Comparing transformation methods for DNA microarray data.

PubMed

Thygesen, Helene H; Zwinderman, Aeilko H

2004-06-17

When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. We used the ratio between biological variance and measurement variance (which is an F-like statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters. The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method.

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

DOE PAGES

Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; ...

2005-01-01

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis -regulatory element discovery. Themore » causal basis for co-regulation is detected through the use of quantitative trait locus mapping.« less

Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline

PubMed Central

Rahmatallah, Yasir; Emmert-Streib, Frank

2016-01-01

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis. Here we provide a brief review of several statistically different GSA approaches (competitive and self-contained) that can be adapted from microarrays practice as well as those specifically designed for RNA-seq. We evaluate their performance (in terms of Type I error rate, power, robustness to the sample size and heterogeneity, as well as the sensitivity to different types of selection biases) on simulated and real RNA-seq data. Not surprisingly, the performance of various GSA approaches depends only on the statistical hypothesis they test and does not depend on whether the test was developed for microarrays or RNA-seq data. Interestingly, we found that competitive methods have lower power as well as robustness to the samples heterogeneity than self-contained methods, leading to poor results reproducibility. We also found that the power of unsupervised competitive methods depends on the balance between up- and down-regulated genes in tested gene sets. These properties of competitive methods have been overlooked before. Our evaluation provides a concise guideline for selecting GSA approaches, best performing under particular experimental settings in the context of RNA-seq. PMID:26342128

Metabolic parameters linked by Phenotype MicroArray to acid resistance profiles of poultry-associated Salmonella enterica.

USDA-ARS?s Scientific Manuscript database

Phenotype microarrays were analyzed for 51 datasets derived from Salmonella enterica. The top 4 serovars associated with poultry products and one associated with turkey, respectively Typhimurium, Enteritidis, Heidelberg, Infantis and Senftenberg, were represented. Datasets were clustered into two ...

MicroRNA-integrated and network-embedded gene selection with diffusion distance.

PubMed

Huang, Di; Zhou, Xiaobo; Lyon, Christopher J; Hsueh, Willa A; Wong, Stephen T C

2010-10-29

Gene network information has been used to improve gene selection in microarray-based studies by selecting marker genes based both on their expression and the coordinate expression of genes within their gene network under a given condition. Here we propose a new network-embedded gene selection model. In this model, we first address the limitations of microarray data. Microarray data, although widely used for gene selection, measures only mRNA abundance, which does not always reflect the ultimate gene phenotype, since it does not account for post-transcriptional effects. To overcome this important (critical in certain cases) but ignored-in-almost-all-existing-studies limitation, we design a new strategy to integrate together microarray data with the information of microRNA, the major post-transcriptional regulatory factor. We also handle the challenges led by gene collaboration mechanism. To incorporate the biological facts that genes without direct interactions may work closely due to signal transduction and that two genes may be functionally connected through multi paths, we adopt the concept of diffusion distance. This concept permits us to simulate biological signal propagation and therefore to estimate the collaboration probability for all gene pairs, directly or indirectly-connected, according to multi paths connecting them. We demonstrate, using type 2 diabetes (DM2) as an example, that the proposed strategies can enhance the identification of functional gene partners, which is the key issue in a network-embedded gene selection model. More importantly, we show that our gene selection model outperforms related ones. Genes selected by our model 1) have improved classification capability; 2) agree with biological evidence of DM2-association; and 3) are involved in many well-known DM2-associated pathways.

Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data

PubMed Central

Kadarmideen, Haja N; Watson-haigh, Nathan S

2012-01-01

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended. PMID:23144540

Customizing microarrays for neuroscience drug discovery.

PubMed

Girgenti, Matthew J; Newton, Samuel S

2007-08-01

Microarray-based gene profiling has become the centerpiece of gene expression studies in the biological sciences. The ability to now interrogate the entire genome using a single chip demonstrates the progress in technology and instrumentation that has been made over the last two decades. Although this unbiased approach provides researchers with an immense quantity of data, obtaining meaningful insight is not possible without intensive data analysis and processing. Custom developed arrays have emerged as a viable and attractive alternative that can take advantage of this robust technology and tailor it to suit the needs and requirements of individual investigations. The ability to simplify data analysis, reduce noise and carefully optimize experimental conditions makes it a suitable tool that can be effectively utilized in neuroscience drug discovery efforts. Furthermore, incorporating recent advancements in fine focusing gene profiling to include specific cellular phenotypes can help resolve the complex cellular heterogeneity of the brain. This review surveys the use of microarray technology in neuroscience paying special attention to customized arrays and their potential in drug discovery. Novel applications of microarrays and ancillary techniques, such as laser microdissection, FAC sorting and RNA amplification, have also been discussed. The notion that a hypothesis-driven approach can be integrated into drug development programs is highlighted.

Integrating Microarray Data and GRNs.

PubMed

Koumakis, L; Potamias, G; Tsiknakis, M; Zervakis, M; Moustakis, V

2016-01-01

With the completion of the Human Genome Project and the emergence of high-throughput technologies, a vast amount of molecular and biological data are being produced. Two of the most important and significant data sources come from microarray gene-expression experiments and respective databanks (e,g., Gene Expression Omnibus-GEO (http://www.ncbi.nlm.nih.gov/geo)), and from molecular pathways and Gene Regulatory Networks (GRNs) stored and curated in public (e.g., Kyoto Encyclopedia of Genes and Genomes-KEGG (http://www.genome.jp/kegg/pathway.html), Reactome (http://www.reactome.org/ReactomeGWT/entrypoint.html)) as well as in commercial repositories (e.g., Ingenuity IPA (http://www.ingenuity.com/products/ipa)). The association of these two sources aims to give new insight in disease understanding and reveal new molecular targets in the treatment of specific phenotypes.Three major research lines and respective efforts that try to utilize and combine data from both of these sources could be identified, namely: (1) de novo reconstruction of GRNs, (2) identification of Gene-signatures, and (3) identification of differentially expressed GRN functional paths (i.e., sub-GRN paths that distinguish between different phenotypes). In this chapter, we give an overview of the existing methods that support the different types of gene-expression and GRN integration with a focus on methodologies that aim to identify phenotype-discriminant GRNs or subnetworks, and we also present our methodology.

Microarray analysis identifies Salmonella genes belonging to the low-shear modeled microgravity regulon

NASA Technical Reports Server (NTRS)

Wilson, James W.; Ramamurthy, Rajee; Porwollik, Steffen; McClelland, Michael; Hammond, Timothy; Allen, Pat; Ott, C. Mark; Pierson, Duane L.; Nickerson, Cheryl A.

2002-01-01

The low-shear environment of optimized rotation suspension culture allows both eukaryotic and prokaryotic cells to assume physiologically relevant phenotypes that have led to significant advances in fundamental investigations of medical and biological importance. This culture environment has also been used to model microgravity for ground-based studies regarding the impact of space flight on eukaryotic and prokaryotic physiology. We have previously demonstrated that low-shear modeled microgravity (LSMMG) under optimized rotation suspension culture is a novel environmental signal that regulates the virulence, stress resistance, and protein expression levels of Salmonella enterica serovar Typhimurium. However, the mechanisms used by the cells of any species, including Salmonella, to sense and respond to LSMMG and identities of the genes involved are unknown. In this study, we used DNA microarrays to elucidate the global transcriptional response of Salmonella to LSMMG. When compared with identical growth conditions under normal gravity (1 x g), LSMMG differentially regulated the expression of 163 genes distributed throughout the chromosome, representing functionally diverse groups including transcriptional regulators, virulence factors, lipopolysaccharide biosynthetic enzymes, iron-utilization enzymes, and proteins of unknown function. Many of the LSMMG-regulated genes were organized in clusters or operons. The microarray results were further validated by RT-PCR and phenotypic analyses, and they indicate that the ferric uptake regulator is involved in the LSMMG response. The results provide important insight about the Salmonella LSMMG response and could provide clues for the functioning of known Salmonella virulence systems or the identification of uncharacterized bacterial virulence strategies.

MGDB: crossing the marker genes of a user microarray with a database of public-microarrays marker genes.

PubMed

Huerta, Mario; Munyi, Marc; Expósito, David; Querol, Enric; Cedano, Juan

2014-06-15

The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different microarrays comparable with tools easy to use for scientists. We have developed these two solutions in our server, implementing a database of microarray marker genes (Marker Genes Data Base). This database contains the marker genes of all GEO microarray datasets and it is updated monthly with the new microarrays from GEO. Thus, researchers can see whether the marker genes of their microarray are marker genes in other microarrays in the database, expanding the analysis of their microarray to the rest of the public microarrays. This solution helps not only to corroborate the conclusions regarding a researcher's microarray but also to identify the phenotype of different subsets of individuals under investigation, to frame the results with microarray experiments from other species, pathologies or tissues, to search for drugs that promote the transition between the studied phenotypes, to detect undesirable side effects of the treatment applied, etc. Thus, the researcher can quickly add relevant information to his/her studies from all of the previous analyses performed in other studies as long as they have been deposited in public repositories. Marker-gene database tool: http://ibb.uab.es/mgdb © The Author 2014. Published by Oxford University Press.

A microarray whole-genome gene expression dataset in a rat model of inflammatory corneal angiogenesis.

PubMed

Mukwaya, Anthony; Lindvall, Jessica M; Xeroudaki, Maria; Peebo, Beatrice; Ali, Zaheer; Lennikov, Anton; Jensen, Lasse Dahl Ejby; Lagali, Neil

2016-11-22

In angiogenesis with concurrent inflammation, many pathways are activated, some linked to VEGF and others largely VEGF-independent. Pathways involving inflammatory mediators, chemokines, and micro-RNAs may play important roles in maintaining a pro-angiogenic environment or mediating angiogenic regression. Here, we describe a gene expression dataset to facilitate exploration of pro-angiogenic, pro-inflammatory, and remodelling/normalization-associated genes during both an active capillary sprouting phase, and in the restoration of an avascular phenotype. The dataset was generated by microarray analysis of the whole transcriptome in a rat model of suture-induced inflammatory corneal neovascularisation. Regions of active capillary sprout growth or regression in the cornea were harvested and total RNA extracted from four biological replicates per group. High quality RNA was obtained for gene expression analysis using microarrays. Fold change of selected genes was validated by qPCR, and protein expression was evaluated by immunohistochemistry. We provide a gene expression dataset that may be re-used to investigate corneal neovascularisation, and may also have implications in other contexts of inflammation-mediated angiogenesis.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview.

PubMed

Skinner, Jeff; Kotliarov, Yuri; Varma, Sudhir; Mine, Karina L; Yambartsev, Anatoly; Simon, Richard; Huyen, Yentram; Morgun, Andrey

2011-07-14

DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

Extracellular Vesicle (EV) Array: microarray capturing of exosomes and other extracellular vesicles for multiplexed phenotyping.

PubMed

Jørgensen, Malene; Bæk, Rikke; Pedersen, Shona; Søndergaard, Evo K L; Kristensen, Søren R; Varming, Kim

2013-01-01

Exosomes are one of the several types of cell-derived vesicles with a diameter of 30-100 nm. These extracellular vesicles are recognized as potential markers of human diseases such as cancer. However, their use in diagnostic tests requires an objective and high-throughput method to define their phenotype and determine their concentration in biological fluids. To identify circulating as well as cell culture-derived vesicles, the current standard is immunoblotting or a flow cytometrical analysis for specific proteins, both of which requires large amounts of purified vesicles. Based on the technology of protein microarray, we hereby present a highly sensitive Extracellular Vesicle (EV) Array capable of detecting and phenotyping exosomes and other extracellular vesicles from unpurified starting material in a high-throughput manner. To only detect the exosomes captured on the EV Array, a cocktail of antibodies against the tetraspanins CD9, CD63 and CD81 was used. These antibodies were selected to ensure that all exosomes captured are detected, and concomitantly excluding the detection of other types of microvesicles. The limit of detection (LOD) was determined on exosomes derived from the colon cancer cell line LS180. It clarified that supernatant from only approximately 10(4) cells was needed to obtain signals or that only 2.5×10(4) exosomes were required for each microarray spot (~1 nL). Phenotyping was performed on plasma (1-10 µL) from 7 healthy donors, which were applied to the EV Array with a panel of antibodies against 21 different cellular surface antigens and cancer antigens. For each donor, there was considerable heterogeneity in the expression levels of individual markers. The protein profiles of the exosomes (defined as positive for CD9, CD63 and CD81) revealed that only the expression level of CD9 and CD81 was approximately equal in the 7 donors. This implies questioning the use of CD63 as a standard exosomal marker since the expression level of this tetraspanin was considerably lower.

[Diagnosis of a case with Williams-Beuren syndrome with nephrocalcinosis using chromosome microarray analysis].

PubMed

Jin, S J; Liu, M; Long, W J; Luo, X P

2016-12-02

Objective: To explore the clinical phenotypes and the genetic cause for a boy with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders. Method: Routine G-banding and chromosome microarray analysis were applied to a child with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders treated in the Department of Pediatrics of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in September 2015 and his parents to conduct the chromosomal karyotype analysis and the whole genome scanning. Deleted genes were searched in the Decipher and NCBI databases, and their relationships with the clinical phenotypes were analyzed. Result: A six-month-old boy was refered to us because of unexplained growth retardation and feeding intolerance.The affected child presented with abnormal manifestation such as special face, umbilical hernia, growth retardation, hypothyroidism, congenital heart disease, right ear sensorineural deafness, hypercalcemia and nephrocalcinosis. The child's karyotype was 46, XY, 16qh + , and his parents' karyotypes were normal. Chromosome microarray analysis revealed a 1 436 kb deletion on the 7q11.23(72701098_74136633) region of the child. This region included 23 protein-coding genes, which were reported to be corresponding to Williams-Beuren syndrome and its certain clinical phenotypes. His parents' results of chromosome microarray analysis were normal. Conclusion: A boy with characteristic manifestation of Williams-Beuren syndrome and rare nephrocalcinosis was diagnosed using chromosome microarray analysis. The deletion on the 7q11.23 might be related to the clinical phenotypes of Williams-Beuren syndrome, yet further studies are needed.

Microarray analysis identifies Salmonella genes belonging to the low-shear modeled microgravity regulon

PubMed Central

Wilson, James W.; Ramamurthy, Rajee; Porwollik, Steffen; McClelland, Michael; Hammond, Timothy; Allen, Pat; Ott, C. Mark; Pierson, Duane L.; Nickerson, Cheryl A.

2002-01-01

The low-shear environment of optimized rotation suspension culture allows both eukaryotic and prokaryotic cells to assume physiologically relevant phenotypes that have led to significant advances in fundamental investigations of medical and biological importance. This culture environment has also been used to model microgravity for ground-based studies regarding the impact of space flight on eukaryotic and prokaryotic physiology. We have previously demonstrated that low-shear modeled microgravity (LSMMG) under optimized rotation suspension culture is a novel environmental signal that regulates the virulence, stress resistance, and protein expression levels of Salmonella enterica serovar Typhimurium. However, the mechanisms used by the cells of any species, including Salmonella, to sense and respond to LSMMG and identities of the genes involved are unknown. In this study, we used DNA microarrays to elucidate the global transcriptional response of Salmonella to LSMMG. When compared with identical growth conditions under normal gravity (1 × g), LSMMG differentially regulated the expression of 163 genes distributed throughout the chromosome, representing functionally diverse groups including transcriptional regulators, virulence factors, lipopolysaccharide biosynthetic enzymes, iron-utilization enzymes, and proteins of unknown function. Many of the LSMMG-regulated genes were organized in clusters or operons. The microarray results were further validated by RT-PCR and phenotypic analyses, and they indicate that the ferric uptake regulator is involved in the LSMMG response. The results provide important insight about the Salmonella LSMMG response and could provide clues for the functioning of known Salmonella virulence systems or the identification of uncharacterized bacterial virulence strategies. PMID:12370447

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

PubMed Central

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates. PMID:26964035

Selection and phenotypic characterization of a core collection of Brachypodium distachyon inbred lines.

PubMed

Tyler, Ludmila; Fangel, Jonatan U; Fagerström, Alexandra Dotson; Steinwand, Michael A; Raab, Theodore K; Willats, William Gt; Vogel, John P

2014-01-14

The model grass Brachypodium distachyon is increasingly used to study various aspects of grass biology. A large and genotypically diverse collection of B. distachyon germplasm has been assembled by the research community. The natural variation in this collection can serve as a powerful experimental tool for many areas of inquiry, including investigating biomass traits. We surveyed the phenotypic diversity in a large collection of inbred lines and then selected a core collection of lines for more detailed analysis with an emphasis on traits relevant to the use of grasses as biofuel and grain crops. Phenotypic characters examined included plant height, growth habit, stem density, flowering time, and seed weight. We also surveyed differences in cell wall composition using near infrared spectroscopy (NIR) and comprehensive microarray polymer profiling (CoMPP). In all cases, we observed extensive natural variation including a two-fold variation in stem density, four-fold variation in ferulic acid bound to hemicellulose, and 1.7-fold variation in seed mass. These characterizations can provide the criteria for selecting diverse lines for future investigations of the genetic basis of the observed phenotypic variation.

Extracellular microvesicle microRNAs in children with sickle cell anaemia with divergent clinical phenotypes.

PubMed

Khalyfa, Abdelnaby; Khalyfa, Ahamed A; Akbarpour, Mahzad; Connes, Phillippe; Romana, Marc; Lapping-Carr, Gabrielle; Zhang, Chunling; Andrade, Jorge; Gozal, David

2016-09-01

Sickle cell anaemia (SCA) is the most frequent genetic haemoglobinopathy, which exhibits a highly variable clinical course characterized by hyper-coagulable and pro-inflammatory states, as well as endothelial dysfunction. Extracellular microvesicles are released into biological fluids and play a role in modifying the functional phenotype of target cells. We hypothesized that potential differences in plasma-derived extracellular microvesicles (EV) function and cargo from SCA patients may underlie divergent clinical trajectories. Plasma EV from SCA patients with mild, intermediate and severe clinical disease course were isolated, and primary endothelial cell cultures were exposed. Endothelial cell activation, monocyte adhesion, barrier disruption and exosome cargo (microRNA microarrays) were assessed. EV disrupted the endothelial barrier and induced expression of adhesion molecules and monocyte adhesion in a SCA severity-dependent manner compared to healthy children. Microarray approaches identified a restricted signature of exosomal microRNAs that readily distinguished severe from mild SCA, as well as from healthy children. The microRNA candidates were further validated using quantitative real time polymerase chain reaction assays, and revealed putative gene targets. Circulating exosomal microRNAs may play important roles in predicting the clinical course of SCA, and in delineation of individually tailored, mechanistically-based clinical treatment approaches of SCA patients in the near future. © 2016 John Wiley & Sons Ltd.

Gene response profiles for Daphnia pulex exposed to the environmental stressor cadmium reveals novel crustacean metallothioneins.

PubMed

Shaw, Joseph R; Colbourne, John K; Davey, Jennifer C; Glaholt, Stephen P; Hampton, Thomas H; Chen, Celia Y; Folt, Carol L; Hamilton, Joshua W

2007-12-21

Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species Daphnia pulex, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant. Our microarray identified genes reported in the literature to be regulated in response to cadmium exposure, suggested functional attributes for genes that share no sequence similarity to proteins in the public databases, and pointed to genes that are likely members of expanded gene families in the Daphnia genome. Genes identified on the microarray also were associated with cadmium induced phenotypes and population-level outcomes that we experimentally determined. A subset of genes regulated in response to cadmium exposure was independently validated using quantitative-realtime (Q-RT)-PCR. These microarray studies led to the discovery of three genes coding for the metal detoxication protein metallothionein (MT). The gene structures and predicted translated sequences of D. pulex MTs clearly place them in this gene family. Yet, they share little homology with previously characterized MTs. The genomic information obtained from this study represents an important first step in characterizing microarray patterns that may be diagnostic to specific environmental contaminants and give insights into their toxicological mechanisms, while also providing a practical tool for evolutionary, ecological, and toxicological functional gene discovery studies. Advances in Daphnia genomics will enable the further development of this species as a model organism for the environmental sciences.

Gene response profiles for Daphnia pulex exposed to the environmental stressor cadmium reveals novel crustacean metallothioneins

PubMed Central

Shaw, Joseph R; Colbourne, John K; Davey, Jennifer C; Glaholt, Stephen P; Hampton, Thomas H; Chen, Celia Y; Folt, Carol L; Hamilton, Joshua W

2007-01-01

Background Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species Daphnia pulex, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant. Results Our microarray identified genes reported in the literature to be regulated in response to cadmium exposure, suggested functional attributes for genes that share no sequence similarity to proteins in the public databases, and pointed to genes that are likely members of expanded gene families in the Daphnia genome. Genes identified on the microarray also were associated with cadmium induced phenotypes and population-level outcomes that we experimentally determined. A subset of genes regulated in response to cadmium exposure was independently validated using quantitative-realtime (Q-RT)-PCR. These microarray studies led to the discovery of three genes coding for the metal detoxication protein metallothionein (MT). The gene structures and predicted translated sequences of D. pulex MTs clearly place them in this gene family. Yet, they share little homology with previously characterized MTs. Conclusion The genomic information obtained from this study represents an important first step in characterizing microarray patterns that may be diagnostic to specific environmental contaminants and give insights into their toxicological mechanisms, while also providing a practical tool for evolutionary, ecological, and toxicological functional gene discovery studies. Advances in Daphnia genomics will enable the further development of this species as a model organism for the environmental sciences. PMID:18154678

Where statistics and molecular microarray experiments biology meet.

PubMed

Kelmansky, Diana M

2013-01-01

This review chapter presents a statistical point of view to microarray experiments with the purpose of understanding the apparent contradictions that often appear in relation to their results. We give a brief introduction of molecular biology for nonspecialists. We describe microarray experiments from their construction and the biological principles the experiments rely on, to data acquisition and analysis. The role of epidemiological approaches and sample size considerations are also discussed.

Methods to study legionella transcriptome in vitro and in vivo.

PubMed

Faucher, Sebastien P; Shuman, Howard A

2013-01-01

The study of transcriptome responses can provide insight into the regulatory pathways and genetic factors that contribute to a specific phenotype. For bacterial pathogens, it can identify putative new virulence systems and shed light on the mechanisms underlying the regulation of virulence factors. Microarrays have been previously used to study gene regulation in Legionella pneumophila. In the past few years a sharp reduction of the costs associated with microarray experiments together with the availability of relatively inexpensive custom-designed commercial microarrays has made microarray technology an accessible tool for the majority of researchers. Here we describe the methodologies to conduct microarray experiments from in vitro and in vivo samples.

Histological Stratification of Thick and Thin Plaque Psoriasis Explores Molecular Phenotypes with Clinical Implications

PubMed Central

Kim, Dong Joo; Brodmerkel, Carrie; Correa da Rosa, Joel; Krueger, James G.; Suárez-Fariñas, Mayte

2015-01-01

Psoriasis, which presents as red, scaly patches on the body, is a common, autoimmune skin disease that affects 2 to 3 percent of the world population. To leverage recent molecular findings into the personalized treatment of psoriasis, we need a strategy that integrates clinical stratification with molecular phenotyping. In this study, we sought to stratify psoriasis patients by histological measurements of epidermal thickness, and to compare their molecular characterizations by gene expression, serum cytokines, and response to biologics. We obtained histological measures of epidermal thickness in a cohort of 609 psoriasis patients, and identified a mixture of two subpopulations—thick and thin plaque psoriasis—from which they were derived. This stratification was verified in a subcohort of 65 patients from a previously published study with significant differences in inflammatory cell infiltrates in the psoriatic skin. Thick and thin plaque psoriasis shared 84.8% of the meta-analysis-derived psoriasis transcriptome, but a stronger dysregulation of the meta-analysis-derived psoriasis transcriptome was seen in thick plaque psoriasis on microarray. RT-PCR revealed that gene expression in thick and thin plaque psoriasis was different not only within psoriatic lesional skin but also in peripheral non-lesional skin. Additionally, differences in circulating cytokines and their changes in response to biologic treatments were found between the two subgroups. All together, we were able to integrate histological stratification with molecular phenotyping as a way of exploring clinical phenotypes with different expression levels of the psoriasis transcriptome and circulating cytokines. PMID:26176783

The Diagnostic Yield of Array Comparative Genomic Hybridization Is High Regardless of Severity of Intellectual Disability/Developmental Delay in Children.

PubMed

D'Arrigo, Stefano; Gavazzi, Francesco; Alfei, Enrico; Zuffardi, Orsetta; Montomoli, Cristina; Corso, Barbara; Buzzi, Erika; Sciacca, Francesca L; Bulgheroni, Sara; Riva, Daria; Pantaleoni, Chiara

2016-05-01

Microarray-based comparative genomic hybridization is a method of molecular analysis that identifies chromosomal anomalies (or copy number variants) that correlate with clinical phenotypes. The aim of the present study was to apply a clinical score previously designated by de Vries to 329 patients with intellectual disability/developmental disorder (intellectual disability/developmental delay) referred to our tertiary center and to see whether the clinical factors are associated with a positive outcome of aCGH analyses. Another goal was to test the association between a positive microarray-based comparative genomic hybridization result and the severity of intellectual disability/developmental delay. Microarray-based comparative genomic hybridization identified structural chromosomal alterations responsible for the intellectual disability/developmental delay phenotype in 16% of our sample. Our study showed that causative copy number variants are frequently found even in cases of mild intellectual disability (30.77%). We want to emphasize the need to conduct microarray-based comparative genomic hybridization on all individuals with intellectual disability/developmental delay, regardless of the severity, because the degree of intellectual disability/developmental delay does not predict the diagnostic yield of microarray-based comparative genomic hybridization. © The Author(s) 2015.

Hybrid microarray based on double biomolecular markers of DNA and carbohydrate for simultaneous genotypic and phenotypic detection of cholera toxin-producing Vibrio cholerae.

PubMed

Shin, Hwa Hui; Seo, Jeong Hyun; Kim, Chang Sup; Hwang, Byeong Hee; Cha, Hyung Joon

2016-05-15

Life-threatening diarrheal cholera is usually caused by water or food contaminated with cholera toxin-producing Vibrio cholerae. For the prevention and surveillance of cholera, it is crucial to rapidly and precisely detect and identify the etiological causes, such as V. cholerae and/or its toxin. In the present work, we propose the use of a hybrid double biomolecular marker (DBM) microarray containing 16S rRNA-based DNA capture probe to genotypically identify V. cholerae and GM1 pentasaccharide capture probe to phenotypically detect cholera toxin. We employed a simple sample preparation method to directly obtain genomic DNA and secreted cholera toxin as target materials from bacterial cells. By utilizing the constructed DBM microarray and prepared samples, V. cholerae and cholera toxin were detected successfully, selectively, and simultaneously; the DBM microarray was able to analyze the pathogenicity of the identified V. cholerae regardless of whether the bacteria produces toxin. Therefore, our proposed DBM microarray is a new effective platform for identifying bacteria and analyzing bacterial pathogenicity simultaneously. Copyright © 2015 Elsevier B.V. All rights reserved.

Genome-Wide Survey on Genomic Variation, Expression Divergence, and Evolution in Two Contrasting Rice Genotypes under High Salinity Stress

PubMed Central

Jiang, Shu-Ye; Ma, Ali; Ramamoorthy, Rengasamy; Ramachandran, Srinivasan

2013-01-01

Expression profiling is one of the most important tools for dissecting biological functions of genes and the upregulation or downregulation of gene expression is sufficient for recreating phenotypic differences. Expression divergence of genes significantly contributes to phenotypic variations. However, little is known on the molecular basis of expression divergence and evolution among rice genotypes with contrasting phenotypes. In this study, we have implemented an integrative approach using bioinformatics and experimental analyses to provide insights into genomic variation, expression divergence, and evolution between salinity-sensitive rice variety Nipponbare and tolerant rice line Pokkali under normal and high salinity stress conditions. We have detected thousands of differentially expressed genes between these two genotypes and thousands of up- or downregulated genes under high salinity stress. Many genes were first detected with expression evidence using custom microarray analysis. Some gene families were preferentially regulated by high salinity stress and might play key roles in stress-responsive biological processes. Genomic variations in promoter regions resulted from single nucleotide polymorphisms, indels (1–10 bp of insertion/deletion), and structural variations significantly contributed to the expression divergence and regulation. Our data also showed that tandem and segmental duplication, CACTA and hAT elements played roles in the evolution of gene expression divergence and regulation between these two contrasting genotypes under normal or high salinity stress conditions. PMID:24121498

Identifying biologically relevant putative mechanisms in a given phenotype comparison

PubMed Central

Hanoudi, Samer; Donato, Michele; Draghici, Sorin

2017-01-01

A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights. PMID:28486531

Roles of the Sodium-Translocating NADH:Quinone Oxidoreductase (Na+-NQR) on Vibrio cholerae Metabolism, Motility and Osmotic Stress Resistance

PubMed Central

Minato, Yusuke; Halang, Petra; Quinn, Matthew J.; Faulkner, Wyatt J.; Aagesen, Alisha M.; Steuber, Julia; Stevens, Jan F.; Häse, Claudia C.

2014-01-01

The Na+ translocating NADH:quinone oxidoreductase (Na+-NQR) is a unique respiratory enzyme catalyzing the electron transfer from NADH to quinone coupled with the translocation of sodium ions across the membrane. Typically, Vibrio spp., including Vibrio cholerae, have this enzyme but lack the proton-pumping NADH:ubiquinone oxidoreductase (Complex I). Thus, Na+-NQR should significantly contribute to multiple aspects of V. cholerae physiology; however, no detailed characterization of this aspect has been reported so far. In this study, we broadly investigated the effects of loss of Na+-NQR on V. cholerae physiology by using Phenotype Microarray (Biolog), transcriptome and metabolomics analyses. We found that the V. cholerae ΔnqrA-F mutant showed multiple defects in metabolism detected by Phenotype Microarray. Transcriptome analysis revealed that the V. cholerae ΔnqrA-F mutant up-regulates 31 genes and down-regulates 55 genes in both early and mid-growth phases. The most up-regulated genes included the cadA and cadB genes, encoding a lysine decarboxylase and a lysine/cadaverine antiporter, respectively. Increased CadAB activity was further suggested by the metabolomics analysis. The down-regulated genes include sialic acid catabolism genes. Metabolomic analysis also suggested increased reductive pathway of TCA cycle and decreased purine metabolism in the V. cholerae ΔnqrA-F mutant. Lack of Na+-NQR did not affect any of the Na+ pumping-related phenotypes of V. cholerae suggesting that other secondary Na+ pump(s) can compensate for Na+ pumping activity of Na+-NQR. Overall, our study provides important insights into the contribution of Na+-NQR to V. cholerae physiology. PMID:24811312

Duffy blood group phenotype-genotype correlations using high-resolution melting analysis PCR and microarray reveal complex cases including a new null FY*A allele: the role for sequencing in genotyping algorithms.

PubMed

Lopez, G H; Morrison, J; Condon, J A; Wilson, B; Martin, J R; Liew, Y-W; Flower, R L; Hyland, C A

2015-10-01

Duffy blood group phenotypes can be predicted by genotyping for single nucleotide polymorphisms (SNPs) responsible for the Fy(a) /Fy(b) polymorphism, for weak Fy(b) antigen, and for the red cell null Fy(a-b-) phenotype. This study correlates Duffy phenotype predictions with serotyping to assess the most reliable procedure for typing. Samples, n = 155 (135 donors and 20 patients), were genotyped by high-resolution melt PCR and by microarray. Samples were in three serology groups: 1) Duffy patterns expected n = 79, 2) weak and equivocal Fy(b) patterns n = 29 and 3) Fy(a-b-) n = 47 (one with anti-Fy3 antibody). Discrepancies were observed for five samples. For two, SNP genotyping predicted weak Fy(b) expression discrepant with Fy(b-) (Group 1 and 3). For three, SNP genotyping predicted Fy(a) , discrepant with Fy(a-b-) (Group 3). DNA sequencing identified silencing mutations in these FY*A alleles. One was a novel FY*A 719delG. One, the sample with the anti-Fy3, was homozygous for a 14-bp deletion (FY*01N.02); a true null. Both the high-resolution melting analysis and SNP microarray assays were concordant and showed genotyping, as well as phenotyping, is essential to ensure 100% accuracy for Duffy blood group assignments. Sequencing is important to resolve phenotype/genotype conflicts which here identified alleles, one novel, that carry silencing mutations. The risk of alloimmunisation may be dependent on this zygosity status. © 2015 International Society of Blood Transfusion.

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

PubMed

Trivedi, Prinal; Edwards, Jode W; Wang, Jelai; Gadbury, Gary L; Srinivasasainagendra, Vinodh; Zakharkin, Stanislav O; Kim, Kyoungmi; Mehta, Tapan; Brand, Jacob P L; Patki, Amit; Page, Grier P; Allison, David B

2005-04-06

Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

flowVS: channel-specific variance stabilization in flow cytometry.

PubMed

Azad, Ariful; Rajwa, Bartek; Pothen, Alex

2016-07-28

Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances. We present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett's likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test. With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays. The homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett's likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.

Microfluidic microarray systems and methods thereof

DOEpatents

West, Jay A. A. [Castro Valley, CA; Hukari, Kyle W [San Ramon, CA; Hux, Gary A [Tracy, CA

2009-04-28

Disclosed are systems that include a manifold in fluid communication with a microfluidic chip having a microarray, an illuminator, and a detector in optical communication with the microarray. Methods for using these systems for biological detection are also disclosed.

Functional comparison of microarray data across multiple platforms using the method of percentage of overlapping functions.

PubMed

Li, Zhiguang; Kwekel, Joshua C; Chen, Tao

2012-01-01

Functional comparison across microarray platforms is used to assess the comparability or similarity of the biological relevance associated with the gene expression data generated by multiple microarray platforms. Comparisons at the functional level are very important considering that the ultimate purpose of microarray technology is to determine the biological meaning behind the gene expression changes under a specific condition, not just to generate a list of genes. Herein, we present a method named percentage of overlapping functions (POF) and illustrate how it is used to perform the functional comparison of microarray data generated across multiple platforms. This method facilitates the determination of functional differences or similarities in microarray data generated from multiple array platforms across all the functions that are presented on these platforms. This method can also be used to compare the functional differences or similarities between experiments, projects, or laboratories.

Production of polyunsaturated fatty acids in yeast Saccharomyces cerevisiae and its relation to alkaline pH tolerance.

PubMed

Yazawa, Hisashi; Iwahashi, Hitoshi; Kamisaka, Yasushi; Kimura, Kazuyoshi; Uemura, Hiroshi

2009-03-01

Saccharomyces cerevisiae produces saturated and monounsaturated fatty acids of 16- and 18-carbon atoms and no polyunsaturated fatty acids (PUFAs) with more than two double bonds. To study the biological significance of PUFAs in yeast, we introduced Kluyveromyces lactis Delta12 fatty acid desaturase (KlFAD2) and omega3 fatty acid desaturase (KlFAD3) genes into S. cerevisiae to produce linoleic and alpha-linolenic acids in S. cerevisiae. The strain producing linoleic and alpha-linolenic acids showed an alkaline pH-tolerant phenotype. DNA microarray analyses showed that the transcription of a set of genes whose expressions are under the repression of Rim101p were downregulated in this strain, suggesting that Rim101p, a transcriptional repressor which governs the ion tolerance, was activated. In line with this activation, the strain also showed elevated resistance to Li(+) and Na(+) ions and to zymolyase, a yeast lytic enzyme preparation containing mainly beta-1,3-glucanase, indicating that the cell wall integrity was also strengthened in this strain. Our findings demonstrate a novel influence of PUFA production on transcriptional control that is likely to play an important role in the early stage of alkaline stress response. The Accession No. for microarray data in the Center for Information Biology Gene Expression database is CBX68.

The Glycan Microarray Story from Construction to Applications.

PubMed

Hyun, Ji Young; Pai, Jaeyoung; Shin, Injae

2017-04-18

Not only are glycan-mediated binding processes in cells and organisms essential for a wide range of physiological processes, but they are also implicated in various pathological processes. As a result, elucidation of glycan-associated biomolecular interactions and their consequences is of great importance in basic biological research and biomedical applications. In 2002, we and others were the first to utilize glycan microarrays in efforts aimed at the rapid analysis of glycan-associated recognition events. Because they contain a number of glycans immobilized in a dense and orderly manner on a solid surface, glycan microarrays enable multiple parallel analyses of glycan-protein binding events while utilizing only small amounts of glycan samples. Therefore, this microarray technology has become a leading edge tool in studies aimed at elucidating roles played by glycans and glycan binding proteins in biological systems. In this Account, we summarize our efforts on the construction of glycan microarrays and their applications in studies of glycan-associated interactions. Immobilization strategies of functionalized and unmodified glycans on derivatized glass surfaces are described. Although others have developed immobilization techniques, our efforts have focused on improving the efficiencies and operational simplicity of microarray construction. The microarray-based technology has been most extensively used for rapid analysis of the glycan binding properties of proteins. In addition, glycan microarrays have been employed to determine glycan-protein interactions quantitatively, detect pathogens, and rapidly assess substrate specificities of carbohydrate-processing enzymes. More recently, the microarrays have been employed to identify functional glycans that elicit cell surface lectin-mediated cellular responses. Owing to these efforts, it is now possible to use glycan microarrays to expand the understanding of roles played by glycans and glycan binding proteins in biological systems.

PRACTICAL STRATEGIES FOR PROCESSING AND ANALYZING SPOTTED OLIGONUCLEOTIDE MICROARRAY DATA

EPA Science Inventory

Thoughtful data analysis is as important as experimental design, biological sample quality, and appropriate experimental procedures for making microarrays a useful supplement to traditional toxicology. In the present study, spotted oligonucleotide microarrays were used to profile...

IMPROVING THE RELIABILITY OF MICROARRAYS FOR TOXICOLOGY RESEARCH: A COLLABORATIVE APPROACH

EPA Science Inventory

Microarray-based gene expression profiling is a critical tool to identify molecular biomarkers of specific chemical stressors. Although current microarray technologies have progressed from their infancy, biological and technical repeatability and reliability are often still limit...

cDNA microarray analysis of esophageal cancer: discoveries and prospects.

PubMed

Shimada, Yutaka; Sato, Fumiaki; Shimizu, Kazuharu; Tsujimoto, Gozoh; Tsukada, Kazuhiro

2009-07-01

Recent progress in molecular biology has revealed many genetic and epigenetic alterations that are involved in the development and progression of esophageal cancer. Microarray analysis has also revealed several genetic networks that are involved in esophageal cancer. However, clinical application of microarray techniques and use of microarray data have not yet occurred. In this review, we focus on the recent developments and problems with microarray analysis of esophageal cancer.

Epigenetic regulation of EFEMP1 in prostate cancer: biological relevance and clinical potential

PubMed Central

Almeida, Mafalda; Costa, Vera L; Costa, Natália R; Ramalho-Carvalho, João; Baptista, Tiago; Ribeiro, Franclim R; Paulo, Paula; Teixeira, Manuel R; Oliveira, Jorge; Lothe, Ragnhild A; Lind, Guro E; Henrique, Rui; Jerónimo, Carmen

2014-01-01

Epigenetic alterations are common in prostate cancer (PCa) and seem to contribute decisively to its initiation and progression. Moreover, aberrant promoter methylation is a promising biomarker for non-invasive screening. Herein, we sought to characterize EFEMP1 as biomarker for PCa, unveiling its biological relevance in prostate carcinogenesis. Microarray analyses of treated PCa cell lines and primary tissues enabled the selection of differentially methylated genes, among which EFEMP1 was further validated by MSP and bisulfite sequencing. Assessment of biomarker performance was accomplished by qMSP. Expression analysis of EFEMP1 and characterization of histone marks were performed in tissue samples and cancer cell lines to determine the impact of epigenetic mechanisms on EFEMP1 transcriptional regulation. Phenotypic assays, using transfected cell lines, permitted the evaluation of EFEMP1’s role in PCa development. EFEMP1 methylation assay discriminated PCa from normal prostate tissue (NPT; P < 0.001, Kruskall–Wallis test) and renal and bladder cancers (96% sensitivity and 98% specificity). EFEMP1 transcription levels inversely correlated with promoter methylation and histone deacetylation, suggesting that both epigenetic mechanisms are involved in gene regulation. Phenotypic assays showed that EFEMP1 de novo expression reduces malignant phenotype of PCa cells. EFEMP1 promoter methylation is prevalent in PCa and accurately discriminates PCa from non-cancerous prostate tissues and other urological neoplasms. This epigenetic alteration occurs early in prostate carcinogenesis and, in association with histone deacetylation, progressively leads to gene down-regulation, fostering cell proliferation, invasion and evasion of apoptosis. PMID:25211630

Phenotypic and genomic responses to titanium dioxide and cerium oxide nanoparticles in Arabidopsis germinants

EPA Science Inventory

The effects of exposure to two nanoparticles (NPs) -titanium dioxide (nano-titania) and cerium oxide (nano-ceria) at 500 mg NPs L-1 on gene expression and growth in Arabidopsis thaliana germinants were studied using microarrays and phenotype studies. After 12 days post treatment,...

PAX3 gene deletion detected by microarray analysis in a girl with hearing loss.

PubMed

Drozniewska, Malgorzata; Haus, Olga

2014-01-01

Deletions of the PAX3 gene have been rarely reported in the literature. Mutations of this gene are a common cause of Waardenburg syndrome type 1 and 3. We report a 16 year old female presenting hearing loss and normal intellectual development, without major features of Waardenburg syndrome type 1, and without family history of the syndrome. Her phenotype, however, overlaps with features of craniofacial-deafness-hand syndrome. Microarray analysis showed ~862 kb de novo deletion at 2q36.1 including PAX3. The above findings suggest that the rearrangement found in our patient appeared de novo and with high probability is a cause of her phenotype.

Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits.

PubMed

Feltus, F Alex

2014-06-01

Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Comparative phenomics and targeted use of genomics reveals variation in carbon and nitrogen assimilation among different Brettanomyces bruxellensis strains.

PubMed

Crauwels, S; Van Assche, A; de Jonge, R; Borneman, A R; Verreth, C; Troels, P; De Samblanx, G; Marchal, K; Van de Peer, Y; Willems, K A; Verstrepen, K J; Curtin, C D; Lievens, B

2015-11-01

Recent studies have suggested a correlation between genotype groups of Brettanomyces bruxellensis and their source of isolation. To further explore this relationship, the objective of this study was to assess metabolic differences in carbon and nitrogen assimilation between different B. bruxellensis strains from three beverages, including beer, wine, and soft drink, using Biolog Phenotype Microarrays. While some similarities of physiology were noted, many traits were variable among strains. Interestingly, some phenotypes were found that could be linked to strain origin, especially for the assimilation of particular α- and β-glycosides as well as α- and β-substituted monosaccharides. Based upon gene presence or absence, an α-glucosidase and β-glucosidase were found explaining the observed phenotypes. Further, using a PCR screen on a large number of isolates, we have been able to specifically link a genomic deletion to the beer strains, suggesting that this region may have a fitness cost for B. bruxellensis in certain fermentation systems such as brewing. More specifically, none of the beer strains were found to contain a β-glucosidase, which may have direct impacts on the ability for these strains to compete with other microbes or on flavor production.

Derivation of Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Teleost Reproductive Axis.

EPA Science Inventory

Oligonucleotide microarrays are a powerful tool for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-based analyses to detect diffe...

IDENTIFICATION OF BIOLOGICALLY RELEVANT GENES USING A DATABASE OF RAT LIVER AND KIDNEY BASELINE GENE EXPRESSION

EPA Science Inventory

Microarray data from independent labs and studies can be compared to potentially identify toxicologically and biologically relevant genes. The Baseline Animal Database working group of HESI was formed to assess baseline gene expression from microarray data derived from control or...

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gardner, Shea N.; McLoughlin, Kevin; Be, Nicholas A.

Venezuelan equine encephalitis virus (VEEV) is a mosquito-borne alphavirus that has caused large outbreaks of severe illness in both horses and humans. New approaches are needed to rapidly infer the origin of a newly discovered VEEV strain, estimate its equine amplification and resultant epidemic potential, and predict human virulence phenotype. We performed whole genome single nucleotide polymorphism (SNP) analysis of all available VEE antigenic complex genomes, verified that a SNP-based phylogeny accurately captured the features of a phylogenetic tree based on multiple sequence alignment, and developed a high resolution genome-wide SNP microarray. We used the microarray to analyze a broadmore » panel of VEEV isolates, found excellent concordance between array- and sequence-based SNP calls, genotyped unsequenced isolates, and placed them on a phylogeny with sequenced genomes. The microarray successfully genotyped VEEV directly from tissue samples of an infected mouse, bypassing the need for viral isolation, culture and genomic sequencing. Lastly, we identified genomic variants associated with serotypes and host species, revealing a complex relationship between genotype and phenotype.« less

Electric-field driven assembly of live bacterial cell microarrays for rapid phenotypic assessment and cell viability testing.

PubMed

Goel, Meenal; Verma, Abhishek; Gupta, Shalini

2018-07-15

Microarray technology to isolate living cells using external fields is a facile way to do phenotypic analysis at the cellular level. We have used alternating current dielectrophoresis (AC-DEP) to drive the assembly of live pathogenic Salmonella typhi (S.typhi) and Escherichia coli (E.coli) bacteria into miniaturized single cell microarrays. The effects of voltage and frequency were optimized to identify the conditions for maximum cell capture which gave an entrapment efficiency of 90% in 60 min. The chip was used for calibration-free estimation of cellular loads in binary mixtures and further applied for rapid and enhanced testing of cell viability in the presence of drug via impedance spectroscopy. Our results using a model antimicrobial sushi peptide showed that the cell viability could be tested down to 5 μg/mL drug concentration under an hour, thus establishing the utility of our system for ultrafast and sensitive detection. Copyright © 2018 Elsevier B.V. All rights reserved.

An efficient method to identify differentially expressed genes in microarray experiments

PubMed Central

Qin, Huaizhen; Feng, Tao; Harding, Scott A.; Tsai, Chung-Jui; Zhang, Shuanglin

2013-01-01

Motivation Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. Results We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. Availability The C++ code to implement the proposed method is available upon request for academic use. PMID:18453554

The Importance of Normalization on Large and Heterogeneous Microarray Datasets

EPA Science Inventory

DNA microarray technology is a powerful functional genomics tool increasingly used for investigating global gene expression in environmental studies. Microarrays can also be used in identifying biological networks, as they give insight on the complex gene-to-gene interactions, ne...

Sensitivity analysis of gene ranking methods in phenotype prediction.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan L; Sonis, Stephen T

2016-12-01

It has become clear that noise generated during the assay and analytical processes has the ability to disrupt accurate interpretation of genomic studies. Not only does such noise impact the scientific validity and costs of studies, but when assessed in the context of clinically translatable indications such as phenotype prediction, it can lead to inaccurate conclusions that could ultimately impact patients. We applied a sequence of ranking methods to damp noise associated with microarray outputs, and then tested the utility of the approach in three disease indications using publically available datasets. This study was performed in three phases. We first theoretically analyzed the effect of noise in phenotype prediction problems showing that it can be expressed as a modeling error that partially falsifies the pathways. Secondly, via synthetic modeling, we performed the sensitivity analysis for the main gene ranking methods to different types of noise. Finally, we studied the predictive accuracy of the gene lists provided by these ranking methods in synthetic data and in three different datasets related to cancer, rare and neurodegenerative diseases to better understand the translational aspects of our findings. In the case of synthetic modeling, we showed that Fisher's Ratio (FR) was the most robust gene ranking method in terms of precision for all the types of noise at different levels. Significance Analysis of Microarrays (SAM) provided slightly lower performance and the rest of the methods (fold change, entropy and maximum percentile distance) were much less precise and accurate. The predictive accuracy of the smallest set of high discriminatory probes was similar for all the methods in the case of Gaussian and Log-Gaussian noise. In the case of class assignment noise, the predictive accuracy of SAM and FR is higher. Finally, for real datasets (Chronic Lymphocytic Leukemia, Inclusion Body Myositis and Amyotrophic Lateral Sclerosis) we found that FR and SAM provided the highest predictive accuracies with the smallest number of genes. Biological pathways were found with an expanded list of genes whose discriminatory power has been established via FR. We have shown that noise in expression data and class assignment partially falsifies the sets of discriminatory probes in phenotype prediction problems. FR and SAM better exploit the principle of parsimony and are able to find subsets with less number of high discriminatory genes. The predictive accuracy and the precision are two different metrics to select the important genes, since in the presence of noise the most predictive genes do not completely coincide with those that are related to the phenotype. Based on the synthetic results, FR and SAM are recommended to unravel the biological pathways that are involved in the disease development. Copyright © 2016 Elsevier Inc. All rights reserved.

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

PubMed

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

Chromosome r(10)(p15.3q26.12) in a newborn child: case report.

PubMed

Gunnarsson, Cecilia; Graffmann, Barbara; Jonasson, Jon

2009-12-07

Ring chromosome 10 is a rare cytogenetic finding. Of the less than 10 reported cases we have found in the literature, none was characterized using high-resolution microarray analysis. Ring chromosomes are frequently unstable due to sister chromatid exchanges and mitotic failures. When mosaicism is present, the interpretation of genotype-phenotype correlations becomes extremely difficult. We report on a newborn girl with growth retardation, microcephaly, congenital heart defects, dysmorphic features and psychomotor retardation. Karyotyping revealed a non-mosaic apparently stable ring chromosome 10 replacing one of the normal homologues in all analyzed metaphases. High-resolution oligonucleotide microarray analysis showed a de novo approximately 12.5 Mb terminal deletion 10q26.12 -> qter and a corresponding 285 kb terminal deletion of 10pter -> p15.3. This case demonstrates that an increased nuchal translucency thickness detected by early ultrasonography should preferably lead to not only QF-PCR for the diagnosis of Down syndrome but also karyotyping. In the future, microarray analysis, which needs further evaluation, might become the method of choice. The clinical phenotype of our patient was in agreement with that of patients with a terminal 10q deletion. For the purpose of genotype-phenotype analysis, there seems to be no need for a "ring syndrome" concept.

Experimental Approaches to Microarray Analysis of Tumor Samples

ERIC Educational Resources Information Center

Furge, Laura Lowe; Winter, Michael B.; Meyers, Jacob I.; Furge, Kyle A.

2008-01-01

Comprehensive measurement of gene expression using high-density nucleic acid arrays (i.e. microarrays) has become an important tool for investigating the molecular differences in clinical and research samples. Consequently, inclusion of discussion in biochemistry, molecular biology, or other appropriate courses of microarray technologies has…

Challenges of microarray applications for microbial detection and gene expression profiling in food

USDA-ARS?s Scientific Manuscript database

Microarray technology represents one of the latest advances in molecular biology. The diverse types of microarrays have been applied to clinical and environmental microbiology, microbial ecology, and in human, veterinary, and plant diagnostics. Since multiple genes can be analyzed simultaneously, ...

A Graphical Systems Model and Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Female Teleost Reproductive Axis

EPA Science Inventory

Oligonucleotide microarrays and other ‘omics’ approaches are powerful tools for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-b...

Gene Expression Browser: Large-Scale and Cross-Experiment Microarray Data Management, Search & Visualization

USDA-ARS?s Scientific Manuscript database

The amount of microarray gene expression data in public repositories has been increasing exponentially for the last couple of decades. High-throughput microarray data integration and analysis has become a critical step in exploring the large amount of expression data for biological discovery. Howeve...

Microarrays Made Simple: "DNA Chips" Paper Activity

ERIC Educational Resources Information Center

Barnard, Betsy

2006-01-01

DNA microarray technology is revolutionizing biological science. DNA microarrays (also called DNA chips) allow simultaneous screening of many genes for changes in expression between different cells. Now researchers can obtain information about genes in days or weeks that used to take months or years. The paper activity described in this article…

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

PubMed

Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

Manufacturing of microarrays.

PubMed

Petersen, David W; Kawasaki, Ernest S

2007-01-01

DNA microarray technology has become a powerful tool in the arsenal of the molecular biologist. Capitalizing on high precision robotics and the wealth of DNA sequences annotated from the genomes of a large number of organisms, the manufacture of microarrays is now possible for the average academic laboratory with the funds and motivation. Microarray production requires attention to both biological and physical resources, including DNA libraries, robotics, and qualified personnel. While the fabrication of microarrays is a very labor-intensive process, production of quality microarrays individually tailored on a project-by-project basis will help researchers shed light on future scientific questions.

The Longhorn Array Database (LAD): An Open-Source, MIAME compliant implementation of the Stanford Microarray Database (SMD)

PubMed Central

Killion, Patrick J; Sherlock, Gavin; Iyer, Vishwanath R

2003-01-01

Background The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms. Description The Longhorn Array Database (LAD) is a MIAME compliant microarray database that operates on PostgreSQL and Linux. It is a fully open source version of the Stanford Microarray Database (SMD), one of the largest microarray databases. LAD is available at Conclusions Our development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data. PMID:12930545

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

Molecular and morphologic correlates of the alternative lengthening of telomeres phenotype in high-grade astrocytomas.

PubMed

Nguyen, Doreen N; Heaphy, Christopher M; de Wilde, Roeland F; Orr, Brent A; Odia, Yazmin; Eberhart, Charles G; Meeker, Alan K; Rodriguez, Fausto J

2013-05-01

Recent studies suggest that the telomere maintenance mechanism known as alternative lengthening of telomeres (ALT) is relatively more common in specific glioma subsets and strongly associated with ATRX mutations. We retrospectively examined 116 high-grade astrocytomas (32 pediatric glioblastomas, 65 adult glioblastomas, 19 anaplastic astrocytomas) with known ALT status using tissue microarrays to identify associations with molecular and phenotypic features. Immunohistochemistry was performed using antibodies against ATRX, DAXX, p53 and IDH1(R132H) mutant protein. EGFR amplification was evaluated by fluorescence in situ hybridization (FISH). Almost half of fibrillary and gemistocytic astrocytomas (44%) demonstrated ALT. Conversely all gliosarcomas (n = 4), epithelioid (n = 2), giant cell (n = 2) and adult small cell astrocytomas (n = 7) were ALT negative. The ALT phenotype was positively correlated with the presence of round cells (P = 0.002), microcysts (P < 0.0002), IDH1 mutant protein (P < 0.0001), ATRX protein loss (P < 0.0001), strong P53 immunostaining (P < 0.0001) and absence of EGFR amplification (P = 0.004). There was no significant correlation with DAXX expression. We conclude that ALT represents a specific phenotype in high-grade astrocytomas with distinctive pathologic and molecular features. Future studies are required to clarify the clinical and biological significance of ALT in high-grade astrocytomas. © 2012 The Authors; Brain Pathology © 2012 International Society of Neuropathology.

Prior knowledge guided active modules identification: an integrated multi-objective approach.

PubMed

Chen, Weiqi; Liu, Jing; He, Shan

2017-03-14

Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

Identification of novel target genes involved in Indian Fanconi anemia patients using microarray.

PubMed

Shyamsunder, Pavithra; Ganesh, Kripa S; Vidyasekar, Prasanna; Mohan, Sheila; Verma, Rama Shanker

2013-12-01

Fanconi anemia (FA) is a genetic disorder characterized by progressive bone marrow failure and a predisposition to cancers. Mutations have been documented in 15 FA genes that participate in the FA-BRCA DNA repair pathway, a fundamental pathway in the development of the disease and the presentation of its characteristic symptoms. Certain symptoms such as oxygen sensitivity, hematological abnormalities and impaired immunity suggest that FA proteins could participate in or independently control other pathways as well. In this study, we identified 9 DNA repair genes that were down regulated in a genome wide analysis of 6 Indian Fanconi anemia patients. Functional clustering of a total of 233 dysregulated genes identified key biological processes that included regulation of transcription, DNA repair, cell cycle and chromosomal organization. Microarray data revealed the down regulation of ATXN3, ARID4A and ETS-1, which were validated by RTPCR in a subsequent sample set of 9 Indian FA patients. Here we report for the first time a gene expression profile of Fanconi anemia patients from the Indian population and a pool of genes that might aid in the acquisition and progression of the FA phenotype. © 2013 Elsevier B.V. All rights reserved.

Evaluating concentration estimation errors in ELISA microarray experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; White, Amanda M.; Varnum, Susan M.

Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Althoughmore » propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.« less

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

Microarray-based mutation detection and phenotypic characterization in Korean patients with retinitis pigmentosa

PubMed Central

Kim, Cinoo; Kim, Kwang Joong; Bok, Jeong; Lee, Eun-Ju; Kim, Dong-Joon; Oh, Ji Hee; Park, Sung Pyo; Shin, Joo Young; Lee, Jong-Young

2012-01-01

Purpose To evaluate microarray-based genotyping technology for the detection of mutations responsible for retinitis pigmentosa (RP) and to perform phenotypic characterization of patients with pathogenic mutations. Methods DNA from 336 patients with RP and 360 controls was analyzed using the GoldenGate assay with microbeads containing 95 previously reported disease-associated mutations from 28 RP genes. Mutations identified by microarray-based genotyping were confirmed by direct sequencing. Segregation analysis and phenotypic characterization were performed in patients with mutations. The disease severity was assessed by visual acuity, electroretinography, optical coherence tomography, and kinetic perimetry. Results Ten RP-related mutations of five RP genes (PRP3 pre-mRNA processing factor 3 homolog [PRPF3], rhodopsin [RHO], phosphodiesterase 6B [PDE6B], peripherin 2 [PRPH2], and retinitis pigmentosa 1 [RP1]) were identified in 26 of the 336 patients (7.7%) and in six of the 360 controls (1.7%). The p.H557Y mutation in PDE6B, which was homozygous in four patients and heterozygous in nine patients, was the most frequent mutation (2.5%). Mutation segregation was assessed in four families. Among the patients with missense mutations, the most severe phenotype occurred in patients with p.D984G in RP1; less severe phenotypes occurred in patients with p.R135W in RHO; a relatively moderate phenotype occurred in patients with p.T494M in PRPF3, p.H557Y in PDE6B, or p.W316G in PRPH2; and a mild phenotype was seen in a patient with p.D190N in RHO. Conclusions The results reveal that the GoldenGate assay may not be an efficient method for molecular diagnosis in RP patients with rare mutations, although it has proven to be reliable and efficient for high-throughput genotyping of single-nucleotide polymorphisms. The clinical features varied according to the mutations. Continuous effort to identify novel RP genes and mutations in a population is needed to improve the efficiency and accuracy of the genetic diagnosis of RP. PMID:23049240

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

PubMed

Ozerov, Ivan V; Lezhnina, Ksenia V; Izumchenko, Evgeny; Artemov, Artem V; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N; Labat, Ivan; West, Michael D; Buzdin, Anton; Cantor, Charles R; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-11-16

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

PubMed Central

Ozerov, Ivan V.; Lezhnina, Ksenia V.; Izumchenko, Evgeny; Artemov, Artem V.; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N.; Labat, Ivan; West, Michael D.; Buzdin, Anton; Cantor, Charles R.; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-01-01

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy. PMID:27848968

Identification of heavy-ion radiation-induced microRNAs in rice

NASA Astrophysics Data System (ADS)

Zhang, Meng; Liang, Shujian; Hang, Xiaoming; Sun, Yeqing

As an excellent model organism for studying the effects of environmental stress, rice was used to assess biological effect of the space radiation environment. Rice abnormal development or growth was observed frequently after seeds space flight. MicroRNAs (miRNAs) are a family of small non-coding regulatory RNAs, which have significant roles in regulating development and stress responses in plant. To identify whether the miRNAs were involved in biological effects of heavy-ion radiation, the germinated seeds of rice were exposed to 20 Gy dose of 12 C heavy-ion radiation which could induce rice development retarded. The microarray was used to monitor rice (Oryza sativa) miRNAs expression profiles under radiation stress. Members of miR164 family and miR156a-j were found up-regulated significantly, and confirmed by relative quantifi-cation real-time PCR. We found that the expression of the miR156 and miR164 increased and targets genes expression decrease was closely bound up with the irradiation rice phenotypes changes.

