Systematic analysis and characterization of long non-coding RNA genes in inflammatory bowel disease.

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Briefings in Functional Genomics Pub Date : 2024-07-19 DOI:10.1093/bfgp/elad044
Rania Velissari, Mirolyuba Ilieva, James Dao, Henry E Miller, Jens Hedelund Madsen, Jan Gorodkin, Masanori Aikawa, Hideshi Ishii, Shizuka Uchida
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Abstract

The cases of inflammatory bowel disease (IBD) are increasing rapidly around the world. Due to the multifactorial causes of IBD, there is an urgent need to understand the pathogenesis of IBD. As such, the usage of high-throughput techniques to profile genetic mutations, microbiome environments, transcriptome and proteome (e.g. lipidome) is increasing to understand the molecular changes associated with IBD, including two major etiologies of IBD: Crohn disease (CD) and ulcerative colitis (UC). In the case of transcriptome data, RNA sequencing (RNA-seq) technique is used frequently. However, only protein-coding genes are analyzed, leaving behind all other RNAs, including non-coding RNAs (ncRNAs) to be unexplored. Among these ncRNAs, long non-coding RNAs (lncRNAs) may hold keys to understand the pathogenesis of IBD as lncRNAs are expressed in a cell/tissue-specific manner and dysregulated in a disease, such as IBD. However, it is rare that RNA-seq data are analyzed for lncRNAs. To fill this gap in knowledge, we re-analyzed RNA-seq data of CD and UC patients compared with the healthy donors to dissect the expression profiles of lncRNA genes. As inflammation plays key roles in the pathogenesis of IBD, we conducted loss-of-function experiments to provide functional data of IBD-specific lncRNA, lung cancer associated transcript 1 (LUCAT1), in an in vitro model of macrophage polarization. To further facilitate the lncRNA research in IBD, we built a web database, IBDB (https://ibd-db.shinyapps.io/IBDB/), to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in IBD patients compared with healthy donors.

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炎症性肠病中长非编码RNA基因的系统分析和表征。
炎症性肠病(IBD)的病例在世界各地迅速增加。由于IBD的多因素原因,迫切需要了解IBD的发病机制。因此,越来越多地使用高通量技术来分析基因突变、微生物组环境、转录组和蛋白质组(如脂质体),以了解与IBD相关的分子变化,包括IBD的两个主要病因:克罗恩病(CD)和溃疡性结肠炎(UC)。在转录组数据的情况下,经常使用RNA测序(RNA-seq)技术。然而,只有蛋白质编码基因被分析,留下了所有其他RNA,包括非编码RNA(ncRNA)有待探索。在这些ncRNA中,长非编码RNA(lncRNA)可能是理解IBD发病机制的关键,因为lncRNA以细胞/组织特异性方式表达,并在疾病(如IBD)中失调。然而,很少对RNA-seq数据进行lncRNA分析。为了填补这一知识空白,我们重新分析了CD和UC患者与健康供体相比的RNA-seq数据,以剖析lncRNA基因的表达谱。由于炎症在IBD的发病机制中起着关键作用,我们进行了功能缺失实验,以在巨噬细胞极化的体外模型中提供IBD特异性lncRNA,即肺癌相关转录物1(LUCAT1)的功能数据。为了进一步促进IBD中lncRNA的研究,我们建立了一个网络数据库IBDB(https://ibd-db.shinyapps.io/IBDB/),为IBD患者与健康供体相比蛋白质编码和lncRNA基因的表达谱分析提供一站式服务。
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来源期刊
Briefings in Functional Genomics
Briefings in Functional Genomics BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
6.30
自引率
2.50%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Briefings in Functional Genomics publishes high quality peer reviewed articles that focus on the use, development or exploitation of genomic approaches, and their application to all areas of biological research. As well as exploring thematic areas where these techniques and protocols are being used, articles review the impact that these approaches have had, or are likely to have, on their field. Subjects covered by the Journal include but are not restricted to: the identification and functional characterisation of coding and non-coding features in genomes, microarray technologies, gene expression profiling, next generation sequencing, pharmacogenomics, phenomics, SNP technologies, transgenic systems, mutation screens and genotyping. Articles range in scope and depth from the introductory level to specific details of protocols and analyses, encompassing bacterial, fungal, plant, animal and human data. The editorial board welcome the submission of review articles for publication. Essential criteria for the publication of papers is that they do not contain primary data, and that they are high quality, clearly written review articles which provide a balanced, highly informative and up to date perspective to researchers in the field of functional genomics.
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