TRED: a transcriptional regulatory element database, new entries and other development

Functional characterization of EI24-induced autophagy in the degradation of RING-domain E3 ligases

Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases.

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells

Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA–TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA–TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

Inferring active regulatory networks from gene expression data using a combination of prior knowledge and enrichment analysis

Background

Under both physiological and pathological conditions gene expression programs are shaped through the interplay of regulatory proteins and their gene targets, interactions between which form intricate gene regulatory networks (GRN). While the assessment of genome-wide expression for the complete set of genes at a given condition has become rather straight-forward and is performed routinely, we are still far from being able to infer the topology of gene regulation simply by analyzing its “descendant” expression profile. In this work we are trying to overcome the existing limitations for the inference and study of such regulatory networks. We are combining our approach with state-of-the-art gene set enrichment analyses in order to create a tool, called Regulatory Network Enrichment Analysis (RNEA) that will prioritize regulatory and functional characteristics of a genome-wide expression experiment.

Results

RNEA combines prior knowledge, originating from manual literature curation and small-scale experimental data, to construct a reference network of interactions and then uses enrichment analysis coupled with a two-level hierarchical parsing of the network, to infer the most relevant subnetwork for a given experimental setting. It is implemented as an R package, currently supporting human and mouse datasets and was herein tested on one test case for each of the two organisms. In both cases, RNEA’s gene set enrichment analysis was comparable to state-of-the-art methodologies. Moreover, through its distinguishing feature of regulatory subnetwork reconstruction, RNEA was able to define the key transcriptional regulators for the studied systems as supported from the literature.

Conclusions

RNEA constitutes a novel computational approach to obtain regulatory interactions directly from a genome-wide expression profile. Its simple implementation, with minimal requirements from the user is coupled with easy-to-parse enrichment lists and a subnetwork file that may be readily visualized to reveal the most important components of the regulatory hierarchy. The combination of prior information and novel concept of a hierarchical reconstruction of regulatory interactions makes RNEA a very useful tool for a first-level interpretation of gene expression profiles.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1040-7) contains supplementary material, which is available to authorized users.

Smad7 interrupts TGF-β signaling in intestinal macrophages and promotes inflammatory activation of these cells during necrotizing enterocolitis

BACKGROUND

Necrotizing enterocolitis (NEC) is an inflammatory bowel necrosis of premature infants. Based on our recent findings of increased Smad7 expression in surgically resected bowel affected by NEC, we hypothesized that NEC macrophages undergo inflammatory activation because increased Smad7 expression renders these cells resistant to normal, gut-specific, transforming growth factor (TGF)-β-mediated suppression of inflammatory pathways.

METHODS

We used surgically resected human NEC tissue, murine models of NEC-like injury, bone marrow-derived and intestinal macrophages, and RAW264.7 cells. Smad7 and IκB kinase-beta (IKK-β) were measured by quantitative PCR, western blots, and immunohistochemistry. Promoter activation was confirmed in luciferase reporter and chromatin immunoprecipitation assays.

RESULTS

NEC macrophages showed increased Smad7 expression, particularly in areas with severe tissue damage and high bacterial load. Lipopolysaccharide-induced Smad7 expression suppressed TGF-β signaling and augmented nuclear factor-kappa B (NF-κB) activation and cytokine production in macrophages. Smad7-mediated NF-κB activation was likely mediated via increased expression of IKK-β, which, further increased Smad7 expression in a feed-forward loop. We show that Smad7 induced IKK-β expression through direct binding to the IKK-β promoter and its transcriptional activation.

CONCLUSION

Smad7 expression in NEC macrophages interrupts TGF-β signaling and promotes NF-κB-mediated inflammatory signaling in these cells through increased expression of IKK-β.

Knockdown of EWSR1/FLI1 expression alters the transcriptome of Ewing sarcoma cells in vitro

Ewing sarcoma breakpoint region 1 (EWSR1) fusion with Friend leukemia integration 1 transcription factor (FLI1) induced by a translocation of chromosome 11 with 22 contributes to Ewing sarcoma development. To date, the precise molecular mechanisms about EWSR1/FLI1 involving in Ewing sarcoma development remains to be defined. This study explored the potential critical gene targets of EWSR1/FLI1 knockdown in Ewing sarcoma cells on the gene expression profile based on online dataset, performed Limma algorithm for differentially expressed genes identification, constructed the transcriptional factor (TF)-gene regulatory network based on integrate transcriptional regulatory element database (TRED). The data showed up- and down-regulation of differentially expressed genes over time and peaked at 72 h after EWSR1/FLI1 knockdown in Ewing sarcoma cells. SMAD3 were up-regulated and FLI1, MYB, E2F1, ETS2, WT1 were down-regulated with more than half of their targets were down-regulated after EWSR1/FLI1 knockdown. The Gene Ontology (GO) and pathway annotation of these differentially expressed genes showed a consistent trend in each group of samples. Totally, there were 355 differentially expressed genes occurring in all five comparison groups of different time points, in which 39 genes constructed a dysregulated TF-gene network in Ewing sarcoma cell line A673 after EWSR1/FLI1 knockdown. These data demonstrated that knockdown of EWSR1/FLI1 expression led to transcriptome changes in Ewing sarcoma cells and that Ewing sarcoma development and progression caused by altered EWSR1/FLI1 expression may be associated with more complex transcriptome changes.

Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data

Background

Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.

Results

In this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.

Network-Based Protein Biomarker Discovery Platforms

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

PAGER: constructing PAGs and new PAG-PAG relationships for network biology

In this article, we described a new database framework to perform integrative “gene-set, network, and pathway analysis” (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene–gene relationships and two types of 3 174 323 PAG–PAG regulatory relationships—co-membership based and regulatory relationship based. To help users assess each PAG’s biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG–PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.

Contact: jakechen@iupui.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Promoter SNPs rs116896264 and rs73933062 form a distinct haplotype and are associated with galectin-4 overexpression in colorectal cancer

Galectin-4 is a member of the galectin family which consists of 15 galactoside-binding proteins. Previously, galectin-4 has been shown to have a role in cancer progression and metastasis and it is found upregulated in many solid tumours, including colorectal cancer (CRC). Recently, the role in the metastatic process was suggested to be via promoting cancer cells to adhere to blood vascular endothelium. In the present study, the regulatory region of LGALS4 (galectin-4) in seven colon cell lines was investigated with respect to genetic variation that could be linked to expression levels and therefore a tumourigenic effect. Interestingly, qRT-PCR and sequencing results revealed that galectin-4 upregulation is associated with SNPs rs116896264 and rs73933062. By use of luciferase reporter- and pull-down assays, we confirmed the association between the gene upregulation and the two SNPs. Also, using pull-down assay followed by mass spectrometry, we found that the presence rs116896264 and rs73933062 is changing transcription factors binding sites. In order to assess the frequencies of the two SNPs among colon cancer patients and healthy individuals, we genotyped 75 colon cancer patients, 12 patients with adenomatous polyposis and 17 patients with ulcerative colitis and we performed data mining in the 1000 genomes databank. We found the two SNPs co-occuring in 21% of 75 CRC patients, 0 out of 12 patients of adenomatous polyposis, and 6 out of 17 patients (35%) with ulcerative colitis. Both in the patient samples and in the 1000 genomes project, the two SNPs were found to co-occur whenever present (D' = 1).

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.

Integrated analysis of global proteome, phosphoproteome, and glycoproteome enables complementary interpretation of disease-related protein networks

Multi-dimensional proteomic analyses provide different layers of protein information, including protein abundance and post-translational modifications. Here, we report an integrated analysis of protein expression, phosphorylation, and N-glycosylation by serial enrichments of phosphorylation and N-glycosylation (SEPG) from the same tissue samples. On average, the SEPG identified 142,106 unmodified peptides of 8,625 protein groups, 18,846 phosphopeptides (15,647 phosphosites), and 4,019 N-glycopeptides (2,634 N-glycosites) in tumor and adjacent normal tissues from three gastric cancer patients. The combined analysis of these data showed that the integrated analysis additively improved the coverages of gastric cancer-related protein networks; phosphoproteome and N-glycoproteome captured predominantly low abundant signal proteins, and membranous or secreted proteins, respectively, while global proteome provided abundances for general population of the proteome. Therefore, our results demonstrate that the SEPG can serve as an effective approach for multi-dimensional proteome analyses, and the holistic profiles of protein expression and PTMs enabled improved interpretation of disease-related networks by providing complementary information.

A method for developing regulatory gene set networks to characterize complex biological systems

Background

Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations.

Results

In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease.

Conclusions

R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?

Computational biology and 'omics' systems sciences are greatly impacting research on common diseases such as cancer. By contrast, dermatology covering an array of skin diseases with high prevalence in society, has received relatively less attention from 'omics' and computational biosciences. We are focusing on psoriasis, a common and debilitating autoimmune disease involving skin and joints. Using computational systems biology and reconstruction, topological, modular, and a novel correlational analyses (based on fold changes) of biological and transcriptional regulatory networks, we analyzed and integrated data from a total of twelve studies from the Gene Expression Omnibus (sample size = 534). Samples represented a comprehensive continuum from lesional and nonlesional skin, as well as bone marrow and dermal mesenchymal stem cells. We identified and propose here a JAK/STAT signaling pathway significant for psoriasis. Importantly, cytokines, interferon-stimulated genes, antimicrobial peptides, among other proteins, were involved in intrinsic parts of the proposed pathway. Several biomarker and therapeutic candidates such as SUB1 are discussed for future experimental studies. The integrative systems biology approach presented here illustrates a comprehensive perspective on the molecular basis of psoriasis. This also attests to the promise of systems biology research in skin diseases, with psoriasis as a systemic component. The present study reports, to the best of our knowledge, the largest set of microarray datasets on psoriasis, to offer new insights into the disease mechanisms with a proposal of a disease pathway. We call for greater computational systems biology research and analyses in dermatology and skin diseases in general.

