GEPAS, a web-based tool for microarray data analysis and interpretation

DExplore: An Online Tool for Detecting Differentially Expressed Genes from mRNA Microarray Experiments

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI's GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance.

Molecular analyses of zebrafish V0v spinal interneurons and identification of transcriptional regulators downstream of Evx1 and Evx2 in these cells

BACKGROUND

V0v spinal interneurons are highly conserved, glutamatergic, commissural neurons that function in locomotor circuits. We have previously shown that Evx1 and Evx2 are required to specify the neurotransmitter phenotype of these cells. However, we still know very little about the gene regulatory networks that act downstream of these transcription factors in V0v cells.

METHODS

To identify candidate members of V0v gene regulatory networks, we FAC-sorted wild-type and evx1;evx2 double mutant zebrafish V0v spinal interneurons and expression-profiled them using microarrays and single cell RNA-seq. We also used in situ hybridization to compare expression of a subset of candidate genes in evx1;evx2 double mutants and wild-type siblings.

RESULTS

Our data reveal two molecularly distinct subtypes of zebrafish V0v spinal interneurons at 48 h and suggest that, by this stage of development, evx1;evx2 double mutant cells transfate into either inhibitory spinal interneurons, or motoneurons. Our results also identify 25 transcriptional regulator genes that require Evx1/2 for their expression in V0v interneurons, plus a further 11 transcriptional regulator genes that are repressed in V0v interneurons by Evx1/2. Two of the latter genes are hmx2 and hmx3a. Intriguingly, we show that Hmx2/3a, repress dI2 interneuron expression of skor1a and nefma, two genes that require Evx1/2 for their expression in V0v interneurons. This suggests that Evx1/2 might regulate skor1a and nefma expression in V0v interneurons by repressing Hmx2/3a expression.

CONCLUSIONS

This study identifies two molecularly distinct subsets of zebrafish V0v spinal interneurons, as well as multiple transcriptional regulators that are strong candidates for acting downstream of Evx1/2 to specify the essential functional characteristics of these cells. Our data further suggest that in the absence of both Evx1 and Evx2, V0v spinal interneurons initially change their neurotransmitter phenotypes from excitatory to inhibitory and then, later, start to express markers of distinct types of inhibitory spinal interneurons, or motoneurons. Taken together, our findings significantly increase our knowledge of V0v and spinal development and move us closer towards the essential goal of identifying the complete gene regulatory networks that specify this crucial cell type.

Molecular Analyses of V0v Spinal Interneurons and Identification of Transcriptional Regulators Downstream of Evx1 and Evx2 in these Cells

Background

V0v spinal interneurons are highly conserved, glutamatergic, commissural neurons that function in locomotor circuits. We have previously shown that Evx1 and Evx2 are required to specify the neurotransmitter phenotype of these cells. However, we still know very little about the gene regulatory networks that act downstream of these transcription factors in V0v cells.

Methods

To identify candidate members of V0v gene regulatory networks, we FAC-sorted WT and evx1;evx2 double mutant zebrafish V0v spinal interneurons and expression-profiled them using microarrays and single cell RNA-seq. We also used in situ hybridization to compare expression of a subset of candidate genes in evx1;evx2 double mutants and wild-type siblings.

Results

Our data reveal two molecularly distinct subtypes of V0v spinal interneurons at 48 h and suggest that, by this stage of development, evx1;evx2 double mutant cells transfate into either inhibitory spinal interneurons, or motoneurons. Our results also identify 25 transcriptional regulator genes that require Evx1/2 for their expression in V0v interneurons, plus a further 11 transcriptional regulator genes that are repressed in V0v interneurons by Evx1/2. Two of the latter genes are hmx2 and hmx3a. Intriguingly, we show that Hmx2/3a, repress dI2 interneuronal expression of skor1a and nefma, two genes that require Evx1/2 for their expression in V0v interneurons. This suggests that Evx1/2 might regulate skor1a and nefma expression in V0v interneurons by repressing Hmx2/3a expression.

Conclusions

This study identifies two molecularly distinct subsets of V0v spinal interneurons, as well as multiple transcriptional regulators that are strong candidates for acting downstream of Evx1/2 to specify the essential functional characteristics of these cells. Our data further suggest that in the absence of both Evx1 and Evx2, V0v spinal interneurons initially change their neurotransmitter phenotypes from excitatory to inhibitory and then, later, start to express markers of distinct types of inhibitory spinal interneurons, or motoneurons. Taken together, our findings significantly increase our knowledge of V0v and spinal development and move us closer towards the essential goal of identifying the complete gene regulatory networks that specify this crucial cell type.

