VisANT: an online visualization and analysis tool for biological interaction data

Integrative analysis of transcriptome and miRNome unveils the key regulatory connections involved in different stages of hepatocellular carcinoma

Dysregulated molecular processes are the major factors that drive and feed the signaling processes involved in carcinogenesis. In recent years, regulation of mRNAs by microRNAs (miRNAs) has been found to play a vital role in many cancers including hepatocellular carcinoma (HCC). However, genomewide studies defining molecular regulatory circuits at both mRNA and miRNA levels are just emerging. To uncover the molecular and functional processes involved in liver tumorigenesis at mRNA and miRNA level, a co-expression-based network of miRNAs was constructed from multiple miRNA profiles. The applicability of the network approach to microRNA expression profiles was assessed. Although the clustering consistency of miRNAs across the profiles was found moderate, miRNA networking has been found informative. Furthermore, microRNA network modules were integrated with the functionally defined mRNA modules derived from an mRNA co-expression network of an earlier study. Three highly clustered regulatory circuits of mRNA-miRNA modules have been identified as involved in hepatocyte, inflammatory-stress and proliferative process activated subcategories of HCC. A subset of the proliferative miRNA module was found clustered in the 14q32.31 chromosomal region. The current integrative network analysis of mRNA-miRNA modules shows the intricate miRNA-mRNA functional circuits and signaling interactions involved in liver tumorigenesis.

Integrative functional genomic delineation of the cascades of transcriptional changes involved in hepatocellular carcinoma progression

Development of targeted therapeutics is still at its early stage for hepatocellular carcinoma (HCC) due to the incomplete understanding of the confounding regulations at signaling pathway level. In this investigation, gene co-expression-based networking and integrative functional genomic modeling of HCC mRNA profiles as signaling processes were employed to understand the complex signaling cascades involved in HCC development toward understanding the avenues for targeted therapeutics. Multiple sets of genes and molecular biological processes involved during HCC development were identified from this integrative analysis: (i) Loss of liver cellular features due to the reduced HNF4A & PPAR signaling in the early stages of HCC, (ii) activated inflammatory and stress signals in the cirrhosis stages and (iii) highly activated cellular proliferation with the activated E2F-MYC oncogenic signaling with the gain of embryonic liver stem cell-like features in the advanced stage tumors. Upon connecting these gene-sets with the established drug sensitivity-related gene signatures, targeted therapeutic strategies for the heterogeneous HCC conditions have been identified. PPAR agonist class of drugs for early stage HCC conditions, anti-inflammatory drugs for cirrhosis and topoisomerase inhibitors for the advanced HCC conditions were inferred. Integrative functional genomic analysis of HCC transcriptome profiles at the context of signaling pathways has defined the key molecular processes involved in HCC development. Further, the study highlights the stage-specific and pathway focused targeted therapeutics for HCC. These findings deserve extensive preclinical explorations toward the establishment of targeted therapeutics.

Stereotypical Metabolic Response to Endoscopic Retrograde Cholangiopancreatography Show Alterations in Pancreatic Function Regardless of Post-Procedure Pancreatitis

OBJECTIVES:

Metabolomics-based diagnosis or prediction of risk may improve patient outcomes and improve understanding of the pathogenesis of acute pancreatitis (AP). Endoscopic retrograde cholangiopancreatography (ERCP) is a risk factor for developing AP. This pilot study examined metabolomes of patients before and after ERCP, hypothesizing that metabolomics could differentiate between patients who did and did not develop post-ERCP pancreatitis, and that biomarkers associated with development of AP could be identified.

METHODS:

Patients at high risk for developing post-ERCP pancreatitis were prospectively enrolled at the University of Minnesota from October 2012 to February 2014. Urine and serum samples were collected before ERCP, 2 h after ERCP, and daily thereafter if patients were admitted to the hospital with AP. Pancreatitis severity was calculated with Bedside Index for Severity in Acute Pancreatitis (BISAP) and Modified Glasgow scores. Patients who developed AP (n=9) were matched to patients who did not develop AP (n=18) by age and gender. Urine and serum metabolites were profiled with nuclear magnetic resonance spectroscopy. Partial least squares discriminant analysis (PLS-DA) was performed to detect changes in metabolic profiles associated with development of pancreatitis. Metabolic networks were constructed to probe functional relationships among metabolites.

RESULTS:

Of the 113 enrolled patients, 9 developed mild AP according to BISAP and modified Glasglow scores. PLS-DA showed common differences between pre- and post-ERCP metabolic profiles in urine and serum regardless of AP status, characterized by increases in serum and urine ketones and serum glucose. Pre-ERCP lipase levels were somewhat elevated in those who went on to develop AP, though this did not reach statistical significance. Metabolic networks differed between patients with AP and those without after ERCP; however, metabolomics did not identify specific prognostic or diagnostic markers of ERCP-induced AP. Aspartate and asparagine were identified as well-connected hubs in post-ERCP serum networks of cases and were correlated with aspartate transaminase (AST) and white blood cell count levels. These features were not evident in controls. Serum aspartate was elevated in AP patients relative to those without AP after ERCP (P=0.03).

CONCLUSIONS:

In this pilot study, ERCP was found to induce global changes in urine and serum metabolomes indicative of alterations in pancreatic function and insulin resistance. This should be taken into consideration in future research on this topic. Post-ERCP serum metabolic networks indicate functional differences surrounding aspartate metabolism between patients with AP and those without. Further study must be done in larger patient populations to test elevated lipase as a prognostic biomarker associated with risk of developing AP and to examine active metabolic mechanisms at work.