Metabolic Capacity of Sinorhizobium (Ensifer) meliloti Strains as Determined by Phenotype MicroArray Analysis▿ †

PubMed Central

Biondi, Emanuele G.; Tatti, Enrico; Comparini, Diego; Giuntini, Elisa; Mocali, Stefano; Giovannetti, Luciana; Bazzicalupo, Marco; Mengoni, Alessio; Viti, Carlo

2009-01-01

Sinorhizobium meliloti is a soil bacterium that fixes atmospheric nitrogen in plant roots. The high genetic diversity of its natural populations has been the subject of extensive analysis. Recent genomic studies of several isolates revealed a high content of variable genes, suggesting a correspondingly large phenotypic differentiation among strains of S. meliloti. Here, using the Phenotype MicroArray (PM) system, hundreds of different growth conditions were tested in order to compare the metabolic capabilities of the laboratory reference strain Rm1021 with those of four natural S. meliloti isolates previously analyzed by comparative genomic hybridization (CGH). The results of PM analysis showed that most phenotypic differences involved carbon source utilization and tolerance to osmolytes and pH, while fewer differences were scored for nitrogen, phosphorus, and sulfur source utilization. Only the variability of the tested strain in tolerance to sodium nitrite and ammonium sulfate of pH 8 was hypothesized to be associated with the genetic polymorphisms detected by CGH analysis. Colony and cell morphologies and the ability to nodulate Medicago truncatula plants were also compared, revealing further phenotypic diversity. Overall, our results suggest that the study of functional (phenotypic) variability of S. meliloti populations is an important and complementary step in the investigation of genetic polymorphism of rhizobia and may help to elucidate rhizobial evolutionary dynamics, including adaptation to diverse environments. PMID:19561177

Characterization of genetic variability of Venezuelan equine encephalitis viruses

DOE PAGES

Gardner, Shea N.; McLoughlin, Kevin; Be, Nicholas A.; ...

2016-04-07

Venezuelan equine encephalitis virus (VEEV) is a mosquito-borne alphavirus that has caused large outbreaks of severe illness in both horses and humans. New approaches are needed to rapidly infer the origin of a newly discovered VEEV strain, estimate its equine amplification and resultant epidemic potential, and predict human virulence phenotype. We performed whole genome single nucleotide polymorphism (SNP) analysis of all available VEE antigenic complex genomes, verified that a SNP-based phylogeny accurately captured the features of a phylogenetic tree based on multiple sequence alignment, and developed a high resolution genome-wide SNP microarray. We used the microarray to analyze a broadmore » panel of VEEV isolates, found excellent concordance between array- and sequence-based SNP calls, genotyped unsequenced isolates, and placed them on a phylogeny with sequenced genomes. The microarray successfully genotyped VEEV directly from tissue samples of an infected mouse, bypassing the need for viral isolation, culture and genomic sequencing. Lastly, we identified genomic variants associated with serotypes and host species, revealing a complex relationship between genotype and phenotype.« less

Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

ERIC Educational Resources Information Center

Tra, Yolande V.; Evans, Irene M.

2010-01-01

"BIO2010" put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on…

ABRF-MARG RESEARCH STUDY: EVALUATION OF SMALL SAMPLE NUCLEIC ACID AMPLIFICATION TECHNOLOGIES FOR GENE EXPRESSION PROFILING

EPA Science Inventory

Microarrays have had a significant impact on many areas of biology. However, there are still many fertile research areas that would benefit from microarray analysis but are limited by the amount of biological material that can be obtained (e.g. samples obtained by small biopsy, f...

Functional Analysis With a Barcoder Yeast Gene Overexpression System

PubMed Central

Douglas, Alison C.; Smith, Andrew M.; Sharifpoor, Sara; Yan, Zhun; Durbic, Tanja; Heisler, Lawrence E.; Lee, Anna Y.; Ryan, Owen; Göttert, Hendrikje; Surendra, Anu; van Dyk, Dewald; Giaever, Guri; Boone, Charles; Nislow, Corey; Andrews, Brenda J.

2012-01-01

Systematic analysis of gene overexpression phenotypes provides an insight into gene function, enzyme targets, and biological pathways. Here, we describe a novel functional genomics platform that enables a highly parallel and systematic assessment of overexpression phenotypes in pooled cultures. First, we constructed a genome-level collection of ~5100 yeast barcoder strains, each of which carries a unique barcode, enabling pooled fitness assays with a barcode microarray or sequencing readout. Second, we constructed a yeast open reading frame (ORF) galactose-induced overexpression array by generating a genome-wide set of yeast transformants, each of which carries an individual plasmid-born and sequence-verified ORF derived from the Saccharomyces cerevisiae full-length EXpression-ready (FLEX) collection. We combined these collections genetically using synthetic genetic array methodology, generating ~5100 strains, each of which is barcoded and overexpresses a specific ORF, a set we termed “barFLEX.” Additional synthetic genetic array allows the barFLEX collection to be moved into different genetic backgrounds. As a proof-of-principle, we describe the properties of the barFLEX overexpression collection and its application in synthetic dosage lethality studies under different environmental conditions. PMID:23050238

Checkpoint Kinase 1 Expression Predicts Poor Prognosis in Nigerian Breast Cancer Patients.

PubMed

Ebili, Henry Okuchukwu; Iyawe, Victoria O; Adeleke, Kikelomo Rachel; Salami, Babatunde Abayomi; Banjo, Adekunbiola Aina; Nolan, Chris; Rakha, Emad; Ellis, Ian; Green, Andrew; Agboola, Ayodeji Olayinka Johnson

2018-02-01

Checkpoint kinase 1 (CHEK1), a DNA damage sensor and cell death pathway stimulator, is regarded as an oncogene in tumours, where its activities are considered essential for tumourigenesis and the survival of cancer cells treated with chemotherapy and radiotherapy. In breast cancer, CHEK1 expression has been associated with an aggressive tumour phenotype, the triple-negative breast cancer subtype, an aberrant response to tamoxifen, and poor prognosis. However, the relevance of CHEK1 expression has, hitherto, not been investigated in an indigenous African population. We therefore aimed to investigate the clinicopathological, biological, and prognostic significance of CHEK1 expression in a cohort of Nigerian breast cancer cases. Tissue microarrays of 207 Nigerian breast cancer cases were tested for CHEK1 expression using immunohistochemistry. The clinicopathological, molecular, and prognostic characteristics of CHEK1-positive tumours were determined using the Chi-squared test and Kaplan-Meier and Cox regression analyses in SPSS Version 16. Nuclear expression of CHEK1 was present in 61% of breast tumours and was associated with tumour size, triple-negative cancer, basal-like phenotype, the epithelial-mesenchymal transition, p53 over-expression, DNA homologous repair pathway dysfunction, and poor prognosis. The rate expression of CHEK1 is high in Nigerian breast cancer cases and is associated with an aggressive phenotype and poor prognosis.

Epigenetic transgenerational inheritance of somatic transcriptomes and epigenetic control regions

PubMed Central

2012-01-01

Background Environmentally induced epigenetic transgenerational inheritance of adult onset disease involves a variety of phenotypic changes, suggesting a general alteration in genome activity. Results Investigation of different tissue transcriptomes in male and female F3 generation vinclozolin versus control lineage rats demonstrated all tissues examined had transgenerational transcriptomes. The microarrays from 11 different tissues were compared with a gene bionetwork analysis. Although each tissue transgenerational transcriptome was unique, common cellular pathways and processes were identified between the tissues. A cluster analysis identified gene modules with coordinated gene expression and each had unique gene networks regulating tissue-specific gene expression and function. A large number of statistically significant over-represented clusters of genes were identified in the genome for both males and females. These gene clusters ranged from 2-5 megabases in size, and a number of them corresponded to the epimutations previously identified in sperm that transmit the epigenetic transgenerational inheritance of disease phenotypes. Conclusions Combined observations demonstrate that all tissues derived from the epigenetically altered germ line develop transgenerational transcriptomes unique to the tissue, but common epigenetic control regions in the genome may coordinately regulate these tissue-specific transcriptomes. This systems biology approach provides insight into the molecular mechanisms involved in the epigenetic transgenerational inheritance of a variety of adult onset disease phenotypes. PMID:23034163

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

Arabidopsis MALE STERILITY1 Encodes a PHD-Type Transcription Factor and Regulates Pollen and Tapetum Development[W

PubMed Central

Ito, Takuya; Nagata, Noriko; Yoshiba, Yoshu; Ohme-Takagi, Masaru; Ma, Hong; Shinozaki, Kazuo

2007-01-01

The Arabidopsis thaliana MALE STERILITY1 (MS1) gene encodes a nuclear protein with Leu zipper–like and PHD-finger motifs and is important for postmeiotic pollen development. Here, we examined MS1 function using both cell biological and molecular biological approaches. We introduced a fusion construct of MS1 and a transcriptional repression domain (MS1-SRDX) into wild-type Arabidopsis, and the transgenic plants showed a semisterile phenotype similar to that of ms1. Since the repression domain can convert various kinds of transcriptional activators to dominant repressors, this suggested that MS1 functioned as a transcriptional activator. The Leu zipper–like region and the PHD motif were required for the MS1 function. Phenotypic analysis of the ms1 mutant and the MS1-SRDX transgenic Arabidopsis indicated that MS1 was involved in formation of pollen exine and pollen cytosolic components as well as tapetum development. Next, we searched for MS1 downstream genes by analyzing publicly available microarray data and identified 95 genes affected by MS1. Using a transgenic ms1 plant showing dexamethasone-inducible recovery of fertility, we further examined whether these genes were immediately downstream of MS1. From these results, we discuss a role of MS1 in pollen and tapetum development and the conservation of MS1 function in flowering plants. PMID:18032630

Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

PubMed Central

2015-01-01

Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies. PMID:24417579

Approximate geodesic distances reveal biologically relevant structures in microarray data.

PubMed

Nilsson, Jens; Fioretos, Thoas; Höglund, Mattias; Fontes, Magnus

2004-04-12

Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.

MRI phenotypes with high neurodegeneration are associated with peripheral blood B-cell changes.

PubMed

Comabella, Manuel; Cantó, Ester; Nurtdinov, Ramil; Río, Jordi; Villar, Luisa M; Picón, Carmen; Castilló, Joaquín; Fissolo, Nicolás; Aymerich, Xavier; Auger, Cristina; Rovira, Alex; Montalban, Xavier

2016-01-15

Little is known about the mechanisms leading to neurodegeneration in multiple sclerosis (MS) and the role of peripheral blood cells in this neurodegenerative component. We aimed to correlate brain radiological phenotypes defined by high and low neurodegeneration with gene expression profiling of peripheral blood mononuclear cells (PBMC) from MS patients. Magnetic resonance imaging (MRI) scans from 64 patients with relapsing-remitting MS (RRMS) were classified into radiological phenotypes characterized by low (N = 27) and high (N = 37) neurodegeneration according to the number of contrast-enhancing lesions, the relative volume of non-enhancing black holes on T1-weighted images, and the brain parenchymal fraction. Gene expression profiling was determined in PBMC using microarrays, and validation of selected genes was performed by polymerase chain reaction (PCR). B-cell immunophenotyping was conducted by flow cytometry. Microarray analysis revealed the B-cell specific genes FCRL1, FCRL2, FCRL5 (Fc receptor-like 1, 2 and 5 respectively), and CD22 as the top differentially expressed genes between patients with high and low neurodegeneration. Levels for these genes were significantly down-regulated in PBMC from patients with MRI phenotypes characterized by high neurodegeneration and microarray findings were validated by PCR. In patients with high neurodegeneration, immunophenotyping showed a significant increase in the expression of the B-cell activation markers CD80 in naïve B cells (CD45+/CD19+/CD27-/IgD+), unswitched memory B cells (CD45+/CD19+/CD27+/IgD+), and switched memory B cells (CD45+/CD19+/CD27+/IgD-), and CD86 in naïve and switched memory B cells. These results suggest that RRMS patients with radiological phenotypes showing high neurodegeneration have changes in B cells characterized by down-regulation of B-cell-specific genes and increased activation status. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The application of DNA microarrays in gene expression analysis.

PubMed

van Hal, N L; Vorst, O; van Houwelingen, A M; Kok, E J; Peijnenburg, A; Aharoni, A; van Tunen, A J; Keijer, J

2000-03-31

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.

Recent progress in making protein microarray through BioLP

NASA Astrophysics Data System (ADS)

Yang, Rusong; Wei, Lian; Feng, Ying; Li, Xiujian; Zhou, Quan

2017-02-01

Biological laser printing (BioLP) is a promising biomaterial printing technique. It has the advantage of high resolution, high bioactivity, high printing frequency and small transported liquid amount. In this paper, a set of BioLP device is design and made, and protein microarrays are printed by this device. It's found that both laser intensity and fluid layer thickness have an influence on the microarrays acquired. Besides, two kinds of the fluid layer coating methods are compared, and the results show that blade coating method is better than well-coating method in BioLP. A microarray of 0.76pL protein microarray and a "NUDT" patterned microarray are printed to testify the printing ability of BioLP.

An Undergraduate Laboratory Exercise to Study the Effect of Darkness on Plant Gene Expression Using DNA Microarray

ERIC Educational Resources Information Center

Chang, Ming-Mei; Briggs, George M.

2007-01-01

DNA microarrays are microscopic arrays on a solid surface, typically a glass slide, on which DNA oligonucleotides are deposited or synthesized in a high-density matrix with a predetermined spatial order. Several types of DNA microarrays have been developed and used for various biological studies. Here, we developed an undergraduate laboratory…

Enhancing interdisciplinary mathematics and biology education: a microarray data analysis course bridging these disciplines.

PubMed

Tra, Yolande V; Evans, Irene M

2010-01-01

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course.

Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

PubMed Central

Evans, Irene M.

2010-01-01

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course. PMID:20810954

Molecular Diversity of Macrophages in Allergic Reaction: Comparison between the Allergenic Modes; Th1- and -Th2-Derived Immune Conditions.

PubMed

Bagheri, Mozhdeh; Dong, Yupeng; Ono, Masao

2015-06-01

Activated macrophages have been classified into classical (M1) and alternative (M2) macrophages. We aimed to establish a method to yield enough number of macrophages to analyze their molecular, biological and immunological functions. We used drugs; adjuvant albumin from chicken egg whites--Imject Alum (OVA-Alum) and OVA Complete Freund Adjuvant (OVA-CFA), to induce macrophages to M2 and M1 respectively. We analyzed the phenotype of purified macrophages induced under these immune conditions, using flow cytometry (FACS) to detect cell-surface molecules and the enzyme-linked immunosorbent assay (ELISA) was used to detect cytokines. The cDNA microarray was employed to measure changes in expression level of cell surface protein between M1 and M2 macrophages. Phenotype analysis of purified macrophages, induced under these immune conditions, showed macrophages induced by OVA-Alum was almost M2 while the proportion of M1 macrophages induced by OVA-CFA was significantly higher. The results also showed higher expression level of macrophage galactose N- acetyl-galactosamine specific lectin-2 protein (MGL1/2-PE), a known M2 macrophage marker, on the surface of Alum-induced macrophages. On the basis of these preliminary data, ELISA results revealed that after macrophage stimulation with lipopolysaccharides (LPS), the level of interleukin (IL)-10 produced by Alum- induced macrophages was higher than the level of IL-10 produced by CFA-induced macrophages. In contrast, the level of tumor necrosis factor-alpha (TNF-α) produced by CFA-induced macrophages was higher than Alum-induced macrophages. The cDNA microarray confirmed previous results and suggest immunoglobulin-like type 2 receptor alpha (Pilra) as a new marker for M1, macrophage galactose N-acetylgalactosamine-specific lectin 2 (Mgl2) as M2 macrophages marker.

Prognostic significance of MYC, BCL2, and BCL6 rearrangements in patients with diffuse large B-cell lymphoma treated with cyclophosphamide, doxorubicin, vincristine, and prednisone plus rituximab.

PubMed

Akyurek, Nalan; Uner, Aysegul; Benekli, Mustafa; Barista, Ibrahim

2012-09-01

Diffuse large B-cell lymphomas (DLBCLs) are a biologically heterogeneous group in which various gene alterations have been reported. The aim of this study was to investigate the frequency and prognostic impact of BCL2, BCL6, and MYC rearrangements in cyclophosphamide, doxorubicin, vincristine, and prednisone plus rituximab (R-CHOP)-treated DLBCL cases. Tissue microarrays were constructed from 239 cases of DLBCL, and the expressions of CD10, BCL6, MUM1/IRF4, and BCL2 were evaluated by immunohistochemistry. MYC, BCL2, and BCL6 rearrangements were investigated by interphase fluorescence in situ hybridization on tissue microarrays. Survival analysis was constructed from 145 R-CHOP-treated patients. MYC, BCL2, and BCL6 rearrangements were detected in 14 (6%), 36 (15%), and 69 (29%) of 239 DLBCL patients. Double or triple rearrangements were detected in 7 (3%) of 239 DLBCL cases. Of these, 4 had BCL2 and MYC, 2 had BCL6 and MYC, and 1 had BCL2, BCL6, and MYC rearrangements. The prognosis of these cases was extremely poor, with a median survival of 9 months. MYC rearrangement was associated with significantly worse overall survival (P = .01), especially for the cases with GC phenotype (P = .009). BCL6 rearrangement also predicted significantly shorter overall survival (P = .04), especially for the non-GC phenotype (P = .03). BCL2 rearrangement had no prognostic impact on outcome. International Prognostic Index (P = .004) and MYC rearrangement (P = .009) were independent poor prognostic factors. Analysis of MYC gene rearrangement along with BCL2 and BCL6 is critical in identifying high-risk patients with poor prognosis. Copyright © 2011 American Cancer Society.

The Influence of LepR Tyrosine Site Mutations on Mouse Ovary Development and Related Gene Expression Changes.

PubMed

Tu, Xiaoyu; Kuang, Zhichao; Gong, Xia; Shi, Yan; Yu, Lin; Shi, Huijuan; Wang, Jian; Sun, Zhaogui

2015-01-01

Leptin exerts many biological functions, such as in metabolism and reproduction, through binding to and activating the leptin receptor, LepRb, which is expressed in many regions of the brain. To better understand the roles of LepR downstream signaling pathways, Y123F mice, which expressed mutant leptin receptors with phenylalanine (F) substituted for three tyrosines (Y) (Tyr985, Tyr1077 and Tyr1138), were generated. The body weight and abdominal fat deposits of Y123F homozygous mice (HOM) were higher than those of wild-type mice (WT). HOM ovaries were atrophic and the follicles developed abnormally; however, the HOM ovaries did not exhibit polycystic phenotypes. Moreover, Y123F HOM adults had no estrous cycle and the blood estrogen concentration remained stable at a low level below detection limit of 5 pg/ml. LepR expression in HOM ovaries was higher than in WT ovaries. Using cDNA Microarrays, the mRNA expressions of 41 genes were increased, and 100 were decreased in HOM vs. WT ovaries, and many signaling pathways were evaluated to be involved significantly. The expressions of 19 genes were validated by real-time quantitative PCR, most of which were consistent with the microarray results. Thus, Y123F HOM mice were suggested as a new animal model of PCOS for research that mainly emphasizes metabolic disorders and anovulation, but not the polycystic phenotype. Meanwhile, using the model, we found that JAK-STAT and hormone biosynthesis pathways were involved in the follicular development and ovulation disorders caused by LepR deficiency in ovaries, although we could not exclude indirect actions from the brain.

The Influence of LepR Tyrosine Site Mutations on Mouse Ovary Development and Related Gene Expression Changes

PubMed Central

Tu, Xiaoyu; Kuang, Zhichao; Gong, Xia; Shi, Yan; Yu, Lin; Shi, Huijuan; Wang, Jian; Sun, Zhaogui

2015-01-01

Leptin exerts many biological functions, such as in metabolism and reproduction, through binding to and activating the leptin receptor, LepRb, which is expressed in many regions of the brain. To better understand the roles of LepR downstream signaling pathways, Y123F mice, which expressed mutant leptin receptors with phenylalanine (F) substituted for three tyrosines (Y) (Tyr985, Tyr1077 and Tyr1138), were generated. The body weight and abdominal fat deposits of Y123F homozygous mice (HOM) were higher than those of wild-type mice (WT). HOM ovaries were atrophic and the follicles developed abnormally; however, the HOM ovaries did not exhibit polycystic phenotypes. Moreover, Y123F HOM adults had no estrous cycle and the blood estrogen concentration remained stable at a low level below detection limit of 5 pg/ml. LepR expression in HOM ovaries was higher than in WT ovaries. Using cDNA Microarrays, the mRNA expressions of 41 genes were increased, and 100 were decreased in HOM vs. WT ovaries, and many signaling pathways were evaluated to be involved significantly. The expressions of 19 genes were validated by real-time quantitative PCR, most of which were consistent with the microarray results. Thus, Y123F HOM mice were suggested as a new animal model of PCOS for research that mainly emphasizes metabolic disorders and anovulation, but not the polycystic phenotype. Meanwhile, using the model, we found that JAK-STAT and hormone biosynthesis pathways were involved in the follicular development and ovulation disorders caused by LepR deficiency in ovaries, although we could not exclude indirect actions from the brain. PMID:26529315

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.

PubMed

De Hertogh, Benoît; De Meulder, Bertrand; Berger, Fabrice; Pierre, Michael; Bareke, Eric; Gaigneaux, Anthoula; Depiereux, Eric

2010-01-11

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods. Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality. Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

PubMed Central

McArt, Darragh G.; Dunne, Philip D.; Blayney, Jaine K.; Salto-Tellez, Manuel; Van Schaeybroeck, Sandra; Hamilton, Peter W.; Zhang, Shu-Dong

2013-01-01

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. PMID:23840550

Cross-study projections of genomic biomarkers: an evaluation in cancer genomics.

PubMed

Lucas, Joseph E; Carvalho, Carlos M; Chen, Julia Ling-Yu; Chi, Jen-Tsan; West, Mike

2009-01-01

Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies--in cancer and other diseases--have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

Multi-level evaluation of Escherichia coli polyphosphate related mutants using global transcriptomic, proteomic and phenomic analyses.

PubMed

Varas, Macarena; Valdivieso, Camilo; Mauriaca, Cecilia; Ortíz-Severín, Javiera; Paradela, Alberto; Poblete-Castro, Ignacio; Cabrera, Ricardo; Chávez, Francisco P

2017-04-01

Polyphosphate (polyP) is a linear biopolymer found in all living cells. In bacteria, mutants lacking polyphosphate kinase 1 (PPK1), the enzyme responsible for synthesis of most polyP, have many structural and functional defects. However, little is known about the causes of these pleiotropic alterations. The link between ppk1 deletion and those numerous phenotypes observed can be the result of complex molecular interactions that can be elucidated via a systems biology approach. By integrating different omics levels (transcriptome, proteome and phenome), we described the functioning of various metabolic pathways among Escherichia coli polyphosphate mutant strains (Δppk1, Δppx, and ΔpolyP). Bioinformatic analyses reveal the complex metabolic and regulatory bases of the phenotypes unique to polyP mutants. Our results suggest that during polyP deficiency (Δppk1 mutant), metabolic pathways needed for energy supply are up-regulated, including fermentation, aerobic and anaerobic respiration. Transcriptomic and q-proteomic contrasting changes between Δppk1 and Δppx mutant strains were observed in those central metabolic pathways and confirmed by using Phenotypic microarrays. In addition, our results suggest a regulatory connection between polyP, second messenger metabolism, alternative Sigma/Anti-Sigma factors and type-II toxin-antitoxin (TA) systems. We suggest a broader role for polyP via regulation of ATP-dependent proteolysis of type II toxin-antitoxin system and alternative Sigma/Anti-Sigma factors, that could explain the multiple structural and functional deficiencies described due to alteration of polyP metabolism. Understanding the interplay of polyP in bacterial metabolism using a systems biology approach can help to improve design of novel antimicrobials toward pathogens. Copyright © 2017 Elsevier B.V. All rights reserved.

Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

PubMed Central

Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

SVS: data and knowledge integration in computational biology.

PubMed

Zycinski, Grzegorz; Barla, Annalisa; Verri, Alessandro

2011-01-01

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.

Phenotypic Profiling of Scedosporium aurantiacum, an Opportunistic Pathogen Colonizing Human Lungs

PubMed Central

Kaur, Jashanpreet; Duan, Shu Yao; Vaas, Lea A. I.; Penesyan, Anahit; Meyer, Wieland; Paulsen, Ian T.; Nevalainen, Helena

2015-01-01

Genotyping studies of Australian Scedosporium isolates have revealed the strong prevalence of a recently described species: Scedosporium aurantiacum. In addition to occurring in the environment, this fungus is also known to colonise the respiratory tracts of cystic fibrosis (CF) patients. A high throughput Phenotype Microarray (PM) analysis using 94 assorted substrates (sugars, amino acids, hexose-acids and carboxylic acids) was carried out for four isolates exhibiting different levels of virulence, determined using a Galleria mellonella infection model. A significant difference was observed in the substrate utilisation patterns of strains displaying differential virulence. For example, certain sugars such as sucrose (saccharose) were utilised only by low virulence strains whereas some sugar derivatives such as D-turanose promoted respiration only in the more virulent strains. Strains with a higher level of virulence also displayed flexibility and metabolic adaptability at two different temperature conditions tested (28 and 37°C). Phenotype microarray data were integrated with the whole-genome sequence data of S. aurantiacum to reconstruct a pathway map for the metabolism of selected substrates to further elucidate differences between the strains. PMID:25811884

Phenotypic profiling of Scedosporium aurantiacum, an opportunistic pathogen colonizing human lungs.

PubMed

Kaur, Jashanpreet; Duan, Shu Yao; Vaas, Lea A I; Penesyan, Anahit; Meyer, Wieland; Paulsen, Ian T; Nevalainen, Helena

2015-01-01

Genotyping studies of Australian Scedosporium isolates have revealed the strong prevalence of a recently described species: Scedosporium aurantiacum. In addition to occurring in the environment, this fungus is also known to colonise the respiratory tracts of cystic fibrosis (CF) patients. A high throughput Phenotype Microarray (PM) analysis using 94 assorted substrates (sugars, amino acids, hexose-acids and carboxylic acids) was carried out for four isolates exhibiting different levels of virulence, determined using a Galleria mellonella infection model. A significant difference was observed in the substrate utilisation patterns of strains displaying differential virulence. For example, certain sugars such as sucrose (saccharose) were utilised only by low virulence strains whereas some sugar derivatives such as D-turanose promoted respiration only in the more virulent strains. Strains with a higher level of virulence also displayed flexibility and metabolic adaptability at two different temperature conditions tested (28 and 37°C). Phenotype microarray data were integrated with the whole-genome sequence data of S. aurantiacum to reconstruct a pathway map for the metabolism of selected substrates to further elucidate differences between the strains.

Nablus mask-like facial syndrome: deletion of chromosome 8q22.1 is necessary but not sufficient to cause the phenotype.

PubMed

Allanson, Judith; Smith, Amanda; Hare, Heather; Albrecht, Beate; Bijlsma, Emilia; Dallapiccola, Bruno; Donti, Emilio; Fitzpatrick, David; Isidor, Bertrand; Lachlan, Katherine; Le Caignec, Cedric; Prontera, Paolo; Raas-Rothschild, Annick; Rogaia, Daniela; van Bon, Bregje; Aradhya, Swaroop; Crocker, Susan F; Jarinova, Olga; McGowan-Jordan, Jean; Boycott, Kym; Bulman, Dennis; Fagerberg, Christina Ringmann

2012-09-01

Nablus mask-like facial syndrome (NMLFS) has many distinctive phenotypic features, particularly tight glistening skin with reduced facial expression, blepharophimosis, telecanthus, bulky nasal tip, abnormal external ear architecture, upswept frontal hairline, and sparse eyebrows. Over the last few years, several individuals with NMLFS have been reported to have a microdeletion of 8q21.3q22.1, demonstrated by microarray analysis. The minimal overlapping region is 93.98-96.22 Mb (hg19). Here we present clinical and microarray data from five singletons and two mother-child pairs who have heterozygous deletions significantly overlapping the region associated with NMLFS. Notably, while one mother and child were said to have mild tightening of facial skin, none of these individuals exhibited reduced facial expression or the classical facial phenotype of NMLFS. These findings indicate that deletion of the 8q21.3q22.1 region is necessary but not sufficient for development of the NMLFS. We discuss possible genetic mechanisms underlying the complex pattern of inheritance for this condition. Copyright © 2012 Wiley Periodicals, Inc.

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data.

PubMed

Tong, Dong Ling; Schierz, Amanda C

2011-09-01

Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ability of the method. These methods also require the microarray data to be preprocessed before analysis takes place. The objective of this study is to develop a hybrid genetic algorithm-neural network (GANN) model that emphasises feature selection and can operate on unpreprocessed microarray data. The GANN is a hybrid model where the fitness value of the genetic algorithm (GA) is based upon the number of samples correctly labelled by a standard feedforward artificial neural network (ANN). The model is evaluated by using two benchmark microarray datasets with different array platforms and differing number of classes (a 2-class oligonucleotide microarray data for acute leukaemia and a 4-class complementary DNA (cDNA) microarray dataset for SRBCTs (small round blue cell tumours)). The underlying concept of the GANN algorithm is to select highly informative genes by co-evolving both the GA fitness function and the ANN weights at the same time. The novel GANN selected approximately 50% of the same genes as the original studies. This may indicate that these common genes are more biologically significant than other genes in the datasets. The remaining 50% of the significant genes identified were used to build predictive models and for both datasets, the models based on the set of genes extracted by the GANN method produced more accurate results. The results also suggest that the GANN method not only can detect genes that are exclusively associated with a single cancer type but can also explore the genes that are differentially expressed in multiple cancer types. The results show that the GANN model has successfully extracted statistically significant genes from the unpreprocessed microarray data as well as extracting known biologically significant genes. We also show that assessing the biological significance of genes based on classification accuracy may be misleading and though the GANN's set of extra genes prove to be more statistically significant than those selected by other methods, a biological assessment of these genes is highly recommended to confirm their functionality. Copyright © 2011 Elsevier B.V. All rights reserved.

The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression.

PubMed

Catto, James W F; Abbod, Maysam F; Wild, Peter J; Linkens, Derek A; Pilarsky, Christian; Rehman, Ishtiaq; Rosario, Derek J; Denzinger, Stefan; Burger, Maximilian; Stoehr, Robert; Knuechel, Ruth; Hartmann, Arndt; Hamdy, Freddie C

2010-03-01

New methods for identifying bladder cancer (BCa) progression are required. Gene expression microarrays can reveal insights into disease biology and identify novel biomarkers. However, these experiments produce large datasets that are difficult to interpret. To develop a novel method of microarray analysis combining two forms of artificial intelligence (AI): neurofuzzy modelling (NFM) and artificial neural networks (ANN) and validate it in a BCa cohort. We used AI and statistical analyses to identify progression-related genes in a microarray dataset (n=66 tumours, n=2800 genes). The AI-selected genes were then investigated in a second cohort (n=262 tumours) using immunohistochemistry. We compared the accuracy of AI and statistical approaches to identify tumour progression. AI identified 11 progression-associated genes (odds ratio [OR]: 0.70; 95% confidence interval [CI], 0.56-0.87; p=0.0004), and these were more discriminate than genes chosen using statistical analyses (OR: 1.24; 95% CI, 0.96-1.60; p=0.09). The expression of six AI-selected genes (LIG3, FAS, KRT18, ICAM1, DSG2, and BRCA2) was determined using commercial antibodies and successfully identified tumour progression (concordance index: 0.66; log-rank test: p=0.01). AI-selected genes were more discriminate than pathologic criteria at determining progression (Cox multivariate analysis: p=0.01). Limitations include the use of statistical correlation to identify 200 genes for AI analysis and that we did not compare regression identified genes with immunohistochemistry. AI and statistical analyses use different techniques of inference to determine gene-phenotype associations and identify distinct prognostic gene signatures that are equally valid. We have identified a prognostic gene signature whose members reflect a variety of carcinogenic pathways that could identify progression in non-muscle-invasive BCa. 2009 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Transcriptome profiling of a Saccharomyces cerevisiae mutant with a constitutively activated Ras/cAMP pathway.

PubMed

Jones, D L; Petty, J; Hoyle, D C; Hayes, A; Ragni, E; Popolo, L; Oliver, S G; Stateva, L I

2003-12-16

Often changes in gene expression levels have been considered significant only when above/below some arbitrarily chosen threshold. We investigated the effect of applying a purely statistical approach to microarray analysis and demonstrated that small changes in gene expression have biological significance. Whole genome microarray analysis of a pde2Delta mutant, constructed in the Saccharomyces cerevisiae reference strain FY23, revealed altered expression of approximately 11% of protein encoding genes. The mutant, characterized by constitutive activation of the Ras/cAMP pathway, has increased sensitivity to stress, reduced ability to assimilate nonfermentable carbon sources, and some cell wall integrity defects. Applying the Munich Information Centre for Protein Sequences (MIPS) functional categories revealed increased expression of genes related to ribosome biogenesis and downregulation of genes in the cell rescue, defense, cell death and aging category, suggesting a decreased response to stress conditions. A reduced level of gene expression in the unfolded protein response pathway (UPR) was observed. Cell wall genes whose expression was affected by this mutation were also identified. Several of the cAMP-responsive orphan genes, upon further investigation, revealed cell wall functions; others had previously unidentified phenotypes assigned to them. This investigation provides a statistical global transcriptome analysis of the cellular response to constitutive activation of the Ras/cAMP pathway.

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

PubMed

Usadel, Björn; Nagel, Axel; Steinhauser, Dirk; Gibon, Yves; Bläsing, Oliver E; Redestig, Henning; Sreenivasulu, Nese; Krall, Leonard; Hannah, Matthew A; Poree, Fabien; Fernie, Alisdair R; Stitt, Mark

2006-12-18

Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Design of microarray experiments for genetical genomics studies.

PubMed

Bueno Filho, Júlio S S; Gilmour, Steven G; Rosa, Guilherme J M

2006-10-01

Microarray experiments have been used recently in genetical genomics studies, as an additional tool to understand the genetic mechanisms governing variation in complex traits, such as for estimating heritabilities of mRNA transcript abundances, for mapping expression quantitative trait loci, and for inferring regulatory networks controlling gene expression. Several articles on the design of microarray experiments discuss situations in which treatment effects are assumed fixed and without any structure. In the case of two-color microarray platforms, several authors have studied reference and circular designs. Here, we discuss the optimal design of microarray experiments whose goals refer to specific genetic questions. Some examples are used to illustrate the choice of a design for comparing fixed, structured treatments, such as genotypic groups. Experiments targeting single genes or chromosomic regions (such as with transgene research) or multiple epistatic loci (such as within a selective phenotyping context) are discussed. In addition, microarray experiments in which treatments refer to families or to subjects (within family structures or complex pedigrees) are presented. In these cases treatments are more appropriately considered to be random effects, with specific covariance structures, in which the genetic goals relate to the estimation of genetic variances and the heritability of transcriptional abundances.

Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears.

PubMed

Rai, Muhammad Farooq; Tycksen, Eric D; Sandell, Linda J; Brophy, Robert H

2018-01-01

Microarrays and RNA-seq are at the forefront of high throughput transcriptome analyses. Since these methodologies are based on different principles, there are concerns about the concordance of data between the two techniques. The concordance of RNA-seq and microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed in clinically derived ligament tissues. To demonstrate the concordance between RNA-seq and microarrays and to assess potential benefits of RNA-seq over microarrays, we assessed differences in transcript expression in anterior cruciate ligament (ACL) tissues based on time-from-injury. ACL remnants were collected from patients with an ACL tear at the time of ACL reconstruction. RNA prepared from torn ACL remnants was subjected to Agilent microarrays (N = 24) and RNA-seq (N = 8). The correlation of biological replicates in RNA-seq and microarrays data was similar (0.98 vs. 0.97), demonstrating that each platform has high internal reproducibility. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarrays values were moderate. The cross-platform concordance for differentially expressed transcripts or enriched pathways was linearly correlated (r = 0.64). RNA-Seq was superior in detecting low abundance transcripts and differentiating biologically critical isoforms. Additional independent validation of transcript expression was undertaken using microfluidic PCR for selected genes. PCR data showed 100% concordance (in expression pattern) with RNA-seq and microarrays data. These findings demonstrate that RNA-seq has advantages over microarrays for transcriptome profiling of ligament tissues when available and affordable. Furthermore, these findings are likely transferable to other musculoskeletal tissues where tissue collection is challenging and cells are in low abundance. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:484-497, 2018. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Gene Expression Profiling Differentiates Autism Case–Controls and Phenotypic Variants of Autism Spectrum Disorders: Evidence for Circadian Rhythm Dysfunction in Severe Autism

PubMed Central

Hu, Valerie W.; Sarachana, Tewarit; Kim, Kyung Soon; Nguyen, AnhThu; Kulkarni, Shreya; Steinberg, Mara E.; Luu, Truong; Lai, Yinglei; Lee, Norman H.

2009-01-01

Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by delayed/abnormal language development, deficits in social interaction, repetitive behaviors and restricted interests. The heterogeneity in clinical presentation of ASD, likely due to different etiologies, complicates genetic/biological analyses of these disorders. DNA microarray analyses were conducted on 116 lymphoblastoid cell lines (LCL) from individuals with idiopathic autism who are divided into three phenotypic subgroups according to severity scores from the commonly used Autism Diagnostic Interview-Revised questionnaire and age-matched, nonautistic controls. Statistical analyses of gene expression data from control LCL against that of LCL from ASD probands identify genes for which expression levels are either quantitatively or qualitatively associated with phenotypic severity. Comparison of the significant differentially expressed genes from each subgroup relative to the control group reveals differentially expressed genes unique to each subgroup as well as genes in common across subgroups. Among the findings unique to the most severely affected ASD group are 15 genes that regulate circadian rhythm, which has been shown to have multiple effects on neurological as well as metabolic functions commonly dysregulated in autism. Among the genes common to all three subgroups of ASD are 20 novel genes mostly in putative noncoding regions, which appear to associate with androgen sensitivity and which may underlie the strong 4:1 bias toward affected males. PMID:19418574

Association of a homozygous nonsense mutation in the ABCA4 (ABCR) gene with cone-rod dystrophy phenotype in an Italian family.

PubMed

Simonelli, Francesca; Testa, Francesco; Zernant, Jana; Nesti, Anna; Rossi, Settimio; Rinaldi, Ernesto; Allikmets, Rando

2004-01-01

Genetic variation in the ABCA4 (ABCR) gene has been associated with several distinct retinal phenotypes, including Stargardt disease/fundus flavimaculatus (STGD/FFM), cone-rod dystrophy (CRD), retinitis pigmentosa (RP) and age-related macular degeneration. The current model of genotype/phenotype association suggests that patients harboring deleterious mutations in both ABCR alleles would develop RP-like retinal pathology. Here we describe ABCA4-associated phenotypes, including a proband with a homozygous nonsense mutation in a family from Southern Italy. The proband had been originally diagnosed with STGD. Ophthalmologic examination included kinetic perimetry, electrophysiological studies and fluorescein angiography. DNA of the affected individual and family members was analyzed for variants in all 50 exons of the ABCA4 gene by screening on the ABCR400 microarray. A homozygous nonsense mutation 2971G>T (G991X) was detected in a patient initially diagnosed with STGD based on funduscopic evidence, including bull's eye depigmentation of the fovea and flecks at the posterior pole extending to the mid-peripheral retina. Since this novel nucleotide substitution results in a truncated, nonfunctional, ABCA4 protein, the patient was examined in-depth for the severity of the disease phenotype. Indeed, subsequent electrophysiological studies determined severely reduced cone amplitude as compared to the rod amplitude, suggesting the diagnosis of CRD. ABCR400 microarray is an efficient tool for determining causal genetic variation, including new mutations. A homozygous protein-truncating mutation in ABCA4 can cause a phenotype ranging from STGD to CRD as diagnosed at an early stage of the disease. Only a combination of comprehensive genotype/phenotype correlation studies will determine the proper diagnosis and prognosis of ABCA4-associated pathology. Copyright 2004 S. Karger AG, Basel

Systems biology meets stress ecology: linking molecular and organismal stress responses in Daphnia magna

PubMed Central

Heckmann, Lars-Henrik; Sibly, Richard M; Connon, Richard; Hooper, Helen L; Hutchinson, Thomas H; Maund, Steve J; Hill, Christopher J; Bouetard, Anthony; Callaghan, Amanda

2008-01-01

Background Ibuprofen and other nonsteroidal anti-inflammatory drugs have been designed to interrupt eicosanoid metabolism in mammals, but little is known of how they affect nontarget organisms. Here we report a systems biology study that simultaneously describes the transcriptomic and phenotypic stress responses of the model crustacean Daphnia magna after exposure to ibuprofen. Results Our findings reveal intriguing similarities in the mode of action of ibuprofen between vertebrates and invertebrates, and they suggest that ibuprofen has a targeted impact on reproduction at the molecular, organismal, and population level in daphnids. Microarray expression and temporal real-time quantitative PCR profiles of key genes suggest early ibuprofen interruption of crustacean eicosanoid metabolism, which appears to disrupt signal transduction affecting juvenile hormone metabolism and oogenesis. Conclusion Combining molecular and organismal stress responses provides a guide to possible chronic consequences of environmental stress for population health. This could improve current environmental risk assessment by providing an early indication of the need for higher tier testing. Our study demonstrates the advantages of a systems approach to stress ecology, in which Daphnia will probably play a major role. PMID:18291039

Genome-wide expression profiling in pediatric septic shock

PubMed Central

Wong, Hector R.

2013-01-01

For nearly a decade, our research group has had the privilege of developing and mining a multi-center, microarray-based, genome-wide expression database of critically ill children (≤ 10 years of age) with septic shock. Using bioinformatic and systems biology approaches, the expression data generated through this discovery-oriented, exploratory approach have been leveraged for a variety of objectives, which will be reviewed. Fundamental observations include wide spread repression of gene programs corresponding to the adaptive immune system, and biologically significant differential patterns of gene expression across developmental age groups. The data have also identified gene expression-based subclasses of pediatric septic shock having clinically relevant phenotypic differences. The data have also been leveraged for the discovery of novel therapeutic targets, and for the discovery and development of novel stratification and diagnostic biomarkers. Almost a decade of genome-wide expression profiling in pediatric septic shock is now demonstrating tangible results. The studies have progressed from an initial discovery-oriented and exploratory phase, to a new phase where the data are being translated and applied to address several areas of clinical need. PMID:23329198

Mutual information estimation reveals global associations between stimuli and biological processes

PubMed Central

Suzuki, Taiji; Sugiyama, Masashi; Kanamori, Takafumi; Sese, Jun

2009-01-01

Background Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. PMID:19208155

Transcriptional master regulator analysis in breast cancer genetic networks.

PubMed

Tovar, Hugo; García-Herrera, Rodrigo; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

2015-12-01

Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Biologic Phenotyping of the Human Small Airway Epithelial Response to Cigarette Smoking

PubMed Central

Tilley, Ann E.; O'Connor, Timothy P.; Hackett, Neil R.; Strulovici-Barel, Yael; Salit, Jacqueline; Amoroso, Nancy; Zhou, Xi Kathy; Raman, Tina; Omberg, Larsson; Clark, Andrew; Mezey, Jason; Crystal, Ronald G.

2011-01-01

Background The first changes associated with smoking are in the small airway epithelium (SAE). Given that smoking alters SAE gene expression, but only a fraction of smokers develop chronic obstructive pulmonary disease (COPD), we hypothesized that assessment of SAE genome-wide gene expression would permit biologic phenotyping of the smoking response, and that a subset of healthy smokers would have a “COPD-like” SAE transcriptome. Methodology/Principal Findings SAE (10th–12th generation) was obtained via bronchoscopy of healthy nonsmokers, healthy smokers and COPD smokers and microarray analysis was used to identify differentially expressed genes. Individual responsiveness to smoking was quantified with an index representing the % of smoking-responsive genes abnormally expressed (ISAE), with healthy smokers grouped into “high” and “low” responders based on the proportion of smoking-responsive genes up- or down-regulated in each smoker. Smokers demonstrated significant variability in SAE transcriptome with ISAE ranging from 2.9 to 51.5%. While the SAE transcriptome of “low” responder healthy smokers differed from both “high” responders and smokers with COPD, the transcriptome of the “high” responder healthy smokers was indistinguishable from COPD smokers. Conclusion/Significance The SAE transcriptome can be used to classify clinically healthy smokers into subgroups with lesser and greater responses to cigarette smoking, even though these subgroups are indistinguishable by clinical criteria. This identifies a group of smokers with a “COPD-like” SAE transcriptome. PMID:21829517

Measuring the effect of inter-study variability on estimating prediction error.

PubMed

Ma, Shuyi; Sung, Jaeyun; Magis, Andrew T; Wang, Yuliang; Geman, Donald; Price, Nathan D

2014-01-01

The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in "batch-effects") and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies. Here we quantify the impact of these combined "study-effects" on a disease signature's predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance. As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification. We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when "sufficient" diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.

Progress in the application of DNA microarrays.

PubMed Central

Lobenhofer, E K; Bushel, P R; Afshari, C A; Hamadeh, H K

2001-01-01

Microarray technology has been applied to a variety of different fields to address fundamental research questions. The use of microarrays, or DNA chips, to study the gene expression profiles of biologic samples began in 1995. Since that time, the fundamental concepts behind the chip, the technology required for making and using these chips, and the multitude of statistical tools for analyzing the data have been extensively reviewed. For this reason, the focus of this review will be not on the technology itself but on the application of microarrays as a research tool and the future challenges of the field. PMID:11673116

Cross species analysis of microarray expression data

PubMed Central

Lu, Yong; Huggins, Peter; Bar-Joseph, Ziv

2009-01-01

Motivation: Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied. Results: In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them. Contact: zivbj@cs.cmu.edu PMID:19357096

Application of phenotypic microarrays to environmental microbiology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borglin, sharon; Joyner, Dominique; DeAngelis, Kristen

2012-01-01

Environmental organisms are extremely diverse and only a small fraction has been successfully cultured in the laboratory. Culture in micro wells provides a method for rapid screening of a wide variety of growth conditions and commercially available plates contain a large number of substrates, nutrient sources, and inhibitors, which can provide an assessment of the phenotype of an organism. This review describes applications of phenotype arrays to anaerobic and thermophilic microorganisms, use of the plates in stress response studies, in development of culture media for newly discovered strains, and for assessment of phenotype of environmental communities. Also discussed are considerationsmore » and challenges in data interpretation and visualization, including data normalization, statistics, and curve fitting.« less

Biological Pathways

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

RECOVERING FILTER-BASED MICROARRAY DATA FOR PATHWAYS ANALYSIS USING A MULTIPOINT ALIGNMENT STRATEGY

EPA Science Inventory

The use of commercial microarrays are rapidly becoming the method of choice for profiling gene expression and assessing various disease states. Research Genetics has provided a series of well defined biological and software tools to the research community for these analyses. Th...

Mining meiosis and gametogenesis with DNA microarrays.

PubMed

Schlecht, Ulrich; Primig, Michael

2003-04-01

Gametogenesis is a key developmental process that involves complex transcriptional regulation of numerous genes including many that are conserved between unicellular eukaryotes and mammals. Recent expression-profiling experiments using microarrays have provided insight into the co-ordinated transcription of several hundred genes during mitotic growth and meiotic development in budding and fission yeast. Furthermore, microarray-based studies have identified numerous loci that are regulated during the cell cycle or expressed in a germ-cell specific manner in eukaryotic model systems like Caenorhabditis elegans, Mus musculus as well as Homo sapiens. The unprecedented amount of information produced by post-genome biology has spawned novel approaches to organizing biological knowledge using currently available information technology. This review outlines experiments that contribute to an emerging comprehensive picture of the molecular machinery governing sexual reproduction in eukaryotes.

Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases

PubMed Central

2011-01-01

Background Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions. Methods An application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs. Results When a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF. Conclusions We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited. PMID:21342584

Deciphering the glycosaminoglycan code with the help of microarrays.

PubMed

de Paz, Jose L; Seeberger, Peter H

2008-07-01

Carbohydrate microarrays have become a powerful tool to elucidate the biological role of complex sugars. Microarrays are particularly useful for the study of glycosaminoglycans (GAGs), a key class of carbohydrates. The high-throughput chip format enables rapid screening of large numbers of potential GAG sequences produced via a complex biosynthesis while consuming very little sample. Here, we briefly highlight the most recent advances involving GAG microarrays built with synthetic or naturally derived oligosaccharides. These chips are powerful tools for characterizing GAG-protein interactions and determining structure-activity relationships for specific sequences. Thereby, they contribute to decoding the information contained in specific GAG sequences.

SRRM4 Expression and the Loss of REST Activity May Promote the Emergence of the Neuroendocrine Phenotype in Castration-Resistant Prostate Cancer.

PubMed

Zhang, Xiaotun; Coleman, Ilsa M; Brown, Lisha G; True, Lawrence D; Kollath, Lori; Lucas, Jared M; Lam, Hung-Ming; Dumpit, Ruth; Corey, Eva; Chéry, Lisly; Lakely, Bryce; Higano, Celestia S; Montgomery, Bruce; Roudier, Martine; Lange, Paul H; Nelson, Peter S; Vessella, Robert L; Morrissey, Colm

2015-10-15

The neuroendocrine phenotype is associated with the development of metastatic castration-resistant prostate cancer (CRPC). Our objective was to characterize the molecular features of the neuroendocrine phenotype in CRPC. Expression of chromogranin A (CHGA), synaptophysin (SYP), androgen receptor (AR), and prostate-specific antigen (PSA) was analyzed by IHC in 155 CRPC metastases from 50 patients and in 24 LuCaP prostate cancer patient-derived xenografts (PDX). Seventy-one of 155 metastases and the 24 LuCaP xenograft lines were analyzed by whole-genome microarrays. REST splicing was verified by PCR. Coexpression of CHGA and SYP in >30% of cells was observed in 22 of 155 metastases (9 patients); 11 of the 22 metastases were AR(+)/PSA(+) (6 patients), 11/22 were AR-/PSA- (4 patients), and 4/24 LuCaP PDXs were AR(-)/PSA(-). By IHC, of the 71 metastases analyzed by whole-genome microarrays, 5 metastases were CHGA(+)/SYP(+)/AR(-), and 5 were CHGA(+)/SYP(+)/AR(+). Only CHGA(+)/SYP(+) metastases had a neuroendocrine transcript signature. The neuronal transcriptional regulator SRRM4 transcript was associated with the neuroendocrine signature in CHGA(+)/SYP(+) metastases and all CHGA(+)/SYP(+) LuCaP xenografts. In addition, expression of SRRM4 in LuCaP neuroendocrine xenografts correlated with a splice variant of REST that lacks the transcriptional repressor domain. (i) Metastatic neuroendocrine status can be heterogeneous in the same patient, (ii) the CRPC neuroendocrine molecular phenotype can be defined by CHGA(+)/SYP(+) dual positivity, (iii) the neuroendocrine phenotype is not necessarily associated with the loss of AR activity, and (iv) the splicing of REST by SRRM4 could promote the neuroendocrine phenotype in CRPC. ©2015 American Association for Cancer Research.

Screening Mammalian Cells on a Hydrogel: Functionalized Small Molecule Microarray.

PubMed

Zhu, Biwei; Jiang, Bo; Na, Zhenkun; Yao, Shao Q

2017-01-01

Mammalian cell-based microarray technology has gained wide attention, for its plethora of promising applications. The platform is able to provide simultaneous information on multiple parameters for a given target, or even multiple target proteins, in a complex biological system. Here we describe the preparation of mammalian cell-based microarrays using selectively captured of human prostate cancer cells (PC-3). This platform was then used in controlled drug release and measuring the associated drug effects on these cancer cells.

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

PubMed Central

Reif, David M.; Israel, Mark A.; Moore, Jason H.

2007-01-01

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org. PMID:19390666

Designing microarray and RNA-Seq experiments for greater systems biology discovery in modern plant genomics.

PubMed

Yang, Chuanping; Wei, Hairong

2015-02-01

Microarray and RNA-seq experiments have become an important part of modern genomics and systems biology. Obtaining meaningful biological data from these experiments is an arduous task that demands close attention to many details. Negligence at any step can lead to gene expression data containing inadequate or composite information that is recalcitrant for pattern extraction. Therefore, it is imperative to carefully consider experimental design before launching a time-consuming and costly experiment. Contemporarily, most genomics experiments have two objectives: (1) to generate two or more groups of comparable data for identifying differentially expressed genes, gene families, biological processes, or metabolic pathways under experimental conditions; (2) to build local gene regulatory networks and identify hierarchically important regulators governing biological processes and pathways of interest. Since the first objective aims to identify the active molecular identities and the second provides a basis for understanding the underlying molecular mechanisms through inferring causality relationships mediated by treatment, an optimal experiment is to produce biologically relevant and extractable data to meet both objectives without substantially increasing the cost. This review discusses the major issues that researchers commonly face when embarking on microarray or RNA-seq experiments and summarizes important aspects of experimental design, which aim to help researchers deliberate how to generate gene expression profiles with low background noise but with more interaction to facilitate novel biological discoveries in modern plant genomics. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

Methylation-Sensitive Amplification Length Polymorphism (MS-AFLP) Microarrays for Epigenetic Analysis of Human Genomes.

PubMed

Alonso, Sergio; Suzuki, Koichi; Yamamoto, Fumiichiro; Perucho, Manuel

2018-01-01

Somatic, and in a minor scale also germ line, epigenetic aberrations are fundamental to carcinogenesis, cancer progression, and tumor phenotype. DNA methylation is the most extensively studied and arguably the best understood epigenetic mechanisms that become altered in cancer. Both somatic loss of methylation (hypomethylation) and gain of methylation (hypermethylation) are found in the genome of malignant cells. In general, the cancer cell epigenome is globally hypomethylated, while some regions-typically gene-associated CpG islands-become hypermethylated. Given the profound impact that DNA methylation exerts on the transcriptional profile and genomic stability of cancer cells, its characterization is essential to fully understand the complexity of cancer biology, improve tumor classification, and ultimately advance cancer patient management and treatment. A plethora of methods have been devised to analyze and quantify DNA methylation alterations. Several of the early-developed methods relied on the use of methylation-sensitive restriction enzymes, whose activity depends on the methylation status of their recognition sequences. Among these techniques, methylation-sensitive amplification length polymorphism (MS-AFLP) was developed in the early 2000s, and successfully adapted from its original gel electrophoresis fingerprinting format to a microarray format that notably increased its throughput and allowed the quantification of the methylation changes. This array-based platform interrogates over 9500 independent loci putatively amplified by the MS-AFLP technique, corresponding to the NotI sites mapped throughout the human genome.

DNA modification study of major depressive disorder: beyond locus-by-locus comparisons.

PubMed

Oh, Gabriel; Wang, Sun-Chong; Pal, Mrinal; Chen, Zheng Fei; Khare, Tarang; Tochigi, Mamoru; Ng, Catherine; Yang, Yeqing A; Kwan, Andrew; Kaminsky, Zachary A; Mill, Jonathan; Gunasinghe, Cerisse; Tackett, Jennifer L; Gottesman, Irving I; Willemsen, Gonneke; de Geus, Eco J C; Vink, Jacqueline M; Slagboom, P Eline; Wray, Naomi R; Heath, Andrew C; Montgomery, Grant W; Turecki, Gustavo; Martin, Nicholas G; Boomsma, Dorret I; McGuffin, Peter; Kustra, Rafal; Petronis, Art

2015-02-01

Major depressive disorder (MDD) exhibits numerous clinical and molecular features that are consistent with putative epigenetic misregulation. Despite growing interest in epigenetic studies of psychiatric diseases, the methodologies guiding such studies have not been well defined. We performed DNA modification analysis in white blood cells from monozygotic twins discordant for MDD, in brain prefrontal cortex, and germline (sperm) samples from affected individuals and control subjects (total N = 304) using 8.1K CpG island microarrays and fine mapping. In addition to the traditional locus-by-locus comparisons, we explored the potential of new analytical approaches in epigenomic studies. In the microarray experiment, we detected a number of nominally significant DNA modification differences in MDD and validated selected targets using bisulfite pyrosequencing. Some MDD epigenetic changes, however, overlapped across brain, blood, and sperm more often than expected by chance. We also demonstrated that stratification for disease severity and age may increase the statistical power of epimutation detection. Finally, a series of new analytical approaches, such as DNA modification networks and machine-learning algorithms using binary and quantitative depression phenotypes, provided additional insights on the epigenetic contributions to MDD. Mapping epigenetic differences in MDD (and other psychiatric diseases) is a complex task. However, combining traditional and innovative analytical strategies may lead to identification of disease-specific etiopathogenic epimutations. Copyright © 2015 Society of Biological Psychiatry. All rights reserved.

c-Kit modifies the inflammatory status of smooth muscle cells

PubMed Central

Song, Lei; Martinez, Laisel; Zigmond, Zachary M.; Hernandez, Diana R.; Lassance-Soares, Roberta M.; Selman, Guillermo

2017-01-01

Background c-Kit is a receptor tyrosine kinase present in multiple cell types, including vascular smooth muscle cells (SMC). However, little is known about how c-Kit influences SMC biology and vascular pathogenesis. Methods High-throughput microarray assays and in silico pathway analysis were used to identify differentially expressed genes between primary c-Kit deficient (KitW/W–v) and control (Kit+/+) SMC. Quantitative real-time RT-PCR and functional assays further confirmed the differences in gene expression and pro-inflammatory pathway regulation between both SMC populations. Results The microarray analysis revealed elevated NF-κB gene expression secondary to the loss of c-Kit that affects both the canonical and alternative NF-κB pathways. Upon stimulation with an oxidized phospholipid as pro-inflammatory agent, c-Kit deficient SMC displayed enhanced NF-κB transcriptional activity, higher phosphorylated/total p65 ratio, and increased protein expression of NF-κB regulated pro-inflammatory mediators with respect to cells from control mice. The pro-inflammatory phenotype of mutant cells was ameliorated after restoring c-Kit activity using lentiviral transduction. Functional assays further demonstrated that c-Kit suppresses NF-κB activity in SMC in a TGFβ-activated kinase 1 (TAK1) and Nemo-like kinase (NLK) dependent manner. Discussion Our study suggests a novel mechanism by which c-Kit suppresses NF-κB regulated pathways in SMC to prevent their pro-inflammatory transformation. PMID:28626608

c-Kit modifies the inflammatory status of smooth muscle cells.

PubMed

Song, Lei; Martinez, Laisel; Zigmond, Zachary M; Hernandez, Diana R; Lassance-Soares, Roberta M; Selman, Guillermo; Vazquez-Padron, Roberto I

2017-01-01

c-Kit is a receptor tyrosine kinase present in multiple cell types, including vascular smooth muscle cells (SMC). However, little is known about how c-Kit influences SMC biology and vascular pathogenesis. High-throughput microarray assays and in silico pathway analysis were used to identify differentially expressed genes between primary c-Kit deficient (Kit W/W-v ) and control (Kit +/+ ) SMC. Quantitative real-time RT-PCR and functional assays further confirmed the differences in gene expression and pro-inflammatory pathway regulation between both SMC populations. The microarray analysis revealed elevated NF-κB gene expression secondary to the loss of c-Kit that affects both the canonical and alternative NF-κB pathways. Upon stimulation with an oxidized phospholipid as pro-inflammatory agent, c-Kit deficient SMC displayed enhanced NF-κB transcriptional activity, higher phosphorylated/total p65 ratio, and increased protein expression of NF-κB regulated pro-inflammatory mediators with respect to cells from control mice. The pro-inflammatory phenotype of mutant cells was ameliorated after restoring c-Kit activity using lentiviral transduction. Functional assays further demonstrated that c-Kit suppresses NF-κB activity in SMC in a TGFβ-activated kinase 1 (TAK1) and Nemo-like kinase (NLK) dependent manner. Our study suggests a novel mechanism by which c-Kit suppresses NF-κB regulated pathways in SMC to prevent their pro-inflammatory transformation.

Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.