Genealyzer: web application for the analysis and comparison of gene expression data

BACKGROUND

Gene expression profiling is a widely adopted method in areas like drug development or functional gene analysis. Microarray data of gene expression experiments is still commonly used and widely available for retrospective analyses. However, due to to changes of the underlying technologies data sets from different technologies are often difficult to compare and thus a multitude of already available data becomes difficult to use. We present a web application that abstracts away mathematical and programmatical details in order to enable a convenient and customizable analysis of microarray data for large-scale reproducibility studies. In addition, the web application provides a feature that allows easy access to large microarray repositories.

RESULTS

Our web application consists of three basic steps which are necessary for a differential gene expression analysis as well as Gene Ontology (GO) enrichment analysis and the comparison of multiple analysis results. Genealyzer can handle Affymetrix data as well as one-channel and two-channel Agilent data. All steps are visualized with meaningful plots. The application offers flexible analysis while being intuitively operable.

CONCLUSIONS

Our web application provides a unified platform for analysing microarray data, while allowing users to compare the results of different technologies and organisms. Beyond reproducibility, this also offers many possibilities for gaining further insights from existing study data, especially since data from different technologies or organisms can also be compared. The web application can be accessed via this URL: https://genealyzer.item.fraunhofer.de/ . Login credentials can be found at the end.

Hmx3a Has Essential Functions in Zebrafish Spinal Cord, Ear and Lateral Line Development

Transcription factors that contain a homeodomain DNA-binding domain have crucial functions in most aspects of cellular function and embryonic development in both animals and plants. Hmx proteins are a subfamily of NK homeodomain-containing proteins that have fundamental roles in development of sensory structures such as the eye and the ear. However, Hmx functions in spinal cord development have not been analyzed. Here, we show that zebrafish (Danio rerio) hmx2 and hmx3a are coexpressed in spinal dI2 and V1 interneurons, whereas hmx3b, hmx1, and hmx4 are not expressed in spinal cord. Using mutational analyses, we demonstrate that, in addition to its previously reported role in ear development, hmx3a is required for correct specification of a subset of spinal interneuron neurotransmitter phenotypes, as well as correct lateral line progression and survival to adulthood. Surprisingly, despite similar expression patterns of hmx2 and hmx3a during embryonic development, zebrafish hmx2 mutants are viable and have no obviously abnormal phenotypes in sensory structures or neurons that require hmx3a In addition, embryos homozygous for deletions of both hmx2 and hmx3a have identical phenotypes to severe hmx3a single mutants. However, mutating hmx2 in hypomorphic hmx3a mutants that usually develop normally, results in abnormal ear and lateral line phenotypes. This suggests that while hmx2 cannot compensate for loss of hmx3a, it does function in these developmental processes, although to a much lesser extent than hmx3a More surprisingly, our mutational analyses suggest that Hmx3a may not require its homeodomain DNA-binding domain for its roles in viability or embryonic development.

Programming Skeletal Muscle Metabolic Flexibility in Offspring of Male Rats in Response to Maternal Consumption of Slow Digesting Carbohydrates during Pregnancy

Skeletal muscle plays a relevant role in metabolic flexibility and fuel usage and the associated muscle metabolic inflexibility due to high-fat diets contributing to obesity and type 2 diabetes. Previous research from our group indicates that a high-fat and rapid-digesting carbohydrate diet during pregnancy promotes an excessive adipogenesis and also increases the risk of non-alcoholic fatty liver disease in the offspring. This effect can be counteracted by diets containing carbohydrates with similar glycemic load but lower digestion rates. To address the role of the skeletal muscle in these experimental settings, pregnant rats were fed high-fat diets containing carbohydrates with similar glycemic load but different digestion rates, a high fat containing rapid-digesting carbohydrates diet (HF/RD diet) or a high fat containing slow-digesting carbohydrates diet (HF/SD diet). After weaning, male offspring were fed a standard diet for 3 weeks (weaning) or 10 weeks (adolescence) and the impact of the maternal HF/RD and HF/SD diets on the metabolism, signaling pathways and muscle transcriptome was analyzed. The HF/SD offspring displayed better muscle features compared with the HF/RD group, showing a higher muscle mass, myosin content and differentiation markers that translated into a greater grip strength. In the HF/SD group, metabolic changes such as a higher expression of fatty acids (FAT/CD36) and glucose (GLUT4) transporters, an enhanced glycogen content, as well as changes in regulatory enzymes such as muscle pyruvate kinase and pyruvate dehydrogenase kinase 4 were found, supporting an increased muscle metabolic flexibility and improved muscle performance. The analysis of signaling pathways was consistent with a better insulin sensitivity in the muscle of the HF/SD group. Furthermore, increased expression of genes involved in pathways leading to muscle differentiation, muscle mass regulation, extracellular matrix content and insulin sensitivity were detected in the HF/SD group when compared with HF/RD animals. In the HF/SD group, the upregulation of the ElaV1/HuR gene could be one of the main regulators in the positive effects of the diet in early programming on the offspring. The long-lasting programming effects of the HF/SD diet during pregnancy may depend on a coordinated gene regulation, modulation of signaling pathways and metabolic flexibility that lead to an improved muscle functionality. The dietary early programming associated to HF/SD diet has synergic and positive crosstalk effects in several tissues, mainly muscle, liver and adipose tissue, contributing to maintain the whole body homeostasis in the offspring.

