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Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

This article is from BMC Proceedings , volume 5 . Abstract Background: The gene networks underlying closure of the optic fissure during vertebrate eye development are not well-understood. We use a novel clustering method based on nonlinear dimension reduction with data labeling to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure. Results: Our nonlinear methods created clusters of genes that mapped onto more specific biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates than conventional linear cluster algorithms. Our new methods build on the advantages of LCM to isolate pure phenotypic populations within complex tissues in order to identify systems biology relationships among critical gene products expressed at lower copy number. Conclusions: The combination of LCM of embryonic organs, gene expression microarrays, and nonlinear dimension reduction with labeling is a potentially useful approach to extract subtle spatial and temporal co-variations within the gene regulatory networks that specify mammalian organogenesis and organ function. Our results motivate further analysis of nonlinear dimension reduction with labeling within other microarray data sets from LCM dissected tissues or other cell specific samples to determine the more general utility of our method for uncovering more specific biological functional relationships.

This article is from Theoretical Biology & Medical Modelling , volume 11 . Abstract Background: Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Methods: Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Results: Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. Conclusions: This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results respect to the SFS approach, independently of the classifier used. Regarding classifiers, even if C-MANTEC did not achieve the best overall results, the performance was competitive with a very robust behaviour in terms of the parameters of the algorithm, and thus it can be considered as a candidate technique for future studies.

This article is from Asian-Australasian Journal of Animal Sciences , volume 25 . Abstract During embryo implantation in pigs, the uterine endometrium undergoes dramatic morphological and functional changes accompanied with dynamic gene expression. Since the greatest amount of embryonic losses occur during this period, it is essential to understand the expression and function of genes in the uterine endometrium. Although many reports have studied gene expression in the uterine endometrium during the estrous cycle and pregnancy, the pattern of global gene expression in the uterine endometrium in response to the presence of a conceptus (embryo/fetus and associated extraembryonic membranes) has not been completely determined. To better understand the expression of pregnancy-specific genes in the endometrium during the implantation period, we analyzed global gene expression in the endometrium on day (D) 12 and D15 of pregnancy and the estrous cycle using a microarray technique in order to identify differentially expressed endometrial genes between D12 of pregnancy and D12 of the estrous cycle and between D15 of pregnancy and D15 of the estrous cycle. Results showed that the global pattern of gene expression varied with pregnancy status. Among 23,937 genes analyzed, 99 and 213 up-regulated genes and 92 and 231 down-regulated genes were identified as differentially expressed genes (DEGs) in the uterine endometrium on D12 and D15 of pregnancy compared to D12 and D15 of the estrous cycle, respectively. Functional annotation clustering analysis showed that those DEGs included genes involved in immunity, steroidogenesis, cell-to-cell interaction, and tissue remodeling. These findings suggest that the implantation process regulates differential endometrial gene expression to support the establishment of pregnancy in pigs. Further analysis of the genes identified in this study will provide insight into the cellular and molecular bases of the implantation process in pigs.

This article is from Molecular Genetics & Genomic Medicine , volume 2 . Abstract Chromosomal microarray analysis (CMA) is now established as the first-tier cytogenetic diagnostic test for fast and accurate detection of chromosomal abnormalities in patients with developmental delay/intellectual disability (DD/ID), multiple congenital anomalies (MCA), and autism spectrum disorders (ASD). We present our experience with using CMA for postnatal and prenatal diagnosis in Estonian patients during 2009–2012. Since 2011, CMA is on the official service list of the Estonian Health Insurance Fund and is performed as the first-tier cytogenetic test for patients with DD/ID, MCA or ASD. A total of 1191 patients were analyzed, including postnatal (1072 [90%] patients and 59 [5%] family members) and prenatal referrals (60 [5%] fetuses). Abnormal results were reported in 298 (25%) patients, with a total of 351 findings (1–3 per individual): 147 (42%) deletions, 106 (30%) duplications, 89 (25%) long contiguous stretches of homozygosity (LCSH) events (>5 Mb), and nine (3%) aneuploidies. Of all findings, 143 (41%) were defined as pathogenic or likely pathogenic; for another 143 findings (41%), most of which were LCSH, the clinical significance remained unknown, while 61 (18%) reported findings can now be reclassified as benign or likely benign. Clinically relevant findings were detected in 126 (11%) patients. However, the proportion of variants of unknown clinical significance was quite high (41% of all findings). It seems that our ability to detect chromosomal abnormalities has far outpaced our ability to understand their role in disease. Thus, the interpretation of CMA findings remains a rather difficult task requiring a close collaboration between clinicians and cytogeneticists.

