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Integrative modeling reveals key chromatin and sequence signatures predicting super-enhancers.

ABSTRACT: Super-enhancers (SEs) are clusters of transcriptional enhancers which control the expression of cell identity and disease-associated genes. Current studies demonstrated the role of multiple factors in SE formation; however, a systematic analysis to assess the relative predictive importance of chromatin and sequence features of SEs and their constituents is lacking. In addition, a predictive model that integrates various types of data to predict SEs has not been established. Here, we integrated diverse types of genomic and epigenomic datasets to identify key signatures of SEs and investigated their predictive importance. Through integrative modeling, we found Cdk8, Cdk9, and Smad3 as new features of SEs, which can define known and new SEs in mouse embryonic stem cells and pro-B cells. We compared six state-of-the-art machine learning models to predict SEs and showed that non-parametric ensemble models performed better as compared to parametric. We validated these models using cross-validation and also independent datasets in four human cell-types. Taken together, our systematic analysis and ranking of features can be used as a platform to define and understand the biology of SEs in other cell-types.

SUBMITTER: Khan A

PROVIDER: S-EPMC6393462 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Integrative modeling reveals key chromatin and sequence signatures predicting super-enhancers.

Khan Aziz A Zhang Xuegong X

Scientific reports 20190227 1

Super-enhancers (SEs) are clusters of transcriptional enhancers which control the expression of cell identity and disease-associated genes. Current studies demonstrated the role of multiple factors in SE formation; however, a systematic analysis to assess the relative predictive importance of chromatin and sequence features of SEs and their constituents is lacking. In addition, a predictive model that integrates various types of data to predict SEs has not been established. Here, we integrated d ...[more]

PMID: 30814546

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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets.MethodsThirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated.ResultsA total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups (C1, C2) via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules.ConclusionsAn integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients.

Dataset Information

Integrative modeling reveals key chromatin and sequence signatures predicting super-enhancers.

Publications

Integrative modeling reveals key chromatin and sequence signatures predicting super-enhancers.

Similar Datasets

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