Long Noncoding RNA MEG3 Interacts with p53 Protein and Regulates Partial p53 Target Genes in Hepatoma Cells

Maternally Expressed Gene 3 (MEG3) encodes a lncRNA which is suggested to function as a tumor suppressor. Previous studies suggested that MEG3 functioned through activation of p53, however, the functional properties of MEG3 remain obscure and their relevance to human diseases is under continuous investigation. Here, we try to illuminate the relationship of MEG3 and p53, and the consequence in hepatoma cells. We find that transfection of expression construct of MEG3 enhances stability and transcriptional activity of p53. Deletion analysis of MEG3 confirms that full length and intact structure of MEG3 are critical for it to activate p53-mediated transactivation. Interestingly, our results demonstrate for the first time that MEG3 can interact with p53 DNA binding domain and various p53 target genes are deregulated after overexpression of MEG3 in hepatoma cells. Furthermore, results of qRT-PCR have shown that MEG3 RNA is lost or reduced in the majority of HCC samples compared with adjacent non-tumorous samples. Ectopic expression of MEG3 in hepatoma cells significantly inhibits proliferation and induces apoptosis. In conclusion, our data demonstrates that MEG3 functions as a tumor suppressor in hepatoma cells through interacting with p53 protein to activate p53-mediated transcriptional activity and influence the expression of partial p53 target genes.

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data.

Database URL: http://www.regnetworkweb.org

Transcriptome Sequencing to Identify Transcription Factor Regulatory Network and Alternative Splicing in Endothelial Cells Under VEGF Stimulation

This study aims to investigate the mechanisms underlying the response of human umbilical vein vascular endothelial cells (HUVECs) to vascular endothelial growth factor (VEGF) stimulation. HUVECs were treated with or without 16 ng/mL VEGF for 4 days, and RNA was extracted from HUVECs. After sequencing and data filtering (tool: NGS QC Toolkit), clean data were mapped to genome hg19 (tool: TopHat2). Thereafter, 154 differentially expressed genes (DEGs) were identified between VEGF group and control group (tool: Cuffdiff), and DEGs were enriched in 11 pathways associated with cytokine receptor interaction and chemokine signaling. Protein-protein interaction network of DEGs was constructed (tool: STRING), and ISG15 and MX1 were hub DEGs. The regulatory network of DEGs and transcription factors (TFs) (tool: TRED database) was also constructed, and CCL2 and FN1 (hub DEGs) were co-regulated by NFKB1 and RELA (hub TFs). Moreover, exon usage and alternative splicing were analyzed (tool: DEXSeq), and the splicing of ADORA2A was altered under VEGF stimulation. VEGF might influence HUVECs proliferation and migration, as well as angiogenesis process by regulating the expression of ISG15, MX1, CCL2, FN1, and ADORA2A. However, more research studies are still required to verify these predictions.

Expression analysis of apolipoprotein E and its associated genes in gastric cancer

Gastric cancer is a common type of cancer worldwide, and has a poor prognosis, in part due to the low rates of early diagnosis and the limited treatment methods available. Apolipoprotein E (ApoE) is involved in exogenous cholesterol transport and may be important in enabling tumor cells to fulfill their high cholesterol requirements. A number of reports have indicated that ApoE affects the development and prognosis of gastric cancer. Therefore, the aim of the present study was to investigate the genes and transcription factors that interact with ApoE during the development of gastric cancer. Using gene expression profiling, the BioGRID database and the transcriptional regulatory element database, gene expression and regulatory networks in gastric cancer tissues and adjacent normal tissues were analyzed. The data demonstrated that eight genes associated with ApoE were differentially expressed, with six of these upregulated and two downregulated. Functionally, these genes were involved in the JAK-STAT cascade, acute-phase response, acute inflammatory response, and the steroid hormone response. Among these ApoE-associated genes, expression of the signal transducer and activator of transcription 2 (STAT2) and STAT3 transcription factors was upregulated. To the best of our knowledge, this is the first study to demonstrate the network of ApoE-related genes and transcription factors in gastric cancer. Additional studies are required in order to confirm these data and to translate the results into the identification of clinical biomarkers and novel treatment strategies for gastric cancer.

MicroRNA and transcription factor mediated regulatory network analysis reveals critical regulators and regulatory modules in myocardial infarction

Myocardial infarction (MI) is a severe coronary artery disease and a leading cause of mortality and morbidity worldwide. However, the molecular mechanisms of MI have yet to be fully elucidated. In this study, we compiled MI-related genes, MI-related microRNAs (miRNAs) and known human transcription factors (TFs), and we then identified 1,232 feed-forward loops (FFLs) among these miRNAs, TFs and their co-regulated target genes through integrating target prediction. By merging these FFLs, the first miRNA and TF mediated regulatory network for MI was constructed, from which four regulators (SP1, ESR1, miR-21-5p and miR-155-5p) and three regulatory modules that might play crucial roles in MI were then identified. Furthermore, based on the miRNA and TF mediated regulatory network and literature survey, we proposed a pathway model for miR-21-5p, the miR-29 family and SP1 to demonstrate their potential co-regulatory mechanisms in cardiac fibrosis, apoptosis and angiogenesis. The majority of the regulatory relations in the model were confirmed by previous studies, which demonstrated the reliability and validity of this miRNA and TF mediated regulatory network. Our study will aid in deciphering the complex regulatory mechanisms involved in MI and provide putative therapeutic targets for MI.