This article is from BMC Bioinformatics , volume 12 . Abstract Background: Clustering-based methods on gene-expression analysis have been shown to be useful in biomedical applications such as cancer subtype discovery. Among them, Matrix factorization (MF) is advantageous for clustering gene expression patterns from DNA microarray experiments, as it efficiently reduces the dimension of gene expression data. Although several MF methods have been proposed for clustering gene expression patterns, a systematic evaluation has not been reported yet. Results: Here we evaluated the clustering performance of orthogonal and non-orthogonal MFs by a total of nine measurements for performance in four gene expression datasets and one well-known dataset for clustering. Specifically, we employed a non-orthogonal MF algorithm, BSNMF (Bi-directional Sparse Non-negative Matrix Factorization), that applies bi-directional sparseness constraints superimposed on non-negative constraints, comprising a few dominantly co-expressed genes and samples together. Non-orthogonal MFs tended to show better clustering-quality and prediction-accuracy indices than orthogonal MFs as well as a traditional method, K-means. Moreover, BSNMF showed improved performance in these measurements. Non-orthogonal MFs including BSNMF showed also good performance in the functional enrichment test using Gene Ontology terms and biological pathways. Conclusions: In conclusion, the clustering performance of orthogonal and non-orthogonal MFs was appropriately evaluated for clustering microarray data by comprehensive measurements. This study showed that non-orthogonal MFs have better performance than orthogonal MFs and K-means for clustering microarray data.

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASAs GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-boxkelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASAs VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASAs GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-boxkelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASAs VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

This article is from Bioinformatics , volume 29 . Abstract Summary: Microarrays are commonly used to detect changes in gene expression between different biological samples. For this purpose, many analysis tools have been developed that offer visualization, statistical analysis and more sophisticated analysis methods. Most of these tools are designed specifically for messenger RNA microarrays. However, today, more and more different microarray platforms are available. Changes in DNA methylation, microRNA expression or even protein phosphorylation states can be detected with specialized arrays. For these microarray technologies, the number of available tools is small compared with mRNA analysis tools. Especially, a joint analysis of different microarray platforms that have been used on the same set of biological samples is hardly supported by most microarray analysis tools. Here, we present InCroMAP, a tool for the analysis and visualization of high-level microarray data from individual or multiple different platforms. Currently, InCroMAP supports mRNA, microRNA, DNA methylation and protein modification datasets. Several methods are offered that allow for an integrated analysis of data from those platforms. The available features of InCroMAP range from visualization of DNA methylation data over annotation of microRNA targets and integrated gene set enrichment analysis to a joint visualization of data from all platforms in the context of metabolic or signalling pathways.Availability: InCroMAP is freely available as Java™ application at www.cogsys.cs.uni-tuebingen.de/software/InCroMAP, including a comprehensive user’s guide and example files.Contact:[email protected] or [email protected]

This article is from Nucleic Acids Research , volume 41 . Abstract Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan–Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management.

This article is from BMC Genomics , volume 12 . Abstract Background: One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward. Results: This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of confirmed that Xcc_CDS002 encoding a novel SIR2-like domain protein is involved in virulence and Xcc_CDS1553 encoding a ArsR family transcription factor is involved in arsenate resistance. Conclusions: Despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.

This article is from BMC Genomics , volume 12 . Abstract Background: One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward. Results: This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of confirmed that Xcc_CDS002 encoding a novel SIR2-like domain protein is involved in virulence and Xcc_CDS1553 encoding a ArsR family transcription factor is involved in arsenate resistance. Conclusions: Despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.

This article is from BMC Genomics , volume 12 . Abstract Background: One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward. Results: This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of confirmed that Xcc_CDS002 encoding a novel SIR2-like domain protein is involved in virulence and Xcc_CDS1553 encoding a ArsR family transcription factor is involved in arsenate resistance. Conclusions: Despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.

