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Technology: Pulldown assay for Transcription factor via catTFRE

  • Jun Qin, Ph.D.
    P Verna and Marrs McLean Department of Biochemistry and Molecular Biology
    Baylor College of Medicine
    Beijing Proteome Research Center (BPRC), Beijing, China

  • Yi Wang, Ph.D.
    Associate Professor


TechDev 7 aims at developing a high-throughput technique that permits identification of endogenous transcription factors at the proteome scale. The tool we use is an affinity reagent that is composed of a synthetic DNA fragment containing a concatenated array of 100 tandem consensus response elements (catTFRE) for binding majority of TF families. Proteins that are bound to the catTFRE are analyzed by label-free quantitative mass spectrometry. We employed a pathway centric approach that target 7 oncogenic pathways in 4 lung cancer cell lines with 10 oncogenic pathway activators; we measure changes in DNA binding activities of transcription factors including nuclear receptors. Well-characterized TFs as well as previously not recognized TFs have been identified. Informative solutions are developed for data filtering and interpretation. While our technique itself is not targeting any particular NR or transcription factor as druggable target, it has the capacity to determine changes in global NR/TF levels when targets are selectively overexpressed/knocked down. We are currently measuring TF alterations in cell lines overexpressing some of the NR targets on KMC priority list. We can find out which transcription factors are activated when a GPCR, an ion channel or a kinase in the dark proteome is activated. The unbiased technology platforms are orthogonal to in silico predictions, and in silico predictions are still extremely challenging. This is perhaps because predictions have been made in gene-centered context-independent manner, whereas many technologies are now gene-independent (at least for kinases and TFs here) and context-specific. More informatics effort should be put into integration of multiple capable technology platforms that assay different aspects of signaling pathways instead of attempting to guide the technology with predictions. Preselection with informatics was more valuable when the technologies were more biased and less sensitive.



Genes Under investigation, Jun Qin, Ph.D.

Submitted 8/2016


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