ReNE: a cytoscape plugin for regulatory network enhancement

A Streamlined Approach to Pathway Analysis from RNA-Sequencing Data

The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the raw data produced can present a formidable challenge for researchers aiming to glean vital information about samples. Though many of the companies that perform RNA-sequencing provide a basic report for the submitted samples, this may not adequately capture particular pathways of interest for sample comparisons. To further assess these data, it can therefore be necessary to utilize various enrichment and mapping software platforms to highlight specific relations. With the wide array of these software platforms available, this can also present a daunting task. The methodology described herein aims to enable researchers new to handling RNA-sequencing data with a streamlined approach to pathway analysis. Additionally, the implemented software platforms are readily available and free to utilize, making this approach viable, even for restrictive budgets. The resulting tables and nodal networks will provide valuable insight into samples and can be used to generate high-quality graphics for publications and presentations.

'One DB to rule them all'-the RING: a Regulatory INteraction Graph combining TFs, genes/proteins, SNPs, diseases and drugs

In the last decade, genomics data have been largely adopted to sketch, study and better understand the complex mechanisms that underlie biological processes. The amount of publicly available data sources has grown accordingly, and several types of regulatory interactions have been collected and documented in literature. Unfortunately, often these efforts do not follow any data naming/interoperability/formatting standards, resulting in high-quality but often uninteroperable heterogeneous data repositories. To efficiently take advantage of the large amount of available data and integrate these heterogeneous sources of information, we built the RING (Regulatory Interaction Graph), an integrative standardized multilevel database of biological interactions able to provide a comprehensive and unmatched high-level perspective on several phenomena that take place in the regulatory cascade and that researchers can use to easily build regulatory networks around entities of interest.

miRNA-seq analysis of human vertebrae provides insight into the mechanism underlying GIOP

High-throughput sequencing (HTS) was recently applied to detect microRNA (miRNA) regulation in age-related osteoporosis. However, miRNA regulation has not been reported in glucocorticoid-induced osteoporosis (GIOP) patients and the mechanism of GIOP remains elusive. To comprehensively analyze the role of miRNA regulation in GIOP based on human vertebrae and to explore the molecular mechanism, a high-throughput sequencing strategy was employed to identify miRNAs involved in GIOP. Twenty-six patients undergoing spinal surgery were included in this study. Six vertebral samples were selected for miRNA sequencing (miRNA-seq) analysis and 26 vertebral samples were verified by qRT-PCR. Bioinformatics was utilized for target prediction, to investigate the regulation of miRNA-mRNA networks, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Six significantly up-regulated miRNAs (including one novel miRNA) and three significantly down-regulated miRNAs were verified via miRNA-seq and verified in the vertebrae of GIOP patients. Up-regulated miRNAs included hsa-miR-214-5p, hsa-miR-10b-5p, hsa-miR-21-5p, hsa-miR-451a, hsa-miR-186-5p, and hsa-miR-novel-chr3_49,413 while down-regulated miRNAs included hsa-let-7f-5p, hsa-let-7a-5p, and hsa-miR-27a-3p. Bioinformatics analysis revealed 5983 and 23,463 predicted targets in the up-regulated and down-regulated miRNAs respectively, using the miRanda, miRBase and TargetScan databases. The target genes of these significantly altered miRNAs were enriched to 1939 GO terms and 84 KEGG pathways. GO terms revealed that up-regulated targets were most enriched in actin filament-based processes (BP), anchoring junction (CC), and cytoskeletal protein binding (MF). Conversely, the down-regulated targets were mostly enriched in multicellular organismal development (BP), intracellular membrane-bounded organelles (CC), and protein binding (MF). Top-10 pathway analysis revealed that the differentially expressed miRNAs in GIOP were closely related to bone metabolism-related pathways such as FoxO, PI3K-Akt, MAPK and Notch signaling pathway. These results suggest that significantly altered miRNAs may play an important role in GIOP by targeting mRNA and regulating biological processes and bone metabolism-related pathways such as MAPK, FoxO, PI3K-Akt and Notch signaling, which provides novel insight into the mechanism of GIOP and lays a good foundation for the prevention and treatment of GIOP.