CXCL14, a Brown Adipokine that Mediates Brown-Fat-to-Macrophage Communication in Thermogenic Adaptation

The beneficial effects of brown adipose tissue (BAT) are attributed to its capacity to oxidize metabolites and produce heat, but recent data suggest that secretory properties of BAT may also be involved. Here, we identify the chemokine CXCL14 (C-X-C motif chemokine ligand-14) as a novel regulatory factor secreted by BAT in response to thermogenic activation. We found that the CXCL14 released by brown adipocytes recruited alternatively activated (M2) macrophages. Cxcl14-null mice exposed to cold showed impaired BAT activity and low recruitment of macrophages, mainly of the M2 phenotype, into BAT. CXCL14 promoted the browning of white fat and ameliorated glucose/insulin homeostasis in high-fat-diet-induced obese mice. Impairment of type 2 cytokine signaling, as seen in Stat6-null mice, blunts the action of CXCL14, promoting adipose tissue browning. We propose that active BAT is a source of CXCL14, which concertedly promotes adaptive thermogenesis via M2 macrophage recruitment, BAT activation, and the browning of white fat.

Lmx1b is required for the glutamatergic fates of a subset of spinal cord neurons

Background

Alterations in neurotransmitter phenotypes of specific neurons can cause imbalances in excitation and inhibition in the central nervous system (CNS), leading to diseases. Therefore, the correct specification and maintenance of neurotransmitter phenotypes is vital. As with other neuronal properties, neurotransmitter phenotypes are often specified and maintained by particular transcription factors. However, the specific molecular mechanisms and transcription factors that regulate neurotransmitter phenotypes remain largely unknown.

Methods

In this paper we use single mutant, double mutant and transgenic zebrafish embryos to elucidate the functions of Lmx1ba and Lmx1bb in the regulation of spinal cord interneuron neurotransmitter phenotypes.

Results

We demonstrate that lmx1ba and lmx1bb are both expressed in zebrafish spinal cord and that lmx1bb is expressed by both V0v cells and dI5 cells. Our functional analyses demonstrate that these transcription factors are not required for neurotransmitter fate specification at early stages of development, but that in embryos with at least two lmx1ba and/or lmx1bb mutant alleles there is a reduced number of excitatory (glutamatergic) spinal interneurons at later stages of development. In contrast, there is no change in the numbers of V0v or dI5 cells. These data suggest that lmx1b-expressing spinal neurons still form normally, but at least a subset of them lose, or do not form, their normal excitatory fates. As the reduction in glutamatergic cells is only seen at later stages of development, Lmx1b is probably required either for the maintenance of glutamatergic fates or to specify glutamatergic phenotypes of a subset of later forming neurons. Using double labeling experiments, we also show that at least some of the cells that lose their normal glutamatergic phenotype are V0v cells. Finally, we also establish that Evx1 and Evx2, two transcription factors that are required for V0v cells to acquire their excitatory neurotransmitter phenotype, are also required for lmx1ba and lmx1bb expression in these cells, suggesting that Lmx1ba and Lmx1bb act downstream of Evx1 and Evx2 in V0v cells.

Conclusions

Lmx1ba and Lmx1bb function at least partially redundantly in the spinal cord and three functional lmx1b alleles are required in zebrafish for correct numbers of excitatory spinal interneurons at later developmental stages. Taken together, our data significantly enhance our understanding of how spinal cord neurotransmitter fates are regulated.

Reliable gene expression profiling of formalin-fixed paraffin-embedded breast cancer tissue (FFPE) using cDNA-mediated annealing, extension, selection, and ligation whole-genome (DASL WG) assay

BACKGROUND

The difficulties in using formalin-fixed and paraffin-embedded (FFPE) tumour specimens for molecular marker studies have hampered progress in translational cancer research. The cDNA-mediated, annealing, selection, extension, and ligation (DASL) assay is a platform for gene expression profiling from FFPE tissue and hence could allow analysis of large collections of tissue with associated clinical data from existing archives, therefore facilitating the development of novel biomarkers.

METHOD

RNA isolated from matched fresh frozen (FF) and FFPE cancer specimens was profiled using both the DASL whole-genome (WG) platform, and Illumina BeadArray's, and results were compared. Samples utilized were obtained from the breast cancer tumour bank held at the Cambridge University Hospitals NHS Foundation Trust.

RESULTS

The number of reliably detected probes was comparable between the DASL and BeadArray platforms, indicating that the source of RNA did not result in a significant difference in the detection rates (Mean probes- 17114 in FFPE & 17400 in FF). There was a significant degree of correlation between replicates within the FF and FFPE sample sets (r (2) = 0.96-0.98) as well as between the two platforms (DASL vs. BeadArray r (2) = range 0.83-0.89). Hierarchical clustering using the most informative probes showed that replicate and matched samples were grouped into the same sub-cluster, regardless of whether RNA was derived from FF or FFPE tissue.

