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Integrative approach to studying LncRNA functions

Integrative approach to studying LncRNA functions
研究 LncRNA 功能的综合方法
批准号:
10119971
负责人:
Xiaobo Zhou
金额:
$30.58万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-15 至 2023-07-31

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中文摘要
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英文摘要
Supplementary to the parent R01GM123037 “Integrative approach to studying LncRNA functions” The recent studies showed that lncRNAs were involved in many neurological diseases such as Alzheimer's disease (AD). First, lncRNAs impact AD pathogenesis because of their diverse functional effects from the epigenetic regulation. For example, β-site amyloid precursor protein cleaving enzyme-1 antisense transcript (BACE1-AS) is the lncRNA transcribed from the opposite strand of β -site APP cleaving enzyme-1 (BACE1) locus. Cell stress increased BACE1-AS levels, which in turn stimulates BACE1 expression, which could enhance APP processing and Ab1-42 production. Elevated Ab1-42 levels can further promote BACE1-AS overexpression and the APP processing cascade in a feedforward manner. Second, accumulating evidence shows that microRNAs play a critical role in the pathogenesis of AD. From the fact that ~ 90% of the SNPs associated with diseases are located in non-coding regions and lncRNA plays a role as a sponge of miRNAs, RNA-level editing, which has the similar effect as the single nucleotide variant in lncRNA, can result in aberrant gene expression regulation in AD. Third, another pathogenic post-transcriptional modification in lncRNA is the alternative splicing (AS). 17A mapped in intron 3 of G-protein-coupled receptor 51 gene (GPR51) undergoes AS. 17A lncRNA is upregulated in AD compared with control and AS event in 17A impairs the GABA B signaling, enhance Aβ secretion. The role and functions of the known lncRNAs have been accumulated. However, there is no deep functional annotation of post-transcriptionally modified lncRNAs that have RNA- editing or alternative splicing events in the AD. Therefore, there is an urgent need to develop new tools for analyzing the potential functional impact of the post-transcriptionally modified lncRNA systemically in the AD genome and predict their regulatory mechanisms. Here, we propose to develop an integrated system for transcriptomic sequencing data-based functional annotation of post-transcriptional modification events of lncRNAs in AD based on the AD sequencing data from ADSP consortium and other NIH controlled AD data sets. Our multidisciplinary team proposed the following aims: (1) We will develop functional mechanism prediction of lncRNAs' RNA-editing events through investigating the gain/loss of the binding sites of miRNAs, which targeting AD genes (we integrated ~ 1,500 AD genes.), due to RNA-editing with checking the expressional impact between RNA-editing +/- groups and studying the changed relative energy of lncRNA secondary structures. (2) We will develop functional mechanism prediction of lncRNAs' AS events through identifying AD-specific/associated AS events in lncRNAs, correlated lncRNA expressions with AD genes' AS isoforms, investigating the gain/loss of miRNA binding sites due to AS event, and SNP/SV-induced AS events. Our study will be focused on AD-related lncRNAs to identify epigenetic impact of these on AD genes and AD pathogenic mechanisms. This supplementary is within the scope of parent R01GM123037 where we proposed to develop Bioinformatics tools to study lncRNA functions for cancer.
期刊论文(2)
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会议论文
DOI: 10.3390/molecules23030697
发表时间: 2018-03-19
期刊: Molecules (Basel, Switzerland)
影响因子: --
作者: [Adjeroh D, Allaga M, Tan J, Lin J, Jiang Y, Abbasi A, Zhou X]
通讯作者: Zhou X
Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)
Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)
Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)
Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)
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