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Related Concept Videos

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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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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Long noncoding RNAs associated with phenotypic severity in multiple sclerosis.

Mehul Gupta1, Kristina Martens1, Luanne M Metz2

  • 1Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.

Multiple Sclerosis and Related Disorders
|September 29, 2019
PubMed
Summary
This summary is machine-generated.

This study identifies four long noncoding RNAs (lncRNAs) as potential biomarkers for predicting multiple sclerosis (MS) severity. These lncRNAs may help personalize MS treatment by indicating disease progression and response to therapy.

Keywords:
BiomarkerDisease severityLong noncoding RNAMultiple sclerosisPhenotype

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Area of Science:

  • Biomarkers
  • Neuroscience
  • Genetics

Background:

  • Multiple sclerosis (MS) is a progressive neurological disease with available treatments to mitigate relapses.
  • Early neuroinflammation reduction may improve long-term MS prognosis.
  • Currently, no reliable biomarker predicts MS severity or guides aggressive therapy decisions.

Purpose of the Study:

  • To identify long noncoding RNAs (lncRNAs) as potential biomarkers for differentiating MS phenotypic severity.
  • To explore the utility of lncRNAs in predicting disease course and guiding treatment strategies for MS patients.

Main Methods:

  • A discovery cohort of 20 MS patients (10 mild, 10 severe phenotype) was analyzed using RNA sequencing on whole blood.
  • Bioinformatic analysis focused on identifying differentially expressed long noncoding RNAs (lncRNAs).
  • Candidate lncRNAs were validated in a separate cohort of 44 MS patients using custom digital droplet RT-qPCR assays.

Main Results:

  • Eight lncRNA candidates were initially identified from the discovery cohort.
  • Four lncRNAs (ENSG00000260302, ENSG00000270972, ENSG00000272512, ENSG00000223387) showed significant differential expression in the validation cohort.
  • These identified lncRNAs are implicated in immune function and potentially MS pathogenesis.

Conclusions:

  • This study represents the first investigation into lncRNA biomarkers for differentiating MS phenotypic severity.
  • Preliminary findings suggest these lncRNAs hold potential for predicting MS disease course.
  • Further research with larger sample sizes is warranted to confirm these findings and guide future MS biomarker studies.