Transcriptional Profiling Uncovers Biologically Significant RNAs and Regulatory Networks in Nucleus Pulposus from Intervertebral Disc Degeneration Patients

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Objective. This study aimed to uncover biologically significant RNAs in nucleus pulposus tissues of human intervertebral disc degeneration (IVDD) by integrated transcriptional profiling. Methods. From the Gene Expression Omnibus (GEO) database, three IVDD-related microarray profiling datasets were retrieved and assessed by intragroup data repeatability test. Then, differentially expressed circRNAs, lncRNAs, mRNAs, and miRNAs were screened in nucleus pulposus tissues between IVDD and control samples via the limma package. Coexpression networks were separately conducted via weighted gene correlation network analysis (WGCNA). Based on the feature RNAs in the IVDD-related modules, IVDD-related circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA networks were conducted. The differentially expressed mRNAs in the two networks were analyzed by protein-protein interaction (PPI) and functional enrichment analyses. Results. By the intragroup data repeatability test, outlier samples were removed. Abnormally expressed RNAs were separately identified in nucleus pulposus between IVDD and controls. Via WGCNA, IVDD-related coexpression modules were constructed and the feature circRNAs, lncRNAs, mRNAs, and miRNAs were identified. Then, the circRNA- and lncRNA-miRNA-mRNA networks were built for IVDD. These mRNAs in the network exhibited complex interactions. Moreover, they were involved in distinct IVDD-related biological processes and pathways such as transcription, cell proliferation, TNF, TGF-β, and HIF signaling pathways. Conclusion. This study revealed biologically significant noncoding RNAs and their complex regulatory networks for IVDD.

Related collections

Most cited references 8

Record: found
Abstract: found
Article: found

Is Open Access

limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

0 comments Cited 12254 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

Jun-Hao Li, Shun Liu, Hui Zhou … (2013)

Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA–RNA and protein–RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285 000 protein–RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10 000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

0 comments Cited 1853 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Cytoscape stringApp: Network analysis and visualization of proteomics data

Nadezhda T. Doncheva, John Morris, Jan Gorodkin … (2018)

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .