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

Transcriptomic characterization of differential gene expression in oral squamous cell carcinoma: a meta-analysis of publicly available microarray data sets

Oral squamous cell carcinoma (OSCC) is a highly prevalent cancer worldwide, and OSCC often goes undiagnosed until advanced disease is present, which contributes to a low survival rate for OSCC patients. The identification of biomarkers for the early detection OSCC and novel therapeutic targets for OSCC treatment is an important research objective. We performed bioinformatics analyses of the gene expression profile of OSCC using microarray data to identify genes that contribute to the development of OSCC. We also predicted the transcription factors involved in the regulation of differential gene expression in OSCC. Our results showed that PI3K, EGFR, STAT1, and CPBP are important contributors to the changes in cellular physiology that occur during the development of OSCC. Therefore, these genes represent potential diagnostic biomarkers and therapeutic targets for OSCC.

Comparison of Normal and Pre-Eclamptic Placental Gene Expression: A Systematic Review with Meta-Analysis

Pre-eclampsia (PE) is a serious multi-factorial disorder of human pregnancy. It is associated with changes in the expression of placental genes. Recent transcription profiling of placental genes with microarray analyses have offered better opportunities to define the molecular pathology of this disorder. However, the extent to which placental gene expression changes in PE is not fully understood. We conducted a systematic review of published PE and normal pregnancy (NP) control placental RNA microarrays to describe the similarities and differences between NP and PE placental gene expression, and examined how these differences could contribute to the molecular pathology of the disease. A total of 167 microarray samples were available for meta-analysis. We found the expression pattern of one group of genes was the same in PE and NP. The review also identified a set of genes (PE unique genes) including a subset, that were significantly (p < 0.05) down-regulated in pre-eclamptic placentae only. Using class prediction analysis, we further identified the expression of 88 genes that were highly associated with PE (p < 0.05), 10 of which (LEP, HTRA4, SPAG4, LHB, TREM1, FSTL3, CGB, INHA, PROCR, and LTF) were significant at p < 0.001. Our review also suggested that about 30% of genes currently being investigated as possibly of importance in PE placenta were not consistently and significantly affected in the PE placentae. We recommend further work to confirm the roles of the PE unique and associated genes, currently not being investigated in the molecular pathology of the disease.

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca.

Transcriptome Analysis of Sunflower Genotypes with Contrasting Oxidative Stress Tolerance Reveals Individual- and Combined- Biotic and Abiotic Stress Tolerance Mechanisms

In nature plants are often simultaneously challenged by different biotic and abiotic stresses. Although the mechanisms underlying plant responses against single stress have been studied considerably, plant tolerance mechanisms under combined stress is not understood. Also, the mechanism used to combat independently and sequentially occurring many number of biotic and abiotic stresses has also not systematically studied. From this context, in this study, we attempted to explore the shared response of sunflower plants to many independent stresses by using meta-analysis of publically available transcriptome data and transcript profiling by quantitative PCR. Further, we have also analyzed the possible role of the genes so identified in contributing to combined stress tolerance. Meta-analysis of transcriptomic data from many abiotic and biotic stresses indicated the common representation of oxidative stress responsive genes. Further, menadione-mediated oxidative stress in sunflower seedlings showed similar pattern of changes in the oxidative stress related genes. Based on this a large scale screening of 55 sunflower genotypes was performed under menadione stress and those contrasting in oxidative stress tolerance were identified. Further to confirm the role of genes identified in individual and combined stress tolerance the contrasting genotypes were individually and simultaneously challenged with few abiotic and biotic stresses. The tolerant hybrid showed reduced levels of stress damage both under combined stress and few independent stresses. Transcript profiling of the genes identified from meta-analysis in the tolerant hybrid also indicated that the selected genes were up-regulated under individual and combined stresses. Our results indicate that menadione-based screening can identify genotypes not only tolerant to multiple number of individual biotic and abiotic stresses, but also the combined stresses.