Utilising the EGFR interactome to identify mechanisms of drug resistance in non-small cell lung cancer - Proof of concept towards a systems pharmacology approach

Drug treatment of epidermal growth factor receptor (EGFR) positive non-small cell lung cancer has improved substantially by targeting activating mutations within the receptor tyrosine kinase domain. However, the development of drug resistance still limits this approach. As root causes, large heterogeneity between tumour entities but also within tumour cells have been suggested. Therefore, approaches to identify these multitude and complex mechanisms are urgently required. Affinity purification coupled with high resolution mass spectrometry was applied to isolate and characterise the EGFR interactome from HCC4006 non-small cell lung cancer cells and their variant HCC4006^rERLO^0.5 adapted to grow in the presence of therapeutically relevant concentrations of erlotinib. Bioinformatics analyses were carried out to identify proteins and their related molecular functions that interact differentially with EGFR in the untreated state or when incubated with erlotinib prior to EGFR activation. Across all experimental conditions 375 proteins were detected to participate in the EGFR interactome, 90% of which constituted a complex protein interaction network that was bioinformatically reconstructed from literature data. Treatment of HCC4006^rERLO^0.5 cells carrying a resistance phenotype to erlotinib was associated with an increase of protein levels of members of the clathrin-associated adaptor protein family AP2 (AP2A1, AP2A2, AP2B1), structural proteins of cytoskeleton rearrangement as well as signalling molecules such as Shc. Validation experiments confirmed activation of the Ras-Raf-Mek-Erk (MAPK)-pathway, of which Shc is an initiating adaptor molecule, in HCC4006^rERLO^0.5 cells. Taken together, differential proteins in the EGFR interactome of HCC4006^rERLO^0.5 cells were identified that could be related to multiple resistance mechanisms including alterations in growth factor receptor expression, cellular remodelling processes suggesting epithelial-to-mesenchymal transition as well as alterations in downstream signalling. Knowledge of these mechanisms is a pivotal step to build an integrative model of drug resistance in a systems pharmacology manner and to be able to investigate the interplay of these mechanisms and ultimately recommend combinatorial treatment strategies to overcome drug resistance.

Integrative functional genomic analysis unveils the differing dysregulated metabolic processes across hepatocellular carcinoma stages

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease and the development of targeted therapeutics is still at an early stage. The 'omics' based genome-wide profiling comprising the transcriptome, miRNome and proteome are highly useful in identifying the deregulated molecular processes involved in hepatocarcinogenesis. One of the end products and processes of the central dogma being the metabolites and metabolic processes mediate the cellular functions. In recent years, metabolomics based investigations have revealed the major deregulated metabolic processes involved in carcinogenesis. However, the integrative analysis of the holistic metabolic processes with genomics is at an early stage. Since the gene-sets are highly useful in assessing the biological processes and pathways, we made an attempt to infer the deregulated cellular metabolic processes involved in HCC by employing metabolism associated gene-set enrichment analysis. Further, the metabolic process enrichment scores were integrated with the transcriptome profiles of HCC. Integrative analysis shows three distinct metabolic deregulations: i) hepatocyte function related molecular processes involving lipid/fatty acid/bile acid synthesis, ii) inflammatory processes with cytokine, sphingolipid & chondriotin sulphate metabolism and iii) enriched nucleotide metabolic process involving purine/pyrimidine & glucose mediated catabolic process, in hepatocarcinogenesis. The three distinct metabolic processes were found to occur both in tumor and liver cancer cell line profiles. Unsupervised hierarchical clustering of the metabolic processes along with clinical sample information has identified two major clusters based on AFP (alpha-fetoprotein) and metastasis. The study reveals the three major regulatory processes involved in HCC stages.

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

miR-434-3p and DNA hypomethylation co-regulate eIF5A1 to increase AChRs and to improve plasticity in SCT rat skeletal muscle

Acetylcholine receptors (AChRs) serve as connections between motor neurons and skeletal muscle and are essential for recovery from spinal cord transection (SCT). Recently, microRNAs have emerged as important potential biotherapeutics for several diseases; however, whether miRNAs operate in the modulation of AChRs remains unknown. We found increased AChRs numbers and function scores in rats with SCT; these increases were reduced following the injection of a eukaryotic translation initiation factor 5A1 (eIF5A1) shRNA lentivirus into the hindlimb muscle. Then, high-throughput screening for microRNAs targeting eIF5A1 was performed, and miR-434-3p was found to be robustly depleted in SCT rat skeletal muscle. Furthermore, a highly conserved miR-434-3p binding site was identified within the mRNA encoding eIF5A1 through bioinformatics analysis and dual-luciferase assay. Overexpression or knockdown of miR-434-3p in vivo demonstrated it was a negative post-transcriptional regulator of eIF5A1 expression and influenced AChRs expression. The microarray-enriched Gene Ontology (GO) terms regulated by miR-434-3p were muscle development terms. Using a lentivirus, one functional gene (map2k6) was confirmed to have a similar function to that of miR-434-3p in GO terms. Finally, HRM and MeDIP-PCR analyses revealed that DNA demethylation also up-regulated eIF5A1 after SCT. Consequently, miR-434-3p/eIF5A1 in muscle is a promising potential biotherapy for SCI repair.

Gene co-expression networks shed light into diseases of brain iron accumulation

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention.

Differential activation of immune/inflammatory response-related co-expression modules in the hippocampus across the major psychiatric disorders

The Stanley Neuropathology Consortium Integrative Database (SNCID, http://sncid.stanleyresearch.org) is a data-mining tool that includes 379 neuropathology data sets from hippocampus, as well as RNA-Seq data measured in 15 well-matched cases in each of four groups: schizophrenia, bipolar disorder (BPD), major depression (MD) and unaffected controls. We analyzed the neuropathology data from the hippocampus to identify those abnormalities that are shared between psychiatric disorders and those that are specific to each disorder. Of the 379 data sets, 20 of them showed a significant abnormality in at least one disorder as compared with unaffected controls. GABAergic markers and synaptic proteins were mainly abnormal in schizophrenia and the two mood disorders, respectively. Two immune/inflammation-related co-expression modules built from RNA-seq data from both schizophrenia and controls combined were associated with disease status, as well as negatively correlated with the GABAergic markers. The correlation between immune-related modules and schizophrenia was replicated using microarray data from an independent tissue collection. Immune/inflammation-related co-expression modules were also built from RNA-seq data from BPD cases or from MD cases but were not preserved when using data from control cases. Moreover, there was no overlap in the genes that comprise the immune/inflammation response-related modules across the different disorders. Thus, there appears to be differential activation of the immune/inflammatory response, as determined by co-expression of genes, which is associated with the major psychiatric disorders and which is also associated with the abnormal neuropathology in the disorders.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Genes located in a chromosomal inversion are correlated with territorial song in white-throated sparrows

The genome of the white-throated sparrow (Zonotrichia albicollis) contains an inversion polymorphism on chromosome 2 that is linked to predictable variation in a suite of phenotypic traits including plumage color, aggression and parental behavior. Differences in gene expression between the two color morphs, which represent the two common inversion genotypes (ZAL2/ZAL2 and ZAL2/ZAL2(m) ), may therefore advance our understanding of the molecular underpinnings of these phenotypes. To identify genes that are differentially expressed between the two morphs and correlated with behavior, we quantified gene expression and terrirorial aggression, including song, in a population of free-living white-throated sparrows. We analyzed gene expression in two brain regions, the medial amygdala (MeA) and hypothalamus. Both regions are part of a 'social behavior network', which is rich in steroid hormone receptors and previously linked with territorial behavior. Using weighted gene co-expression network analyses, we identified modules of genes that were correlated with both morph and singing behavior. The majority of these genes were located within the inversion, showing the profound effect of the inversion on the expression of genes captured by the rearrangement. These modules were enriched with genes related to retinoic acid signaling and basic cellular functioning. In the MeA, the most prominent pathways were those related to steroid hormone receptor activity. Within these pathways, the only gene encoding such a receptor was ESR1 (estrogen receptor 1), a gene previously shown to predict song rate in this species. The set of candidate genes we identified may mediate the effects of a chromosomal inversion on territorial behavior.

