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Abstract Inducible protein knockdowns are excellent tools to test the function of essential proteins in short time scales and to capture the role of proteins in dynamic events. Current approaches destroy or sequester proteins by exploiting plant biological mechanisms such as the activity of photoreceptors for optogenetics or auxin-mediated ubiquitination in auxin degrons. It follows that these are not applicable for plants as light and auxin are strong signals for plant cells. We describe here an inducible protein degradation system in plants named E3-DART for E3-targeted Degradation of Plant Proteins. The E3-DART system is based on the specific and well-characterized interaction between the Salmonella secreted protein H1 (SspH1) and its human target protein kinase N1 (PKN1). This system harnesses the E3 catalytic activity of SspH1 and the SspH1-binding activity of the Homology Region 1b (HR1b) domain from PKN1. Using Nicotiana benthamiana and Arabidopsis (Arabidopsis thaliana), we show that a chimeric protein containing the Leucine-Rich Repeat (LRR) and novel E3 ligase (NEL) domains of SspH1 efficiently targets protein fusions of varying sizes containing HR1b for degradation. Target protein degradation was induced by transcriptional control of the chimeric E3 ligase using a glucocorticoid transactivation system and target protein depletion was detected as early as 3 h after induction. This system could be used to study the loss of any plant protein with high temporal resolution and may become an important tool in plant cell biology.
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Small-molecule inducible transcriptional control in mammalian cells
Tools for tuning transcription in mammalian cells have broad applications, from basic biological discovery to human gene therapy. While precise control over target gene transcription via dosing with small molecules (drugs) is highly sought, the design of such inducible systems that meets required performance metrics poses a great challenge in mammalian cell synthetic biology. Important characteristics include tight and tunable gene expression with a low background, minimal drug toxicity, and orthogonality. Here, we review small-molecule-inducible transcriptional control devices that have demonstrated success in mammalian cells and mouse models. Most of these systems employ natural or designed ligand-binding protein domains to directly or indirectly communicate with transcription machinery at a target sequence, via carefully constructed fusions. Example fusions include those to transcription activator-like effectors (TALEs), DNA-targeting proteins (e.g. dCas systems) fused to transactivating domains, and recombinases. Similar to the architecture of Type I nuclear receptors, many of the systems are designed such that the transcriptional controller is excluded from the nucleus in the absence of an inducer. Techniques that use ligand-induced proteolysis and antibody-based chemically induced dimerizers are also described. Collectively, these transcriptional control devices take advantage of a variety of recently developed molecular biology tools and cell biology insights and represent both proof of concept (e.g. targeting reporter gene expression) and disease-targeting studies.
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- Award ID(s):
- 1705464
- PAR ID:
- 10195217
- Date Published:
- Journal Name:
- Critical Reviews in Biotechnology
- ISSN:
- 0738-8551
- Page Range / eLocation ID:
- 1 to 20
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators’ activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/07388551.2020.1808583