PubMed

Assawamakin, Anunchai; Prueksaaroon, Supakit; Kulawonganunchai, Supasak; Shaw, Philip James; Varavithya, Vara; Ruangrajitpakorn, Taneth; Tongsima, Sissades

2013-01-01

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

The prevalence of chromosomal deletions relating to developmental delay and/or intellectual disability in human euploid blastocysts.

PubMed

He, Wenyin; Sun, Xiaofang; Liu, Lian; Li, Man; Jin, Hua; Wang, Wei-Hua

2014-01-01

Chromosomal anomalies in human embryos produced by in vitro fertilization are very common, which include numerical (aneuploidy) and structural (deletion, duplication or others) anomalies. Our previous study indicated that chromosomal deletion(s) is the most common structural anomaly accounting for approximately 8% of euploid blastocysts. It is still unknown if these deletions in human euploid blastocysts have clinical significance. In this study, we analyzed 15 previously diagnosed euploid blastocysts that had chromosomal deletion(s) using Agilent oligonucleotide DNA microarray platform and localized the gene location in each deletion. Then, we used OMIM gene map and phenotype database to investigate if these deletions are related with some important genes that cause genetic diseases, especially developmental delay or intellectual disability. As results, we found that the detectable chromosomal deletion size with Agilent microarray is above 2.38 Mb, while the deletions observed in human blastocysts are between 11.6 to 103 Mb. With OMIM gene map and phenotype database information, we found that deletions can result in loss of 81-464 genes. Out of these genes, 34-149 genes are related with known genetic problems. Furthermore, we found that 5 out of 15 samples lost genes in the deleted region, which were related to developmental delay and/or intellectual disability. In conclusion, our data indicates that all human euploid blastocysts with chromosomal deletion(s) are abnormal and transfer of these embryos may cause birth defects and/or developmental and intellectual disabilities. Therefore, the embryos with chromosomal deletion revealed by DNA microarray should not be transferred to the patients, or further gene map and/or phenotype seeking is necessary before making a final decision.

In-vitro analysis of Quantum Molecular Resonance effects on human mesenchymal stromal cells

PubMed Central

Sella, Sabrina; Adami, Valentina; Amati, Eliana; Bernardi, Martina; Chieregato, Katia; Gatto, Pamela; Menarin, Martina; Pozzato, Alessandro; Pozzato, Gianantonio; Astori, Giuseppe

2018-01-01

Electromagnetic fields play an essential role in cellular functions interfering with cellular pathways and tissue physiology. In this context, Quantum Molecular Resonance (QMR) produces waves with a specific form at high-frequencies (4–64 MHz) and low intensity through electric fields. We evaluated the effects of QMR stimulation on bone marrow derived mesenchymal stromal cells (MSC). MSC were treated with QMR for 10 minutes for 4 consecutive days for 2 weeks at different nominal powers. Cell morphology, phenotype, multilineage differentiation, viability and proliferation were investigated. QMR effects were further investigated by cDNA microarray validated by real-time PCR. After 1 and 2 weeks of QMR treatment morphology, phenotype and multilineage differentiation were maintained and no alteration of cellular viability and proliferation were observed between treated MSC samples and controls. cDNA microarray analysis evidenced more transcriptional changes on cells treated at 40 nominal power than 80 ones. The main enrichment lists belonged to development processes, regulation of phosphorylation, regulation of cellular pathways including metabolism, kinase activity and cellular organization. Real-time PCR confirmed significant increased expression of MMP1, PLAT and ARHGAP22 genes while A2M gene showed decreased expression in treated cells compared to controls. Interestingly, differentially regulated MMP1, PLAT and A2M genes are involved in the extracellular matrix (ECM) remodelling through the fibrinolytic system that is also implicated in embryogenesis, wound healing and angiogenesis. In our model QMR-treated MSC maintained unaltered cell phenotype, viability, proliferation and the ability to differentiate into bone, cartilage and adipose tissue. Microarray analysis may suggest an involvement of QMR treatment in angiogenesis and in tissue regeneration probably through ECM remodelling. PMID:29293552

Genome image programs: visualization and interpretation of Escherichia coli microarray experiments.

PubMed

Zimmer, Daniel P; Paliy, Oleg; Thomas, Brian; Gyaneshwar, Prasad; Kustu, Sydney

2004-08-01

We have developed programs to facilitate analysis of microarray data in Escherichia coli. They fall into two categories: manipulation of microarray images and identification of known biological relationships among lists of genes. A program in the first category arranges spots from glass-slide DNA microarrays according to their position in the E. coli genome and displays them compactly in genome order. The resulting genome image is presented in a web browser with an image map that allows the user to identify genes in the reordered image. Another program in the first category aligns genome images from two or more experiments. These images assist in visualizing regions of the genome with common transcriptional control. Such regions include multigene operons and clusters of operons, which are easily identified as strings of adjacent, similarly colored spots. The images are also useful for assessing the overall quality of experiments. The second category of programs includes a database and a number of tools for displaying biological information about many E. coli genes simultaneously rather than one gene at a time, which facilitates identifying relationships among them. These programs have accelerated and enhanced our interpretation of results from E. coli DNA microarray experiments. Examples are given. Copyright 2004 Genetics Society of America

Integrated in silico and biological validation of the blocking effect of Cot-1 DNA on Microarray-CGH.

PubMed

Kang, Seung-Hui; Park, Chan Hee; Jeung, Hei Cheul; Kim, Ki-Yeol; Rha, Sun Young; Chung, Hyun Cheol

2007-06-01

In array-CGH, various factors may act as variables influencing the result of experiments. Among them, Cot-1 DNA, which has been used as a repetitive sequence-blocking agent, may become an artifact-inducing factor in BAC array-CGH. To identify the effect of Cot-1 DNA on Microarray-CGH experiments, Cot-1 DNA was labeled directly and Microarray-CGH experiments were performed. The results confirmed that probes which hybridized more completely with Cot-1 DNA had a higher sequence similarity to the Alu element. Further, in the sex-mismatched Microarray-CGH experiments, the variation and intensity in the fluorescent signal were reduced in the high intensity probe group in which probes were better hybridized with Cot-1 DNA. Otherwise, those of the low intensity probe group showed no alterations regardless of Cot-1 DNA. These results confirmed by in silico methods that Cot-1 DNA could block repetitive sequences in gDNA and probes. In addition, it was confirmed biologically that the blocking effect of Cot-1 DNA could be presented via its repetitive sequences, especially Alu elements. Thus, in contrast to BAC-array CGH, the use of Cot-1 DNA is advantageous in controlling experimental variation in Microarray-CGH.

Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

PubMed Central

Weniger, Markus; Engelmann, Julia C; Schultz, Jörg

2007-01-01

Background Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. Results We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at . Conclusion GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at . PMID:17543125

Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data.

PubMed

Tsou, Ann-Ping; Sun, Yi-Ming; Liu, Chia-Lin; Huang, Hsien-Da; Horng, Jorng-Tzong; Tsai, Meng-Feng; Liu, Baw-Juine

2006-07-01

Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.

The Vitis vinifera C-repeat binding protein 4 (VvCBF4) transcriptional factor enhances freezing tolerance in wine grape.

PubMed

Tillett, Richard L; Wheatley, Matthew D; Tattersall, Elizabeth A R; Schlauch, Karen A; Cramer, Grant R; Cushman, John C

2012-01-01

Chilling and freezing can reduce significantly vine survival and fruit set in Vitis vinifera wine grape. To overcome such production losses, a recently identified grapevine C-repeat binding factor (CBF) gene, VvCBF4, was overexpressed in grape vine cv. 'Freedom' and found to improve freezing survival and reduced freezing-induced electrolyte leakage by up to 2 °C in non-cold-acclimated vines. In addition, overexpression of this transgene caused a reduced growth phenotype similar to that observed for CBF overexpression in Arabidopsis and other species. Both freezing tolerance and reduced growth phenotypes were manifested in a transgene dose-dependent manner. To understand the mechanistic basis of VvCBF4 transgene action, one transgenic line (9-12) was genotyped using microarray-based mRNA expression profiling. Forty-seven and 12 genes were identified in unstressed transgenic shoots with either a >1.5-fold increase or decrease in mRNA abundance, respectively. Comparison of mRNA changes with characterized CBF regulons in woody and herbaceous species revealed partial overlaps, suggesting that CBF-mediated cold acclimation responses are widely conserved. Putative VvCBF4-regulon targets included genes with functions in cell wall structure, lipid metabolism, epicuticular wax formation and stress-responses suggesting that the observed cold tolerance and dwarf phenotypes are the result of a complex network of diverse functional determinants. © 2011 The Authors. Plant Biotechnology Journal © 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd.

Differential gene expression of two extreme honey bee (Apis mellifera) colonies showing varroa tolerance and susceptibility.

PubMed

Jiang, S; Robertson, T; Mostajeran, M; Robertson, A J; Qiu, X

2016-06-01

Varroa destructor, an ectoparasitic mite of honey bees (Apis mellifera), is the most serious pest threatening the apiculture industry. In our honey bee breeding programme, two honey bee colonies showing extreme phenotypes for varroa tolerance/resistance (S88) and susceptibility (G4) were identified by natural selection from a large gene pool over a 6-year period. To investigate potential defence mechanisms for honey bee tolerance to varroa infestation, we employed DNA microarray and real time quantitative (PCR) analyses to identify differentially expressed genes in the tolerant and susceptible colonies at pupa and adult stages. Our results showed that more differentially expressed genes were identified in the tolerant bees than in bees from the susceptible colony, indicating that the tolerant colony showed an increased genetic capacity to respond to varroa mite infestation. In both colonies, there were more differentially expressed genes identified at the pupa stage than at the adult stage, indicating that pupa bees are more responsive to varroa infestation than adult bees. Genes showing differential expression in the colony phenotypes were categorized into several groups based on their molecular functions, such as olfactory signalling, detoxification processes, exoskeleton formation, protein degradation and long-chain fatty acid metabolism, suggesting that these biological processes play roles in conferring varroa tolerance to naturally selected colonies. Identification of differentially expressed genes between the two colony phenotypes provides potential molecular markers for selecting and breeding varroa-tolerant honey bees. © 2016 The Royal Entomological Society.

Evidence of inflammatory immune signaling in chronic fatigue syndrome: A pilot study of gene expression in peripheral blood.

PubMed

Aspler, Anne L; Bolshin, Carly; Vernon, Suzanne D; Broderick, Gordon

2008-09-26

Genomic profiling of peripheral blood reveals altered immunity in chronic fatigue syndrome (CFS) however interpretation remains challenging without immune demographic context. The object of this work is to identify modulation of specific immune functional components and restructuring of co-expression networks characteristic of CFS using the quantitative genomics of peripheral blood. Gene sets were constructed a priori for CD4+ T cells, CD8+ T cells, CD19+ B cells, CD14+ monocytes and CD16+ neutrophils from published data. A group of 111 women were classified using empiric case definition (U.S. Centers for Disease Control and Prevention) and unsupervised latent cluster analysis (LCA). Microarray profiles of peripheral blood were analyzed for expression of leukocyte-specific gene sets and characteristic changes in co-expression identified from topological evaluation of linear correlation networks. Median expression for a set of 6 genes preferentially up-regulated in CD19+ B cells was significantly lower in CFS (p = 0.01) due mainly to PTPRK and TSPAN3 expression. Although no other gene set was differentially expressed at p < 0.05, patterns of co-expression in each group differed markedly. Significant co-expression of CD14+ monocyte with CD16+ neutrophil (p = 0.01) and CD19+ B cell sets (p = 0.00) characterized CFS and fatigue phenotype groups. Also in CFS was a significant negative correlation between CD8+ and both CD19+ up-regulated (p = 0.02) and NK gene sets (p = 0.08). These patterns were absent in controls. Dissection of blood microarray profiles points to B cell dysfunction with coordinated immune activation supporting persistent inflammation and antibody-mediated NK cell modulation of T cell activity. This has clinical implications as the CD19+ genes identified could provide robust and biologically meaningful basis for the early detection and unambiguous phenotyping of CFS.

Promise and progress in environmental genomics: a status report on the applications of gene expression-based microarray studies in ecologically relevant fish species.

PubMed

Hook, S E

2010-12-01

The advent of any new technology is typically met with great excitement. So it was a few years ago, when the combination of advances in sequencing technology and the development of microarray technology made measurements of global gene expression in ecologically relevant species possible. Many of the review papers published around that time promised that these new technologies would revolutionize environmental biology as they had revolutionized medicine and related fields. A few years have passed since these technological advancements have been made, and the use of microarray studies in non-model fish species has been adopted in many laboratories internationally. Has the relatively widespread adoption of this technology really revolutionized the fields of environmental biology, including ecotoxicology, aquaculture and ecology, as promised? Or have these studies merely become a novelty and a potential distraction for scientists addressing environmentally relevant questions? In this review, the promises made in early review papers, in particular about the advances that the use of microarrays would enable, are summarized; these claims are compared to the results of recent studies to determine whether the forecasted changes have materialized. Some applications, as discussed in the paper, have been realized and have led to advances in their field, others are still under development. © 2010 CSIRO. Journal of Fish Biology © 2010 The Fisheries Society of the British Isles.

Signal amplification by rolling circle amplification on DNA microarrays

PubMed Central

Nallur, Girish; Luo, Chenghua; Fang, Linhua; Cooley, Stephanie; Dave, Varshal; Lambert, Jeremy; Kukanskis, Kari; Kingsmore, Stephen; Lasken, Roger; Schweitzer, Barry

2001-01-01

While microarrays hold considerable promise in large-scale biology on account of their massively parallel analytical nature, there is a need for compatible signal amplification procedures to increase sensitivity without loss of multiplexing. Rolling circle amplification (RCA) is a molecular amplification method with the unique property of product localization. This report describes the application of RCA signal amplification for multiplexed, direct detection and quantitation of nucleic acid targets on planar glass and gel-coated microarrays. As few as 150 molecules bound to the surface of microarrays can be detected using RCA. Because of the linear kinetics of RCA, nucleic acid target molecules may be measured with a dynamic range of four orders of magnitude. Consequently, RCA is a promising technology for the direct measurement of nucleic acids on microarrays without the need for a potentially biasing preamplification step. PMID:11726701

Transcriptional profiling identifies the long noncoding RNA plasmacytoma variant translocation (PVT1) as a novel regulator of the asthmatic phenotype in human airway smooth muscle.

PubMed

Austin, Philip J; Tsitsiou, Eleni; Boardman, Charlotte; Jones, Simon W; Lindsay, Mark A; Adcock, Ian M; Chung, Kian Fan; Perry, Mark M

2017-03-01

The mechanism underlying nonsevere and severe asthma remains unclear, although it is commonly associated with increased airway smooth muscle (ASM) mass. Long noncoding RNAs (lncRNAs) are known to be important in regulating healthy primary airway smooth muscle cells (ASMCs), whereas changed expression has been observed in CD8 T cells from patients with severe asthma. Primary ASMCs were isolated from healthy subjects (n = 9) and patients classified as having nonsevere (n = 9) or severe (n = 9) asthma. ASMCs were exposed to dexamethasone and FCS. mRNA and lncRNA expression was measured by using a microarray and quantitative real-time PCR. Bioinformatic analysis was used to examine relevant biological pathways. Finally, the lncRNA plasmacytoma variant translocation 1 (PVT1) was inhibited by transfection of primary ASMCs with small interfering RNAs, and the effect on ASMC phenotype was examined. The mRNA expression profile was significantly different between patient groups after exposure to dexamethasone and FCS, and these were associated with biological pathways that might be relevant to the pathogenesis of asthma, including cellular proliferation and pathways associated with glucocorticoid activity. We also observed a significant change in lncRNA expression, yet the expression of only one lncRNA (PVT1) is decreased in patients with corticosteroid-sensitive nonsevere asthma and increased in patients with corticosteroid-insensitive severe asthma. Subsequent targeting studies demonstrated the importance of this lncRNA in controlling both proliferation and IL-6 release in ASMCs from patients with severe asthma. lncRNAs are associated with the aberrant phenotype observed in ASMCs from asthmatic patients. Targeting PVT1 might be effective in reducing airway remodeling in asthmatic patients. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Ionotropic glutamate receptors mediate inducible defense in the water flea Daphnia pulex.

PubMed

Miyakawa, Hitoshi; Sato, Masanao; Colbourne, John K; Iguchi, Taisen

2015-01-01

Phenotypic plasticity is the ability held in many organisms to produce different phenotypes with a given genome in response to environmental stimuli, such as temperature, nutrition and various biological interactions. It seems likely that environmental signals induce a variety of mechanistic responses that influence ontogenetic processes. Inducible defenses, in which prey animals alter their morphology, behavior and/or other traits to help protect against direct or latent predation threats, are among the most striking examples of phenotypic plasticity. The freshwater microcrustacean Daphnia pulex forms tooth-like defensive structures, "neckteeth," in response to chemical cues or signals, referred to as "kairomones," in this case released from phantom midge larvae, a predator of D. pulex. To identify factors involved in the reception and/or transmission of a kairomone, we used microarray analysis to identify genes up-regulated following a short period of exposure to the midge kairomone. In addition to identifying differentially expressed genes of unknown function, we also found significant up-regulation of genes encoding ionotropic glutamate receptors, which are known to be involved in neurotransmission in many animal species. Specific antagonists of these receptors strongly inhibit the formation of neckteeth in D. pulex, although agonists did not induce neckteeth by themselves, indicating that ionotropic glutamate receptors are necessary but not sufficient for early steps of neckteeth formation in D. pulex. Moreover, using co-exposure of D. pulex to antagonists and juvenile hormone (JH), which physiologically mediates neckteeth formation, we found evidence suggesting that the inhibitory effect of antagonists is not due to direct inhibition of JH synthesis/secretion. Our findings not only provide a candidate molecule required for the inducible defense response in D. pulex, but also will contribute to the understanding of complex mechanisms underlying the recognition of environmental changes, which form the basis of phenotypic plasticity.

Pure mechanistic analysis of additive neuroprotective effects between baicalin and jasminoidin in ischemic stroke mice.

PubMed

Wang, Peng-Qian; Liu, Qiong; Xu, Wen-Juan; Yu, Ya-Nan; Zhang, Ying-Ying; Li, Bing; Liu, Jun; Wang, Zhong

2018-06-01

Both baicalin (BA) and jasminoidin (JA) are active ingredients in Chinese herb medicine Scutellaria baicalensis and Fructus gardeniae, respectively. They have been shown to exert additive neuroprotective action in ischemic stroke models. In this study we used transcriptome analysis to explore the pure therapeutic mechanisms of BA, JA and their combination (BJ) contributing to phenotype variation and reversal of pathological processes. Mice with middle cerebral artery obstruction were treated with BA, JA, their combination (BJ), or concha margaritifera (CM). Cerebral infarct volume was examined to determine the effect of these compounds on phenotype. Using the hippocampus microarray and ingenuity pathway analysis (IPA) software, we exacted the differentially expressed genes, networks, pathways, and functions in positive-phenotype groups (BA, JA and BJ) by comparing with the negative-phenotype group (CM). In the BA, JA, and BJ groups, a total of 7, 4, and 11 specific target molecules, 1, 1, and 4 networks, 51, 59, and 18 canonical pathways and 70, 53, and 64 biological functions, respectively, were identified. Pure therapeutic mechanisms of BA and JA were mainly overlapped in specific target molecules, functions and pathways, which were related to the nervous system, inflammation and immune response. The specific mechanisms of BA and JA were associated with apoptosis and cancer-related signaling and endocrine and hormone regulation, respectively. In the BJ group, novel target profiles distinct from mono-therapies were revealed, including 11 specific target molecules, 10 functions, and 10 pathways, the majority of which were related to a virus-mediated immune response. The pure additive effects between BA and JA were based on enhanced action in virus-mediated immune response. This pure mechanistic analysis may provide a clearer outline of the target profiles of multi-target compounds and combination therapies.

A MICROARRAY ANALYSIS OF GENE EXPRESSION IN THE EMBRYONIC FORELIMB OF THE C57BL/6J MOUSE REVEALS SIGNIFICANT ALTERATIONS METABOLIC AND DEVELOPMENTAL REGULATION FOLLOWING ETHANOL EXPOSURE.

EPA Science Inventory

The observation of transcriptional changes following embryonic ethanol exposure may provide significant insights into the biological response to ethanol exposure. In this study, we used microarray analysis to examine the transcriptional response of the developing limb to a dose ...

Chromosomal microarray analysis in developmental delay and intellectual disability with comorbid conditions.

PubMed

Fan, Yanjie; Wu, Yanming; Wang, Lili; Wang, Yu; Gong, Zhuwen; Qiu, Wenjuan; Wang, Jingmin; Zhang, Huiwen; Ji, Xing; Ye, Jun; Han, Lianshu; Jin, Xingming; Shen, Yongnian; Li, Fei; Xiao, Bing; Liang, Lili; Zhang, Xia; Liu, Xiaomin; Gu, Xuefan; Yu, Yongguo

2018-05-24

Developmental delay (DD) and intellectual disability (ID) are frequently associated with a broad spectrum of additional phenotypes. Chromosomal microarray analysis (CMA) has been recommended as a first-tier test for DD/ID in general, whereas the diagnostic yield differs significantly among DD/ID patients with different comorbid conditions. To investigate the genotype-phenotype correlation, we examined the characteristics of identified pathogenic copy number variations (pCNVs) and compared the diagnostic yields among patient subgroups with different co-occurring conditions. This study is a retrospective review of CMA results generated from a mixed cohort of 710 Chinese patients with DD/ID. A total of 247 pCNVs were identified in 201 patients (28%). A large portion of these pCNVs were copy number losses, and the size of copy number losses was generally smaller than gains. The diagnostic yields were significantly higher in subgroups with co-occurring congenital heart defects (55%), facial dysmorphism (39%), microcephaly (34%) or hypotonia (35%), whereas co-occurring conditions of skeletal malformation (26%), brain malformation (24%) or epilepsy (24%) did not alter the yield. In addition, the diagnostic yield nominally correlated with ID severity. Varied yields exist in DD/ID patients with different phenotypic presentation. The presence of comorbid conditions can be among factors to consider when planning CMA.

Phenotypic Analysis of Korean Patients with Abnormal Chromosomal Microarray in Patients with Unexplained Developmental Delay/Intellectual Disability.

PubMed

Kim, Hyo Jeong; Park, Chang Il; Lim, Jae Woo; Lee, Gyung Min; Cho, Eunhae; Kim, Hyon J

2018-05-01

The present study aimed to investigate chromosomal microarray (CMA) and clinical data in patients with unexplained developmental delay/intellectual disability (DD/ID) accompanying dysmorphism, congenital anomalies, or epilepsy. We also aimed to evaluate phenotypic clues in patients with pathogenic copy number variants (CNVs). We collected clinical and CMA data from patients at Konyang University Hospital between September 2013 and October 2014. We included patients who had taken the CMA test to evaluate the etiology of unexplained DD/ID. All of the 50 patients identified had DD/ID. Thirty-nine patients had dysmorphism, 19 patients suffered from epilepsy, and 12 patients had congenital anomalies. Twenty-nine of the 50 patients (58%) showed abnormal results. Eighteen (36%) were considered to have pathogenic CNVs. Dysmorphism (p=0.028) was significantly higher in patients with pathogenic CNVs than in those with normal CMA. Two or more clinical features were presented by 61.9% (13/21) of the patients with normal CMA and by 83.3% (15/18) of the patients with pathogenic CMA. Dysmorphism can be a phenotypic clue to pathogenic CNVs. Furthermore, pathogenic CNV might be more frequently found if patients have two or more clinical features in addition to DD/ID. © Copyright: Yonsei University College of Medicine 2018.

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

PubMed

Bhanot, Gyan; Alexe, Gabriela; Levine, Arnold J; Stolovitzky, Gustavo

2005-01-01

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

t4 Workshop Report*

PubMed Central

Bouhifd, Mounir; Beger, Richard; Flynn, Thomas; Guo, Lining; Harris, Georgina; Hogberg, Helena; Kaddurah-Daouk, Rima; Kamp, Hennicke; Kleensang, Andre; Maertens, Alexandra; Odwin-DaCosta, Shelly; Pamies, David; Robertson, Donald; Smirnova, Lena; Sun, Jinchun; Zhao, Liang; Hartung, Thomas

2017-01-01

Summary Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however – from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining – is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based transcriptomics, where consensus on quality assurance and reporting standards has been fostered over the last two decades, quality assurance of metabolomics is only now emerging. Regulatory use in safety sciences, and even proper scientific use of these technologies, demand quality assurance. In an effort to promote this discussion, an expert workshop discussed the quality assurance needs of metabolomics. The goals for this workshop were 1) to consider the challenges associated with metabolomics as an emerging science, with an emphasis on its application in toxicology and 2) to identify the key issues to be addressed in order to establish and implement quality assurance procedures in metabolomics-based toxicology. Consensus has still to be achieved regarding best practices to make sure sound, useful, and relevant information is derived from these new tools. PMID:26536290

Microarray-integrated optoelectrofluidic immunoassay system

PubMed Central

Han, Dongsik

2016-01-01

A microarray-based analytical platform has been utilized as a powerful tool in biological assay fields. However, an analyte depletion problem due to the slow mass transport based on molecular diffusion causes low reaction efficiency, resulting in a limitation for practical applications. This paper presents a novel method to improve the efficiency of microarray-based immunoassay via an optically induced electrokinetic phenomenon by integrating an optoelectrofluidic device with a conventional glass slide-based microarray format. A sample droplet was loaded between the microarray slide and the optoelectrofluidic device on which a photoconductive layer was deposited. Under the application of an AC voltage, optically induced AC electroosmotic flows caused by a microarray-patterned light actively enhanced the mass transport of target molecules at the multiple assay spots of the microarray simultaneously, which reduced tedious reaction time from more than 30 min to 10 min. Based on this enhancing effect, a heterogeneous immunoassay with a tiny volume of sample (5 μl) was successfully performed in the microarray-integrated optoelectrofluidic system using immunoglobulin G (IgG) and anti-IgG, resulting in improved efficiency compared to the static environment. Furthermore, the application of multiplex assays was also demonstrated by multiple protein detection. PMID:27190571

Microarray-integrated optoelectrofluidic immunoassay system.

PubMed

Han, Dongsik; Park, Je-Kyun

2016-05-01

A microarray-based analytical platform has been utilized as a powerful tool in biological assay fields. However, an analyte depletion problem due to the slow mass transport based on molecular diffusion causes low reaction efficiency, resulting in a limitation for practical applications. This paper presents a novel method to improve the efficiency of microarray-based immunoassay via an optically induced electrokinetic phenomenon by integrating an optoelectrofluidic device with a conventional glass slide-based microarray format. A sample droplet was loaded between the microarray slide and the optoelectrofluidic device on which a photoconductive layer was deposited. Under the application of an AC voltage, optically induced AC electroosmotic flows caused by a microarray-patterned light actively enhanced the mass transport of target molecules at the multiple assay spots of the microarray simultaneously, which reduced tedious reaction time from more than 30 min to 10 min. Based on this enhancing effect, a heterogeneous immunoassay with a tiny volume of sample (5 μl) was successfully performed in the microarray-integrated optoelectrofluidic system using immunoglobulin G (IgG) and anti-IgG, resulting in improved efficiency compared to the static environment. Furthermore, the application of multiplex assays was also demonstrated by multiple protein detection.

Teaching bioinformatics and neuroinformatics by using free web-based tools.

PubMed

Grisham, William; Schottler, Natalie A; Valli-Marill, Joanne; Beck, Lisa; Beatty, Jackson

2010-01-01

This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes-narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

An overview of the Progenika ID CORE XT: an automated genotyping platform based on a fluidic microarray system.

PubMed

Goldman, Mindy; Núria, Núria; Castilho, Lilian M

2015-01-01

Automated testing platforms facilitate the introduction of red cell genotyping of patients and blood donors. Fluidic microarray systems, such as Luminex XMAP (Austin, TX), are used in many clinical applications, including HLA and HPA typing. The Progenika ID CORE XT (Progenika Biopharma-Grifols, Bizkaia, Spain) uses this platform to analyze 29 polymorphisms determining 37 antigens in 10 blood group systems. Once DNA has been extracted, processing time is approximately 4 hours. The system is highly automated and includes integrated analysis software that produces a file and a report with genotype and predicted phenotype results.

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks1[C][W][OA

PubMed Central

Ficklin, Stephen P.; Luo, Feng; Feltus, F. Alex

2010-01-01

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes. PMID:20668062

The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks.

PubMed

Ficklin, Stephen P; Luo, Feng; Feltus, F Alex

2010-09-01

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes.

Microarray missing data imputation based on a set theoretic framework and biological knowledge.

PubMed

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2006-01-01

Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.

CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

PubMed Central

Li, Yang; Liu, Jun S.; Mootha, Vamsi K.

2017-01-01

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active. PMID:28719601

Development of a Cross-Disciplinary Investigative Model for the Introduction of Microarray Techniques at Non-R1 Undergraduate Institutions

ERIC Educational Resources Information Center

Walker, David E.; Lutz, Gary P.; Alvarez, Consuelo J.

2008-01-01

Integrating advanced biological techniques into instruction at non-R1 institutions can prove to be a challenge. Here, we report the creation of a model for the introduction of gene expression microarray technology into a research laboratory. A student assessment tool was used to evaluate: (1) technical skill development; (2) cross-disciplinary…

What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins

PubMed Central

Hutchins, James R. A.

2014-01-01

The genomic era has enabled research projects that use approaches including genome-scale screens, microarray analysis, next-generation sequencing, and mass spectrometry–based proteomics to discover genes and proteins involved in biological processes. Such methods generate data sets of gene, transcript, or protein hits that researchers wish to explore to understand their properties and functions and thus their possible roles in biological systems of interest. Recent years have seen a profusion of Internet-based resources to aid this process. This review takes the viewpoint of the curious biologist wishing to explore the properties of protein-coding genes and their products, identified using genome-based technologies. Ten key questions are asked about each hit, addressing functions, phenotypes, expression, evolutionary conservation, disease association, protein structure, interactors, posttranslational modifications, and inhibitors. Answers are provided by presenting the latest publicly available resources, together with methods for hit-specific and data set–wide information retrieval, suited to any genome-based analytical technique and experimental species. The utility of these resources is demonstrated for 20 factors regulating cell proliferation. Results obtained using some of these are discussed in more depth using the p53 tumor suppressor as an example. This flexible and universally applicable approach for characterizing experimental hits helps researchers to maximize the potential of their projects for biological discovery. PMID:24723265

Integrated Analysis of Pharmacologic, Clinical, and SNP Microarray Data using Projection onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing

PubMed Central

Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun; Campana, Dario; Evans, William E.; Pui, Ching-Hon; Relling, Mary V.

2010-01-01

Powerful methods for integrated analysis of multiple biological data sets are needed to maximize interpretation capacity and acquire meaningful knowledge. We recently developed Projection Onto the Most Interesting Statistical Evidence (PROMISE). PROMISE is a statistical procedure that incorporates prior knowledge about the biological relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical, and genome-wide genotype data that incorporating knowledge about the biological relationships among pharmacologic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE is a powerful statistical tool for identifying genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables. PMID:21516175

A perspective on microarrays: current applications, pitfalls, and potential uses

PubMed Central

Jaluria, Pratik; Konstantopoulos, Konstantinos; Betenbaugh, Michael; Shiloach, Joseph

2007-01-01

With advances in robotics, computational capabilities, and the fabrication of high quality glass slides coinciding with increased genomic information being available on public databases, microarray technology is increasingly being used in laboratories around the world. In fact, fields as varied as: toxicology, evolutionary biology, drug development and production, disease characterization, diagnostics development, cellular physiology and stress responses, and forensics have benefiting from its use. However, for many researchers not familiar with microarrays, current articles and reviews often address neither the fundamental principles behind the technology nor the proper designing of experiments. Although, microarray technology is relatively simple, conceptually, its practice does require careful planning and detailed understanding of the limitations inherently present. Without these considerations, it can be exceedingly difficult to ascertain valuable information from microarray data. Therefore, this text aims to outline key features in microarray technology, paying particular attention to current applications as outlined in recent publications, experimental design, statistical methods, and potential uses. Furthermore, this review is not meant to be comprehensive, but rather substantive; highlighting important concepts and detailing steps necessary to conduct and interpret microarray experiments. Collectively, the information included in this text will highlight the versatility of microarray technology and provide a glimpse of what the future may hold. PMID:17254338

A multilevel Lab on chip platform for DNA analysis.

PubMed

Marasso, Simone Luigi; Giuri, Eros; Canavese, Giancarlo; Castagna, Riccardo; Quaglio, Marzia; Ferrante, Ivan; Perrone, Denis; Cocuzza, Matteo

2011-02-01

Lab-on-chips (LOCs) are critical systems that have been introduced to speed up and reduce the cost of traditional, laborious and extensive analyses in biological and biomedical fields. These ambitious and challenging issues ask for multi-disciplinary competences that range from engineering to biology. Starting from the aim to integrate microarray technology and microfluidic devices, a complex multilevel analysis platform has been designed, fabricated and tested (All rights reserved-IT Patent number TO2009A000915). This LOC successfully manages to interface microfluidic channels with standard DNA microarray glass slides, in order to implement a complete biological protocol. Typical Micro Electro Mechanical Systems (MEMS) materials and process technologies were employed. A silicon/glass microfluidic chip and a Polydimethylsiloxane (PDMS) reaction chamber were fabricated and interfaced with a standard microarray glass slide. In order to have a high disposable system all micro-elements were passive and an external apparatus provided fluidic driving and thermal control. The major microfluidic and handling problems were investigated and innovative solutions were found. Finally, an entirely automated DNA hybridization protocol was successfully tested with a significant reduction in analysis time and reagent consumption with respect to a conventional protocol.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

PubMed

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Characterization of a complex chromosomal rearrangement using chromosome, FISH, and microarray assays in a girl with multiple congenital abnormalities and developmental delay.

PubMed

Hemmat, Morteza; Yang, Xiaojing; Chan, Patricia; McGough, Robert A; Ross, Leslie; Mahon, Loretta W; Anguiano, Arturo L; Boris, Wang T; Elnaggar, Mohamed M; Wang, Jia-Chi J; Strom, Charles M; Boyar, Fatih Z

2014-01-01

Complex chromosomal rearrangements (CCRs) are balanced or unbalanced structural rearrangements involving three or more cytogenetic breakpoints on two or more chromosomal pairs. The phenotypic anomalies in such cases are attributed to gene disruption, superimposed cryptic imbalances in the genome, and/or position effects. We report a 14-year-old girl who presented with multiple congenital anomalies and developmental delay. Chromosome and FISH analysis indicated a highly complex chromosomal rearrangement involving three chromosomes (3, 7 and 12), seven breakpoints as a result of one inversion, two insertions, and two translocations forming three derivative chromosomes. Additionally, chromosomal microarray study (CMA) revealed two submicroscopic deletions at 3p12.3 (467 kb) and 12q13.12 (442 kb). We postulate that microdeletion within the ROBO1 gene at 3p12.3 may have played a role in the patient's developmental delay, since it has potential activity-dependent role in neurons. Additionally, factors other than genomic deletions such as loss of function or position effects may also contribute to the abnormal phenotype in our patient.

Comparative study of classification algorithms for immunosignaturing data

PubMed Central

2012-01-01

Background High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. Results We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy. Conclusions ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties. PMID:22720696

Transcriptome Analysis of Human Injured Meniscus Reveals a Distinct Phenotype of Meniscus Degeneration with Aging

PubMed Central

Rai, Muhammad Farooq; Patra, Debabrata; Sandell, Linda J.; Brophy, Robert H.

2013-01-01

Objective Meniscus tears are associated with a heightened risk for osteoarthritis. We aimed to advance our understanding of the metabolic state of human injured meniscus at the time of arthroscopic partial meniscectomy through transcriptome-wide analysis of gene expression in relation to patient age and degree of cartilage chondrosis. Methods The degree of chondrosis of knee cartilage was recorded at the time of meniscectomy in symptomatic patients without radiographic osteoarthritis. RNA preparations from resected menisci (N=12) were subjected to transcriptome-wide microarray and QuantiGene Plex analyses. The relative changes in gene expression variation with age and chondrosis were analyzed and integrated biological processes were investigated computationally. Results We identified a set of genes in torn meniscus that were differentially expressed with age and chondrosis. There were 866 genes differentially regulated (≥1.5-fold; P<0.05) with age and 49 with chondrosis. In older patients, genes associated with cartilage and skeletal development and extracellular matrix synthesis were repressed while those involved in immune response, inflammation, cell cycle, and cellular proliferation were stimulated. With chondrosis, genes representing cell catabolism (cAMP catabolic process) and tissue and endothelial cell development were repressed and those involved in T cell differentiation and apoptosis were elevated. Conclusion Differences in age-related gene expression suggest that in older adults, meniscal cells might de-differentiate and initiate a proliferative phenotype. Conversely, meniscal cells in younger patients appear to respond to injury, but maintain the differentiated phenotype. Definitive molecular signatures identified in damaged meniscus could be segregated largely with age and, to a lesser extent, with chondrosis. PMID:23658108

Cruella: developing a scalable tissue microarray data management system.

PubMed

Cowan, James D; Rimm, David L; Tuck, David P

2006-06-01

Compared with DNA microarray technology, relatively little information is available concerning the special requirements, design influences, and implementation strategies of data systems for tissue microarray technology. These issues include the requirement to accommodate new and different data elements for each new project as well as the need to interact with pre-existing models for clinical, biological, and specimen-related data. To design and implement a flexible, scalable tissue microarray data storage and management system that could accommodate information regarding different disease types and different clinical investigators, and different clinical investigation questions, all of which could potentially contribute unforeseen data types that require dynamic integration with existing data. The unpredictability of the data elements combined with the novelty of automated analysis algorithms and controlled vocabulary standards in this area require flexible designs and practical decisions. Our design includes a custom Java-based persistence layer to mediate and facilitate interaction with an object-relational database model and a novel database schema. User interaction is provided through a Java Servlet-based Web interface. Cruella has become an indispensable resource and is used by dozens of researchers every day. The system stores millions of experimental values covering more than 300 biological markers and more than 30 disease types. The experimental data are merged with clinical data that has been aggregated from multiple sources and is available to the researchers for management, analysis, and export. Cruella addresses many of the special considerations for managing tissue microarray experimental data and the associated clinical information. A metadata-driven approach provides a practical solution to many of the unique issues inherent in tissue microarray research, and allows relatively straightforward interoperability with and accommodation of new data models.

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

PubMed Central

2014-01-01

Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved. PMID:24444313

Fabrication of Carbohydrate Microarrays by Boronate Formation.

PubMed

Adak, Avijit K; Lin, Ting-Wei; Li, Ben-Yuan; Lin, Chun-Cheng

2017-01-01

The interactions between soluble carbohydrates and/or surface displayed glycans and protein receptors are essential to many biological processes and cellular recognition events. Carbohydrate microarrays provide opportunities for high-throughput quantitative analysis of carbohydrate-protein interactions. Over the past decade, various techniques have been implemented for immobilizing glycans on solid surfaces in a microarray format. Herein, we describe a detailed protocol for fabricating carbohydrate microarrays that capitalizes on the intrinsic reactivity of boronic acid toward carbohydrates to form stable boronate diesters. A large variety of unprotected carbohydrates ranging in structure from simple disaccharides and trisaccharides to considerably more complex human milk and blood group (oligo)saccharides have been covalently immobilized in a single step on glass slides, which were derivatized with high-affinity boronic acid ligands. The immobilized ligands in these microarrays maintain the receptor-binding activities including those of lectins and antibodies according to the structures of their pendant carbohydrates for rapid analysis of a number of carbohydrate-recognition events within 30 h. This method facilitates the direct construction of otherwise difficult to obtain carbohydrate microarrays from underivatized glycans.

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

PubMed

Robinson, Mark D; McCarthy, Davis J; Smyth, Gordon K

2010-01-01

It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer.

PubMed

Zhao, Yiru Chen; Madak Erdogan, Zeynep

2016-03-17

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective mining of the information in the large-scale datasets that are obtained from gene expression microarrays, deep sequencing experiments or metabolic profiling is essential to uncover and then effectively target the critical regulators of diseased cell phenotypes. Estrogen Receptor α (ERα) is one of the master transcription factors regulating the gene programs that are important for estrogen responsive breast cancers. In order to understand to role of ERα signaling in breast cancer metabolism we utilized transcriptomic, cistromic and metabolomic data from MCF-7 cells treated with estradiol. In this report we described generation of samples for RNA-Seq, ChIP-Seq and metabolomics experiments and the integrative computational analysis of the obtained data. This approach is useful in delineating novel molecular mechanisms and gene regulatory circuits that are regulated by a particular transcription factor which impacts metabolism of normal or diseased cells.

A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.

PubMed

Chang, Tzu-Hao; Wu, Shih-Lin; Wang, Wei-Jen; Horng, Jorng-Tzong; Chang, Cheng-Wei

2014-01-01

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

High density DNA microarrays: algorithms and biomedical applications.

PubMed

Liu, Wei-Min

2004-08-01

DNA microarrays are devices capable of detecting the identity and abundance of numerous DNA or RNA segments in samples. They are used for analyzing gene expressions, identifying genetic markers and detecting mutations on a genomic scale. The fundamental chemical mechanism of DNA microarrays is the hybridization between probes and targets due to the hydrogen bonds of nucleotide base pairing. Since the cross hybridization is inevitable, and probes or targets may form undesirable secondary or tertiary structures, the microarray data contain noise and depend on experimental conditions. It is crucial to apply proper statistical algorithms to obtain useful signals from noisy data. After we obtained the signals of a large amount of probes, we need to derive the biomedical information such as the existence of a transcript in a cell, the difference of expression levels of a gene in multiple samples, and the type of a genetic marker. Furthermore, after the expression levels of thousands of genes or the genotypes of thousands of single nucleotide polymorphisms are determined, it is usually important to find a small number of genes or markers that are related to a disease, individual reactions to drugs, or other phenotypes. All these applications need careful data analyses and reliable algorithms.

Mode of action from dose-response microarray data: case study using 10 environmental chemicals

EPA Science Inventory

Ligand-activated nuclear receptors regulate many biological processes through complex interactions with biological macromolecules. Certain xenobiotics alter nuclear receptor signaling through direct or indirect interactions. Defining the mode of action of such xenobiotics is di...

Prenatal metformin exposure in mice programs the metabolic phenotype of the offspring during a high fat diet at adulthood.

PubMed

Salomäki, Henriikka; Vähätalo, Laura H; Laurila, Kirsti; Jäppinen, Norma T; Penttinen, Anna-Maija; Ailanen, Liisa; Ilyasizadeh, Juan; Pesonen, Ullamari; Koulu, Markku

2013-01-01

The antidiabetic drug metformin is currently used prior and during pregnancy for polycystic ovary syndrome, as well as during gestational diabetes mellitus. We investigated the effects of prenatal metformin exposure on the metabolic phenotype of the offspring during adulthood in mice. Metformin (300 mg/kg) or vehicle was administered orally to dams on regular diet from the embryonic day E0.5 to E17.5. Gene expression profiles in liver and brain were analysed from 4-day old offspring by microarray. Body weight development and several metabolic parameters of offspring were monitored both during regular diet (RD-phase) and high fat diet (HFD-phase). At the end of the study, two doses of metformin or vehicle were given acutely to mice at the age of 20 weeks, and Insig-1 and GLUT4 mRNA expressions in liver and fat tissue were analysed using qRT-PCR. Metformin exposed fetuses were lighter at E18.5. There was no effect of metformin on the maternal body weight development or food intake. Metformin exposed offspring gained more body weight and mesenteric fat during the HFD-phase. The male offspring also had impaired glucose tolerance and elevated fasting glucose during the HFD-phase. Moreover, the expression of GLUT4 mRNA was down-regulated in epididymal fat in male offspring prenatally exposed to metformin. Based on the microarray and subsequent qRT-PCR analyses, the expression of Insig-1 was changed in the liver of neonatal mice exposed to metformin prenatally. Furthermore, metformin up-regulated the expression of Insig-1 later in development. Gene set enrichment analysis based on preliminary microarray data identified several differentially enriched pathways both in control and metformin exposed mice. The present study shows that prenatal metformin exposure causes long-term programming effects on the metabolic phenotype during high fat diet in mice. This should be taken into consideration when using metformin as a therapeutic agent during pregnancy.

DNA microarray technology in nutraceutical and food safety.

PubMed

Liu-Stratton, Yiwen; Roy, Sashwati; Sen, Chandan K

2004-04-15

The quality and quantity of diet is a key determinant of health and disease. Molecular diagnostics may play a key role in food safety related to genetically modified foods, food-borne pathogens and novel nutraceuticals. Functional outcomes in biology are determined, for the most part, by net balance between sets of genes related to the specific outcome in question. The DNA microarray technology offers a new dimension of strength in molecular diagnostics by permitting the simultaneous analysis of large sets of genes. Automation of assay and novel bioinformatics tools make DNA microarrays a robust technology for diagnostics. Since its development a few years ago, this technology has been used for the applications of toxicogenomics, pharmacogenomics, cell biology, and clinical investigations addressing the prevention and intervention of diseases. Optimization of this technology to specifically address food safety is a vast resource that remains to be mined. Efforts to develop diagnostic custom arrays and simplified bioinformatics tools for field use are warranted.

Use of Bayesian Networks to Probabilistically Model and Improve the Likelihood of Validation of Microarray Findings by RT-PCR

PubMed Central

English, Sangeeta B.; Shih, Shou-Ching; Ramoni, Marco F.; Smith, Lois E.; Butte, Atul J.

2014-01-01

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation. PMID:18790084

Phenotype discovery by gene expression profiling: mapping of biological processes linked to BMP-2-mediated osteoblast differentiation.

PubMed

Balint, Eva; Lapointe, David; Drissi, Hicham; van der Meijden, Caroline; Young, Daniel W; van Wijnen, Andre J; Stein, Janet L; Stein, Gary S; Lian, Jane B

2003-05-15

Understanding physiological control of osteoblast differentiation necessitates characterization of the regulatory signals that initiate the events directing a cell to lineage commitment and establishing competency for bone formation. The bone morphogenetic protein, BMP-2, a member of the TGFbeta superfamily, induces osteoblast differentiation and functions through the Smad signal transduction pathway during in vivo bone formation. However, the molecular targets of BMP-mediated gene transcription during the process of osteoblast differentiation have not been comprehensively identified. In the present study, BMP-2 responsive factors involved in the early stages of commitment and differentiation to the osteoblast phenotype were analyzed by microarray gene expression profiling in samples ranging from 1 to 24 h following BMP-2 dependent differentiation of C2C12 premyoblasts into the osteogenic lineage. A total of 1,800 genes were responsive to BMP-2 and expression was modulated from 3- to 14-fold for less than 100 genes during the time course. Approximately 50% of these 100 genes are either up- or downregulated. Major events associated with phenotypic changes towards the osteogenic lineage were identified from hierarchical and functional clustering analyses. BMP-2 immediately responsive genes (1-4 h), which exhibited either transient or sustained expression, reflect activation and repression of non-osseous BMP-2 developmental systems. This initial response was followed by waves of expression of nuclear proteins and developmental regulatory factors including inhibitors of DNA binding, Runx2, C/EBP, Zn finger binding proteins, forkhead, and numerous homeobox proteins (e.g., CDP/cut, paired, distaless, Hox) which are expressed at characterized stages during osteoblast differentiation. A sequential profile of genes mediating changes in cell morphology, cell growth, and basement membrane formation is observed as a secondary transient early response (2-8 h). Commitment to the osteogenic phenotype is recognized by 8 h, reflected by downregulation of most myogenic-related genes and induction of a spectrum of signaling proteins and enzymes facilitating synthesis and assembly of an extracellular skeletal environment. These genes included collagens Type I and VI and the small leucine rich repeat family of proteoglycans (e.g., decorin, biglycan, osteomodulin, fibromodulin, and osteoadherin/osteoglycin) that reached peak expression at 24 h. With extracellular matrix development, the bone phenotype was further established from 16 to 24 h by induction of genes for cell adhesion and communication and enzymes that organize the bone ECM. Our microarray analysis resulted in the discovery of a class of genes, initially described in relation to differentiation of astrocytes and oligodendrocytes that are functionally coupled to signals for cellular extensions. They include nexin, neuropilin, latexin, neuroglian, neuron specific gene 1, and Ulip; suggesting novel roles for these genes in the bone microenvironment. This global analysis identified a multistage molecular and cellular cascade that supports BMP-2-mediated osteoblast differentiation. Copyright 2003 Wiley-Liss, Inc.

A biomimetic algorithm for the improved detection of microarray features

NASA Astrophysics Data System (ADS)

Nicolau, Dan V., Jr.; Nicolau, Dan V.; Maini, Philip K.

2007-02-01

One the major difficulties of microarray technology relate to the processing of large and - importantly - error-loaded images of the dots on the chip surface. Whatever the source of these errors, those obtained in the first stage of data acquisition - segmentation - are passed down to the subsequent processes, with deleterious results. As it has been demonstrated recently that biological systems have evolved algorithms that are mathematically efficient, this contribution attempts to test an algorithm that mimics a bacterial-"patented" algorithm for the search of available space and nutrients to find, "zero-in" and eventually delimitate the features existent on the microarray surface.

Questioning the utility of pooling samples in microarray experiments with cell lines.

PubMed

Lusa, L; Cappelletti, V; Gariboldi, M; Ferrario, C; De Cecco, L; Reid, J F; Toffanin, S; Gallus, G; McShane, L M; Daidone, M G; Pierotti, M A

2006-01-01

We describe a microarray experiment using the MCF-7 breast cancer cell line in two different experimental conditions for which the same number of independent pools as the number of individual samples was hybridized on Affymetrix GeneChips. Unexpectedly, when using individual samples, the number of probe sets found to be differentially expressed between treated and untreated cells was about three times greater than that found using pools. These findings indicate that pooling samples in microarray experiments where the biological variability is expected to be small might not be helpful and could even decrease one's ability to identify differentially expressed genes.

Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.

PubMed

Ma, Chuang; Wang, Xiangfeng

2012-09-01

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey's biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

Identifying novel glioma associated pathways based on systems biology level meta-analysis.

PubMed

Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong

2013-01-01

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Application of the Gini Correlation Coefficient to Infer Regulatory Relationships in Transcriptome Analysis[W][OA

PubMed Central

Ma, Chuang; Wang, Xiangfeng

2012-01-01

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey’s biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses. PMID:22797655

Principles of gene microarray data analysis.

PubMed

Mocellin, Simone; Rossi, Carlo Riccardo

2007-01-01

The development of several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and gene microarray, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complex cascade of molecular events leading to tumor development and progression. The availability of such large amounts of information has shifted the attention of scientists towards a nonreductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, gene microarray has become prominent because it is easier to use, does not require large-scale DNA sequencing, and allows for the parallel quantification of thousands of genes from multiple samples. Gene microarray technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor. Therefore, it is of paramount importance for both researchers and clinicians to know the principles underlying the analysis of the huge amount of data generated with microarray technology.

Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation

PubMed Central

Hu, Wenchao; Liu, Yuting; Yan, Jun

2014-01-01

Alternative polyadenylation (APA) is a post-transcriptional mechanism to generate diverse mRNA transcripts with different 3′UTRs from the same gene. In this study, we systematically searched for the APA events with differential expression in public mouse microarray data. Hundreds of genes with over-represented differential APA events and the corresponding experiments were identified. We further revealed that global APA differential expression occurred prevalently in tissues such as brain comparing to peripheral tissues, and biological processes such as development, differentiation and immune responses. Interestingly, we also observed widespread differential APA events in RNA-binding protein (RBP) genes such as Rbm3, Eif4e2 and Elavl1. Given the fact that RBPs are considered as the main regulators of differential APA expression, we constructed a co-expression network between APAs and RBPs using the microarray data. Further incorporation of CLIP-seq data of selected RBPs showed that Nova2 represses and Mbnl1 promotes the polyadenylation of closest poly(A) sites respectively. Altogether, our study is the first microarray meta-analysis in a mammal on the regulation of APA by RBPs that integrated massive mRNA expression data under a wide-range of biological conditions. Finally, we present our results as a comprehensive resource in an online website for the research community. PMID:24622240

In vitro study of the effects of ELF electric fields on gene expression in human epidermal cells.

PubMed

Collard, Jean-Francois; Mertens, Benjamin; Hinsenkamp, Maurice

2011-01-01

An acceleration of differentiation, at the expense of proliferation, is observed after exposure of various biological models to low frequency and low amplitude electric and electromagnetic fields. Following these results showing significant modifications, we try to identify the biological mechanism involved at the cell level through microarray screening. For this study, we use epidermis cultures harvested from human abdominoplasty. Two platinum electrodes are used to apply the electric signal. The gene expressions of 38,500 well-characterized human genes are analyzed using Affymetrix(®) microarray U133 Plus 2.0 chips. The protocol is repeated on three different patients. After three periods of exposure, a total of 24 chips have been processed. After the application of ELF electric fields, the microarray analysis confirms a modification of the gene expression of epidermis cells. Particularly, four up-regulated genes (DKK1, TXNRD1, ATF3, and MME) and one down-regulated gene (MACF1) are involved in the regulation of proliferation and differentiation. Expression of these five genes was also confirmed by real-time rtPCR in all samples used for microarray analysis. These results corroborate an acceleration of cell differentiation at the expense of cell proliferation. © 2010 Wiley-Liss, Inc.

Customizing chemotherapy for colon cancer: the potential of gene expression profiling.

PubMed

Mariadason, John M; Arango, Diego; Augenlicht, Leonard H

2004-06-01

The value of gene expression profiling, or microarray analysis, for the classification and prognosis of multiple forms of cancer is now clearly established. For colon cancer, expression profiling can readily discriminate between normal and tumor tissue, and to some extent between tumors of different histopathological stage and prognosis. While a definitive in vivo study demonstrating the potential of this methodology for predicting response to chemotherapy is presently lacking, the ability of microarrays to distinguish other subtleties of colon cancer phenotype, as well as recent in vitro proof-of-principle experiments utilizing colon cancer cell lines, illustrate the potential of this methodology for predicting the probability of response to specific chemotherapeutic agents. This review discusses some of the recent advances in the use of microarray analysis for understanding and distinguishing colon cancer subtypes, and attempts to identify challenges that need to be overcome in order to achieve the goal of using gene expression profiling for customizing chemotherapy in colon cancer.

Potentials and capabilities of the Extracellular Vesicle (EV) Array.

PubMed

Jørgensen, Malene Møller; Bæk, Rikke; Varming, Kim

2015-01-01

Extracellular vesicles (EVs) and exosomes are difficult to enrich or purify from biofluids, hence quantification and phenotyping of these are tedious and inaccurate. The multiplexed, highly sensitive and high-throughput platform of the EV Array presented by Jørgensen et al., (J Extracell Vesicles, 2013; 2: 10) has been refined regarding the capabilities of the method for characterization and molecular profiling of EV surface markers. Here, we present an extended microarray platform to detect and phenotype plasma-derived EVs (optimized for exosomes) for up to 60 antigens without any enrichment or purification prior to analysis.

Plant-pathogen interactions: what microarray tells about it?

PubMed

Lodha, T D; Basak, J

2012-01-01

Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant-pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant-pathogen interaction, and ends with the future prospects of this technology.

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

PubMed

Richard, Arianne C; Lyons, Paul A; Peters, James E; Biasci, Daniele; Flint, Shaun M; Lee, James C; McKinney, Eoin F; Siegel, Richard M; Smith, Kenneth G C

2014-08-04

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions.

PubMed

van Haaften, Rachel I M; Luceri, Cristina; van Erk, Arie; Evelo, Chris T A

2009-06-01

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

Identification of differentially expressed genes and false discovery rate in microarray studies.

PubMed

Gusnanto, Arief; Calza, Stefano; Pawitan, Yudi

2007-04-01

To highlight the development in microarray data analysis for the identification of differentially expressed genes, particularly via control of false discovery rate. The emergence of high-throughput technology such as microarrays raises two fundamental statistical issues: multiplicity and sensitivity. We focus on the biological problem of identifying differentially expressed genes. First, multiplicity arises due to testing tens of thousands of hypotheses, rendering the standard P value meaningless. Second, known optimal single-test procedures such as the t-test perform poorly in the context of highly multiple tests. The standard approach of dealing with multiplicity is too conservative in the microarray context. The false discovery rate concept is fast becoming the key statistical assessment tool replacing the P value. We review the false discovery rate approach and argue that it is more sensible for microarray data. We also discuss some methods to take into account additional information from the microarrays to improve the false discovery rate. There is growing consensus on how to analyse microarray data using the false discovery rate framework in place of the classical P value. Further research is needed on the preprocessing of the raw data, such as the normalization step and filtering, and on finding the most sensitive test procedure.

Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion.

PubMed

Steger, Doris; Berry, David; Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander

2011-01-01

The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.

Systematic Spatial Bias in DNA Microarray Hybridization Is Caused by Probe Spot Position-Dependent Variability in Lateral Diffusion

PubMed Central

Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander

2011-01-01

Background The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. Methodology/Principal Findings This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Conclusions Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization. PMID:21858215

Differentiation and activation of equine monocyte-derived dendritic cells are not correlated with CD206 or CD83 expression

PubMed Central

Moyo, Nathifa A; Marchi, Emanuele; Steinbach, Falko

2013-01-01

Dendritic cells (DC) are the main immune mediators inducing primary immune responses. DC generated from monocytes (MoDC) are a model system to study the biology of DC in vitro, as they represent inflammatory DC in vivo. Previous studies on the generation of MoDC in horses indicated that there was no distinct difference between immature and mature DC and that the expression profile was distinctly different from humans, where CD206 is expressed on immature MoDC whereas CD83 is expressed on mature MoDC. Here we describe the kinetics of equine MoDC differentiation and activation, analysing both phenotypic and functional characteristics. Blood monocytes were first differentiated with equine granulocyte–macrophage colony-stimulating factor and interleukin-4 generating immature DC (iMoDC). These cells were further activated with a cocktail of cytokines including interferon-γ) but not CD40 ligand to obtain mature DC (mMoDC). To determine the expression of a broad range of markers for which no monoclonal antibodies were available to analyse the protein expression, microarray and quantitative PCR analysis were performed to carry out gene expression analysis. This study demonstrates that equine iMoDC and mMoDC can be distinguished both phenotypically and functionally but the expression pattern of some markers including CD206 and CD83 is dissimilar to the human system. PMID:23461413

Microarray Analysis and Mutagenesis of the Biological Control Agent Pseudomonas fluorescens Pf-5

USDA-ARS?s Scientific Manuscript database

The biological control agent Pseudomonas fluorescens Pf-5 suppresses seedling emergence diseases caused by soilborne fungi and Oomycetes. Pf-5 produces at least ten secondary metabolites. These include hydrogen cyanide, pyrrolnitrin, pyoluteorin and 2,4-diacetylphloroglucinol, which have known funct...

Anti-protozoal and anti-bacterial antibiotics that inhibit protein synthesis kill cancer subtypes enriched for stem cell-like properties.

PubMed

Cuyàs, Elisabet; Martin-Castillo, Begoña; Corominas-Faja, Bruna; Massaguer, Anna; Bosch-Barrera, Joaquim; Menendez, Javier A

2015-01-01

Key players in translational regulation such as ribosomes might represent powerful, but hitherto largely unexplored, targets to eliminate drug-refractory cancer stem cells (CSCs). A recent study by the Lisanti group has documented how puromycin, an old antibiotic derived from Streptomyces alboniger that inhibits ribosomal protein translation, can efficiently suppress CSC states in tumorspheres and monolayer cultures. We have used a closely related approach based on Biolog Phenotype Microarrays (PM), which contain tens of lyophilized antimicrobial drugs, to assess the chemosensitivity profiles of breast cancer cell lines enriched for stem cell-like properties. Antibiotics directly targeting active sites of the ribosome including emetine, puromycin and cycloheximide, inhibitors of ribosome biogenesis such as dactinomycin, ribotoxic stress agents such as daunorubicin, and indirect inhibitors of protein synthesis such as acriflavine, had the largest cytotoxic impact against claudin-low and basal-like breast cancer cells. Thus, biologically aggressive, treatment-resistant breast cancer subtypes enriched for stem cell-like properties exhibit exacerbated chemosensitivities to anti-protozoal and anti-bacterial antibiotics targeting protein synthesis. These results suggest that old/existing microbicides might be repurposed not only as new cancer therapeutics, but also might provide the tools and molecular understanding needed to develop second-generation inhibitors of ribosomal translation to eradicate CSC traits in tumor tissues.