Molecular Portrait of Oral Tongue Squamous Cell Carcinoma Shown by Integrative Meta-Analysis of Expression Profiles with Validations

Oral Tongue Squamous cell carcinoma (OTSCC), the most frequently affected oral cancer sub-site, is associated with a poor therapeutic outcome and survival despite aggressive multi- modality management. Till date, there are no established biomarkers to indicate prognosis and outcome in patients presenting with tongue cancer. There is an urgent need for reliable molecular prognostic factors to enable identification of patients with high risk of recurrence and treatment failure in OTSCC management. In the current study, we present the meta-analysis of OTSCC microarray based gene expression profiles, deriving a comprehensive molecular portrait of tongue cancer biology, showing the relevant genes and pathways which can be pursued further to derive novel, tailored therapeutics as well as for prognostication. We have studied 5 gene expression profiling data sets available on exclusively oral tongue subsite comprising of sample size; n = 190, consisting of 111 tumors and 79 normals. The meta- analysis results showed 2405 genes differentially regulated comparing OTSCC tumor and normal. The top up regulated genes were found to be involved in Extracellular matrix degradation (ECM) and Epithelial to mesenchymal transition (EMT) pathways. The top down regulated genes were found to be involved in detoxication pathways. We validated the results in clinical samples (n = 206), comprising of histologically normals (n = 10), prospective (n = 29) and retrospective (n = 167) OTSCC by evaluating MMP9 and E-cadherin gene expression by qPCR and immunohistochemistry. Consistent with meta-analysis results, MMP9 mRNA expression was significantly up regulated in OTSCC primary tumors compared to normals. MMP9 protein over expression was found to be a significant predictor of poor prognosis, disease recurrence and poor Disease Free Survival (DFS) in OTSCC patients. Analysis by univariate and multivariate Cox proportional hazard model showed patients with loss of E-cadherin expression in OTSCC tumors having a poorer DFS (HR = 1.566; P value = 0.045) and poorer Overall Survival (OS) (HR = 1.224; P value = 0.003) respectively. Combined over-expression of MMP9 and loss of E-cadherin membrane positivity in the invasive tumor front (ITF) of OTSCC had a significant association with poorer DFS (Log Rank = 16.040; P value = 0.001). These results suggest that along with known clinical indicators of prognosis like occult node positivity, assessment of MMP9 and E-cadherin expression at ITF can be useful to identify patients at high risk and requiring a more intensive treatment strategy for OTSCC. Meta-analysis study of gene expression profiles indicates that OTSCC is a disease of ECM degradation leading to activated EMT processes implying the aggressive nature of the disease. The triggers for these processes should be studied further. Newer clinical application with agents that can inhibit the mediators of ECM degradation may be a key to achieving clinical control of invasion and metastasis of OTSCC.

Comprehensive Transcriptome Meta-analysis to Characterize Host Immune Responses in Helminth Infections

Helminth infections affect more than a third of the world’s population. Despite very broad phylogenetic differences among helminth parasite species, a systemic Th2 host immune response is typically associated with long-term helminth infections, also known as the “helminth effect”. Many investigations have been carried out to study host gene expression profiles during helminth infections. The objective of this study is to determine if there is a common transcriptomic signature characteristic of the helminth effect across multiple helminth species and tissue types. To this end, we performed a comprehensive meta-analysis of publicly available gene expression datasets. After data processing and adjusting for study-specific effects, we identified ~700 differentially expressed genes that are changed consistently during helminth infections. Functional enrichment analyses indicate that upregulated genes are predominantly involved in various immune functions, including immunomodulation, immune signaling, inflammation, pathogen recognition and antigen presentation. Down-regulated genes are mainly involved in metabolic process, with only a few of them are involved in immune regulation. This common immune gene signature confirms previous observations and indicates that the helminth effect is robust across different parasite species as well as host tissue types. To the best of our knowledge, this study is the first comprehensive meta-analysis of host transcriptome profiles during helminth infections.

Many studies have been conducted to understand the immune modulatory effects in helminth infections. To determine whether there is a common transcriptomic signature characteristic of the helminth effect, we performed a comprehensive meta-analysis of publicly available gene expression datasets. The results revealed a distinct pattern of gene expression that is consistent across multiple helminth species and host tissue types, with upregulated genes dominated by those involved in immune regulation, Th2 immunity and inflammatory responses.

Cancer Metabolomics and the Human Metabolome Database

The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world's largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.

Gene expression signatures, pathways and networks in carotid atherosclerosis

BACKGROUND

Embolism from unstable atheromas in the carotid bifurcation is a major cause of stroke. Here, we analysed gene expression in endarterectomies from patients with symptomatic (S) and asymptomatic (AS) carotid stenosis to identify pathways linked to plaque instability.

