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Updated: Jan 10, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
Published on: April 10, 2018
Long Non-Coding RNAs in Multiple Sclerosis-Differential Expression and Functional Implications.
Kaalindi Misra1, Aishwary Nerkar1, Ferdinando Clarelli1
1Laboratory of Human Genetics of Neurological Disorders, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy.
Long non-coding RNAs (lncRNAs) show consistent but context-specific changes in Multiple Sclerosis (MS). This review highlights key lncRNAs and pathways, calling for standardized research to advance diagnostics and therapeutics.
Area of Science:
- Immunogenomics
- Molecular Biology
- Neuroimmunology
Background:
- Long non-coding RNAs (lncRNAs) are emerging regulators of immune responses.
- lncRNAs may have diagnostic and therapeutic potential in autoimmune diseases like Multiple Sclerosis (MS).
- Existing research on lncRNAs in MS is fragmented and geographically limited.
Purpose of the Study:
- To systematically review and evaluate studies on lncRNA expression in MS.
- To assess the consistency of findings across different studies.
- To synthesize the functional roles of frequently studied lncRNAs in MS.
Main Methods:
- Systematic review of 51 studies (2010-2024) using PRISMA guidelines.
- Searched major databases (PubMed, Scopus, Embase, Web of Science).
- Included studies on adult MS with ≥20 participants, analyzing lncRNAs in biological samples via qRT-PCR, RNA-seq, or microarrays.
Main Results:
- Identified 77 unique lncRNAs, with MALAT1, GAS5, MEG3, and H19 showing consistent dysregulation in MS.
- Other lncRNAs (e.g., THRIL, IFNG-AS1) displayed context-dependent expression.
- Functional analysis implicated pathways like NF-κB, STAT3, and IFN-γ/Th1 signaling.
Conclusions:
- Reproducible and context-specific lncRNA dysregulation in MS is evident.
- Highlights the need for transcriptome-wide studies, standardized methods, and multi-center validation.
- Current research limitations include geographic bias, preselection bias, and methodological heterogeneity.
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