Comparison of three microsatellite analysis methods for detecting genetic diversity in Phytophthora sojae (Stramenopila: Oomycete)

Analysis of an organism’s genetic diversity requires a method that gives reliable, reproducible results. Microsatellites are robust markers, however, detection of allele sizes can be difficult with some systems as well as consistency among laboratories. In this study, our two laboratories used 219 isolates of Phytophthora sojae to compare three microsatellite methods. Two capillary electrophoresis methods, the Applied Biosystems 3730 Genetic Analyzer and the CEQ 8000 Genetic Analysis system, detected an average of 2.4-fold more alleles compared to gel electrophoresis with a mean of 8.8 and 3.6 alleles per locus using capillary and gel methods, respectively. The two capillary methods were comparable, although allele sizes differed consistently by an average of 3.2 bp across isolates. Differences between capillary methods could be overcome if reference standard DNA genotypes are shared between collaborating laboratories

Accurate Estimates of Microarray Target Concentration from a Simple Sequence-Independent Langmuir Model

Background: Microarray technology is a commonly used tool for assessing global gene expression. Many models for estimation of target concentration based on observed microarray signal have been proposed, but, in general, these models have been complex and platform-dependent. Principal Findings: We introduce a universal Langmuir model for estimation of absolute target concentration from microarray experiments. We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray. The model also accurately predicts concentration for the MAQC data sets. This approach significantly reduces the computational complexity of quantitative target concentration estimates. Conclusions: Using a simple form of the Langmuir isotherm model, with a minimum of parameters and assumptions, and without explicit modeling of individual probe properties, we were able to recover absolute transcript concentrations with high R 2 on four different array platforms. The results obtained here suggest that with a ‘‘spiked-in’ ’ concentration serie

Query Large Scale Microarray Compendium Datasets Using a Model-Based Bayesian Approach with Variable Selection

In microarray gene expression data analysis, it is often of interest to identify genes that share similar expression profiles with a particular gene such as a key regulatory protein. Multiple studies have been conducted using various correlation measures to identify co-expressed genes. While working well for small datasets, the heterogeneity introduced from increased sample size inevitably reduces the sensitivity and specificity of these approaches. This is because most co-expression relationships do not extend to all experimental conditions. With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge. We develop a model-based gene expression query algorithm built under the Bayesian model selection framework. It is capable of detecting co-expression profiles under a subset of samples/experimental conditions. In addition, it allows linearly transformed expression patterns to be recognized and is robust against sporadic outliers in the data. Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets. Our simulation studies suggest that this method outperforms existing correlation coefficients or mutual information-based query tools. When we apply this new method to the Escherichia coli microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors

Characterization and simulation of cDNA microarray spots using a novel mathematical model

<p>Abstract</p> <p>Background</p> <p>The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images.</p> <p>Results</p> <p>We developed a governing equation of cDNA deposition during evaporation of a drop in the microarray spotting process. The governing equation included four parameters: the surface site density on the support, the extrapolated equilibrium constant for the binding of cDNA molecules with surface sites on glass slides, the macromolecular interaction factor, and the volume constant of a drop of cDNA solution. We simulated cDNA deposition from the single model equation by varying the value of the parameters. The morphology of the resulting cDNA deposit can be classified into three types: a doughnut shape, a peak shape, and a volcano shape. The spot morphology can be changed into a flat shape by varying the experimental conditions while considering the parameters of the governing equation of cDNA deposition. The four parameters were estimated by fitting the governing equation to the real microarray images. With the results of the simulation and the parameter estimation, the phenomenon of the formation of cDNA deposits in each type was investigated.</p> <p>Conclusion</p> <p>This study explains how various spot shapes can exist and suggests which parameters are to be adjusted for obtaining a good spot. This system is able to explore the cDNA microarray spotting process in a predictable, manageable and descriptive manner. We hope it can provide a way to predict the incidents that can occur during a real cDNA microarray experiment, and produce useful data for several research applications involving cDNA microarrays.</p

Differential gene expression between wild-type and Gulo-deficient mice supplied with vitamin C