Systems biology approach to studying proliferation-dependent prognostic subnetworks in breast cancer

Tumor proliferative capacity is a major biological correlate of breast tumor metastatic potential. In this paper, we developed a systems approach to investigate associations among gene expression patterns, representative protein-protein interactions, and the potential for clinical metastases, to uncover novel survival-related subnetwork signatures as a function of tumor proliferative potential. Based on the statistical associations between gene expression patterns and patient outcomes, we identified three groups of survival prognostic subnetwork signatures (SPNs) corresponding to three proliferation levels. We discovered 8 SPNs in the high proliferation group, 8 SPNs in the intermediate proliferation group, and 6 SPNs in the low proliferation group. We observed little overlap of SPNs between the three proliferation groups. The enrichment analysis revealed that most SPNs were enriched in distinct signaling pathways and biological processes. The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors. Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner. These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients.

Emerging role of transcription factor-microRNA-target gene feed-forward loops in cancer

Transcriptional regulatory networks are biological network motifs that act in accordance with each other to play decisive roles in the pathological processes of cancer. One of the most common types, the feed-forward loop (FFL), has recently attracted interest. Three connected deregulated nodes, a transcription factor (TF), its downstream microRNA (miRNA) and their shared target gene can make up a class of cancer-involved FFLs as ≥1 of the 3 can act individually as a bona fide oncogene or a tumor suppressor. Numerous notable elements, such as p53, miR-17-92 cluster and cyclins, are proven members of their respective FFLs. Databases of interaction prediction, verification of experimental methods and confirmation of loops have been continually emerging during recent years. Development of TF-miRNA-target loops may help understand the mechanism of tumorgenesis at a higher level and explain the discovery and screening of the therapeutic target for drug exploitation.

Principles for the organization of gene-sets

A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.

A novel patient-derived tumorgraft model with TRAF1-ALK anaplastic large-cell lymphoma translocation

Although anaplastic large-cell lymphomas (ALCL) carrying anaplastic lymphoma kinase (ALK) have a relatively good prognosis, aggressive forms exist. We have identified a novel translocation, causing the fusion of the TRAF1 and ALK genes, in one patient who presented with a leukemic ALK+ ALCL (ALCL-11). To uncover the mechanisms leading to high-grade ALCL, we developed a human patient-derived tumorgraft (hPDT) line. Molecular characterization of primary and PDT cells demonstrated the activation of ALK and nuclear factor kB (NFkB) pathways. Genomic studies of ALCL-11 showed the TP53 loss and the in vivo subclonal expansion of lymphoma cells, lacking PRDM1/Blimp1 and carrying c-MYC gene amplification. The treatment with proteasome inhibitors of TRAF1-ALK cells led to the downregulation of p50/p52 and lymphoma growth inhibition. Moreover, a NFkB gene set classifier stratified ALCL in distinct subsets with different clinical outcome. Although a selective ALK inhibitor (CEP28122) resulted in a significant clinical response of hPDT mice, nevertheless the disease could not be eradicated. These data indicate that the activation of NFkB signaling contributes to the neoplastic phenotype of TRAF1-ALK ALCL. ALCL hPDTs are invaluable tools to validate the role of druggable molecules, predict therapeutic responses and implement patient specific therapies.

Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model

Scoring the correlation between two genes by their shared properties is a common and basic work in biological study. A prospective way to score this correlation is to quantify the overlap between the two sets of homogeneous properties of the two genes. However the proper model has not been decided, here we focused on studying the quantification of overlap and proposed a more effective model after theoretically compared 7 existing models. We defined three characteristic parameters (d, R, r) of an overlap, which highlight essential differences among the 7 models and grouped them into two classes. Then the pros and cons of the two groups of model were fully examined by their solution space in the (d, R, r) coordinate system. Finally we proposed a new model called OScal (Overlap Score calculator), which was modified on Poisson distribution (one of 7 models) to avoid its disadvantages. Tested in assessing gene relation using different data, OScal performs better than existing models. In addition, OScal is a basic mathematic model, with very low computation cost and few restrictive conditions, so it can be used in a wide-range of research areas to measure the overlap or similarity of two entities.

Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

BACKGROUND

Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets.

RESULTS

Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods.

CONCLUSION

The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.

Gene target specificity of the Super Elongation Complex (SEC) family: how HIV-1 Tat employs selected SEC members to activate viral transcription

The AF4/FMR2 proteins AFF1 and AFF4 act as a scaffold to assemble the Super Elongation Complex (SEC) that strongly activates transcriptional elongation of HIV-1 and cellular genes. Although they can dimerize, it is unclear whether the dimers exist and function within a SEC in vivo. Furthermore, it is unknown whether AFF1 and AFF4 function similarly in mediating SEC-dependent activation of diverse genes. Providing answers to these questions, our current study shows that AFF1 and AFF4 reside in separate SECs that display largely distinct gene target specificities. While the AFF1-SEC is more potent in supporting HIV-1 transactivation by the viral Tat protein, the AFF4-SEC is more important for HSP70 induction upon heat shock. The functional difference between AFF1 and AFF4 in Tat-transactivation has been traced to a single amino acid variation between the two proteins, which causes them to enhance the affinity of Tat for P-TEFb, a key SEC component, with different efficiency. Finally, genome-wide analysis confirms that the genes regulated by AFF1-SEC and AFF4-SEC are largely non-overlapping and perform distinct functions. Thus, the SEC represents a family of related complexes that exist to increase the regulatory diversity and gene control options during transactivation of diverse cellular and viral genes.