This article is from BMC Genomics , volume 12 . Abstract Background: One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward. Results: This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of confirmed that Xcc_CDS002 encoding a novel SIR2-like domain protein is involved in virulence and Xcc_CDS1553 encoding a ArsR family transcription factor is involved in arsenate resistance. Conclusions: Despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.

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This article is from BMC Bioinformatics , volume 11 . Abstract Background: An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Results: Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. Conclusions: GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

This article is from Bioinformatics , volume 30 . Abstract Motivation: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected.Results: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis.Availability: Project URL: http://www.nanocan.org/miracle/Contact: [email protected] information:Supplementary data are available at Bioinformatics online.

Background : Microarray technology has revolutionized the way genomic analysis has been performed. High-throughput data acquisition, brought up a challenge in data comprehension i.e. in gene expression. Methods : k-means cluster obtained after analysis of miRNA expression data have been sorted by an algorithmic procedure. Results : The proposed method managed to sort k-means centroids and manifest a more simple way of drawing conclusions on studied tumor samples. miRNAs were unraveled that changed in expression levels with respect to tumor aggressiveness. Conclusions : In the present work we presented a new and simple approach in data analysis using a new analysis approach, which we termed sorted-k-means analysis.

This article is from PLoS ONE , volume 9 . Abstract The European clam, Ruditapes decussatus is a species with a high commercial importance in Portugal and other Southern European countries. Its production is almost exclusively based on natural recruitment, which is subject to high annual fluctuations. Increased knowledge of the natural reproductive cycle of R. decussatus and its molecular mechanisms would be particularly important in providing new highly valuable genomic information for better understanding the regulation of reproduction in this economically important aquaculture species. In this study, the transcriptomic bases of R. decussatus reproduction have been analysed using a custom oligonucleotide microarray representing 51,678 assembled contigs. Microarray analyses were performed in four gonadal maturation stages from two different Portuguese wild populations, characterized by different responses to spawning induction when used as progenitors in hatchery. A comparison between the two populations elucidated a specific pathway involved in the recognition signals and binding between the oocyte and components of the sperm plasma membrane. We suggest that this pathway can explain part of the differences in terms of spawning induction success between the two populations. In addition, sexes and reproductive stages were compared and a correlation between mRNA levels and gonadal area was investigated. The lists of differentially expressed genes revealed that sex explains most of the variance in gonadal gene expression. Additionally, genes like Foxl2, vitellogenin, condensing 2, mitotic apparatus protein p62, Cep57, sperm associated antigens 6, 16 and 17, motile sperm domain containing protein 2, sperm surface protein Sp17, sperm flagellar proteins 1 and 2 and dpy-30, were identified as being correlated with the gonad area and therefore supposedly with the number and/or the size of the gametes produced.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. Results: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… Conclusions: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

This article is from Journal of Bone Metabolism , volume 21 . Abstract Background: Cyclo-oxygenase-2 (Cox-2) is an inflammatory mediator that is necessary for the tissue repair, including bone fracture healing. Although the application of Cox-2 gene therapy to a murine closed femoral fracture has accelerated bony union, but the beneficial effect was not observed until the endochondral stage of bone repair that is well after the inflammatory stage normally subsides. Methods: To identify the molecular pathways through which Cox-2 regulates fracture healing, we examined gene expression profile in fracture tissues in response to Cox-2 gene therapy during the endochondral bone repair phase. Cox-2 gene therapy was applied to the closed murine femur fracture model. Microarray analysis was performed at 10 days post-fracture to examine global gene expression profile in the fracture tissues during the endochondral bone repair phase. The entire repertoire of significantly expressed genes was examined by gene set enrichment analysis, and the most up-regulated individual genes were evaluated further. Results: The genes that normally promote inflammation were under-represented in the microarray analysis, and the expression of several inflammatory chemokines was significantly down-regulated. There was an up-regulation of two key transcription factor genes that regulate hematopoiesis and erythropoiesis. More surprisingly, there was no significant up-regulation in the genes that are normally involved in angiogenesis or bone formation. However, the expression of two tissue remodeling genes was up-regulated. Conclusions: The down-regulation of the inflammatory genes in response to Cox-2 gene therapy was unexpected, given the pro-inflammatory role of prostaglandins. Cox-2 gene therapy could promote bony union through hematopoietic precursor proliferation during endochondral bone repair and thereby enhances subsequently fracture callus remodeling that leads to bony union of the fracture gap.

