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坏死性小肠结肠炎中的基因表达谱分析揭示了与克罗恩病中所报道的相同的通路。

Gene expression profiling in necrotizing enterocolitis reveals pathways common to those reported in Crohn's disease.

作者信息

Tremblay Éric, Thibault Marie-Pier, Ferretti Emanuela, Babakissa Corentin, Bertelle Valérie, Bettolli Marcos, Burghardt Karolina Maria, Colombani Jean-François, Grynspan David, Levy Emile, Lu Peng, Mayer Sandeep, Ménard Daniel, Mouterde Olivier, Renes Ingrid B, Seidman Ernest G, Beaulieu Jean-François

机构信息

Department of Anatomy and Cell Biology, Faculté de Médecine et Sciences de la Santé, Université de Sherbrooke, 3001, 12th Avec North, J1H 5N4, Sherbrooke, QC, Canada.

Division of Neonatology, Department of Pediatrics, CHEO, Ottawa, ON, Canada.

出版信息

BMC Med Genomics. 2016 Jan 22;9:6. doi: 10.1186/s12920-016-0166-9.

DOI:10.1186/s12920-016-0166-9
PMID:26801768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4722613/
Abstract

BACKGROUND

Necrotizing enterocolitis (NEC) is the most frequent life-threatening gastrointestinal disease experienced by premature infants in neonatal intensive care units. The challenge for neonatologists is to detect early clinical manifestations of NEC. One strategy would be to identify specific markers that could be used as early diagnostic tools to identify preterm infants most at risk of developing NEC or in the event of a diagnostic dilemma of suspected disease. As a first step in this direction, we sought to determine the specific gene expression profile of NEC.

METHODS

Deep sequencing (RNA-Seq) was used to establish the gene expression profiles in ileal samples obtained from preterm infants diagnosed with NEC and non-NEC conditions. Data were analyzed with Ingenuity Pathway Analysis and ToppCluster softwares.

RESULTS

Data analysis indicated that the most significant functional pathways over-represented in NEC neonates were associated with immune functions, such as altered T and B cell signaling, B cell development, and the role of pattern recognition receptors for bacteria and viruses. Among the genes that were strongly modulated in neonates with NEC, we observed a significant degree of similarity when compared with those reported in Crohn's disease, a chronic inflammatory bowel disease.

CONCLUSIONS

Gene expression profile analysis revealed a predominantly altered immune response in the intestine of NEC neonates. Moreover, comparative analysis between NEC and Crohn's disease gene expression repertoires revealed a surprisingly high degree of similarity between these two conditions suggesting a new avenue for identifying NEC biomarkers.

摘要

背景

坏死性小肠结肠炎(NEC)是新生儿重症监护病房中早产儿最常见的危及生命的胃肠道疾病。新生儿科医生面临的挑战是检测NEC的早期临床表现。一种策略是识别特定标志物,可将其用作早期诊断工具,以识别最易发生NEC的早产儿,或在疑似疾病的诊断困境中使用。作为朝着这个方向迈出的第一步,我们试图确定NEC的特定基因表达谱。

方法

使用深度测序(RNA测序)来建立从诊断为NEC和非NEC的早产儿回肠样本中的基因表达谱。数据使用Ingenuity Pathway Analysis和ToppCluster软件进行分析。

结果

数据分析表明,NEC新生儿中过度表达的最显著功能通路与免疫功能相关,如T和B细胞信号改变、B细胞发育以及细菌和病毒模式识别受体的作用。在NEC新生儿中强烈调节的基因中,与慢性炎症性肠病克罗恩病中报道的基因相比,我们观察到显著程度的相似性。

结论

基因表达谱分析揭示了NEC新生儿肠道中主要改变的免疫反应。此外,NEC和克罗恩病基因表达库之间的比较分析显示这两种疾病之间存在惊人的高度相似性,这为识别NEC生物标志物开辟了一条新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/f25611481581/12920_2016_166_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/04eec3d2b4e9/12920_2016_166_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/ec9eae0f5f40/12920_2016_166_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/15e6cdb3e940/12920_2016_166_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/80963cc2897d/12920_2016_166_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/f25611481581/12920_2016_166_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/04eec3d2b4e9/12920_2016_166_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/ec9eae0f5f40/12920_2016_166_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/15e6cdb3e940/12920_2016_166_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/80963cc2897d/12920_2016_166_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2921/4722613/f25611481581/12920_2016_166_Fig5_HTML.jpg

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