Anti-protozoal and anti-bacterial antibiotics that inhibit protein synthesis kill cancer subtypes enriched for stem cell-like properties

PubMed Central

Cuyàs, Elisabet; Martin-Castillo, Begoña; Corominas-Faja, Bruna; Massaguer, Anna; Bosch-Barrera, Joaquim; Menendez, Javier A

2015-01-01

Key players in translational regulation such as ribosomes might represent powerful, but hitherto largely unexplored, targets to eliminate drug-refractory cancer stem cells (CSCs). A recent study by the Lisanti group has documented how puromycin, an old antibiotic derived from Streptomyces alboniger that inhibits ribosomal protein translation, can efficiently suppress CSC states in tumorspheres and monolayer cultures. We have used a closely related approach based on Biolog Phenotype Microarrays (PM), which contain tens of lyophilized antimicrobial drugs, to assess the chemosensitivity profiles of breast cancer cell lines enriched for stem cell-like properties. Antibiotics directly targeting active sites of the ribosome including emetine, puromycin and cycloheximide, inhibitors of ribosome biogenesis such as dactinomycin, ribotoxic stress agents such as daunorubicin, and indirect inhibitors of protein synthesis such as acriflavine, had the largest cytotoxic impact against claudin-low and basal-like breast cancer cells. Thus, biologically aggressive, treatment-resistant breast cancer subtypes enriched for stem cell-like properties exhibit exacerbated chemosensitivities to anti-protozoal and anti-bacterial antibiotics targeting protein synthesis. These results suggest that old/existing microbicides might be repurposed not only as new cancer therapeutics, but also might provide the tools and molecular understanding needed to develop second-generation inhibitors of ribosomal translation to eradicate CSC traits in tumor tissues. PMID:25970790

Predicting Phenotypes from Genetic Crosses: A Mathematical Concept to Help Struggling Biology Students

ERIC Educational Resources Information Center

Baurhoo, Neerusha; Darwish, Shireef

2012-01-01

Predicting phenotypic outcomes from genetic crosses is often very difficult for biology students, especially those with learning disabilities. With our mathematical concept, struggling students in inclusive biology classrooms are now better equipped to solve genetic problems and predict phenotypes, because of improved understanding of dominance…

Microarray Bactericidal Testing of Natural Products Against Yersinia intermedia and Bacillus anthracis

DTIC Science & Technology

2002-01-01

Based Preservation Systems and Probiotic Bacteria. In Food Microbiology: Fundamentals and Frontiers. M. P. Doyle, L.R. Beuchat and T.J. Montville...Microarray Bactericidal Testing of Natural Products Against Yersinia intermedia and Bacillus anthracis I.J. Fry1, F.K. Lee2, A. Turetsky2 and J.J...effective protection against biological warfare agents (BWA’s), natural products with a historical record of bactericidal efficacy such as

BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

PubMed

Angelini, Claudia; Cutillo, Luisa; De Canditiis, Daniela; Mutarelli, Margherita; Pensky, Marianna

2008-10-06

Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. The software package BATS (Bayesian Analysis of Time Series) presented here implements the methodology described above. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles when at least 5-6 time points are available. The package has a user-friendly interface. BATS successfully manages various technical difficulties which arise in time-course microarray experiments, such as a small number of observations, non-uniform sampling intervals and replicated or missing data. BATS is a free user-friendly software for the analysis of both simulated and real microarray time course experiments. The software, the user manual and a brief illustrative example are freely available online at the BATS website: http://www.na.iac.cnr.it/bats.

Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology.

PubMed

Malenke, J R; Milash, B; Miller, A W; Dearing, M D

2013-07-01

Massively parallel sequencing has enabled the creation of novel, in-depth genetic tools for nonmodel, ecologically important organisms. We present the de novo transcriptome sequencing, analysis and microarray development for a vertebrate herbivore, the woodrat (Neotoma spp.). This genus is of ecological and evolutionary interest, especially with respect to ingestion and hepatic metabolism of potentially toxic plant secondary compounds. We generated a liver transcriptome of the desert woodrat (Neotoma lepida) using the Roche 454 platform. The assembled contigs were well annotated using rodent references (99.7% annotation), and biotransformation function was reflected in the gene ontology. The transcriptome was used to develop a custom microarray (eArray, Agilent). We tested the microarray with three experiments: one across species with similar habitat (thus, dietary) niches, one across species with different habitat niches and one across populations within a species. The resulting one-colour arrays had high technical and biological quality. Probes designed from the woodrat transcriptome performed significantly better than functionally similar probes from the Norway rat (Rattus norvegicus). There were a multitude of expression differences across the woodrat treatments, many of which related to biotransformation processes and activities. The pattern and function of the differences indicate shared ecological pressures, and not merely phylogenetic distance, play an important role in shaping gene expression profiles of woodrat species and populations. The quality and functionality of the woodrat transcriptome and custom microarray suggest these tools will be valuable for expanding the scope of herbivore biology, as well as the exploration of conceptual topics in ecology. © 2013 John Wiley & Sons Ltd.

Molecular biological identification of Babesia, Theileria, and Anaplasma species in cattle in Egypt using PCR assays, gene sequence analysis and a novel DNA microarray.

PubMed

El-Ashker, Maged; Hotzel, Helmut; Gwida, Mayada; El-Beskawy, Mohamed; Silaghi, Cornelia; Tomaso, Herbert

2015-01-30

In this preliminary study, a novel DNA microarray system was tested for the diagnosis of bovine piroplasmosis and anaplasmosis in comparison with microscopy and PCR assay results. In the Dakahlia Governorate, Egypt, 164 cattle were investigated for the presence of piroplasms and Anaplasma species. All investigated cattle were clinically examined. Blood samples were screened for the presence of blood parasites using microscopy and PCR assays. Seventy-one animals were acutely ill, whereas 93 were apparently healthy. In acutely ill cattle, Babesia/Theileria species (n=11) and Anaplasma marginale (n=10) were detected. Mixed infections with Babesia/Theileria spp. and A. marginale were present in two further cases. A. marginale infections were also detected in apparently healthy subjects (n=23). The results of PCR assays were confirmed by DNA sequencing. All samples that were positive by PCR for Babesia/Theileria spp. gave also positive results in the microarray analysis. The microarray chips identified Babesia bovis (n=12) and Babesia bigemina (n=2). Cattle with babesiosis were likely to have hemoglobinuria and nervous signs when compared to those with anaplasmosis that frequently had bloody feces. We conclude that clinical examination in combination with microscopy are still very useful in diagnosing acute cases of babesiosis and anaplasmosis, but a combination of molecular biological diagnostic assays will detect even asymptomatic carriers. In perspective, parallel detection of Babesia/Theileria spp. and A. marginale infections using a single microarray system will be a valuable improvement. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Linking microarray reporters with protein functions.

PubMed

Gaj, Stan; van Erk, Arie; van Haaften, Rachel I M; Evelo, Chris T A

2007-09-26

The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways. This paper proposes a novel method that aims to improve microarray reporter annotation by BLASTing the original reporter sequences against a species-specific EMBL subset, that was derived from and crosslinked back to the highly curated UniProt database. The resulting alignments were filtered using high quality alignment criteria and further compared with the outcome of a more traditional approach, where reporter sequences were BLASTed against EnsEMBL followed by locating the corresponding protein (UniProt) entry for the high quality hits. Combining the results of both methods resulted in successful annotation of > 58% of all reporter sequences with UniProt IDs on two commercial array platforms, increasing the amount of Incyte reporters that could be coupled to Gene Ontology terms from 32.7% to 58.3% and to a local GenMAPP pathway from 9.6% to 16.7%. For Agilent, 35.3% of the total reporters are now linked towards GO nodes and 7.1% on local pathways. Our methods increased the annotation quality of microarray reporter sequences and allowed us to visualize more reporters using pathway visualization tools. Even in cases where the original reporter annotation showed the correct description the new identifiers often allowed improved pathway and Gene Ontology linking. These methods are freely available at http://www.bigcat.unimaas.nl/public/publications/Gaj_Annotation/.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view. PMID:23515240

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

A probabilistic framework for microarray data analysis: fundamental probability models and statistical inference.

PubMed

Ogunnaike, Babatunde A; Gelmi, Claudio A; Edwards, Jeremy S

2010-05-21

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Identification of the TFII-I family target genes in the vertebrate genome.

PubMed

Chimge, Nyam-Osor; Makeyev, Aleksandr V; Ruddle, Frank H; Bayarsaihan, Dashzeveg

2008-07-01

GTF2I and GTF2IRD1 encode members of the TFII-I transcription factor family and are prime candidates in the Williams syndrome, a complex neurodevelopmental disorder. Our previous expression microarray studies implicated TFII-I proteins in the regulation of a number of genes critical in various aspects of cell physiology. Here, we combined bioinformatics and microarray results to identify TFII-I downstream targets in the vertebrate genome. These results were validated by chromatin immunoprecipitation and siRNA analysis. The collected evidence revealed the complexity of TFII-I-mediated processes that involve distinct regulatory networks. Altogether, these results lead to a better understanding of specific molecular events, some of which may be responsible for the Williams syndrome phenotype.

Mothers' appreciation of chromosomal microarray analysis for autism spectrum disorder.

PubMed

Giarelli, Ellen; Reiff, Marian

2015-10-01

The aim of this study was to examine mothers' experiences with chromosomal microarray analysis (CMA) for a child with autism spectrum disorder (ASD). This is a descriptive qualitative study using thematic content analysis of in-depth interview with 48 mothers of children who had genetic testing for ASD. The principal theme, "something is missing," included missing knowledge about genetics, information on use of the results, explanations of the relevance to the diagnosis, and relevance to life-long care. Two subordinate themes were (a) disappreciation of the helpfulness of scientific information to explain the diagnosis, and (b) returning to personal experience for interpretation. The test "appreciated" in value when results could be linked to the phenotype. © 2015, Wiley Periodicals, Inc.

Phenotypic analysis of prostate-infiltrating lymphocytes reveals TH17 and Treg skewing.

PubMed

Sfanos, Karen Sandell; Bruno, Tullia C; Maris, Charles H; Xu, Lauren; Thoburn, Christopher J; DeMarzo, Angelo M; Meeker, Alan K; Isaacs, William B; Drake, Charles G

2008-06-01

Pathologic examination of prostate glands removed from patients with prostate cancer commonly reveals infiltrating CD4+ and CD8+ T cells. Little is known about the phenotype of these cells, despite accumulating evidence suggesting a potential role for chronic inflammation in the etiology of prostate cancer. We developed a technique that samples the majority of the peripheral prostate through serial needle aspirates. CD4+ prostate-infiltrating lymphocytes (PIL) were isolated using magnetic beads and analyzed for subset skewing using both flow cytometry and quantitative reverse transcription-PCR. The transcriptional profile of fluorescence-activated cell sorted prostate-infiltrating regulatory T cells (CD4+, CD25+, GITR+) was compared with naïve, peripheral blood T cells using microarray analysis. CD4+ PIL showed a paucity of TH2 (interleukin-4-secreting) cells, a surprising finding given the generally accepted association of these cells with chronic, smoldering inflammation. Instead, CD4+ PIL seemed to be skewed towards a regulatory Treg phenotype (FoxP3+) as well as towards the TH17 phenotype (interleukin-17+). We also found that a preponderance of TH17-mediated inflammation was associated with a lower pathologic Gleason score. These protein level data were reflected at the message level, as analyzed by quantitative reverse transcription-PCR. Microarray analysis of pooled prostate-infiltrating T(reg) revealed expected Treg-associated transcripts (FoxP3, CTLA-4, GITR, LAG-3) as well as a number of unique cell surface markers that may serve as additional Treg markers. Taken together, these data suggest that TH17 and/or Treg CD4+ T cells (rather than TH2 T cells) may be involved in the development or progression of prostate cancer.

Phenotypic Analysis of Prostate-Infiltrating Lymphocytes Reveals TH17 and Treg Skewing

PubMed Central

Sfanos, Karen Sandell; Bruno, Tullia C.; Maris, Charles H.; Xu, Lauren; Thoburn, Christopher J.; DeMarzo, Angelo M.; Meeker, Alan K.; Isaacs, William B.; Drake, Charles G.

2011-01-01

Purpose Pathologic examination of prostate glands removed from patients with prostate cancer commonly reveals infiltrating CD4+ and CD8+ T cells. Little is known about the phenotype of these cells, despite accumulating evidence suggesting a potential role for chronic inflammation in the etiology of prostate cancer. Experimental Design We developed a technique that samples the majority of the peripheral prostate through serial needle aspirates. CD4+ prostate-infiltrating lymphocytes (PIL) were isolated using magnetic beads and analyzed for subset skewing using both flow cytometry and quantitative reverse transcription-PCR. The transcriptional profile of fluorescence-activated cell sorted prostate-infiltrating regulatory T cells (CD4+, CD25+, GITR+) was compared with naïve, peripheral blood T cells using microarray analysis. Results CD4+ PIL showed a paucity of TH2 (interleukin-4– secreting) cells, a surprising finding given the generally accepted association of these cells with chronic, smoldering inflammation. Instead, CD4+ PIL seemed to be skewed towards a regulatory Treg phenotype (FoxP3+) as well as towards the TH17 phenotype (interleukin-17+). We also found that a preponderance of TH17-mediated inflammation was associated with a lower pathologic Gleason score. These protein level data were reflected at the message level, as analyzed by quantitative reverse transcription-PCR. Microarray analysis of pooled prostate-infiltrating Treg revealed expected Treg-associated transcripts (FoxP3, CTLA-4, GITR, LAG-3) as well as a number of unique cell surface markers that may serve as additional Treg markers. Conclusion Taken together, these data suggest that TH17 and/or Treg CD4+ T cells (rather than TH2 T cells) may be involved in the development or progression of prostate cancer. PMID:18519750

Polymer microarray technology for stem cell engineering

PubMed Central

Coyle, Robert; Jia, Jia; Mei, Ying

2015-01-01

Stem cells hold remarkable promise for applications in tissue engineering and disease modeling. During the past decade, significant progress has been made in developing soluble factors (e.g., small molecules and growth factors) to direct stem cells into a desired phenotype. However, the current lack of suitable synthetic materials to regulate stem cell activity has limited the realization of the enormous potential of stem cells. This can be attributed to a large number of materials properties (e.g., chemical structures and physical properties of materials) that can affect stem cell fate. This makes it challenging to design biomaterials to direct stem cell behavior. To address this, polymer microarray technology has been developed to rapidly identify materials for a variety of stem cell applications. In this article, we summarize recent developments in polymer array technology and their applications in stem cell engineering. Statement of significance Stem cells hold remarkable promise for applications in tissue engineering and disease modeling. In the last decade, significant progress has been made in developing chemically defined media to direct stem cells into a desired phenotype. However, the current lack of the suitable synthetic materials to regulate stem cell activities has been limiting the realization of the potential of stem cells. This can be attributed to the number of variables in material properties (e.g., chemical structures and physical properties) that can affect stem cells. Polymer microarray technology has shown to be a powerful tool to rapidly identify materials for a variety of stem cell applications. Here we summarize recent developments in polymer array technology and their applications in stem cell engineering. PMID:26497624

An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data.

PubMed

Jin, Guangxu; Zhao, Hong; Zhou, Xiaobo; Wong, Stephen T C

2011-07-01

Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data. We addressed the downstream signaling network of the targets for the two individual drugs used in the pairwise combinations and applied EPN to the identified targeted signaling network. In EPN, drugs and signaling molecules are assigned to different types of places, while drug doses and molecular expressions are denoted by color tokens. The changes of molecular expressions caused by treatments of drugs are simulated by two actions of EPN: firing and blasting. Firing is to transit the drug and molecule tokens from one node or place to another, and blasting is to reduce the number of molecule tokens by drug tokens in a molecule node. The goal of EPN is to mediate the state characterized by control condition without any treatment to that of treatment and to depict the drug effects on molecules by the drug tokens. We applied EPN to our generated pairwise drug combination microarray data. The synergistic predictions using EPN are consistent with those predicted using phenotypic response data. The molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism. The software implemented in Python 2.7 programming language is available from request. stwong@tmhs.org.

Identification of genes associated with the long-gut-persistence phenotype of the probiotic Lactobacillus johnsonii strain NCC533 using a combination of genomics and transcriptome analysis.

PubMed

Denou, Emmanuel; Pridmore, Raymond David; Berger, Bernard; Panoff, Jean-Michel; Arigoni, Fabrizio; Brüssow, Harald

2008-05-01

Lactobacillus johnsonii strains NCC533 and ATCC 33200 (the type strain of this species) differed significantly in gut residence time (12 versus 5 days) after oral feeding to mice. Genes affecting the long gut residence time of the probiotic strain NCC533 were targeted for analysis. We hypothesized that genes specific for this strain, which are expressed during passage of the bacterium through the gut, affect the phenotype. When the DNA of the type strain was hybridized against a microarray of the sequenced NCC533 strain, we identified 233 genes that were specific for the long-gut-persistence isolate. Whole-genome transcription analysis of the NCC533 strain using the microarray format identified 174 genes that were strongly and consistently expressed in the jejunum of mice monocolonized with this strain. Fusion of the two microarray data sets identified three gene loci that were both expressed in vivo and specific to the long-gut-persistence isolate. The identified genes included LJ1027 and LJ1028, two glycosyltransferase genes in the exopolysaccharide synthesis operon; LJ1654 to LJ1656, encoding a sugar phosphotransferase system (PTS) transporter annotated as mannose PTS; and LJ1680, whose product shares 30% amino acid identity with immunoglobulin A proteases from pathogenic bacteria. Knockout mutants were tested in vivo. The experiments revealed that deletion of LJ1654 to LJ1656 and LJ1680 decreased the gut residence time, while a mutant with a deleted exopolysaccharide biosynthesis cluster had a slightly increased residence time.

Epigenomics

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

Cloning

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

Chromosomes

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

Transcriptome

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data.

PubMed

He, Xianmin; Wei, Qing; Sun, Meiqian; Fu, Xuping; Fan, Sichang; Li, Yao

2006-05-01

Biological techniques such as Array-Comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH) and affymetrix single nucleotide pleomorphism (SNP) array have been used to detect cytogenetic aberrations. However, on genomic scale, these techniques are labor intensive and time consuming. Comparative genomic microarray analysis (CGMA) has been used to identify cytogenetic changes in hepatocellular carcinoma (HCC) using gene expression microarray data. However, CGMA algorithm can not give precise localization of aberrations, fails to identify small cytogenetic changes, and exhibits false negatives and positives. Locally un-weighted smoothing cytogenetic aberrations prediction (LS-CAP) based on local smoothing and binomial distribution can be expected to address these problems. LS-CAP algorithm was built and used on HCC microarray profiles. Eighteen cytogenetic abnormalities were identified, among them 5 were reported previously, and 12 were proven by CGH studies. LS-CAP effectively reduced the false negatives and positives, and precisely located small fragments with cytogenetic aberrations.

Characterization of a complex chromosomal rearrangement using chromosome, FISH, and microarray assays in a girl with multiple congenital abnormalities and developmental delay

PubMed Central

2014-01-01

Complex chromosomal rearrangements (CCRs) are balanced or unbalanced structural rearrangements involving three or more cytogenetic breakpoints on two or more chromosomal pairs. The phenotypic anomalies in such cases are attributed to gene disruption, superimposed cryptic imbalances in the genome, and/or position effects. We report a 14-year-old girl who presented with multiple congenital anomalies and developmental delay. Chromosome and FISH analysis indicated a highly complex chromosomal rearrangement involving three chromosomes (3, 7 and 12), seven breakpoints as a result of one inversion, two insertions, and two translocations forming three derivative chromosomes. Additionally, chromosomal microarray study (CMA) revealed two submicroscopic deletions at 3p12.3 (467 kb) and 12q13.12 (442 kb). We postulate that microdeletion within the ROBO1 gene at 3p12.3 may have played a role in the patient’s developmental delay, since it has potential activity-dependent role in neurons. Additionally, factors other than genomic deletions such as loss of function or position effects may also contribute to the abnormal phenotype in our patient. PMID:25478007

Identification and characterization of nuclear genes involved in photosynthesis in Populus

PubMed Central

2014-01-01

Background The gap between the real and potential photosynthetic rate under field conditions suggests that photosynthesis could potentially be improved. Nuclear genes provide possible targets for improving photosynthetic efficiency. Hence, genome-wide identification and characterization of the nuclear genes affecting photosynthetic traits in woody plants would provide key insights on genetic regulation of photosynthesis and identify candidate processes for improvement of photosynthesis. Results Using microarray and bulked segregant analysis strategies, we identified differentially expressed nuclear genes for photosynthesis traits in a segregating population of poplar. We identified 515 differentially expressed genes in this population (FC ≥ 2 or FC ≤ 0.5, P < 0.05), 163 up-regulated and 352 down-regulated. Real-time PCR expression analysis confirmed the microarray data. Singular Enrichment Analysis identified 48 significantly enriched GO terms for molecular functions (28), biological processes (18) and cell components (2). Furthermore, we selected six candidate genes for functional examination by a single-marker association approach, which demonstrated that 20 SNPs in five candidate genes significantly associated with photosynthetic traits, and the phenotypic variance explained by each SNP ranged from 2.3% to 12.6%. This revealed that regulation of photosynthesis by the nuclear genome mainly involves transport, metabolism and response to stimulus functions. Conclusions This study provides new genome-scale strategies for the discovery of potential candidate genes affecting photosynthesis in Populus, and for identification of the functions of genes involved in regulation of photosynthesis. This work also suggests that improving photosynthetic efficiency under field conditions will require the consideration of multiple factors, such as stress responses. PMID:24673936

Gene expression profiling in the adult Down syndrome brain.

PubMed

Lockstone, H E; Harris, L W; Swatton, J E; Wayland, M T; Holland, A J; Bahn, S

2007-12-01

The mechanisms by which trisomy 21 leads to the characteristic Down syndrome (DS) phenotype are unclear. We used whole genome microarrays to characterize for the first time the transcriptome of human adult brain tissue (dorsolateral prefrontal cortex) from seven DS subjects and eight controls. These data were coanalyzed with a publicly available dataset from fetal DS tissue and functional profiling was performed to identify the biological processes central to DS and those that may be related to late onset pathologies, particularly Alzheimer disease neuropathology. A total of 685 probe sets were differentially expressed between adult DS and control brains at a stringent significance threshold (adjusted p value (q) < 0.005), 70% of these being up-regulated in DS. Over 25% of genes on chromosome 21 were differentially expressed in comparison to a median of 4.4% for all chromosomes. The unique profile of up-regulation on chromosome 21, consistent with primary dosage effects, was accompanied by widespread transcriptional disruption. The critical Alzheimer disease gene, APP, located on chromosome 21, was not found to be up-regulated in adult brain by microarray or QPCR analysis. However, numerous other genes functionally linked to APP processing were dysregulated. Functional profiling of genes dysregulated in both fetal and adult datasets identified categories including development (notably Notch signaling and Dlx family genes), lipid transport, and cellular proliferation. In the adult brain these processes were concomitant with cytoskeletal regulation and vesicle trafficking categories, and increased immune response and oxidative stress response, which are likely linked to the development of Alzheimer pathology in individuals with DS.

Big Results from Small Samples: Evaluation of Amplification Protocols for Gene Expression Profiling

EPA Science Inventory

Microarrays have revolutionized many areas of biology due to our technical ability to quantify tens of thousands of transcripts within a single experiment. However, there are still many areas that cannot benefit from this technology due to the amount of biological material needed...

Genetic Mapping

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice.

PubMed

Bruno, D L; Ganesamoorthy, D; Schoumans, J; Bankier, A; Coman, D; Delatycki, M; Gardner, R J M; Hunter, M; James, P A; Kannu, P; McGillivray, G; Pachter, N; Peters, H; Rieubland, C; Savarirayan, R; Scheffer, I E; Sheffield, L; Tan, T; White, S M; Yeung, A; Bowman, Z; Ngo, C; Choy, K W; Cacheux, V; Wong, L; Amor, D J; Slater, H R

2009-02-01

Microarray genome analysis is realising its promise for improving detection of genetic abnormalities in individuals with mental retardation and congenital abnormality. Copy number variations (CNVs) are now readily detectable using a variety of platforms and a major challenge is the distinction of pathogenic from ubiquitous, benign polymorphic CNVs. The aim of this study was to investigate replacement of time consuming, locus specific testing for specific microdeletion and microduplication syndromes with microarray analysis, which theoretically should detect all known syndromes with CNV aetiologies as well as new ones. Genome wide copy number analysis was performed on 117 patients using Affymetrix 250K microarrays. 434 CNVs (195 losses and 239 gains) were found, including 18 pathogenic CNVs and 9 identified as "potentially pathogenic". Almost all pathogenic CNVs were larger than 500 kb, significantly larger than the median size of all CNVs detected. Segmental regions of loss of heterozygosity larger than 5 Mb were found in 5 patients. Genome microarray analysis has improved diagnostic success in this group of patients. Several examples of recently discovered "new syndromes" were found suggesting they are more common than previously suspected and collectively are likely to be a major cause of mental retardation. The findings have several implications for clinical practice. The study revealed the potential to make genetic diagnoses that were not evident in the clinical presentation, with implications for pretest counselling and the consent process. The importance of contributing novel CNVs to high quality databases for genotype-phenotype analysis and review of guidelines for selection of individuals for microarray analysis is emphasised.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Microarray-based characterization of differential gene expression during vocal fold wound healing in rats

PubMed Central

Welham, Nathan V.; Ling, Changying; Dawson, John A.; Kendziorski, Christina; Thibeault, Susan L.; Yamashita, Masaru

2015-01-01

The vocal fold (VF) mucosa confers elegant biomechanical function for voice production but is susceptible to scar formation following injury. Current understanding of VF wound healing is hindered by a paucity of data and is therefore often generalized from research conducted in skin and other mucosal systems. Here, using a previously validated rat injury model, expression microarray technology and an empirical Bayes analysis approach, we generated a VF-specific transcriptome dataset to better capture the system-level complexity of wound healing in this specialized tissue. We measured differential gene expression at 3, 14 and 60 days post-injury compared to experimentally naïve controls, pursued functional enrichment analyses to refine and add greater biological definition to the previously proposed temporal phases of VF wound healing, and validated the expression and localization of a subset of previously unidentified repair- and regeneration-related genes at the protein level. Our microarray dataset is a resource for the wider research community and has the potential to stimulate new hypotheses and avenues of investigation, improve biological and mechanistic insight, and accelerate the identification of novel therapeutic targets. PMID:25592437

Estimation of gene induction enables a relevance-based ranking of gene sets.

PubMed

Bartholomé, Kilian; Kreutz, Clemens; Timmer, Jens

2009-07-01

In order to handle and interpret the vast amounts of data produced by microarray experiments, the analysis of sets of genes with a common biological functionality has been shown to be advantageous compared to single gene analyses. Some statistical methods have been proposed to analyse the differential gene expression of gene sets in microarray experiments. However, most of these methods either require threshhold values to be chosen for the analysis, or they need some reference set for the determination of significance. We present a method that estimates the number of differentially expressed genes in a gene set without requiring a threshold value for significance of genes. The method is self-contained (i.e., it does not require a reference set for comparison). In contrast to other methods which are focused on significance, our approach emphasizes the relevance of the regulation of gene sets. The presented method measures the degree of regulation of a gene set and is a useful tool to compare the induction of different gene sets and place the results of microarray experiments into the biological context. An R-package is available.

eIF4E and eIF4GI have distinct and differential imprints on multiple myeloma's proteome and signaling

PubMed Central

Attar-Schneider, Oshrat; Pasmanik-Chor, Metsada; Tartakover-Matalon, Shelly

2015-01-01

Accumulating data indicate translation plays a role in cancer biology, particularly its rate limiting stage of initiation. Despite this evolving recognition, the function and importance of specific translation initiation factors is unresolved. The eukaryotic translation initiation complex eIF4F consists of eIF4E and eIF4G at a 1:1 ratio. Although it is expected that they display interdependent functions, several publications suggest independent mechanisms. This study is the first to directly assess the relative contribution of eIF4F components to the expressed cellular proteome, transcription factors, microRNAs, and phenotype in a malignancy known for extensive protein synthesis-multiple myeloma (MM). Previously, we have shown that eIF4E/eIF4GI attenuation (siRNA/Avastin) deleteriously affected MM cells' fate and reduced levels of eIF4E/eIF4GI established targets. Here, we demonstrated that eIF4E/eIF4GI indeed have individual influences on cell proteome. We used an objective, high throughput assay of mRNA microarrays to examine the significance of eIF4E/eIF4GI silencing to several cellular facets such as transcription factors, microRNAs and phenotype. We showed different imprints for eIF4E and eIF4GI in all assayed aspects. These results promote our understanding of the relative contribution and importance of eIF4E and eIF4GI to the malignant phenotype and shed light on their function in eIF4F translation initiation complex. PMID:25717031

Morphologic, phenotypic, and transcriptomic characterization of classically and alternatively activated canine blood-derived macrophages in vitro

PubMed Central

Heinrich, Franziska; Lehmbecker, Annika; Raddatz, Barbara B.; Kegler, Kristel; Tipold, Andrea; Stein, Veronika M.; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang; Ulrich, Reiner

2017-01-01

Macrophages are a heterogeneous cell population playing a pivotal role in tissue homeostasis and inflammation, and their phenotype strongly depends on the micromilieu. Despite its increasing importance as a translational animal model for human diseases, there is a considerable gap of knowledge with respect to macrophage polarization in dogs. The present study comprehensively investigated the morphologic, phenotypic, and transcriptomic characteristics of unstimulated (M0), M1- (GM-CSF, LPS, IFNγ-stimulated) and M2- (M-CSF, IL-4-stimulated)-polarized canine blood-derived macrophages in vitro. Scanning electron microscopy revealed distinct morphologies of polarized macrophages with formation of multinucleated cells in M2-macrophages, while immunofluorescence employing literature-based prototype-antibodies against CD16, CD32, iNOS, MHC class II (M1-markers), CD163, CD206, and arginase-1 (M2-markers) demonstrated that only CD206 was able to discriminate M2-macrophages from both other phenotypes, highlighting this molecule as a promising marker for canine M2-macrophages. Global microarray analysis revealed profound changes in the transcriptome of polarized canine macrophages. Functional analysis pointed out that M1-polarization was associated with biological processes such as “respiratory burst”, whereas M2-polarization was associated with processes such as “mitosis”. Literature-based marker gene selection revealed only minor overlaps in the gene sets of the dog compared to prototype markers of murine and human macrophages. Biomarker selection using supervised clustering suggested latexin (LXN) and membrane-spanning 4-domains, subfamily A, member 2 (MS4A2) to be the most powerful predicting biomarkers for canine M1- and M2-macrophages, respectively. Immunofluorescence for both markers demonstrated expression of both proteins by macrophages in vitro but failed to reveal differences between canine M1 and M2-macrophages. The present study provides a solid basis for future studies upon the role of macrophage polarization in spontaneous diseases of the dog, a species that has emerging importance for translational research. PMID:28817687

Ionotropic Glutamate Receptors Mediate Inducible Defense in the Water Flea Daphnia pulex

PubMed Central

Miyakawa, Hitoshi; Sato, Masanao; Colbourne, John K.; Iguchi, Taisen

2015-01-01

Phenotypic plasticity is the ability held in many organisms to produce different phenotypes with a given genome in response to environmental stimuli, such as temperature, nutrition and various biological interactions. It seems likely that environmental signals induce a variety of mechanistic responses that influence ontogenetic processes. Inducible defenses, in which prey animals alter their morphology, behavior and/or other traits to help protect against direct or latent predation threats, are among the most striking examples of phenotypic plasticity. The freshwater microcrustacean Daphnia pulex forms tooth-like defensive structures, “neckteeth,” in response to chemical cues or signals, referred to as “kairomones,” in this case released from phantom midge larvae, a predator of D. pulex. To identify factors involved in the reception and/or transmission of a kairomone, we used microarray analysis to identify genes up-regulated following a short period of exposure to the midge kairomone. In addition to identifying differentially expressed genes of unknown function, we also found significant up-regulation of genes encoding ionotropic glutamate receptors, which are known to be involved in neurotransmission in many animal species. Specific antagonists of these receptors strongly inhibit the formation of neckteeth in D. pulex, although agonists did not induce neckteeth by themselves, indicating that ionotropic glutamate receptors are necessary but not sufficient for early steps of neckteeth formation in D. pulex. Moreover, using co-exposure of D. pulex to antagonists and juvenile hormone (JH), which physiologically mediates neckteeth formation, we found evidence suggesting that the inhibitory effect of antagonists is not due to direct inhibition of JH synthesis/secretion. Our findings not only provide a candidate molecule required for the inducible defense response in D. pulex, but also will contribute to the understanding of complex mechanisms underlying the recognition of environmental changes, which form the basis of phenotypic plasticity. PMID:25799112

Cloud-scale genomic signals processing classification analysis for gene expression microarray data.

PubMed

Harvey, Benjamin; Soo-Yeon Ji

2014-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA sequence data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological inference by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale classification analysis of microarray data using Wavelet thresholding in a Cloud environment to identify significantly expressed features. This paper proposes a novel methodology that uses Wavelet based Denoising to initialize a threshold for determination of significantly expressed genes for classification. Additionally, this research was implemented and encompassed within cloud-based distributed processing environment. The utilization of Cloud computing and Wavelet thresholding was used for the classification 14 tumor classes from the Global Cancer Map (GCM). The results proved to be more accurate than using a predefined p-value for differential expression classification. This novel methodology analyzed Wavelet based threshold features of gene expression in a Cloud environment, furthermore classifying the expression of samples by analyzing gene patterns, which inform us of biological processes. Moreover, enabling researchers to face the present and forthcoming challenges that may arise in the analysis of data in functional genomics of large microarray datasets.

Microarray gene-expression study in fibroblast and lymphoblastoid cell lines from antipsychotic-naïve first-episode schizophrenia patients.

PubMed

Gassó, Patricia; Mas, Sergi; Rodríguez, Natalia; Boloc, Daniel; García-Cerro, Susana; Bernardo, Miquel; Lafuente, Amalia; Parellada, Eduard

2017-12-01

Schizophrenia (SZ) is a chronic psychiatric disorder whose onset of symptoms occurs in late adolescence and early adulthood. The etiology is complex and involves important gene-environment interactions. Microarray gene-expression studies on SZ have identified alterations in several biological processes. The heterogeneity in the results can be attributed to the use of different sample types and other important confounding factors including age, illness chronicity and antipsychotic exposure. The aim of the present microarray study was to analyze, for the first time to our knowledge, differences in gene expression profiles in 18 fibroblast (FCLs) and 14 lymphoblastoid cell lines (LCLs) from antipsychotic-naïve first-episode schizophrenia (FES) patients and healthy controls. We used an analytical approach based on protein-protein interaction network construction and functional annotation analysis to identify the biological processes that are altered in SZ. Significant differences in the expression of 32 genes were found when LCLs were assessed. The network and gene set enrichment approach revealed the involvement of similar biological processes in FCLs and LCLs, including apoptosis and related biological terms such as cell cycle, autophagy, cytoskeleton organization and response to stress and stimulus. Metabolism and other processes, including signal transduction, kinase activity and phosphorylation, were also identified. These results were replicated in two independent cohorts using the same analytical approach. This provides more evidence for altered apoptotic processes in antipsychotic-naïve FES patients and other important biological functions such as cytoskeleton organization and metabolism. The convergent results obtained in both peripheral cell models support their usefulness for transcriptome studies on SZ. Copyright © 2017 Elsevier Ltd. All rights reserved.

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data

PubMed Central

Glez-Peña, Daniel; Álvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-01

Background Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. Results DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. Conclusion DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released. PMID:19178723

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data.

PubMed

Glez-Peña, Daniel; Alvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-29

Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released.

A novel universal DNA labeling and amplification system for rapid microarray-based detection of 117 antibiotic resistance genes in Gram-positive bacteria.

PubMed

Strauss, Christian; Endimiani, Andrea; Perreten, Vincent

2015-01-01

A rapid and simple DNA labeling system has been developed for disposable microarrays and has been validated for the detection of 117 antibiotic resistance genes abundant in Gram-positive bacteria. The DNA was fragmented and amplified using phi-29 polymerase and random primers with linkers. Labeling and further amplification were then performed by classic PCR amplification using biotinylated primers specific for the linkers. The microarray developed by Perreten et al. (Perreten, V., Vorlet-Fawer, L., Slickers, P., Ehricht, R., Kuhnert, P., Frey, J., 2005. Microarray-based detection of 90 antibiotic resistance genes of gram-positive bacteria. J.Clin.Microbiol. 43, 2291-2302.) was improved by additional oligonucleotides. A total of 244 oligonucleotides (26 to 37 nucleotide length and with similar melting temperatures) were spotted on the microarray, including genes conferring resistance to clinically important antibiotic classes like β-lactams, macrolides, aminoglycosides, glycopeptides and tetracyclines. Each antibiotic resistance gene is represented by at least 2 oligonucleotides designed from consensus sequences of gene families. The specificity of the oligonucleotides and the quality of the amplification and labeling were verified by analysis of a collection of 65 strains belonging to 24 species. Association between genotype and phenotype was verified for 6 antibiotics using 77 Staphylococcus strains belonging to different species and revealed 95% test specificity and a 93% predictive value of a positive test. The DNA labeling and amplification is independent of the species and of the target genes and could be used for different types of microarrays. This system has also the advantage to detect several genes within one bacterium at once, like in Staphylococcus aureus strain BM3318, in which up to 15 genes were detected. This new microarray-based detection system offers a large potential for applications in clinical diagnostic, basic research, food safety and surveillance programs for antimicrobial resistance. Copyright © 2014 Elsevier B.V. All rights reserved.

ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses

PubMed Central

Stokes, Todd H; Torrance, JT; Li, Henry; Wang, May D

2008-01-01

Background A survey of microarray databases reveals that most of the repository contents and data models are heterogeneous (i.e., data obtained from different chip manufacturers), and that the repositories provide only basic biological keywords linking to PubMed. As a result, it is difficult to find datasets using research context or analysis parameters information beyond a few keywords. For example, to reduce the "curse-of-dimension" problem in microarray analysis, the number of samples is often increased by merging array data from different datasets. Knowing chip data parameters such as pre-processing steps (e.g., normalization, artefact removal, etc), and knowing any previous biological validation of the dataset is essential due to the heterogeneity of the data. However, most of the microarray repositories do not have meta-data information in the first place, and do not have a a mechanism to add or insert this information. Thus, there is a critical need to create "intelligent" microarray repositories that (1) enable update of meta-data with the raw array data, and (2) provide standardized archiving protocols to minimize bias from the raw data sources. Results To address the problems discussed, we have developed a community maintained system called ArrayWiki that unites disparate meta-data of microarray meta-experiments from multiple primary sources with four key features. First, ArrayWiki provides a user-friendly knowledge management interface in addition to a programmable interface using standards developed by Wikipedia. Second, ArrayWiki includes automated quality control processes (caCORRECT) and novel visualization methods (BioPNG, Gel Plots), which provide extra information about data quality unavailable in other microarray repositories. Third, it provides a user-curation capability through the familiar Wiki interface. Fourth, ArrayWiki provides users with simple text-based searches across all experiment meta-data, and exposes data to search engine crawlers (Semantic Agents) such as Google to further enhance data discovery. Conclusions Microarray data and meta information in ArrayWiki are distributed and visualized using a novel and compact data storage format, BioPNG. Also, they are open to the research community for curation, modification, and contribution. By making a small investment of time to learn the syntax and structure common to all sites running MediaWiki software, domain scientists and practioners can all contribute to make better use of microarray technologies in research and medical practices. ArrayWiki is available at . PMID:18541053

A genome-wide 20 K citrus microarray for gene expression analysis

PubMed Central

Martinez-Godoy, M Angeles; Mauri, Nuria; Juarez, Jose; Marques, M Carmen; Santiago, Julia; Forment, Javier; Gadea, Jose

2008-01-01

Background Understanding of genetic elements that contribute to key aspects of citrus biology will impact future improvements in this economically important crop. Global gene expression analysis demands microarray platforms with a high genome coverage. In the last years, genome-wide EST collections have been generated in citrus, opening the possibility to create new tools for functional genomics in this crop plant. Results We have designed and constructed a publicly available genome-wide cDNA microarray that include 21,081 putative unigenes of citrus. As a functional companion to the microarray, a web-browsable database [1] was created and populated with information about the unigenes represented in the microarray, including cDNA libraries, isolated clones, raw and processed nucleotide and protein sequences, and results of all the structural and functional annotation of the unigenes, like general description, BLAST hits, putative Arabidopsis orthologs, microsatellites, putative SNPs, GO classification and PFAM domains. We have performed a Gene Ontology comparison with the full set of Arabidopsis proteins to estimate the genome coverage of the microarray. We have also performed microarray hybridizations to check its usability. Conclusion This new cDNA microarray replaces the first 7K microarray generated two years ago and allows gene expression analysis at a more global scale. We have followed a rational design to minimize cross-hybridization while maintaining its utility for different citrus species. Furthermore, we also provide access to a website with full structural and functional annotation of the unigenes represented in the microarray, along with the ability to use this site to directly perform gene expression analysis using standard tools at different publicly available servers. Furthermore, we show how this microarray offers a good representation of the citrus genome and present the usefulness of this genomic tool for global studies in citrus by using it to catalogue genes expressed in citrus globular embryos. PMID:18598343

Linking microarray reporters with protein functions

PubMed Central

Gaj, Stan; van Erk, Arie; van Haaften, Rachel IM; Evelo, Chris TA

2007-01-01

Background The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways. Results This paper proposes a novel method that aims to improve microarray reporter annotation by BLASTing the original reporter sequences against a species-specific EMBL subset, that was derived from and crosslinked back to the highly curated UniProt database. The resulting alignments were filtered using high quality alignment criteria and further compared with the outcome of a more traditional approach, where reporter sequences were BLASTed against EnsEMBL followed by locating the corresponding protein (UniProt) entry for the high quality hits. Combining the results of both methods resulted in successful annotation of > 58% of all reporter sequences with UniProt IDs on two commercial array platforms, increasing the amount of Incyte reporters that could be coupled to Gene Ontology terms from 32.7% to 58.3% and to a local GenMAPP pathway from 9.6% to 16.7%. For Agilent, 35.3% of the total reporters are now linked towards GO nodes and 7.1% on local pathways. Conclusion Our methods increased the annotation quality of microarray reporter sequences and allowed us to visualize more reporters using pathway visualization tools. Even in cases where the original reporter annotation showed the correct description the new identifiers often allowed improved pathway and Gene Ontology linking. These methods are freely available at http://www.bigcat.unimaas.nl/public/publications/Gaj_Annotation/. PMID:17897448

Library of molecular associations: curating the complex molecular basis of liver diseases.

PubMed

Buchkremer, Stefan; Hendel, Jasmin; Krupp, Markus; Weinmann, Arndt; Schlamp, Kai; Maass, Thorsten; Staib, Frank; Galle, Peter R; Teufel, Andreas

2010-03-20

Systems biology approaches offer novel insights into the development of chronic liver diseases. Current genomic databases supporting systems biology analyses are mostly based on microarray data. Although these data often cover genome wide expression, the validity of single microarray experiments remains questionable. However, for systems biology approaches addressing the interactions of molecular networks comprehensive but also highly validated data are necessary. We have therefore generated the first comprehensive database for published molecular associations in human liver diseases. It is based on PubMed published abstracts and aimed to close the gap between genome wide coverage of low validity from microarray data and individual highly validated data from PubMed. After an initial text mining process, the extracted abstracts were all manually validated to confirm content and potential genetic associations and may therefore be highly trusted. All data were stored in a publicly available database, Library of Molecular Associations http://www.medicalgenomics.org/databases/loma/news, currently holding approximately 1260 confirmed molecular associations for chronic liver diseases such as HCC, CCC, liver fibrosis, NASH/fatty liver disease, AIH, PBC, and PSC. We furthermore transformed these data into a powerful resource for molecular liver research by connecting them to multiple biomedical information resources. Together, this database is the first available database providing a comprehensive view and analysis options for published molecular associations on multiple liver diseases.

Characterization of regulatory pathways in Xylella fastidiosa: genes and phenotypes controlled by algU.

PubMed

Shi, Xiang Yang; Dumenyo, C Korsi; Hernandez-Martinez, Rufina; Azad, Hamid; Cooksey, Donald A

2007-11-01

Many virulence genes in plant bacterial pathogens are coordinately regulated by "global" regulatory genes. Conducting DNA microarray analysis of bacterial mutants of such genes, compared with the wild type, can help to refine the list of genes that may contribute to virulence in bacterial pathogens. The regulatory gene algU, with roles in stress response and regulation of the biosynthesis of the exopolysaccharide alginate in Pseudomonas aeruginosa and many other bacteria, has been extensively studied. The role of algU in Xylella fastidiosa, the cause of Pierce's disease of grapevines, was analyzed by mutation and whole-genome microarray analysis to define its involvement in aggregation, biofilm formation, and virulence. In this study, an algU::nptII mutant had reduced cell-cell aggregation, attachment, and biofilm formation and lower virulence in grapevines. Microarray analysis showed that 42 genes had significantly lower expression in the algU::nptII mutant than in the wild type. Among these are several genes that could contribute to cell aggregation and biofilm formation, as well as other physiological processes such as virulence, competition, and survival.

Comparing microarrays and next-generation sequencing technologies for microbial ecology research.

PubMed

Roh, Seong Woon; Abell, Guy C J; Kim, Kyoung-Ho; Nam, Young-Do; Bae, Jin-Woo

2010-06-01

Recent advances in molecular biology have resulted in the application of DNA microarrays and next-generation sequencing (NGS) technologies to the field of microbial ecology. This review aims to examine the strengths and weaknesses of each of the methodologies, including depth and ease of analysis, throughput and cost-effectiveness. It also intends to highlight the optimal application of each of the individual technologies toward the study of a particular environment and identify potential synergies between the two main technologies, whereby both sample number and coverage can be maximized. We suggest that the efficient use of microarray and NGS technologies will allow researchers to advance the field of microbial ecology, and importantly, improve our understanding of the role of microorganisms in their various environments.

Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays

PubMed Central

2010-01-01

Background Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. Results We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. Conclusions MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions. PMID:20509979

Correlational analysis for identifying genes whose regulation contributes to chronic neuropathic pain

PubMed Central

Persson, Anna-Karin; Gebauer, Mathias; Jordan, Suzana; Metz-Weidmann, Christiane; Schulte, Anke M; Schneider, Hans-Christoph; Ding-Pfennigdorff, Danping; Thun, Jonas; Xu, Xiao-Jun; Wiesenfeld-Hallin, Zsuzsanna; Darvasi, Ariel; Fried, Kaj; Devor, Marshall

2009-01-01

Background Nerve injury-triggered hyperexcitability in primary sensory neurons is considered a major source of chronic neuropathic pain. The hyperexcitability, in turn, is thought to be related to transcriptional switching in afferent cell somata. Analysis using expression microarrays has revealed that many genes are regulated in the dorsal root ganglion (DRG) following axotomy. But which contribute to pain phenotype versus other nerve injury-evoked processes such as nerve regeneration? Using the L5 spinal nerve ligation model of neuropathy we examined differential changes in gene expression in the L5 (and L4) DRGs in five mouse strains with contrasting susceptibility to neuropathic pain. We sought genes for which the degree of regulation correlates with strain-specific pain phenotype. Results In an initial experiment six candidate genes previously identified as important in pain physiology were selected for in situ hybridization to DRG sections. Among these, regulation of the Na+ channel α subunit Scn11a correlated with levels of spontaneous pain behavior, and regulation of the cool receptor Trpm8 correlated with heat hypersensibility. In a larger scale experiment, mRNA extracted from individual mouse DRGs was processed on Affymetrix whole-genome expression microarrays. Overall, 2552 ± 477 transcripts were significantly regulated in the axotomized L5DRG 3 days postoperatively. However, in only a small fraction of these was the degree of regulation correlated with pain behavior across strains. Very few genes in the "uninjured" L4DRG showed altered expression (24 ± 28). Conclusion Correlational analysis based on in situ hybridization provided evidence that differential regulation of Scn11a and Trpm8 contributes to across-strain variability in pain phenotype. This does not, of course, constitute evidence that the others are unrelated to pain. Correlational analysis based on microarray data yielded a larger "look-up table" of genes whose regulation likely contributes to pain variability. While this list is enriched in genes of potential importance for pain physiology, and is relatively free of the bias inherent in the candidate gene approach, additional steps are required to clarify which transcripts on the list are in fact of functional importance. PMID:19228393

Sampling Daphnia's expressed genes: preservation, expansion and invention of crustacean genes with reference to insect genomes

PubMed Central

Colbourne, John K; Eads, Brian D; Shaw, Joseph; Bohuski, Elizabeth; Bauer, Darren J; Andrews, Justen

2007-01-01

Background Functional and comparative studies of insect genomes have shed light on the complement of genes, which in part, account for shared morphologies, developmental programs and life-histories. Contrasting the gene inventories of insects to those of the nematodes provides insight into the genomic changes responsible for their diversification. However, nematodes have weak relationships to insects, as each belongs to separate animal phyla. A better outgroup to distinguish lineage specific novelties would include other members of Arthropoda. For example, crustaceans are close allies to the insects (together forming Pancrustacea) and their fascinating aquatic lifestyle provides an important comparison for understanding the genetic basis of adaptations to life on land versus life in water. Results This study reports on the first characterization of cDNA libraries and sequences for the model crustacean Daphnia pulex. We analyzed 1,546 ESTs of which 1,414 represent approximately 787 nuclear genes, by measuring their sequence similarities with insect and nematode proteomes. The provisional annotation of genes is supported by expression data from microarray studies described in companion papers. Loci expected to be shared between crustaceans and insects because of their mutual biological features are identified, including genes for reproduction, regulation and cellular processes. We identify genes that are likely derived within Pancrustacea or lost within the nematodes. Moreover, lineage specific gene family expansions are identified, which suggest certain biological demands associated with their ecological setting. In particular, up to seven distinct ferritin loci are found in Daphnia compared to three in most insects. Finally, a substantial fraction of the sampled gene transcripts shares no sequence similarity with those from other arthropods. Genes functioning during development and reproduction are comparatively well conserved between crustaceans and insects. By contrast, genes that were responsive to environmental conditions (metal stress) and not sex-biased included the greatest proportion of genes with no matches to insect proteomes. Conclusion This study along with associated microarray experiments are the initial steps in a coordinated effort by the Daphnia Genomics Consortium to build the necessary genomic platform needed to discover genes that account for the phenotypic diversity within the genus and to gain new insights into crustacean biology. This effort will soon include the first crustacean genome sequence. PMID:17612412

Reusable conductimetric array of interdigitated microelectrodes for the readout of low-density microarrays.

PubMed

Mallén, Maria; Díaz-González, María; Bonilla, Diana; Salvador, Juan P; Marco, María P; Baldi, Antoni; Fernández-Sánchez, César

2014-06-17

Low-density protein microarrays are emerging tools in diagnostics whose deployment could be primarily limited by the cost of fluorescence detection schemes. This paper describes an electrical readout system of microarrays comprising an array of gold interdigitated microelectrodes and an array of polydimethylsiloxane microwells, which enabled multiplexed detection of up to thirty six biological events on the same substrate. Similarly to fluorescent readout counterparts, the microarray can be developed on disposable glass slide substrates. However, unlike them, the presented approach is compact and requires a simple and inexpensive instrumentation. The system makes use of urease labeled affinity reagents for developing the microarrays and is based on detection of conductivity changes taking place when ionic species are generated in solution due to the catalytic hydrolysis of urea. The use of a polydimethylsiloxane microwell array facilitates the positioning of the measurement solution on every spot of the microarray. Also, it ensures the liquid tightness and isolation from the surrounding ones during the microarray readout process, thereby avoiding evaporation and chemical cross-talk effects that were shown to affect the sensitivity and reliability of the system. The performance of the system is demonstrated by carrying out the readout of a microarray for boldenone anabolic androgenic steroid hormone. Analytical results are comparable to those obtained by fluorescent scanner detection approaches. The estimated detection limit is 4.0 ng mL(-1), this being below the threshold value set by the World Anti-Doping Agency and the European Community. Copyright © 2014 Elsevier B.V. All rights reserved.

Chromosomal loss of 3q26.3-3q26.32, involving a partial neuroligin 1 deletion, identified by genomic microarray in a child with microcephaly, seizure disorder, and severe intellectual disability.

PubMed

Millson, Alison; Lagrave, Danielle; Willis, Mary J H; Rowe, Leslie R; Lyon, Elaine; South, Sarah T

2012-01-01

Neuroligin 1 (NLGN1) is one of five members of the neuroligin gene family and may represent a candidate gene for neurological disorders, as members of this family are involved in formation and remodeling of central nervous system synapses. NLGN1 is expressed predominantly in the central nervous system, where it dimerizes and then binds with β-neurexin to form a functional synapse. Mutations in neurexin 1 (NRXN1) as well as two other members of the neuroligin family, NLGN3 and NLGN4, have been associated with autism and mutations in NLGN4 have also been associated with intellectual disability, seizures, and EEG abnormalities. Genomic microarray is recommended for the detection of chromosomal gains or losses in patients with intellectual disability and multiple congenital anomalies. Results of uncertain significance are not uncommon. Parental studies can provide additional information by demonstrating that the imbalance is either de novo or inherited, and therefore is more or less likely to be causative of the clinical phenotype. However, the possibility that even inherited deletions and duplications may play a role in the phenotype of the proband cannot be excluded as many copy number variants associated with neurodevelopmental conditions show incomplete penetrance and may be inherited from an unaffected parent. Here, we report on a patient with a 2.2 Mb deletion at 3q26.3-3q26.32-encompassing the terminal end of NLGN1 and the entire NAALADL2 gene-detected by genomic microarray, and confirmed by FISH and real-time quantitative PCR. The same size deletion was subsequently found in her healthy, asymptomatic, adult mother. Copyright © 2011 Wiley Periodicals, Inc.

VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine).

PubMed

Wong, Darren C J; Sweetman, Crystal; Drew, Damian P; Ford, Christopher M

2013-12-16

Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis) whereby the recovered sub-networks reconfirm established plant gene functions and also identify novel associations. Together, we present valuable insights into grapevine transcriptional regulation by developing network models applicable to researchers in their prioritisation of gene candidates, for on-going study of biological processes related to grapevine development, metabolism and stress responses.

Carbon utilization profiles of river bacterial strains facing sole carbon sources suggest metabolic interactions.

PubMed

Goetghebuer, Lise; Servais, Pierre; George, Isabelle F

2017-05-01

Microbial communities play a key role in water self-purification. They are primary drivers of biogenic element cycles and ecosystem processes. However, these communities remain largely uncharacterized. In order to understand the diversity-heterotrophic activity relationship facing sole carbon sources, we assembled a synthetic community composed of 20 'typical' freshwater bacterial species mainly isolated from the Zenne River (Belgium). The carbon source utilization profiles of each individual strain and of the mixed community were measured in Biolog Phenotype MicroArrays PM1 and PM2A microplates that allowed testing 190 different carbon sources. Our results strongly suggest interactions occurring between our planktonic strains as our synthetic community showed metabolic properties that were not displayed by its single components. Finally, the catabolic performances of the synthetic community and a natural community from the same sampling site were compared. The synthetic community behaved like the natural one and was therefore representative of the latter in regard to carbon source consumption. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Causal inference in biology networks with integrated belief propagation.

PubMed

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Mining TCGA Data Using Boolean Implications

PubMed Central

Sinha, Subarna; Tsang, Emily K.; Zeng, Haoyang; Meister, Michela; Dill, David L.

2014-01-01

Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/. PMID:25054200

Imipenem Treatment Induces Expression of Important Genes and Phenotypes in a Resistant Acinetobacter baumannii Isolate

PubMed Central

AbuBakar, Sazaly; Cerqueira, Gustavo Maia; Al-Haroni, Mohammed; Pang, Sui Ping

2015-01-01

Acinetobacter baumannii has emerged as a notorious multidrug-resistant pathogen, and development of novel control measures is of the utmost importance. Understanding the factors that play a role in drug resistance may contribute to the identification of novel therapeutic targets. Pili are essential for A. baumannii adherence to and biofilm formation on abiotic surfaces as well as virulence. In the present study, we found that biofilm formation was significantly induced in an imipenem-resistant (Impr) strain treated with a subinhibitory concentration of antibiotic compared to that in an untreated control and an imipenem-susceptible (Imps) isolate. Using microarray and quantitative PCR analyses, we observed that several genes responsible for the synthesis of type IV pili were significantly upregulated in the Impr but not in the Imps isolate. Notably, this finding is corroborated by an increase in the motility of the Impr strain. Our results suggest that the ability to overproduce colonization factors in response to imipenem treatment confers biological advantage to A. baumannii and may contribute to clinical success. PMID:26666943

Imipenem Treatment Induces Expression of Important Genes and Phenotypes in a Resistant Acinetobacter baumannii Isolate.

PubMed

Dhabaan, Ghulam Nasser; AbuBakar, Sazaly; Cerqueira, Gustavo Maia; Al-Haroni, Mohammed; Pang, Sui Ping; Hassan, Hamimah

2015-12-14

Acinetobacter baumannii has emerged as a notorious multidrug-resistant pathogen, and development of novel control measures is of the utmost importance. Understanding the factors that play a role in drug resistance may contribute to the identification of novel therapeutic targets. Pili are essential for A. baumannii adherence to and biofilm formation on abiotic surfaces as well as virulence. In the present study, we found that biofilm formation was significantly induced in an imipenem-resistant (Imp(r)) strain treated with a subinhibitory concentration of antibiotic compared to that in an untreated control and an imipenem-susceptible (Imp(s)) isolate. Using microarray and quantitative PCR analyses, we observed that several genes responsible for the synthesis of type IV pili were significantly upregulated in the Imp(r) but not in the Imp(s) isolate. Notably, this finding is corroborated by an increase in the motility of the Imp(r) strain. Our results suggest that the ability to overproduce colonization factors in response to imipenem treatment confers biological advantage to A. baumannii and may contribute to clinical success. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Emerging semantics to link phenotype and environment

PubMed Central

Bunker, Daniel E.; Buttigieg, Pier Luigi; Cooper, Laurel D.; Dahdul, Wasila M.; Domisch, Sami; Franz, Nico M.; Jaiswal, Pankaj; Lawrence-Dill, Carolyn J.; Midford, Peter E.; Mungall, Christopher J.; Ramírez, Martín J.; Specht, Chelsea D.; Vogt, Lars; Vos, Rutger Aldo; Walls, Ramona L.; White, Jeffrey W.; Zhang, Guanyang; Deans, Andrew R.; Huala, Eva; Lewis, Suzanna E.; Mabee, Paula M.

2015-01-01

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments. PMID:26713234

Emerging semantics to link phenotype and environment.

PubMed

Thessen, Anne E; Bunker, Daniel E; Buttigieg, Pier Luigi; Cooper, Laurel D; Dahdul, Wasila M; Domisch, Sami; Franz, Nico M; Jaiswal, Pankaj; Lawrence-Dill, Carolyn J; Midford, Peter E; Mungall, Christopher J; Ramírez, Martín J; Specht, Chelsea D; Vogt, Lars; Vos, Rutger Aldo; Walls, Ramona L; White, Jeffrey W; Zhang, Guanyang; Deans, Andrew R; Huala, Eva; Lewis, Suzanna E; Mabee, Paula M

2015-01-01

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

Emerging semantics to link phenotype and environment

DOE PAGES

Thessen, Anne E.; Bunker, Daniel E.; Buttigieg, Pier Luigi; ...

