alexa Prediction of Gene Activity in Early B Cell Development Based on an Integrative Multi-Omics Analysis
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Prediction of Gene Activity in Early B Cell Development Based on an Integrative Multi-Omics Analysis

Mohammad Heydarian1,2, Teresa Romeo Luperchio1,2, Jevon Cutler1,2,3, Christopher J. Mitchell1,3, Min-Sik Kim1,3, Akhilesh Pandey1,3, Barbara Sollner-Webb1 and Karen Reddy1,2*

1Johns Hopkins University, Department of Biological Chemistry, 725 North Wolfe Street, Baltimore, USA

2Johns Hopkins University, Center for Epigenetics, 855 North Wolfe Street, Baltimore, USA

3Johns Hopkins University, McKusick-Nathans Institute of Genetic Medicine, 733 North, Broadway Avenue, Baltimore, USA

*Corresponding Author:
Karen Reddy
Johns Hopkins University
Department of Biological Chemistry
725 North Wolfe Street, Baltimore
MD 21205, USA
Tel: 443-287-7216
E-mail: [email protected]

Received Date: November 30, 2013; Accepted Date: February 12, 2014; Published Date: February 17, 2014

Citation: Heydarian M, Luperchio TR, Cutler J, Mitchell CJ, Kim MS, et al. (2014) Prediction of Gene Activity in Early B Cell Development Based on an Integrative Multi-Omics Analysis. J Proteomics Bioinform 7:050-063. doi: 10.4172/jpb.1000302

Copyright: © 2014 Heydarian M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

An increasingly common method for predicting gene activity is genome-wide chromatin immuno-precipitation of ‘active’ chromatin modifications followed by massively parallel sequencing (ChIP-seq). In order to understand better the relationship between developmentally regulated chromatin landscapes and regulation of early B cell development, we determined how differentially active promoter regions were able predict relative RNA and protein levels at the pre-pro-B and pro-B stages. Herein, we describe a novel ChIP-seq quantification method (cRPKM) to identify active promoters and a multi-omics approach that compares promoter chromatin status with ongoing active transcription (GRO-seq), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ). We demonstrate that active chromatin modifications at promoters are good indicators of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted the differential abundance of proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in abundance of their cognate proteins. As expected, this large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post- transcriptional mechanisms. Strikingly, the most differentially abundant protein in our B-cell development system, 2410004B18Rik, was regulated by a posttranscriptional mechanism, which further analyses indicated was mediated by a micro RNA. These data highlight how this integrated multi-omics data set can be a useful resource in uncovering regulatory mechanisms. This data can be accessed at: https://usegalaxy.org/u/thereddylab/p/prediction-of-gene-activity-based-on-an-integrative-multiomics- analysis

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