Zeitschrift für Proteomik und Bioinformatik
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Prediction of Gene Activity in Early B Cell Development Based on an Integrative Multi-Omics Analysis

Mohammad Heydarian, Teresa Romeo Luperchio, Jevon Cutler, Christopher J. Mitchell, Min-Sik Kim, Akhilesh Pandey, Barbara Sollner-Webb and Karen Reddy

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