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

A novel Anxa2-interacting protein Ebp1 inhibits cancer proliferation and invasion by suppressing Anxa2 protein level

Anxa2 is dysregulated in many types of carcinomas and implicated in several pivotal biological functions, such as angiogenesis, cell proliferation, invasion, and metastasis. We previously demonstrated that upregulation of Anxa2 enhances the proliferation and invasion of breast cancer cells. However, the detailed mechanism remains unclear. In this study, co-immunoprecipitation and LC-MS/MS-based interactome approach were employed to screen potential Anxa2 binding proteins. A total of 312 proteins were identified as candidate Anxa2 interacting partners. Using Gene Ontology, pathway annotation, and protein-protein interaction analyses, we constructed a connected network for Anxa2 interacting proteins, and Ebp1 may function as a "hub" in the Anxa2 interaction network. Moreover, Ebp1 knockdown resulted in enhanced cell proliferation and invasion, as well as increased expression of Anxa2. Furthermore, the abundance of cyclin D1 and the phosphorylation of Erk1/2 were increased in Ebp1 inhibited cells. This finding is consistent with a previous study, in which upregulation of Anxa2 results in an increased cyclin D1 expression and Erk1/2 activation. Our results suggest a novel function of Ebp1 as a binding protein and negative regulator of Anxa2. The functional association between Anxa2 and EBP1 may also participate in regulating cancer cell proliferation and invasion, thereby contributing to cancer progression.

Extended LineSets: a visualization technique for the interactive inspection of biological pathways

BACKGROUND

Biologists make use of pathway visualization tools for a range of tasks, including investigating inter-pathway connectivity and retrieving details about biological entities and interactions. Some of these tasks require an understanding of the hierarchical nature of elements within the pathway or the ability to make comparisons between multiple pathways. We introduce a technique inspired by LineSets that enables biologists to fulfill these tasks more effectively.

RESULTS

We introduce a novel technique, Extended LineSets, to facilitate new explorations of biological pathways. Our technique incorporates intuitive graphical representations of different levels of information and includes a well-designed set of user interactions for selecting, filtering, and organizing biological pathway data gathered from multiple databases.

CONCLUSIONS

Based on interviews with domain experts and an analysis of two use cases, we show that our technique provides functionality not currently enabled by current techniques, and moreover that it helps biologists to better understand both inter-pathway connectivity and the hierarchical structure of biological elements within the pathways.

Cancer as the Disintegration of Robustness: Population-Level Variance in Gene Expression Identifies Key Differences Between Tobacco- and HPV-Associated Oropharyngeal Carcinogenesis

CONTEXT

Oropharyngeal squamous cell carcinoma is associated both with tobacco use and with human papillomavirus (HPV) infection. It is argued that carcinogen-driven tumorigenesis is a distinct disease from its virally driven counterpart. We hypothesized that tumorigenesis is the result of a loss of genotypic robustness resulting in an increase in phenotypic variation in tumors compared with adjacent histologically normal tissues, and that carcinogen-driven tumorigenesis results in greater variation than its virally driven counterpart.

OBJECTIVES

To examine the loss of robustness in carcinogen-driven and virally driven oropharyngeal squamous cell carcinoma samples, and to identify potential pathways involved.

DESIGN

We used coefficients of variation for messenger RNA and microRNA expression to measure the loss of robustness in oropharyngeal squamous cell carcinoma samples. Tumors were compared with matched normal tissues, and were further categorized by HPV and patient smoking status. Weighted gene coexpression networks were constructed for genes with highly variable expression among the HPV⁻ tumors from smokers.

RESULTS

We observed more genes with variable messenger RNA expression in tumors compared with normal tissues, regardless of HPV and smoking status, and more microRNAs with variable expression in HPV⁻ and HPV⁺ tumors from smoking patients than from nonsmokers. For both the messenger RNA and microRNA data, we observed more variance among HPV⁻ tumors from smokers compared with HPV⁺ tumors from nonsmokers. The gene coexpression network construction highlighted pathways that have lost robustness in carcinogen-induced tumors but appear stable in virally induced tumors.

CONCLUSIONS

Using coefficients of variation and coexpression networks, we identified multiple altered pathways that may play a role in carcinogen-driven tumorigenesis.

PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways

Background

High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists.

Results

We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management.

Conclusions

PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at http://raven.anr.udel.edu/~sunliang/PathRings and the source code is hosted on Github: https://github.com/ivcl/PathRings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0585-1) contains supplementary material, which is available to authorized users.

Spatiotemporal dynamics of the postnatal developing primate brain transcriptome

Developmental changes in the temporal and spatial regulation of gene expression drive the emergence of normal mature brain function, while disruptions in these processes underlie many neurodevelopmental abnormalities. To solidify our foundational knowledge of such changes in a primate brain with an extended period of postnatal maturation like in human, we investigated the whole-genome transcriptional profiles of rhesus monkey brains from birth to adulthood. We found that gene expression dynamics are largest from birth through infancy, after which gene expression profiles transition to a relatively stable state by young adulthood. Biological pathway enrichment analysis revealed that genes more highly expressed at birth are associated with cell adhesion and neuron differentiation, while genes more highly expressed in juveniles and adults are associated with cell death. Neocortex showed significantly greater differential expression over time than subcortical structures, and this trend likely reflects the protracted postnatal development of the cortex. Using network analysis, we identified 27 co-expression modules containing genes with highly correlated expression patterns that are associated with specific brain regions, ages or both. In particular, one module with high expression in neonatal cortex and striatum that decreases during infancy and juvenile development was significantly enriched for autism spectrum disorder (ASD)-related genes. This network was enriched for genes associated with axon guidance and interneuron differentiation, consistent with a disruption in the formation of functional cortical circuitry in ASD.