METHODS

Microarrays were prepared from plaques (n = 127) and peripheral blood samples (n = 96) of S and AS patients. Gene set enrichment, pathway mapping and network analyses of differentially expressed genes were performed.

RESULTS

These studies revealed upregulation of haemoglobin metabolism (P = 2.20E-05) and bone resorption (P = 9.63E-04) in S patients. Analysis of subgroups of patients indicated enrichment of calcification and osteoblast differentiation in S patients on statins, as well as inflammation and apoptosis in plaques removed >1 month compared to <2 weeks after symptom. By prediction profiling, a panel of 30 genes, mostly transcription factors, discriminated between plaques from S versus AS patients with 78% accuracy. By meta-analysis, common gene networks associated with atherosclerosis mapped to hypoxia, chemokines, calcification, actin cytoskeleton and extracellular matrix. A set of dysregulated genes (LMOD1, SYNPO2, PLIN2 and PPBP) previously not described in atherosclerosis were identified from microarrays and validated by quantitative PCR and immunohistochemistry.

CONCLUSIONS

Our findings confirmed a central role for inflammation and proteases in plaque instability, and highlighted haemoglobin metabolism and bone resorption as important pathways. Subgroup analysis suggested prolonged inflammation following the symptoms of plaque instability and calcification as a possible stabilizing mechanism by statins. In addition, transcriptional regulation may play an important role in the determination of plaque phenotype. The results from this study will serve as a basis for further exploration of molecular signatures in carotid atherosclerosis.

Role of innate immunity-triggered pathways in the pathogenesis of Sickle Cell Disease: a meta-analysis of gene expression studies

Despite the detailed characterization of the inflammatory and endothelial changes observed in Sickle Cell Disease (SCD), the hierarchical relationship between elements involved in the pathogenesis of this complex disease is yet to be described. Meta-analyses of gene expression studies from public repositories represent a novel strategy, capable to identify key mediators in complex diseases. We performed several meta-analyses of gene expression studies involving SCD, including studies with patient samples, as well as in-vitro models of the disease. Meta-analyses were performed with the Inmex bioinformatics tool, based on the RankProd package, using raw gene expression data. Functional gene set analysis was performed using more than 60 gene-set libraries. Our results demonstrate that the well-characterized association between innate immunity, hemostasis, angiogenesis and heme metabolism with SCD is also consistently observed at the transcriptomic level, across independent studies. The enrichment of genes and pathways associated with innate immunity and damage repair-associated pathways supports the model of erythroid danger-associated molecular patterns (DAMPs) as key mediators of the pathogenesis of SCD. Our study also generated a novel database of candidate genes, pathways and transcription factors not previously associated with the pathogenesis of SCD that warrant further investigation in models and patients of SCD.

The use of 'Omics technology to rationally improve industrial mammalian cell line performance

Biologics represent an increasingly important class of therapeutics, with 7 of the 10 top selling drugs from 2013 being in this class. Furthermore, health authority approval of biologics in the immuno-oncology space is expected to transform treatment of patients with debilitating and deadly diseases. The growing importance of biologics in the healthcare field has also resulted in the recent approvals of several biosimilars. These recent developments, combined with pressure to provide treatments at lower costs to payers, are resulting in increasing need for the industry to quickly and efficiently develop high yielding, robust processes for the manufacture of biologics with the ability to control quality attributes within narrow distributions. Achieving this level of manufacturing efficiency and the ability to design processes capable of regulating growth, death and other cellular pathways through manipulation of media, feeding strategies, and other process parameters will undoubtedly be facilitated through systems biology tools generated in academic and public research communities. Here we discuss the intersection of systems biology, 'Omics technologies, and mammalian bioprocess sciences. Specifically, we address how these methods in conjunction with traditional monitoring techniques represent a unique opportunity to better characterize and understand host cell culture state, shift from an empirical to rational approach to process development and optimization of bioreactor cultivation processes. We summarize the following six key areas: (i) research applied to parental, non-recombinant cell lines; (ii) systems level datasets generated with recombinant cell lines; (iii) datasets linking phenotypic traits to relevant biomarkers; (iv) data depositories and bioinformatics tools; (v) in silico model development, and (vi) examples where these approaches have been used to rationally improve cellular processes. We critically assess relevant and state of the art research being conducted in academic, government and industrial laboratories. Furthermore, we apply our expertise in bioprocess to define a potential model for integration of these systems biology approaches into biologics development.