CONCLUSION

Both FF and FFPE material generated reproducible gene expression profiles, although there was more noise in profiles from FFPE specimens. We have shown that the DASL WG platform is suitable for profiling formalin-fixed paraffin-embedded samples, but robust bioinformatics analysis is required.

Viral Strain-Specific Differential Alterations in Arabidopsis Developmental Patterns

Turnip mosaic virus (TuMV) infections affect many Arabidopsis developmental traits. This paper analyzes, at different levels, the development-related differential alterations induced by different strains of TuMV, represented by isolates UK 1 and JPN 1. The genomic sequence of JPN 1 TuMV isolate revealed highest divergence in the P1 and P3 viral cistrons, upon comparison with the UK 1 sequence. Infectious viral chimeras covering the whole viral genome uncovered the P3 cistron as a major viral determinant of development alterations, excluding the involvement of the PIPO open reading frame. However, constitutive transgenic expression of P3 in Arabidopsis did not induce developmental alterations nor modulate the strong effects induced by the transgenic RNA silencing suppressor HC-Pro from either strain. This highlights the importance of studying viral determinants within the context of actual viral infections. Transcriptomic and interactomic analyses at different stages of plant development revealed large differences in the number of genes affected by the different infections at medium infection times but no significant differences at very early times. Biological functions affected by UK 1 (the most severe strain) included mainly stress response and transport. Most cellular components affected cell-wall transport or metabolism. Hubs in the interactome were affected upon infection.

Emergent Transcriptomic Technologies and Their Role in the Discovery of Biomarkers of Liver Transplant Tolerance

Liver transplantation offers a unique window into transplant immunology due, in part, to the considerable proportion of recipients who develop immunological tolerance to their allograft. Biomarkers are able to identify and predict such a state of tolerance, and thereby able to establish suitable candidates for the minimization of hazardous immunosuppressive therapies, are not only of great potential clinical benefit but might also shed light on the immunological mechanisms underlying tolerance and rejection. Here, we review the emergent transcriptomic technologies serving as drivers of biomarker discovery, we appraise efforts to identify a molecular signature of liver allograft tolerance, and we consider the implications of this work on the mechanistic understanding of immunological tolerance.

NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data

Meta-analysis of gene expression data sets is increasingly performed to help identify robust molecular signatures and to gain insights into underlying biological processes. The complicated nature of such analyses requires both advanced statistics and innovative visualization strategies to support efficient data comparison, interpretation and hypothesis generation. NetworkAnalyst (http://www.networkanalyst.ca) is a comprehensive web-based tool designed to allow bench researchers to perform various common and complex meta-analyses of gene expression data via an intuitive web interface. By coupling well-established statistical procedures with state-of-the-art data visualization techniques, NetworkAnalyst allows researchers to easily navigate large complex gene expression data sets to determine important features, patterns, functions and connections, thus leading to the generation of new biological hypotheses. This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta-analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expression data sets. NetworkAnalyst is designed to be accessible to biologists rather than to specialist bioinformaticians. The complete protocol can be executed in ∼1.5 h. Compared with other similar web-based tools, NetworkAnalyst offers a unique visual analytics experience that enables data analysis within the context of protein-protein interaction networks, heatmaps or chord diagrams. All of these analysis methods provide the user with supporting statistical and functional evidence.

MetaboAnalyst 3.0--making metabolomics more meaningful

MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.

Babelomics 5.0: functional interpretation for new generations of genomic data

Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

Gene expression profile induced by ovariectomy in bone marrow of mice: a functional approach to identify new candidate genes associated to osteoporosis risk in women

Osteoporosis is a multifactorial skeletal pathology with a main genetic component. To date, however, the majority of genes associated with this pathology remain unknown since genes cataloged to date only explain a part of the heritability of bone phenotypes. In the present study, we have used a genome-wide gene expression approach by means of microarrays to identify new candidate genes involved in the physiopathology of osteoporosis, using as a model the ovariectomized (OVX) mice by comparing global bone marrow gene expression of the OVX mice with those of SHAM operated mice. One hundred and eighty transcripts were found to be differentially expressed between groups. The analysis showed 23 significant regulatory networks, of which the top five canonical pathways included B-cell development, primary immunodeficiency signaling, PI3K signaling in B-cells, phospholipase C signaling, and FcgRIIB signaling in B-cells. Twelve differentially expressed genes were validated by MALDI-TOF mass spectrometry with good reproducibility. Finally, the association to bone phenotypes of SNPs in genes whose expression was increased (IL7R and CD79A) or decreased (GPX3 and IRAK3) by OVX in mice was analyzed in a cohort of 706 postmenopausal women. We detected an association of a SNP in a gene involved in the detoxification of free radicals like glutathione peroxidase 3 (GPX3) with femoral neck BMD (rs8177447, P=0.043) and two SNPs in the Ig-alpha protein of the B-cell antigen component gene (CD79A) with lumbar spine BMD (rs3810153 and rs1428922, P=0.016 and P=0.001, respectively). These results reinforce the role of antioxidant pathways and of B-cells in bone metabolism. Furthermore, it shows that a genome-wide gene expression approach in animal models is a useful method for detecting genes associated to BMD and osteoporosis risk in humans.