Transcriptional profiles of bovine in vivo pre-implantation development

Background

During mammalian pre-implantation embryonic development dramatic and orchestrated changes occur in gene transcription. The identification of the complete changes has not been possible until the development of the Next Generation Sequencing Technology.

Results

Here we report comprehensive transcriptome dynamics of single matured bovine oocytes and pre-implantation embryos developed in vivo. Surprisingly, more than half of the estimated 22,000 bovine genes, 11,488 to 12,729 involved in more than 100 pathways, is expressed in oocytes and early embryos. Despite the similarity in the total numbers of genes expressed across stages, the nature of the expressed genes is dramatically different. A total of 2,845 genes were differentially expressed among different stages, of which the largest change was observed between the 4- and 8-cell stages, demonstrating that the bovine embryonic genome is activated at this transition. Additionally, 774 genes were identified as only expressed/highly enriched in particular stages of development, suggesting their stage-specific roles in embryogenesis. Using weighted gene co-expression network analysis, we found 12 stage-specific modules of co-expressed genes that can be used to represent the corresponding stage of development. Furthermore, we identified conserved key members (or hub genes) of the bovine expressed gene networks. Their vast association with other embryonic genes suggests that they may have important regulatory roles in embryo development; yet, the majority of the hub genes are relatively unknown/under-studied in embryos. We also conducted the first comparison of embryonic expression profiles across three mammalian species, human, mouse and bovine, for which RNA-seq data are available. We found that the three species share more maternally deposited genes than embryonic genome activated genes. More importantly, there are more similarities in embryonic transcriptomes between bovine and humans than between humans and mice, demonstrating that bovine embryos are better models for human embryonic development.

Conclusions

This study provides a comprehensive examination of gene activities in bovine embryos and identified little-known potential master regulators of pre-implantation development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-756) contains supplementary material, which is available to authorized users.

IL-32 is a molecular marker of a host defense network in human tuberculosis

Tuberculosis is a leading cause of infectious disease-related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ- and IL-15-induced "defense response" genes. IL-32 induced the vitamin D-dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15-induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15-induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis.

Functional characterization of breast cancer using pathway profiles

BACKGROUND

The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.

METHODS

We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.

RESULTS

Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.

CONCLUSIONS

Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

Insights from cerebellar transcriptomic analysis into the pathogenesis of ataxia

IMPORTANCE

The core clinical and neuropathological feature of the autosomal dominant spinocerebellar ataxias (SCAs) is cerebellar degeneration. Mutations in the known genes explain only 50% to 60% of SCA cases. To date, no effective treatments exist, and the knowledge of drug-treatable molecular pathways is limited. The examination of overlapping mechanisms and the interpretation of how ataxia genes interact will be important in the discovery of potential disease-modifying agents.

OBJECTIVES

To address the possible relationships among known SCA genes, predict their functions, identify overlapping pathways, and provide a framework for candidate gene discovery using whole-transcriptome expression data.

DESIGN, SETTING, AND PARTICIPANTS

We have used a systems biology approach based on whole-transcriptome gene expression analysis. As part of the United Kingdom Brain Expression Consortium, we analyzed the expression profile of 788 brain samples obtained from 101 neuropathologically healthy individuals (10 distinct brain regions each). Weighted gene coexpression network analysis was used to cluster 24 SCA genes into gene coexpression modules in an unsupervised manner. The overrepresentation of SCA transcripts in modules identified in the cerebellum was assessed. Enrichment analysis was performed to infer the functions and molecular pathways of genes in biologically relevant modules.

MAIN OUTCOMES AND MEASURES

Molecular functions and mechanisms implicating SCA genes, as well as lists of relevant coexpressed genes as potential candidates for novel SCA causative or modifier genes.

RESULTS

Two cerebellar gene coexpression modules were statistically enriched in SCA transcripts (P = .021 for the tan module and P = 2.87 × 10-5 for the light yellow module) and contained established granule and Purkinje cell markers, respectively. One module includes genes involved in the ubiquitin-proteasome system and contains SCA genes usually associated with a complex phenotype, while the other module encloses many genes important for calcium homeostasis and signaling and contains SCA genes associated mostly with pure ataxia.

CONCLUSIONS AND RELEVANCE

Using normal gene expression in the human brain, we identified significant cell types and pathways in SCA pathogenesis. The overrepresentation of genes involved in calcium homeostasis and signaling may indicate an important target for therapy in the future. Furthermore, the gene networks provide new candidate genes for ataxias or novel genes that may be critical for cerebellar function.

NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules

Background

Network representations of cell-biological signaling processes frequently contain large numbers of interacting molecular and multi-molecular components that can exist in, and switch between, multiple biochemical and/or structural states. In addition, the interaction categories (associations, dissociations and transformations) in such networks cannot satisfactorily be mapped onto simple arrows connecting pairs of components since their specifications involve information such as reaction rates and conditions with regard to the states of the interacting components. This leads to the challenge of having to reconcile competing objectives: providing a high-level overview without omitting relevant information, and showing interaction specifics while not overwhelming users with too much detail displayed simultaneously. This problem is typically addressed by splitting the information required to understand a reaction network model into several categories that are rendered separately through combinations of visualizations and/or textual and tabular elements, requiring modelers to consult several sources to obtain comprehensive insights into the underlying assumptions of the model.

Results

We report the development of an application, the Simmune NetworkViewer, that visualizes biochemical reaction networks using iconographic representations of protein interactions and the conditions under which the interactions take place using the same symbols that were used to specify the underlying model with the Simmune Modeler. This approach not only provides a coherent model representation but, moreover, following the principle of “overview first, zoom and filter, then details-on-demand,” can generate an overview visualization of the global network and, upon user request, presents more detailed views of local sub-networks and the underlying reaction rules for selected interactions. This visual integration of information would be difficult to achieve with static network representations or approaches that use scripted model specifications without offering simple but detailed symbolic representations of molecular interactions, their conditions and consequences in terms of biochemical modifications.

Conclusions

The Simmune NetworkViewer provides concise, yet comprehensive visualizations of reaction networks created in the Simmune framework. In the near future, by adopting the upcoming SBML standard for encoding multi-component, multi-state molecular complexes and their interactions as input, the NetworkViewer will, moreover, be able to offer such visualization for any rule-based model that can be exported to that standard.