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

BACKGROUND

Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer.

METHODS

We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes "Prior interaction map + TCGA data → Posterior interaction map."

RESULTS

Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC.

CONCLUSIONS

Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

Regulatory networks in retinal ischemia-reperfusion injury

BACKGROUND

Retinal function is ordered by interactions between transcriptional and posttranscriptional regulators at the molecular level. These regulators include transcription factors (TFs) and posttranscriptional factors such as microRNAs (miRs). Some studies propose that miRs predominantly target the TFs rather than other types of protein coding genes and such studies suggest a possible interconnection of these two regulators in co-regulatory networks.

RESULTS

Our lab has generated mRNA and miRNA microarray expression data to investigate time-dependent changes in gene expression, following induction of ischemia-reperfusion (IR) injury in the rat retina. Data from different reperfusion time points following retinal IR-injury were analyzed. Paired expression data for miRNA-target gene (TG), TF-TG, miRNA-TF were used to identify regulatory loop motifs whose expressions were altered by the IR injury paradigm. These loops were subsequently integrated into larger regulatory networks and biological functions were assayed. Systematic analyses of the networks have provided new insights into retinal gene regulation in the early and late periods of IR. We found both overlapping and unique patterns of molecular expression at the two time points. These patterns can be defined by their characteristic molecular motifs as well as their associated biological processes. We highlighted the regulatory elements of miRs and TFs associated with biological processes in the early and late phases of ischemia-reperfusion injury.

CONCLUSIONS

The etiology of retinal ischemia-reperfusion injury is orchestrated by complex and still not well understood gene networks. This work represents the first large network analysis to integrate miRNA and mRNA expression profiles in context of retinal ischemia. It is likely that an appreciation of such regulatory networks will have prognostic potential. In addition, the computational framework described in this study can be used to construct miRNA-TF interactive systems networks for various diseases/disorders of the retina and other tissues.

REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies

BACKGROUND

Genes encoding transcription factors that constitute gene-regulatory networks and maternal factors accumulating in egg cytoplasm are two classes of essential genes that play crucial roles in developmental processes. Transcription factors control the expression of their downstream target genes by interacting with cis-regulatory elements. Maternal factors initiate embryonic developmental programs by regulating the expression of zygotic genes and various other events during early embryogenesis.

RESULTS

This article documents the transcription factors of 77 metazoan species as well as human and mouse maternal factors. We improved the previous method using a statistical approach adding Gene Ontology information to Pfam based identification of transcription factors. This method detects previously un-discovered transcription factors. The novel features of this database are: (1) It includes both transcription factors and maternal factors, although the number of species, in which maternal factors are listed, is limited at the moment. (2) Ontological representation at the cell, tissue, organ, and system levels has been specially designed to facilitate development studies. This is the unique feature in our database and is not available in other transcription factor databases.

CONCLUSIONS

A user-friendly web interface, REGULATOR ( http://www.bioinformatics.org/regulator/ ), which can help researchers to efficiently identify, validate, and visualize the data analyzed in this study, are provided. Using this web interface, users can browse, search, and download detailed information on species of interest, genes, transcription factor families, or developmental ontology terms.

Transcription factor decoy: a pre-transcriptional approach for gene downregulation purpose in cancer

Gene therapy as a therapeutic approach has been the dream for many scientists around the globe. Many strategies have been proposed and applied for this purpose, yet the void for a functional safe method is still apparent. Since most of the diseases are caused by undesirable upregulation (oncogenes) or downregulation (tumor suppressor genes) of genes, major gene therapy's techniques affect gene expression. Most of the methods are used in post-transcriptional level such as RNA inhibitory (RNAi) and splice-switching oligonucleotides (SSOs). RNAi blocks messenger RNA (mRNA) translation by mRNA degradation or interruption between attachments of mRNA with ribosomes' subunits. However, one of the novel methods is the usage of transcription factor targeted decoys. DNA decoys are the new generation of functional gene downregulatory oligonucleotides which compete with specific binding sites of transcription factors. Considering the exponential growth of this technique in both in vitro and in vivo studies, in this paper, we aim to line out the description, design, and application of decoys in research and therapy.