Reanalysis of the gene expression profile in chronic pancreatitis via bioinformatics methods

Background

Diagnosis at an early stage of chronic pancreatitis (CP) is challenging. It has been reported that microRNAs (miRNAs) are increasingly found and applied as targets for the diagnosis and treatment of various cancers. However, to the best of our knowledge, few published papers have described the role of miRNAs in the diagnosis of CP.

Method

We downloaded gene expression profile data from the Gene Expression Omnibus and identified differentially expressed genes (DEGs) between CP and normal samples of Harlan mice and Jackson Laboratory mice. Common DEGs were filtered out, and the semantic similarities of gene classes were calculated using the GOSemSim software package. The gene class with the highest functional consistency was selected, and then the Lists2Networks web-based system was used to analyse regulatory relationships between miRNAs and gene classes. The functional enrichment of the gene classes was assessed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation terms.

Results

A total of 405 common upregulated DEGs and 7 common downregulated DEGs were extracted from the two kinds of mice. Gene cluster D was selected from the common upregulated DEGs because it had the highest semantic similarity. miRNA 124a (miR-124a) was found to have a significant regulatory relationship with cluster D, and DEGs such as CHSY1 and ABCC4 were found to be regulated by miR-124a. The GO term of response to DNA damage stimulus and the pathway of Escherichia coli infection were significantly enriched in cluster D.

Conclusion

DNA damage and E. coli infection might play important roles in CP pathogenesis. In addition, miR-124a might be a potential target for the diagnosis and treatment of CP.

NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration

Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca.

PaGenBase: a pattern gene database for the global and dynamic understanding of gene function

Pattern genes are a group of genes that have a modularized expression behavior under serial physiological conditions. The identification of pattern genes will provide a path toward a global and dynamic understanding of gene functions and their roles in particular biological processes or events, such as development and pathogenesis. In this study, we present PaGenBase, a novel repository for the collection of tissue- and time-specific pattern genes, including specific genes, selective genes, housekeeping genes and repressed genes. The PaGenBase database is now freely accessible at http://bioinf.xmu.edu.cn/PaGenBase/. In the current version (PaGenBase 1.0), the database contains 906,599 pattern genes derived from the literature or from data mining of more than 1,145,277 gene expression profiles in 1,062 distinct samples collected from 11 model organisms. Four statistical parameters were used to quantitatively evaluate the pattern genes. Moreover, three methods (quick search, advanced search and browse) were designed for rapid and customized data retrieval. The potential applications of PaGenBase are also briefly described. In summary, PaGenBase will serve as a resource for the global and dynamic understanding of gene function and will facilitate high-level investigations in a variety of fields, including the study of development, pathogenesis and novel drug discovery.

Transcriptome profiling of radiata pine branches reveals new insights into reaction wood formation with implications in plant gravitropism

BACKGROUND

Formation of compression (CW) and opposite wood (OW) in branches and bent trunks is an adaptive feature of conifer trees in response to various displacement forces, such as gravity, wind, snow and artificial bending. Several previous studies have characterized tracheids, wood and gene transcription in artificially or naturally bent conifer trunks. These studies have provided molecular basis of reaction wood formation in response to bending forces and gravity stimulus. However, little is known about reaction wood formation and gene transcription in conifer branches under gravity stress. In this study SilviScan® technology was used to characterize tracheid and wood traits in radiate pine (Pinus radiata D. Don) branches and genes differentially transcribed in CW and OW were investigated using cDNA microarrays.

RESULTS

CW drastically differed from OW in tracheids and wood traits with increased growth, thicker tracheid walls, larger microfibril angle (MFA), higher density and lower stiffness. However, CW and OW tracheids had similar diameters in either radial or tangential direction. Thus, gravity stress largely influenced wood growth, secondary wall deposition, cellulose microfibril orientation and wood properties, but had little impact on primary wall expansion. Microarray gene transcription revealed about 29% of the xylem transcriptomes were significantly altered in CW and OW sampled in both spring and autumn, providing molecular evidence for the drastic variation in tracheid and wood traits. Genes involved in cell division, cellulose biosynthesis, lignin deposition, and microtubules were mostly up-regulated in CW, conferring its greater growth, thicker tracheid walls, higher density, larger MFA and lower stiffness. However, genes with roles in cell expansion and primary wall formation were differentially transcribed in CW and OW, respectively, implicating their similar diameters of tracheid walls and different tracheid lengths. Interestingly, many genes related to hormone and calcium signalling as well as various environmental stresses were exclusively up-regulated in CW, providing important clues for earlier molecular signatures of reaction wood formation under gravity stimulus.

