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Updated: May 14, 2026
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
Published on: June 27, 2020
Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced
Shao-shan Carol Huang1, David C Clarke, Sara J C Gosline
1Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Abstract:
Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118-310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.
Insights
This study introduces a novel network approach to link proteomic and transcriptomic data, identifying potential drug targets for glioblastoma. The method prioritizes compounds like ICG-001 and PKF118-310, showing promise in altering cancer cell viability.
Area of Science:
- Molecular Biology
- Systems Biology
- Bioinformatics
Background:
- Cellular signal transduction involves complex protein modifications and gene expression changes.
- Existing high-throughput methods study individual stages but miss interconnections.
- Integrating multi-omics data is crucial for understanding cellular processes and identifying therapeutic targets.
Purpose of the Study:
- To develop a network-based approach for integrating multi-omics data (epigenomic, phosphoproteomic, transcriptome).
- To identify critical links between proteomic and transcriptomic changes.
- To prioritize potential therapeutic targets for glioblastoma multiforme (GBM).
Main Methods:
- Developed a method to link chromatin accessibility changes to gene expression.
- Integrated epigenomic, phosphoproteomic, and transcriptome data using a network model.
- Used small molecules to target highly connected network nodes and assessed their impact on cell viability.
- Employed ChIP-Seq to map genome-wide binding sites of p300.
Main Results:
- Successfully integrated multi-omics data to build a regulatory network.
- Identified novel potential therapeutic targets, including CREBBP and CTNNB1, for GBM.
- Demonstrated that targeting network nodes with small molecules can alter glioblastoma cell viability.
- p300 was identified as a highly connected transcriptional co-regulator with a potential role in tumorigenesis.
Conclusions:
- The developed network approach effectively integrates diverse high-throughput datasets.
- This method can prioritize therapeutic targets missed by analyzing data in isolation.
- The findings highlight ICG-001 and PKF118-310 as promising agents for GBM treatment.
- The general method is applicable to various high-throughput datasets for building regulatory networks.
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