Ma Huang Tang ameliorates bronchial asthma symptoms through the TLR9 pathway

CONTEXT

Ma Huang Tang (MHT) has been used to treat influenza, fever, bronchial asthma, etc. as a traditional Chinese medication. However, the anti-inflammation mechanism of MHT remains unclear.

OBJECTIVE

The study identifies the possible mechanisms of MHT on ovalbumin (OVA)-induced acute bronchial asthma in mice.

MATERIALS AND METHODS

First, an asthma-related protein-protein interaction (PPI) network was constructed. And then, the acute bronchial asthma mice models were established by exposing to aerosolized 1% ovalbumin for 30 min/day for 1 week, and the mice were administered 2.0, 4.0, or 8.0 g/kg of MHT daily. To evaluate therapeutic effect, sensitization time, abdominal breathing time, eosinophils in bronchoalveolar lavage fluid, and tissue and trachea pathology were examined. Related genes were measured using RNA sequencing (RNA-seq). The expression levels of TLR9 in lung and trachea tissues were determined by immunohistochemical staining.

RESULTS

MHT had a LD₅₀ = 19.2 g/kg against asthma, while MHT at high doses (8 g/kg) effectively extended the sensitization time and abdominal breathing time and alleviated OVA-induced eosinophilic airway inflammation and mitigated pathological changes. The RNA-seq assay showed that the high-dose MHT resulted in a significant decrease in the levels of TLR9, TRAF6, TAB2, etc. in the lung tissue. Immunohistochemical assay confirmed the down-regulated of TLR9. Molecular docking revealed that six MHT compounds potentially mediated the TLR9 signaling pathway.

DISCUSSION AND CONCLUSIONS

MHT could mitigate the pathological changes of acute asthma-like syndrome through inhibition of the TLR9 pathway. Results of this study may provide a reference for the development of a novel therapy for patients with allergic asthma.

Computing of Low Shear Stress-Driven Endothelial Gene Network Involved in Early Stages of Atherosclerotic Process

Background

In the pathogenesis of atherosclerosis, a central role is represented by endothelial inflammation with influx of chemokine-mediated leukocytes in the vascular wall. Aim of this study was to analyze the effect of different shear stresses on endothelial gene expression and compute gene network involved in atherosclerotic disease, in particular to homeostasis, inflammatory cell migration, and apoptotic processes.

Methods

HUVECs were subjected to shear stress of 1, 5, and 10 dyne/cm² in a Flow Bioreactor for 24 hours to compare gene expression modulation. Total RNA was analyzed by Affymetrix technology and the expression of two specific genes (CXCR4 and ICAM-1) was validated by RT-PCR. To highlight possible regulations between genes and as further validation, a bioinformatics analysis was performed.

Results

At low shear stress (1 dyne/cm²) we observed the following: (a) strong upregulation of CXCR4; (b) mild upregulation of Caspase-8; (c) mild downregulation of ICAM-1; (d) marked downexpression of TNFAIP3. Bioinformatics analysis showed the presence of network composed by 59 new interactors (14 transcription factors and 45 microRNAs) appearing strongly related to shear stress.

Conclusions

The significant modulation of these genes at low shear stress and their close relationships through transcription factors and microRNAs suggest that all may promote an initial inflamed endothelial cell phenotype, favoring the atherosclerotic disease.

rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining

BACKGROUND

Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage.

RESULT

To address this issue, we propose a new visualization tool rNAV 2.0, for detecting and filtering bacterial sRNA targets for regulatory networks. rNAV is designed to cope with a variety of biological constraints, including the gene annotations, the conserved regions of interaction or specific patterns of regulation. Depending on the application, these constraints can be variously combined to analyze the target candidates, prioritized for instance by a known conserved interaction region, or because of a common function.

CONCLUSION

The standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates.

Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles

Background

There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape (http://cytoscape.org/) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged ‘network evolution’ found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results.

Results

Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface.

Conclusion

Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1427-5) contains supplementary material, which is available to authorized users.

Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs

Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide important insights into the mechanisms of complex human diseases caused by miRNA regulation. Therefore, it is important to identify miRNA synergism and study miRNA characteristics in miRNA synergistic regulatory networks. A number of methods have been proposed to identify miRNA synergism. However, most of the methods only use downstream target genes of miRNAs to infer miRNA synergism when miRNAs can also be regulated by upstream transcription factors (TFs) at the transcriptional level. Additionally, most methods are based on statistical associations identified from data without considering the causal nature of gene regulation. In this paper, we present a causality based framework, called mirSRN (miRNA synergistic regulatory network), to infer miRNA synergism in human molecular systems by considering both downstream miRNA targets and upstream TF regulation. We apply the proposed framework to two real world datasets and discover that almost all the top 10 miRNAs with the largest node degree in the mirSRNs are associated with different human diseases, including cancer, and that the mirSRNs are approximately scale-free and small-world networks. We also find that most miRNAs in the networks are frequently synergistic with other miRNAs, and miRNAs related to the same disease are likely to be synergistic and in a cluster linked to a biological function. Synergistic miRNA pairs show higher co-expression level, and may have potential functional relationships indicating collaboration between the miRNAs. Functional validation of the identified synergistic miRNAs demonstrates that these miRNAs cause different kinds of diseases. These results deepen our understanding of the biological meaning of miRNA synergism.

Identification of miRNAs Potentially Involved in Bronchiolitis Obliterans Syndrome: A Computational Study

The pathogenesis of Bronchiolitis Obliterans Syndrome (BOS), the main clinical phenotype of chronic lung allograft dysfunction, is poorly understood. Recent studies suggest that epigenetic regulation of microRNAs might play a role in its development. In this paper we present the application of a complex computational pipeline to perform enrichment analysis of miRNAs in pathways applied to the study of BOS. The analysis considered the full set of miRNAs annotated in miRBase (version 21), and applied a sequence of filtering approaches and statistical analyses to reduce this set and to score the candidate miRNAs according to their potential involvement in BOS development. Dysregulation of two of the selected candidate miRNAs–miR-34a and miR-21 –was clearly shown in in-situ hybridization (ISH) on five explanted human BOS lungs and on a rat model of acute and chronic lung rejection, thus definitely identifying miR-34a and miR-21 as pathogenic factors in BOS and confirming the effectiveness of the computational pipeline.

Identification and characterization of microRNAs in Eucheuma denticulatum by high-throughput sequencing and bioinformatics analysis

Eucheuma denticulatum, an economically and industrially important red alga, is a valuable marine resource. Although microRNAs (miRNAs) play an essential role in gene post-transcriptional regulation, no research has been conducted to identify and characterize miRNAs in E. denticulatum. In this study, we identified 134 miRNAs (133 conserved miRNAs and one novel miRNA) from 2,997,135 small-RNA reads by high-throughput sequencing combined with bioinformatics analysis. BLAST searching against miRBase uncovered 126 potential miRNA families. A conservation and diversity analysis of predicted miRNA families in different plant species was performed by comparative alignment and homology searching. A total of 4 and 13 randomly selected miRNAs were respectively validated by northern blotting and stem-loop reverse transcription PCR, thereby demonstrating the reliability of the miRNA sequencing data. Altogether, 871 potential target genes were predicted using psRobot and TargetFinder. Target genes classification and enrichment were conducted based on Gene Ontology analysis. The functions of target gene products and associated metabolic pathways were predicted by Kyoto Encyclopedia of Genes and Genomes pathway analysis. A Cytoscape network was constructed to explore the interrelationships of miRNAs, miRNA-target genes and target genes. A large number of miRNAs with diverse target genes will play important roles for further understanding some essential biological processes in E. denticulatum. The uncovered information can serve as an important reference for the protection and utilization of this unique red alga in the future.