Advances in 1st trimester aneuploidy screening have increased the need for early prenatal diagnostic testing. There are only 2 prenatal diagnostic tests : CVS and amniocentesis. In this podcast, we will review these procedures and rates of potential complications. We will also summarize the difference between microarray chromosomal analysis and conventional karyotype.

Advances in 1st trimester aneuploidy screening have increased the need for early prenatal diagnostic testing. There are only 2 prenatal diagnostic tests : CVS and amniocentesis. In this podcast, we will review these procedures and rates of potential complications. We will also summarize the difference between microarray chromosomal analysis and conventional karyotype.

This article is from Experimental and Therapeutic Medicine , volume 5 . Abstract Curcumin, or diferuloylmethane, is a major chemical component of turmeric (Curcuma longa Linn.) that has been consumed as a dietary spice through the ages. This yellow-colored polyphenol has a notably wide range of beneficial properties, including anti-inflammatory, antioxidant, antitumoral, anti-invasive and anti-metastatic activity. In the present study, microarray gene expression analysis was applied to identify the curcumin-regulated genes in a highly invasive human breast carcinoma cell line (MDA-MB 231). Cells were cultured with curcumin (20 μM) for 24 h; total RNA was isolated and hybridized to Whole Human Genome Microarray slides. Gene set enrichment analyses on our whole genome expression data revealed downregulation of the EGF pathway elements following curcumin treatment. Furthermore, gene network analysis identified a significantly relevant network among the differentially expressed genes, centered on the EGR1 and FOS genes. The members of these pathways and networks play an essential role in the regulation of cancer cell growth and development; the majority exhibited decreased expression levels following treatment with curcumin. These observations suggest that curcumin is an excellent candidate for the prevention and treatment of breast cancer.

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, $\hat{L}(D|M)$, and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, $\hat{L}(D_0|M)$. Typically, the computational burden for obtaining $\hat{L}(D_0|M)$ is immense, often exceeding the limits of computing available resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, $\hat{L}(D|M)$, and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, $\hat{L}(D_0|M)$. Typically, the computational burden for obtaining $\hat{L}(D_0|M)$ is immense, often exceeding the limits of computing available resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, $\hat{L}(D|M)$, and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, $\hat{L}(D_0|M)$. Typically, the computational burden for obtaining $\hat{L}(D_0|M)$ is immense, often exceeding the limits of computing available resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.

The increasing availability of high throughput data arising from gene expression studies leads to the necessity of methods for summarizing the available information. As annotation quality improves it is becoming common to rely on the Gene Ontology (GO) to build functional profiles that characterize a set of genes using the frequency of use of each GO term or group of terms in the array. In this work we describe a statistical model for such profiles, provide methods to compare profiles and develop inferential procedures to assess this comparison. An R-package implementing the methods is available.

The increasing availability of high throughput data arising from gene expression studies leads to the necessity of methods for summarizing the available information. As annotation quality improves it is becoming common to rely on the Gene Ontology (GO) to build functional profiles that characterize a set of genes using the frequency of use of each GO term or group of terms in the array. In this work we describe a statistical model for such profiles, provide methods to compare profiles and develop inferential procedures to assess this comparison. An R-package implementing the methods is available.

The increasing availability of high throughput data arising from gene expression studies leads to the necessity of methods for summarizing the available information. As annotation quality improves it is becoming common to rely on the Gene Ontology (GO) to build functional profiles that characterize a set of genes using the frequency of use of each GO term or group of terms in the array. In this work we describe a statistical model for such profiles, provide methods to compare profiles and develop inferential procedures to assess this comparison. An R-package implementing the methods is available.

In this paper, we present a new approach for analyzing gene expression data that builds on topological characteristics of time series. Our goal is to identify cell cycle regulated genes in micro array dataset. We construct a point cloud out of time series using delay coordinate embeddings. Persistent homology is utilized to analyse the topology of the point cloud for detection of periodicity. This novel technique is accurate and robust to noise, missing data points and varying sampling intervals. Our experiments using Yeast Saccharomyces cerevisiae dataset substantiate the capabilities of the proposed method.