Transcriptional landscape of the prenatal human brain

The anatomical and functional architecture of the human brain is largely determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and postmitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and human-expanded outer subventricular zones. Both germinal and postmitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in frontal lobe. Finally, many neurodevelopmental disorder and human evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.

Using VisANT to Analyze Networks

VisANT is a Web-based workbench for the integrative analysis of biological networks with unique features such as exploratory navigation of interaction network and multi-scale visualization and inference with integrated hierarchical knowledge. It provides functionalities for convenient construction, visualization, and analysis of molecular and higher order networks based on functional (e.g., expression profiles, phylogenetic profiles) and physical (e.g., yeast two-hybrid, chromatin-immunoprecipitation and drug target) relations from either the Predictome database or user-defined data sets. Analysis capabilities include network structure analysis, overrepresentation analysis, expression enrichment analysis etc. Additionally, network can be saved, accessed, and shared online. VisANT is able to develop and display meta-networks for meta-nodes that are structural complexes or pathways or any kind of subnetworks. Further, VisANT supports a growing number of standard exchange formats and database referencing standards, e.g., PSI-MI, KGML, BioPAX, SBML(in progress) Multiple species are supported to the extent that interactions or associations are available (i.e., public datasets or Predictome database).

Anticancer properties of distinct antimalarial drug classes

We have tested five distinct classes of established and experimental antimalarial drugs for their anticancer potential, using a panel of 91 human cancer lines. Three classes of drugs: artemisinins, synthetic peroxides and DHFR (dihydrofolate reductase) inhibitors effected potent inhibition of proliferation with IC50s in the nM- low µM range, whereas a DHODH (dihydroorotate dehydrogenase) and a putative kinase inhibitor displayed no activity. Furthermore, significant synergies were identified with erlotinib, imatinib, cisplatin, dasatinib and vincristine. Cluster analysis of the antimalarials based on their differential inhibition of the various cancer lines clearly segregated the synthetic peroxides OZ277 and OZ439 from the artemisinin cluster that included artesunate, dihydroartemisinin and artemisone, and from the DHFR inhibitors pyrimethamine and P218 (a parasite DHFR inhibitor), emphasizing their shared mode of action. In order to further understand the basis of the selectivity of these compounds against different cancers, microarray-based gene expression data for 85 of the used cell lines were generated. For each compound, distinct sets of genes were identified whose expression significantly correlated with compound sensitivity. Several of the antimalarials tested in this study have well-established and excellent safety profiles with a plasma exposure, when conservatively used in malaria, that is well above the IC50s that we identified in this study. Given their unique mode of action and potential for unique synergies with established anticancer drugs, our results provide a strong basis to further explore the potential application of these compounds in cancer in pre-clinical or and clinical settings.

Conserved molecular signatures of neurogenesis in the hippocampal subgranular zone of rodents and primates

The neurogenic potential of the subgranular zone (SGZ) of the hippocampal dentate gyrus is likely to be regulated by molecular cues arising from its complex heterogeneous cellular environment. Through transcriptome analysis using laser microdissection coupled with DNA microarrays, in combination with analysis of genome-wide in situ hybridization data, we identified 363 genes selectively enriched in adult mouse SGZ. These genes reflect expression in the different constituent cell types, including progenitor and dividing cells, immature granule cells, astrocytes, oligodendrocytes and GABAergic interneurons. Similar transcriptional profiling in the rhesus monkey dentate gyrus across postnatal development identified a highly overlapping set of SGZ-enriched genes, which can be divided based on temporal profiles to reflect maturation of glia versus granule neurons. Furthermore, we identified a neurogenesis-related gene network with decreasing postnatal expression that is highly correlated with the declining number of proliferating cells in dentate gyrus over postnatal development. Many of the genes in this network showed similar postnatal downregulation in mouse, suggesting a conservation of molecular mechanisms underlying developmental and adult neurogenesis in rodents and primates. Conditional deletion of Sox4 and Sox11, encoding two neurogenesis-related transcription factors central in this network, produces a mouse with no hippocampus, confirming the crucial role for these genes in regulating hippocampal neurogenesis.

Gene expression profiling by mRNA sequencing reveals increased expression of immune/inflammation-related genes in the hippocampus of individuals with schizophrenia

Whole-genome expression profiling in postmortem brain tissue has recently provided insight into the pathophysiology of schizophrenia. Previous microarray and RNA-Seq studies identified several biological processes including synaptic function, mitochondrial function and immune/inflammation response as altered in the cortex of subjects with schizophrenia. Now using RNA-Seq data from the hippocampus, we have identified 144 differentially expressed genes in schizophrenia cases as compared with unaffected controls. Immune/inflammation response was the main biological process over-represented in these genes. The upregulation of several of these genes, IFITM1, IFITM2, IFITM3, APOL1 (Apolipoprotein L1), ADORA2A (adenosine receptor 2A), IGFBP4 and CD163 were validated in the schizophrenia subjects using data from the SNCID database and with quantitative RT-PCR. We identified a co-expression module associated with schizophrenia that includes the majority of differentially expressed genes related to immune/inflammation response as well as with the density of parvalbumin-containing neurons in the hippocampus. The results indicate that abnormal immune/inflammation response in the hippocampus may underlie the pathophysiology of schizophrenia and may be associated with abnormalities in the parvalbumin-containing neurons that lead to the cognitive deficits of the disease.

Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing

Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.

Clinically relevant mutant DNA gyrase alters supercoiling, changes the transcriptome, and confers multidrug resistance

Bacterial DNA is maintained in a supercoiled state controlled by the action of topoisomerases. Alterations in supercoiling affect fundamental cellular processes, including transcription. Here, we show that substitution at position 87 of GyrA of Salmonella influences sensitivity to antibiotics, including nonquinolone drugs, alters global supercoiling, and results in an altered transcriptome with increased expression of stress response pathways. Decreased susceptibility to multiple antibiotics seen with a GyrA Asp87Gly mutant was not a result of increased efflux activity or reduced reactive-oxygen production. These data show that a frequently observed and clinically relevant substitution within GyrA results in altered expression of numerous genes, including those important in bacterial survival of stress, suggesting that GyrA mutants may have a selective advantage under specific conditions. Our findings help contextualize the high rate of quinolone resistance in pathogenic strains of bacteria and may partly explain why such mutant strains are evolutionarily successful.

