Identifying hidden drivers of heterogeneous inflammatory diseases

Identifying hidden drivers of heterogeneous inflammatory diseases
复制标题

识别异质炎症性疾病的隐藏驱动因素

DOI:
10.1101/2020.07.25.221309
复制
发表时间:
2020
期刊:
bioRxiv
影响因子:
--
通讯作者:
Eyerich K
Eyerich K
中科院分区:
--
文献类型:
--
作者:
Batra R;Garzorz-Stark N;Lauffer F;Jargosch M;Pilz AC;Roenneberg S;Schäbitz A;Böhner A;Seiringer P;Thomas J;Fereydouni B;Kutkaite G;Menden M;Tsoi LC;Gudjonsson JE;Theis FJ;Biedermann T;Schmidt-Weber CB;Müller N;Eyerich S;Eyerich K

文献摘要

参考文献

相似文献

慢性炎性疾病的特征在于遗传易感性和组织特异性免疫应答之间的复杂相互作用。这种异质性使诊断和利用组学方法改善疾病管理,开发更有效的治疗方法和应用精准医学的能力变得复杂。使用皮肤炎症作为模型,我们开发了一种方法,该方法将深层临床表型信息(表型组学)与病变和非病变皮肤(564个样本)的转录组数据相结合,以识别临床相关的基因特征。它使我们发现了迄今为止尚未探索的因素,包括中性粒细胞侵袭中的CCAAT增强子结合蛋白β(CEBPB)和致病性上皮对炎症反应中的PCAAT肿瘤转化2(PTTG 2)。这些因素通过使用基因修饰的人类皮肤等效物、迁移试验和原位成像进行验证。因此,通过深度临床表型和组学数据的有意义的整合,我们揭示了临床相关生物过程的隐藏驱动因素。
Chronic inflammatory diseases are characterized by complex interactions between genetic predisposition and tissue-specific immune responses. This heterogeneity complicates diagnoses and the ability to exploit omics approaches to improve disease management, develop more effective therapeutics, and apply precision medicine. Using skin inflammation as a model, we developed a method that integrates deep clinical phenotyping information (phenomics) with transcriptome data of lesional and non-lesional skin (564 samples) to identify clinically-relevant gene signatures. It led us to discover so-far unexplored factors, including CCAAT Enhancer-Binding Protein Beta (CEBPB) in neutrophil invasion, and Pituitary Tumor-Transforming 2 (PTTG2) in the pathogenic epithelial response to inflammation. These factors were validated using genetically-modified human skin equivalents, migration assays, andin situimaging. Thus, by meaningful integration of deep clinical phenotyping and omics data we reveal hidden drivers of clinically-relevant biological processes.
DOI: 10.1038/s41540-019-0099-y
发表时间: 2019-07-09
影响因子: 4
作者:
Koh, Hiromi W. L.;Fermin, Damian;Choi, Hyungwon
通讯作者: Choi, Hyungwon
RFamide 相关肽(A. J. Kastin (ed))
DOI: --
发表时间: 2006
期刊:
影响因子: --
作者:
松岡 達;皿井伸明;城 日加里;野間昭典;He Yi;N.Chartrel et al.
通讯作者: N.Chartrel et al.
DOI: 10.1038/nature21056
发表时间: 2017-02-02
期刊: Nature
影响因子: 64.8
作者:
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者: Thrun S
DOI: 10.1038/nature05505
发表时间: 2007-02-08
期刊: NATURE
影响因子: 64.8
作者:
Zheng, Yan;Danilenko, Dimitry M.;Ouyang, Wenjun
通讯作者: Ouyang, Wenjun
DeepWAS:使用深度学习直接整合监管信息来实现多变量基因型-表型关联
DOI: 10.1101/069096
发表时间: 2016
期刊: bioRxiv
影响因子: --
作者:
Eraslan;Arloth;Martins;Iurato;Czamara;Binder;Mueller
通讯作者: Mueller