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Related Experiment Video

Updated: Nov 1, 2025

Novel RNA-Binding Proteins Isolation by the RaPID Methodology
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Methods to Study Protein-Binding to Pseudogene Transcripts.

Eusebio Chiefari1, Biagio Arcidiacono1, Maria Mirabelli1

  • 1Department of Health Sciences, University of Catanzaro "Magna Græcia", Catanzaro, Italy.

Methods in Molecular Biology (Clifton, N.J.)
|June 24, 2021
PubMed
Summary

Processed pseudogenes can affect parental gene expression by competing for RNA-binding proteins (RBPs). Understanding these interactions, like HMGA1-p/HMGA1 with αCP1, aids in disease biomarker discovery.

Keywords:
HMGA1-pImmunoprecipitationRNA-EMSARNA-binding proteinαCP1

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Area of Science:

  • Molecular Biology
  • Genomics
  • Biochemistry

Background:

  • Processed pseudogenes share high sequence similarity with parental coding genes.
  • This similarity leads to competition for posttranscriptional regulators, including RNA-binding proteins (RBPs).
  • Dysregulation of this competition can alter protein synthesis, potentially causing pathological consequences.

Purpose of the Study:

  • To investigate the interactions between pseudogene-parental gene RNA pairs and RBPs.
  • To provide protocols for studying these interactions, focusing on the HMGA1-p/HMGA1 pair and the protein αCP1.
  • To introduce bioinformatic tools for predicting RBP binding sites on pseudogene transcripts.

Main Methods:

  • Plasmid-based molecular cloning.
  • RNA-electrophoretic mobility shift assay (EMSA) for analyzing protein-RNA interactions.
  • Overview of RNA immunoprecipitation (RIP) procedures.
  • Development of novel bioinformatic tools for RBP binding site prediction.

Main Results:

  • Detailed protocols for molecular cloning and EMSA are provided.
  • The interaction between the HMGA1-p/HMGA1 RNA pair and the protein αCP1 was investigated.
  • Novel bioinformatic tools for predicting RBP binding sites on pseudogenes were presented.

Conclusions:

  • Understanding pseudogene-RBP interactions is crucial for elucidating disease mechanisms.
  • These interactions may offer insights into complex diseases like cancer and diabetes.
  • Identified interactions and tools can aid in discovering novel predictive and prognostic biomarkers.