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piRNA - Piwi-interacting RNAs

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PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...

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Related Experiment Video

Updated: May 4, 2026

Detection of MicroRNAs in Microglia by Real-time PCR in Normal CNS and During Neuroinflammation

Detection of MicroRNAs in Microglia by Real-time PCR in Normal CNS and During Neuroinflammation

Published on: July 23, 2012

Exploring miRNAs' Based Modeling Approach for Predicting PIRA in Multiple Sclerosis: A Comprehensive Analysis.

Tommaso Gosetti di Sturmeck¹, Leonardo Malimpensa², Gina Ferrazzano³

¹European Brain Research Institute (EBRI) Rita Levi-Montalcini, 00161 Rome, Italy.

International Journal of Molecular Sciences

|June 27, 2024

Summary

Biomarkers in microRNA profiles can predict multiple sclerosis progression independent of relapse activity. This aids in personalizing treatment strategies for patients with relapsing-remitting multiple sclerosis.

Keywords:

miRNA modeling approach multiple sclerosis progression independent of relapse activity (PIRA)

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Area of Science:

Neuroimmunology
Biomarker Discovery
Genomics

Background:

Multiple sclerosis (MS) pathophysiology involves inflammation and neurodegeneration.
Disease Modifying Therapies (DMTs) reduce relapses but not all disability progression.
Progression Independent of Relapse Activity (PIRA) significantly impacts long-term disability.

Purpose of the Study:

Identify microRNA (miRNA) biomarkers to predict PIRA in relapsing-remitting MS (RRMS).
Develop models for stratifying patients based on disease trajectory.
Enhance personalized treatment strategies for early MS stages.

Main Methods:

Analyzed miRNA profiles from peripheral blood mononuclear cells of 25 RRMS patients before and after high-efficacy DMTs.
Utilized Agilent microarrays for miRNA profiling.
Employed multivariate logistic and linear models with randomization tests for analysis.

Main Results:

Selected a subset of miRNAs correlated with PIRA and Expanded Disability Status Scale (EDSS) changes.
Developed a robust logistic model predicting PIRA status with high accuracy (AUC = 0.971).
Built a multilinear model using four miRNA predictors to describe EDSS changes.

Conclusions:

Multivariate modeling of miRNA expression shows promise for predicting MS disability progression.
Baseline miRNA profiles can stratify patients regarding PIRA risk.
These findings support the development of personalized therapeutic approaches for MS.