Identification of APOBEC3B promoter elements responsible for activation by human papillomavirus type 16 E6

Recent cancer genomics studies have identified mutation patterns characteristic of APOBEC3B (A3B) in multiple cancers, including cervical cancer, which is caused by human papillomavirus (HPV) infection. A3B expression is upregulated by HPV E6/E7 oncoproteins, implying a crucial role for A3B upregulation in HPV-induced carcinogenesis. Here, we explored the molecular mechanisms underlying the activation of the A3B promoter by E6. Luciferase reporter assays with a series of deleted fragments of the human A3B promoter in normal immortalized human keratinocytes (NIKS) identified two functional regions in the promoter: the distal region (from -200 to -51), which is required for basal promoter activity, and the proximal region (from +1 to +45), which exerts an inhibitory effect on gene expression. Each promoter region was found to contain an E6-responsive element(s). Disruption of an AT-rich motif located between +10 and +16 abrogated the proximal-region-mediated activation of the A3B promoter by E6. DNA pull-down assays revealed that a cellular zinc-finger protein, ZNF384, binds to the AT-rich motif in the A3B promoter, and chromatin immunoprecipitation assays confirmed that ZNF384 binds to the A3B promoter in cells. ZNF384 knockdown reduced the A3B mRNA levels in NIKS expressing E6, but not in the parental NIKS, indicating that ZNF384 contributes to A3B upregulation by E6, but not to basal A3B expression. The exogenous expression of ZNF384 led to the activation of the A3B promoter in NIKS. Collectively, these results indicate that E6 activates the A3B promoter through the distal and proximal regions, and that ZNF384 is required for the proximal-region-mediated activation of A3B.

ING3 protein expression profiling in normal human tissues suggest its role in cellular growth and self-renewal

Members of the INhibitor of Growth (ING) family of proteins act as readers of the epigenetic code through specific recognition of the trimethylated form of lysine 4 of histone H3 (H3K4Me3) by their plant homeodomains. The founding member of the family, ING1, was initially identified as a tumor suppressor with altered regulation in a variety of cancer types. While alterations in ING1 and ING4 levels have been reported in a variety of cancer types, little is known regarding ING3 protein levels in normal or transformed cells due to a lack of reliable immunological tools. In this study we present the characterization of a new monoclonal antibody we have developed against ING3 that specifically recognizes human and mouse ING3. The antibody works in western blots, immunofluorescence, immunoprecipitation and immunohistochemistry. Using this antibody we show that ING3 is most highly expressed in small intestine, bone marrow and epidermis, tissues in which cells undergo rapid proliferation and renewal. Consistent with this observation, we show that ING3 is expressed at significantly higher levels in proliferating versus quiescent epithelial cells. These data suggest that ING3 levels may serve as a surrogate for growth rate, and suggest possible roles for ING3 in growth and self renewal and related diseases such as cancer.

NPD1-mediated stereoselective regulation of BIRC3 expression through cREL is decisive for neural cell survival

Neuroprotectin D1 (NPD1), a docosahexaenoic acid (DHA)-derived mediator, induces cell survival in uncompensated oxidative stress (OS), neurodegenerations or ischemic stroke. The molecular principles underlying this protection remain unresolved. We report here that, in retinal pigment epithelial cells, NPD1 induces nuclear translocation and cREL synthesis that, in turn, mediates BIRC3 transcription. NPD1 activates NF-κB by an alternate route to canonical signaling, so the opposing effects of TNFR1 and NPD1 on BIRC3 expression are not due to interaction/s between NF-κB pathways. RelB expression follows a similar pattern as BIRC3, indicating that NPD1 also is required to activate cREL-mediated RelB expression. These results suggest that cREL, which follows a periodic pattern augmented by the lipid mediator, regulates a cluster of NPD1-dependent genes after cREL nuclear translocation. BIRC3 silencing prevents NPD1 induction of survival against OS. Moreover, brain NPD1 biosynthesis and selective neuronal BIRC3 abundance are increased by DHA after experimental ischemic stroke followed by remarkable neurological recovery. Thus, NPD1 bioactivity governs key counter-regulatory gene transcription decisive for retinal and brain neural cell integrity when confronted with potential disruptions of homeostasis.

Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges

Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.

Triple negative breast cancer: a multi-omics network discovery strategy for candidate targets and driving pathways

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.

Gene expression profiling analysis of castration-resistant prostate cancer

Background

Prostate cancer is a global health issue. Usually, men with metastatic disease will progress to castration-resistant prostate cancer (CRPC). We aimed to identify the differentially expressed genes (DEGs) in tumor samples from non-castrated and castrated men from LNCaP Orthotopic xenograft models of prostate cancer and to study the mechanisms of CRPC.

Material/Methods

In this work, GSE46218 containing 4 samples from non-castrated men and 4 samples from castrated men was downloaded from Gene Expression Omnibus. We identified DEGs using limma Geoquery in R, the Robust Multi-array Average (RMA) method in Bioconductor, and Bias methods, followed by constructing an integrated regulatory network involving DEGs, miRNAs, and TFs using Cytoscape. Then, we analyzed network motifs of the integrated gene regulatory network using FANMOD. We selected regulatory modules corresponding to network motifs from the integrated regulatory network by Perl script. We preformed gene ontology (GO) and pathway enrichment analysis of DEGs in the regulatory modules using DAVID.