CONCLUSIONS

The first comprehensive investigation of tracheid characteristics, wood properties and gene transcription in branches of a conifer species revealed more accurate and new insights into reaction wood formation in response to gravity stress. The identified differentially transcribed genes with diverse functions conferred or implicated drastic CW and OW variation observed in radiata pine branches. These genes are excellent candidates for further researches on the molecular mechanisms of reaction wood formation with a view to plant gravitropism.

Molecular toxicity identification evaluation (mTIE) approach predicts chemical exposure in Daphnia magna

Daphnia magna is a bioindicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression data set for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors and inorganic and organic chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.

INMEX--a web-based tool for integrative meta-analysis of expression data

The widespread applications of various ‘omics’ technologies in biomedical research together with the emergence of public data repositories have resulted in a plethora of data sets for almost any given physiological state or disease condition. Properly combining or integrating these data sets with similar basic hypotheses can help reduce study bias, increase statistical power and improve overall biological understanding. However, the difficulties in data management and the complexities of analytical approaches have significantly limited data integration to enable meta-analysis. Here, we introduce integrative meta-analysis of expression data (INMEX), a user-friendly web-based tool designed to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner. INMEX is freely available at http://www.inmex.ca.

Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease

Background

This study aims to expand knowledge of the complex process of myocardial infarction (MI) through the application of a systems-based approach.

Methods

We generated a gene co-expression network from microarray data originating from a mouse model of MI. We characterized it on the basis of connectivity patterns and independent biological information. The potential clinical novelty and relevance of top predictions were assessed in the context of disease classification models. Models were validated using independent gene expression data from mouse and human samples.

Results

The gene co-expression network consisted of 178 genes and 7298 associations. The network was dissected into statistically and biologically meaningful communities of highly interconnected and co-expressed genes. Among the most significant communities, one was distinctly associated with molecular events underlying heart repair after MI (P < 0.05). Col5a2, a gene previously not specifically linked to MI response but responsible for the classic type of Ehlers-Danlos syndrome, was found to have many and strong co-expression associations within this community (11 connections with ρ > 0.85). To validate the potential clinical application of this discovery, we tested its disease discriminatory capacity on independently generated MI datasets from mice and humans. High classification accuracy and concordance was achieved across these evaluations with areas under the receiving operating characteristic curve above 0.8.

Conclusion

Network-based approaches can enable the discovery of clinically-interesting predictive insights that are accurate and robust. Col5a2 shows predictive potential in MI, and in principle may represent a novel candidate marker for the identification and treatment of ischemic cardiovascular disease.

AMDA 2.13: A major update for automated cross-platform microarray data analysis

Microarray platforms require analytical pipelines with modules for data pre-processing including data normalization, statistical analysis for identification of differentially expressed genes, cluster analysis, and functional annotation. We previously developed the Automated Microarray Data Analysis (AMDA, version 2.3.5) pipeline to process Affymetrix 3' IVT GeneChips. The availability of newer technologies that demand open-source tools for microarray data analysis has impelled us to develop an updated multi-platform version, AMDA 2.13. It includes additional quality control metrics, annotation-driven (annotation grade of Affymetrix NetAffx) and signal-driven (Inter-Quartile Range) gene filtering, and approaches to experimental design. To enhance understanding of biological data, differentially expressed genes have been mapped into KEGG pathways. Finally, a more stable and user-friendly interface was designed to integrate the requirements for different platforms. AMDA 2.13 allows the analysis of Affymetrix (cartridges and plates) and whole transcript probe design (Gene 1.0/1.1 ST and Exon 1.0 ST GeneChips), Illumina Bead Arrays, and one-channel Agilent 4×44 arrays. Relative to early versions, it supports various experimental designs and delivers more insightful biological understanding and up-to-date annotations.

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules.

Workflows for microarray data processing in the Kepler environment

BACKGROUND

Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services.

RESULTS

We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R/BioConductor scripting approaches to pipeline design. Finally, we suggest that microarray data processing task workflows may provide a basis for future example-based comparison of different workflow systems.

CONCLUSIONS

We provide a set of tools and complete workflows for microarray data analysis in the Kepler environment, which has the advantages of offering graphical, clear display of conceptual steps and parameters and the ability to easily integrate other resources such as remote data and web services.