Fluoroquinolones are a powerful group of antibiotics that target bacterial enzymes involved in helping bacteria maintain the conformation of their chromosome. Mutations in the target enzymes allow bacteria to become resistant to these antibiotics, and fluoroquinolone resistance is common. We show here that these mutations also provide protection against a broad range of other antimicrobials by triggering a defensive stress response in the cell. This work suggests that fluoroquinolone resistance mutations may be beneficial under a range of conditions.

Network2Canvas: network visualization on a canvas with enrichment analysis

MOTIVATION

Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than ∼100 nodes and ∼200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a node's brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses.

AVAILABILITY

N2C is freely available at http://www.maayanlab.net/N2C and is open source.

CONTACT

avi.maayan@mssm.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies

With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly important role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate the convenient network analysis of diseases, therapies, genes and drugs. With improved understanding of the mechanisms of complex diseases and drug actions through network analysis, novel drug methods (e.g., drug repositioning, multi-target drug and combination therapy) can be designed. More specifically, the new update includes (i) integrated search and navigation of disease and drug hierarchies; (ii) integrated disease–gene, therapy–drug and drug–target association to aid the network construction and filtering; (iii) annotation of genes/drugs using disease/therapy information; (iv) prediction of associated diseases/therapies for a given set of genes/drugs using enrichment analysis; (v) network transformation to support construction of versatile network of drugs, genes, diseases and therapies; (vi) enhanced user interface using docking windows to allow easy customization of node and edge properties with build-in legend node to distinguish different node type. VisANT is freely available at: http://visant.bu.edu.

Genes and pathways underlying regional and cell type changes in Alzheimer's disease

Background

Transcriptional studies suggest Alzheimer's disease (AD) involves dysfunction of many cellular pathways, including synaptic transmission, cytoskeletal dynamics, energetics, and apoptosis. Despite known progression of AD pathologies, it is unclear how such striking regional vulnerability occurs, or which genes play causative roles in disease progression.

Methods

To address these issues, we performed a large-scale transcriptional analysis in the CA1 and relatively less vulnerable CA3 brain regions of individuals with advanced AD and nondemented controls. In our study, we assessed differential gene expression across region and disease status, compared our results to previous studies of similar design, and performed an unbiased co-expression analysis using weighted gene co-expression network analysis (WGCNA). Several disease genes were identified and validated using qRT-PCR.

Results

We find disease signatures consistent with several previous microarray studies, then extend these results to show a relationship between disease status and brain region. Specifically, genes showing decreased expression with AD progression tend to show enrichment in CA3 (and vice versa), suggesting transcription levels may reflect a region's vulnerability to disease. Additionally, we find several candidate vulnerability (ABCA1, MT1H, PDK4, RHOBTB3) and protection (FAM13A1, LINGO2, UNC13C) genes based on expression patterns. Finally, we use a systems-biology approach based on WGCNA to uncover disease-relevant expression patterns for major cell types, including pathways consistent with a key role for early microglial activation in AD.

Conclusions

These results paint a picture of AD as a multifaceted disease involving slight transcriptional changes in many genes between regions, coupled with a systemic immune response, gliosis, and neurodegeneration. Despite this complexity, we find that a consistent picture of gene expression in AD is emerging.

Creating a "pro-survival" phenotype through epigenetic modulation

BACKGROUND

We demonstrated recently that treatment with suberoylanilide hydroxamic acid (SAHA), a histone deacetylase inhibitor, improved survival in a rodent model of lipopolysaccharide (LPS)-induced endotoxic shock. The precise mechanisms, however, have not been well-defined. The aim of this study was to investigate the impact of SAHA treatment on gene expression profiles at an early stage of shock.

METHODS

Male C57BL/6J mice were treated with or without SAHA (50 mg/kg, IP), followed by a lethal dose of LPS (20 mg/kg, IP) and a second dose of SAHA. Lungs of the animals (LPS and SAHA+LPS groups; n = 3 per group) were harvested 3 hours post-LPS insult. Sham mice (no LPS and no SAHA) served as controls. RNA was isolated from the tissues and gene expression was analyzed using Affymatrix microarray (23,000 genes). A lower confidence bound of fold change was determined for comparison of LPS versus SAHA + LPS, and genes with a lower confidence bound of >2 were considered to be differentially expressed. Reverse transcriptase polymerase chain reaction, Western blotting, and tissue staining were performed to verify the key changes. Network graphs were used to determine gene interaction and biologic relevance.

RESULTS

The expression of many genes known to be involved in septic pathophysiology changed after the LPS insult. Interestingly, a number of genes not implicated previously in the septic response were also altered. SAHA treatment attenuated expression of several key genes involved in inflammation. It also decreased neutrophil infiltration in lungs and histologic evidence of acute lung injury. Further analysis confirmed genes engaged in the cellular and humoral arms of the innate immune system were specifically inhibited by SAHA. Gene network analysis identified numerous molecules for the potential development of targeted therapies.

CONCLUSION

Administration of SAHA in a rodent model of LPS shock rapidly modulates gene transcription, with an attenuation of inflammatory mediators derived from both arms (cellular and humoral) of the innate immune system. This may be a novel mechanism responsible for the survival advantage seen with SAHA treatment.

Identification of flotillin-1 as a novel biomarker for lymph node metastasis and prognosis of lung adenocarcinoma by quantitative plasma membrane proteome analysis

To identify a novel lung adenocarcinoma (AdC) biomarker, iTRAQ-tagging combined with 2D LC-MS/MS analysis was used to identify differentially expressed plasma membrane (PM) proteins in primary lung AdCs and paraneoplastic normal lung tissues (PNLTs). As a result, 36 differentially expressed membrane proteins were identified. Two differential PM proteins flotillin-1 and caveolin-1 were selectively validated by Western blotting. As there has been no report on the association of flotillin-1 with lung AdC, immunohistochemistry was further performed to detect the expression of flotillin-1 in the archival tissue specimens including 42 cases of PNLTs, 62 cases of primary lung AdCs with lymph node metastasis (LNM AdCs), and 46 cases of primary lung AdCs without lymph node metastasis (non-LNM AdCs), and the correlation of flotillin-1 expression levels in lung AdCs with clinicopathological features and clinical outcomes were evaluated. The results showed that up-regulation of flotillin-1 expression in lung AdCs was significantly correlated with advanced clinical stage, lymph node metastasis, increased postoperative relapse and decreased overall survival. Cox regression analysis revealed that the expressional level of flotillin-1 was an independent prognostic factor. The data suggest that flotillin-1 is a potential novel biomarker for lymph node metastasis and prognosis of lung AdC, and flotillin-1 up-regulation might play an important role in the pathogenesis of lung AdC.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery

Background

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.