This article is from Molecular Cytogenetics , volume 7 . Abstract Background: Chromosome abnormalities that segregate with a disease phenotype can facilitate the identification of disease loci and genes. The relationship between chromosome 18 anomalies with severe intellectual disability has attracted the attention of cytogeneticists worldwide. Duplications of the X chromosome can cause intellectual disability in females with variable phenotypic effects, due in part to variations in X-inactivation patterns. Additionally, deletions of the 7qter region are associated with a range of phenotypes. Results: We report the first case of de novo microdeletion at 7q and 18p, 18q partial trisomy, microduplication at Xp associated to intellectual disability in a Brazilian child, presenting a normal karyotype. Karyotyping showed any chromosome alteration. Chromosomal microarray analysis detected a de novo microdeletion at 18p11.32 and 18q partial trisomy, an inherited microdeletion at 7q31.1 and a de novo microduplication at Xp22.33p21.3. Conclusions: Our report illustrates a case that presents complex genomic imbalances which may contribute to a severe clinical phenotypes. The rare and complex phenotypes have to be investigated to define the subsets and allow the phenotypes classification.

This article is from Molecular Cytogenetics , volume 7 . Abstract Background: Chromosome abnormalities that segregate with a disease phenotype can facilitate the identification of disease loci and genes. The relationship between chromosome 18 anomalies with severe intellectual disability has attracted the attention of cytogeneticists worldwide. Duplications of the X chromosome can cause intellectual disability in females with variable phenotypic effects, due in part to variations in X-inactivation patterns. Additionally, deletions of the 7qter region are associated with a range of phenotypes. Results: We report the first case of de novo microdeletion at 7q and 18p, 18q partial trisomy, microduplication at Xp associated to intellectual disability in a Brazilian child, presenting a normal karyotype. Karyotyping showed any chromosome alteration. Chromosomal microarray analysis detected a de novo microdeletion at 18p11.32 and 18q partial trisomy, an inherited microdeletion at 7q31.1 and a de novo microduplication at Xp22.33p21.3. Conclusions: Our report illustrates a case that presents complex genomic imbalances which may contribute to a severe clinical phenotypes. The rare and complex phenotypes have to be investigated to define the subsets and allow the phenotypes classification.

This article is from BMC Genomics , volume 15 . Abstract Background: Regulation of gene expression plays a central role in embryonic development. Early stages are controlled by gametic transcripts, which are subsequently substituted with transcripts from the genome of the zygote. Transcriptomic analyses provide an efficient approach to explore the temporal gene expression profiles in embryos and to search for the developmental regulators. We report a study of early Atlantic cod development that used a genome-wide oligonucleotide microarray to examine the composition and putative roles of polyadenylated transcripts. Results: The analyses were carried out in unfertilized oocytes, newly fertilized oocytes and embryos at the stages of mid-blastula transition and segmentation. Numerous genes transcribed in oocytes are involved in multiple aspects of cell maintenance and protection, including metabolism, signal perception and transduction, RNA processing, cell cycle, defense against pathogens and DNA damage. Transcripts found in unfertilized oocytes also encoded a large number of proteins implicated in cell adherence, tight junction and focal adhesion, suggesting high complexity in terms of structure and cellular interactions in embryos prior to midblastula transition (MBT). Prezygotic transcripts included multiple regulators that are most likely involved in developmental processes that take place long after fertilization, such as components of ErbB, hedgehog, notch, retinoid, TGFb, VEGF and Wnt signaling pathways, as well as transcripts involved in the development of nervous system. The major event of MBT was the activation of a large group of histones and other genes that modify chromatin structure preceding massive gene expression changes. A hallmark of events observed during segmentation was the induction of multiple transcription factors, including a large group of homeobox proteins in pace with decay of a large fraction of maternal transcripts. Microarray analyses detected a suite of master developmental regulators that control differentiation and maintenance of diverse cell lineages. Conclusions: Transcriptome profiling of the early stages in Atlantic cod revealed the presence of transcripts involved in patterning and development of tissues and organs long before activation of the zygotic genome. The switch from maternal to zygotic developmental programs is associated with large-scale modification of chromosomes. Electronic supplementary material: The online version of this article (doi:10.1186/1471-2164-15-594) contains supplementary material, which is available to authorized users.