2015-12-14

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies aremore » well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.« less

In vitro Increased Respiratory Activity of Selected Oral Bacteria May Explain Competitive and Collaborative Interactions in the Oral Microbiome

PubMed Central

Hernandez-Sanabria, Emma; Slomka, Vera; Herrero, Esteban R.; Kerckhof, Frederiek-Maarten; Zaidel, Lynette; Teughels, Wim; Boon, Nico

2017-01-01

Understanding the driving forces behind the shifts in the ecological balance of the oral microbiota will become essential for the future management and treatment of periodontitis. As the use of competitive approaches for modulating bacterial outgrowth is unexplored in the oral ecosystem, our study aimed to investigate both the associations among groups of functional compounds and the impact of individual substrates on selected members of the oral microbiome. We employed the Phenotype Microarray high-throughput technology to analyse the microbial cellular phenotypes of 15 oral bacteria. Multivariate statistical analysis was used to detect respiratory activity triggers and to assess similar metabolic activities. Carbon and nitrogen were relevant for the respiration of health-associated bacteria, explaining competitive interactions when grown in biofilms. Carbon, nitrogen, and peptides tended to decrease the respiratory activity of all pathobionts, but not significantly. None of the evaluated compounds significantly increased activity of pathobionts at both 24 and 48 h. Additionally, metabolite requirements of pathobionts were dissimilar, suggesting that collective modulation of their respiratory activity may be challenging. Flow cytometry indicated that the metabolic activity detected in the Biolog plates may not be a direct result of the number of bacterial cells. In addition, damage to the cell membrane may not influence overall respiratory activity. Our methodology confirmed previously reported competitive and collaborative interactions among bacterial groups, which could be used either as marker of health status or as targets for modulation of the oral environment. PMID:28638806

Fast gene ontology based clustering for microarray experiments.

PubMed

Ovaska, Kristian; Laakso, Marko; Hautaniemi, Sampsa

2008-11-21

Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Comparison of global brain gene expression profiles between inbred long-sleep and inbred short-sleep mice by high-density gene array hybridization.

PubMed

Xu, Y; Ehringer, M; Yang, F; Sikela, J M

2001-06-01

Inbred long-sleep (ILS) and short-sleep (ISS) mice show significant central nervous system-mediated differences in sleep time for sedative dose of ethanol and are frequently used as a rodent model for ethanol sensitivity. In this study, we have used complementary DNA (cDNA) array hybridization methodology to identify genes that are differentially expressed between the brains of ILS and ISS mice. To carry out this analysis, we used both the gene discovery array (GDA) and the Mouse GEM 1 Microarray. GDA consists of 18,378 nonredundant mouse cDNA clones on a single nylon filter. Complex probes were prepared from total brain mRNA of ILS or ISS mice by using reverse transcription and 33P labeling. The labeled probes were hybridized in parallel to the gene array filters. Data from GDA experiments were analyzed with SQL-Plus and Oracle 8. The GEM microarray includes 8,730 sequence-verified clones on a glass chip. Two fluorescently labeled probes were used to hybridize a microarray simultaneously. Data from GEM experiments were analyzed by using the GEMTools software package (Incyte). Differentially expressed genes identified from each method were confirmed by relative quantitative reverse transcription-polymerase chain reaction (RT-PCR). A total of 41 genes or expressed sequence tags (ESTs) display significant expression level differences between brains of ILS and ISS mice after GDA, GEM1 hybridization, and quantitative RT-PCR confirmation. Among them, 18 clones were expressed higher in ILS mice, and 23 clones were expressed higher in ISS mice. The individual gene or EST's function and mapping information have been analyzed. This study identified 41 genes that are differentially expressed between brains of ILS and ISS mice. Some of them may have biological relevance in mediation of phenotypic variation between ILS and ISS mice for ethanol sensitivity. This study also demonstrates that parallel gene expression comparison with high-density cDNA arrays is a rapid and efficient way to discover potential genes and pathways involved in alcoholism and alcohol-related physiologic processes.

GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction.

PubMed

Zhang, Yaogong; Liu, Jiahui; Liu, Xiaohu; Hong, Yuxiang; Fan, Xin; Huang, Yalou; Wang, Yuan; Xie, Maoqiang

2018-04-24

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC[Formula: see text]NMF). Specifically, first we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchical phenotypic data as weighted graph constraint for a better phenotype understanding. Second, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us with the intuition that genes in a group probably result in similar phenotypes. The model not only allows us to achieve a high-grade prediction performance, but also helps us to learn interpretable representation of genes and phenotypes simultaneously to facilitate future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC[Formula: see text]NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-arts methods.

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform.

PubMed

Salehi, Reza; Tsoi, Stephen C M; Colazo, Marcos G; Ambrose, Divakar J; Robert, Claude; Dyck, Michael K

2017-01-30

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo development due to their similar developmental process. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. Microarray technology allows quantitative measurement and gene expression profiling of transcript levels on a genome-wide basis. One of the main decisions that have to be made when planning a microarray experiment is whether to use a one- or two-color approach. Two-color design increases technical replication, minimizes variability, improves sensitivity and accuracy as well as allows having loop designs, defining the common reference samples. Although microarray is a powerful biological tool, there are potential pitfalls that can attenuate its power. Hence, in this technical paper we demonstrate an optimized protocol for RNA extraction, amplification, labeling, hybridization of the labeled amplified RNA to the array, array scanning and data analysis using the two-color analysis strategy.

DNA Microarray Wet Lab Simulation Brings Genomics into the High School Curriculum

PubMed Central

Zanta, Carolyn A.; Heyer, Laurie J.; Kittinger, Ben; Gabric, Kathleen M.; Adler, Leslie

2006-01-01

We have developed a wet lab DNA microarray simulation as part of a complete DNA microarray module for high school students. The wet lab simulation has been field tested with high school students in Illinois and Maryland as well as in workshops with high school teachers from across the nation. Instead of using DNA, our simulation is based on pH indicators, which offer many ideal teaching characteristics. The simulation requires no specialized equipment, is very inexpensive, is very reliable, and takes very little preparation time. Student and teacher assessment data indicate the simulation is popular with both groups, and students show significant learning gains. We include many resources with this publication, including all prelab introductory materials (e.g., a paper microarray activity), the student handouts, teachers notes, and pre- and postassessment tools. We did not test the simulation on other student populations, but based on teacher feedback, the simulation also may fit well in community college and in introductory and nonmajors' college biology curricula. PMID:17146040

Clustering gene expression data based on predicted differential effects of GV interaction.

PubMed

Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

DNA microarray wet lab simulation brings genomics into the high school curriculum.

PubMed

Campbell, A Malcolm; Zanta, Carolyn A; Heyer, Laurie J; Kittinger, Ben; Gabric, Kathleen M; Adler, Leslie; Schulz, Barbara

2006-01-01

We have developed a wet lab DNA microarray simulation as part of a complete DNA microarray module for high school students. The wet lab simulation has been field tested with high school students in Illinois and Maryland as well as in workshops with high school teachers from across the nation. Instead of using DNA, our simulation is based on pH indicators, which offer many ideal teaching characteristics. The simulation requires no specialized equipment, is very inexpensive, is very reliable, and takes very little preparation time. Student and teacher assessment data indicate the simulation is popular with both groups, and students show significant learning gains. We include many resources with this publication, including all prelab introductory materials (e.g., a paper microarray activity), the student handouts, teachers notes, and pre- and postassessment tools. We did not test the simulation on other student populations, but based on teacher feedback, the simulation also may fit well in community college and in introductory and nonmajors' college biology curricula.

Vaginal microbial flora analysis by next generation sequencing and microarrays; can microbes indicate vaginal origin in a forensic context?

PubMed

Benschop, Corina C G; Quaak, Frederike C A; Boon, Mathilde E; Sijen, Titia; Kuiper, Irene

2012-03-01

Forensic analysis of biological traces generally encompasses the investigation of both the person who contributed to the trace and the body site(s) from which the trace originates. For instance, for sexual assault cases, it can be beneficial to distinguish vaginal samples from skin or saliva samples. In this study, we explored the use of microbial flora to indicate vaginal origin. First, we explored the vaginal microbiome for a large set of clinical vaginal samples (n = 240) by next generation sequencing (n = 338,184 sequence reads) and found 1,619 different sequences. Next, we selected 389 candidate probes targeting genera or species and designed a microarray, with which we analysed a diverse set of samples; 43 DNA extracts from vaginal samples and 25 DNA extracts from samples from other body sites, including sites in close proximity of or in contact with the vagina. Finally, we used the microarray results and next generation sequencing dataset to assess the potential for a future approach that uses microbial markers to indicate vaginal origin. Since no candidate genera/species were found to positively identify all vaginal DNA extracts on their own, while excluding all non-vaginal DNA extracts, we deduce that a reliable statement about the cellular origin of a biological trace should be based on the detection of multiple species within various genera. Microarray analysis of a sample will then render a microbial flora pattern that is probably best analysed in a probabilistic approach.

Microarray analysis of differentially expressed genes engaged in fruit development between Prunus mume and Prunus armeniaca.

PubMed

Li, Xiaoying; Korir, Nicholas Kibet; Liu, Lili; Shangguan, Lingfei; Wang, Yuzhu; Han, Jian; Chen, Ming; Fang, Jinggui

2012-11-15

Microarray analysis is a technique that can be employed to provide expression profiles of single genes and new insights to elucidate the biological mechanisms responsible for fruit development. To evaluate expression of genes mostly engaged in fruit development between Prunus mume and Prunus armeniaca, we first identified differentially expressed transcripts along the entire fruit life cycle by using microarrays spotted with 10,641 ESTs collected from P. mume and other Prunus EST sequences. A total of 1418 ESTs were selected after quality control of microarray spots and analysis for differential gene expression patterns during fruit development of P. mume and P. Armeniaca. From these, 707 up-regulated and 711 down-regulated genes showing more than two-fold differences in expression level were annotated by GO based on biological processes, molecular functions and cellular components. These differentially expressed genes were found to be involved in several important pathways of carbohydrate, galactose, and starch and sucrose metabolism as well as in biosynthesis of other secondary metabolites via KEGG. This could provide detailed information on the fruit quality differences during development and ripening of these two species. With the results obtained, we provide a practical database for comprehensive understanding of molecular events during fruit development and also lay a theoretical foundation for the cloning of genes regulating in a series of important rate-limiting enzymes involved in vital metabolic pathways during fruit development. Copyright © 2012 Elsevier GmbH. All rights reserved.

geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.

PubMed

Reboiro-Jato, Miguel; Arrais, Joel P; Oliveira, José Luis; Fdez-Riverola, Florentino

2014-01-30

The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools. geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research. geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at http://sing.ei.uvigo.es/GC/.

Systematic validation and atomic force microscopy of non-covalent short oligonucleotide barcode microarrays.

PubMed

Cook, Michael A; Chan, Chi-Kin; Jorgensen, Paul; Ketela, Troy; So, Daniel; Tyers, Mike; Ho, Chi-Yip

2008-02-06

Molecular barcode arrays provide a powerful means to analyze cellular phenotypes in parallel through detection of short (20-60 base) unique sequence tags, or "barcodes", associated with each strain or clone in a collection. However, costs of current methods for microarray construction, whether by in situ oligonucleotide synthesis or ex situ coupling of modified oligonucleotides to the slide surface are often prohibitive to large-scale analyses. Here we demonstrate that unmodified 20mer oligonucleotide probes printed on conventional surfaces show comparable hybridization signals to covalently linked 5'-amino-modified probes. As a test case, we undertook systematic cell size analysis of the budding yeast Saccharomyces cerevisiae genome-wide deletion collection by size separation of the deletion pool followed by determination of strain abundance in size fractions by barcode arrays. We demonstrate that the properties of a 13K unique feature spotted 20 mer oligonucleotide barcode microarray compare favorably with an analogous covalently-linked oligonucleotide array. Further, cell size profiles obtained with the size selection/barcode array approach recapitulate previous cell size measurements of individual deletion strains. Finally, through atomic force microscopy (AFM), we characterize the mechanism of hybridization to unmodified barcode probes on the slide surface. These studies push the lower limit of probe size in genome-scale unmodified oligonucleotide microarray construction and demonstrate a versatile, cost-effective and reliable method for molecular barcode analysis.

Insights into the degradation capacities of Amycolatopsis tucumanensis DSM 45259 guided by microarray data.

PubMed

Bourguignon, Natalia; Bargiela, Rafael; Rojo, David; Chernikova, Tatyana N; de Rodas, Sara A López; García-Cantalejo, Jesús; Näther, Daniela J; Golyshin, Peter N; Barbas, Coral; Ferrero, Marcela; Ferrer, Manuel

2016-12-01

The analysis of catabolic capacities of microorganisms is currently often achieved by cultivation approaches and by the analysis of genomic or metagenomic datasets. Recently, a microarray system designed from curated key aromatic catabolic gene families and key alkane degradation genes was designed. The collection of genes in the microarray can be exploited to indicate whether a given microbe or microbial community is likely to be functionally connected with certain degradative phenotypes, without previous knowledge of genome data. Herein, this microarray was applied to capture new insights into the catabolic capacities of copper-resistant actinomycete Amycolatopsis tucumanensis DSM 45259. The array data support the presumptive ability of the DSM 45259 strain to utilize single alkanes (n-decane and n-tetradecane) and aromatics such as benzoate, phthalate and phenol as sole carbon sources, which was experimentally validated by cultivation and mass spectrometry. Interestingly, while in strain DSM 45259 alkB gene encoding an alkane hydroxylase is most likely highly similar to that found in other actinomycetes, the genes encoding benzoate 1,2-dioxygenase, phthalate 4,5-dioxygenase and phenol hydroxylase were homologous to proteobacterial genes. This suggests that strain DSM 45259 contains catabolic genes distantly related to those found in other actinomycetes. Together, this study not only provided new insight into the catabolic abilities of strain DSM 45259, but also suggests that this strain contains genes uncommon within actinomycetes.

Optimal consistency in microRNA expression analysis using reference-gene-based normalization.

PubMed

Wang, Xi; Gardiner, Erin J; Cairns, Murray J

2015-05-01

Normalization of high-throughput molecular expression profiles secures differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches. While the same general principles apply to microRNA (miRNA) normalization, there is mounting evidence that global shifts in their expression patterns occur in specific circumstances, which pose a challenge for normalizing miRNA expression data. As an alternative to global normalization, which has the propensity to flatten large trends, normalization against constitutively expressed reference genes presents an advantage through their relative independence. Here we investigated the performance of reference-gene-based (RGB) normalization for differential miRNA expression analysis of microarray expression data, and compared the results with other normalization methods, including: quantile, variance stabilization, robust spline, simple scaling, rank invariant, and Loess regression. The comparative analyses were executed using miRNA expression in tissue samples derived from subjects with schizophrenia and non-psychiatric controls. We proposed a consistency criterion for evaluating methods by examining the overlapping of differentially expressed miRNAs detected using different partitions of the whole data. Based on this criterion, we found that RGB normalization generally outperformed global normalization methods. Thus we recommend the application of RGB normalization for miRNA expression data sets, and believe that this will yield a more consistent and useful readout of differentially expressed miRNAs, particularly in biological conditions characterized by large shifts in miRNA expression.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example. PMID:23515190

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

Brief Guide to Genomics: DNA, Genes and Genomes

MedlinePlus

... Sheets A Brief Guide to Genomics About NHGRI Research About the International HapMap Project Biological Pathways Chromosome Abnormalities Chromosomes Cloning Comparative Genomics DNA Microarray Technology DNA Sequencing Deoxyribonucleic Acid ( ...

Microscopy Images as Interactive Tools in Cell Modeling and Cell Biology Education

ERIC Educational Resources Information Center

Araujo-Jorge, Tania C.; Cardona, Tania S.; Mendes, Claudia L. S.; Henriques-Pons, Andrea; Meirelles, Rosane M. S.; Coutinho, Claudia M. L. M.; Aguiar, Luiz Edmundo V.; Meirelles, Maria de Nazareth L.; de Castro, Solange L.; Barbosa, Helene S.; Luz, Mauricio R. M. P.

2004-01-01

The advent of genomics, proteomics, and microarray technology has brought much excitement to science, both in teaching and in learning. The public is eager to know about the processes of life. In the present context of the explosive growth of scientific information, a major challenge of modern cell biology is to popularize basic concepts of…

Genome Consortium for Active Teaching: Meeting the Goals of BIO2010

PubMed Central

Ledbetter, Mary Lee S.; Hoopes, Laura L.M.; Eckdahl, Todd T.; Heyer, Laurie J.; Rosenwald, Anne; Fowlks, Edison; Tonidandel, Scott; Bucholtz, Brooke; Gottfried, Gail

2007-01-01

The Genome Consortium for Active Teaching (GCAT) facilitates the use of modern genomics methods in undergraduate education. Initially focused on microarray technology, but with an eye toward diversification, GCAT is a community working to improve the education of tomorrow's life science professionals. GCAT participants have access to affordable microarrays, microarray scanners, free software for data analysis, and faculty workshops. Microarrays provided by GCAT have been used by 141 faculty on 134 campuses, including 21 faculty that serve large numbers of underrepresented minority students. An estimated 9480 undergraduates a year will have access to microarrays by 2009 as a direct result of GCAT faculty workshops. Gains for students include significantly improved comprehension of topics in functional genomics and increased interest in research. Faculty reported improved access to new technology and gains in understanding thanks to their involvement with GCAT. GCAT's network of supportive colleagues encourages faculty to explore genomics through student research and to learn a new and complex method with their undergraduates. GCAT is meeting important goals of BIO2010 by making research methods accessible to undergraduates, training faculty in genomics and bioinformatics, integrating mathematics into the biology curriculum, and increasing participation by underrepresented minority students. PMID:17548873

Genome Consortium for Active Teaching: meeting the goals of BIO2010.

PubMed

Campbell, A Malcolm; Ledbetter, Mary Lee S; Hoopes, Laura L M; Eckdahl, Todd T; Heyer, Laurie J; Rosenwald, Anne; Fowlks, Edison; Tonidandel, Scott; Bucholtz, Brooke; Gottfried, Gail

2007-01-01

The Genome Consortium for Active Teaching (GCAT) facilitates the use of modern genomics methods in undergraduate education. Initially focused on microarray technology, but with an eye toward diversification, GCAT is a community working to improve the education of tomorrow's life science professionals. GCAT participants have access to affordable microarrays, microarray scanners, free software for data analysis, and faculty workshops. Microarrays provided by GCAT have been used by 141 faculty on 134 campuses, including 21 faculty that serve large numbers of underrepresented minority students. An estimated 9480 undergraduates a year will have access to microarrays by 2009 as a direct result of GCAT faculty workshops. Gains for students include significantly improved comprehension of topics in functional genomics and increased interest in research. Faculty reported improved access to new technology and gains in understanding thanks to their involvement with GCAT. GCAT's network of supportive colleagues encourages faculty to explore genomics through student research and to learn a new and complex method with their undergraduates. GCAT is meeting important goals of BIO2010 by making research methods accessible to undergraduates, training faculty in genomics and bioinformatics, integrating mathematics into the biology curriculum, and increasing participation by underrepresented minority students.

Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

PubMed Central

Johnstone, Daniel M.; Riveros, Carlos; Heidari, Moones; Graham, Ross M.; Trinder, Debbie; Berretta, Regina; Olynyk, John K.; Scott, Rodney J.; Moscato, Pablo; Milward, Elizabeth A.

2013-01-01

While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation—if not adequately minimized by effective normalization—may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes. PMID:27605185

The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series

PubMed Central

DuBois, Debra C; Piel, William H; Jusko, William J

2008-01-01

High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest. PMID:15212590

Variation of gene expression in Bacillus subtilis samples of fermentation replicates.

PubMed

Zhou, Ying; Yu, Wen-Bang; Ye, Bang-Ce

2011-06-01

The application of comprehensive gene expression profiling technologies to compare wild and mutated microorganism samples or to assess molecular differences between various treatments has been widely used. However, little is known about the normal variation of gene expression in microorganisms. In this study, an Agilent customized microarray representing 4,106 genes was used to quantify transcript levels of five-repeated flasks to assess normal variation in Bacillus subtilis gene expression. CV analysis and analysis of variance were employed to investigate the normal variance of genes and the components of variance, respectively. The results showed that above 80% of the total variation was caused by biological variance. For the 12 replicates, 451 of 4,106 genes exhibited variance with CV values over 10%. The functional category enrichment analysis demonstrated that these variable genes were mainly involved in cell type differentiation, cell type localization, cell cycle and DNA processing, and spore or cyst coat. Using power analysis, the minimal biological replicate number for a B. subtilis microarray experiment was determined to be six. The results contribute to the definition of the baseline level of variability in B. subtilis gene expression and emphasize the importance of replicate microarray experiments.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

PubMed

Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

2009-12-21

Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

Identification of a transcriptional signature for the wound healing continuum

PubMed Central

Peake, Matthew A; Caley, Mathew; Giles, Peter J; Wall, Ivan; Enoch, Stuart; Davies, Lindsay C; Kipling, David; Thomas, David W; Stephens, Phil

2014-01-01

There is a spectrum/continuum of adult human wound healing outcomes ranging from the enhanced (nearly scarless) healing observed in oral mucosa to scarring within skin and the nonhealing of chronic skin wounds. Central to these outcomes is the role of the fibroblast. Global gene expression profiling utilizing microarrays is starting to give insight into the role of such cells during the healing process, but no studies to date have produced a gene signature for this wound healing continuum. Microarray analysis of adult oral mucosal fibroblast (OMF), normal skin fibroblast (NF), and chronic wound fibroblast (CWF) at 0 and 6 hours post-serum stimulation was performed. Genes whose expression increases following serum exposure in the order OMF < NF < CWF are candidates for a negative/impaired healing phenotype (the dysfunctional healing group), whereas genes with the converse pattern are potentially associated with a positive/preferential healing phenotype (the enhanced healing group). Sixty-six genes in the enhanced healing group and 38 genes in the dysfunctional healing group were identified. Overrepresentation analysis revealed pathways directly and indirectly associated with wound healing and aging and additional categories associated with differentiation, development, and morphogenesis. Knowledge of this wound healing continuum gene signature may in turn assist in the therapeutic assessment/treatment of a patient's wounds. PMID:24844339

Genome-wide expression profiling of in vivo-derived bloodstream parasite stages and dynamic analysis of mRNA alterations during synchronous differentiation in Trypanosoma brucei

PubMed Central

Kabani, Sarah; Fenn, Katelyn; Ross, Alan; Ivens, Al; Smith, Terry K; Ghazal, Peter; Matthews, Keith

2009-01-01

Background Trypanosomes undergo extensive developmental changes during their complex life cycle. Crucial among these is the transition between slender and stumpy bloodstream forms and, thereafter, the differentiation from stumpy to tsetse-midgut procyclic forms. These developmental events are highly regulated, temporally reproducible and accompanied by expression changes mediated almost exclusively at the post-transcriptional level. Results In this study we have examined, by whole-genome microarray analysis, the mRNA abundance of genes in slender and stumpy forms of T.brucei AnTat1.1 cells, and also during their synchronous differentiation to procyclic forms. In total, five biological replicates representing the differentiation of matched parasite populations derived from five individual mouse infections were assayed, with RNAs being derived at key biological time points during the time course of their synchronous differentiation to procyclic forms. Importantly, the biological context of these mRNA profiles was established by assaying the coincident cellular events in each population (surface antigen exchange, morphological restructuring, cell cycle re-entry), thereby linking the observed gene expression changes to the well-established framework of trypanosome differentiation. Conclusion Using stringent statistical analysis and validation of the derived profiles against experimentally-predicted gene expression and phenotypic changes, we have established the profile of regulated gene expression during these important life-cycle transitions. The highly synchronous nature of differentiation between stumpy and procyclic forms also means that these studies of mRNA profiles are directly relevant to the changes in mRNA abundance within individual cells during this well-characterised developmental transition. PMID:19747379

PGMapper: a web-based tool linking phenotype to genes.

PubMed

Xiong, Qing; Qiu, Yuhui; Gu, Weikuan

2008-04-01

With the availability of whole genome sequence in many species, linkage analysis, positional cloning and microarray are gradually becoming powerful tools for investigating the links between phenotype and genotype or genes. However, in these methods, causative genes underlying a quantitative trait locus, or a disease, are usually located within a large genomic region or a large set of genes. Examining the function of every gene is very time consuming and needs to retrieve and integrate the information from multiple databases or genome resources. PGMapper is a software tool for automatically matching phenotype to genes from a defined genome region or a group of given genes by combining the mapping information from the Ensembl database and gene function information from the OMIM and PubMed databases. PGMapper is currently available for candidate gene search of human, mouse, rat, zebrafish and 12 other species. Available online at http://www.genediscovery.org/pgmapper/index.jsp.

Molecular Insights on Post-chemotherapy Retinoblastoma by Microarray Gene Expression Analysis

PubMed Central

Nalini, Venkatesan; Segu, Ramya; Deepa, Perinkulam Ravi; Khetan, Vikas; Vasudevan, Madavan; Krishnakumar, Subramanian

2013-01-01

Purpose Management of Retinoblastoma (RB), a pediatric ocular cancer is limited by drug-resistance and drug-dosage related side effects during chemotherapy. Molecular de-regulation in post-chemotherapy RB tumors was investigated. Materials and Methods cDNA microarray analysis of two post-chemotherapy and one pre-chemotherapy RB tumor tissues was performed, followed by Principle Component Analysis, Gene ontology, Pathway Enrichment analysis and Biological Analysis Network (BAN) modeling. The drug modulation role of two significantly up-regulated genes (p≤0.05) − Ect2 (Epithelial-cell-transforming-sequence-2), and PRAME (preferentially-expressed-Antigen-in-Melanoma) was assessed by qRT-PCR, immunohistochemistry and cell viability assays. Results Differential up-regulation of 1672 genes and down-regulation of 2538 genes was observed in RB tissues (relative to normal adult retina), while 1419 genes were commonly de-regulated between pre-chemotherapy and post- chemotherapy RB. Twenty one key gene ontology categories, pathways, biomarkers and phenotype groups harboring 250 differentially expressed genes were dys-regulated (EZH2, NCoR1, MYBL2, RB1, STAMN1, SYK, JAK1/2, STAT1/2, PLK2/4, BIRC5, LAMN1, Ect2, PRAME and ABCC4). Differential molecular expressions of PRAME and Ect2 in RB tumors with and without chemotherapy were analyzed. There was neither up- regulation of MRP1, nor any significant shift in chemotherapeutic IC50, in PRAME over-expressed versus non-transfected RB cells. Conclusion Cell cycle regulatory genes were dys-regulated post-chemotherapy. Ect2 gene was expressed in response to chemotherapy-induced stress. PRAME does not contribute to drug resistance in RB, yet its nuclear localization and BAN information, points to its possible regulatory role in RB. PMID:24092970

Adipose tissue transcriptome changes during obesity development in female dogs.

PubMed

Grant, Ryan W; Vester Boler, Brittany M; Ridge, Tonya K; Graves, Thomas K; Swanson, Kelly S

2011-03-29

During the development of obesity, adipose tissue undergoes major expansion and remodeling, but the biological processes involved in this transition are not well understood. The objective of this study was to analyze global gene expression profiles of adipose tissue in dogs, fed a high-fat diet, during the transition from a lean to obese phenotype. Nine female beagles (4.09 ± 0.64 yr; 8.48 ± 0.35 kg) were randomized to ad libitum feeding or body weight maintenance. Subcutaneous adipose tissue biopsy, blood, and dual x-ray absorptiometry measurements were collected at 0, 4, 8, 12, and 24 wk of feeding. Serum was analyzed for glucose, insulin, fructosamine, triglycerides, free fatty acids, adiponectin, and leptin. Formalin-fixed adipose tissue was used for determination of adipocyte size. Adipose RNA samples were hybridized to Affymetrix Canine 2.0 microarrays. Statistical analysis, using repeated-measures ANOVA, showed ad libitum feeding increased (P < 0.05) body weight (0 wk, 8.36 ± 0.34 kg; 24 wk, 14.64 ± 0.34 kg), body fat mass (0 wk, 1.36 ± 0.24 kg; 24 wk, 6.52 ± 0.24 kg), adipocyte size (0 wk, 114.66 ± 17.38 μm(2); 24 wk, 320.97 ± 0.18.17 μm(2)), and leptin (0 wk, 0.8 ± 1.0 ng/ml; 24 wk, 12.9 ± 1.0 ng/ml). Microarrays displayed 1,665 differentially expressed genes in adipose tissue as weight increased. Alterations were seen in adipose tissue homeostatic processes including metabolism, oxidative stress, mitochondrial homeostasis, and extracellular matrix. Adipose transcriptome changes highlight the dynamic and adaptive response to ad libitum feeding and obesity development.

Transcriptome profiling in engrailed-2 mutant mice reveals common molecular pathways associated with autism spectrum disorders.

PubMed

Sgadò, Paola; Provenzano, Giovanni; Dassi, Erik; Adami, Valentina; Zunino, Giulia; Genovesi, Sacha; Casarosa, Simona; Bozzi, Yuri

2013-12-19

Transcriptome analysis has been used in autism spectrum disorder (ASD) to unravel common pathogenic pathways based on the assumption that distinct rare genetic variants or epigenetic modifications affect common biological pathways. To unravel recurrent ASD-related neuropathological mechanisms, we took advantage of the En2-/- mouse model and performed transcriptome profiling on cerebellar and hippocampal adult tissues. Cerebellar and hippocampal tissue samples from three En2-/- and wild type (WT) littermate mice were assessed for differential gene expression using microarray hybridization followed by RankProd analysis. To identify functional categories overrepresented in the differentially expressed genes, we used integrated gene-network analysis, gene ontology enrichment and mouse phenotype ontology analysis. Furthermore, we performed direct enrichment analysis of ASD-associated genes from the SFARI repository in our differentially expressed genes. Given the limited number of animals used in the study, we used permissive criteria and identified 842 differentially expressed genes in En2-/- cerebellum and 862 in the En2-/- hippocampus. Our functional analysis revealed that the molecular signature of En2-/- cerebellum and hippocampus shares convergent pathological pathways with ASD, including abnormal synaptic transmission, altered developmental processes and increased immune response. Furthermore, when directly compared to the repository of the SFARI database, our differentially expressed genes in the hippocampus showed enrichment of ASD-associated genes significantly higher than previously reported. qPCR was performed for representative genes to confirm relative transcript levels compared to those detected in microarrays. Despite the limited number of animals used in the study, our bioinformatic analysis indicates the En2-/- mouse is a valuable tool for investigating molecular alterations related to ASD.

Prognostic significance of membrane-associated mucins 1 and 4 in gastric adenocarcinoma.

PubMed

Hwang, Ilseon; Kang, Yu Na; Kim, Jin Young; DO, Young Rok; Song, Hong Suk; Park, Keon Uk

2012-08-01

Aberrant expression of mucins is likely associated with cancer biology as alterations in the expression and/or glycosylation patterns of various mucins have been noted. Expression of the mucin family in gastric cancers has been reported in numerous studies, but the results are conflicting. Therefore, we investigated the potential use of mucin (MUC)1 and 4 as prognostic markers in gastric cancer according to histological subtype. Three-hundred and sixty-five gastric adenocarcinoma patients who underwent surgical resection were selected for this study. Among the 365 gastric cancer samples tested here, 34% consisted of early gastric cancer and 66% were advanced. In terms of location, 68.7% of the cohort had intestinal-type cancer and 30.7% had diffuse-type. We constructed tissue microarrays with formalin-fixed paraffin-embedded blocks of gastric cancer and these micro-arrays were evaluated for phenotypic expression of MUC1/4 using monoclonal antibodies. Two-hundred and ninety-two patients (92.7%) were positive for MUC1 and 216 (60.5%) were positive for MUC4. MUC1 expression was not correlated with any other clinicopathological variables such as age, gender, depth of invasion, lymph node metastasis, Lauren classification or recurrence. However, loss of MUC4 expression was significantly correlated with recurrence (p=0.033). MUC4 expression was also significantly correlated with better disease-free survival (p=0.049) and particularly in the intestinal-type (p=0.018). Our present findings demonstrated that loss of MUC4 expression can be used as a prognostic marker in gastric cancer. Loss of MUC4 expression is a prognostic indicator of increased recurrence and poor disease-free survival in patients with gastric cancer.

Microarray analysis of gene expression patterns of high lycopene tomato generated from seeds after long-term space flight

NASA Astrophysics Data System (ADS)

Lu, Jinying; Ren, Chunxiao; Pan, Yi; Nechitailo, Galina S.; Liu, Min

Lycopene content is a most vital trait of tomatoes due to the role of lycopene in reducing the risk of some kinds of cancers. In this experiment, we gained a high lycopene (hl) tomato (named HY-2), after seven generations of self-cross selection, from seeds Russian MNP-1 carried in Russia MIR space station for six years. HPLC result showed that the lycopene content was 1.6 times more than that in Russian MNP-1 (the wild type). Microarray analysis presented the general profile of differential expressed genes at the tomato developmental stage of 7DPB (days post breaker). One hundred and forty three differential expression genes were identified according to the following criterion: the average changes were no less than 1.5 folds with q-value (similar to FDR) less than 0.05 or changes were no less than 1.5 folds in all three biological replications. Most of the differential expressed genes were mainly involved in metabolism, response to stimulus, biosynthesis, development and regulation. Particularly, we discussed the genes involved in protein metabolism, response to unfolded protein, carotenoid biosynthesis and photosynthesis that might be related to the fruit development and the accumulation of lycopene. What's more, we conducted QRT-PCR validation of five key genes (Fps, CrtL-b, CrtR-b, Zep and Nxs) in the lycopene biosynthesis pathway through time courses and that provided the direct molecular evidence for the hl phenotype. Our results demonstrate that long-term space flight, as a rarely used tool, can positively cause some beneficial mutations in the seeds and thus to help to generate a high quality variety, combined with ground selections.

An XRCC4 Splice Mutation Associated With Severe Short Stature, Gonadal Failure, and Early-Onset Metabolic Syndrome

PubMed Central

de Bruin, Christiaan; Mericq, Verónica; Andrew, Shayne F.; van Duyvenvoorde, Hermine A.; Verkaik, Nicole S.; Losekoot, Monique; Porollo, Aleksey; Garcia, Hernán; Kuang, Yi; Hanson, Dan; Clayton, Peter; van Gent, Dik C.; Wit, Jan M.; Hwa, Vivian

2015-01-01

Context: Severe short stature can be caused by defects in numerous biological processes including defects in IGF-1 signaling, centromere function, cell cycle control, and DNA damage repair. Many syndromic causes of short stature are associated with medical comorbidities including hypogonadism and microcephaly. Objective: To identify an underlying genetic etiology in two siblings with severe short stature and gonadal failure. Design: Clinical phenotyping, genetic analysis, complemented by in vitro functional studies of the candidate gene. Setting: An academic pediatric endocrinology clinic. Patients or Other Participants: Two adult siblings (male patient [P1] and female patient 2 [P2]) presented with a history of severe postnatal growth failure (adult heights: P1, −6.8 SD score; P2, −4 SD score), microcephaly, primary gonadal failure, and early-onset metabolic syndrome in late adolescence. In addition, P2 developed a malignant gastrointestinal stromal tumor at age 28. Intervention(s): Single nucleotide polymorphism microarray and exome sequencing. Results: Combined microarray analysis and whole exome sequencing of the two affected siblings and one unaffected sister identified a homozygous variant in XRCC4 as the probable candidate variant. Sanger sequencing and mRNA studies revealed a splice variant resulting in an in-frame deletion of 23 amino acids. Primary fibroblasts (P1) showed a DNA damage repair defect. Conclusions: In this study we have identified a novel pathogenic variant in XRCC4, a gene that plays a critical role in non-homologous end-joining DNA repair. This finding expands the spectrum of DNA damage repair syndromes to include XRCC4 deficiency causing severe postnatal growth failure, microcephaly, gonadal failure, metabolic syndrome, and possibly tumor predisposition. PMID:25742519

The MGED Ontology: a resource for semantics-based description of microarray experiments.

PubMed

Whetzel, Patricia L; Parkinson, Helen; Causton, Helen C; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Game, Laurence; Heiskanen, Mervi; Morrison, Norman; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Taylor, Chris; White, Joseph; Stoeckert, Christian J

2006-04-01

The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). Stoeckrt@pcbi.upenn.edu Supplementary data are available at Bioinformatics online.

Development and assessment of whole-genome oligonucleotide microarrays to analyze an anaerobic microbial community and its responses to oxidative stress.

PubMed

Scholten, Johannes C M; Culley, David E; Nie, Lei; Munn, Kyle J; Chow, Lely; Brockman, Fred J; Zhang, Weiwen

2007-06-29

The application of DNA microarray technology to investigate multiple-species microbial communities presents great challenges. In this study, we reported the design and quality assessment of four whole genome oligonucleotide microarrays for two syntroph bacteria, Desulfovibrio vulgaris and Syntrophobacter fumaroxidans, and two archaeal methanogens, Methanosarcina barkeri, and Methanospirillum hungatei, and their application to analyze global gene expression in a four-species microbial community in response to oxidative stress. In order to minimize the possibility of cross-hybridization, cross-genome comparison was performed to assure all probes unique to each genome so that the microarrays could provide species-level resolution. Microarray quality was validated by the good reproducibility of experimental measurements of multiple biological and analytical replicates. This study showed that S. fumaroxidans and M. hungatei responded to the oxidative stress with up-regulation of several genes known to be involved in reactive oxygen species (ROS) detoxification, such as catalase and rubrerythrin in S. fumaroxidans and thioredoxin and heat shock protein Hsp20 in M. hungatei. However, D. vulgaris seemed to be less sensitive to the oxidative stress as a member of a four-species community, since no gene involved in ROS detoxification was up-regulated. Our work demonstrated the successful application of microarrays to a multiple-species microbial community, and our preliminary results indicated that this approach could provide novel insights on the metabolism within microbial communities.

Partial sleep deprivation activates the DNA damage response (DDR) and the senescence-associated secretory phenotype (SASP) in aged adult humans.

PubMed

Carroll, Judith E; Cole, Steven W; Seeman, Teresa E; Breen, Elizabeth C; Witarama, Tuff; Arevalo, Jesusa M G; Ma, Jeffrey; Irwin, Michael R

2016-01-01

Age-related disease risk has been linked to short sleep duration and sleep disturbances; however, the specific molecular pathways linking sleep loss with diseases of aging are poorly defined. Key cellular events seen with aging, which are thought to contribute to disease, may be particularly sensitive to sleep loss. We tested whether one night of partial sleep deprivation (PSD) would increase leukocyte gene expression indicative of DNA damage responses (DDR), the senescence-associated secretory phenotype (SASP), and senescence indicator p16(INK4a) in older adult humans, who are at increased risk for cellular senescence. Community-dwelling older adults aged 61-86years (n=29; 48% male) underwent an experimental partial sleep deprivation (PSD) protocol over 4 nights, including adaptation, an uninterrupted night of sleep, partial sleep deprivation (sleep restricted 3-7AM), and a subsequent full night of sleep. Blood samples were obtained each morning to assess peripheral blood mononuclear cell (PBMC) gene expression using Illumina HT-12 arrays. Analyses of microarray results revealed that SASP (p<.05) and DDR (p=.08) gene expression were elevated from baseline to PSD nights. Gene expression changes were also observed from baseline to PSD in NFKB2, NBS1 and CHK2 (all p's<.05). The senescence marker p16(INK4a) (CDKN2A) was increased 1day after PSD compared to baseline (p<.01), however confirmatory RT-PCR did not replicate this finding. One night of partial sleep deprivation activates PBMC gene expression patterns consistent with biological aging in this older adult sample. PSD enhanced the SASP and increased the accumulation of damage that initiates cell cycle arrest and promotes cellular senescence. These findings causally link sleep deprivation to the molecular processes associated with biological aging. Copyright © 2015 Elsevier Inc. All rights reserved.

EurEAs_Gplex--A new SNaPshot assay for continental population discrimination and gender identification.

PubMed

Daca-Roszak, P; Pfeifer, A; Żebracka-Gala, J; Jarząb, B; Witt, M; Ziętkiewicz, E

2016-01-01

Assays that allow analysis of the biogeographic origin of biological samples in a standard forensic laboratory have to target a small number of highly differentiating markers. Such markers should be easy to multiplex and the assay must perform well in the degraded and scarce biological material. SNPs localized in the genome regions, which in the past were subjected to differential selective pressure in various populations, are the most widely used markers in the studies of biogeographic affiliation. SNPs reflecting biogeographic differences not related to any phenotypic traits are not sufficiently explored. The goal of our study was to identify a small set of SNPs not related to any known pigmentation/phenotype-specific genes, which would allow efficient discrimination between populations of Europe and East Asia. The selection of SNPs was based on the comparative analysis of representative European and Chinese/Japanese samples (B-lymphocyte cell lines), genotyped using the Infinium HumanOmniExpressExome microarray (Illumina). The classifier, consisting of 24 unlinked SNPs (24-SNP classifier), was selected. The performance of a 14-SNP subset of this classifier (14-SNP subclassifier) was tested using genotype data from several populations. The 14-SNP subclassifier differentiated East Asians, Europeans and Africans with ∼100% accuracy; Palestinians, representative of the Middle East, clustered with Europeans, while Amerindians and Pakistani were placed between East Asian and European populations. Based on these results, we have developed a SNaPshot assay (EurEAs_Gplex) for genotyping SNPs from the 14-SNP subclassifier, combined with an additional marker for gender identification. Forensic utility of the EurEAs_Gplex was verified using degraded and low quantity DNA samples. The performance of the EurEAs_Gplex was satisfactory when using degraded DNA; tests using low quantity DNA samples revealed a previously not described source of genotyping errors, potentially important for any SNaPshot-based assays. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The multiscale backbone of the human phenotype network based on biological pathways.

PubMed

Darabos, Christian; White, Marquitta J; Graham, Britney E; Leung, Derek N; Williams, Scott M; Moore, Jason H

2014-01-25

Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

New Statistics for Testing Differential Expression of Pathways from Microarray Data

NASA Astrophysics Data System (ADS)

Siu, Hoicheong; Dong, Hua; Jin, Li; Xiong, Momiao

Exploring biological meaning from microarray data is very important but remains a great challenge. Here, we developed three new statistics: linear combination test, quadratic test and de-correlation test to identify differentially expressed pathways from gene expression profile. We apply our statistics to two rheumatoid arthritis datasets. Notably, our results reveal three significant pathways and 275 genes in common in two datasets. The pathways we found are meaningful to uncover the disease mechanisms of rheumatoid arthritis, which implies that our statistics are a powerful tool in functional analysis of gene expression data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thessen, Anne E.; Bunker, Daniel E.; Buttigieg, Pier Luigi

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies aremore » well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.« less

Relevance of TNBS-Colitis in Rats: A Methodological Study with Endoscopic, Histologic and Transcriptomic Characterization and Correlation to IBD

PubMed Central

Brenna, Øystein; Furnes, Marianne W.; Drozdov, Ignat; van Beelen Granlund, Atle; Flatberg, Arnar; Sandvik, Arne K.; Zwiggelaar, Rosalie T. M.; Mårvik, Ronald; Nordrum, Ivar S.; Kidd, Mark; Gustafsson, Björn I.

2013-01-01

Background Rectal instillation of trinitrobenzene sulphonic acid (TNBS) in ethanol is an established model for inflammatory bowel disease (IBD). We aimed to 1) set up a TNBS-colitis protocol resulting in an endoscopic and histologic picture resembling IBD, 2) study the correlation between endoscopic, histologic and gene expression alterations at different time points after colitis induction, and 3) compare rat and human IBD mucosal transcriptomic data to evaluate whether TNBS-colitis is an appropriate model of IBD. Methodology/Principal Findings Five female Sprague Daley rats received TNBS diluted in 50% ethanol (18 mg/0.6 ml) rectally. The rats underwent colonoscopy with biopsy at different time points. RNA was extracted from rat biopsies and microarray was performed. PCR and in situ hybridization (ISH) were done for validation of microarray results. Rat microarray profiles were compared to human IBD expression profiles (25 ulcerative colitis Endoscopic score demonstrated mild to moderate colitis after three and seven days, but declined after twelve days. Histologic changes corresponded with the endoscopic appearance. Over-represented Gene Ontology Biological Processes included: Cell Adhesion, Immune Response, Lipid Metabolic Process, and Tissue Regeneration. IL-1α, IL-1β, TLR2, TLR4, PRNP were all significantly up-regulated, while PPARγ was significantly down-regulated. Among genes with highest fold change (FC) were SPINK4, LBP, ADA, RETNLB and IL-1α. The highest concordance in differential expression between TNBS and IBD transcriptomes was three days after colitis induction. ISH and PCR results corresponded with the microarray data. The most concordantly expressed biologically relevant pathways included TNF signaling, Cell junction organization, and Interleukin-1 processing. Conclusions/Significance Endoscopy with biopsies in TNBS-colitis is useful to follow temporal changes of inflammation visually and histologically, and to acquire tissue for gene expression analyses. TNBS-colitis is an appropriate model to study specific biological processes in IBD. PMID:23382912

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

Genomic profiling using array comparative genomic hybridization define distinct subtypes of diffuse large b-cell lymphoma: a review of the literature

PubMed Central

2012-01-01

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin Lymphoma comprising of greater than 30% of adult non-Hodgkin Lymphomas. DLBCL represents a diverse set of lymphomas, defined as diffuse proliferation of large B lymphoid cells. Numerous cytogenetic studies including karyotypes and fluorescent in situ hybridization (FISH), as well as morphological, biological, clinical, microarray and sequencing technologies have attempted to categorize DLBCL into morphological variants, molecular and immunophenotypic subgroups, as well as distinct disease entities. Despite such efforts, most lymphoma remains undistinguishable and falls into DLBCL, not otherwise specified (DLBCL-NOS). The advent of microarray-based studies (chromosome, RNA, gene expression, etc) has provided a plethora of high-resolution data that could potentially facilitate the finer classification of DLBCL. This review covers the microarray data currently published for DLBCL. We will focus on these types of data; 1) array based CGH; 2) classical CGH; and 3) gene expression profiling studies. The aims of this review were three-fold: (1) to catalog chromosome loci that are present in at least 20% or more of distinct DLBCL subtypes; a detailed list of gains and losses for different subtypes was generated in a table form to illustrate specific chromosome loci affected in selected subtypes; (2) to determine common and distinct copy number alterations among the different subtypes and based on this information, characteristic and similar chromosome loci for the different subtypes were depicted in two separate chromosome ideograms; and, (3) to list re-classified subtypes and those that remained indistinguishable after review of the microarray data. To the best of our knowledge, this is the first effort to compile and review available literatures on microarray analysis data and their practical utility in classifying DLBCL subtypes. Although conventional cytogenetic methods such as Karyotypes and FISH have played a major role in classification schemes of lymphomas, better classification models are clearly needed to further understanding the biology, disease outcome and therapeutic management of DLBCL. In summary, microarray data reviewed here can provide better subtype specific classifications models for DLBCL. PMID:22967872

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.

PubMed

Vaas, Lea A I; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Complete genome sequence and phenotype microarray analysis of Cronobacter sakazakii SP291: a persistent isolate cultured from a powdered infant formula production facility.

PubMed

Yan, Qiongqiong; Power, Karen A; Cooney, Shane; Fox, Edward; Gopinath, Gopal R; Grim, Christopher J; Tall, Ben D; McCusker, Matthew P; Fanning, Séamus

2013-01-01

Outbreaks of human infection linked to the powdered infant formula (PIF) food chain and associated with the bacterium Cronobacter, are of concern to public health. These bacteria are regarded as opportunistic pathogens linked to life-threatening infections predominantly in neonates, with an under developed immune system. Monitoring the microbiological ecology of PIF production sites is an important step in attempting to limit the risk of contamination in the finished food product. Cronobacter species, like other microorganisms can adapt to the production environment. These organisms are known for their desiccation tolerance, a phenotype that can aid their survival in the production site and PIF itself. In evaluating the genome data currently available for Cronobacter species, no sequence information has been published describing a Cronobacter sakazakii isolate found to persist in a PIF production facility. Here we report on the complete genome sequence of one such isolate, Cronobacter sakazakii SP291 along with its phenotypic characteristics. The genome of C. sakazakii SP291 consists of a 4.3-Mb chromosome (56.9% GC) and three plasmids, denoted as pSP291-1, [118.1-kb (57.2% GC)], pSP291-2, [52.1-kb (49.2% GC)], and pSP291-3, [4.4-kb (54.0% GC)]. When C. sakazakii SP291 was compared to the reference C. sakazakii ATCC BAA-894, which is also of PIF origin, the annotated genome data identified two interesting functional categories, comprising of genes related to the bacterial stress response and resistance to antimicrobial and toxic compounds. Using a phenotypic microarray (PM), we provided a full metabolic profile comparing C. sakazakii SP291 and the previously sequenced C. sakazakii ATCC BAA-894. These data extend our understanding of the genome of this important neonatal pathogen and provides further insights into the genotypes associated with features that can contribute to its persistence in the PIF environment.

Complete genome sequence and phenotype microarray analysis of Cronobacter sakazakii SP291: a persistent isolate cultured from a powdered infant formula production facility

PubMed Central

Yan, Qiongqiong; Power, Karen A.; Cooney, Shane; Fox, Edward; Gopinath, Gopal R.; Grim, Christopher J.; Tall, Ben D.; McCusker, Matthew P.; Fanning, Séamus

2013-01-01

Outbreaks of human infection linked to the powdered infant formula (PIF) food chain and associated with the bacterium Cronobacter, are of concern to public health. These bacteria are regarded as opportunistic pathogens linked to life-threatening infections predominantly in neonates, with an under developed immune system. Monitoring the microbiological ecology of PIF production sites is an important step in attempting to limit the risk of contamination in the finished food product. Cronobacter species, like other microorganisms can adapt to the production environment. These organisms are known for their desiccation tolerance, a phenotype that can aid their survival in the production site and PIF itself. In evaluating the genome data currently available for Cronobacter species, no sequence information has been published describing a Cronobacter sakazakii isolate found to persist in a PIF production facility. Here we report on the complete genome sequence of one such isolate, Cronobacter sakazakii SP291 along with its phenotypic characteristics. The genome of C. sakazakii SP291 consists of a 4.3-Mb chromosome (56.9% GC) and three plasmids, denoted as pSP291-1, [118.1-kb (57.2% GC)], pSP291-2, [52.1-kb (49.2% GC)], and pSP291-3, [4.4-kb (54.0% GC)]. When C. sakazakii SP291 was compared to the reference C. sakazakii ATCC BAA-894, which is also of PIF origin, the annotated genome data identified two interesting functional categories, comprising of genes related to the bacterial stress response and resistance to antimicrobial and toxic compounds. Using a phenotypic microarray (PM), we provided a full metabolic profile comparing C. sakazakii SP291 and the previously sequenced C. sakazakii ATCC BAA-894. These data extend our understanding of the genome of this important neonatal pathogen and provides further insights into the genotypes associated with features that can contribute to its persistence in the PIF environment. PMID:24032028

Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

PubMed Central

Vaas, Lea A. I.; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

Background The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. Methodology The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. Conclusions We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data. PMID:22536335

Two stable variants of Burkholderia pseudomallei strain MSHR5848 express broadly divergent in vitro phenotypes associated with their virulence differences.

PubMed

Shea, A A; Bernhards, R C; Cote, C K; Chase, C J; Koehler, J W; Klimko, C P; Ladner, J T; Rozak, D A; Wolcott, M J; Fetterer, D P; Kern, S J; Koroleva, G I; Lovett, S P; Palacios, G F; Toothman, R G; Bozue, J A; Worsham, P L; Welkos, S L

2017-01-01

Burkholderia pseudomallei (Bp), the agent of melioidosis, causes disease ranging from acute and rapidly fatal to protracted and chronic. Bp is highly infectious by aerosol, can cause severe disease with nonspecific symptoms, and is naturally resistant to multiple antibiotics. However, no vaccine exists. Unlike many Bp strains, which exhibit random variability in traits such as colony morphology, Bp strain MSHR5848 exhibited two distinct and relatively stable colony morphologies on sheep blood agar plates: a smooth, glossy, pale yellow colony and a flat, rough, white colony. Passage of the two variants, designated "Smooth" and "Rough", under standard laboratory conditions produced cultures composed of > 99.9% of the single corresponding type; however, both could switch to the other type at different frequencies when incubated in certain nutritionally stringent or stressful growth conditions. These MSHR5848 derivatives were extensively characterized to identify variant-associated differences. Microscopic and colony morphology differences on six differential media were observed and only the Rough variant metabolized sugars in selective agar. Antimicrobial susceptibilities and lipopolysaccharide (LPS) features were characterized and phenotype microarray profiles revealed distinct metabolic and susceptibility disparities between the variants. Results using the phenotype microarray system narrowed the 1,920 substrates to a subset which differentiated the two variants. Smooth grew more rapidly in vitro than Rough, yet the latter exhibited a nearly 10-fold lower lethal dose for mice than Smooth. Finally, the Smooth variant was phagocytosed and replicated to a greater extent and was more cytotoxic than Rough in macrophages. In contrast, multiple locus sequence type (MLST) analysis, ribotyping, and whole genome sequence analysis demonstrated the variants' genetic conservation; only a single consistent genetic difference between the two was identified for further study. These distinct differences shown by two variants of a Bp strain will be leveraged to better understand the mechanism of Bp phenotypic variability and to possibly identify in vitro markers of infection.

A Molecular Perspective on Systematics, Taxonomy and Classification Amazonian Discus Fishes of the Genus Symphysodon

PubMed Central

Amado, Manuella Villar; Farias, Izeni P.; Hrbek, Tomas

2011-01-01

With the goal of contributing to the taxonomy and systematics of the Neotropical cichlid fishes of the genus Symphysodon, we analyzed 336 individuals from 24 localities throughout the entire distributional range of the genus. We analyzed variation at 13 nuclear microsatellite markers, and subjected the data to Bayesian analysis of genetic structure. The results indicate that Symphysodon is composed of four genetic groups: group PURPLE—phenotype Heckel and abacaxi; group GREEN—phenotype green; group RED—phenotype blue and brown; and group PINK—populations of Xingú and Cametá. Although the phenotypes blue and brown are predominantly biological group RED, they also have substantial contributions from other biological groups, and the patterns of admixture of the two phenotypes are different. The two phenotypes are further characterized by distinct and divergent mtDNA haplotype groups, and show differences in mean habitat use measured as pH and conductivity. Differences in mean habitat use is also observed between most other biological groups. We therefore conclude that Symphysodon comprises five evolutionary significant units: Symphysodon discus (Heckel and abacaxi phenotypes), S. aequifasciatus (brown phenotype), S. tarzoo (green phenotype), Symphysodon sp. 1 (blue phenotype) and Symphysodon sp. 2 (Xingú group). PMID:21811676

A Robust Unified Approach to Analyzing Methylation and Gene Expression Data

PubMed Central

Khalili, Abbas; Huang, Tim; Lin, Shili

2009-01-01

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines. PMID:20161265

SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency

PubMed Central

Yi, Ming; Stephens, Robert M.

2008-01-01

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data. PMID:18818771

Genotyping microarray (gene chip) for the ABCR (ABCA4) gene.

PubMed

Jaakson, K; Zernant, J; Külm, M; Hutchinson, A; Tonisson, N; Glavac, D; Ravnik-Glavac, M; Hawlina, M; Meltzer, M R; Caruso, R C; Testa, F; Maugeri, A; Hoyng, C B; Gouras, P; Simonelli, F; Lewis, R A; Lupski, J R; Cremers, F P M; Allikmets, R

2003-11-01

Genetic variation in the ABCR (ABCA4) gene has been associated with five distinct retinal phenotypes, including Stargardt disease/fundus flavimaculatus (STGD/FFM), cone-rod dystrophy (CRD), and age-related macular degeneration (AMD). Comparative genetic analyses of ABCR variation and diagnostics have been complicated by substantial allelic heterogeneity and by differences in screening methods. To overcome these limitations, we designed a genotyping microarray (gene chip) for ABCR that includes all approximately 400 disease-associated and other variants currently described, enabling simultaneous detection of all known ABCR variants. The ABCR genotyping microarray (the ABCR400 chip) was constructed by the arrayed primer extension (APEX) technology. Each sequence change in ABCR was included on the chip by synthesis and application of sequence-specific oligonucleotides. We validated the chip by screening 136 confirmed STGD patients and 96 healthy controls, each of whom we had analyzed previously by single strand conformation polymorphism (SSCP) technology and/or heteroduplex analysis. The microarray was >98% effective in determining the existing genetic variation and was comparable to direct sequencing in that it yielded many sequence changes undetected by SSCP. In STGD patient cohorts, the efficiency of the array to detect disease-associated alleles was between 54% and 78%, depending on the ethnic composition and degree of clinical and molecular characterization of a cohort. In addition, chip analysis suggested a high carrier frequency (up to 1:10) of ABCR variants in the general population. The ABCR genotyping microarray is a robust, cost-effective, and comprehensive screening tool for variation in one gene in which mutations are responsible for a substantial fraction of retinal disease. The ABCR chip is a prototype for the next generation of screening and diagnostic tools in ophthalmic genetics, bridging clinical and scientific research. Copyright 2003 Wiley-Liss, Inc.

Discovery of Possible Gene Relationships through the Application of Self-Organizing Maps to DNA Microarray Databases

PubMed Central

Chavez-Alvarez, Rocio; Chavoya, Arturo; Mendez-Vazquez, Andres

2014-01-01

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms. PMID:24699245

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

PubMed

Wu, Mon-Ju; Mwangi, Benson; Bauer, Isabelle E; Passos, Ives C; Sanches, Marsal; Zunta-Soares, Giovana B; Meyer, Thomas D; Hasan, Khader M; Soares, Jair C

2017-01-15

Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders. Copyright © 2016 Elsevier Inc. All rights reserved.

A Novel Method to Screen for Dominant Negative ATM Mutations in Familial Breast Cancer

DTIC Science & Technology

2005-04-01

carry dominant negative mutation in ATM due to natural variation amongst LCLs. Microarrays have been performed to determine differences in gene expression... genes that are altered in their expression in ATMmutation carriers. The validation of this data in carriers of different ATM mutation indicated that the...heterozygous carriers of T727 1 G mutation display a gene expression phenotype that appears identical to carriers of protein truncating mutations in

Finding Our Way through Phenotypes

PubMed Central

Deans, Andrew R.; Lewis, Suzanna E.; Huala, Eva; Anzaldo, Salvatore S.; Ashburner, Michael; Balhoff, James P.; Blackburn, David C.; Blake, Judith A.; Burleigh, J. Gordon; Chanet, Bruno; Cooper, Laurel D.; Courtot, Mélanie; Csösz, Sándor; Cui, Hong; Dahdul, Wasila; Das, Sandip; Dececchi, T. Alexander; Dettai, Agnes; Diogo, Rui; Druzinsky, Robert E.; Dumontier, Michel; Franz, Nico M.; Friedrich, Frank; Gkoutos, George V.; Haendel, Melissa; Harmon, Luke J.; Hayamizu, Terry F.; He, Yongqun; Hines, Heather M.; Ibrahim, Nizar; Jackson, Laura M.; Jaiswal, Pankaj; James-Zorn, Christina; Köhler, Sebastian; Lecointre, Guillaume; Lapp, Hilmar; Lawrence, Carolyn J.; Le Novère, Nicolas; Lundberg, John G.; Macklin, James; Mast, Austin R.; Midford, Peter E.; Mikó, István; Mungall, Christopher J.; Oellrich, Anika; Osumi-Sutherland, David; Parkinson, Helen; Ramírez, Martín J.; Richter, Stefan; Robinson, Peter N.; Ruttenberg, Alan; Schulz, Katja S.; Segerdell, Erik; Seltmann, Katja C.; Sharkey, Michael J.; Smith, Aaron D.; Smith, Barry; Specht, Chelsea D.; Squires, R. Burke; Thacker, Robert W.; Thessen, Anne; Fernandez-Triana, Jose; Vihinen, Mauno; Vize, Peter D.; Vogt, Lars; Wall, Christine E.; Walls, Ramona L.; Westerfeld, Monte; Wharton, Robert A.; Wirkner, Christian S.; Woolley, James B.; Yoder, Matthew J.; Zorn, Aaron M.; Mabee, Paula

2015-01-01

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility. PMID:25562316

Finding our way through phenotypes.