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

Meta-Analysis of Multiple Sclerosis Microarray Data Reveals Dysregulation in RNA Splicing Regulatory Genes

Abnormalities in RNA metabolism and alternative splicing (AS) are emerging as important players in complex disease phenotypes. In particular, accumulating evidence suggests the existence of pathogenic links between multiple sclerosis (MS) and altered AS, including functional studies showing that an imbalance in alternatively-spliced isoforms may contribute to disease etiology. Here, we tested whether the altered expression of AS-related genes represents a MS-specific signature. A comprehensive comparative analysis of gene expression profiles of publicly-available microarray datasets (190 MS cases, 182 controls), followed by gene-ontology enrichment analysis, highlighted a significant enrichment for differentially-expressed genes involved in RNA metabolism/AS. In detail, a total of 17 genes were found to be differentially expressed in MS in multiple datasets, with CELF1 being dysregulated in five out of seven studies. We confirmed CELF1 downregulation in MS (p = 0.0015) by real-time RT-PCRs on RNA extracted from blood cells of 30 cases and 30 controls. As a proof of concept, we experimentally verified the unbalance in alternatively-spliced isoforms in MS of the NFAT5 gene, a putative CELF1 target. In conclusion, for the first time we provide evidence of a consistent dysregulation of splicing-related genes in MS and we discuss its possible implications in modulating specific AS events in MS susceptibility genes.

Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis

Acquired resistance to lapatinib is a highly problematic clinical barrier that has to be overcome for a successful cancer treatment. Despite efforts to determine the mechanisms underlying acquired lapatinib resistance (ALR), no definitive genetic factors have been reported to be solely responsible for the acquired resistance in breast cancer. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets related to breast cancer with ALR, using the R-based RankProd package. From the meta-analysis, we were able to identify a total of 990 differentially expressed genes (DEGs, 406 upregulated, 584 downregulated) that are potentially associated with ALR. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs showed that "response to organic substance" and "p53 signaling pathway" may be largely involved in ALR process. Of these, many of the top 50 upregulated and downregulated DEGs were found in oncogenesis of various tumors and cancers. For the top 50 DEGs, we constructed the gene coexpression and protein-protein interaction networks from a huge database of well-known molecular interactions. By integrative analysis of two systemic networks, we condensed the total number of DEGs to six common genes (LGALS1, PRSS23, PTRF, FHL2, TOB1, and SOCS2). Furthermore, these genes were confirmed in functional module eigens obtained from the weighted gene correlation network analysis of total DEGs in the microarray datasets ("GSE16179" and "GSE52707"). Our integrative meta-analysis could provide a comprehensive perspective into complex mechanisms underlying ALR in breast cancer and a theoretical support for further chemotherapeutic studies.

Systems vaccinology: a promise for the young and the poor

As a child, the risk of suffering and dying from infection is higher the younger you are; and higher, the less developed a region you are born in. Childhood vaccination programmes have greatly reduced mortality around the world, but least so for the very young among the very poor of the world. This appears partly owing to suboptimal vaccine effectiveness. Unfortunately, although most vaccines are administered to the newborn and very young infant (less than or equal to two months), we know the least about their host response to vaccination. We thus currently lack the knowledge to guide efforts aimed at improving vaccine effectiveness in this vulnerable population. Systems vaccinology, the study of molecular networks activated by immunization, has begun to provide unprecedented insights into mechanisms leading to vaccine-induced protection from infection or disease. However, all published reports of systems vaccinology have focused on either adults or at most children and older infants, not those most in need, i.e. newborns and very young infants. Given that the tools of systems vaccinology work perfectly well with very small sample volumes, it is time we deliver the promise that systems vaccinology holds for those most in need of vaccine-mediated protection from infection.