This article is from PLoS ONE , volume 9 . Abstract LncRNAs, which represent one of the most highly expressed classes of ncRNAs in the brain, are becoming increasingly interesting with regard to brain functions and disorders. However, changes in the expression of regulatory lncRNAs in Major Depressive Disorder (MDD) have not yet been reported. Using microarrays, we profiled the expression of 34834 lncRNAs and 39224 mRNAs in peripheral blood sampled from MDD patients as well as demographically-matched controls. Among these, we found that 2007 lncRNAs and 1667 mRNAs were differentially expressed, 17 of which were documented as depression-related gene in previous studies. Gene Ontology (GO) and pathway analyses indicated that the biological functions of differentially expressed mRNAs were related to fundamental metabolic processes and neurodevelopment diseases. To investigate the potential regulatory roles of the differentially expressed lncRNAs on the mRNAs, we also constructed co-expression networks composed of the lncRNAs and mRNAs, which shows significant correlated patterns of expression. In the MDD-derived network, there were a greater number of nodes and connections than that in the control-derived network. The lncRNAs located at chr10:874695-874794, chr10:75873456-75873642, and chr3:47048304-47048512 may be important factors regulating the expression of mRNAs as they have previously been reported associations with MDD. This study is the first to explore genome-wide lncRNA expression and co-expression with mRNA patterns in MDD using microarray technology. We identified circulating lncRNAs that are aberrantly expressed in MDD and the results suggest that lncRNAs may contribute to the molecular pathogenesis of MDD.

This article is from PLoS ONE , volume 9 . Abstract Four systems of cyclic somatic embryogenesis of Siberian ginseng (Eleutherococcus senticosus Maxim) were used to study the mechanism of embryonic cell cluster generation. The first, direct somatic embryo induction (DSEI), generates secondary embryos directly from the primary somatic embryos; the second, direct embryogenic cell cluster induction (DEC)), induces embryogenic cell clusters directly from somatic embryos in agar medium. Subsequently, we found that when DEC-derived somatic embryos are transferred to suspension culture or a bioreactor culture, only somatic embryos are induced, and embryogenic cell clusters cannot form. Therefore, these new lines were named DEC cultured by liquid medium (ECS) and DEC cultured by bioreactor (ECB), respectively. Transmission electron microscopy showed that DEC epidermal cells contained a variety of inclusions, distinct from other lines. A cDNA library of DEC was constructed, and 1,948 gene clusters were obtained and used as probes. RNA was prepared from somatic embryos from each of the four lines and hybridized to a microarray. In DEC, 7 genes were specifically upregulated compared with the other three lines, and 4 genes were downregulated. EsXTH1 and EsPLT1, which were among the genes upregulated in DEC, were cloned using the rapid amplification of cDNA ends (RACE). Real-time quantitative PCR showed EsXTH1 was more highly expressed in DEC than in other lines throughout the culture cycle, and EsPLT1 expression in DEC increased as culture duration increased, but remained at a low expression level in other lines. These results suggest that EsXTH1 and EsPLT1 may be the essential genes that play important roles during the induction of embryogenic cell clusters.

This article is from PLoS ONE , volume 8 . Abstract Motivation: DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Results: Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub’s leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

This article is from Sensors (Basel, Switzerland) , volume 12 . Abstract Chemical contaminants in food have caused serious health issues in both humans and animals. Microarray technology is an advanced technique suitable for the analysis of chemical contaminates. In particular, immuno-microarray approach is one of the most promising methods for chemical contaminants analysis. The use of microarrays for the analysis of chemical contaminants is the subject of this review. Fabrication strategies and detection methods for chemical contaminants are discussed in detail. Application to the analysis of mycotoxins, biotoxins, pesticide residues, and pharmaceutical residues is also described. Finally, future challenges and opportunities are discussed.