Meta-Analysis of Global Transcriptomics Suggests that Conserved Genetic Pathways are Responsible for Quercetin and Tannic Acid Mediated Longevity in C. elegans

Recent research has highlighted that the polyphenols Quercetin and Tannic acid are capable of extending the lifespan of Caenorhabditis elegans. To gain a deep understanding of the underlying molecular genetics, we analyzed the global transcriptional patterns of nematodes exposed to three concentrations of Quercetin or Tannic acid, respectively. By means of an intricate meta-analysis it was possible to compare the transcriptomes of polyphenol exposure to recently published datasets derived from (i) longevity mutants or (ii) infection. This detailed comparative in silico analysis facilitated the identification of compound specific and overlapping transcriptional profiles and allowed the prediction of putative mechanistic models of Quercetin and Tannic acid mediated longevity. Lifespan extension due to Quercetin was predominantly driven by the metabolome, TGF-beta signaling, Insulin-like signaling, and the p38 MAPK pathway and Tannic acid's impact involved, in part, the amino acid metabolism and was modulated by the TGF-beta and the p38 MAPK pathways. DAF-12, which integrates TGF-beta and Insulin-like downstream signaling, and genetic players of the p38 MAPK pathway therefore seem to be crucial regulators for both polyphenols. Taken together, this study underlines how meta-analyses can provide an insight of molecular events that go beyond the traditional categorization into gene ontology-terms and Kyoto encyclopedia of genes and genomes-pathways. It also supports the call to expand the generation of comparative and integrative databases, an effort that is currently still in its infancy.

Transcriptome data analysis for cell culture processes

In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.

Prerequisites, performance and profits of transcriptional profiling the abiotic stress response

During the last decade, microarrays became a routine tool for the analysis of transcripts in the model plant Arabidopsis thaliana and the crop plant species rice, poplar or barley. The overwhelming amount of data generated by gene expression studies is a valuable resource for every scientist. Here, we summarize the most important findings about the abiotic stress responses in plants. Interestingly, conserved patterns of gene expression responses have been found that are common between different abiotic stresses or that are conserved between different plant species. However, the individual histories of each plant affect the inter-comparability between experiments already before the onset of the actual stress treatment. This review outlines multiple aspects of microarray technology and highlights some of the benefits, limitations and also pitfalls of the technique. This article is part of a Special Issue entitled: Plant gene regulation in response to abiotic stress.

Apolipoprotein D alters the early transcriptional response to oxidative stress in the adult cerebellum

The lipocalin Apolipoprotein D (ApoD), known to protect the nervous system against oxidative stress (OS) in model organisms, is up-regulated early in the mouse brain in response to the ROS generator paraquat. However, the processes triggered by this up-regulation have not been explored. We present here a study of the effect of ApoD on the early transcriptional changes upon OS in the mouse cerebellum using microarray profiling. ApoD-KO and transgenic mice over-expressing ApoD in neurons are compared to wild-type controls. In control conditions, ApoD affects the transcriptional profile of neuron and oligodendrocyte-specific genes involved in neuronal excitability, synaptic function, and myelin homeostasis. When challenged with paraquat, the absence of ApoD modifies the response of genes mainly related to OS management and myelination. Interestingly, the over-expression of ApoD in neurons almost completely abolishes the early transcriptional response to OS. We independently evaluate the expression of protein kinase Cδ, a gene up-regulated by OS only in the ApoD-KO cerebellum, and find it over-expressed in cultured ApoD-KO primary astrocytes, which points to a role for ApoD in astrocyte-microglia signaling. Our results support the hypothesis that ApoD is necessary for a proper response of the nervous system against physiological and pathological OS.

GRISSOM platform: enabling distributed processing and management of biological data through fusion of grid and web technologies

Transcriptomic technologies have a critical impact in the revolutionary changes that reshape biological research. Through the recruitment of novel high-throughput instrumentation and advanced computational methodologies, an unprecedented wealth of quantitative data is produced. Microarray experiments are considered high-throughput, both in terms of data volumes (data intensive) and processing complexity (computationally intensive). In this paper, we present grids for in silico systems biology and medicine (GRISSOM), a web-based application that exploits GRID infrastructures for distributed data processing and management, of DNA microarrays (cDNA, Affymetrix, Illumina) through a generic, consistent, computational analysis framework. GRISSOM performs versatile annotation and integrative analysis tasks, through the use of third-party application programming interfaces, delivered as web services. In parallel, by conforming to service-oriented architectures, it can be encapsulated in other biomedical processing workflows, with the help of workflow enacting software, like Taverna Workbench, thus rendering access to its algorithms, transparent and generic. GRISSOM aims to set a generic paradigm of efficient metamining that promotes translational research in biomedicine, through the fusion of grid and semantic web computing technologies.

Insights into global mechanisms and disease by gene expression profiling

Transcriptomics has played an essential role as proof of concept in the development of experimental and bioinformatics approaches for the generation and analysis of Omics data. We are giving an introduction on how large-scale technologies for gene expression profiling, especially microarrays, have changed the view from studying single molecular events to a systems level view of global mechanisms in a cell, the biological processes, and their pathological mutations. The main platforms available for gene expression profiling (from microarrays to RNA-seq) are presented and the general concepts that need to be taken into account for proper data analysis in order to extract objective and general conclusions from transcriptomics experiments are introduced. We also describe the available main bioinformatics resources used for this purpose.