Results

We identified total 443 DEGs. We built an integrated regulatory network, found three motifs (motif 1, motif 2 and motif 3), and got two function modules (module 1 corresponded to motif 1, and module 2 corresponded to motif 2). Several GO terms (such as regulation of cell proliferation, positive regulation of macromolecule metabolic process, phosphorylation, and phosphorus metabolic process) and two pathways (pathway in cancer and Melanoma) were enriched. Furthermore, some significant DEGs (such as CAV1, LYN, FGFR3 and FGFR3) were related to CPRC development.

Conclusions

These genes might play important roles in the development and progression of CRPC.

Genomic expression profiling of NK cells in health and disease

NK cells are important components of innate and adaptive immunity. Functionally, they play key roles in host defense against tumors and infectious pathogens. Within the past few years, genomic-scale experiments have provided us with a plethora of gene expression data that reveal an extensive molecular and biological map underlying gene expression programs. In order to better explore and take advantage of existing datasets, we review here the genomic expression profiles of NK cells and their subpopulations in resting or stimulated states, in diseases, and in different organs; moreover, we contrast these expression data to those of other lymphocytes. We have also compiled a comprehensive list of genomic profiling studies of both human and murine NK cells in this review.

A crowd-sourcing approach for the construction of species-specific cell signaling networks

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.

Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict.

Contact:ebilal@us.ibm.com or gustavo@us.ibm.com.

Supplementary information:Supplementary data are available at Bioinformatics online.

Large-scale modeling of condition-specific gene regulatory networks by information integration and inference

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η² (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.

A systems biology approach identified different regulatory networks targeted by KSHV miR-K12-11 in B cells and endothelial cells

Background

Kaposi’s sarcoma associated herpes virus (KSHV) is associated with tumors of endothelial and lymphoid origin. During latent infection, KSHV expresses miR-K12-11, an ortholog of the human tumor gene hsa-miR-155. Both gene products are microRNAs (miRNAs), which are important post-transcriptional regulators that contribute to tissue specific gene expression. Advances in target identification technologies and molecular interaction databases have allowed a systems biology approach to unravel the gene regulatory networks (GRNs) triggered by miR-K12-11 in endothelial and lymphoid cells. Understanding the tissue specific function of miR-K12-11 will help to elucidate underlying mechanisms of KSHV pathogenesis.

Results

Ectopic expression of miR-K12-11 differentially affected gene expression in BJAB cells of lymphoid origin and TIVE cells of endothelial origin. Direct miRNA targeting accounted for a small fraction of the observed transcriptome changes: only 29 genes were identified as putative direct targets of miR-K12-11 in both cell types. However, a number of commonly affected biological pathways, such as carbohydrate metabolism and interferon response related signaling, were revealed by gene ontology analysis. Integration of transcriptome profiling, bioinformatic algorithms, and databases of protein-protein interactome from the ENCODE project identified different nodes of GRNs utilized by miR-K12-11 in a tissue-specific fashion. These effector genes, including cancer associated transcription factors and signaling proteins, amplified the regulatory potential of a single miRNA, from a small set of putative direct targets to a larger set of genes.

Conclusions

This is the first comparative analysis of miRNA-K12-11’s effects in endothelial and B cells, from tissues infected with KSHV in vivo. MiR-K12-11 was able to broadly modulate gene expression in both cell types. Using a systems biology approach, we inferred that miR-K12-11 establishes its GRN by both repressing master TFs and influencing signaling pathways, to counter the host anti-viral response and to promote proliferation and survival of infected cells. The targeted GRNs are more reproducible and informative than target gene identification, and our approach can be applied to other regulatory factors of interest.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-668) contains supplementary material, which is available to authorized users.

Study on the activity of the signaling pathways regulating hepatocytes from G0 phase into G1 phase during rat liver regeneration

Under normal physiological conditions, the majority of hepatocytes are in the functional state (G0 phase). After injury or liver partial hepatectomy (PH), hepatocytes are rapidly activated to divide. To understand the mechanism underlying hepatocyte G0/G1 transition during rat liver regeneration, we used the Rat Genome 230 2.0 Array to determine the expression changes of genes, then searched the GO and NCBI databases for genes associated with the G0/G1 transition, and QIAGEN and KEGG databases for the G0/G1 transition signaling pathways. We used expression profile function (E t ) to calculate the activity level of the known G0/G1 transition signal pathways, and Ingenuity Pathway Analysis 9.0 (IPA) to determine the interactions among these signaling pathways. The results of our study show that the activity of the signaling pathways of HGF, IL-10 mediated by p38MAPK, IL-6 mediated by STAT3, and JAK/STAT mediated by Ras/ERK and STAT3 are significantly increased during the priming phase (2-6 h after PH) of rat liver regeneration. This leads us to conclude that during rat liver regeneration, the HGF, IL-10, IL-6 and JAK/STAT signaling pathways play a major role in promoting hepatocyte G0/G1 transition in the regenerating liver.

A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein-protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

Pathway bridge based multiobjective optimization approach for lurking pathway prediction

Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1-TGFβR1-SMAD2/3-SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβsignal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis.

