como o MAGE-ML. Este documento visa apresentar o padrão MAGE-ML para aqueles que ainda não se utilizam desse recurso e gostariam de aprender um pouco a respeito.

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

The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports

BACKGROUND:The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies.METHODS:Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests.RESULTS:The participants were white of European descent and from the FHS Original and Offspring Cohorts (examination 1 Offspring mean age 32 +/- 9 years, 54% women). This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequency [greater than or equal to] 10%, genotype call rate [greater than or equal to] 80%, Hardy-Weinberg equilibrium p-value [greater than or equal to] 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.CONCLUSION:We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication.

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.

BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally "validated" in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.

Functional annotation signatures of disease susceptibility loci improve SNP association analysis.

BACKGROUND: Genetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan. RESULTS: We developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations. CONCLUSIONS: We show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.