Emerging Putative Associations between Non-Coding RNAs and Protein-Coding Genes in Neuropathic Pain: Added Value from Reusing Microarray Data

Abstract

Regeneration of injured nerves is likely occurring in the peripheral nervous system, but not in the central nervous system. Although protein-coding gene expression has been assessed during nerve regeneration, little is currently known about the role of non-coding RNAs (ncRNAs). This leaves open questions about the potential effects of ncRNAs at transcriptome level. Due to the limited availability of human neuropathic pain (NP) data, we have identified the most comprehensive time-course gene expression profile referred to sciatic nerve (SN) injury and studied in a rat model using two neuronal tissues, namely dorsal root ganglion (DRG) and SN. We have developed a methodology to identify differentially expressed bioentities starting from microarray probes and repurposing them to annotate ncRNAs, while analyzing the expression profiles of protein-coding genes. The approach is designed to reuse microarray data and perform first profiling and then meta-analysis through three main steps. First, we used contextual analysis to identify what we considered putative or potential protein-coding targets for selected ncRNAs. Relevance was therefore assigned to differential expression of neighbor protein-coding genes, with neighborhood defined by a fixed genomic distance from long or antisense ncRNA loci, and of parental genes associated with pseudogenes. Second, connectivity among putative targets was used to build networks, in turn useful to conduct inference at interactomic scale. Last, network paths were annotated to assess relevance to NP. We found significant differential expression in long-intergenic ncRNAs (32 lincRNAs in SN and 8 in DRG), antisense RNA (31 asRNA in SN and 12 in DRG), and pseudogenes (456 in SN and 56 in DRG). In particular, contextual analysis centered on pseudogenes revealed some targets with known association to neurodegeneration and/or neurogenesis processes. While modules of the olfactory receptors were clearly identified in protein–protein interaction networks, other connectivity paths were identified between proteins already investigated in studies on disorders, such as Parkinson, Down syndrome, Huntington disease, and Alzheimer. Our findings suggest the importance of reusing gene expression data by meta-analysis approaches.

Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes

“…Interesting evidences appear from the network in Fig. 5C (similar methodology was recently proposed in a different disease context 38 ). Formed from the DE parental genes associated with expressed pseudogenes (including DE), the network consists of interacting proteins retrieved from STRING V10.0 39 ( www.string-db.org ), also annotated with GeneCards: the Human Gene Database 40 ( www.genecards.org ).…”

Bioinformatic analysis of microRNA expression in Huntington's disease

“…In addition, we found another miRNA, hsa-miR-549a, was upregulated in HD patients and could regulate five target genes. When reviewing the literature, we found that GMNN ( 50 ), TNFAIP18 ( 51 ), LAPTM5 ( 52 ), and RGS18 ( 53 ) played roles in the pathogenesis of neurodegenerative diseases through various mechanisms, including regulation of transcription factors and affecting autophagy and endoplasmic reticulum stress pathways and might be involved in the pathogenesis of HD. Other affected miRNAs found in our study including hsa-miR-144-3p, hsa-miR-10b-3p, hsa-miR-10a-5p, and hsa-miR-483-5p have been reported as differentially expressed in HD samples; hsa-miR-10b-3p showed a significant association with CAG length in HD ( 6 ).…”

The reuse of public datasets in the life sciences: potential risks and rewards

“…Reuse of data saves time and money, thus is more economical, and can offer the opportunity to overcome the restraints of limited experiments, high costs and technical difficulties (Raju, Tsinoremas & Capobianco, 2016;Curty et al, 2017). In medicine, the reliance on expensive experimental research when a wealth of existing data is available has been criticised and reuse of existing data and information was presented as a solution (Wade, 2014).…”

“…Reuse of microarray data for meta-analyses including the investigation of non-coding RNAs (Raju, Tsinoremas & Capobianco, 2016) Microarray technology is not comprehensive for example in comparison to RNA-Seq; the study is limited to known non-coding RNAs and is not suitable for the detection of new non-coding RNAs (Raju, Tsinoremas & Capobianco, 2016).…”