Results

atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis.

Conclusion

atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Application of approximate pattern matching in two dimensional spaces to grid layout for biochemical network maps

Background

For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process.

Results

We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor) algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts.

Conclusions

Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.

mrhl RNA, a long noncoding RNA, negatively regulates Wnt signaling through its protein partner Ddx5/p68 in mouse spermatogonial cells

Meiotic recombination hot spot locus (mrhl) RNA is a nuclear enriched long noncoding RNA encoded in the mouse genome and expressed in testis, liver, spleen, and kidney. mrhl RNA silencing in Gc1-Spg cells, derived from mouse spermatogonial cells, resulted in perturbation of expression of genes belonging to cell adhesion, cell signaling and development, and differentiation, among which many were of the Wnt signaling pathway. A weighted gene coexpression network generated nine coexpression modules, which included TCF4, a key transcription factor involved in Wnt signaling. Activation of Wnt signaling upon mrhl RNA downregulation was demonstrated by beta-catenin nuclear localization, beta-catenin-TCF4 interaction, occupancy of beta-catenin at the promoters of Wnt target genes, and TOP/FOP-luciferase assay. Northwestern blot and RNA pulldown experiments identified Ddx5/p68 as one of the interacting proteins of mrhl RNA. Downregulation of mrhl RNA resulted in the cytoplasmic translocation of tyrosine-phosphorylated p68. Concomitant downregulation of both mrhl RNA and p68 prevented the nuclear translocation of beta-catenin. mrhl RNA was downregulated on Wnt3a treatment in Gc1-Spg cells. This study shows that mrhl RNA plays a negative role in Wnt signaling in mouse spermatogonial cells through its interaction with p68.

State of the art in silico tools for the study of signaling pathways in cancer

In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided.

Association between SNPs and gene expression in multiple regions of the human brain

Identifying the genetic cis associations between DNA variants (single-nucleotide polymorphisms (SNPs)) and gene expression in brain tissue may be a promising approach to find functionally relevant pathways that contribute to the etiology of psychiatric disorders. In this study, we examined the association between genetic variations and gene expression in prefrontal cortex, hippocampus, temporal cortex, thalamus and cerebellum in subjects with psychiatric disorders and in normal controls. We identified cis associations between 648 transcripts and 6725 SNPs in the various brain regions. Several SNPs showed brain regional-specific associations. The expression level of only one gene, PDE4DIP, was associated with a SNP, rs12124527, in all the brain regions tested here. From our data, we generated a list of brain cis expression quantitative trait loci (eQTL) genes that we compared with a list of schizophrenia candidate genes downloaded from the Schizophrenia Forum (SZgene) database (http://www.szgene.org/). Of the SZgene candidate genes, we found that the expression levels of four genes, HTR2A, PLXNA2, SRR and TCF4, were significantly associated with cis SNPs in at least one brain region tested. One gene, SRR, was also involved in a coexpression module that we found to be associated with disease status. In addition, a substantial number of cis eQTL genes were also involved in the module, suggesting eQTL analysis of brain tissue may identify more reliable susceptibility genes for schizophrenia than case-control genetic association analyses. In an attempt to facilitate the identification of genetic variations that may underlie the etiology of major psychiatric disorders, we have integrated the brain eQTL results into a public and online database, Stanley Neuropathology Consortium Integrative Database (SNCID; http://sncid.stanleyresearch.org).

Molecular microcircuitry underlies functional specification in a basal ganglia circuit dedicated to vocal learning

Similarities between speech and birdsong make songbirds advantageous for investigating the neurogenetics of learned vocal communication--a complex phenotype probably supported by ensembles of interacting genes in cortico-basal ganglia pathways of both species. To date, only FoxP2 has been identified as critical to both speech and birdsong. We performed weighted gene coexpression network analysis on microarray data from singing zebra finches to discover gene ensembles regulated during vocal behavior. We found ∼2,000 singing-regulated genes comprising three coexpression groups unique to area X, the basal ganglia subregion dedicated to learned vocalizations. These contained known targets of human FOXP2 and potential avian targets. We validated biological pathways not previously implicated in vocalization. Higher-order gene coexpression patterns, rather than expression levels, molecularly distinguish area X from the ventral striato-pallidum during singing. The previously unknown structure of singing-driven networks enables prioritization of molecular interactors that probably bear on human motor disorders, especially those affecting speech.

Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes

Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D_ABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10⁻⁴). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10⁻⁴); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10⁻⁴) and BMI–adjusted waist-to-hip ratio (P = 2.4×10⁻⁴). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.

Metabolic Syndrome (MetS) is a highly prevalent disorder with considerable public health concern, but its underlying genetic factors remain elusive. Given that most cellular components exert their functions through interactions with other cellular components, even the largest of genome-wide association (GWA) studies may often not detect their effects, nor necessarily provide insight into the complex molecular mechanisms of the disease. Rather than focusing on individual genes, the analysis of coexpression networks can be used for finding clusters (modules) of correlated expression levels across samples. In this study, we used a gene network–based approach for integrating clinical MetS, genotypic, and gene expression data from abdominal and gluteal adipose tissue and whole blood. We identified modules of genes related to MetS significantly enriched for immune response and oxidative phosphorylation pathways. We tested SNPs for association with MetS–associated expression (eSNPs), and tested prioritised eSNPs for association with MetS–related phenotypes in two large-scale GWA datasets. We identified two loci, neither of which had reached genome-wide significance levels in GWAs, associated with expression levels of RARRES2 and HLA-DRB1 and with MetS–related phenotypes, demonstrating that the integrated analysis of genotype and expression data from relevant multiple tissues can identify novel associations with complex traits such as MetS.

Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the Continuity of Care Record standard

OBJECTIVES

The current volume and complexity of genetic tests, and the molecular genetics knowledge and health knowledge related to interpretation of the results of those tests, are rapidly outstripping the ability of individual clinicians to recall, understand and convey to their patients information relevant to their care. The tailoring of molecular genetics knowledge and health knowledge in clinical settings is important both for the provision of personalized medicine and to reduce clinician information overload. In this paper we describe the incorporation, customization and demonstration of molecular genetic data (mainly sequence variants), molecular genetics knowledge and health knowledge into a standards-based electronic health record (EHR) prototype developed specifically for this study.

