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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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Autism Spectrum Disorder01:19

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Modelling cell type-specific lncRNA regulatory network in autism with Cycle.

Chenchen Xiong1,2, Mingfang Zhang3, Haolin Yang1

  • 1School of Engineering, Dali University, Dali, Yunnan, China.

BMC Bioinformatics
|September 28, 2024
PubMed
Summary

This study introduces Cycle, a new method to map cell type-specific long non-coding RNA regulation in autism spectrum disorder (ASD). Cycle reveals unique regulatory patterns in ASD cell types, offering insights into disease heterogeneity.

Keywords:
Autism spectrum disorderLncRNALncRNA regulationMRNASingle-cell RNA sequencing

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Area of Science:

  • Neuroscience
  • Genetics
  • Bioinformatics

Background:

  • Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with significant genetic diversity.
  • Long non-coding RNAs (lncRNAs) are crucial regulators in neurological functions and diseases like ASD.
  • Current methods analyzing bulk transcriptomics overlook cell type-specific lncRNA regulation in ASD.

Purpose of the Study:

  • To develop a computational method for constructing cell type-specific lncRNA regulatory networks in ASD.
  • To investigate the unique lncRNA regulatory landscape across different cell types in ASD.
  • To identify key regulatory modules and hubs associated with ASD.

Main Methods:

  • Development of Cycle (Cell type-specific lncRNA regulatory network) tool.
  • Analysis of cell type-specific transcriptomics data in ASD.
  • Construction of cell type-specific lncRNA regulatory networks and cell similarity networks.

Main Results:

  • Each ASD cell type exhibits distinct lncRNA regulation.
  • Cell type-specific lncRNA regulatory networks display scale-free and small-world properties.
  • Identified 19 rewired and 11 stable modules, and 8 rewired and 3 stable hubs related to ASD.
  • Discovered a correlation between ASD cell type similarity and network connection strength.

Conclusions:

  • Cycle is a promising method for modeling cell type-specific lncRNA regulation in ASD.
  • The findings provide insights into the heterogeneity of lncRNA regulation in different ASD cell types.
  • This approach aids in understanding the molecular mechanisms underlying ASD.