The aim of this study was to test the hypothesis that hepatic vitamin C (VC) levels in VC deficient mice rescued with high doses of VC supplements still do not reach the optimal levels present in wild-type mice. For this, we used a mouse scurvy model (sfx) in which the L-gulonolactone oxidase gene (Gulo) is deleted. Six age- (6 weeks old) and gender- (female) matched wild-type (WT) and sfx mice (rescued by administering 500 mg of VC/L) were used as the control (WT) and treatment (MT) groups (n = 3 for each group), respectively. Total hepatic RNA was used in triplicate microarray assays for each group. EDGE software was used to identify differentially expressed genes and transcriptomic analysis was used to assess the potential genetic regulation of Gulo gene expression. Hepatic VC concentrations in MT mice were significantly lower than in WT mice, even though there were no morphological differences between the two groups. In MT mice, 269 differentially expressed transcripts were detected (≥ twice the difference between MT and WT mice), including 107 up-regulated and 162 down-regulated genes. These differentially expressed genes included stress-related and exclusively/predominantly hepatocyte genes. Transcriptomic analysis identified a major locus on chromosome 18 that regulates Gulo expression. Since three relevant oxidative genes are located within the critical region of this locus we suspect that they are involved in the down-regulation of oxidative activity in sfx mice

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background: The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings: 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions: Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe ‘‘percent bound’ ’ value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for shor

Genome-Wide Analysis of Histone H3 Lysine9 Modifications in Human Mesenchymal Stem Cell Osteogenic Differentiation

Mesenchymal stem cells (MSCs) possess self-renewal and multi-lineage differentiation potentials. It has been established that epigenetic mechanisms such as histone modifications could be critical for determining the fate of stem cells. In this study, full human genome promoter microarrays and expression microarrays were used to explore the roles of histone modifications (H3K9Ac and H3K9Me2) upon the induction of MSC osteogenic differentiation. Our results revealed that the enrichment of H3K9Ac was decreased globally at the gene promoters, whereas the number of promoters enriched with H3K9Me2 was increased evidently upon osteogenic induction. By a combined analysis of data from both ChIP-on-chip and expression microarrays, a number of differentially expressed genes regulated by H3K9Ac and/or H3K9Me2 were identified, implicating their roles in several biological events, such as cell cycle withdraw and cytoskeleton reconstruction that were essential to differentiation process. In addition, our results showed that the vitamin D receptor played a trans-repression role via alternations of H3K9Ac and H3K9Me2 upon MSC osteogenic differentiation. Data from this study suggested that gene activation and silencing controlled by changes of H3K9Ac and H3K9Me2, respectively, were crucial to MSC osteogenic differentiation

Aberrant expression of RAB1A in human tongue cancer

This study was designed to identify specific gene expression changes in tongue squamous cell carcinomas (TSCCs) compared with normal tissues using in-house cDNA microarray that comprised of 2304 full-length cDNAs from a cDNA library prepared from normal oral tissues, primary oral cancers, and oral cancer cell lines. The genes identified by our microarray system were further analysed at the mRNA or protein expression level in a series of clinical samples by real-time quantitative reverse transcriptase–polymerase chain reaction (qRT–PCR) analysis and imuunohositochemistry. The microarray analysis identified a total of 16 genes that were significantly upregulated in common among four TSCC specimens. Consistent with the results of the microarray, increased mRNA levels of selected genes with known molecular functions were found in the four TSCCs. Among genes identified, Rab1a, a member of the Ras oncogene family, was further analysed for its protein expression in 54 TSCCs and 13 premalignant lesions. We found a high prevalence of Rab1A-overexpression not only in TSCCs (98%) but also in premalignant lesions (93%). Thus, our results suggest that rapid characterisation of the target gene(s) for TSCCs can be accomplished using our in-house cDNA microarray analysis combined with the qRT–PCR and immunohistochemistry, and that the Rab1A is a potential biomarker of tongue carcinogenesis

Accurate molecular classification of cancer using simple rules

<p>Abstract</p> <p>Background</p> <p>One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible.</p> <p>Methods</p> <p>We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets.</p> <p>Results</p> <p>We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods.</p> <p>Conclusion</p> <p>In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.</p