PubMed

Deans, Andrew R; Lewis, Suzanna E; Huala, Eva; Anzaldo, Salvatore S; Ashburner, Michael; Balhoff, James P; Blackburn, David C; Blake, Judith A; Burleigh, J Gordon; Chanet, Bruno; Cooper, Laurel D; Courtot, Mélanie; Csösz, Sándor; Cui, Hong; Dahdul, Wasila; Das, Sandip; Dececchi, T Alexander; Dettai, Agnes; Diogo, Rui; Druzinsky, Robert E; Dumontier, Michel; Franz, Nico M; Friedrich, Frank; Gkoutos, George V; Haendel, Melissa; Harmon, Luke J; Hayamizu, Terry F; He, Yongqun; Hines, Heather M; Ibrahim, Nizar; Jackson, Laura M; Jaiswal, Pankaj; James-Zorn, Christina; Köhler, Sebastian; Lecointre, Guillaume; Lapp, Hilmar; Lawrence, Carolyn J; Le Novère, Nicolas; Lundberg, John G; Macklin, James; Mast, Austin R; Midford, Peter E; Mikó, István; Mungall, Christopher J; Oellrich, Anika; Osumi-Sutherland, David; Parkinson, Helen; Ramírez, Martín J; Richter, Stefan; Robinson, Peter N; Ruttenberg, Alan; Schulz, Katja S; Segerdell, Erik; Seltmann, Katja C; Sharkey, Michael J; Smith, Aaron D; Smith, Barry; Specht, Chelsea D; Squires, R Burke; Thacker, Robert W; Thessen, Anne; Fernandez-Triana, Jose; Vihinen, Mauno; Vize, Peter D; Vogt, Lars; Wall, Christine E; Walls, Ramona L; Westerfeld, Monte; Wharton, Robert A; Wirkner, Christian S; Woolley, James B; Yoder, Matthew J; Zorn, Aaron M; Mabee, Paula

2015-01-01

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

[Phenotype-genotype correlation analysis of 12 cases with Angelman/Prader-Willi syndrome].

PubMed

Chen, Chen; Peng, Ying; Xia, Yan; Li, Haoxian; Zhu, Huimin; Pan, Qian; Yin, Fei; Wu, Lingqian

2014-12-01

To investigate the genotype-phenotype correlation in patients with Angelman syndrome/Prader-Willi syndrome (AS/PWS) and assess the application value of high-resolution single nucleotide polymorphism microarrays (SNP array) for such diseases. Twelve AS/PWS patients were diagnosed through SNP array, fluorescence in situ hybridization (FISH) and karyotype analysis. Clinical characteristics were analyzed. Deletions ranging from 4.8 Mb to 7.0 Mb on chromosome 15q11.2-13 were detected in 11 patients. Uniparental disomy (UPD) was detected in only 1 patient. Patients with deletions could be divided into 2 groups, including 7 cases with class I and 4 with class II. The two groups however had no significant phenotypic difference. The UPD patient had relatively better development and language ability. Deletions of 6 patients were confirmed by FISH to be of de novo in origin. The risk to their sibs was determined to be less than 1%. The phenotypic differences between AS/PWS patients with class I and class II deletion need to be further studied. SNP array is useful in detecting and distinguishing of patients with deletion or UPD. This method may be applied for studying the genotype-phenotype association and the mechanism underlying AS/PWS.

BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

PubMed

Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

2009-02-15

BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

Microarray analysis of gene expression patterns in the leaf during potato tuberization in the potato somatic hybrid Solanum tuberosum and Solanum etuberosum.

PubMed

Tiwari, Jagesh Kumar; Devi, Sapna; Sundaresha, S; Chandel, Poonam; Ali, Nilofer; Singh, Brajesh; Bhardwaj, Vinay; Singh, Bir Pal

2015-06-01

Genes involved in photoassimilate partitioning and changes in hormonal balance are important for potato tuberization. In the present study, we investigated gene expression patterns in the tuber-bearing potato somatic hybrid (E1-3) and control non-tuberous wild species Solanum etuberosum (Etb) by microarray. Plants were grown under controlled conditions and leaves were collected at eight tuber developmental stages for microarray analysis. A t-test analysis identified a total of 468 genes (94 up-regulated and 374 down-regulated) that were statistically significant (p ≤ 0.05) and differentially expressed in E1-3 and Etb. Gene Ontology (GO) characterization of the 468 genes revealed that 145 were annotated and 323 were of unknown function. Further, these 145 genes were grouped based on GO biological processes followed by molecular function and (or) PGSC description into 15 gene sets, namely (1) transport, (2) metabolic process, (3) biological process, (4) photosynthesis, (5) oxidation-reduction, (6) transcription, (7) translation, (8) binding, (9) protein phosphorylation, (10) protein folding, (11) ubiquitin-dependent protein catabolic process, (12) RNA processing, (13) negative regulation of protein, (14) methylation, and (15) mitosis. RT-PCR analysis of 10 selected highly significant genes (p ≤ 0.01) confirmed the microarray results. Overall, we show that candidate genes induced in leaves of E1-3 were implicated in tuberization processes such as transport, carbohydrate metabolism, phytohormones, and transcription/translation/binding functions. Hence, our results provide an insight into the candidate genes induced in leaf tissues during tuberization in E1-3.

Archetypal analysis of diverse Pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways

PubMed Central

2013-01-01

Background Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data. The aim of this study is to introduce a method for DNA microarray analysis that provides an intuitive interpretation of data through dimension reduction and pattern recognition. We present the first “Archetypal Analysis” of global gene expression. The analysis is based on microarray data from five integrated studies of Pseudomonas aeruginosa isolated from the airways of cystic fibrosis patients. Results Our analysis clustered samples into distinct groups with comprehensible characteristics since the archetypes representing the individual groups are closely related to samples present in the data set. Significant changes in gene expression between different groups identified adaptive changes of the bacteria residing in the cystic fibrosis lung. The analysis suggests a similar gene expression pattern between isolates with a high mutation rate (hypermutators) despite accumulation of different mutations for these isolates. This suggests positive selection in the cystic fibrosis lung environment, and changes in gene expression for these isolates are therefore most likely related to adaptation of the bacteria. Conclusions Archetypal analysis succeeded in identifying adaptive changes of P. aeruginosa. The combination of clustering and matrix factorization made it possible to reveal minor similarities among different groups of data, which other analytical methods failed to identify. We suggest that this analysis could be used to supplement current methods used to analyze DNA microarray data. PMID:24059747

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

PubMed

Mansourian, Robert; Mutch, David M; Antille, Nicolas; Aubert, Jerome; Fogel, Paul; Le Goff, Jean-Marc; Moulin, Julie; Petrov, Anton; Rytz, Andreas; Voegel, Johannes J; Roberts, Matthew-Alan

2004-11-01

Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure. The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-gamma treated skin cells. In this experiment, up to nine replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared with both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness and confidence in gene selection. A subset of the selected genes were validated by real-time reverse transcription-polymerase chain reaction (RT-PCR). All these results suggest that GEA methodology is (i) suitable for selection of differentially expressed genes in microarray data, (ii) intuitive and computationally efficient and (iii) especially advantageous under conditions of low k. The GEA code for R software is freely available upon request to authors.

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.

PubMed

Jani, Saurin D; Argraves, Gary L; Barth, Jeremy L; Argraves, W Scott

2010-04-01

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. 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. 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/.

Transcriptomic response to prolonged ethanol production in the cyanobacterium Synechocystis sp. PCC6803

PubMed Central

2014-01-01

Background The production of biofuels in photosynthetic microalgae and cyanobacteria is a promising alternative to the generation of fuels from fossil resources. To be economically competitive, producer strains need to be established that synthesize the targeted product at high yield and over a long time. Engineering cyanobacteria into forced fuel producers should considerably interfere with overall cell homeostasis, which in turn might counteract productivity and sustainability of the process. Therefore, in-depth characterization of the cellular response upon long-term production is of high interest for the targeted improvement of a desired strain. Results The transcriptome-wide response to continuous ethanol production was examined in Synechocystis sp. PCC6803 using high resolution microarrays. In two independent experiments, ethanol production rates of 0.0338% (v/v) ethanol d-1 and 0.0303% (v/v) ethanol d-1 were obtained over 18 consecutive days, measuring two sets of biological triplicates in fully automated photobioreactors. Ethanol production caused a significant (~40%) delay in biomass accumulation, the development of a bleaching phenotype and a down-regulation of light harvesting capacity. However, microarray analyses performed at day 4, 7, 11 and 18 of the experiment revealed only three mRNAs with a strongly modified accumulation level throughout the course of the experiment. In addition to the overexpressed adhA (slr1192) gene, this was an approximately 4 fold reduction in cpcB (sll1577) and 3 to 6 fold increase in rps8 (sll1809) mRNA levels. Much weaker modifications of expression level or modifications restricted to day 18 of the experiment were observed for genes involved in carbon assimilation (Ribulose bisphosphate carboxylase and Glutamate decarboxylase). Molecular analysis of the reduced cpcB levels revealed a post-transcriptional processing of the cpcBA operon mRNA leaving a truncated mRNA cpcA* likely not competent for translation. Moreover, western blots and zinc-enhanced bilin fluorescence blots confirmed a severe reduction in the amounts of both phycocyanin subunits, explaining the cause of the bleaching phenotype. Conclusions Changes in gene expression upon induction of long-term ethanol production in Synechocystis sp. PCC6803 are highly specific. In particular, we did not observe a comprehensive stress response as might have been expected. PMID:24502290

The genotype-phenotype map of an evolving digital organism.

PubMed

Fortuna, Miguel A; Zaman, Luis; Ofria, Charles; Wagner, Andreas

2017-02-01

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

The genotype-phenotype map of an evolving digital organism

PubMed Central

Zaman, Luis; Wagner, Andreas

2017-01-01

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable. PMID:28241039

Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips

PubMed Central

Lee, Yun-Shien; Chen, Chun-Houh; Tsai, Chi-Neu; Tsai, Chia-Lung; Chao, Angel; Wang, Tzu-Hao

2009-01-01

Interlaboratory comparison of microarray data, even when using the same platform, imposes several challenges to scientists. RNA quality, RNA labeling efficiency, hybridization procedures and data-mining tools can all contribute variations in each laboratory. In Affymetrix GeneChips, about 11–20 different 25-mer oligonucleotides are used to measure the level of each transcript. Here, we report that ‘labeling extension values (LEVs)’, which are correlation coefficients between probe intensities and probe positions, are highly correlated with the gene expression levels (GEVs) on eukayotic Affymetrix microarray data. By analyzing LEVs and GEVs in the publicly available 2414 cel files of 20 Affymetrix microarray types covering 13 species, we found that correlations between LEVs and GEVs only exist in eukaryotic RNAs, but not in prokaryotic ones. Surprisingly, Affymetrix results of the same specimens that were analyzed in different laboratories could be clearly differentiated only by LEVs, leading to the identification of ‘laboratory signatures’. In the examined dataset, GSE10797, filtering out high-LEV genes did not compromise the discovery of biological processes that are constructed by differentially expressed genes. In conclusion, LEVs provide a new filtering parameter for microarray analysis of gene expression and it may improve the inter- and intralaboratory comparability of Affymetrix GeneChips data. PMID:19295132

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

PubMed Central

Brodsky, Leonid; Leontovich, Andrei; Shtutman, Michael; Feinstein, Elena

2004-01-01

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides’ areas characterized by an abnormal concentration of low/high differential expression values, which we define as ‘patterns of differentials’. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile’s quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis. PMID:14999086

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Comparison of DNA Microarray, Loop-Mediated Isothermal Amplification (LAMP) and Real-Time PCR with DNA Sequencing for Identification of Fusarium spp. Obtained from Patients with Hematologic Malignancies.

PubMed

de Souza, Marcela; Matsuzawa, Tetsuhiro; Sakai, Kanae; Muraosa, Yasunori; Lyra, Luzia; Busso-Lopes, Ariane Fidelis; Levin, Anna Sara Shafferman; Schreiber, Angélica Zaninelli; Mikami, Yuzuru; Gonoi, Tohoru; Kamei, Katsuhiko; Moretti, Maria Luiza; Trabasso, Plínio

2017-08-01

The performance of three molecular biology techniques, i.e., DNA microarray, loop-mediated isothermal amplification (LAMP), and real-time PCR were compared with DNA sequencing for properly identification of 20 isolates of Fusarium spp. obtained from blood stream as etiologic agent of invasive infections in patients with hematologic malignancies. DNA microarray, LAMP and real-time PCR identified 16 (80%) out of 20 samples as Fusarium solani species complex (FSSC) and four (20%) as Fusarium spp. The agreement among the techniques was 100%. LAMP exhibited 100% specificity, while DNA microarray, LAMP and real-time PCR showed 100% sensitivity. The three techniques had 100% agreement with DNA sequencing. Sixteen isolates were identified as FSSC by sequencing, being five Fusarium keratoplasticum, nine Fusarium petroliphilum and two Fusarium solani. On the other hand, sequencing identified four isolates as Fusarium non-solani species complex (FNSSC), being three isolates as Fusarium napiforme and one isolate as Fusarium oxysporum. Finally, LAMP proved to be faster and more accessible than DNA microarray and real-time PCR, since it does not require a thermocycler. Therefore, LAMP signalizes as emerging and promising methodology to be used in routine identification of Fusarium spp. among cases of invasive fungal infections.

Analytical Protein Microarrays: Advancements Towards Clinical Applications

PubMed Central

Sauer, Ursula

2017-01-01

Protein microarrays represent a powerful technology with the potential to serve as tools for the detection of a broad range of analytes in numerous applications such as diagnostics, drug development, food safety, and environmental monitoring. Key features of analytical protein microarrays include high throughput and relatively low costs due to minimal reagent consumption, multiplexing, fast kinetics and hence measurements, and the possibility of functional integration. So far, especially fundamental studies in molecular and cell biology have been conducted using protein microarrays, while the potential for clinical, notably point-of-care applications is not yet fully utilized. The question arises what features have to be implemented and what improvements have to be made in order to fully exploit the technology. In the past we have identified various obstacles that have to be overcome in order to promote protein microarray technology in the diagnostic field. Issues that need significant improvement to make the technology more attractive for the diagnostic market are for instance: too low sensitivity and deficiency in reproducibility, inadequate analysis time, lack of high-quality antibodies and validated reagents, lack of automation and portable instruments, and cost of instruments necessary for chip production and read-out. The scope of the paper at hand is to review approaches to solve these problems. PMID:28146048

Systematic Validation and Atomic Force Microscopy of Non-Covalent Short Oligonucleotide Barcode Microarrays

PubMed Central

Cook, Michael A.; Chan, Chi-Kin; Jorgensen, Paul; Ketela, Troy; So, Daniel; Tyers, Mike; Ho, Chi-Yip

2008-01-01

Background Molecular barcode arrays provide a powerful means to analyze cellular phenotypes in parallel through detection of short (20–60 base) unique sequence tags, or “barcodes”, associated with each strain or clone in a collection. However, costs of current methods for microarray construction, whether by in situ oligonucleotide synthesis or ex situ coupling of modified oligonucleotides to the slide surface are often prohibitive to large-scale analyses. Methodology/Principal Findings Here we demonstrate that unmodified 20mer oligonucleotide probes printed on conventional surfaces show comparable hybridization signals to covalently linked 5′-amino-modified probes. As a test case, we undertook systematic cell size analysis of the budding yeast Saccharomyces cerevisiae genome-wide deletion collection by size separation of the deletion pool followed by determination of strain abundance in size fractions by barcode arrays. We demonstrate that the properties of a 13K unique feature spotted 20 mer oligonucleotide barcode microarray compare favorably with an analogous covalently-linked oligonucleotide array. Further, cell size profiles obtained with the size selection/barcode array approach recapitulate previous cell size measurements of individual deletion strains. Finally, through atomic force microscopy (AFM), we characterize the mechanism of hybridization to unmodified barcode probes on the slide surface. Conclusions/Significance These studies push the lower limit of probe size in genome-scale unmodified oligonucleotide microarray construction and demonstrate a versatile, cost-effective and reliable method for molecular barcode analysis. PMID:18253494

The laboratory-clinician team: a professional call to action to improve communication and collaboration for optimal patient care in chromosomal microarray testing.

PubMed

Wain, Karen E; Riggs, Erin; Hanson, Karen; Savage, Melissa; Riethmaier, Darlene; Muirhead, Andrea; Mitchell, Elyse; Packard, Bethanny Smith; Faucett, W Andrew

2012-10-01

The International Standards for Cytogenomic Arrays (ISCA) Consortium is a worldwide collaborative effort dedicated to optimizing patient care by improving the quality of chromosomal microarray testing. The primary effort of the ISCA Consortium has been the development of a database of copy number variants (CNVs) identified during the course of clinical microarray testing. This database is a powerful resource for clinicians, laboratories, and researchers, and can be utilized for a variety of applications, such as facilitating standardized interpretations of certain CNVs across laboratories or providing phenotypic information for counseling purposes when published data is sparse. A recognized limitation to the clinical utility of this database, however, is the quality of clinical information available for each patient. Clinical genetic counselors are uniquely suited to facilitate the communication of this information to the laboratory by virtue of their existing clinical responsibilities, case management skills, and appreciation of the evolving nature of scientific knowledge. We intend to highlight the critical role that genetic counselors play in ensuring optimal patient care through contributing to the clinical utility of the ISCA Consortium's database, as well as the quality of individual patient microarray reports provided by contributing laboratories. Current tools, paper and electronic forms, created to maximize this collaboration are shared. In addition to making a professional commitment to providing complete clinical information, genetic counselors are invited to become ISCA members and to become involved in the discussions and initiatives within the Consortium.

Microarray RNA expression analysis of cerebral white matter lesions reveals changes in multiple functional pathways.

PubMed

Simpson, Julie E; Hosny, Ola; Wharton, Stephen B; Heath, Paul R; Holden, Hazel; Fernando, Malee S; Matthews, Fiona; Forster, Gill; O'Brien, John T; Barber, Robert; Kalaria, Raj N; Brayne, Carol; Shaw, Pamela J; Lewis, Claire E; Ince, Paul G

2009-02-01

White matter lesions (WML) in brain aging are linked to dementia and depression. Ischemia contributes to their pathogenesis but other mechanisms may contribute. We used RNA microarray analysis with functional pathway grouping as an unbiased approach to investigate evidence for additional pathogenetic mechanisms. WML were identified by MRI and pathology in brains donated to the Medical Research Council Cognitive Function and Ageing Study Cognitive Function and Aging Study. RNA was extracted to compare WML with nonlesional white matter samples from cases with lesions (WM[L]), and from cases with no lesions (WM[C]) using RNA microarray and pathway analysis. Functional pathways were validated for selected genes by quantitative real-time polymerase chain reaction and immunocytochemistry. We identified 8 major pathways in which multiple genes showed altered RNA transcription (immune regulation, cell cycle, apoptosis, proteolysis, ion transport, cell structure, electron transport, metabolism) among 502 genes that were differentially expressed in WML compared to WM[C]. In WM[L], 409 genes were altered involving the same pathways. Genes selected to validate this microarray data all showed the expected changes in RNA levels and immunohistochemical expression of protein. WML represent areas with a complex molecular phenotype. From this and previous evidence, WML may arise through tissue ischemia but may also reflect the contribution of additional factors like blood-brain barrier dysfunction. Differential expression of genes in WM[L] compared to WM[C] indicate a "field effect" in the seemingly normal surrounding white matter.

Microarray and FISH-based genotype-phenotype analysis of 22 Japanese patients with Wolf-Hirschhorn syndrome.

PubMed

Shimizu, Kenji; Wakui, Keiko; Kosho, Tomoki; Okamoto, Nobuhiko; Mizuno, Seiji; Itomi, Kazuya; Hattori, Shigeto; Nishio, Kimio; Samura, Osamu; Kobayashi, Yoshiyuki; Kako, Yuko; Arai, Takashi; Tsutomu, Oh-ishi; Kawame, Hiroshi; Narumi, Yoko; Ohashi, Hirofumi; Fukushima, Yoshimitsu

2014-03-01

Wolf-Hirschhorn syndrome (WHS) is a contiguous gene deletion syndrome of the distal 4p chromosome, characterized by craniofacial features, growth impairment, intellectual disability, and seizures. Although genotype-phenotype correlation studies have previously been published, several important issues remain to be elucidated including seizure severity. We present detailed clinical and molecular-cytogenetic findings from a microarray and fluorescence in situ hybridization (FISH)-based genotype-phenotype analysis of 22 Japanese WHS patients, the first large non-Western series. 4p deletions were terminal in 20 patients and interstitial in two, with deletion sizes ranging from 2.06 to 29.42 Mb. The new Wolf-Hirschhorn syndrome critical region (WHSCR2) was deleted in all cases, and duplication of other chromosomal regions occurred in four. Complex mosaicism was identified in two cases: two different 4p terminal deletions; a simple 4p terminal deletion and an unbalanced translocation with the same 4p breakpoint. Seizures began in infancy in 33% (2/6) of cases with small (<6 Mb) deletions and in 86% (12/14) of cases with larger deletions (>6 Mb). Status epilepticus occurred in 17% (1/6) with small deletions and in 87% (13/15) with larger deletions. Renal hypoplasia or dysplasia and structural ocular anomalies were more prevalent in those with larger deletions. A new susceptible region for seizure occurrence is suggested between 0.76 and 1.3 Mb from 4 pter, encompassing CTBP1 and CPLX1, and distal to the previously-supposed candidate gene LETM1. The usefulness of bromide therapy for seizures and additional clinical features including hypercholesterolemia are also described. © 2013 Wiley Periodicals, Inc.

Perimysial fibroblasts of extraocular muscle, as unique as the muscle fibers.

PubMed

Kusner, Linda L; Young, Andrew; Tjoe, Steven; Leahy, Patrick; Kaminski, Henry J

2010-01-01

Extraocular muscle (EOM) has a distinct skeletal muscle phenotype. The hypothesis for the study was that fibroblasts support the unique EOM phenotype and that perimysial fibroblasts derived from EOM have properties that distinguish them from fibroblasts derived from other skeletal muscle. Perimysial fibroblasts from leg muscle (LM-Fibro) and EOM (EOM-Fibro) of mice were derived and maintained in culture. EOM- and LM-Fibro were assessed morphologically and for vimentin, smooth muscle actin, and Thy-1 immunoreactivity. DNA microarray analysis was performed on LM- and EOM-Fibro grown in conditions that support myoblast differentiation. To assess trophic interactions, co-cultures of myoblasts from established cell lines, CL-EOM and CL-LM with, EOM- or LM-Fibro were performed in direct contact and in a permeable filter support culture. The degree of myotube maturation was assessed by the percentage of myotubes with more than three myonuclei per myotube. EOM- and LM-Fibro cells exhibited distinct morphologies. Both cell types proliferated as a monolayer and expressed vimentin. Fifty-five percent (SD 4.4%) of EOM-Fibro were Thy-1 positive compared with only 24% (SD 4.4%) of LM-Fibro. DNA microarray analysis demonstrated differential expression of structural, immune response, and metabolism-related genes between EOM- and LM-Fibro. Co-cultures demonstrated that mature myotube formation in EOM-derived cell lines was supported to a greater extent by EOM-Fibro than by LM-Fibro, compared with CL-EOM grown with LM-Fibro. Fibroblasts from EOM demonstrate distinct properties that distinguish them from leg muscle-derived fibroblasts. The distinct properties of EOM-Fibro may support the unique EOM phenotype and contribute to their differential involvement in disease.

Metabolic parameters linked by phenotype microarray to acid resistance profiles of poultry-associated Salmonella enterica.

PubMed

Guard, Jean; Rothrock, Michael J; Shah, Devendra H; Jones, Deana R; Gast, Richard K; Sanchez-Ingunza, Roxana; Madsen, Melissa; El-Attrache, John; Lungu, Bwalya

Phenotype microarrays were analyzed for 51 datasets derived from Salmonella enterica. The top 4 serotypes associated with poultry products and one associated with turkey, respectively Typhimurium, Enteritidis, Heidelberg, Infantis and Senftenberg, were represented. Datasets were partitioned initially into two clusters based on ranking by values at pH 4.5 (PM10 A03). Negative control wells were used to establish 90 respiratory units as the point differentiating acid resistance from sensitive strains. Thus, 24 isolates that appeared most acid-resistant were compared initially to 27 that appeared most acid-sensitive (24 × 27 format). Paired cluster analysis was also done and it included the 7 most acid-resistant and -sensitive datasets (7 × 7 format). Statistical analyses of ranked data were then calculated in order of standard deviation, probability value by the Student's t-test and a measure of the magnitude of difference called effect size. Data were reported as significant if, by order of filtering, the following parameters were calculated: i) a standard deviation of 24 respiratory units or greater from all datasets for each chemical, ii) a probability value of less than or equal to 0.03 between clusters and iii) an effect size of at least 0.50 or greater between clusters. Results suggest that between 7.89% and 23.16% of 950 chemicals differentiated acid-resistant isolates from sensitive ones, depending on the format applied. Differences were more evident at the extremes of phenotype using the subset of data in the paired 7 × 7 format. Results thus provide a strategy for selecting compounds for additional research, which may impede the emergence of acid-resistant Salmonella enterica in food. Published by Elsevier Masson SAS.

Downstream effects of ROCK signaling in cultured human corneal stromal cells: microarray analysis of gene expression.

PubMed

Harvey, Stephen A K; Anderson, Susan C; SundarRaj, Nirmala

2004-07-01

Rho-associated coiled-coil-containing protein kinase (ROCK) is a downstream target of Rho GTPase signaling and regulates the assembly of stress fibers. Previous reports indicate that Rho/ROCK signaling is involved in the regulation of several cellular processes, some of which may be cell-type specific and are probably critical to corneal stromal cell activation. The present study identified ROCK-regulated gene expression in corneal stromal cells. Corneal stromal cells derived from eyes of three different donors were cultured to yield the following designated phenotypes: baseline fibroblasts (DMEM with 10% serum), activated fibroblasts (10% serum+bFGF+heparin), and myofibroblasts (1% serum+TGF-beta 1). Cells were exposed to the ROCK inhibitor Y-27632 or vehicle for 12 hours, and transcript levels altered by ROCK inhibition were identified with oligonucleotide microarrays (GeneChips; Affymetrix, Santa Clara, CA). In these phenotypes, Y-27632 caused marked (twofold or more) increases or decreases in 14/4, 12/3, and 15/10 transcripts. In both fibroblast groups Y-27632-treatment increased expression of endothelin receptors and of parathyroid hormone-like hormone. The upregulation of alpha-smooth muscle actin in myofibroblasts was attenuated by Y-27632. Combining data from all groups identified ROCK-supported (Y-27632 inhibitable) expression of 10 transcripts, including ribonucleotide reductase M2, the cyclin B1-CDC2-CKS2 system, and four mitotic spindle-associated proteins. ROCK inhibition causes broad inhibition of DNA synthesis and mitosis and causes changes that are different between (bFGF-activated) fibroblasts and (TGF-beta 1-induced) myofibroblasts. Thus, Rho/ROCK signaling regulates both common and distinct downstream events in corneal stromal cells activated (differentiated) to fibroblast or myofibroblast phenotype.

Cell cycle arrest and gene expression profiling of testis in mice exposed to fluoride.

PubMed

Su, Kai; Sun, Zilong; Niu, Ruiyan; Lei, Ying; Cheng, Jing; Wang, Jundong

2017-05-01

Exposure to fluoride results in low reproductive capacity; however, the mechanism underlying the impact of fluoride on male productive system still remains obscure. To assess the potential toxicity in testis of mice administrated with fluoride, global genome microarray and real-time PCR were performed to detect and identify the altered transcriptions. The results revealed that 763 differentially expressed genes were identified, including 330 up-regulated and 433 down-regulated genes, which were involved in spermatogenesis, apoptosis, DNA damage, DNA replication, and cell differentiation. Twelve differential expressed genes were selected to confirm the microarray results using real-time PCR, and the result kept the same tendency with that of microarray. Furthermore, compared with the control group, more apoptotic spermatogenic cells were observed in the fluoride group, and the spermatogonium were markedly increased in S phase and decreased in G2/M phase by fluoride. Our findings suggested global genome microarray provides an insight into the reproductive toxicity induced by fluoride, and several important biological clues for further investigations. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 1558-1565, 2017. © 2016 Wiley Periodicals, Inc.

Implementation of plaid model biclustering method on microarray of carcinoma and adenoma tumor gene expression data

NASA Astrophysics Data System (ADS)

Ardaneswari, Gianinna; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

A Tumor is an abnormal growth of cells that serves no purpose. Carcinoma is a tumor that grows from the top of the cell membrane and the organ adenoma is a benign tumor of the gland-like cells or epithelial tissue. In the field of molecular biology, the development of microarray technology is used in the data store of disease genetic expression. For each of microarray gene, an amount of information is stored for each trait or condition. In gene expression data clustering can be done with a bicluster algorithm, thats clustering method which not only the objects to be clustered, but also the properties or condition of the object. This research proposed Plaid Model Biclustering as one of biclustering method. In this study, we discuss the implementation of Plaid Model Biclustering Method on microarray of Carcinoma and Adenoma tumor gene expression data. From the experimental results, we found three biclusters are formed by Carcinoma gene expression data and four biclusters are formed by Adenoma gene expression data.

Prediction of Microbial Infection of Cultured Cells Using DNA Microarray Gene-Expression Profiles of Host Responses

PubMed Central

Park, Yu Rang; Chung, Tae Su; Lee, Young Joo; Song, Yeong Wook; Lee, Eun Young; Sohn, Yeo Won; Song, Sukgil; Park, Woong Yang

2012-01-01

Infection by microorganisms may cause fatally erroneous interpretations in the biologic researches based on cell culture. The contamination by microorganism in the cell culture is quite frequent (5% to 35%). However, current approaches to identify the presence of contamination have many limitations such as high cost of time and labor, and difficulty in interpreting the result. In this paper, we propose a model to predict cell infection, using a microarray technique which gives an overview of the whole genome profile. By analysis of 62 microarray expression profiles under various experimental conditions altering cell type, source of infection and collection time, we discovered 5 marker genes, NM_005298, NM_016408, NM_014588, S76389, and NM_001853. In addition, we discovered two of these genes, S76389, and NM_001853, are involved in a Mycolplasma-specific infection process. We also suggest models to predict the source of infection, cell type or time after infection. We implemented a web based prediction tool in microarray data, named Prediction of Microbial Infection (http://www.snubi.org/software/PMI). PMID:23091307

Integration of Neuroimaging and Microarray Datasets through Mapping and Model-Theoretic Semantic Decomposition of Unstructured Phenotypes

PubMed Central

Pantazatos, Spiro P.; Li, Jianrong; Pavlidis, Paul; Lussier, Yves A.

2009-01-01

An approach towards heterogeneous neuroscience dataset integration is proposed that uses Natural Language Processing (NLP) and a knowledge-based phenotype organizer system (PhenOS) to link ontology-anchored terms to underlying data from each database, and then maps these terms based on a computable model of disease (SNOMED CT®). The approach was implemented using sample datasets from fMRIDC, GEO, The Whole Brain Atlas and Neuronames, and allowed for complex queries such as “List all disorders with a finding site of brain region X, and then find the semantically related references in all participating databases based on the ontological model of the disease or its anatomical and morphological attributes”. Precision of the NLP-derived coding of the unstructured phenotypes in each dataset was 88% (n = 50), and precision of the semantic mapping between these terms across datasets was 98% (n = 100). To our knowledge, this is the first example of the use of both semantic decomposition of disease relationships and hierarchical information found in ontologies to integrate heterogeneous phenotypes across clinical and molecular datasets. PMID:20495688

Microarray analysis reveals overlapping and specific transcriptional responses to different plant hormones in rice

PubMed Central

Garg, Rohini; Tyagi, Akhilesh K.; Jain, Mukesh

2012-01-01

Hormones exert pleiotropic effects on plant growth and development throughout the life cycle. Many of these effects are mediated at molecular level via altering gene expression. In this study, we investigated the exogenous effect of plant hormones, including auxin, cytokinin, abscisic acid, ethylene, salicylic acid and jasmonic acid, on the transcription of rice genes at whole genome level using microarray. Our analysis identified a total of 4171 genes involved in several biological processes, whose expression was altered significantly in the presence of different hormones. Further, 28% of these genes exhibited overlapping transcriptional responses in the presence of any two hormones, indicating crosstalk among plant hormones. In addition, we identified genes showing only a particular hormone-specific response, which can be used as hormone-specific markers. The results of this study will facilitate further studies in hormone biology in rice. PMID:22827941

CoPub: a literature-based keyword enrichment tool for microarray data analysis.

PubMed

Frijters, Raoul; Heupers, Bart; van Beek, Pieter; Bouwhuis, Maurice; van Schaik, René; de Vlieg, Jacob; Polman, Jan; Alkema, Wynand

2008-07-01

Medline is a rich information source, from which links between genes and keywords describing biological processes, pathways, drugs, pathologies and diseases can be extracted. We developed a publicly available tool called CoPub that uses the information in the Medline database for the biological interpretation of microarray data. CoPub allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs. CoPub is freely accessible at http://services.nbic.nl/cgi-bin/copub/CoPub.pl.

Mutational robustness accelerates the origin of novel RNA phenotypes through phenotypic plasticity.

PubMed

Wagner, Andreas

2014-02-18

Novel phenotypes can originate either through mutations in existing genotypes or through phenotypic plasticity, the ability of one genotype to form multiple phenotypes. From molecules to organisms, plasticity is a ubiquitous feature of life, and a potential source of exaptations, adaptive traits that originated for nonadaptive reasons. Another ubiquitous feature is robustness to mutations, although it is unknown whether such robustness helps or hinders the origin of new phenotypes through plasticity. RNA is ideal to address this question, because it shows extensive plasticity in its secondary structure phenotypes, a consequence of their continual folding and unfolding, and these phenotypes have important biological functions. Moreover, RNA is to some extent robust to mutations. This robustness structures RNA genotype space into myriad connected networks of genotypes with the same phenotype, and it influences the dynamics of evolving populations on a genotype network. In this study I show that both effects help accelerate the exploration of novel phenotypes through plasticity. My observations are based on many RNA molecules sampled at random from RNA sequence space, and on 30 biological RNA molecules. They are thus not only a generic feature of RNA sequence space but are relevant for the molecular evolution of biological RNA. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Quantitative phenotyping via deep barcode sequencing.

PubMed

Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

2009-10-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Framework for Parallel Preprocessing of Microarray Data Using Hadoop

PubMed Central

2018-01-01

Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and not able to handle a large number of datasets with thousands of experiments. Parallel processing can be used to address the above-mentioned issues. Hadoop is a well-known and ideal distributed file system framework that provides a parallel environment to run the experiment. In this research, for the first time, the capability of Hadoop and statistical power of R have been leveraged to parallelize the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on cluster containing 5 nodes, while each node has 16 cores and 16 GB memory. It compares efficiency and the performance of parallelized RMA using Hadoop with parallelized RMA using affyPara package as well as sequential RMA. The result shows the speed-up rate of the proposed approach outperforms the sequential approach and affyPara approach. PMID:29796018

The developmental genetics of biological robustness

PubMed Central

Mestek Boukhibar, Lamia; Barkoulas, Michalis

2016-01-01

Background Living organisms are continuously confronted with perturbations, such as environmental changes that include fluctuations in temperature and nutrient availability, or genetic changes such as mutations. While some developmental systems are affected by such challenges and display variation in phenotypic traits, others continue consistently to produce invariable phenotypes despite perturbation. This ability of a living system to maintain an invariable phenotype in the face of perturbations is termed developmental robustness. Biological robustness is a phenomenon observed across phyla, and studying its mechanisms is central to deciphering the genotype–phenotype relationship. Recent work in yeast, animals and plants has shown that robustness is genetically controlled and has started to reveal the underlying mechinisms behind it. Scope and Conclusions Studying biological robustness involves focusing on an important property of developmental traits, which is the phenotypic distribution within a population. This is often neglected because the vast majority of developmental biology studies instead focus on population aggregates, such as trait averages. By drawing on findings in animals and yeast, this Viewpoint considers how studies on plant developmental robustness may benefit from strict definitions of what is the developmental system of choice and what is the relevant perturbation, and also from clear distinctions between gene effects on the trait mean and the trait variance. Recent advances in quantitative developmental biology and high-throughput phenotyping now allow the design of targeted genetic screens to identify genes that amplify or restrict developmental trait variance and to study how variation propagates across different phenotypic levels in biological systems. The molecular characterization of more quantitative trait loci affecting trait variance will provide further insights into the evolution of genes modulating developmental robustness. The study of robustness mechanisms in closely related species will address whether mechanisms of robustness are evolutionarily conserved. PMID:26292993

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

PubMed Central

2009-01-01

Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. Conclusions We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment. PMID:20025723

An approach for identification of unknown viruses using sequencing-by-hybridization.

PubMed

Katoski, Sarah E; Meyer, Hermann; Ibrahim, Sofi

2015-09-01

Accurate identification of biological threat agents, especially RNA viruses, in clinical or environmental samples can be challenging because the concentration of viral genomic material in a given sample is usually low, viral genomic RNA is liable to degradation, and RNA viruses are extremely diverse. A two-tiered approach was used for initial identification, then full genomic characterization of 199 RNA viruses belonging to virus families Arenaviridae, Bunyaviridae, Filoviridae, Flaviviridae, and Togaviridae. A Sequencing-by-hybridization (SBH) microarray was used to tentatively identify a viral pathogen then, the identity is confirmed by guided next-generation sequencing (NGS). After optimization and evaluation of the SBH and NGS methodologies with various virus species and strains, the approach was used to test the ability to identify viruses in blinded samples. The SBH correctly identified two Ebola viruses in the blinded samples within 24 hr, and by using guided amplicon sequencing with 454 GS FLX, the identities of the viruses in both samples were confirmed. SBH provides at relatively low-cost screening of biological samples against a panel of viral pathogens that can be custom-designed on a microarray. Once the identity of virus is deduced from the highest hybridization signal on the SBH microarray, guided (amplicon) NGS sequencing can be used not only to confirm the identity of the virus but also to provide further information about the strain or isolate, including a potential genetic manipulation. This approach can be useful in situations where natural or deliberate biological threat incidents might occur and a rapid response is required. © 2015 Wiley Periodicals, Inc.

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells

PubMed Central

Maouche, Seraya; Poirier, Odette; Godefroy, Tiphaine; Olaso, Robert; Gut, Ivo; Collet, Jean-Phillipe; Montalescot, Gilles; Cambien, François

2008-01-01

Background In this study we assessed the respective ability of Affymetrix and Illumina microarray methodologies to answer a relevant biological question, namely the change in gene expression between resting monocytes and macrophages derived from these monocytes. Five RNA samples for each type of cell were hybridized to the two platforms in parallel. In addition, a reference list of differentially expressed genes (DEG) was generated from a larger number of hybridizations (mRNA from 86 individuals) using the RNG/MRC two-color platform. Results Our results show an important overlap of the Illumina and Affymetrix DEG lists. In addition, more than 70% of the genes in these lists were also present in the reference list. Overall the two platforms had very similar performance in terms of biological significance, evaluated by the presence in the DEG lists of an excess of genes belonging to Gene Ontology (GO) categories relevant for the biology of monocytes and macrophages. Our results support the conclusion of the MicroArray Quality Control (MAQC) project that the criteria used to constitute the DEG lists strongly influence the degree of concordance among platforms. However the importance of prioritizing genes by magnitude of effect (fold change) rather than statistical significance (p-value) to enhance cross-platform reproducibility recommended by the MAQC authors was not supported by our data. Conclusion Functional analysis based on GO enrichment demonstrates that the 2 compared technologies delivered very similar results and identified most of the relevant GO categories enriched in the reference list. PMID:18578872

EST resources and establishment and validation of a 16k cDNA microarray from Atlantic cod (Gadus morhua).

PubMed

Edvardsen, Rolf B; Malde, Ketil; Mittelholzer, Christian; Taranger, Geir Lasse; Nilsen, Frank

2011-03-01

The Atlantic cod, Gadus morhua, is an important species both for traditional fishery and increasingly also in fish farming. The Atlantic cod is also under potential threat from various environmental changes such as pollution and climate change, but the biological impact of such changes are not well known, in particular when it comes to sublethal effects that can be difficult to assert. Modern molecular and genomic approaches have revolutionized biological research during the last decade, and offer new avenues to study biological functions and e.g. the impact of anthropogenic activities at different life-stages for a given organism. In order to develop genomic data and genomic tools for Atlantic cod we conducted a program were we constructed 20 cDNA libraries, and produced and analyzed 44006 expressed sequence tags (ESTs) from these. Several tissues are represented in the multiple cDNA libraries, that differ in either sexual maturation or immulogical stimulation. This approach allowed us to identify genes that are expressed in particular tissues, life-stages or in response to specific stimuli, and also gives us information about potential functions of the transcripts. The ESTs were used to construct a 16k cDNA microarray to further investigate the cod transcriptome. Microarray analyses were preformed on pylorus, pituitary gland, spleen and testis of sexually maturing male cod. The four different tissues displayed tissue specific transcriptomes demonstrating that the cDNA array is working as expected and will prove to be a powerful tool in further experiments. Copyright © 2010 Elsevier Inc. All rights reserved.

Conceptual Foundations of Systems Biology Explaining Complex Cardiac Diseases.

PubMed

Louridas, George E; Lourida, Katerina G

2017-02-21

Systems biology is an important concept that connects molecular biology and genomics with computing science, mathematics and engineering. An endeavor is made in this paper to associate basic conceptual ideas of systems biology with clinical medicine. Complex cardiac diseases are clinical phenotypes generated by integration of genetic, molecular and environmental factors. Basic concepts of systems biology like network construction, modular thinking, biological constraints (downward biological direction) and emergence (upward biological direction) could be applied to clinical medicine. Especially, in the field of cardiology, these concepts can be used to explain complex clinical cardiac phenotypes like chronic heart failure and coronary artery disease. Cardiac diseases are biological complex entities which like other biological phenomena can be explained by a systems biology approach. The above powerful biological tools of systems biology can explain robustness growth and stability during disease process from modulation to phenotype. The purpose of the present review paper is to implement systems biology strategy and incorporate some conceptual issues raised by this approach into the clinical field of complex cardiac diseases. Cardiac disease process and progression can be addressed by the holistic realistic approach of systems biology in order to define in better terms earlier diagnosis and more effective therapy.

Development and application of an antibody-based protein microarray to assess physiological stress in grizzly bears (Ursus arctos).

PubMed

Carlson, Ruth I; Cattet, Marc R L; Sarauer, Bryan L; Nielsen, Scott E; Boulanger, John; Stenhouse, Gordon B; Janz, David M

2016-01-01

A novel antibody-based protein microarray was developed that simultaneously determines expression of 31 stress-associated proteins in skin samples collected from free-ranging grizzly bears (Ursus arctos) in Alberta, Canada. The microarray determines proteins belonging to four broad functional categories associated with stress physiology: hypothalamic-pituitary-adrenal axis proteins, apoptosis/cell cycle proteins, cellular stress/proteotoxicity proteins and oxidative stress/inflammation proteins. Small skin samples (50-100 mg) were collected from captured bears using biopsy punches. Proteins were isolated and labelled with fluorescent dyes, with labelled protein homogenates loaded onto microarrays to hybridize with antibodies. Relative protein expression was determined by comparison with a pooled standard skin sample. The assay was sensitive, requiring 80 µg of protein per sample to be run in triplicate on the microarray. Intra-array and inter-array coefficients of variation for individual proteins were generally <10 and <15%, respectively. With one exception, there were no significant differences in protein expression among skin samples collected from the neck, forelimb, hindlimb and ear in a subsample of n = 4 bears. This suggests that remotely delivered biopsy darts could be used in future sampling. Using generalized linear mixed models, certain proteins within each functional category demonstrated altered expression with respect to differences in year, season, geographical sampling location within Alberta and bear biological parameters, suggesting that these general variables may influence expression of specific proteins in the microarray. Our goal is to apply the protein microarray as a conservation physiology tool that can detect, evaluate and monitor physiological stress in grizzly bears and other species at risk over time in response to environmental change.

Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

PubMed

Chavan, Shweta S; Bauer, Michael A; Peterson, Erich A; Heuck, Christoph J; Johann, Donald J

2013-01-01

Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches. Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets. Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline. A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets.

PubMed

Park, Inho; Lee, Kwang H; Lee, Doheon

2010-06-15

Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. Supplementary data are available at Bioinformatics online.

Lithium-Responsive Seizure-Like Hyperexcitability Is Caused by a Mutation in the Drosophila Voltage-Gated Sodium Channel Gene paralytic

PubMed Central

Kasuya, Junko; Ueda, Atsushi; Iyengar, Atulya; Wu, Chun-Fang

2016-01-01

Abstract Shudderer (Shu) is an X-linked dominant mutation in Drosophila melanogaster identified more than 40 years ago. A previous study showed that Shu caused spontaneous tremors and defects in reactive climbing behavior, and that these phenotypes were significantly suppressed when mutants were fed food containing lithium, a mood stabilizer used in the treatment of bipolar disorder (Williamson, 1982). This unique observation suggested that the Shu mutation affects genes involved in lithium-responsive neurobiological processes. In the present study, we identified Shu as a novel mutant allele of the voltage-gated sodium (Nav) channel gene paralytic (para). Given that hypomorphic para alleles and RNA interference–mediated para knockdown reduced the severity of Shu phenotypes, Shu was classified as a para hypermorphic allele. We also demonstrated that lithium could improve the behavioral abnormalities displayed by other Nav mutants, including a fly model of the human generalized epilepsy with febrile seizures plus. Our electrophysiological analysis of Shu showed that lithium treatment did not acutely suppress Nav channel activity, indicating that the rescue effect of lithium resulted from chronic physiological adjustments to this drug. Microarray analysis revealed that lithium significantly alters the expression of various genes in Shu, including those involved in innate immune responses, amino acid metabolism, and oxidation-reduction processes, raising the interesting possibility that lithium-induced modulation of these biological pathways may contribute to such adjustments. Overall, our findings demonstrate that Nav channel mutants in Drosophila are valuable genetic tools for elucidating the effects of lithium on the nervous system in the context of neurophysiology and behavior. PMID:27844061

Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated With Acute Renal Allograft Rejection

PubMed Central

Shin, Heesun; Günther, Oliver; Hollander, Zsuzsanna; Wilson-McManus, Janet E.; Ng, Raymond T.; Balshaw, Robert; Keown, Paul A.; McMaster, Robert; McManus, Bruce M.; Isbel, Nicole M.; Knoll, Greg; Tebbutt, Scott J.

2014-01-01

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature. PMID:24526836

Pathogen profiling for disease management and surveillance.

PubMed

Sintchenko, Vitali; Iredell, Jonathan R; Gilbert, Gwendolyn L

2007-06-01

The usefulness of rapid pathogen genotyping is widely recognized, but its effective interpretation and application requires integration into clinical and public health decision-making. How can pathogen genotyping data best be translated to inform disease management and surveillance? Pathogen profiling integrates microbial genomics data into communicable disease control by consolidating phenotypic identity-based methods with DNA microarrays, proteomics, metabolomics and sequence-based typing. Sharing data on pathogen profiles should facilitate our understanding of transmission patterns and the dynamics of epidemics.

EZH2 is a mediator of EWS/FLI1 driven tumor growth and metastasis blocking endothelial and neuro-ectodermal differentiation

PubMed Central

Richter, Günther H. S.; Plehm, Stephanie; Fasan, Annette; Rössler, Sabine; Unland, Rebekka; Bennani-Baiti, Idriss M.; Hotfilder, Marc; Löwel, Diana; von Luettichau, Irene; Mossbrugger, Ilona; Quintanilla-Martinez, Leticia; Kovar, Heinrich; Staege, Martin S.; Müller-Tidow, Carsten; Burdach, Stefan

2009-01-01

Ewing tumors (ET) are highly malignant, localized in bone or soft tissue, and are molecularly defined by ews/ets translocations. DNA microarray analysis revealed a relationship of ET to both endothelium and fetal neural crest. We identified expression of histone methyltransferase enhancer of Zeste, Drosophila, Homolog 2 (EZH2) to be increased in ET. Suppressive activity of EZH2 maintains stemness in normal and malignant cells. Here, we found EWS/FLI1 bound to the EZH2 promoter in vivo, and induced EZH2 expression in ET and mesenchymal stem cells. Down-regulation of EZH2 by RNA interference in ET suppressed oncogenic transformation by inhibiting clonogenicity in vitro. Similarly, tumor development and metastasis was suppressed in immunodeficient Rag2−/−γC−/− mice. EZH2-mediated gene silencing was shown to be dependent on histone deacetylase (HDAC) activity. Subsequent microarray analysis of EZH2 knock down, HDAC-inhibitor treatment and confirmation in independent assays revealed an undifferentiated phenotype maintained by EZH2 in ET. EZH2 regulated stemness genes such as nerve growth factor receptor (NGFR), as well as genes involved in neuroectodermal and endothelial differentiation (EMP1, EPHB2, GFAP, and GAP43). These data suggest that EZH2 might have a central role in ET pathology by shaping the oncogenicity and stem cell phenotype of this tumor. PMID:19289832

Honey bee aggression supports a link between gene regulation and behavioral evolution.

PubMed

Alaux, Cédric; Sinha, Saurabh; Hasadsri, Linda; Hunt, Greg J; Guzmán-Novoa, Ernesto; DeGrandi-Hoffman, Gloria; Uribe-Rubio, José Luis; Southey, Bruce R; Rodriguez-Zas, Sandra; Robinson, Gene E

2009-09-08

A prominent theory states that animal phenotypes arise by evolutionary changes in gene regulation, but the extent to which this theory holds true for behavioral evolution is not known. Because "nature and nurture" are now understood to involve hereditary and environmental influences on gene expression, we studied whether environmental influences on a behavioral phenotype, i.e., aggression, could have evolved into inherited differences via changes in gene expression. Here, with microarray analysis of honey bees, we show that aggression-related genes with inherited patterns of brain expression are also environmentally regulated. There were expression differences in the brain for hundreds of genes between the highly aggressive Africanized honey bee compared with European honey bee (EHB) subspecies. Similar results were obtained for EHB in response to exposure to alarm pheromone (which provokes aggression) and when comparing old and young bees (aggressive tendencies increase with age). There was significant overlap of the gene lists generated from these three microarray experiments. Moreover, there was statistical enrichment of several of the same cis regulatory motifs in promoters of genes on all three gene lists. Aggression shows a remarkably robust brain molecular signature regardless of whether it occurs because of inherited, age-related, or environmental (social) factors. It appears that one element in the evolution of different degrees of aggressive behavior in honey bees involved changes in regulation of genes that mediate the response to alarm pheromone.

Parental effects and the evolution of phenotypic memory.

PubMed

Kuijper, B; Johnstone, R A

2016-02-01

Despite growing evidence for nongenetic inheritance, the ecological conditions that favour the evolution of heritable parental or grandparental effects remain poorly understood. Here, we systematically explore the evolution of parental effects in a patch-structured population with locally changing environments. When selection favours the production of a mix of offspring types, this mix differs according to the parental phenotype, implying that parental effects are favoured over selection for bet-hedging in which the mixture of offspring phenotypes produced does not depend on the parental phenotype. Positive parental effects (generating a positive correlation between parental and offspring phenotype) are favoured in relatively stable habitats and when different types of local environment are roughly equally abundant, and can give rise to long-term parental inheritance of phenotypes. By contrast, unstable habitats can favour negative parental effects (generating a negative correlation between parental and offspring phenotype), and under these circumstances, even slight asymmetries in the abundance of local environmental states select for marked asymmetries in transmission fidelity. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa.

PubMed

Ficklin, Stephen P; Feltus, Frank Alex

2013-01-01

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

PubMed Central

Ficklin, Stephen P.; Feltus, Frank Alex

2013-01-01

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance. PMID:23874666

Using NASA's GeneLab for VESGEN Systems Analysis of Vascular Phenotypes from Stress and Other Signaling Pathways

NASA Technical Reports Server (NTRS)

Parsons-Wingerter, P.; Weitzel, Alexander; Vyas, R. J.; Murray, M. C.; Vickerman, M. B.; Bhattacharya, S.; Wyatt, S. E.

2016-01-01

One fundamental requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants, is a complex, fractally branching vascular system. NASA's VESsel GENeration Analysis (VESGEN) software maps and quantifies vascular trees, networks, and tree-network composites according to weighted physiological rules such as vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport requires a complex distributed system of highly regulated laminar flow. Microvascular branching rules within vertebrates, dicot leaves and the other organisms therefore display many similarities. A unifying perspective is that vascular patterning offers a useful readout of molecular signaling that necessarily integrates these complex pathways. VESGEN has elucidated changes in vascular pattern resulting from inflammatory, developmental and other signaling within numerous tissues and major model organisms studied for Space Biology. For a new VESGEN systems approach, we analyzed differential gene expression in leaves of Arabidopsis thaliana reported by GeneLab (GLDS-7) for spaceflight. Vascularrelated changes in leaf gene expression were identified that can potentially be phenocopied by mutants in ground-based experiments. To link transcriptional, protein and other molecular change with phenotype, alterations in the spatial and dynamic dimensions of vascular patterns for Arabidopsis leaves and other model species are being co-localized with signaling patterns of single molecular expression analyzed as information dimensions. Previously, Drosophila microarray data returned from space suggested significant changes in genes related to wing venation development that include EGF, Notch, Hedghog, Wingless and Dpp signaling. Phenotypes of increasingly abnormal ectopic wing venation in the (non-spaceflight) Drosophila wing generated by overexpression of a Notch antagonist were analyzed by VESGEN. Other VESGEN research applications include the mouse retina, GI and coronary vessels, avian placental analogs and translational studies in the astronaut retina related to health challenges for long-duration missions.

NASAs VESGEN: Systems Analysis of Vascular Phenotypes from Stress and Other Signaling Pathways Using GeneLab.

NASA Technical Reports Server (NTRS)

Parsons-Wingerter, Patricia A.; Weitzel, Alexander; Vyas, Ruchi J.; Murray, Matthew C.; Wyatt, Sarah E.

2016-01-01

One fundamental requirement shared by humans with all higher terrestrial life forms, including insect wings, higher land plants and other vertebrates, is a complex, fractally branching vascular system. NASA's VESsel GENeration Analysis (VESGEN) software maps and quantifies vascular trees, networks, and tree-network composites according to weighted physiological rules such as vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport requires a complex distributed system of highly regulated laminar flow. Microvascular branching rules within vertebrates, dicot leaves and the other organisms therefore display many similarities. One unifying perspective is that vascular patterning offers a useful readout that necessarily integrates complex molecular signaling pathways. VESGEN has elucidated changes in vascular pattern resulting from inflammatory, stress response, developmental and other signaling within numerous tissues and major model organisms studied for Space Biology. For a new VESGEN systems approach, we analyzed differential gene expression in leaves of Arabidopsis thaliana reported by GeneLab (GLDS-7) for spaceflight. Vascular-related changes in leaf gene expression were identified that can potentially be phenocopied by mutants in ground-based experiments. To link transcriptional, protein and other molecular change with phenotype, alterations in the Euclidean and dynamic dimensions (x,y,t) of vascular patterns for Arabidopsis leaves and other model species are being co-localized with signaling patterns of single molecular expression analyzed as information dimensions (i,j,k,...). Previously, Drosophila microarray data returned from space suggested significant changes in genes related to wing venation development that include EGF, Notch, Hedghog, Wingless and Dpp signaling. Phenotypes of increasingly abnormal ectopic wing venation in the (non-spaceflight) Drosophila wing generated by overexpression of a Notch antagonist were analyzed by VESGEN. Other VESGEN research applications include the mouse retina, GI and coronary vessels, avian placental analogs and translational studies in the astronaut retina related to health challenges for long-duration missions.

Isolation, characterization, and molecular regulation of muscle stem cells

PubMed Central

Fukada, So-ichiro; Ma, Yuran; Ohtani, Takuji; Watanabe, Yoko; Murakami, Satoshi; Yamaguchi, Masahiko

2013-01-01

Skeletal muscle has great regenerative capacity which is dependent on muscle stem cells, also known as satellite cells. A loss of satellite cells and/or their function impairs skeletal muscle regeneration and leads to a loss of skeletal muscle power; therefore, the molecular mechanisms for maintaining satellite cells in a quiescent and undifferentiated state are of great interest in skeletal muscle biology. Many studies have demonstrated proteins expressed by satellite cells, including Pax7, M-cadherin, Cxcr4, syndecan3/4, and c-met. To further characterize satellite cells, we established a method to directly isolate satellite cells using a monoclonal antibody, SM/C-2.6. Using SM/C-2.6 and microarrays, we measured the genes expressed in quiescent satellite cells and demonstrated that Hesr3 may complement Hesr1 in generating quiescent satellite cells. Although Hesr1- or Hesr3-single knockout mice show a normal skeletal muscle phenotype, including satellite cells, Hesr1/Hesr3-double knockout mice show a gradual decrease in the number of satellite cells and increase in regenerative defects dependent on satellite cell numbers. We also observed that a mouse's genetic background affects the regenerative capacity of its skeletal muscle and have established a line of DBA/2-background mdx mice that has a much more severe phenotype than the frequently used C57BL/10-mdx mice. The phenotype of DBA/2-mdx mice also seems to depend on the function of satellite cells. In this review, we summarize the methodology of direct isolation, characterization, and molecular regulation of satellite cells based on our results. The relationship between the regenerative capacity of satellite cells and progression of muscular disorders is also summarized. In the last part, we discuss application of the accumulating scientific information on satellite cells to treatment of patients with muscular disorders. PMID:24273513

Genome-Wide Profile of Pleural Mesothelioma versus Parietal and Visceral Pleura: The Emerging Gene Portrait of the Mesothelioma Phenotype

PubMed Central

Røe, Oluf Dimitri; Anderssen, Endre; Helge, Eli; Pettersen, Caroline Hild; Olsen, Karina Standahl; Sandeck, Helmut; Haaverstad, Rune; Lundgren, Steinar; Larsson, Erik

2009-01-01

Background Malignant pleural mesothelioma is considered an almost incurable tumour with increasing incidence worldwide. It usually develops in the parietal pleura, from mesothelial lining or submesothelial cells, subsequently invading the visceral pleura. Chromosomal and genomic aberrations of mesothelioma are diverse and heterogenous. Genome-wide profiling of mesothelioma versus parietal and visceral normal pleural tissue could thus reveal novel genes and pathways explaining its aggressive phenotype. Methodology and Principal Findings Well-characterised tissue from five mesothelioma patients and normal parietal and visceral pleural samples from six non-cancer patients were profiled by Affymetrix oligoarray of 38 500 genes. The lists of differentially expressed genes tested for overrepresentation in KEGG PATHWAYS (Kyoto Encyclopedia of Genes and Genomes) and GO (gene ontology) terms revealed large differences of expression between visceral and parietal pleura, and both tissues differed from mesothelioma. Cell growth and intrinsic resistance in tumour versus parietal pleura was reflected in highly overexpressed cell cycle, mitosis, replication, DNA repair and anti-apoptosis genes. Several genes of the “salvage pathway” that recycle nucleobases were overexpressed, among them TYMS, encoding thymidylate synthase, the main target of the antifolate drug pemetrexed that is active in mesothelioma. Circadian rhythm genes were expressed in favour of tumour growth. The local invasive, non-metastatic phenotype of mesothelioma, could partly be due to overexpression of the known metastasis suppressors NME1 and NME2. Down-regulation of several tumour suppressor genes could contribute to mesothelioma progression. Genes involved in cell communication were down-regulated, indicating that mesothelioma may shield itself from the immune system. Similarly, in non-cancer parietal versus visceral pleura signal transduction, soluble transporter and adhesion genes were down-regulated. This could represent a genetical platform of the parietal pleura propensity to develop mesothelioma. Conclusions Genome-wide microarray approach using complex human tissue samples revealed novel expression patterns, reflecting some important features of mesothelioma biology that should be further explored. PMID:19662092

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong

2014-12-10

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverifiedmore » reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.« less

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

PubMed Central

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

2014-01-01

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates. PMID:25540776

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

PubMed

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

2014-01-01

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

Long non-coding RNA CASP5 promotes the malignant phenotypes of human glioblastoma multiforme.