Integrative meta-analysis of differentially expressed genes in osteoarthritis using microarray technology

The aim of the present study was to identify differentially expressed (DE) genes in patients with osteoarthritis (OA), and biological processes associated with changes in gene expression that occur in this disease. Using the INMEX (integrative meta-analysis of expression data) software tool, a meta-analysis of publicly available microarray Gene Expression Omnibus (GEO) datasets of OA was performed. Gene ontology (GO) enrichment analysis was performed in order to detect enriched functional attributes based on gene-associated GO terms. Three GEO datasets, containing 137 patients with OA and 52 healthy controls, were included in the meta-analysis. The analysis identified 85 genes that were consistently differentially expressed in OA (30 genes were upregulated and 55 genes were downregulated). The upregulated gene with the lowest P-value (P=5.36E-07) was S-phase kinase-associated protein 2, E3 ubiquitin protein ligase (SKP2). The downregulated gene with the lowest P-value (P=4.42E-09) was Proline rich 5 like (PRR5L). Among the 210 GO terms that were associated with the set of DE genes, the most significant two enrichments were observed in the GO categories of 'Immune response', with a P-value of 0.000129438, and 'Immune effectors process', with a P-value of 0.000288619. The current meta-analysis identified genes that were consistently DE in OA, in addition to biological pathways associated with changes in gene expression that occur during OA, which may provide insight into the molecular mechanisms underlying the pathogenesis of this disease.

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.

Identification of hepatocellular carcinoma-associated hub genes and pathways by integrated microarray analysis

AIMS AND BACKGROUND

Hepatocellular carcinoma (HCC) is a dismal malignancy associated with multiple molecular changes. The purpose of this study was to identify the differentially expressed genes and analyze the biological processes related to HCC.

METHODS AND STUDY DESIGN

Datasets of HCC were obtained from the NCBI Gene Expression Omnibus. Integrated analysis of differentially expressed genes was performed using the INMEX program. Then Gene Ontology enrichment analyses and pathway analysis were performed based on the Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes. A protein-protein interaction network was constructed using the Cytoscape software; the netwerk served to find hub genes for HCC. Real-time RT-PCR was used to validate the microarray data for hub genes.

RESULTS

We identified 273 genes that were differentially expressed in HCC. Gene Ontology enrichment analyses revealed response to cadmium ion, cellular response to cadmium ion, and cellular response to zinc ion for these genes. Pathway analysis showed that significant pathways included fatty acid metabolism, butanoate metabolism, and PPAR signaling pathway. The protein-protein interaction network indicated that CDH1, ECHS1, ACAA1, MT2A, and MYC were important genes which participated in many interactions. Experimental validation of the role of four upregulated genes (ECHS1, ACAA1, MT2A and MYC) in the progression of HCC was carried out.

CONCLUSIONS

Our study displayed genes that were consistently differentially expressed in HCC. The biological pathways and protein-protein interaction networks associated with those genes were also identified. We predicted that CDH1, ECHS1, ACAA1, MT2A, and MYC might be target genes for diagnosing HCC.

Network-based metaanalysis identifies HNF4A and PTBP1 as longitudinally dynamic biomarkers for Parkinson's disease

Environmental and genetic factors are likely to be involved in the pathogenesis of Parkinson's disease (PD), the second most prevalent neurodegenerative disease among the elderly. Network-based metaanalysis of four independent microarray studies identified the hepatocyte nuclear factor 4 alpha (HNF4A), a transcription factor associated with gluconeogenesis and diabetes, as a central regulatory hub gene up-regulated in blood of PD patients. In parallel, the polypyrimidine tract binding protein 1 (PTBP1), involved in the stabilization and mRNA translation of insulin, was identified as the most down-regulated gene. Quantitative PCR assays revealed that HNF4A and PTBP1 mRNAs were up- and down-regulated, respectively, in blood of 51 PD patients and 45 controls nested in the Diagnostic and Prognostic Biomarkers for Parkinson's Disease. These results were confirmed in blood of 50 PD patients compared with 46 healthy controls nested in the Harvard Biomarker Study. Relative abundance of HNF4A mRNA correlated with the Hoehn and Yahr stage at baseline, suggesting its clinical utility to monitor disease severity. Using both markers, PD patients were classified with 90% sensitivity and 80% specificity. Longitudinal performance analysis demonstrated that relative abundance of HNF4A and PTBP1 mRNAs significantly decreased and increased, respectively, in PD patients during the 3-y follow-up period. The inverse regulation of HNF4A and PTBP1 provides a molecular rationale for the altered insulin signaling observed in PD patients. The longitudinally dynamic biomarkers identified in this study may be useful for monitoring disease-modifying therapies for PD.

