Construction of a novel mRNA-miRNA-lncRNA/circRNA triple subnetwork associated with immunity and aging in intervertebral disc degeneration

OBJECTIVE

Intervertebral disk degeneration (IVDD) is one of the most common causes of low back pain. However, in the etiology of IVDD, the specific method by which nucleus pulposus (NP) cell senescence and the immune response induce disease is uncertain.

METHODS

Gene Expression Omnibus database was used to find differentially expressed genes (DEGs), differentially expressed miRNAs (DE miRNAs), differentially expressed lncRNAs (DE lncRNAs), and differentially expressed circRNAs (DE circRNAs). Functional enrichment analysis was performed through Enrichr database. Potential regulatory miRNAs, lncRNAs and circRNAs of mRNAs were predicted by ENCORI and circBank, respectively.

RESULTS

We identified 198 upregulated and 131 downregulated genes, 39 upregulated and 22 downregulated miRNAs, 2152 upregulated and 564 downregulated lncRNAs, and 352 upregulated and 279 downregulated circRNAs as DEGs, DE miRNAs, DE lncRNAs, DE circRNAs, respectively. Functional enrichment analysis revealed that they were significantly enriched in Toll-like receptor signaling route and the NF-kappa B signaling pathway. An mRNA-miRNA-lncRNA/circRNA network linked to the pathogenesis of NP cells in IVDD was constructed based on node degree and differential expression level. Eight immune-related DEGs (6 upregulated and 2 downregulated genes) and five aging-related DEGs (3 upregulated and 2 downregulated genes) were identified in the critical network.

CONCLUSION

We established a novel immune-related and aging-related triple regulatory network of mRNA-miRNA-lncRNA/circRNA ceRNA, among which all RNAs may be utilized as the pathogenesis biomarker of NP cells in IVDD.

GSA Central—A web platform to perform, learn, and discuss gene set analysis

Gene Set Analysis (GSA) is one of the most commonly used strategies to analyze omics data. Hundreds of GSA-related papers have been published, giving birth to a GSA field in Bioinformatics studies. However, as the field grows, it is becoming more difficult to obtain a clear view of all available methods, resources, and their quality. In this paper, we introduce a web platform called “GSA Central” which, as its name indicates, acts as a focal point to centralize GSA information and tools useful to beginners, average users, and experts in the GSA field. “GSA Central” contains five different resources: A Galaxy instance containing GSA tools (“Galaxy-GSA”), a portal to educational material (“GSA Classroom”), a comprehensive database of articles (“GSARefDB”), a set of benchmarking tools (“GSA BenchmarKING”), and a blog (“GSA Blog”). We expect that “GSA Central” will become a useful resource for users looking for introductory learning, state-of-the-art updates, method/tool selection guidelines and insights, tool usage, tool integration under a Galaxy environment, tool design, and tool validation/benchmarking. Moreover, we expect this kind of platform to become an example of a “thematic platform” containing all the resources that people in the field might need, an approach that could be extended to other bioinformatics topics or scientific fields.

Functional Enrichment Analysis of Regulatory Elements

Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by the Wallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Popularity and performance of bioinformatics software: the case of gene set analysis

Background

Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies.

Results

Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods.

Conclusions

The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04124-5.

miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson’s disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

In silico Analysis Excavates A Novel Competing Endogenous RNA Subnetwork in Adolescent Idiopathic Scoliosis

Background and Objective: Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional deformity of the spine. Mesenchymal stem cells (MSCs) regulate bone mass homeostasis in AIS, which might be related to the pathogenesis of AIS. However, the mRNA–miRNA–lncRNA network linked to the regulation of the genetic pathogenesis of MSCs remains unknown.

Methods: We conducted an exhaustive literature search of PubMed, EMBASE, and the Gene Expression Omnibus database to find differentially expressed genes (DEGs), differentially expressed miRNAs (DE miRNAs), and differentially expressed lncRNAs (DE lncRNAs). Functional enrichment analysis was performed through Enrichr database. Protein–protein interaction (PPI) network was constructed using STRING database, and hub genes were identified by CytoHubba. Potential regulatory miRNAs and lncRNAs of mRNAs were predicted by miRTarBase and RNA22, respectively.

Results: We identified 551 upregulated and 476 downregulated genes, 42 upregulated and 12 downregulated miRNAs, and 345 upregulated and 313 downregulated lncRNAs as DEGs, DE miRNAs, and DE lncRNAs, respectively. Functional enrichment analysis revealed that they were significantly enriched in protein deglutamylation and regulation of endoplasmic reticulum unfolded protein response. According to node degree, one upregulated hub gene and eight downregulated hub genes were identified. After drawing the Venn diagrams and matching to Cytoscape, an mRNA–miRNA–lncRNA network linked to the pathogenesis of MSCs in AIS was constructed.

Conclusion: We established a novel triple regulatory network of mRNA–miRNA–lncRNA ceRNA, among which all RNAs may be utilized as the pathogenesis biomarker of MSCs in AIS.