Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping

Reece‐Hoyes, John S.; Diallo, Alos; Lajoie, Bryan R.; Kent, Amanda; Shrestha, Shaleen; Kadreppa, Sreenath; Pesyna, Colin; Dekker, Job; Myers, Chad L.; Walhout, Albertha J.M.

doi:10.1038/nmeth.1748

Cited by 111 publications

(183 citation statements)

References 34 publications

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“…The promoter:HIS3 sequences were integrated into the genome of the haploid yeast (Saccharomyces cerevisiae) strain YM4271, and the pDEST-AD-CRF6 was transformed into the strain Ya1867 as previously described to create the bait and preys, respectively (Deplancke et al, 2006;Reece-Hoyes et al, 2011). The transformed haploid baits were grown on minimum selective media containing all necessary amino acids except His (synthetic dropout [SD-HT]), and the transformed haploid preys were selected on SD-T (Trp).…”

Section: Yeast One-hybridmentioning

confidence: 99%

Cytokinin Response Factor 6 Represses Cytokinin-Associated Genes during Oxidative Stress

et al. 2016

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Cytokinin is a phytohormone that is well known for its roles in numerous plant growth and developmental processes, yet it has also been linked to abiotic stress response in a less defined manner. Arabidopsis (Arabidopsis thaliana) Cytokinin Response Factor 6 (CRF6) is a cytokinin-responsive AP2/ERF-family transcription factor that, through the cytokinin signaling pathway, plays a key role in the inhibition of dark-induced senescence. CRF6 expression is also induced by oxidative stress, and here we show a novel function for CRF6 in relation to oxidative stress and identify downstream transcriptional targets of CRF6 that are repressed in response to oxidative stress. Analysis of transcriptomic changes in wild-type and crf6 mutant plants treated with H 2 O 2 identified CRF6-dependent differentially expressed transcripts, many of which were repressed rather than induced. Moreover, many repressed genes also show decreased expression in 35S:CRF6 overexpressing plants. Together, these findings suggest that CRF6 functions largely as a transcriptional repressor. Interestingly, among the H 2 O 2 repressed CRF6-dependent transcripts was a set of five genes associated with cytokinin processes: (signaling) ARR6, ARR9, ARR11, (biosynthesis) LOG7, and (transport) ABCG14. We have examined mutants of these cytokinin-associated target genes to reveal novel connections to oxidative stress. Further examination of CRF6-DNA interactions indicated that CRF6 may regulate its targets both directly and indirectly. Together, this shows that CRF6 functions during oxidative stress as a negative regulator to control this cytokinin-associated module of CRF6-dependent genes and establishes a novel connection between cytokinin and oxidative stress response.The frequent environmental changes to which a plant is subject can lead to physiological alterations and disruption of normal metabolism. In particular, the energetic reactions that take place in chloroplasts, peroxisomes, and mitochondria are susceptible to dysfunction, which results in production of excessive levels of reactive oxygen species (ROS). In fact, many common abiotic stress conditions encountered in agriculture, including temperature extremes, drought, soil salinity, and air pollution, are known to include an oxidative stress component (Gill and Tuteja, 2010). Cellular levels of ROS are carefully maintained at relatively low levels through a wide range of scavenging and detoxification mechanisms. However, if the balance between ROS production and removal is shifted too far toward production (e.g. under stress conditions), cellular damage can occur as a result of oxidation of macromolecules such as lipids, proteins, and nucleic acids (Mittler, 2002;Gill and Tuteja, 2010). Accumulation of ROS beyond some threshold triggers cell death as a response. Therefore, ROS are thought to serve as indicators of oxidative stress within a cell but may also

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Section: Yeast One-hybridmentioning

confidence: 99%

Cytokinin Response Factor 6 Represses Cytokinin-Associated Genes during Oxidative Stress

et al. 2016

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“…For example, Marbach et al (2012) and Beyer et al (2006) both combined coexpression data with in vivo binding data from ChIP, evolutionary conservation, and PWM models of binding specificity. The latter can be derived by several experimental methods, including protein binding arrays (Zhu et al 2009), Selex ( Jolma et al 2010), and yeast one-hybrid assays (Reece-Hoyes et al 2011). Interestingly, the analysis by Beyer et al (2006), which included a comprehensive yeast ChIP data set (Harbison et al 2004), identified only 5245 TF-target relationships that they considered ''high confidence.''…”

Section: Building a More Comprehensive Networkmentioning

confidence: 99%

Mapping functional transcription factor networks from gene expression data

et al. 2013

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A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein–DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.

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“…26,42,58 The C. elegans RBP library is currently small with 40 RBPs, but we anticipate expanding this library as we have done previously for our transcription factor collection. 59 In the future, we also anticipate streamlining the PRIMA pipeline such that we can make the process higher throughput, similar to yeast one-and-two hybrid assays used for the study of protein-DNA and protein-protein interactions, respectively. 59, 60 We have not tested 3 0 UTRs longer than 437 nucleotides.…”

Section: Limitationsmentioning

confidence: 99%

“…59 In the future, we also anticipate streamlining the PRIMA pipeline such that we can make the process higher throughput, similar to yeast one-and-two hybrid assays used for the study of protein-DNA and protein-protein interactions, respectively. 59, 60 We have not tested 3 0 UTRs longer than 437 nucleotides. It is important to note that most 3 0 UTRs in C. elegans are shorter, 6 indicating that PRIMA should be broadly applicable to this organism's RNA-RBP interactome.…”

Section: Limitationsmentioning

confidence: 99%

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PRIMA: a gene-centered, RNA-to-protein method for mapping RNA-protein interactions

et al. 2017

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Interactions between RNA binding proteins (RBPs) and mRNAs are critical to post-transcriptional gene regulation. Eukaryotic genomes encode thousands of mRNAs and hundreds of RBPs. However, in contrast to interactions between transcription factors (TFs) and DNA, the interactome between RBPs and RNA has been explored for only a small number of proteins and RNAs. This is largely because the focus has been on using 'protein-centered' (RBP-to-RNA) interaction mapping methods that identify the RNAs with which an individual RBP interacts. While powerful, these methods cannot as of yet be applied to the entire RBPome. Moreover, it may be desirable for a researcher to identify the repertoire of RBPs that can interact with an mRNA of interest-in a 'gene-centered' manner-yet few such techniques are available. Here, we present Protein-RNA Interaction Mapping Assay (PRIMA) with which an RNA 'bait' can be tested versus multiple RBP 'preys' in a single experiment. PRIMA is a translation-based assay that examines interactions in the yeast cytoplasm, the cellular location of mRNA translation. We show that PRIMA can be used with small RNA elements, as well as with full-length Caenorhabditis elegans 3 0 UTRs. PRIMA faithfully recapitulated numerous well-characterized RNA-RBP interactions and also identified novel interactions, some of which were confirmed in vivo. We envision that PRIMA will provide a complementary tool to expand the depth and scale with which the RNA-RBP interactome can be explored.

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Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping

Cited by 111 publications

References 34 publications

Cytokinin Response Factor 6 Represses Cytokinin-Associated Genes during Oxidative Stress

Cytokinin Response Factor 6 Represses Cytokinin-Associated Genes during Oxidative Stress

Mapping functional transcription factor networks from gene expression data

PRIMA: a gene-centered, RNA-to-protein method for mapping RNA-protein interactions

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