Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2

Abstract Alzheimer’s disease (AD) is an age-related neurodegenerative disorder with regulatory RNAs playing significant roles in its etiology. Circular RNAs (CircRNA) are enriched in human brains and contribute to AD progression. Many circRNA isoforms derived from same gene loci share common back sp...

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Main Authors: Fengdi Zhao, Yangping Li, Li Chen, Bing Yao
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Communications Biology
Online Access:https://doi.org/10.1038/s42003-024-07060-1
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author Fengdi Zhao
Yangping Li
Li Chen
Bing Yao
author_facet Fengdi Zhao
Yangping Li
Li Chen
Bing Yao
author_sort Fengdi Zhao
collection DOAJ
description Abstract Alzheimer’s disease (AD) is an age-related neurodegenerative disorder with regulatory RNAs playing significant roles in its etiology. Circular RNAs (CircRNA) are enriched in human brains and contribute to AD progression. Many circRNA isoforms derived from same gene loci share common back splicing sites, thus often form clusters and work as a group to additively regulate their downstream targets. Unfortunately, the coordinated role of clustered circRNAs is often overlooked in individual circRNA differential expression (DE) analysis. To address these challenges, we develop circMeta2, a computational tool designed to perform DE analysis focused on circRNA clusters, equipped with modules tailored for both a small sample of biological replicates and a large-scale population study. Using circMeta2, we identify brain region-specific circRNA clusters from six distinct brain regions in the ENCODE datasets, as well as brain region-specific alteration of circRNA clusters signatures associated with AD from Mount Sinai brain bank (MSBB) AD study. We also illustrate how AD-associated circRNA clusters within and across different brain regions work coordinately to contribute to AD etiology by impacting miRNA-mediated gene expression and identified key circRNA clusters that associated with AD progression and severity. Our study demonstrates circMeta2 as a highly accuracy and robust tool for analyzing circRNA clusters, offering valuable molecular insights into AD pathology.
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spelling doaj-art-b294eb761c754e6691741b06a82608f52025-08-20T02:17:53ZengNature PortfolioCommunications Biology2399-36422024-10-017111410.1038/s42003-024-07060-1Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2Fengdi Zhao0Yangping Li1Li Chen2Bing Yao3Department of Biostatistics, University of FloridaDepartment of Human Genetics, Emory University School of MedicineDepartment of Biostatistics, University of FloridaDepartment of Human Genetics, Emory University School of MedicineAbstract Alzheimer’s disease (AD) is an age-related neurodegenerative disorder with regulatory RNAs playing significant roles in its etiology. Circular RNAs (CircRNA) are enriched in human brains and contribute to AD progression. Many circRNA isoforms derived from same gene loci share common back splicing sites, thus often form clusters and work as a group to additively regulate their downstream targets. Unfortunately, the coordinated role of clustered circRNAs is often overlooked in individual circRNA differential expression (DE) analysis. To address these challenges, we develop circMeta2, a computational tool designed to perform DE analysis focused on circRNA clusters, equipped with modules tailored for both a small sample of biological replicates and a large-scale population study. Using circMeta2, we identify brain region-specific circRNA clusters from six distinct brain regions in the ENCODE datasets, as well as brain region-specific alteration of circRNA clusters signatures associated with AD from Mount Sinai brain bank (MSBB) AD study. We also illustrate how AD-associated circRNA clusters within and across different brain regions work coordinately to contribute to AD etiology by impacting miRNA-mediated gene expression and identified key circRNA clusters that associated with AD progression and severity. Our study demonstrates circMeta2 as a highly accuracy and robust tool for analyzing circRNA clusters, offering valuable molecular insights into AD pathology.https://doi.org/10.1038/s42003-024-07060-1
spellingShingle Fengdi Zhao
Yangping Li
Li Chen
Bing Yao
Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2
Communications Biology
title Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2
title_full Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2
title_fullStr Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2
title_full_unstemmed Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2
title_short Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer’s disease using circMeta2
title_sort identification of brain region specific landscape and functions of clustered circrnas in alzheimer s disease using circmeta2
url https://doi.org/10.1038/s42003-024-07060-1
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