This article is from PLoS ONE , volume 9 . Abstract The rapid evolution of toxin resistance in animals has important consequences for the ecology of species and our economy. Pesticide resistance in insects has been a subject of intensive study; however, very little is known about how Drosophila species became resistant to natural toxins with ecological relevance, such as α-amanitin that is produced in deadly poisonous mushrooms. Here we performed a microarray study to elucidate the genes, chromosomal loci, molecular functions, biological processes, and cellular components that contribute to the α-amanitin resistance phenotype in Drosophila melanogaster. We suggest that toxin entry blockage through the cuticle, phase I and II detoxification, sequestration in lipid particles, and proteolytic cleavage of α-amanitin contribute in concert to this quantitative trait. We speculate that the resistance to mushroom toxins in D. melanogaster and perhaps in mycophagous Drosophila species has evolved as cross-resistance to pesticides, other xenobiotic substances, or environmental stress factors.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results: To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions: GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results: To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions: GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

This article is from BMC Genomics , volume 14 . Abstract Background: Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players in the complex networks of gene regulation, and cancer miRNA expression studies have evidenced a direct involvement of miRNAs in cancer progression. Results: In this study, we have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by reverse transcription quantitative PCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomiRs) among regulated miRNAs. Conclusions: We propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes.

This article is from European Journal of Medical Research , volume 18 . Abstract Background: Osteoporosis affects 200 million people worldwide and places an enormous economic burden on society. We aim to identify the feature genes that are related to osteoprotegerin in osteoporosis and to perform function analysis with DNA microarray from human bone marrow. Methods: We downloaded the gene expression profile GSE35957 from Gene Expression Omnibus database including nine gene chips from bone marrow mesenchymal stem cells of five osteoporotic and four non-osteoporotic subjects. The differentially expressed genes between normal and disease samples were identified by LIMMA package in R language. The interactions among the osteoprotegerin gene (OPG) and differentially expressed genes were searched and visualized by Cytoscape. MCODE and Bingo were used to perform module analysis. Finally, GENECODIS was used to obtain enriched pathways of genes in an interaction network. Results: A total of 656 genes were identified as differentially expressed genes between osteoporotic and non-osteoporotic samples. IL17RC, COL1A1, and ESR1 were identified to interact with OPG directly from the protein-protein interaction network. A module containing ERS1 was screened out, and this module was most significantly enriched in organ development. Pathway enrichment analysis suggested genes in the interaction network were related to focal adhesion. Conclusions: The expression pattern of IL17RC, COL1A1, and ESR1 can be useful in osteoporosis detection, which may help in identifying those populations at high risk for osteoporosis, and in directing treatment of osteoporosis.

This article is from Experimental and Therapeutic Medicine , volume 7 . Abstract The purpose of this study was to identify the feature genes that are associated with non-union skeletal fractures using samples of normal union and non-union skeletal fracture microarray data. The gene expression profile GSE494 was downloaded from the Gene Expression Omnibus database and included 12 samples based on three different platforms (GPL92, GPL93 and GPL8300). Each of the platforms had four sets of expression data, two from normal union skeletal fracture samples and two from non-union skeletal fracture samples. The differentially expressed genes within the three platforms of expression data were identified using packages in R language and the differentially expressed genes common to the three platforms were selected. The selected common differentially expressed genes were further analyzed using bioinformatic methods. The software HitPredict was used to search interactions of the common differentially expressed genes and then FuncAssociate was used to conduct a functional analysis of the genes in the interaction network. Further, the associated pathways were identified using the software WebGestalt. Under the three different platforms, GPL92, GPL93 and GPL8300, the numbers of differentially expressed genes identified were 531, 418 and 914, respectively. The common gene CLU and its interacting genes were most significantly associated with the regulation of sterol transport and the osteoclast differentiation pathway. Upregulation of the gene CLU was identified by comparing data for normal union and non-union skeletal fracture samples. According to the function of CLU and its interacting genes, it was concluded that they inhibit the normal healing process following a fracture, and result in non-union skeletal fractures through the regulation of sterol transport and the pathways of differentiation in osteoclasts.

This article is from BMC Research Notes , volume 5 . Abstract Background: The role of n-3 fatty acids in prevention of breast cancer is well recognized, but the underlying molecular mechanisms are still unclear. In view of the growing need for early detection of breast cancer, Graham et al. (2010) studied the microarray gene expression in histologically normal epithelium of subjects with or without breast cancer. We conducted a secondary analysis of this dataset with a focus on the genes (n = 47) involved in fat and lipid metabolism. We used stepwise multivariate logistic regression analyses, volcano plots and false discovery rates for association analyses. We also conducted meta-analyses of other microarray studies using random effects models for three outcomes--risk of breast cancer (380 breast cancer patients and 240 normal subjects), risk of metastasis (430 metastatic compared to 1104 non-metastatic breast cancers) and risk of recurrence (484 recurring versus 890 non-recurring breast cancers). Results: The HADHA gene [hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase (trifunctional protein), alpha subunit] was significantly under-expressed in breast cancer; more so in those with estrogen receptor-negative status. Our meta-analysis showed an 18.4%-26% reduction in HADHA expression in breast cancer. Also, there was an inconclusive but consistent under-expression of HADHA in subjects with metastatic and recurring breast cancers. Conclusions: Involvement of mitochondria and the mitochondrial trifunctional protein (encoded by HADHA gene) in breast carcinogenesis is known. Our results lend additional support to the possibility of this involvement. Further, our results suggest that targeted subset analysis of large genome-based datasets can provide interesting association signals.

This article is from Physiological Reports , volume 1 . Abstract Analysis of global gene expression in mesenteric control and collateral arteries was used to investigate potential molecules, pathways, and mechanisms responsible for impaired collateral growth in the Spontaneously Hypertensive Rat (SHR). A fundamental difference was observed in overall gene expression pattern in SHR versus Wistar Kyoto (WKY) collaterals; only 6% of genes altered in collaterals were similar between rat strains. Ingenuity® Pathway Analysis (IPA) identified major differences between WKY and SHR in networks and biological functions related to cell growth and proliferation and gene expression. In SHR control arteries, several mechano-sensitive and redox-dependent transcription regulators were downregulated including JUN (−5.2×, P = 0.02), EGR1 (−4.1×, P = 0.01), and NFĸB1 (−1.95×, P = 0.04). Predicted binding sites for NFĸB and AP-1 were present in genes altered in WKY but not SHR collaterals. Immunostaining showed increased NFĸB nuclear translocation in collateral arteries of WKY and apocynin-treated SHR, but not in untreated SHR. siRNA for the p65 subunit suppressed collateral growth in WKY, confirming a functional role of NFkB. Canonical pathways identified by IPA in WKY but not SHR included nitric oxide and renin–angiotensin system signaling. The angiotensin type 1 receptor (AGTR1) exhibited upregulation in WKY collaterals, but downregulation in SHR; pharmacological blockade of AGTR1 with losartan prevented collateral luminal expansion in WKY. Together, these results suggest that collateral growth impairment results from an abnormality in a fundamental regulatory mechanism that occurs at a level between signal transduction and gene transcription and implicate redox-dependent modulation of mechano-sensitive transcription factors such as NFĸB as a potential mechanism.

This article is from Nucleic Acids Research , volume 39 . Abstract A role for BRCA1 in the direct and indirect regulation of transcription is well established. However, a comprehensive view of the degree to which BRCA1 impacts transcriptional regulation on a genome-wide level has not been defined. We performed genome-wide expression profiling and ChIP-chip analysis, comparison of which revealed that although BRCA1 depletion results in transcriptional changes in 1294 genes, only 44 of these are promoter bound by BRCA1. However, 27% of these transcripts were linked to transcriptional regulation possibly explaining the large number of indirect transcriptional changes observed by microarray analysis. We show that no specific consensus sequence exists for BRCA1 DNA binding but rather demonstrate the presence of a number of known and novel transcription factor (TF)- binding sites commonly found on BRCA1 bound promoters. Co-immunoprecipitations confirmed that BRCA1 interacts with a number of these TFs including AP2-α, PAX2 and ZF5. Finally, we show that BRCA1 is bound to a subset of promoters of genes that are not altered by BRCA1 loss, but are transcriptionally regulated in a BRCA1-dependent manner upon DNA damage. These data suggest a model, whereby BRCA1 is present on defined promoters as part of an inactive complex poised to respond to various genotoxic stimuli.

This article is from BMC Bioinformatics , volume 12 . Abstract Background: The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation. Results: We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories. Conclusions: RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.

This article is from BMC Bioinformatics , volume 12 . Abstract Background: The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation. Results: We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories. Conclusions: RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.