Transcriptome changes in grapevine (Vitis vinifera L.) cv. Malbec leaves induced by ultraviolet-B radiation

BACKGROUND

Ultraviolet-B radiation (UV-B, 280-315 nm) is a natural component of sunlight, which has numerous regulatory effects on plant physiology. The nature of the response to UV-B is dependent on fluence rate, dose, duration and wavelength of the UV-B treatment. Some reports have analyzed the changes in gene expression caused by UV-B light on several plant species using microarray technology. However, there is no information on the transcriptome response triggered by UV-B in grapevine. In this paper we investigate the gene expression responses of leaves from in vitro cultured Vitis vinifera cv. Malbec plants subjected to the same dose of biologically effective UV-B radiation (4.75 kJ m-2 d-1) administered at two different fluence rates (16 h at ≅ 8.25 μW cm-2, 4 h at ≅ 33 μW cm-2) using a new custom made GrapeGen Affymetrix GeneChip®.

RESULTS

The number of genes modulated by high fluence rate UV-B doubled the number of genes modulated by low fluence UV-B. Their functional analyses revealed several functional categories commonly regulated by both UV-B treatments as well as categories more specifically modulated depending on UV-B fluence rate. General protective responses, namely the induction of pathways regulating synthesis of UV-B absorbing compounds such as the Phenylpropanoid pathway, the induction of different antioxidant defense systems and the activation of pathways commonly associated with pathogen defense and abiotic stress responses seem to play critical roles in grapevine responses against UV-B radiation. Furthermore, high fluence rate UV-B seemed to specifically modulate additional pathways and processes in order to protect grapevine plantlets against UV-B-induced oxidative stress, stop the cell cycle progression, and control protein degradation. On the other hand, low fluence rate UV-B regulated the expression of specific responses in the metabolism of auxin and abscisic acid as well as in the modification of cell walls that could be involved in UV-B acclimation-like processes.

CONCLUSION

Our results show the UV-B radiation effects on the leaf transcriptome of grapevine (Vitis vinifera cv. Malbec) plantlets. Functional categories commonly modulated under both UV-B treatments as well as transcripts specifically regulated in an UV-B-intensity dependent way were identified. While high fluence rate UV-B had regulatory effects mainly on defense or general multiple-stress responses pathways, low fluence rate UV-B promoted the expression of genes that could be involved in UV-B protection or the amelioration of the UV-B-induced damage. This study also provides an extensive list of genes regulating multiple metabolic pathways involved in the response of grapevine to UV-B that can be used for future researches.

Seasonal reorganization of the xylem transcriptome at different tree ages reveals novel insights into wood formation in Pinus radiata

*Seasonal wood development produces earlywood (EW) and latewood (LW) with distinct properties. The molecular mechanisms controlling EW and LW formation at different tree ages are poorly understood. *Seasonal reorganization of the xylem transcriptome was investigated in Pinus radiata at four tree ages using cDNA microarrays. Transcriptome profiles were compared with seasonal wood variation measured by SilviScan (CSIRO, Clayton, Australia). *The xylem transcriptome was considerably reorganized during seasonal change, and this reorganization showed a maturation-related pattern. The greater reorganization occurred at the transition (30%) and juvenile (21%) stages, but it declined with tree maturity (11-13%). However, this pattern does not correlate well with maturation-related patterns of seasonal wood variation. In total, 319 seasonal-responsive xylem candidate genes were identified. Many transcripts involved in primary and secondary wall biosynthesis were preferentially accumulated in EW and LW, respectively. A large proportion (45-81%) of the candidate genes are preferentially regulated at a single age and their transcript abundance may influence maturation-related patterns of seasonal wood variation. *Seasonal reorganization of the xylem transcriptome is significantly affected by tree age. Physiological changes at the transition stage may contribute to its greater seasonal transcriptome reorganization. Identified stage-preferential xylem transcripts could influence seasonal wood variation at different tree ages.

Integrative genomic and proteomic analyses identify targets for Lkb1-deficient metastatic lung tumors

In mice, Lkb1 deletion and activation of Kras(G12D) results in lung tumors with a high penetrance of lymph node and distant metastases. We analyzed these primary and metastatic de novo lung cancers with integrated genomic and proteomic profiles, and have identified gene and phosphoprotein signatures associated with Lkb1 loss and progression to invasive and metastatic lung tumors. These studies revealed that SRC is activated in Lkb1-deficient primary and metastatic lung tumors, and that the combined inhibition of SRC, PI3K, and MEK1/2 resulted in synergistic tumor regression. These studies demonstrate that integrated genomic and proteomic analyses can be used to identify signaling pathways that may be targeted for treatment.

Serial Expression Analysis: a web tool for the analysis of serial gene expression data

Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.