PubMed

Zhou, Yali; Dai, Wei; Wang, Handong; Pan, Hao; Wang, Qiang

2018-06-12

Long non-coding RNAs (lncRNAs) have been demonstrated to be intensively involved in the development of various carcinomas, including glioblastoma multiforme (GBM). However, only a few of them have been well characterized. LncRNA CASP5 have been found to be up-regulated in GBM tissues compared with normal tissues in a microarray-based lncRNA profiling study. In the present study, we further explored the biological role of lncRNA CASP5 in GBM. We examined the expression level of lncRNA CASP5 in GBM tissues as well as GBM cell lines. CCK-8 assay, flow cytometric analysis, western blotting, orthotopic GBM model as well as transwell assay were performed to investigate the biological role of CASP5. We observed that lncRNA CASP5 was highly expressed in GBM tissues and cell lines. Knockdown of CASP5 greatly inhibited GBM proliferation and resulted in G1 cell cycle arrest along with higher apoptosis ratios in vitro and in vivo, while overexpression led to the opposite phenomenon. Furthermore, the migration and invasion ability of GBM cells were significantly decreased after CASP5 down-regulation, while increased migration and invasion can be observed after CASP5 up-regulation. We demonstrate for the first time the potential oncogenic role of lncRNA CASP5 which may be helpful for identifying novel therapeutic targets in GBM. Copyright © 2018 Elsevier Inc. All rights reserved.

WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

PubMed Central

Yi, Ming; Horton, Jay D; Cohen, Jonathan C; Hobbs, Helen H; Stephens, Robert M

2006-01-01

Background Analysis of High Throughput (HTP) Data such as microarray and proteomics data has provided a powerful methodology to study patterns of gene regulation at genome scale. A major unresolved problem in the post-genomic era is to assemble the large amounts of data generated into a meaningful biological context. We have developed a comprehensive software tool, WholePathwayScope (WPS), for deriving biological insights from analysis of HTP data. Result WPS extracts gene lists with shared biological themes through color cue templates. WPS statistically evaluates global functional category enrichment of gene lists and pathway-level pattern enrichment of data. WPS incorporates well-known biological pathways from KEGG (Kyoto Encyclopedia of Genes and Genomes) and Biocarta, GO (Gene Ontology) terms as well as user-defined pathways or relevant gene clusters or groups, and explores gene-term relationships within the derived gene-term association networks (GTANs). WPS simultaneously compares multiple datasets within biological contexts either as pathways or as association networks. WPS also integrates Genetic Association Database and Partial MedGene Database for disease-association information. We have used this program to analyze and compare microarray and proteomics datasets derived from a variety of biological systems. Application examples demonstrated the capacity of WPS to significantly facilitate the analysis of HTP data for integrative discovery. Conclusion This tool represents a pathway-based platform for discovery integration to maximize analysis power. The tool is freely available at . PMID:16423281

Directed evolution and synthetic biology applications to microbial systems.

PubMed

Bassalo, Marcelo C; Liu, Rongming; Gill, Ryan T

2016-06-01

Biotechnology applications require engineering complex multi-genic traits. The lack of knowledge on the genetic basis of complex phenotypes restricts our ability to rationally engineer them. However, complex phenotypes can be engineered at the systems level, utilizing directed evolution strategies that drive whole biological systems toward desired phenotypes without requiring prior knowledge of the genetic basis of the targeted trait. Recent developments in the synthetic biology field accelerates the directed evolution cycle, facilitating engineering of increasingly complex traits in biological systems. In this review, we summarize some of the most recent advances in directed evolution and synthetic biology that allows engineering of complex traits in microbial systems. Then, we discuss applications that can be achieved through engineering at the systems level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genome‐Wide MicroRNA and Gene Analysis of Mesenchymal Stem Cell Chondrogenesis Identifies an Essential Role and Multiple Targets for miR‐140‐5p

PubMed Central

Tselepi, Maria; Gómez, Rodolfo; Woods, Steven; Hui, Wang; Smith, Graham R.; Shanley, Daryl P.; Clark, Ian M.; Young, David A.

2015-01-01

Abstract microRNAs (miRNAs) are abundantly expressed in development where they are critical determinants of cell differentiation and phenotype. Accordingly miRNAs are essential for normal skeletal development and chondrogenesis in particular. However, the question of which miRNAs are specific to the chondrocyte phenotype has not been fully addressed. Using microarray analysis of miRNA expression during mesenchymal stem cell chondrogenic differentiation and detailed examination of the role of essential differentiation factors, such as SOX9, TGF‐β, and the cell condensation phase, we characterize the repertoire of specific miRNAs involved in chondrocyte development, highlighting in particular miR‐140 and miR‐455. Further with the use of mRNA microarray data we integrate miRNA expression and mRNA expression during chondrogenesis to underline the particular importance of miR‐140, especially the ‐5p strand. We provide a detailed identification and validation of direct targets of miR‐140‐5p in both chondrogenesis and adult chondrocytes with the use of microarray and 3′UTR analysis. This emphasizes the diverse array of targets and pathways regulated by miR‐140‐5p. We are also able to confirm previous experimentally identified targets but, additionally, identify a novel positive regulation of the Wnt signaling pathway by miR‐140‐5p. Wnt signaling has a complex role in chondrogenesis and skeletal development and these findings illustrate a previously unidentified role for miR‐140‐5p in regulation of Wnt signaling in these processes. Together these developments further highlight the role of miRNAs during chondrogenesis to improve our understanding of chondrocyte development and guide cartilage tissue engineering. Stem Cells 2015;33:3266–3280 PMID:26175215

Recovering from iron deficiency chlorosis in near-isogenic soybeans: a microarray study.

PubMed

O'Rourke, Jamie A; Graham, Michelle A; Vodkin, Lila; Gonzalez, Delkin Orlando; Cianzio, Silvia R; Shoemaker, Randy C

2007-05-01

Iron deficiency chlorosis (IDC) in soybeans has proven to be a perennial problem in the calcareous soils of the U.S. upper Midwest. A historically difficult trait to study in fields, the use of hydroponics in a controlled greenhouse environment has provided a mechanism to study genetic variation while limiting environmental complications. IDC susceptible plants growing in calcareous soils and in iron-controlled hydroponic experiments often exhibit a characteristic chlorotic phenotype early in the growing season but are able to re-green later in the season. To examine the changes in gene expression of these plants, near-isogenic lines, iron efficient PI548553 (Clark) and iron inefficient PI547430 (IsoClark), developed for their response to iron deficiency stress [USDA, ARS, National Genetic Resources Program, Germplasm Resources Information Network - GRIN. (Online Database) National Germplasm Resources Laboratory, Beltsville, MD, 2004. Available: http://www.ars.grin.gov/cgi-bin/npgs/html/acc_search.pl?accid=PI+547430. [22] were grown in iron-deficient hydroponic conditions for one week, then transferred to iron sufficient conditions for another week. This induced a phenotypic response mimicking the growth of the plants in the field; initial chlorosis followed by re-greening. RNA was isolated from root tissue and transcript profiles were examined between the two near-isogenic lines using publicly available cDNA microarrays. By alleviating the iron deficiency stress our expectation was that plants would return to baseline expression levels. However, the microarray comparison identified four cDNAs that were under-expressed by a two-fold or greater difference in the iron inefficient plant compared to the iron efficient plant. This differential expression was re-examined and confirmed by real time PCR experimentation. Control experiments showed that these genes are not differentially expressed in plants grown continually under iron rich hydroponic conditions. The expression differences suggest potential residual effects of iron deficiency on plant health.

The search for the neurobiological basis of vulnerability to drug abuse: using microarrays to investigate the role of stress and individual differences.

PubMed

Kabbaj, M; Evans, S; Watson, S J; Akil, H

2004-01-01

Basic neurobiological studies have led to great progress in our understanding of the mechanisms of action of drugs of abuse. Much has been learned about the brain response from the moment a psychoactive drug enters the organism onwards, including the psychological, neurobiological and peripheral effects of repeated drug administration, withdrawal and re-exposure. However, to relate this knowledge to the human experience requires further research on the antecedents of drug-taking behavior and the factors that predispose particular individuals to drug seeking and drug abuse. Thus, it is important to address several issues at the fundamental level: (1) Why are some individuals more vulnerable to drugs of abuse more than others? Is there a broader dimension or dimensions of emotional reactivity that contribute to this difference in vulnerability? (2) What is the effect of psychosocial stress on drug-seeking and drug-taking behavior, and are the effects distinct across individuals? (3) Since both drug-taking behavior and stress have sustained and pervasive effects on the brain, can we use microarrays to discern the "neural signature" or "neural phenotype" associated with these processes, and can we distinguish this signature across individuals with differing propensities to taking drugs? In the present paper, we summarize some of our early attempts at addressing these questions. We rely on animal studies aimed at characterizing the emotional and stress reactivity of rats with different propensities to self-administer drugs (high responders and low responders); we briefly describe the effect of a psychosocial stressor on these animals; we then detail a study using microarray technology aimed at investigating the "neural phenotype" associated with social defeat stress in the high vs. low responder animals. This "discovery" approach is used as a starting place for identifying novel mechanisms that might alter the vulnerability of different individuals to drug-seeking behavior. The power and limits of this approach, and its future directions, are discussed within this general framework.

Chromosomal microarray analysis of Bulgarian patients with epilepsy and intellectual disability.

PubMed

Peycheva, Valentina; Kamenarova, Kunka; Ivanova, Neviana; Stamatov, Dimitar; Avdjieva-Tzavella, Daniela; Alexandrova, Iliana; Zhelyazkova, Sashka; Pacheva, Iliana; Dimova, Petya; Ivanov, Ivan; Litvinenko, Ivan; Bozhinova, Veneta; Tournev, Ivailo; Simeonov, Emil; Mitev, Vanyo; Jordanova, Albena; Kaneva, Radka

2018-08-15

High resolution chromosomal microarray analysis (CMA) has facilitated the identification of small chromosomal rearrangements throughout the genome, associated with various neurodevelopmental phenotypes, including ID/DD. Recently, it became evident that intellectual disability (ID)/developmental delay (DD) can occur with associated co-morbidities like epileptic seizures, autism and additional congenital anomalies. These observations require whole genome approach in order to detect the genetic causes of these complex disorders. In this study, we examined 92 patients of Bulgarian origin at age between 1 and 22 years with ID, generalized epilepsy, autistic signs and congenital anomalies. CMA was carried out using SurePrint G3 Human CGH Microarray Kit, 4 × 180 K and SurePrint G3 Unrestricted CGH ISCA v2, 4 × 180 K oligo platforms. Referral indications for selection of the patients were the presence of generalized refractory seizures disorders and co-morbid ID. Clearly pathogenic copy number variations (CNVs) were detected in eight patients (8.7%) from our cohort. Additionally, possibly pathogenic rearrangements of unclear clinical significance were detected in six individuals (6.5%), which make for an overall diagnostic yield of 15.2% among our cohort of patients. We report here the patients with clearly pathogenic CNVs, discuss the potential causality of the possibly pathogenic CNVs and make genotype - phenotype correlations. One novel possibly pathogenic heterozygous deletion in 15q22.31 region was detected in a case with ID/DD. Additionally, whole APBA2 gene duplication in 15q13.1 was found in three generations of a family with epilepsy, ID and psychiatric abnormalities. The results from this study allow us to define the genetic diagnosis in a subset of Bulgarian patients and improve the genetic counseling of the affected families. To our knowledge, this is the first aCGH evaluation of a Bulgarian cohort of children with epilepsy and ID so far. Copyright © 2018 Elsevier B.V. All rights reserved.

The Choice of the Filtering Method in Microarrays Affects the Inference Regarding Dosage Compensation of the Active X-Chromosome

PubMed Central

Zeller, Tanja; Wild, Philipp S.; Truong, Vinh; Trégouët, David-Alexandre; Munzel, Thomas; Ziegler, Andreas; Cambien, François; Blankenberg, Stefan; Tiret, Laurence

2011-01-01

Background The hypothesis of dosage compensation of genes of the X chromosome, supported by previous microarray studies, was recently challenged by RNA-sequencing data. It was suggested that microarray studies were biased toward an over-estimation of X-linked expression levels as a consequence of the filtering of genes below the detection threshold of microarrays. Methodology/Principal Findings To investigate this hypothesis, we used microarray expression data from circulating monocytes in 1,467 individuals. In total, 25,349 and 1,156 probes were unambiguously assigned to autosomes and the X chromosome, respectively. Globally, there was a clear shift of X-linked expressions toward lower levels than autosomes. We compared the ratio of expression levels of X-linked to autosomal transcripts (X∶AA) using two different filtering methods: 1. gene expressions were filtered out using a detection threshold irrespective of gene chromosomal location (the standard method in microarrays); 2. equal proportions of genes were filtered out separately on the X and on autosomes. For a wide range of filtering proportions, the X∶AA ratio estimated with the first method was not significantly different from 1, the value expected if dosage compensation was achieved, whereas it was significantly lower than 1 with the second method, leading to the rejection of the hypothesis of dosage compensation. We further showed in simulated data that the choice of the most appropriate method was dependent on biological assumptions regarding the proportion of actively expressed genes on the X chromosome comparative to the autosomes and the extent of dosage compensation. Conclusion/Significance This study shows that the method used for filtering out lowly expressed genes in microarrays may have a major impact according to the hypothesis investigated. The hypothesis of dosage compensation of X-linked genes cannot be firmly accepted or rejected using microarray-based data. PMID:21912656

Vertical silicon nanowires as a universal platform for delivering biomolecules into living cells

PubMed Central

Shalek, Alex K.; Robinson, Jacob T.; Karp, Ethan S.; Lee, Jin Seok; Ahn, Dae-Ro; Yoon, Myung-Han; Sutton, Amy; Jorgolli, Marsela; Gertner, Rona S.; Gujral, Taranjit S.; MacBeath, Gavin; Yang, Eun Gyeong; Park, Hongkun

2010-01-01

A generalized platform for introducing a diverse range of biomolecules into living cells in high-throughput could transform how complex cellular processes are probed and analyzed. Here, we demonstrate spatially localized, efficient, and universal delivery of biomolecules into immortalized and primary mammalian cells using surface-modified vertical silicon nanowires. The method relies on the ability of the silicon nanowires to penetrate a cell’s membrane and subsequently release surface-bound molecules directly into the cell’s cytosol, thus allowing highly efficient delivery of biomolecules without chemical modification or viral packaging. This modality enables one to assess the phenotypic consequences of introducing a broad range of biological effectors (DNAs, RNAs, peptides, proteins, and small molecules) into almost any cell type. We show that this platform can be used to guide neuronal progenitor growth with small molecules, knock down transcript levels by delivering siRNAs, inhibit apoptosis using peptides, and introduce targeted proteins to specific organelles. We further demonstrate codelivery of siRNAs and proteins on a single substrate in a microarray format, highlighting this technology’s potential as a robust, monolithic platform for high-throughput, miniaturized bioassays. PMID:20080678

Alternative RNA splicing of the MEAF6 gene facilitates neuroendocrine prostate cancer progression.

PubMed

Lee, Ahn R; Li, Yinan; Xie, Ning; Gleave, Martin E; Cox, Michael E; Collins, Colin C; Dong, Xuesen

2017-04-25

Although potent androgen receptor pathway inhibitors (ARPI) improve overall survival of metastatic prostate cancer patients, treatment-induced neuroendocrine prostate cancer (t-NEPC) as a consequence of the selection pressures of ARPI is becoming a more common clinical issue. Improved understanding of the molecular biology of t-NEPC is essential for the development of new effective management approaches for t-NEPC. In this study, we identify a splice variant of the MYST/Esa1-associated factor 6 (MEAF6) gene, MEAF6-1, that is highly expressed in both t-NEPC tumor biopsies and neuroendocrine cell lines of prostate and lung cancers. We show that MEAF6-1 splicing is stimulated by neuronal RNA splicing factor SRRM4. Rather than inducing neuroendocrine trans-differentiation of cells in prostate adenocarcinoma, MEAF6-1 upregulation stimulates cell proliferation, anchorage-independent cell growth, invasion and xenograft tumor growth. Gene microarray identifies that these MEAF6-1 actions are in part mediated by the ID1 and ID3 genes. These findings suggest that the MEAF6-1 variant does not induce neuroendocrine differentiation of prostate cancer cells, but rather facilitates t-NEPC progression by increasing the proliferation rate of cells that have acquired neuroendocrine phenotypes.

Liprin-α4 as a Possible New Therapeutic Target for Pancreatic Cancer.

PubMed

Yamasaki, Akio; Nakayama, Kazunori; Imaizumi, Akira; Kawamoto, Makoto; Fujimura, Akiko; Oyama, Yasuhiro; Nagai, Shuntaro; Yanai, Kosuke; Onishi, Hideya

2017-12-01

In pancreatic cancer, where the microenvironment is extremely hypoxic, analyzing signal transduction under hypoxia is thought to be significantly important. By investigating microarray analysis of pancreatic cancer cells cultured under both normoxia and hypoxia, we found that the expression of leukocyte common antigen-related (LAR)-interacting protein (liprin)-α4 was extremely increased under hypoxia compared to under normoxia. In the present study, the biological significance of liprin-α4 in pancreatic cancer was investigated and whether liprin-α4 has potential as a therapeutic target for pancreatic cancer was estimated. Suppression of liprin-α4 reduced proliferation of pancreatic cancer cells both in vitro and in vivo. Inhibition of liprin-α4 also reduced invasiveness through the suppression of endothelial-mesenchymal transition. Stimulation by liprin-α4 was through phosphoinositide 3-kinase and mitogen-activated protein kinase signaling pathways. Liprin-α4 plays a pivotal role in inducing malignant phenotypes such as increased proliferation and invasion in pancreatic cancer, and that liprin-α4 could be a new effective therapeutic target for pancreatic cancer. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Supervised normalization of microarrays

PubMed Central

Mecham, Brigham H.; Nelson, Peter S.; Storey, John D.

2010-01-01

Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundances is ‘normalization’, the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date. Results: We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods. Availability: An R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org). Contact: jstorey@princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20363728

Microarrays--new possibilities for detecting biological factors hazardous for humans and animals, and for use in environmental protection.

PubMed

Mirski, Tomasz; Bartoszcze, Michał; Bielawska-Drózd, Agata; Gryko, Romuald; Kocik, Janusz; Niemcewicz, Marcin; Chomiczewski, Krzysztof

2016-01-01

Both the known biological agents that cause infectious diseases, as well as modified (ABF-Advanced Biological Factors) or new, emerging agents pose a significant diagnostic problem using previously applied methods, both classical, as well as based on molecular biology methods. The latter, such as PCR and real-time PCR, have significant limitations, both quantitative (low capacity), and qualitative (limited number of targets). The article discusses the results of studies on using the microarray method for the identification of viruses (e.g. Orthopoxvirus group, noroviruses, influenza A and B viruses, rhino- and enteroviruses responsible for the FRI (Febrile Respiratory Illness), European bunyaviruses, and SARS-causing viruses), and bacteria (Mycobacterium spp., Yersinia spp., Campylobacter spp., Streptococcus pneumoniae, Salmonella typhi, Salmonella enterica, Staphylococcus aureus, Neisseria meningitidis, Clostridium difficile , Helicobacter pylori), including multiple antibiotic-resistant strains. The method allows for the serotyping and genotyping of bacteria, and is useful in the diagnosis of genetically modified agents. It allows the testing of thousands of genes in one experiment. In addition to diagnosis, it is applicable for gene expression studies, analysis of the function of genes, microorganisms virulence, and allows the detection of even single mutations. The possibility of its operational application in epidemiological surveillance, and in the detection of disease outbreak agents is demonstrated.

Retinal cell responses to elevated intraocular pressure: a gene array comparison between the whole retina and retinal ganglion cell layer.

PubMed

Guo, Ying; Cepurna, William O; Dyck, Jennifer A; Doser, Tom A; Johnson, Elaine C; Morrison, John C

2010-06-01

To determine and compare gene expression patterns in the whole retina and retinal ganglion cell layer (RGCL) in a rodent glaucoma model. IOP was unilaterally elevated in Brown Norway rats (N = 26) by injection of hypertonic saline and monitored for 5 weeks. A cDNA microarray was used on whole retinas from one group of eyes with extensive optic nerve injury and on RGCL isolated by laser capture microdissection (LCM) from another group with comparable injury, to determine the significantly up- or downregulated genes and gene categories in both groups. Expression changes of selected genes were examined by quantitative reverse transcription-PCR (qPCR) to verify microarray results. Microarray analysis of the whole retina identified 632 genes with significantly changed expression (335 up, 297 down), associated with 9 upregulated and 3 downregulated biological processes. In contrast, the RGCL microarray yielded 3726 genes with significantly changed expression (2003 up, 1723 down), including 60% of those found in whole retina. Thirteen distinct upregulated biological processes were identified in the RGCL, dominated by protein synthesis. Among 11 downregulated processes, axon extension and dendrite morphogenesis and generation of precursor metabolism and energy were uniquely identified in the RGCL. qPCR confirmed significant changes in 6 selected messages in whole retina and 11 in RGCL. Increased Atf3, the most upregulated gene in the RGCL, was confirmed by immunohistochemistry of RGCs. Isolation of RGCL by LCM allows a more refined detection of gene response to elevated pressure and improves the potential of determining cellular mechanisms in RGCs and their supporting cells that could be targets for enhancing RGC survival.

AN ECOLOGICAL PERSPECTIVE OF GENOMICS: ASSESSING ECOLOGICAL RISK THROUGH PARTNERSHIPS

EPA Science Inventory

The application of new molecular biological tools to environmental toxicology was discussed at an international workshop attended by
approximately 60 government, academic, and industrial scientists. The sequencing of the human genome, development of microarrays and
DNA chip...

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications.

PubMed

Otero, José Manuel; Vongsangnak, Wanwipa; Asadollahi, Mohammad A; Olivares-Hernandes, Roberto; Maury, Jérôme; Farinelli, Laurent; Barlocher, Loïc; Osterås, Magne; Schalk, Michel; Clark, Anthony; Nielsen, Jens

2010-12-22

The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications

PubMed Central

2010-01-01

Background The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. Results In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. Conclusions With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk. PMID:21176163

Calorie restriction ameliorates neurodegenerative phenotypes in forebrain-specific presenilin-1 and presenilin-2 double knockout mice.

PubMed

Wu, Pu; Shen, Qian; Dong, Suzhen; Xu, Zhiliang; Tsien, Joe Z; Hu, Yinghe

2008-10-01

Conditional double knockout of presenilin-1 and presenilin-2 (cDKO) in forebrain of mice led to brain atrophy, tau hyperphosphorylation, synaptic dysfunction and cognitive deficit. These brain changes recapitulated most of the neurodegenerative phenotypes of Alzheimer's disease (AD). In this report, we have investigated the effects of 4-month calorie restriction (CR) regimen on different phenotypes in cDKO mice. We found that CR improved novel object recognition and contextual fear conditioning memory in the cDKO mice. Histological and biochemical analysis showed that CR attenuated ventricle enlargement, caspase-3 activation and astrogliosis. In addition, the induction of tau hyperphosphorylation in the cDKO mice was reduced by CR, possibly through reduction of p25 accumulation and aberrant CDK5 activation. Finally, DNA microarray analysis demonstrated that CR could increase the expression of neurogenesis related genes and decrease the expression of inflammation related genes in the hippocampus of cDKO mice. The possible molecular mechanisms of the CR effects on alleviating AD pathogenesis have been discussed.

Clinical comparison of overlapping deletions of 19p13.3.

PubMed

Risheg, Hiba; Pasion, Romela; Sacharow, Stephanie; Proud, Virginia; Immken, LaDonna; Schwartz, Stuart; Tepperberg, Jim H; Papenhausen, Peter; Tan, Tiong Y; Andrieux, Joris; Plessis, Ghislaine; Amor, David J; Keitges, Elisabeth A

2013-05-01

We present three patients with overlapping interstitial deletions of 19p13.3 identified by high resolution SNP microarray analysis. All three had a similar phenotype characterized by intellectual disability or developmental delay, structural heart abnormalities, large head relative to height and weight or macrocephaly, and minor facial anomalies. Deletion sizes ranged from 792 Kb to 1.0 Mb and included a common region arr [hg19] 19p13.3 (3,814,392-4,136,989), containing eight genes: ZFR2, ATCAY, NMRK2, DAPK3, EEF2, PIAS4, ZBTB7A, MAP2K2, and two non-coding RNA's MIR637 and SNORDU37. The patient phenotypes were compared with three previous single patient reports with similar interstitial 19p13.3 deletions and six additional patients from the DECIPHER and ISCA databases to determine if a common haploinsufficient phenotype for the region can be established. Copyright © 2013 Wiley Periodicals, Inc.

A multiplex reverse transcription PCR and automated electronic microarray assay for detection and differentiation of seven viruses affecting swine.

PubMed

Erickson, A; Fisher, M; Furukawa-Stoffer, T; Ambagala, A; Hodko, D; Pasick, J; King, D P; Nfon, C; Ortega Polo, R; Lung, O

2018-04-01

Microarray technology can be useful for pathogen detection as it allows simultaneous interrogation of the presence or absence of a large number of genetic signatures. However, most microarray assays are labour-intensive and time-consuming to perform. This study describes the development and initial evaluation of a multiplex reverse transcription (RT)-PCR and novel accompanying automated electronic microarray assay for simultaneous detection and differentiation of seven important viruses that affect swine (foot-and-mouth disease virus [FMDV], swine vesicular disease virus [SVDV], vesicular exanthema of swine virus [VESV], African swine fever virus [ASFV], classical swine fever virus [CSFV], porcine respiratory and reproductive syndrome virus [PRRSV] and porcine circovirus type 2 [PCV2]). The novel electronic microarray assay utilizes a single, user-friendly instrument that integrates and automates capture probe printing, hybridization, washing and reporting on a disposable electronic microarray cartridge with 400 features. This assay accurately detected and identified a total of 68 isolates of the seven targeted virus species including 23 samples of FMDV, representing all seven serotypes, and 10 CSFV strains, representing all three genotypes. The assay successfully detected viruses in clinical samples from the field, experimentally infected animals (as early as 1 day post-infection (dpi) for FMDV and SVDV, 4 dpi for ASFV, 5 dpi for CSFV), as well as in biological material that were spiked with target viruses. The limit of detection was 10 copies/μl for ASFV, PCV2 and PRRSV, 100 copies/μl for SVDV, CSFV, VESV and 1,000 copies/μl for FMDV. The electronic microarray component had reduced analytical sensitivity for several of the target viruses when compared with the multiplex RT-PCR. The integration of capture probe printing allows custom onsite array printing as needed, while electrophoretically driven hybridization generates results faster than conventional microarrays that rely on passive hybridization. With further refinement, this novel, rapid, highly automated microarray technology has potential applications in multipathogen surveillance of livestock diseases. © 2017 Her Majesty the Queen in Right of Canada • Transboundary and Emerging Diseases.

Rapid and simultaneous detection of ricin, staphylococcal enterotoxin B and saxitoxin by chemiluminescence-based microarray immunoassay.

PubMed

Szkola, A; Linares, E M; Worbs, S; Dorner, B G; Dietrich, R; Märtlbauer, E; Niessner, R; Seidel, M

2014-11-21

Simultaneous detection of small and large molecules on microarray immunoassays is a challenge that limits some applications in multiplex analysis. This is the case for biosecurity, where fast, cheap and reliable simultaneous detection of proteotoxins and small toxins is needed. Two highly relevant proteotoxins, ricin (60 kDa) and bacterial toxin staphylococcal enterotoxin B (SEB, 30 kDa) and the small phycotoxin saxitoxin (STX, 0.3 kDa) are potential biological warfare agents and require an analytical tool for simultaneous detection. Proteotoxins are successfully detected by sandwich immunoassays, whereas competitive immunoassays are more suitable for small toxins (<1 kDa). Based on this need, this work provides a novel and efficient solution based on anti-idiotypic antibodies for small molecules to combine both assay principles on one microarray. The biotoxin measurements are performed on a flow-through chemiluminescence microarray platform MCR3 in 18 minutes. The chemiluminescence signal was amplified by using a poly-horseradish peroxidase complex (polyHRP), resulting in low detection limits: 2.9 ± 3.1 μg L(-1) for ricin, 0.1 ± 0.1 μg L(-1) for SEB and 2.3 ± 1.7 μg L(-1) for STX. The developed multiplex system for the three biotoxins is completely novel, relevant in the context of biosecurity and establishes the basis for research on anti-idiotypic antibodies for microarray immunoassays.

Microarray analysis of genes differentially expressed in HepG2 cells cultured in simulated microgravity: preliminary report

NASA Technical Reports Server (NTRS)

Khaoustov, V. I.; Risin, D.; Pellis, N. R.; Yoffe, B.; McIntire, L. V. (Principal Investigator)

2001-01-01

Developed at NASA, the rotary cell culture system (RCCS) allows the creation of unique microgravity environment of low shear force, high-mass transfer, and enables three-dimensional (3D) cell culture of dissimilar cell types. Recently we demonstrated that a simulated microgravity is conducive for maintaining long-term cultures of functional hepatocytes and promote 3D cell assembly. Using deoxyribonucleic acid (DNA) microarray technology, it is now possible to measure the levels of thousands of different messenger ribonucleic acids (mRNAs) in a single hybridization step. This technique is particularly powerful for comparing gene expression in the same tissue under different environmental conditions. The aim of this research was to analyze gene expression of hepatoblastoma cell line (HepG2) during early stage of 3D-cell assembly in simulated microgravity. For this, mRNA from HepG2 cultured in the RCCS was analyzed by deoxyribonucleic acid microarray. Analyses of HepG2 mRNA by using 6K glass DNA microarray revealed changes in expression of 95 genes (overexpression of 85 genes and downregulation of 10 genes). Our preliminary results indicated that simulated microgravity modifies the expression of several genes and that microarray technology may provide new understanding of the fundamental biological questions of how gravity affects the development and function of individual cells.

Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis.

PubMed

Harvey, Benjamin Simeon; Ji, Soo-Yeon

2017-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring forth oncological inference to the bioinformatics community through the analysis of large-scale cancer genomic (LSCG) DNA and mRNA microarray data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological interpretation by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale distributed parallel (CSDP) separable 1-D wavelet decomposition technique for denoising through differential expression thresholding and classification of LSCG microarray data. This research presents a novel methodology that utilizes a CSDP separable 1-D method for wavelet-based transformation in order to initialize a threshold which will retain significantly expressed genes through the denoising process for robust classification of cancer patients. Additionally, the overall study was implemented and encompassed within CSDP environment. The utilization of cloud computing and wavelet-based thresholding for denoising was used for the classification of samples within the Global Cancer Map, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas. The results proved that separable 1-D parallel distributed wavelet denoising in the cloud and differential expression thresholding increased the computational performance and enabled the generation of higher quality LSCG microarray datasets, which led to more accurate classification results.

Ontology-based, Tissue MicroArray oriented, image centered tissue bank

PubMed Central

Viti, Federica; Merelli, Ivan; Caprera, Andrea; Lazzari, Barbara; Stella, Alessandra; Milanesi, Luciano

2008-01-01

Background Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information. Results In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data. Conclusions Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes. PMID:18460177

High-throughput screening of T7 phage display and protein microarrays as a methodological approach for the identification of IgE-reactive components.

PubMed

San Segundo-Acosta, Pablo; Garranzo-Asensio, María; Oeo-Santos, Carmen; Montero-Calle, Ana; Quiralte, Joaquín; Cuesta-Herranz, Javier; Villalba, Mayte; Barderas, Rodrigo

2018-05-01

Olive pollen and yellow mustard seeds are major allergenic sources with high clinical relevance. To aid with the identification of IgE-reactive components, the development of sensitive methodological approaches is required. Here, we have combined T7 phage display and protein microarrays for the identification of allergenic peptides and mimotopes from olive pollen and mustard seeds. The identification of these allergenic sequences involved the construction and biopanning of T7 phage display libraries of mustard seeds and olive pollen using sera from allergic patients to both biological sources together with the construction of phage microarrays printed with 1536 monoclonal phages from the third/four rounds of biopanning. The screening of the phage microarrays with individual sera from allergic patients enabled the identification of 10 and 9 IgE-reactive unique amino acid sequences from olive pollen and mustard seeds, respectively. Five immunoreactive amino acid sequences displayed on phages were selected for their expression as His6-GST tag fusion proteins and validation. After immunological characterization, we assessed the IgE-reactivity of the constructs. Our results show that protein microarrays printed with T7 phages displaying peptides from allergenic sources might be used to identify allergenic components -peptides, proteins or mimotopes- through their screening with specific IgE antibodies from allergic patients. Copyright © 2018 Elsevier B.V. All rights reserved.

Global monitoring of autumn gene expression within and among phenotypically divergent populations of Sitka spruce (Picea sitchensis).

PubMed

Holliday, Jason A; Ralph, Steven G; White, Richard; Bohlmann, Jörg; Aitken, Sally N

2008-01-01

Cold acclimation in conifers is a complex process, the timing and extent of which reflects local adaptation and varies widely along latitudinal gradients for many temperate and boreal tree species. Despite their ecological and economic importance, little is known about the global changes in gene expression that accompany autumn cold acclimation in conifers. Using three populations of Sitka spruce (Picea sitchensis) spanning the species range, and a Picea cDNA microarray with 21,840 unique elements, within- and among-population gene expression was monitored during the autumn. Microarray data were validated for selected genes using real-time PCR. Similar numbers of genes were significantly twofold upregulated (1257) and downregulated (967) between late summer and early winter. Among those upregulated were dehydrins, pathogenesis-related/antifreeze genes, carbohydrate and lipid metabolism genes, and genes involved in signal transduction and transcriptional regulation. Among-population microarray hybridizations at early and late autumn time points revealed substantial variation in the autumn transcriptome, some of which may reflect local adaptation. These results demonstrate the complexity of cold acclimation in conifers, highlight similarities and differences to cold tolerance in annual plants, and provide a solid foundation for functional and genetic studies of this important adaptive process.

Integrative analysis of RUNX1 downstream pathways and target genes

PubMed Central

Michaud, Joëlle; Simpson, Ken M; Escher, Robert; Buchet-Poyau, Karine; Beissbarth, Tim; Carmichael, Catherine; Ritchie, Matthew E; Schütz, Frédéric; Cannon, Ping; Liu, Marjorie; Shen, Xiaofeng; Ito, Yoshiaki; Raskind, Wendy H; Horwitz, Marshall S; Osato, Motomi; Turner, David R; Speed, Terence P; Kavallaris, Maria; Smyth, Gordon K; Scott, Hamish S

2008-01-01

Background The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia. Results Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1) cell lines with RUNX1 mutations from FPD-AML patients, 2) over-expression of RUNX1 and CBFβ, and 3) Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes) significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFβ. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes. Conclusion This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease progression in both familial and sporadic leukemia as well as therapeutic implications. PMID:18671852

Detecting novel genes with sparse arrays

PubMed Central

Haiminen, Niina; Smit, Bart; Rautio, Jari; Vitikainen, Marika; Wiebe, Marilyn; Martinez, Diego; Chee, Christine; Kunkel, Joe; Sanchez, Charles; Nelson, Mary Anne; Pakula, Tiina; Saloheimo, Markku; Penttilä, Merja; Kivioja, Teemu

2014-01-01

Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. PMID:20691772

High-throughput investigation of endothelial-to-mesenchymal transformation (EndMT) with combinatorial cellular microarrays.

PubMed

Wang, Zongjie; Calpe, Blaise; Zerdani, Jalil; Lee, Youngsang; Oh, Jonghyun; Bae, Hojae; Khademhosseini, Ali; Kim, Keekyoung

2016-07-01

In the developing heart, a specific subset of endocardium undergoes an endothelial-to-mesenchymal transformation (EndMT) thus forming nascent valve leaflets. Extracellular matrix (ECM) proteins and growth factors (GFs) play important roles in regulating EndMT but the combinatorial effect of GFs with ECM proteins is less well understood. Here we use microscale engineering techniques to create single, binary, and tertiary component microenvironments to investigate the combinatorial effects of ECM proteins and GFs on the attachment and transformation of adult ovine mitral valve endothelial cells to a mesenchymal phenotype. With the combinatorial microenvironment microarrays, we utilized 60 different combinations of ECM proteins (Fibronectin, Collagen I, II, IV, Laminin) and GFs (TGF-β1, bFGF, VEGF) and were able to identify new microenvironmental conditions capable of modulating EndMT in MVECs. Experimental results indicated that TGF-β1 significantly upregulated the EndMT while either bFGF or VEGF downregulated EndMT process markedly. Also, ECM proteins could influence both the attachment of MVECs and the response of MVECs to GFs. In terms of attachment, fibronectin is significantly better for the adhesion of MVECs among the five tested proteins. Overall collagen IV and fibronectin appeared to play important roles in promoting EndMT process. Great consistency between macroscale and microarrayed experiments and present studies demonstrates that high-throughput cellular microarrays are a promising approach to study the regulation of EndMT in valvular endothelium. Biotechnol. Bioeng. 2016;113: 1403-1412. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Using pathway modules as targets for assay development in xenobiotic screening

EPA Science Inventory

Toxicology and pharmaceutical research is increasingly making use of high throughout-screening (HTS) methods to assess the effects of chemicals on molecular pathways, cells and tissues. Whole-genome microarray analysis provides broad information on the response of biological syst...

Fabrication of high quality cDNA microarray using a small amount of cDNA.

PubMed

Park, Chan Hee; Jeong, Ha Jin; Jung, Jae Jun; Lee, Gui Yeon; Kim, Sang-Chul; Kim, Tae Soo; Yang, Sang Hwa; Chung, Hyun Cheol; Rha, Sun Young

2004-05-01

DNA microarray technology has become an essential part of biological research. It enables the genome-scale analysis of gene expression in various types of model systems. Manufacturing high quality cDNA microarrays of microdeposition type depends on some key factors including a printing device, spotting pins, glass slides, spotting solution, and humidity during spotting. UsingEthe Microgrid II TAS model printing device, this study defined the optimal conditions for producing high density, high quality cDNA microarrays with the least amount of cDNA product. It was observed that aminosilane-modified slides were superior to other types of surface modified-slides. A humidity of 30+/-3% in a closed environment and the overnight drying of the spotted slides gave the best conditions for arraying. In addition, the cDNA dissolved in 30% DMSO gave the optimal conditions for spotting compared to the 1X ArrayIt, 3X SSC and 50% DMSO. Lastly, cDNA in the concentration range of 100-300 ng/ micro l was determined to be best for arraying and post-processing. Currently, the printing system in this study yields reproducible 9000 spots with a spot size 150 mm diameter, and a 200 nm spot spacing.

Galectins are human milk glycan receptors

PubMed Central

Noll, Alexander J; Gourdine, Jean-Philippe; Yu, Ying; Lasanajak, Yi; Smith, David F; Cummings, Richard D

2016-01-01

The biological recognition of human milk glycans (HMGs) is poorly understood. Because HMGs are rich in galactose we explored whether they might interact with human galectins, which bind galactose-containing glycans and are highly expressed in epithelial cells and other cell types. We screened a number of human galectins for their binding to HMGs on a shotgun glycan microarray consisting of 247 HMGs derived from human milk, as well as to a defined HMG microarray. Recombinant human galectins (hGal)-1, -3, -4, -7, -8 and -9 bound selectively to glycans, with each galectin recognizing a relatively unique binding motif; by contrast hGal-2 did not recognize HMGs, but did bind to the human blood group A Type 2 determinants on other microarrays. Unlike other galectins, hGal-7 preferentially bound to glycans expressing a terminal Type 1 (Galβ1-3GlcNAc) sequence, a motif that had eluded detection on non-HMG glycan microarrays. Interactions with HMGs were confirmed in a solution setting by isothermal titration microcalorimetry and hapten inhibition experiments. These results demonstrate that galectins selectively bind to HMGs and suggest the possibility that galectin–HMG interactions may play a role in infant immunity. PMID:26747425

A Quick and Parallel Analytical Method Based on Quantum Dots Labeling for ToRCH-Related Antibodies

NASA Astrophysics Data System (ADS)

Yang, Hao; Guo, Qing; He, Rong; Li, Ding; Zhang, Xueqing; Bao, Chenchen; Hu, Hengyao; Cui, Daxiang

2009-12-01

Quantum dot is a special kind of nanomaterial composed of periodic groups of II-VI, III-V or IV-VI materials. Their high quantum yield, broad absorption with narrow photoluminescence spectra and high resistance to photobleaching, make them become a promising labeling substance in biological analysis. Here, we report a quick and parallel analytical method based on quantum dots for ToRCH-related antibodies including Toxoplasma gondii, Rubella virus, Cytomegalovirus and Herpes simplex virus type 1 (HSV1) and 2 (HSV2). Firstly, we fabricated the microarrays with the five kinds of ToRCH-related antigens and used CdTe quantum dots to label secondary antibody and then analyzed 100 specimens of randomly selected clinical sera from obstetric outpatients. The currently prevalent enzyme-linked immunosorbent assay (ELISA) kits were considered as “golden standard” for comparison. The results show that the quantum dots labeling-based ToRCH microarrays have comparable sensitivity and specificity with ELISA. Besides, the microarrays hold distinct advantages over ELISA test format in detection time, cost, operation and signal stability. Validated by the clinical assay, our quantum dots-based ToRCH microarrays have great potential in the detection of ToRCH-related pathogens.

Tomato Expression Database (TED): a suite of data presentation and analysis tools

PubMed Central

Fei, Zhangjun; Tang, Xuemei; Alba, Rob; Giovannoni, James

2006-01-01

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150 000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at . PMID:16381976

Tomato Expression Database (TED): a suite of data presentation and analysis tools.

PubMed

Fei, Zhangjun; Tang, Xuemei; Alba, Rob; Giovannoni, James

2006-01-01

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150,000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at http://ted.bti.cornell.edu.

Apparently low reproducibility of true differential expression discoveries in microarray studies.

PubMed

Zhang, Min; Yao, Chen; Guo, Zheng; Zou, Jinfeng; Zhang, Lin; Xiao, Hui; Wang, Dong; Yang, Da; Gong, Xue; Zhu, Jing; Li, Yanhui; Li, Xia

2008-09-15

Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries. Based on a statistical model, we show that even in technical replicate tests using identical samples, it is highly likely that the selected DEG lists will be very inconsistent in the presence of small measurement variations. Therefore, the apparently low reproducibility of DEG detection from current technical replicate tests does not indicate low quality of microarray technology. We also demonstrate that heterogeneous biological variations existing in real cancer data will further reduce the overall reproducibility of DEG detection. Nevertheless, in small subsamples from both simulated and real data, the actual false discovery rate (FDR) for each DEG list tends to be low, suggesting that each separately determined list may comprise mostly true DEGs. Rather than simply counting the overlaps of the discovery lists from different studies for a complex disease, novel metrics are needed for evaluating the reproducibility of discoveries characterized with correlated molecular changes. Supplementaty information: Supplementary data are available at Bioinformatics online.

Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus).

PubMed

Mancia, Annalaura; Abelli, Luigi; Kucklick, John R; Rowles, Teresa K; Wells, Randall S; Balmer, Brian C; Hohn, Aleta A; Baatz, John E; Ryan, James C

2015-02-01

It is increasingly common to monitor the marine environment and establish geographic trends of environmental contamination by measuring contaminant levels in animals from higher trophic levels. The health of an ecosystem is largely reflected in the health of its inhabitants. As an apex predator, the common bottlenose dolphin (Tursiops truncatus) can reflect the health of near shore marine ecosystems, and reflect coastal threats that pose risk to human health, such as legacy contaminants or marine toxins, e.g. polychlorinated biphenyls (PCBs) and brevetoxins. Major advances in the understanding of dolphin biology and the unique adaptations of these animals in response to the marine environment are being made as a result of the development of cell-lines for use in in vitro experiments, the production of monoclonal antibodies to recognize dolphin proteins, the development of dolphin DNA microarrays to measure global gene expression and the sequencing of the dolphin genome. These advances may play a central role in understanding the complex and specialized biology of the dolphin with regard to how this species responds to an array of environmental insults. This work presents the creation, characterization and application of a new molecular tool to better understand the complex and unique biology of the common bottlenose dolphin and its response to environmental stress and infection. A dolphin oligo microarray representing 24,418 unigene sequences was developed and used to analyze blood samples collected from 69 dolphins during capture-release health assessments at five geographic locations (Beaufort, NC, Sarasota Bay, FL, Saint Joseph Bay, FL, Sapelo Island, GA and Brunswick, GA). The microarray was validated and tested for its ability to: 1) distinguish male from female dolphins; 2) differentiate dolphins inhabiting different geographic locations (Atlantic coasts vs the Gulf of Mexico); and 3) study in detail dolphins resident in one site, the Georgia coast, known to be heavily contaminated by Aroclor 1268, an uncommon polychlorinated (PCB) mixture. The microarray was able to distinguish dolphins by sex, geographic location, and corroborate previously published health irregularities for the Georgia dolphins. Genes involved in xenobiotic metabolism, development/differentiation and oncogenic pathways were found to be differentially expressed in GA dolphins. The report bridges the advancements in dolphin genome sequencing to the first step towards providing a cost-effective means to screen for indicators of chemical toxin exposure as well as disease status in top level predators. Copyright © 2014 Elsevier B.V. All rights reserved.

EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments

PubMed Central

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-01-01

Background The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE3 was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results EDGE3 has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE3 is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion Here, we present EDGE3, an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE3 provides a means for managing RNA samples and arrays during the hybridization process. EDGE3 is freely available for download at . PMID:19732451

EDGE(3): a web-based solution for management and analysis of Agilent two color microarray experiments.

PubMed

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-09-04

The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Here, we present EDGE(3), an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE(3) provides a means for managing RNA samples and arrays during the hybridization process. EDGE(3) is freely available for download at http://edge.oncology.wisc.edu/.

Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: rationale and design of the "Genetic Loci and the Burden of Atherosclerotic Lesions" study.

PubMed

Voros, Szilard; Maurovich-Horvat, Pal; Marvasty, Idean B; Bansal, Aruna T; Barnes, Michael R; Vazquez, Gustavo; Murray, Sarah S; Voros, Viktor; Merkely, Bela; Brown, Bradley O; Warnick, G Russell

2014-01-01

Complex biological networks of atherosclerosis are largely unknown. The main objective of the Genetic Loci and the Burden of Atherosclerotic Lesions study is to assemble comprehensive biological networks of atherosclerosis using advanced cardiovascular imaging for phenotyping, a panomic approach to identify underlying genomic, proteomic, metabolomic, and lipidomic underpinnings, analyzed by systems biology-driven bioinformatics. By design, this is a hypothesis-free unbiased discovery study collecting a large number of biologically related factors to examine biological associations between genomic, proteomic, metabolomic, lipidomic, and phenotypic factors of atherosclerosis. The Genetic Loci and the Burden of Atherosclerotic Lesions study (NCT01738828) is a prospective, multicenter, international observational study of atherosclerotic coronary artery disease. Approximately 7500 patients are enrolled and undergo non-contrast-enhanced coronary calcium scanning by CT for the detection and quantification of coronary artery calcium, as well as coronary artery CT angiography for the detection and quantification of plaque, stenosis, and overall coronary artery disease burden. In addition, patients undergo whole genome sequencing, DNA methylation, whole blood-based transcriptome sequencing, unbiased proteomics based on mass spectrometry, as well as metabolomics and lipidomics on a mass spectrometry platform. The study is analyzed in 3 subsequent phases, and each phase consists of a discovery cohort and an independent validation cohort. For the primary analysis, the primary phenotype will be the presence of any atherosclerotic plaque, as detected by cardiac CT. Additional phenotypic analyses will include per patient maximal luminal stenosis defined as 50% and 70% diameter stenosis. Single-omic and multi-omic associations will be examined for each phenotype; putative biomarkers will be assessed for association, calibration, discrimination, and reclassification. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Reconstructing the temporal ordering of biological samples using microarray data.

PubMed

Magwene, Paul M; Lizardi, Paul; Kim, Junhyong

2003-05-01

Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.

PubMed

Davis, Sean; Meltzer, Paul S

2007-07-15

Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus (Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140,000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genomics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data. GEOquery is available as part of the BioConductor project.

Development of an ELISA microarray assay for the sensitive and simultaneous detection of ten biodefense toxins.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jenko, Kathryn; Zhang, Yanfeng; Kostenko, Yulia

Plant and microbial toxins are considered bioterrorism threat agents because of their extreme toxicity and/or ease of availability. Additionally, some of these toxins are increasingly responsible for accidental food poisonings. The current study utilized an ELISA-based protein antibody microarray for the multiplexed detection of ten biothreat toxins, botulinum neurotoxins (BoNT) A, B, C, D, E, F, ricin, shiga toxins 1 and 2 (Stx), and staphylococcus enterotoxin B (SEB), in buffer and complex biological matrices. The multiplexed assay displayed a sensitivity of 1.3 pg/mL (BoNT/A, BoNT/B, SEB, Stx-1 and Stx-2), 3.3 pg/mL (BoNT/C, BoNT/E, BoNT/F) and 8.2 pg/mL (BoNT/D, ricin). Allmore » assays demonstrated high accuracy (75-120 percent recovery) and reproducibility (most coefficients of variation < 20%). Quantification curves for the ten toxins were also evaluated in clinical samples (serum, plasma, nasal fluid, saliva, stool, and urine) and environmental samples (apple juice, milk and baby food) with overall minimal matrix effects. The multiplex assays were highly specific, with little crossreactivity observed between the selected toxin antibodies. The results demonstrate a multiplex microarray that improves current immunoassay sensitivity for biological warfare agents in buffer, clinical, and environmental samples.« less

Reverse engineering biological networks :applications in immune responses to bio-toxins.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineermore » regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.« less

Prediction of gene expression in embryonic structures of Drosophila melanogaster.

PubMed

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-07-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

PubMed Central

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-01-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. PMID:17658945

Transcriptional profiling of host gene expression in chicken embryo lung cells infected with laryngotracheitis virus

PubMed Central

2010-01-01

Background Infection by infectious laryngotracheitis virus (ILTV; gallid herpesvirus 1) causes acute respiratory diseases in chickens often with high mortality. To better understand host-ILTV interactions at the host transcriptional level, a microarray analysis was performed using 4 × 44 K Agilent chicken custom oligo microarrays. Results Microarrays were hybridized using the two color hybridization method with total RNA extracted from ILTV infected chicken embryo lung cells at 0, 1, 3, 5, and 7 days post infection (dpi). Results showed that 789 genes were differentially expressed in response to ILTV infection that include genes involved in the immune system (cytokines, chemokines, MHC, and NF-κB), cell cycle regulation (cyclin B2, CDK1, and CKI3), matrix metalloproteinases (MMPs) and cellular metabolism. Differential expression for 20 out of 789 genes were confirmed by quantitative reverse transcription-PCR (qRT-PCR). A bioinformatics tool (Ingenuity Pathway Analysis) used to analyze biological functions and pathways on the group of 789 differentially expressed genes revealed that 21 possible gene networks with intermolecular connections among 275 functionally identified genes. These 275 genes were classified into a number of functional groups that included cancer, genetic disorder, cellular growth and proliferation, and cell death. Conclusion The results of this study provide comprehensive knowledge on global gene expression, and biological functionalities of differentially expressed genes in chicken embryo lung cells in response to ILTV infections. PMID:20663125

Profiling cellular protein complexes by proximity ligation with dual tag microarray readout.

PubMed

Hammond, Maria; Nong, Rachel Yuan; Ericsson, Olle; Pardali, Katerina; Landegren, Ulf

2012-01-01

Patterns of protein interactions provide important insights in basic biology, and their analysis plays an increasing role in drug development and diagnostics of disease. We have established a scalable technique to compare two biological samples for the levels of all pairwise interactions among a set of targeted protein molecules. The technique is a combination of the proximity ligation assay with readout via dual tag microarrays. In the proximity ligation assay protein identities are encoded as DNA sequences by attaching DNA oligonucleotides to antibodies directed against the proteins of interest. Upon binding by pairs of antibodies to proteins present in the same molecular complexes, ligation reactions give rise to reporter DNA molecules that contain the combined sequence information from the two DNA strands. The ligation reactions also serve to incorporate a sample barcode in the reporter molecules to allow for direct comparison between pairs of samples. The samples are evaluated using a dual tag microarray where information is decoded, revealing which pairs of tags that have become joined. As a proof-of-concept we demonstrate that this approach can be used to detect a set of five proteins and their pairwise interactions both in cellular lysates and in fixed tissue culture cells. This paper provides a general strategy to analyze the extent of any pairwise interactions in large sets of molecules by decoding reporter DNA strands that identify the interacting molecules.

Analytical chemistry at the interface between materials science and biology

NASA Astrophysics Data System (ADS)

O'Brien, Janese Christine

This work describes several research efforts that lie at the new interfaces between analytical chemistry and other disciplines, namely materials science and biology. In the materials science realm, the search for new materials that may have useful or unique chromatographic properties motivated the synthesis and characterization of electrically conductive sol-gels. In the biology realm, the search for new surface fabrication schemes that would permit or even improve the detection of specific biological reactions motivated the design of miniaturized biological arrays. Collectively, this work represents some of analytical chemistry's newest forays into these disciplines. This dissertation is divided into six chapters. Chapter 1 is an introductory chapter that provides background information pertinent to several key aspects of the work contained in this dissertation. Chapter 2 describes the synthesis and characterization of electrically conductive sol-gels derived from the acid-catalyzed hydrolysis of a vanadium alkoxide. Specifically, this chapter describes our attempts to increase the conductivity of vanadium sol-gels by optimizing the acidic and drying conditions used during synthesis. Chapter 3 reports the construction of novel antigenic immunosensing platforms of increased epitope density using Fab'-SH antibody fragments on gold. Here, X-ray photoelectron spectroscopy (XPS), thin-layer cell (TLC) and confocal fluorescence spectroscopies, and scanning force microscopy (SFM) are employed to characterize the fragment-substrate interaction, to quantify epitope density, and to demonstrate fragment viability and specificity. Chapter 4 presents a novel method for creating and interrogating double-stranded DNA (dsDNA) microarrays suitable for screening protein:dsDNA interactions. Using the restriction enzyme ECoR1, we demonstrate the ability of the atomic force microscope (AFM) to detect changes in topography that result from the enzymatic cleavage of dsDNA microarrays containing the correct recognition sequence. Chapter 5 explores more fully the microarray fabrication process described in Chapter 4. Specifically, experiments characterizing the effect of deposition conditions on oligonucleotide topography and as well as those that describe array density optimization are presented. Chapter 6 presents general conclusions from the work recorded in this dissertation and speculates on its extension.

RNA-seq based transcriptomic map reveals new insights into mouse salivary gland development and maturation.

PubMed

Gluck, Christian; Min, Sangwon; Oyelakin, Akinsola; Smalley, Kirsten; Sinha, Satrajit; Romano, Rose-Anne

2016-11-16

Mouse models have served a valuable role in deciphering various facets of Salivary Gland (SG) biology, from normal developmental programs to diseased states. To facilitate such studies, gene expression profiling maps have been generated for various stages of SG organogenesis. However these prior studies fall short of capturing the transcriptional complexity due to the limited scope of gene-centric microarray-based technology. Compared to microarray, RNA-sequencing (RNA-seq) offers unbiased detection of novel transcripts, broader dynamic range and high specificity and sensitivity for detection of genes, transcripts, and differential gene expression. Although RNA-seq data, particularly under the auspices of the ENCODE project, have covered a large number of biological specimens, studies on the SG have been lacking. To better appreciate the wide spectrum of gene expression profiles, we isolated RNA from mouse submandibular salivary glands at different embryonic and adult stages. In parallel, we processed RNA-seq data for 24 organs and tissues obtained from the mouse ENCODE consortium and calculated the average gene expression values. To identify molecular players and pathways likely to be relevant for SG biology, we performed functional gene enrichment analysis, network construction and hierarchal clustering of the RNA-seq datasets obtained from different stages of SG development and maturation, and other mouse organs and tissues. Our bioinformatics-based data analysis not only reaffirmed known modulators of SG morphogenesis but revealed novel transcription factors and signaling pathways unique to mouse SG biology and function. Finally we demonstrated that the unique SG gene signature obtained from our mouse studies is also well conserved and can demarcate features of the human SG transcriptome that is different from other tissues. Our RNA-seq based Atlas has revealed a high-resolution cartographic view of the dynamic transcriptomic landscape of the mouse SG at various stages. These RNA-seq datasets will complement pre-existing microarray based datasets, including the Salivary Gland Molecular Anatomy Project by offering a broader systems-biology based perspective rather than the classical gene-centric view. Ultimately such resources will be valuable in providing a useful toolkit to better understand how the diverse cell population of the SG are organized and controlled during development and differentiation.

Differential transcriptomic profiles effected by oil palm phenolics indicate novel health outcomes

PubMed Central

2011-01-01

Background Plant phenolics are important nutritional antioxidants which could aid in overcoming chronic diseases such as cardiovascular disease and cancer, two leading causes of death in the world. The oil palm (Elaeis guineensis) is a rich source of water-soluble phenolics which have high antioxidant activities. This study aimed to identify the in vivo effects and molecular mechanisms involved in the biological activities of oil palm phenolics (OPP) during healthy states via microarray gene expression profiling, using mice supplemented with a normal diet as biological models. Results Having confirmed via histology, haematology and clinical biochemistry analyses that OPP is not toxic to mice, we further explored the gene expression changes caused by OPP through statistical and functional analyses using Illumina microarrays. OPP showed numerous biological activities in three major organs of mice, the liver, spleen and heart. In livers of mice given OPP, four lipid catabolism genes were up-regulated while five cholesterol biosynthesis genes were down-regulated, suggesting that OPP may play a role in reducing cardiovascular disease. OPP also up-regulated eighteen blood coagulation genes in spleens of mice. OPP elicited gene expression changes similar to the effects of caloric restriction in the hearts of mice supplemented with OPP. Microarray gene expression fold changes for six target genes in the three major organs tested were validated with real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and the correlation of fold changes obtained with these two techniques was high (R2 = 0.9653). Conclusions OPP showed non-toxicity and various pleiotropic effects in mice. This study implies the potential application of OPP as a valuable source of wellness nutraceuticals, and further suggests the molecular mechanisms as to how dietary phenolics work in vivo. PMID:21864415

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

PubMed Central

Müller, Christian; Schillert, Arne; Röthemeier, Caroline; Trégouët, David-Alexandre; Proust, Carole; Binder, Harald; Pfeiffer, Norbert; Beutel, Manfred; Lackner, Karl J.; Schnabel, Renate B.; Tiret, Laurence; Wild, Philipp S.; Blankenberg, Stefan

2016-01-01

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data. PMID:27272489

Differential transcriptomic profiles effected by oil palm phenolics indicate novel health outcomes.