METHODS

We extended the CCR (Continuity of Care Record), an existing EHR standard for representing clinical data, to include molecular genetic data. An EHR prototype was built based on the extended CCR and designed to display relevant molecular genetics knowledge and health knowledge from an existing knowledge base for cystic fibrosis (OntoKBCF). We reconstructed test records from published case reports and represented them in the CCR schema. We then used the EHR to dynamically filter molecular genetics knowledge and health knowledge from OntoKBCF using molecular genetic data and clinical data from the test cases.

RESULTS

The molecular genetic data were successfully incorporated in the CCR by creating a category of laboratory results called "Molecular Genetics" and specifying a particular class of test ("Gene Mutation Test") in this category. Unlike other laboratory tests reported in the CCR, results of tests in this class required additional attributes ("Molecular Structure" and "Molecular Position") to support interpretation by clinicians. These results, along with clinical data (age, sex, ethnicity, diagnostic procedures, and therapies) were used by the EHR to filter and present molecular genetics knowledge and health knowledge from OntoKBCF.

CONCLUSIONS

This research shows a feasible model for delivering patient sequence variants and presenting tailored molecular genetics knowledge and health knowledge via a standards-based EHR system prototype. EHR standards can be extended to include the necessary patient data (as we have demonstrated in the case of the CCR), while knowledge can be obtained from external knowledge bases that are created and maintained independently from the EHR. This approach can form the basis for a personalized medicine framework, a more comprehensive standards-based EHR system and a potential platform for advancing translational research by both disseminating results and providing opportunities for new insights into phenotype-genotype relationships.

Identification of candidate biomarkers for early detection of human lung squamous cell cancer by quantitative proteomics

To discover novel biomarkers for early detection of human lung squamous cell cancer (LSCC) and explore possible mechanisms of LSCC carcinogenesis, iTRAQ-tagging combined with two dimensional liquid chromatography tandem MS analysis was used to identify differentially expressed proteins in human bronchial epithelial carcinogenic process using laser capture microdissection-purified normal bronchial epithelium (NBE), squamous metaplasia (SM), atypical hyperplasia (AH), carcinoma in situ (CIS) and invasive LSCC. As a result, 102 differentially expressed proteins were identified, and three differential proteins (GSTP1, HSPB1 and CKB) showing progressively expressional changes in the carcinogenic process were selectively validated by Western blotting. Immunohistochemistry was performed to detect the expression of the three proteins in an independent set of paraffin-embedded archival specimens including various stage tissues of bronchial epithelial carcinogenesis, and their ability for early detection of LSCC was evaluated by receiver operating characteristic analysis. The results showed that the combination of the three proteins could perfectly discriminate NBE from preneoplastic lesions (SM, AH and CIS) from invasive LSCC, achieving a sensitivity of 96% and a specificity of 92% in discriminating NBE from preneoplatic lesions, a sensitivity of 100% and a specificity of 98% in discriminating NBE from invasive LSCC, and a sensitivity of 92% and a specificity of 91% in discriminating preneoplastic lesions from invasive LSCC, respectively. Furthermore, we knocked down GSTP1 in immortalized human bronchial epithelial cell line 16HBE cells, and then measured their susceptibility to carcinogen benzo(a)pyrene-induced cell transformation. The results showed that GSTP1 knockdown significantly increased the efficiency of benzo(a)pyrene-induced 16HBE cell transformation. The present data first time show that GSTP1, HSPB1 and CKB are novel potential biomarkers for early detection of LSCC, and GSTP1 down-regulation is involved in human bronchial epithelial carcinogenesis.

Interactive, graph-based visual analysis of high-dimensional, multi-parameter fluorescence microscopy data in toponomics

In Toponomics, the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction. The most advanced imaging technique is robot-driven multi-parameter fluorescence microscopy. This technique is capable of co-mapping hundreds of proteins and their distribution and assembly in protein clusters across a cell or tissue sample by running cycles of fluorescence tagging with monoclonal antibodies or other affinity reagents, imaging, and bleaching in situ. The imaging results in complex multi-parameter data composed of one slice or a 3D volume per affinity reagent. Biologists are particularly interested in the localization of co-occurring proteins, the frequency of co-occurrence and the distribution of co-occurring proteins across the cell. We present an interactive visual analysis approach for the evaluation of multi-parameter fluorescence microscopy data in toponomics. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The feature specification result is linked to all views establishing a focus+context visualization in 3D. In a new attribute view, we integrate techniques from graph visualization. Each node in the graph represents an affinity reagent while each edge represents two co-occurring affinity reagent bindings. The graph visualization is enhanced by glyphs which encode specific properties of the binding. The graph view is equipped with brushing facilities. By brushing in the spatial and attribute domain, the biologist achieves a better understanding of the function protein patterns of a cell. Furthermore, an interactive table view is integrated which summarizes unique fluorescence patterns. We discuss our approach with respect to a cell probe containing lymphocytes and a prostate tissue section.

GraphCrunch 2: Software tool for network modeling, alignment and clustering

Background

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype.

Results

We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks.

Conclusions

GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.

The Protein Information and Property Explorer 2: gaggle-like exploration of biological proteomic data within one webpage

The Protein Information and Property Explorer 2 (PIPE2) is an enhanced software program and updated web application that aims at providing the proteomic researcher a simple, intuitive user interface through which to begin inquiry into the biological significance of a list of proteins typically produced by MS/MS proteomic processing software. PIPE2 includes an improved interface, new data visualization options, and new data analysis methods for combining disparate, but related, data sets. In particular, PIPE2 has been enhanced to handle multi-dimensional data such as protein abundance, gene expression, and/or interaction data. The current architecture of PIPE2, modeled after that of Gaggle (a programming infrastructure for interoperability between separately developed software tools), contains independent functional units that can be instantiated and pieced together at the user's discretion to form a pipelined analysis workflow. Among these functional units is the Network Viewer component, which adds rich network analysis capabilities to the suite of existing proteomic web resources. Additionally, PIPE2 implements a framework within which new analysis procedures can be easily deployed and distributed over the World Wide Web. PIPE2 is available as a web service at http://pipe2.systemsbiology.net/.

Constructing a fish metabolic network model

We report the construction of a genome-wide fish metabolic network model, MetaFishNet, and its application to analyzing high throughput gene expression data. This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at http://metafishnet.appspot.com.