The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells

The transcription factor BATF is required for the differentiation of interleukin 17 (IL-17)-producing helper T cells (TH17 cells) and follicular helper T cells (TFH cells). Here we identified a fundamental role for BATF in regulating the differentiation of effector of CD8(+) T cells. BATF-deficient CD8(+) T cells showed profound defects in effector population expansion and underwent proliferative and metabolic catastrophe early after encountering antigen. BATF, together with the transcription factors IRF4 and Jun proteins, bound to and promoted early expression of genes encoding lineage-specific transcription-factors (T-bet and Blimp-1) and cytokine receptors while paradoxically repressing genes encoding effector molecules (IFN-γ and granzyme B). Thus, BATF amplifies T cell antigen receptor (TCR)-dependent expression of transcription factors and augments the propagation of inflammatory signals but restrains the expression of genes encoding effector molecules. This checkpoint prevents irreversible commitment to an effector fate until a critical threshold of downstream transcriptional activity has been achieved.

Using a large-scale knowledge database on reactions and regulations to propose key upstream regulators of various sets of molecules participating in cell metabolism

Background

Most of the existing methods to analyze high-throughput data are based on gene ontology principles, providing information on the main functions and biological processes. However, these methods do not indicate the regulations behind the biological pathways. A critical point in this context is the extraction of information from many possible relationships between the regulated genes, and its combination with biochemical regulations. This study aimed at developing an automatic method to propose a reasonable number of upstream regulatory candidates from lists of various regulated molecules by confronting experimental data with encyclopedic information.

Results

A new formalism of regulated reactions combining biochemical transformations and regulatory effects was proposed to unify the different mechanisms contained in knowledge libraries. Based on a related causality graph, an algorithm was developed to propose a reasonable set of upstream regulators from lists of target molecules. Scores were added to candidates according to their ability to explain the greatest number of targets or only few specific ones. By testing 250 lists of target genes as inputs, each with a known solution, the success of the method to provide the expected transcription factor among 50 or 100 proposed regulatory candidates, was evaluated to 62.6% and 72.5% of the situations, respectively. An additional prioritization among candidates might be further realized by adding functional ontology information. The benefit of this strategy was proved by identifying PPAR isotypes and their partners as the upstream regulators of a list of experimentally-identified targets of PPARA, a pivotal transcriptional factor in lipid oxidation. The proposed candidates participated in various biological functions that further enriched the original information. The efficiency of the method in merging reactions and regulations was also illustrated by identifying gene candidates participating in glucose homeostasis from an input list of metabolites involved in cell glycolysis.

Conclusion

This method proposes a reasonable number of regulatory candidates for lists of input molecules that may include transcripts of genes and metabolites. The proposed upstream regulators are the transcription factors themselves and protein complexes, so that a multi-level description of how cell metabolism is regulated is obtained.

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability.

Tumour cells acquire the ability to divide and multiply indefinitely whereas normal cells can undergo only a limited number of divisions. The switch to immortalisation of the tumour cell is dependent on maintaining the integrity of telomere DNA which forms chromosome ends and is achieved through activation of the telomerase enzyme by turning on synthesis of the TERT gene, which is usually silenced in normal cells. Suppressing telomerase is toxic to cancer cells and it is widely believed that understanding TERT regulation could lead to potential cancer therapies. Previous studies have identified many of the factors which individually contribute to activate or repress TERT levels in cancer cells. However, transcription factors do not behave in isolation in cells, but rather as a complex co-operative network displaying inter-regulation. Therefore, full understanding of TERT regulation will require a broader view of the transcriptional network. In this paper we take a computational modelling approach to study TERT regulation at the network level. We tested interactions between 14 TERT-regulatory factors in an ovarian cancer cell line using a screening approach and developed a model to analyse which network interventions were able to silence TERT.

Altered expression of hypoxia-inducible factor-1α (HIF-1α) and its regulatory genes in gastric cancer tissues

Tissue hypoxia induces reprogramming of cell metabolism and may result in normal cell transformation and cancer progression. Hypoxia-inducible factor 1-alpha (HIF-1α), the key transcription factor, plays an important role in gastric cancer development and progression. This study aimed to investigate the underlying regulatory signaling pathway in gastric cancer using gastric cancer tissue specimens. The integration of gene expression profile and transcriptional regulatory element database (TRED) was pursued to identify HIF-1α ↔ NFκB1 → BRCA1 → STAT3 ← STAT1 gene pathways and their regulated genes. The data showed that there were 82 differentially expressed genes that could be regulated by these five transcription factors in gastric cancer tissues and these genes formed 95 regulation modes, among which seven genes (MMP1, TIMP1, TLR2, FCGR3A, IRF1, FAS, and TFF3) were hub molecules that are regulated at least by two of these five transcription factors simultaneously and were associated with hypoxia, inflammation, and immune disorder. Real-Time PCR and western blot showed increasing of HIF-1α in mRNA and protein levels as well as TIMP1, TFF3 in mRNA levels in gastric cancer tissues. The data are the first study to demonstrate HIF-1α-regulated transcription factors and their corresponding network genes in gastric cancer. Further study with a larger sample size and more functional experiments is needed to confirm these data and then translate into clinical biomarker discovery and treatment strategy for gastric cancer.