Identification of potential transcriptomic markers in developing ankylosing spondylitis: a meta-analysis of gene expression profiles

The goal of this study was to identify potential transcriptomic markers in developing ankylosing spondylitis by a meta-analysis of multiple public microarray datasets. Using the INMEX (integrative meta-analysis of expression data) program, we performed the meta-analysis to identify consistently differentially expressed (DE) genes in ankylosing spondylitis and further performed functional interpretation (gene ontology analysis and pathway analysis) of the DE genes identified in the meta-analysis. Three microarray datasets (26 cases and 29 controls in total) were collected for meta-analysis. 905 consistently DE genes were identified in ankylosing spondylitis, among which 482 genes were upregulated and 423 genes were downregulated. The upregulated gene with the smallest combined rank product (RP) was GNG11 (combined RP = 299.64). The downregulated gene with the smallest combined RP was S100P (combined RP = 335.94). In the gene ontology (GO) analysis, the most significantly enriched GO term was “immune system process” (P = 3.46 × 10⁻²⁶). The most significant pathway identified in the pathway analysis was antigen processing and presentation (P = 8.40 × 10⁻⁵). The consistently DE genes in ankylosing spondylitis and biological pathways associated with those DE genes identified provide valuable information for studying the pathophysiology of ankylosing spondylitis.

Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjögren's syndrome uncovered through gene expression meta-analysis

Introduction

Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and Sjögren’s syndrome (SjS) are inflammatory systemic autoimmune diseases (SADs) that share several clinical and pathological features. The shared biological mechanisms are not yet fully characterized. The objective of this study was to perform a meta-analysis using publicly available gene expression data about the three diseases to identify shared gene expression signatures and overlapping biological processes.

Methods

Previously reported gene expression datasets were selected and downloaded from the Gene Expression Omnibus database. Normalization and initial preprocessing were performed using the statistical programming language R and random effects model–based meta-analysis was carried out using INMEX software. Functional analysis of over- and underexpressed genes was done using the GeneCodis tool.

Results

The gene expression meta-analysis revealed a SAD signature composed of 371 differentially expressed genes in patients and healthy controls, 187 of which were underexpressed and 184 overexpressed. Many of these genes have previously been reported as significant biomarkers for individual diseases, but others provide new clues to the shared pathological state. Functional analysis showed that overexpressed genes were involved mainly in immune and inflammatory responses, mitotic cell cycles, cytokine-mediated signaling pathways, apoptotic processes, type I interferon–mediated signaling pathways and responses to viruses. Underexpressed genes were involved primarily in inhibition of protein synthesis.

Conclusions

We define a common gene expression signature for SLE, RA and SjS. The analysis of this signature revealed relevant biological processes that may play important roles in the shared development of these pathologies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0489-x) contains supplementary material, which is available to authorized users.

A network approach to clinical intervention in neurodegenerative diseases

Network biology has become a powerful tool to dissect the molecular mechanisms triggering neurodegeneration. Recent developments in network biology have led to the discovery of disease-causing genes, diagnostic biomarkers, and therapeutic targets for several neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases. Network-based approaches have provided the molecular rationale for the relationship among cancer, diabetes, and neurodegenerative diseases, and have uncovered unexpected links between apparently unrelated diseases. Here, we summarize the recent advances in network biology to untangle the molecular underpinnings giving rise to the most prevalent neurodegenerative diseases. We propose that network analysis provides a feasible and practical tool for identifying biologically meaningful biomarkers and potential therapeutic targets for clinical intervention in neurodegenerative diseases.

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data

MOTIVATION

Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context.

RESULTS

This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches.

AVAILABILITY AND IMPLEMENTATION

R code for jNMFMA is available for non-commercial use via http://micblab.iim.ac.cn/Download/.

CONTACT

hqwang@ustc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

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.

