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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Related Experiment Video

Updated: Mar 12, 2026

The Sciatic Nerve Cuffing Model of Neuropathic Pain in Mice
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Emerging Putative Associations between Non-Coding RNAs and Protein-Coding Genes in Neuropathic Pain: Added Value from

Hemalatha B Raju1, Nicholas F Tsinoremas2, Enrico Capobianco3

  • 1Center for Computational Science, University of Miami Miller School of Medicine, Miami, FL, USA; Human Genetics and Genomic Graduate Program, University of Miami Miller School of Medicine, Miami, FL, USA.

Frontiers in Neurology
|November 3, 2016
PubMed
Summary

This study reveals the significant role of non-coding RNAs (ncRNAs) in nerve regeneration after injury. By repurposing microarray data, researchers identified differentially expressed ncRNAs and their protein targets, offering new insights into neuropathic pain mechanisms.

Keywords:
differential expressionmicroarray data reusenetworksneuropathic painnon-coding RNAspathway analysistime-course profiling

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Area of Science:

  • Neuroscience
  • Genomics
  • Bioinformatics

Background:

  • Nerve regeneration differs between the peripheral and central nervous systems.
  • The role of non-coding RNAs (ncRNAs) in nerve regeneration is largely unknown.
  • Limited human neuropathic pain (NP) data necessitates novel analytical approaches.

Purpose of the Study:

  • To investigate the role of ncRNAs in nerve regeneration using a rat sciatic nerve (SN) injury model.
  • To develop a methodology for identifying differentially expressed ncRNAs and their protein targets from existing microarray data.
  • To analyze the transcriptome-level effects of ncRNAs on neuropathic pain.

Main Methods:

  • Developed a novel methodology to identify differentially expressed ncRNAs and protein-coding genes from microarray data.
  • Utilized contextual analysis to identify potential protein-coding targets for ncRNAs based on genomic proximity.
  • Constructed gene networks from putative targets to infer interactomic relationships and assess relevance to NP.

Main Results:

  • Identified significant differential expression of long intergenic ncRNAs (lincRNAs), antisense RNAs (asRNAs), and pseudogenes in both SN and dorsal root ganglion (DRG) tissues.
  • Contextual analysis of pseudogenes revealed targets associated with neurodegeneration and neurogenesis.
  • Network analysis identified connections to known disease-related proteins and pathways, including olfactory receptors.

Conclusions:

  • ncRNAs, particularly pseudogenes, play a significant role in nerve injury and regeneration.
  • Repurposing gene expression data through meta-analysis is a valuable approach for understanding complex biological processes like neuropathic pain.
  • Findings provide a foundation for further research into ncRNA-based diagnostics and therapeutics for neurological disorders.