PubMed

Leow, Soon-Sen; Sekaran, Shamala Devi; Sundram, Kalyana; Tan, YewAi; Sambanthamurthi, Ravigadevi

2011-08-25

Plant phenolics are important nutritional antioxidants which could aid in overcoming chronic diseases such as cardiovascular disease and cancer, two leading causes of death in the world. The oil palm (Elaeis guineensis) is a rich source of water-soluble phenolics which have high antioxidant activities. This study aimed to identify the in vivo effects and molecular mechanisms involved in the biological activities of oil palm phenolics (OPP) during healthy states via microarray gene expression profiling, using mice supplemented with a normal diet as biological models. Having confirmed via histology, haematology and clinical biochemistry analyses that OPP is not toxic to mice, we further explored the gene expression changes caused by OPP through statistical and functional analyses using Illumina microarrays. OPP showed numerous biological activities in three major organs of mice, the liver, spleen and heart. In livers of mice given OPP, four lipid catabolism genes were up-regulated while five cholesterol biosynthesis genes were down-regulated, suggesting that OPP may play a role in reducing cardiovascular disease. OPP also up-regulated eighteen blood coagulation genes in spleens of mice. OPP elicited gene expression changes similar to the effects of caloric restriction in the hearts of mice supplemented with OPP. Microarray gene expression fold changes for six target genes in the three major organs tested were validated with real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and the correlation of fold changes obtained with these two techniques was high (R2 = 0.9653). OPP showed non-toxicity and various pleiotropic effects in mice. This study implies the potential application of OPP as a valuable source of wellness nutraceuticals, and further suggests the molecular mechanisms as to how dietary phenolics work in vivo.

Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.

PubMed

Zadran, Sohila; Remacle, Francoise; Levine, Raphael

2014-01-01

Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free energy amongst microRNAs (miRNAs), results in a system deviating from a non-cancer state to the GBM cancer -specific phenotypic state. Utilizing global miRNA microarray expression data of normal and GBM patients tumors, surprisal analysis characterizes a miRNA system response capable of distinguishing GBM samples from normal tissue biopsy samples. We indicate that the miRNAs contributing to this system behavior is a disease phenotypic state specific to GBM and is therefore a unique GBM-specific thermodynamic signature. MiRNAs implicated in the regulation of stochastic signaling processes crucial in the hallmarks of human cancer, dominate this GBM-cancer phenotypic state. With this theory, we were able to distinguish with high fidelity GBM patients solely by monitoring the dynamics of miRNAs present in patients' biopsy samples. We anticipate that the GBM-specific thermodynamic signature will provide a critical translational tool in better characterizing cancer types and in the development of future therapeutics for GBM.

Arabidopsis Hormone Database: a comprehensive genetic and phenotypic information database for plant hormone research in Arabidopsis

PubMed Central

Peng, Zhi-yu; Zhou, Xin; Li, Linchuan; Yu, Xiangchun; Li, Hongjiang; Jiang, Zhiqiang; Cao, Guangyu; Bai, Mingyi; Wang, Xingchun; Jiang, Caifu; Lu, Haibin; Hou, Xianhui; Qu, Lijia; Wang, Zhiyong; Zuo, Jianru; Fu, Xiangdong; Su, Zhen; Li, Songgang; Guo, Hongwei

2009-01-01

Plant hormones are small organic molecules that influence almost every aspect of plant growth and development. Genetic and molecular studies have revealed a large number of genes that are involved in responses to numerous plant hormones, including auxin, gibberellin, cytokinin, abscisic acid, ethylene, jasmonic acid, salicylic acid, and brassinosteroid. Here, we develop an Arabidopsis hormone database, which aims to provide a systematic and comprehensive view of genes participating in plant hormonal regulation, as well as morphological phenotypes controlled by plant hormones. Based on data from mutant studies, transgenic analysis and gene ontology (GO) annotation, we have identified a total of 1026 genes in the Arabidopsis genome that participate in plant hormone functions. Meanwhile, a phenotype ontology is developed to precisely describe myriad hormone-regulated morphological processes with standardized vocabularies. A web interface (http://ahd.cbi.pku.edu.cn) would allow users to quickly get access to information about these hormone-related genes, including sequences, functional category, mutant information, phenotypic description, microarray data and linked publications. Several applications of this database in studying plant hormonal regulation and hormone cross-talk will be presented and discussed. PMID:19015126

Arabidopsis Hormone Database: a comprehensive genetic and phenotypic information database for plant hormone research in Arabidopsis.

PubMed

Peng, Zhi-yu; Zhou, Xin; Li, Linchuan; Yu, Xiangchun; Li, Hongjiang; Jiang, Zhiqiang; Cao, Guangyu; Bai, Mingyi; Wang, Xingchun; Jiang, Caifu; Lu, Haibin; Hou, Xianhui; Qu, Lijia; Wang, Zhiyong; Zuo, Jianru; Fu, Xiangdong; Su, Zhen; Li, Songgang; Guo, Hongwei

2009-01-01

Plant hormones are small organic molecules that influence almost every aspect of plant growth and development. Genetic and molecular studies have revealed a large number of genes that are involved in responses to numerous plant hormones, including auxin, gibberellin, cytokinin, abscisic acid, ethylene, jasmonic acid, salicylic acid, and brassinosteroid. Here, we develop an Arabidopsis hormone database, which aims to provide a systematic and comprehensive view of genes participating in plant hormonal regulation, as well as morphological phenotypes controlled by plant hormones. Based on data from mutant studies, transgenic analysis and gene ontology (GO) annotation, we have identified a total of 1026 genes in the Arabidopsis genome that participate in plant hormone functions. Meanwhile, a phenotype ontology is developed to precisely describe myriad hormone-regulated morphological processes with standardized vocabularies. A web interface (http://ahd.cbi.pku.edu.cn) would allow users to quickly get access to information about these hormone-related genes, including sequences, functional category, mutant information, phenotypic description, microarray data and linked publications. Several applications of this database in studying plant hormonal regulation and hormone cross-talk will be presented and discussed.

Genome-wide polymorphisms and development of a microarray platform to detect genetic variations in Plasmodium yoelii.

PubMed

Nair, Sethu C; Pattaradilokrat, Sittiporn; Zilversmit, Martine M; Dommer, Jennifer; Nagarajan, Vijayaraj; Stephens, Melissa T; Xiao, Wenming; Tan, John C; Su, Xin-Zhuan

2014-01-01

The rodent malaria parasite Plasmodium yoelii is an important model for studying malaria immunity and pathogenesis. One approach for studying malaria disease phenotypes is genetic mapping, which requires typing a large number of genetic markers from multiple parasite strains and/or progeny from genetic crosses. Hundreds of microsatellite (MS) markers have been developed to genotype the P. yoelii genome; however, typing a large number of MS markers can be labor intensive, time consuming, and expensive. Thus, development of high-throughput genotyping tools such as DNA microarrays that enable rapid and accurate large-scale genotyping of the malaria parasite will be highly desirable. In this study, we sequenced the genomes of two P. yoelii strains (33X and N67) and obtained a large number of single nucleotide polymorphisms (SNPs). Based on the SNPs obtained, we designed sets of oligonucleotide probes to develop a microarray that could interrogate ∼11,000 SNPs across the 14 chromosomes of the parasite in a single hybridization. Results from hybridizations of DNA samples of five P. yoelii strains or cloned lines (17XNL, YM, 33X, N67 and N67C) and two progeny from a genetic cross (N67×17XNL) to the microarray showed that the array had a high call rate (∼97%) and accuracy (99.9%) in calling SNPs, providing a simple and reliable tool for typing the P. yoelii genome. Our data show that the P. yoelii genome is highly polymorphic, although isogenic pairs of parasites were also detected. Additionally, our results indicate that the 33X parasite is a progeny of 17XNL (or YM) and an unknown parasite. The highly accurate and reliable microarray developed in this study will greatly facilitate our ability to study the genetic basis of important traits and the disease it causes. Published by Elsevier B.V.

High-throughput microarray technology in diagnostics of enterobacteria based on genome-wide probe selection and regression analysis.

PubMed

Friedrich, Torben; Rahmann, Sven; Weigel, Wilfried; Rabsch, Wolfgang; Fruth, Angelika; Ron, Eliora; Gunzer, Florian; Dandekar, Thomas; Hacker, Jörg; Müller, Tobias; Dobrindt, Ulrich

2010-10-21

The Enterobacteriaceae comprise a large number of clinically relevant species with several individual subspecies. Overlapping virulence-associated gene pools and the high overall genome plasticity often interferes with correct enterobacterial strain typing and risk assessment. Array technology offers a fast, reproducible and standardisable means for bacterial typing and thus provides many advantages for bacterial diagnostics, risk assessment and surveillance. The development of highly discriminative broad-range microbial diagnostic microarrays remains a challenge, because of marked genome plasticity of many bacterial pathogens. We developed a DNA microarray for strain typing and detection of major antimicrobial resistance genes of clinically relevant enterobacteria. For this purpose, we applied a global genome-wide probe selection strategy on 32 available complete enterobacterial genomes combined with a regression model for pathogen classification. The discriminative power of the probe set was further tested in silico on 15 additional complete enterobacterial genome sequences. DNA microarrays based on the selected probes were used to type 92 clinical enterobacterial isolates. Phenotypic tests confirmed the array-based typing results and corroborate that the selected probes allowed correct typing and prediction of major antibiotic resistances of clinically relevant Enterobacteriaceae, including the subspecies level, e.g. the reliable distinction of different E. coli pathotypes. Our results demonstrate that the global probe selection approach based on longest common factor statistics as well as the design of a DNA microarray with a restricted set of discriminative probes enables robust discrimination of different enterobacterial variants and represents a proof of concept that can be adopted for diagnostics of a wide range of microbial pathogens. Our approach circumvents misclassifications arising from the application of virulence markers, which are highly affected by horizontal gene transfer. Moreover, a broad range of pathogens have been covered by an efficient probe set size enabling the design of high-throughput diagnostics.

Development and validation of a gene expression oligo microarray for the gilthead sea bream (Sparus aurata).

PubMed

Ferraresso, Serena; Vitulo, Nicola; Mininni, Alba N; Romualdi, Chiara; Cardazzo, Barbara; Negrisolo, Enrico; Reinhardt, Richard; Canario, Adelino V M; Patarnello, Tomaso; Bargelloni, Luca

2008-12-03

Aquaculture represents the most sustainable alternative of seafood supply to substitute for the declining marine fisheries, but severe production bottlenecks remain to be solved. The application of genomic technologies offers much promise to rapidly increase our knowledge on biological processes in farmed species and overcome such bottlenecks. Here we present an integrated platform for mRNA expression profiling in the gilthead sea bream (Sparus aurata), a marine teleost of great importance for aquaculture. A public data base was constructed, consisting of 19,734 unique clusters (3,563 contigs and 16,171 singletons). Functional annotation was obtained for 8,021 clusters. Over 4,000 sequences were also associated with a GO entry. Two 60mer probes were designed for each gene and in-situ synthesized on glass slides using Agilent SurePrint technology. Platform reproducibility and accuracy were assessed on two early stages of sea bream development (one-day and four days old larvae). Correlation between technical replicates was always > 0.99, with strong positive correlation between paired probes. A two class SAM test identified 1,050 differentially expressed genes between the two developmental stages. Functional analysis suggested that down-regulated transcripts (407) in older larvae are mostly essential/housekeeping genes, whereas tissue-specific genes are up-regulated in parallel with the formation of key organs (eye, digestive system). Cross-validation of microarray data was carried out using quantitative qRT-PCR on 11 target genes, selected to reflect the whole range of fold-change and both up-regulated and down-regulated genes. A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates. Good concordance between qRT-PCR and microarray data was observed between 2- and 7-fold change, while fold-change compression in the microarray was present for differences greater than 10-fold in the qRT-PCR. A highly reliable oligo-microarray platform was developed and validated for the gilthead sea bream despite the presently limited knowledge of the species transcriptome. Because of the flexible design this array will be able to accommodate additional probes as soon as novel unique transcripts are available.

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed Central

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-01-01

Background Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Results Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. Conclusion In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects. PMID:12962547

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-09-08

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

AN ECOLOGICAL PERSPECTIVE OF GENOMICS: ASSESSING ECOLOGICAL RISK THROUGH PARTNERSHIPS

EPA Science Inventory

A workshop attended by approximately 60 scientists from around the world met to discuss the application of new molecular biology tools to issues in environmental toxicology and chemistry. With the sequencing of the human genome, development of microarrays and DNA chips, and devel...

How Can We Use Bioinformatics to Predict Which Agents Will Cause Birth Defects?

EPA Science Inventory

The availability of genomic sequences from a growing number of human and model organisms has provided an explosion of data, information, and knowledge regarding biological systems and disease processes. High-throughput technologies such as DNA and protein microarray biochips are ...

Strategies for cell manipulation and skeletal tissue engineering using high-throughput polymer blend formulation and microarray techniques.

PubMed

Khan, Ferdous; Tare, Rahul S; Kanczler, Janos M; Oreffo, Richard O C; Bradley, Mark

2010-03-01

A combination of high-throughput material formulation and microarray techniques were synergistically applied for the efficient analysis of the biological functionality of 135 binary polymer blends. This allowed the identification of cell-compatible biopolymers permissive for human skeletal stem cell growth in both in vitro and in vivo applications. The blended polymeric materials were developed from commercially available, inexpensive and well characterised biodegradable polymers, which on their own lacked both the structural requirements of a scaffold material and, critically, the ability to facilitate cell growth. Blends identified here proved excellent templates for cell attachment, and in addition, a number of blends displayed remarkable bone-like architecture and facilitated bone regeneration by providing 3D biomimetic scaffolds for skeletal cell growth and osteogenic differentiation. This study demonstrates a unique strategy to generate and identify innovative materials with widespread application in cell biology as well as offering a new reparative platform strategy applicable to skeletal tissues. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

From Saccharomyces cerevisiae to human: The important gene co-expression modules.

PubMed

Liu, Wei; Li, Li; Ye, Hua; Chen, Haiwei; Shen, Weibiao; Zhong, Yuexian; Tian, Tian; He, Huaqin

2017-08-01

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae . Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.

A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities.

PubMed

Soneson, Charlotte; Fontes, Magnus

2012-01-01

Analysis of multivariate data sets from, for example, microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper, we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward comparison between any 2 lists. It can also be used to generate new more stable gene rankings incorporating more information from the experimental data. Using 2 microarray data sets, we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance of the rankings.

New insights about host response to smallpox using microarray data.

PubMed

Esteves, Gustavo H; Simoes, Ana C Q; Souza, Estevao; Dias, Rodrigo A; Ospina, Raydonal; Venancio, Thiago M

2007-08-24

Smallpox is a lethal disease that was endemic in many parts of the world until eradicated by massive immunization. Due to its lethality, there are serious concerns about its use as a bioweapon. Here we analyze publicly available microarray data to further understand survival of smallpox infected macaques, using systems biology approaches. Our goal is to improve the knowledge about the progression of this disease. We used KEGG pathways annotations to define groups of genes (or modules), and subsequently compared them to macaque survival times. This technique provided additional insights about the host response to this disease, such as increased expression of the cytokines and ECM receptors in the individuals with higher survival times. These results could indicate that these gene groups could influence an effective response from the host to smallpox. Macaques with higher survival times clearly express some specific pathways previously unidentified using regular gene-by-gene approaches. Our work also shows how third party analysis of public datasets can be important to support new hypotheses to relevant biological problems.

Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery.

PubMed

Drawnel, Faye Marie; Zhang, Jitao David; Küng, Erich; Aoyama, Natsuyo; Benmansour, Fethallah; Araujo Del Rosario, Andrea; Jensen Zoffmann, Sannah; Delobel, Frédéric; Prummer, Michael; Weibel, Franziska; Carlson, Coby; Anson, Blake; Iacone, Roberto; Certa, Ulrich; Singer, Thomas; Ebeling, Martin; Prunotto, Marco

2017-05-18

Today, novel therapeutics are identified in an environment which is intrinsically different from the clinical context in which they are ultimately evaluated. Using molecular phenotyping and an in vitro model of diabetic cardiomyopathy, we show that by quantifying pathway reporter gene expression, molecular phenotyping can cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously. Molecular phenotyping was applicable to compounds with a range of binding specificities and triaged false positives derived from high-content screening assays. The technique identified a class of calcium-signaling modulators that can reverse disease-regulated pathways and phenotypes, which was validated by structurally distinct compounds of relevant classes. Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade. Copyright © 2017 Elsevier Ltd. All rights reserved.

Microarray profiling analysis uncovers common molecular mechanisms of rubella virus, human cytomegalovirus, and herpes simplex virus type 2 infections in ECV304 cells.

PubMed

Mo, X; Xu, L; Yang, Q; Feng, H; Peng, J; Zhang, Y; Yuan, W; Wang, Y; Li, Y; Deng, Y; Wan, Y; Chen, Z; Li, F; Wu, X

2011-08-01

To study the common molecular mechanisms of various viruses infections that might result in congential cardiovascular diseases in perinatal period, changes in mRNA expression levels of ECV304 cells infected by rubella virus (RUBV), human cytomegalovirus (HCMV), and herpes simplex virus type 2 (HSV-2) were analyzed using a microarray system representing 18,716 human genes. 99 genes were found to exhibit differential expression (80 up-regulated and 19 down-regulated). Biological process analysis showed that 33 signaling pathways including 22 genes were relevant significantly to RV, HCMV and HSV-II infections. Of these 33 biological processes, 28 belong to one-gene biological processes and 5 belong to multiple-gene biological processes. Gene annotation indicated that the 5 multiple-gene biological processes including regulation of cell growth, collagen fibril organization, mRNA transport, cell adhesion and regulation of cell shape, and seven down- or up-regulated genes [CRIM1 (cysteine rich transmembrane BMP regulator 1), WISP2 (WNT1 inducible signaling pathway protein 2), COL12A1 (collagen, type XII, alpha 1), COL11A2 (collagen, type XI, alpha 2), CNTN5 (contactin 5), DDR1 (discoidin domain receptor tyrosine kinase 1), VEGF (vascular endothelial growth factor precursor)], are significantly correlated to RUBV, HCMV and HSV-2 infections in ECV304 cells. The results obtained in this study suggested the common molecular mechanisms of viruses infections that might result in congential cardiovascular diseases.

Early constraints in sexual dimorphism: survival benefits of feminized phenotypes.

PubMed

López-Rull, I; Vergara, P; Martínez-Padilla, J; Fargallo, J A

2016-02-01

Sexual dimorphism (SD) has evolved in response to selection pressures that differ between sexes. Since such pressures change across an individual's life, SD may vary within age classes. Yet, little is known about how selection on early phenotypes may drive the final SD observed in adults. In many dimorphic species, juveniles resemble adult females rather than adult males, meaning that out of the selective pressures established by sexual selection feminized phenotypes may be adaptive. If true, fitness benefits of early female-like phenotypes may constrain the expression of male phenotypes in adulthood. Using the common kestrel Falco tinnunculus as a study model, we evaluated the fitness advantages of expressing more feminized phenotypes at youth. Although more similar to adult females than to adult males, common kestrel fledglings are still sexually dimorphic in size and coloration. Integrating morphological and chromatic variables, we analysed the phenotypic divergence between sexes as a measure of how much each individual looks like the sex to which it belongs (phenotypic sexual resemblance, PSR). We then tested the fitness benefits associated with PSR by means of the probability of recruitment in the population. We found a significant interaction between PSR and sex, showing that in both sexes more feminized phenotypes recruited more into the population than less feminized phenotypes. Moreover, males showed lower PSR than females and a higher proportion of incorrect sex classifications. These findings suggest that the mechanisms in males devoted to resembling female phenotypes in youth, due to a trend to increase fitness through more feminized phenotypes, may provide a mechanism to constrain the SD in adulthood. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

Rare genomic rearrangement in a boy with Williams-Beuren syndrome associated to XYY syndrome and intriguing behavior.

PubMed

Dutra, Roberta L; Piazzon, Flavia B; Zanardo, Évelin A; Costa, Thais Virginia Moura Machado; Montenegro, Marília M; Novo-Filho, Gil M; Dias, Alexandre T; Nascimento, Amom M; Kim, Chong Ae; Kulikowski, Leslie D

2015-12-01

Williams-Beuren syndrome (WBS) is caused by a hemizygous contiguous gene microdeletion of 1.55-1.84 Mb at 7q11.23 region. Approximately, 28 genes have been shown to contribute to classical phenotype of SWB with presence of dysmorphic facial features, supravalvular aortic stenosis (SVAS), intellectual disability, and overfriendliness. With the use of Microarray-based comparative genomic hybridization and other molecular cytogenetic techniques, is possible define with more accuracy partial or atypical deletion and refine the genotype-phenotype correlation. Here, we report on a rare genomic structural rearrangement in a boy with atypical deletion in 7q11.23 and XYY syndrome with characteristic clinical signs, but not sufficient for the diagnosis of WBS. Cytogenetic analysis of G-banding showed a karyotype 47,XYY. Analysis of DNA with the technique of MLPA (Multiplex Ligation-dependent Probe Amplification) using kits a combination of kits (P064, P036, P070, and P029) identified an atypical deletion on 7q11.23. In addition, high resolution SNP Oligonucleotide Microarray Analysis (SNP-array) confirmed the alterations found by MLPA and revealed others pathogenic CNVs, in the chromosomes 7 and X. The present report demonstrates an association not yet described in literature, between Williams-Beuren syndrome and 47,XYY. The identification of atypical deletion in 7q11.23 concomitant to additional pathogenic CNVs in others genomic regions allows a better comprehension of clinical consequences of atypical genomic rearrangements. © 2015 Wiley Periodicals, Inc.

Uniparental Disomy of Chromosome 15 in Two Cases by Chromosome Microarray: A Lesson Worth Thinking.

PubMed

Liu, Shu; Zhang, Kaihui; Song, Fengling; Yang, Yali; Lv, Yuqiang; Gao, Min; Liu, Yi; Gai, Zhongtao

2017-01-01

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are neurogenetic disorders caused by loss of function of the imprinted genes at 15q11q13. A 5-7 Mb paternal/maternal deletion of chromosomal region 15q11.2q13 is the major genetic cause of PWS/AS, but in a small group of patients, the PWS/AS phenotype can result from maternal/paternal uniparental disomy (UPD) of chromosome 15. Various mechanisms leading to UPD include gametic complementation, trisomy rescue, and compensatory UPD, which can be inferred from the pattern of uniparental heterodisomy (heteroUPD) or uniparental isodisomy (isoUPD). However, heteroUPD and isoUPD, especially mixed heteroUPD and isoUPD, are very rare in patients with PWS/AS. Here, we report 2 children with PWS/AS caused by mixed segmental heteroUPD 15 and isoUPD 15 which failed to be identified by chromosome microarray (CMA) but could be detected by other molecular genetic methods. The present report unravels the mechanism of mixed iso/heteroUPD 15 in PWS/AS and phenotype-genotype correlations. Moreover, our study suggests that CMA is prone to misdiagnosis for imprinting disorders such as PWS/AS, though it is considered a highly useful tool for copy number variations. As a result, other molecular detection methods, such as methylation analysis and STR marker analysis for UPD, should be supplementary used in this situation. © 2017 S. Karger AG, Basel.

Optimized Probe Masking for Comparative Transcriptomics of Closely Related Species

PubMed Central

Poeschl, Yvonne; Delker, Carolin; Trenner, Jana; Ullrich, Kristian Karsten; Quint, Marcel; Grosse, Ivo

2013-01-01

Microarrays are commonly applied to study the transcriptome of specific species. However, many available microarrays are restricted to model organisms, and the design of custom microarrays for other species is often not feasible. Hence, transcriptomics approaches of non-model organisms as well as comparative transcriptomics studies among two or more species often make use of cost-intensive RNAseq studies or, alternatively, by hybridizing transcripts of a query species to a microarray of a closely related species. When analyzing these cross-species microarray expression data, differences in the transcriptome of the query species can cause problems, such as the following: (i) lower hybridization accuracy of probes due to mismatches or deletions, (ii) probes binding multiple transcripts of different genes, and (iii) probes binding transcripts of non-orthologous genes. So far, methods for (i) exist, but these neglect (ii) and (iii). Here, we propose an approach for comparative transcriptomics addressing problems (i) to (iii), which retains only transcript-specific probes binding transcripts of orthologous genes. We apply this approach to an Arabidopsis lyrata expression data set measured on a microarray designed for Arabidopsis thaliana, and compare it to two alternative approaches, a sequence-based approach and a genomic DNA hybridization-based approach. We investigate the number of retained probe sets, and we validate the resulting expression responses by qRT-PCR. We find that the proposed approach combines the benefit of sequence-based stringency and accuracy while allowing the expression analysis of much more genes than the alternative sequence-based approach. As an added benefit, the proposed approach requires probes to detect transcripts of orthologous genes only, which provides a superior base for biological interpretation of the measured expression responses. PMID:24260119

Quantitative phenotyping via deep barcode sequencing

PubMed Central

Smith, Andrew M.; Heisler, Lawrence E.; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J.; Chee, Mark; Roth, Frederick P.; Giaever, Guri; Nislow, Corey

2009-01-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or “Bar-seq,” outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that ∼20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene–environment interactions on a genome-wide scale. PMID:19622793

Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data.

PubMed

Xu, Lingyang; Hou, Yali; Bickhart, Derek M; Song, Jiuzhou; Liu, George E

2013-06-25

Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be utilized for copy number detection. Substantial progress has been made in array design and CNV calling algorithms and at least 10 comparison studies in humans have been published to assess them. In this review, we first survey the literature on existing microarray platforms and CNV calling algorithms. We then examine a number of CNV calling tools to evaluate their impacts using bovine high-density SNP data. Large incongruities in the results from different CNV calling tools highlight the need for standardizing array data collection, quality assessment and experimental validation. Only after careful experimental design and rigorous data filtering can the impacts of CNVs on both normal phenotypic variability and disease susceptibility be fully revealed.

A universal reference sample derived from clone vector for improved detection of differential gene expression

PubMed Central

Khan, Rishi L; Gonye, Gregory E; Gao, Guang; Schwaber, James S

2006-01-01

Background Using microarrays by co-hybridizing two samples labeled with different dyes enables differential gene expression measurements and comparisons across slides while controlling for within-slide variability. Typically one dye produces weaker signal intensities than the other often causing signals to be undetectable. In addition, undetectable spots represent a large problem for two-color microarray designs and most arrays contain at least 40% undetectable spots even when labeled with reference samples such as Stratagene's Universal Reference RNAs™. Results We introduce a novel universal reference sample that produces strong signal for all spots on the array, increasing the average fraction of detectable spots to 97%. Maximizing detectable spots on the reference image channel also decreases the variability of microarray data allowing for reliable detection of smaller differential gene expression changes. The reference sample is derived from sequence contained in the parental EST clone vector pT7T3D-Pac and is called vector RNA (vRNA). We show that vRNA can also be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This reference sample can be made inexpensively in large quantities as a renewable resource that is consistent across experiments. Conclusion Results of this study show that vRNA provides a useful universal reference that yields high signal for almost all spots on a microarray, reduces variation and allows for comparisons between experiments and laboratories. Further, it can be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This type of reference allows for detection of small changes in differential expression while reference designs in general allow for large-scale multivariate experimental designs. vRNA in combination with reference designs enable systems biology microarray experiments of small physiologically relevant changes. PMID:16677381

Analyzing gene perturbation screens with nested effects models in R and bioconductor.

PubMed

Fröhlich, Holger; Beissbarth, Tim; Tresch, Achim; Kostka, Dennis; Jacob, Juby; Spang, Rainer; Markowetz, F

2008-11-01

Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs. Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org.

Carbapenemase-producing Enterobacteriaceae: a 2-year surveillance in a hospital in Iaşi, Romania.

PubMed

Braun, Sascha D; Dorneanu, Olivia S; Vremeră, Teodora; Reißig, Annett; Monecke, Stefan; Ehricht, Ralf

2016-01-01

Limited information is currently available about the prevalence of carbapenemase-producing Enterobacteriaceae (CPE) in Romania. Routine tests of 1,993 clinical isolates at a hospital in Iaşi yielded 46 isolates that were resistant to carbapenems. All 46 isolates were phenotypically and genotypically analyzed using VITEK-2 and DNA microarray-based assays. Isolates were assigned to Klebsiella pneumoniae and Enterobacter cloacae. For 39 isolates, carbapenem resistance was confirmed and 37 harbored at least one carbapenem resistance gene. Two isolates were probably resistant due to AmpC β-lactamases in combination with a porin loss. The overall concordance between detected phenotype and genotype was 95%. Our data show that carbapenemase-producing isolates with different underlying resistance mechanisms are still rare in Iaşi, but the global rise of CPE warrants intensified surveillance.

The Cognitive and Behavioral Phenotypes of Individuals with CHRNA7 Duplications.

PubMed

Gillentine, M A; Berry, L N; Goin-Kochel, R P; Ali, M A; Ge, J; Guffey, D; Rosenfeld, J A; Hannig, V; Bader, P; Proud, M; Shinawi, M; Graham, B H; Lin, A; Lalani, S R; Reynolds, J; Chen, M; Grebe, T; Minard, C G; Stankiewicz, P; Beaudet, A L; Schaaf, C P

2017-03-01

Chromosome 15q11q13 is among the least stable regions in the genome due to its highly complex genomic architecture. Low copy repeat elements at 15q13.3 facilitate recurrent copy number variants (CNVs), with deletions established as pathogenic and CHRNA7 implicated as a candidate gene. However, the pathogenicity of duplications of CHRNA7 is unclear, as they are found in affected probands as well as in reportedly healthy parents and unaffected control individuals. We evaluated 18 children with microduplications involving CHRNA7, identified by clinical chromosome microarray analysis (CMA). Comprehensive phenotyping revealed high prevalence of developmental delay/intellectual disability, autism spectrum disorder, and attention deficit/hyperactivity disorder. As CHRNA7 duplications are the most common CNVs identified by clinical CMA, this study provides anticipatory guidance for those involved with care of affected individuals.

Systems biology of cancer biomarker detection.

PubMed

Mitra, Sanga; Das, Smarajit; Chakrabarti, Jayprokas

2013-01-01

Cancer systems-biology is an ever-growing area of research due to explosion of data; how to mine these data and extract useful information is the problem. To have an insight on carcinogenesis one need to systematically mine several resources, such as databases, microarray and next-generation sequences. This review encompasses management and analysis of cancer data, databases construction and data deposition, whole transcriptome and genome comparison, analysing results from high throughput experiments to uncover cellular pathways and molecular interactions, and the design of effective algorithms to identify potential biomarkers. Recent technical advances such as ChIP-on-chip, ChIP-seq and RNA-seq can be applied to get epigenetic information transformed into a high-throughput endeavour to which systems biology and bioinformatics are making significant inroads. The data from ENCODE and GENCODE projects available through UCSC genome browser can be considered as benchmark for comparison and meta-analysis. A pipeline for integrating next generation sequencing data, microarray data, and putting them together with the existing database is discussed. The understanding of cancer genomics is changing the way we approach cancer diagnosis and treatment. To give a better understanding of utilizing available resources' we have chosen oral cancer to show how and what kind of analysis can be done. This review is a computational genomic primer that provides a bird's eye view of computational and bioinformatics' tools currently available to perform integrated genomic and system biology analyses of several carcinoma.

Rational design of peptide affinity ligands for the purification of therapeutic enzymes.

PubMed

Trasatti, John P; Woo, James; Ladiwala, Asif; Cramer, Steven; Karande, Pankaj

2018-04-25

Non-mAb biologics represent a growing class of therapeutics under clinical development. Although affinity chromatography is a potentially attractive approach for purification, the development of platform technologies, such as Protein A for mAbs, has been challenging due to the inherent chemical and structural diversity of these molecules. Here, we present our studies on the rapid development of peptide affinity ligands for the purification of biologics using a prototypical enzyme therapeutic in clinical use. Employing a suite of de novo rational and combinatorial design strategies we designed and screened a library of peptides on microarray platforms for their ability to bind to the target with high affinity and selectivity in cell culture fluid. Lead peptides were evaluated on resin in batch conditions and compared with a commercially available resin to evaluate their efficacy. Two lead candidates identified from microarray studies provided high binding capacity to the target while demonstrating high selectivity against culture contaminants and product variants compared to a commercial resin system. These findings provide a proof-of-concept for developing affinity peptide-based bioseparations processes for a target biologic. Peptide affinity ligand design and screening approaches presented in this work can also be easily translated to other biologics of interest. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 2018. © 2018 American Institute of Chemical Engineers.

Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

PubMed

Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-03-09

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold ([Formula: see text]). These phenotypes are more heritable ([Formula: see text]) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. Copyright © 2015 Brown et al.

Low-Density microarray technologies for rapid human norovirus genotyping

USDA-ARS?s Scientific Manuscript database

Human noroviruses (HuNoV) are the most common cause of food borne disease and viruses are likely responsible for a large proportion of foodborne diseases of unknown etiology. Recent advancements in molecular biology, bioinformatics, epidemiology, and risk analysis have aided the study of these agent...

Emerging molecular phenotypes of asthma

PubMed Central

Ray, Anuradha; Oriss, Timothy B.

2014-01-01

Although asthma has long been considered a heterogeneous disease, attempts to define subgroups of asthma have been limited. In recent years, both clinical and statistical approaches have been utilized to better merge clinical characteristics, biology, and genetics. These combined characteristics have been used to define phenotypes of asthma, the observable characteristics of a patient determined by the interaction of genes and environment. Identification of consistent clinical phenotypes has now been reported across studies. Now the addition of various 'omics and identification of specific molecular pathways have moved the concept of clinical phenotypes toward the concept of molecular phenotypes. The importance of these molecular phenotypes is being confirmed through the integration of molecularly targeted biological therapies. Thus the global term asthma is poised to become obsolete, being replaced by terms that more specifically identify the pathology associated with the disease. PMID:25326577

Similar patterns of frequency-dependent selection on animal personalities emerge in three species of social spiders.

PubMed

Lichtenstein, J L L; Pruitt, J N

2015-06-01

Frequency-dependent selection is thought to be a major contributor to the maintenance of phenotypic variation. We tested for frequency-dependent selection on contrasting behavioural strategies, termed here 'personalities', in three species of social spiders, each thought to represent an independent evolutionary origin of sociality. The evolution of sociality in the spider genus Anelosimus is consistently met with the emergence of two temporally stable discrete personality types: an 'aggressive' or 'docile' form. We assessed how the foraging success of each phenotype changes as a function of its representation within a colony. We did this by creating experimental colonies of various compositions (six aggressives, three aggressives and three dociles, one aggressive and five dociles, six dociles), maintaining them in a common garden for 3 weeks, and tracking the mass gained by individuals of either phenotype. We found that both the docile and aggressive phenotypes experienced their greatest mass gain in mixed colonies of mostly docile individuals. However, the performance of both phenotypes decreased as the frequency of the aggressive phenotype increased. Nearly identical patterns of phenotype-specific frequency dependence were recovered in all three species. Naturally occurring colonies of these spiders exhibit mixtures dominated by the docile phenotype, suggesting that these spiders may have evolved mechanisms to maintain the compositions that maximize the success of the colony without compromising the expected reproductive output of either phenotype. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

Transcriptomics as a tool for assessing the scalability of mammalian cell perfusion systems.

PubMed

Jayapal, Karthik P; Goudar, Chetan T

2014-01-01

DNA microarray-based transcriptomics have been used to determine the time course of laboratory and manufacturing-scale perfusion bioreactors in an attempt to characterize cell physiological state at these two bioreactor scales. Given the limited availability of genomic data for baby hamster kidney (BHK) cells, a Chinese hamster ovary (CHO)-based microarray was used following a feasibility assessment of cross-species hybridization. A heat shock experiment was performed using both BHK and CHO cells and resulting DNA microarray data were analyzed using a filtering criteria of perfect match (PM)/single base mismatch (MM) > 1.5 and PM-MM > 50 to exclude probes with low specificity or sensitivity for cross-species hybridizations. For BHK cells, 8910 probe sets (39 %) passed the cutoff criteria, whereas 12,961 probe sets (56 %) passed the cutoff criteria for CHO cells. Yet, the data from BHK cells allowed distinct clustering of heat shock and control samples as well as identification of biologically relevant genes as being differentially expressed, indicating the utility of cross-species hybridization. Subsequently, DNA microarray analysis was performed on time course samples from laboratory- and manufacturing-scale perfusion bioreactors that were operated under the same conditions. A majority of the variability (37 %) was associated with the first principal component (PC-1). Although PC-1 changed monotonically with culture duration, the trends were very similar in both the laboratory and manufacturing-scale bioreactors. Therefore, despite time-related changes to the cell physiological state, transcriptomic fingerprints were similar across the two bioreactor scales at any given instance in culture. Multiple genes were identified with time-course expression profiles that were very highly correlated (> 0.9) with bioprocess variables of interest. Although the current incomplete annotation limits the biological interpretation of these observations, their full potential may be realized in due course when richer genomic data become available. By taking a pragmatic approach of transcriptome fingerprinting, we have demonstrated the utility of systems biology to support the comparability of laboratory and manufacturing-scale perfusion systems. Scale-down model qualification is the first step in process characterization and hence is an integral component of robust regulatory filings. Augmenting the current paradigm, which relies primarily on cell culture and product quality information, with gene expression data can help make a substantially stronger case for similarity. With continued advances in systems biology approaches, we expect them to be seamlessly integrated into bioprocess development, which can translate into more robust and high yielding processes that can ultimately reduce cost of care for patients.

Is this the real time for genomics?

PubMed

Guarnaccia, Maria; Gentile, Giulia; Alessi, Enrico; Schneider, Claudio; Petralia, Salvatore; Cavallaro, Sebastiano

2014-01-01

In the last decades, molecular biology has moved from gene-by-gene analysis to more complex studies using a genome-wide scale. Thanks to high-throughput genomic technologies, such as microarrays and next-generation sequencing, a huge amount of information has been generated, expanding our knowledge on the genetic basis of various diseases. Although some of this information could be transferred to clinical diagnostics, the technologies available are not suitable for this purpose. In this review, we will discuss the drawbacks associated with the use of traditional DNA microarrays in diagnostics, pointing out emerging platforms that could overcome these obstacles and offer a more reproducible, qualitative and quantitative multigenic analysis. New miniaturized and automated devices, called Lab-on-Chip, begin to integrate PCR and microarray on the same platform, offering integrated sample-to-result systems. The introduction of this kind of innovative devices may facilitate the transition of genome-based tests into clinical routine. Copyright © 2014. Published by Elsevier Inc.

A Sol-gel Integrated Dual-readout Microarray Platform for Quantification and Identification of Prostate-specific Antigen.

PubMed

Lee, SangWook; Lee, Jong Hyun; Kwon, Hyuck Gi; Laurell, Thomas; Jeong, Ok Chan; Kim, Soyoun

2018-01-01

Here, we report a sol-gel integrated affinity microarray for on-chip matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) that enables capture and identification of prostate?specific antigen (PSA) in samples. An anti-PSA antibody (H117) was mixed with a sol?gel, and the mixture was spotted onto a porous silicon (pSi) surface without additional surface modifications. The antibody easily penetrates the sol-gel macropore fluidic network structure, making possible high affinities. To assess the capture affinity of the platform, we performed a direct assay using fluorescein isothiocyanate-labeled PSA. Pure PSA was subjected to on-chip MALDI-TOF-MS analysis, yielding three clear mass peptide peaks (m/z = 1272, 1407, and 1872). The sol-gel microarray platform enables dual readout of PSA both fluorometric and MALDI-TOF MS analysis in biological samples. Here we report a useful method for a means for discovery of biomarkers in complex body fluids.

Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

PubMed Central

Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; Haaland, D. M.; Timlin, J. A.; Elbourne, L. D. H.; Palenik, B.; Paulsen, I. T.

2009-01-01

Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in part to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition. PMID:19404483

Gene Expression Omnibus (GEO): Microarray data storage, submission, retrieval, and analysis

PubMed Central

Barrett, Tanya

2006-01-01

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a MIAME- (Minimum Information About a Microarray Experiment) supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1,500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly Web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data, at the level of individual genes or entire studies. This chapter describes how the data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/. PMID:16939800

Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

DOE PAGES

Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; ...

2009-01-01

Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in partmore » to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition.« less

Spectral gene set enrichment (SGSE).

PubMed

Frost, H Robert; Li, Zhigang; Moore, Jason H

2015-03-03

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracy-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Unsupervised gene set testing can provide important information about the biological signal held in high-dimensional genomic data sets. Because it uses the association between gene sets and samples PCs to generate a measure of unsupervised enrichment, the SGSE method is independent of cluster or network creation algorithms and, most importantly, is able to utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely to represent noise.

Association between the CpG island methylator phenotype and its prognostic significance in primary pulmonary adenocarcinoma.

PubMed

Koh, Young Wha; Chun, Sung-Min; Park, Young-Soo; Song, Joon Seon; Lee, Geon Kook; Khang, Shin Kwang; Jang, Se Jin

2016-08-01

Aberrant methylation of promoter CpG islands is one of the most important inactivation mechanisms for tumor suppressor and tumor-related genes. Previous studies using genome-wide DNA methylation microarray analysis have suggested the existence of a CpG island methylator phenotype (CIMP) in lung adenocarcinomas. Although the biological behavior of these tumors varies according to tumor stage, no large-scale study has examined the CIMP in lung adenocarcinoma patients according to tumor stage. Furthermore, there have been no reported results regarding the clinical significance of each of the six CIMP markers. To examine the CIMP in patients with pulmonary adenocarcinoma after a surgical resection, we performed methylation analysis of six genes (CCNA1, ACAN, GFRA1, EDARADD, MGC45800, and p16 (INK4A)) in 230 pulmonary adenocarcinoma cases using the SEQUENOM MassARRAY platform. Fifty-four patients (28 %, 54/191) were in the CIMP-high (CIMP-H) group associated with high nodal stage (P = 0.007), the presence of micropapillary or solid histology (P = 0.003), and the absence of an epidermal growth factor receptor (EGFR) mutation (P = 0.002). By multivariate analysis, CIMP was an independent prognostic marker for overall survival (OS) and disease-specific survival (P = 0.03 and P = 0.43, respectively). In the stage I subgroups alone, CIMP-H patients had lower OS rates than the CIMP-low (CIMP-L) group (P = 0.041). Of the six CIMP markers, ACAN alone was significantly associated with patient survival. CIMP predicted the risk of progression independently of clinicopathological variables and enables the stratification of pulmonary adenocarcinoma patients, particularly among stage I cases.

The MGED ontology: a framework for describing functional genomics experiments.

PubMed

Stoeckert, Christian J; Parkinson, Helen

2003-01-01

The Microarray Gene Expression Data (MGED) society was formed with an initial focus on experiments involving microarray technology. Despite the diversity of applications, there are common concepts used and a common need to capture experimental information in a standardized manner. In building the MGED ontology, it was recognized that it would be impractical to cover all the different types of experiments on all the different types of organisms by listing and defining all the types of organisms and their properties. Our solution was to create a framework for describing microarray experiments with an initial focus on the biological sample and its manipulation. For concepts that are common for many species, we could provide a manageable listing of controlled terms. For concepts that are species-specific or whose values cannot be readily listed, we created an 'OntologyEntry' concept that referenced an external resource. The MGED ontology is a work in progress that needs additional instances and particularly needs constraints to be added. The ontology currently covers the experimental sample and design, and we have begun capturing aspects of the microarrays themselves as well. The primary application of the ontology will be to develop forms for entering information into databases, and consequently allowing queries, taking advantage of the structure provided by the ontology. The application of an ontology of experimental conditions extends beyond microarray experiments and, as the scope of MGED includes other aspects of functional genomics, so too will the MGED ontology.

Bacterial identification and subtyping using DNA microarray and DNA sequencing.

PubMed

Al-Khaldi, Sufian F; Mossoba, Magdi M; Allard, Marc M; Lienau, E Kurt; Brown, Eric D

2012-01-01

The era of fast and accurate discovery of biological sequence motifs in prokaryotic and eukaryotic cells is here. The co-evolution of direct genome sequencing and DNA microarray strategies not only will identify, isotype, and serotype pathogenic bacteria, but also it will aid in the discovery of new gene functions by detecting gene expressions in different diseases and environmental conditions. Microarray bacterial identification has made great advances in working with pure and mixed bacterial samples. The technological advances have moved beyond bacterial gene expression to include bacterial identification and isotyping. Application of new tools such as mid-infrared chemical imaging improves detection of hybridization in DNA microarrays. The research in this field is promising and future work will reveal the potential of infrared technology in bacterial identification. On the other hand, DNA sequencing by using 454 pyrosequencing is so cost effective that the promise of $1,000 per bacterial genome sequence is becoming a reality. Pyrosequencing technology is a simple to use technique that can produce accurate and quantitative analysis of DNA sequences with a great speed. The deposition of massive amounts of bacterial genomic information in databanks is creating fingerprint phylogenetic analysis that will ultimately replace several technologies such as Pulsed Field Gel Electrophoresis. In this chapter, we will review (1) the use of DNA microarray using fluorescence and infrared imaging detection for identification of pathogenic bacteria, and (2) use of pyrosequencing in DNA cluster analysis to fingerprint bacterial phylogenetic trees.

Exploiting fluorescence for multiplex immunoassays on protein microarrays

NASA Astrophysics Data System (ADS)

Herbáth, Melinda; Papp, Krisztián; Balogh, Andrea; Matkó, János; Prechl, József

2014-09-01

Protein microarray technology is becoming the method of choice for identifying protein interaction partners, detecting specific proteins, carbohydrates and lipids, or for characterizing protein interactions and serum antibodies in a massively parallel manner. Availability of the well-established instrumentation of DNA arrays and development of new fluorescent detection instruments promoted the spread of this technique. Fluorescent detection has the advantage of high sensitivity, specificity, simplicity and wide dynamic range required by most measurements. Fluorescence through specifically designed probes and an increasing variety of detection modes offers an excellent tool for such microarray platforms. Measuring for example the level of antibodies, their isotypes and/or antigen specificity simultaneously can offer more complex and comprehensive information about the investigated biological phenomenon, especially if we take into consideration that hundreds of samples can be measured in a single assay. Not only body fluids, but also cell lysates, extracted cellular components, and intact living cells can be analyzed on protein arrays for monitoring functional responses to printed samples on the surface. As a rapidly evolving area, protein microarray technology offers a great bulk of information and new depth of knowledge. These are the features that endow protein arrays with wide applicability and robust sample analyzing capability. On the whole, protein arrays are emerging new tools not just in proteomics, but glycomics, lipidomics, and are also important for immunological research. In this review we attempt to summarize the technical aspects of planar fluorescent microarray technology along with the description of its main immunological applications.

Bayes multiple decision functions.

PubMed

Wu, Wensong; Peña, Edsel A

2013-01-01

This paper deals with the problem of simultaneously making many ( M ) binary decisions based on one realization of a random data matrix X . M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting through simulation studies. The procedure is also applied to a subset of a microarray data set from a colon cancer study.

Low-Molecular-Weight Protein Tyrosine Phosphatase Predicts Prostate Cancer Outcome by Increasing the Metastatic Potential.

PubMed

Ruela-de-Sousa, Roberta R; Hoekstra, Elmer; Hoogland, A Marije; Queiroz, Karla C Souza; Peppelenbosch, Maikel P; Stubbs, Andrew P; Pelizzaro-Rocha, Karin; van Leenders, Geert J L H; Jenster, Guido; Aoyama, Hiroshi; Ferreira, Carmen V; Fuhler, Gwenny M

2016-04-01

Low-risk patients suffering from prostate cancer (PCa) are currently placed under active surveillance rather than undergoing radical prostatectomy. However, clear parameters for selecting the right patient for each strategy are not available, and new biomarkers and treatment modalities are needed. Low-molecular-weight protein tyrosine phosphatase (LMWPTP) could present such a target. To correlate expression levels of LMWPTP in primary PCa to clinical outcome, and determine the role of LMWPTP in prostate tumor cell biology. Acid phosphatase 1, soluble (ACP1) expression was analyzed on microarray data sets, which were subsequently used in Ingenuity Pathway Analysis. Immunohistochemistry was performed on a tissue microarray containing material of 481 PCa patients whose clinicopathologic data were recorded. PCa cell line models were used to investigate the role of LMWPTP in cell proliferation, migration, adhesion, and anoikis resistance. The association between LMWPTP expression and clinical and pathologic outcomes was calculated using chi-square correlations and multivariable Cox regression analysis. Functional consequences of LMWPTP overexpression or downregulation were determined using migration and adhesion assays, confocal microscopy, Western blotting, and proliferation assays. LMWPTP expression was significantly increased in human PCa and correlated with earlier recurrence of disease (hazard ratio [HR]:1.99; p<0.001) and reduced patient survival (HR: 1.53; p=0.04). Unbiased Ingenuity analysis comparing cancer and normal prostate suggests migratory propensities in PCa. Indeed, overexpression of LMWPTP increases PCa cell migration, anoikis resistance, and reduces activation of focal adhesion kinase/paxillin, corresponding to decreased adherence. Overexpression of LMWPTP in PCa confers a malignant phenotype with worse clinical outcome. Prospective follow-up should determine the clinical potential of LMWPTP overexpression. These findings implicate low-molecular-weight protein tyrosine phosphatase as a novel oncogene in prostate cancer and could offer the possibility of using this protein as biomarker or target for treatment of this disease. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Developmental mechanisms underlying variation in craniofacial disease and evolution.

PubMed

Fish, Jennifer L

2016-07-15

Craniofacial disease phenotypes exhibit significant variation in penetrance and severity. Although many genetic contributions to phenotypic variation have been identified, genotype-phenotype correlations remain imprecise. Recent work in evolutionary developmental biology has exposed intriguing developmental mechanisms that potentially explain incongruities in genotype-phenotype relationships. This review focuses on two observations from work in comparative and experimental animal model systems that highlight how development structures variation. First, multiple genetic inputs converge on relatively few developmental processes. Investigation of when and how variation in developmental processes occurs may therefore help predict potential genetic interactions and phenotypic outcomes. Second, genetic mutation is typically associated with an increase in phenotypic variance. Several models outlining developmental mechanisms underlying mutational increases in phenotypic variance are discussed using Satb2-mediated variation in jaw size as an example. These data highlight development as a critical mediator of genotype-phenotype correlations. Future research in evolutionary developmental biology focusing on tissue-level processes may help elucidate the "black box" between genotype and phenotype, potentially leading to novel treatment, earlier diagnoses, and better clinical consultations for individuals affected by craniofacial anomalies. Copyright © 2015 Elsevier Inc. All rights reserved.

Role of Arabidopsis ABF1/3/4 during det1 germination in salt and osmotic stress conditions.

PubMed

Fernando, V C Dilukshi; Al Khateeb, Wesam; Belmonte, Mark F; Schroeder, Dana F

2018-05-01

Arabidopsis det1 mutants exhibit salt and osmotic stress resistant germination. This phenotype requires HY5, ABF1, ABF3, and ABF4. While DE-ETIOLATED 1 (DET1) is well known as a negative regulator of light development, here we describe how det1 mutants also exhibit altered responses to salt and osmotic stress, specifically salt and mannitol resistant germination. LONG HYPOCOTYL 5 (HY5) positively regulates both light and abscisic acid (ABA) signalling. We found that hy5 suppressed the det1 salt and mannitol resistant germination phenotype, thus, det1 stress resistant germination requires HY5. We then queried publically available microarray datasets to identify genes downstream of HY5 that were differentially expressed in det1 mutants. Our analysis revealed that ABA regulated genes, including ABA RESPONSIVE ELEMENT BINDING FACTOR 3 (ABF3), are downregulated in det1 seedlings. We found that ABF3 is induced by salt in wildtype seeds, while homologues ABF4 and ABF1 are repressed, and all three genes are underexpressed in det1 seeds. We then investigated the role of ABF3, ABF4, and ABF1 in det1 phenotypes. Double mutant analysis showed that abf3, abf4, and abf1 all suppress the det1 salt/osmotic stress resistant germination phenotype. In addition, abf1 suppressed det1 rapid water loss and open stomata phenotypes. Thus interactions between ABF genes contribute to det1 salt/osmotic stress response phenotypes.

Complementary techniques: validation of gene expression data by quantitative real time PCR.

PubMed

Provenzano, Maurizio; Mocellin, Simone

2007-01-01

Microarray technology can be considered the most powerful tool for screening gene expression profiles of biological samples. After data mining, results need to be validated with highly reliable biotechniques allowing for precise quantitation of transcriptional abundance of identified genes. Quantitative real time PCR (qrt-PCR) technology has recently reached a level of sensitivity, accuracy and practical ease that support its use as a routine bioinstrumentation for gene level measurement. Currently, qrt-PCR is considered by most experts the most appropriate method to confirm or confute microarray-generated data. The knowledge of the biochemical principles underlying qrt-PCR as well as some related technical issues must be beard in mind when using this biotechnology.

Loss of the Mechanotransducer Zyxin Promotes a Synthetic Phenotype of Vascular Smooth Muscle Cells

PubMed Central

Ghosh, Subhajit; Kollar, Branislav; Nahar, Taslima; Suresh Babu, Sahana; Wojtowicz, Agnieszka; Sticht, Carsten; Gretz, Norbert; Wagner, Andreas H; Korff, Thomas; Hecker, Markus

2015-01-01

Background Exposure of vascular smooth muscle cells (VSMCs) to excessive cyclic stretch such as in hypertension causes a shift in their phenotype. The focal adhesion protein zyxin can transduce such biomechanical stimuli to the nucleus of both endothelial cells and VSMCs, albeit with different thresholds and kinetics. However, there is no distinct vascular phenotype in young zyxin-deficient mice, possibly due to functional redundancy among other gene products belonging to the zyxin family. Analyzing zyxin function in VSMCs at the cellular level might thus offer a better mechanistic insight. We aimed to characterize zyxin-dependent changes in gene expression in VSMCs exposed to biomechanical stretch and define the functional role of zyxin in controlling the resultant VSMC phenotype. Methods and Results DNA microarray analysis was used to identify genes and pathways that were zyxin regulated in static and stretched human umbilical artery–derived and mouse aortic VSMCs. Zyxin-null VSMCs showed a remarkable shift to a growth-promoting, less apoptotic, promigratory and poorly contractile phenotype with ≈90% of the stretch-responsive genes being zyxin dependent. Interestingly, zyxin-null cells already seemed primed for such a synthetic phenotype, with mechanical stretch further accentuating it. This could be accounted for by higher RhoA activity and myocardin-related transcription factor-A mainly localized to the nucleus of zyxin-null VSMCs, and a condensed and localized accumulation of F-actin upon stretch. Conclusions At the cellular level, zyxin is a key regulator of stretch-induced gene expression. Loss of zyxin drives VSMCs toward a synthetic phenotype, a process further consolidated by exaggerated stretch. PMID:26071033

Stanniocalcin 2 is an estrogen-responsive gene coexpressed with the estrogen receptor in human breast cancer.

PubMed

Bouras, Toula; Southey, Melissa C; Chang, Andy C; Reddel, Roger R; Willhite, Dorian; Glynne, Richard; Henderson, Michael A; Armes, Jane E; Venter, Deon J

2002-03-01

Differences in gene expression are likely to explain the phenotypic variation between hormone-responsive and hormone-unresponsive breast cancers. In this study, DNA microarray analysis of approximately 10,000 known genes and 25,000 expressed sequence tag clusters was performed to identify genes induced by estrogen and repressed by the pure antiestrogen ICI 182 780 in vitro that correlated with estrogen receptor (ER) expression in primary breast carcinomas in vivo. Stanniocalcin (STC) 2 was identified as one of the genes that fulfilled these criteria. DNA microarray hybridization showed a 3-fold induction of STC2 mRNA expression in MCF-7 cells in < or = 3 h of estrogen exposure and a 3-fold repression in the presence of antiestrogen (one-way ANOVA, P < 0.0005). In 13 ER-positive and 12 ER-negative breast carcinomas, the microarray-derived mRNA levels observed for STC2 correlated with tumor ER mRNA (Pearson's correlation, r = 0.85; P < 0.0001) and ER protein status (Spearman's rank correlation, r = 0.73; P < 0.0001). The expression profile of STC2 was further confirmed by in situ hybridization and immunohistochemistry on a larger cohort of 236 unselected breast carcinomas using tissue microarrays. STC2 mRNA and protein expression were found to be associated with tumor ER status (Fisher's exact test, P < 0.005). The related gene, STC1, was also examined and shown to be associated with ER status in breast carcinomas (Fisher's exact test, P < 0.05). This study demonstrates the feasibility of using global gene expression data derived from an in vitro model to pinpoint novel estrogen-responsive genes of potential clinical relevance.

Androgen receptor expression in breast cancer in relation to molecular phenotype: results from the Nurses' Health Study.

PubMed

Collins, Laura C; Cole, Kimberly S; Marotti, Jonathan D; Hu, Rong; Schnitt, Stuart J; Tamimi, Rulla M

2011-07-01

Previous studies have demonstrated that androgen receptor is expressed in many breast cancers, but its expression in relation to the various breast cancer subtypes as defined by molecular profiling has not been studied in detail. We constructed tissue microarrays from 3093 breast cancers that developed in women enrolled in the Nurses' Health Study. Tissue microarray sections were immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6, epidermal growth factor receptor (EGFR) and androgen receptor (ER). Immunostain results were used to categorize each cancer as luminal A or B, HER2 and basal like. The relationships between androgen receptor expression and molecular subtype were analyzed. Overall, 77% of the invasive breast carcinomas were androgen receptor positive. Among 2171 invasive cancers, 64% were luminal A, 15% luminal B, 6% HER2 and 11% basal like. The frequency of androgen receptor expression varied significantly across the molecular phenotypes (P<0.0001). In particular, androgen receptor expression was commonly observed in luminal A (91%) and B (68%) cancers, but was less frequently seen in HER2 cancers (59%). Despite being defined by the absence of ER and PR expression and being considered hormonally unresponsive, 32% of basal-like cancers expressed androgen receptor. Among 246 cases of ductal carcinoma in situ, 86% were androgen receptor positive, but the frequency of androgen receptor expression differed significantly across the molecular phenotypes (P=0.001), and high nuclear grade lesions were less likely to be androgen receptor positive compared with lower-grade lesions. Androgen receptor expression is most commonly seen in luminal A and B invasive breast cancers. However, expression of androgen receptor is also seen in approximately one-third of basal-like cancers, providing further evidence that basal-like cancers represent a heterogeneous group. Our findings raise the possibility that targeting the androgen receptor pathway may represent a novel therapeutic approach to the management of patients with basal-like cancers.

MBD5 haploinsufficiency is associated with sleep disturbance and disrupts circadian pathways common to Smith-Magenis and fragile X syndromes.

PubMed

Mullegama, Sureni V; Pugliesi, Loren; Burns, Brooke; Shah, Zalak; Tahir, Raiha; Gu, Yanghong; Nelson, David L; Elsea, Sarah H

2015-06-01

Individuals with autism spectrum disorders (ASD) who have an identifiable single-gene neurodevelopmental disorder (NDD), such as fragile X syndrome (FXS, FMR1), Smith-Magenis syndrome (SMS, RAI1), or 2q23.1 deletion syndrome (del 2q23.1, MBD5) share phenotypic features, including a high prevalence of sleep disturbance. We describe the circadian deficits in del 2q23.1 through caregiver surveys in which we identify several frequent sleep anomalies, including night/early awakenings, coughing/snoring loudly, and difficulty falling asleep. We couple these findings with studies on the molecular analysis of the circadian deficits associated with haploinsufficiency of MBD5 in which circadian gene mRNA levels of NR1D2, PER1, PER2, and PER3 were altered in del 2q23.1 lymphoblastoid cell lines (LCLs), signifying that haploinsufficiency of MBD5 can result in dysregulation of circadian rhythm gene expression. These findings were further supported by expression microarrays of MBD5 siRNA knockdown cells that showed significantly altered expression of additional circadian rhythm signaling pathway genes. Based on the common sleep phenotypes observed in del 2q23.1, SMS, and FXS patients, we explored the possibility that MBD5, RAI1, and FMR1 function in overlapping circadian rhythm pathways. Bioinformatic analysis identified conserved putative E boxes in MBD5 and RAI1, and expression levels of NR1D2 and CRY2 were significantly reduced in patient LCLs. Circadian and mTOR signaling pathways, both associated with sleep disturbance, were altered in both MBD5 and RAI1 knockdown microarray data, overlapping with findings associated with FMR1. These data support phenotypic and molecular overlaps across these syndromes that may be exploited to provide therapeutic intervention for multiple disorders.