Meta-analysis of gene expression profiles to predict response to biologic agents in rheumatoid arthritis

Our aim was to identify differentially expressed (DE) genes and biological processes that may help predict patient response to biologic agents for rheumatoid arthritis (RA). Using the INMEX (integrative meta-analysis of expression data) software tool, we performed a meta-analysis of publicly available microarray Gene Expression Omnibus (GEO) datasets that examined patient response to biologic therapy for RA. Three GEO datasets, containing 79 responders and 34 non-responders, were included in the meta-analysis. We identified 1,374 genes that were consistently differentially expressed in responders vs. non-responders (651 up-regulated and 723 down-regulated). The up-regulated gene with the smallest p value (p=0.000192) was ASCC2 (Activating Signal Cointegrator 1 Complex Subunit 2), and the up-regulated gene with the largest fold change (average log fold change=-0.75869, p=0.000206) was KLRC3 (Killer Cell Lectin-Like Receptor Subfamily C, Member 3). The down-regulated gene with the smallest p value (p=0.000195) was MPL (Myeloproliferative Leukemia Virus Oncogene). Among the 236 GO terms associated with the set of DE genes, the most significantly enriched was "CTP biosynthetic process" (GO:0006241; p=0.000454). Our meta-analysis identified genes that were consistently DE in responders vs. non-responders, as well as biological pathways associated with this set of genes. These results provide insight into the molecular mechanisms underlying responsiveness to biologic therapy for RA.

Gap junction modulation and its implications for heart function

Gap junction communication (GJC) mediated by connexins is critical for heart function. To gain insight into the causal relationship of molecular mechanisms of disease pathology, it is important to understand which mechanisms contribute to impairment of gap junctional communication. Here, we present an update on the known modulators of connexins, including various interaction partners, kinases, and signaling cascades. This gap junction network (GJN) can serve as a blueprint for data mining approaches exploring the growing number of publicly available data sets from experimental and clinical studies.

Array data extractor (ADE): a LabVIEW program to extract and merge gene array data

Background

Large data sets from gene expression array studies are publicly available offering information highly valuable for research across many disciplines ranging from fundamental to clinical research. Highly advanced bioinformatics tools have been made available to researchers, but a demand for user-friendly software allowing researchers to quickly extract expression information for multiple genes from multiple studies persists.

Findings

Here, we present a user-friendly LabVIEW program to automatically extract gene expression data for a list of genes from multiple normalized microarray datasets. Functionality was tested for 288 class A G protein-coupled receptors (GPCRs) and expression data from 12 studies comparing normal and diseased human hearts. Results confirmed known regulation of a beta 1 adrenergic receptor and further indicate novel research targets.

Conclusions

Although existing software allows for complex data analyses, the LabVIEW based program presented here, “Array Data Extractor (ADE)”, provides users with a tool to retrieve meaningful information from multiple normalized gene expression datasets in a fast and easy way. Further, the graphical programming language used in LabVIEW allows applying changes to the program without the need of advanced programming knowledge.

Meta-analysis of differentially expressed genes in primary Sjogren's syndrome by using microarray

INTRODUCTION

The purpose of this study was to identify differentially expressed (DE) genes and biological processes associated with changes in gene expression in primary Sjogren's syndrome (pSS).

METHODS

We performed a meta-analysis using the INMEX program (integrative meta-analysis of expression data) of publicly available microarray GEO datasets of pSS. We performed Gene Ontology (GO) enrichment analyses and pathway analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG).

RESULTS

Three GEO datasets including 37 cases and 33 controls were available for the meta-analysis. We identified 179 genes across the studies which were consistently DE in pSS (146 up-regulated and 33 down-regulated). The up-regulated gene with the largest effect size (ES) (ES = -2.4228) was SELL (selectin L), whose product is required for the binding and subsequent rolling of leucocytes on endothelial cells to facilitate their migration into secondary lymphoid organs and inflammation sites. The most significant enrichment was in the immune response GO category (P = 2.52 × 10(-25)). The most significant pathway in our KEGG analysis was Epstein-Barr virus infection (P = 9.91 × 10(-06)).

CONCLUSIONS

Our meta-analysis demonstrated genes that were consistently DE and biological pathways associated with gene expression changes with pSS.

INVEX--a web-based tool for integrative visualization of expression data

SUMMARY

Gene expression or metabolomics data generated from clinical settings are often associated with multiple metadata (i.e. diagnosis, genotype, gender, etc.). It is of great interest to analyze and to visualize the data in these contexts. Here, we introduce INVEX-a novel web-based tool that integrates the server-side capabilities for data analysis with the browse-based technology for data visualization. INVEX has two key features: (i) flexible differential expression analysis for a wide variety of experimental designs; and (ii) interactive visualization within the context of metadata and biological annotations. INVEX has built-in support for gene/metabolite annotation and a fully functional heatmap builder.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://www.invex.ca.