Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters

McIsaac, R. Scott; Oakes, Benjamin L.; Botstein, David; Noyes, Marcus B.

doi:10.3791/51153

Cited by 11 publications

(10 citation statements)

References 26 publications

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“…The reporter plasmid (pMVS102, BAC1017) contains the P3 promoter (McIsaac et al, 2013b) driving a fast maturing GFP variant (Cormack et al, 1996; Iizuka et al, 2011) in a ClonNAT marked pFA6 plasmid (Longtine et al, 1998). We PCR amplified GFP from me118 (Sherman et al, 2015) (CP17.P3 CP17.P4), synthesized the the P3 promoter described in McIsaac et al 2013 (gBlock, IDT) and added them to a ClonNAT marked pFA6 plasmid (pMVS008, MSP11 a gift from Michael Springer) digested with BamHI-HF and AscI with Gibson assembly (NEB E2611L).…”

Section: Star Methodsmentioning

confidence: 99%

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

et al. 2018

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Transcriptional activation domains are essential for gene regulation, but their intrinsic disorder and low primary sequence conservation have made it difficult to identify the amino acid composition features that underlie their activity. Here, we describe a rational mutagenesis scheme that deconvolves the function of four activation domain sequence features-acidity, hydrophobicity, intrinsic disorder, and short linear motifs-by quantifying the activity of thousands of variants in vivo and simulating their conformational ensembles using an all-atom Monte Carlo approach. Our results with a canonical activation domain from the Saccharomyces cerevisiae transcription factor Gcn4 reconcile existing observations into a unified model of its function: the intrinsic disorder and acidic residues keep two hydrophobic motifs from driving collapse. Instead, the most-active variants keep their aromatic residues exposed to the solvent. Our results illustrate how the function of intrinsically disordered proteins can be revealed by high-throughput rational mutagenesis.

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Section: Star Methodsmentioning

confidence: 99%

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

et al. 2018

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“…Recently, some of us released a data set in which the expression of nearly every yeast TF was induced from a very low level to a high level (http://idea.research.calicolabs.com) (Hackett et al 2020). This was accomplished by expressing ZEV, an estradiol-activated artificial TF, and replacing the promoter of the gene to be induced with a ZEV-responsive promoter (McIsaac et al 2013(McIsaac et al , 2014. (Some of the TFs were induced using an earlier iteration of the artificial TF called GEV [McIsaac et al 2011], but we refer to the data set as ZEV for convenience.)…”

Section: Most Bound Genes In the Harbison Chip Data Are Not Responsivmentioning

confidence: 99%

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

et al. 2020

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“…Recently, Hackett et al released a data set in which the expression of nearly every yeast TF was induced from a very low level to a high level [17]. This was accomplished by expressing ZEV, an estradiol-activated artificial TF, and replacing the promoter of the gene to be induced with a ZEV-responsive promoter [18, 19]. (Some of the TFs were induced using an earlier iteration of the artificial TF called GEV [20], but we refer to the data set as ZEV for convenience.)…”

Section: Resultsmentioning

confidence: 99%

Mapping Transcription Factor Networks By Comparing Tf Binding Locations To Tf Perturbation Responses

Kang¹,

Patel²,

Shively³

et al. 2019

Preprint

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26 Background: A transcription-factor (TF) network map indicates the direct, functional targets of 27 each TF --the genes it regulates by binding to their cis-regulatory DNA. Data on the genomic 28 binding locations of each TF and the transcriptional responses to perturbations of its activity, 29 such as overexpressing it, could support TF network mapping. Systematic data sets of both 30 types exist for yeast and for human K562 and HEK293 cells. 31 Results: In previous data, most TF binding sites appear to be non-functional, so one cannot 32 take the genes in whose promoters a TF binds as its direct, functional (DF) targets. Taking the 33 genes that are both bound by a TF and responsive to a perturbation of it as its DF targets 34 (intersection algorithm) is also not safe, as we show by deriving a new lower bound on the 35 expected false discovery rate of the intersection algorithm. When there are many non-functional 36 binding sites and many indirect targets, non-functional sites are expected to occur in the cis-37 regulatory DNA of indirect targets by chance. Dual threshold optimization, a new method for 38 setting significance thresholds on binding and response data, improves the intersection 39 algorithm, as does post-processing perturbation-response data with NetProphet 2.0. A 40 comprehensive new data set measuring the transcriptional response shortly after inducing 41 overexpression of a TF also helps, as does transposon calling cards, a new method for 42 identifying TF binding locations. 43 Conclusions: The combination of dual threshold optimization and NetProphet greatly expands 44 the high-confidence TF network map in both yeast and human. In yeast, measuring the 45 response shortly after inducing TF overexpression and measuring binding locations by using 46 transposon calling cards improve the network synergistically. 47 48

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Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters

Cited by 11 publications

References 26 publications

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

Mapping Transcription Factor Networks By Comparing Tf Binding Locations To Tf Perturbation Responses

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