Phosphorylation of CBP20 Links MicroRNA to Root Growth in the Ethylene Response

Zhang, Fan; Wang, Likai; Lim, Jae Yun; Kim, Tae-Wook; Pyo, Youngjae; Sung, Sibum; Shin, Chanseok; Qiao, Hong

doi:10.1371/journal.pgen.1006437

Cited by 36 publications

(26 citation statements)

References 61 publications

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“…The yeast two-hybrid assay was performed according to the instructions provided with the ProQuest Two-Hybrid System (Invitrogen) and were previously published (Zhang et al, 2016a(Zhang et al, , 2016b. Briefly, BD-HDACs and AD-EIN2-C, AD-ENAP1, AD-EIN3, or AD-SRT1/SRT2 were cotransformed into the yeast strain AH109 (Clontech).…”

Section: Yeast Two-hybrid Assaymentioning

confidence: 99%

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Histone Deacetylases SRT1 and SRT2 Interact with ENAP1 to Mediate Ethylene-Induced Transcriptional Repression

Zhang

Wang

et al. 2018

Plant Cell

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Ethylene plays pleiotropic roles in plant growth, plant development, and stress responses. Although the effects of ethylene on plants are well documented, little is known about molecular-level events that result in transcriptional repression during the ethylene response. In this study, we found that two histone deacetylases, SRT1 and SRT2, interact with ENAP1, which associates with EIN2 in the nucleus. Genetic and transcriptome analyses revealed that SRT1 and SRT2 are required for negative regulation of certain ethylene-responsive genes. The acetylation of HISTONE3 at K9 (H3K9Ac) is specifically regulated by SRT1 and SRT2 in ethylene-repressed genes. In addition, the srt1 srt2 double mutation in Arabidopsis thaliana suppresses both the ENAP1ox and the EIN3ox constitutive ethylene response phenotypes, and the ethylene-induced transcriptional repression observed in EIN3ox plants is derepressed in the EIN3ox/srt1 srt2 mutant. SRT2 and ENAP1 both bind to promoter regions of genes negatively regulated by ethylene, reducing H3K9Ac levels and resulting in transcriptional repression. This work establishes a mechanism by which histone deacetylases SRT1 and SRT2 interact with ENAP1 to mediate transcriptional repression by regulating the levels of H3K9 acetylation in the ethylene signaling.

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Section: Yeast Two-hybrid Assaymentioning

confidence: 99%

“…The RNA library construction was performed as previously described (Zhang et al, 2016b). Briefly, total RNA was isolated from 3-d-old srt1-1 srt2-1 double mutant etiolated seedlings treated with air or ethylene gas for 4 h prior to harvest using TRIzol reagent (Invitrogen).…”

Section: Rna Library Construction and Deep Sequencingmentioning

confidence: 99%

Histone Deacetylases SRT1 and SRT2 Interact with ENAP1 to Mediate Ethylene-Induced Transcriptional Repression

Zhang

Wang

et al. 2018

Plant Cell

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“…Phosphorylation appears to be involved in regulating most of the metabolic and physiological pathways in plants, including: defense (Jones et al, 2006 ; Nühse et al, 2007 ), RNA metabolism (de la Fuente van Bentem et al, 2006 ), carbon metabolism (Wu et al, 2014 ), and root growth (Zhang et al, 2013 , 2016 ).…”

Section: Phosphorylation: Importance In Regulation Of Plant Processesmentioning

confidence: 99%

Monitoring of Plant Protein Post-translational Modifications Using Targeted Proteomics

2018

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Protein post-translational modifications (PTMs) are among the fastest and earliest of plant responses to changes in the environment, making the mechanisms and dynamics of PTMs an important area of plant science. One of the most studied PTMs is protein phosphorylation. This review summarizes the use of targeted proteomics for the elucidation of the biological functioning of plant PTMs, and focuses primarily on phosphorylation. Since phosphorylated peptides have a low abundance, usually complex enrichment protocols are required for their research. Initial identification is usually performed with discovery phosphoproteomics, using high sensitivity mass spectrometers, where as many phosphopeptides are measured as possible. Once a PTM site is identified, biological characterization can be addressed with targeted proteomics. In targeted proteomics, Selected/Multiple Reaction Monitoring (S/MRM) is traditionally coupled to simple, standard protein digestion protocols, often omitting the enrichment step, and relying on triple-quadruple mass spectrometer. The use of synthetic peptides as internal standards allows accurate identification, avoiding cross-reactivity typical for some antibody based approaches. Importantly, internal standards allow absolute peptide quantitation, reported down to 0.1 femtomoles, also useful for determination of phospho-site occupancy. S/MRM is advantageous in situations where monitoring and diagnostics of peptide PTM status is needed for many samples, as it has faster sample processing times, higher throughput than other approaches, and excellent quantitation and reproducibility. Furthermore, the number of publicly available data-bases with plant PTM discovery data is growing, facilitating selection of modified peptides and design of targeted proteomics workflows. Recent instrument developments result in faster scanning times, inclusion of ion-trap instruments leading to parallel reaction monitoring- which further facilitates S/MRM experimental design. Finally, recent combination of data independent and data dependent spectra acquisition means that in addition to anticipated targeted data, spectra can now be queried for unanticipated information. The potential for future applications in plant biology is outlined.

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“…Differentially expressed genes (DEGs) have been widely used to understand not only gene function but also the molecular mechanisms underlying different biological processes. A number of methods have been developed to analyze differential gene expression, such as Real-time reverse transcription PCR (qRT-PCR) [ 1 ], cDNA microarray analysis [ 2 ], whole genome tiling array [ 3 – 5 ], RNA sequencing (RNA-seq) [ 6 – 8 ]. As a result of the low cost of next generation sequencing technologies and its remarkable power and accuracy, RNA-seq has become the most popular method for DEGs analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Signal transduction of ethylene has been studied for more than 2 decades, mainly with the model plant Arabidopsis , and a linear signal transduction pathway has been proposed [ 25 , 26 ]. Ethylene regulated genes have been determined using RNA-seq in Arabidopsis etiolated seedlings [ 6 , 8 , 27 , 28 ], in which many genes have been confirmed to be regulated by ethylene treatment, such as CONSTITUTIVE TRIPLE RESPONSE 1 ( CTR1 ) [ 29 ], EIN3-BINDING F BOX PROTEIN 2 ( EBF2 ) [ 30 ], ETHYLENE RESPONSE 2 ( ETR2 ) [ 31 ] etc. However, some well-known ethylene regulated genes, namely EIN3-BINDING F BOX PROTEIN 1 ( EBF1 ) [ 30 ], WRKY 25 [ 32 ], WRK 26 [ 32 ] were not detected by RNA-seq.…”

Section: Introductionmentioning

confidence: 99%

RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes

Wang

Sung

et al. 2018

BMC Genomics

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BackgroundAlthough different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity. Machine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set. ML–based differential network analysis has been applied to predict stress-responsive genes through learning the patterns of 32 expression characteristics of known stress-related genes. In addition, the epigenetic regulation plays critical roles in gene expression, therefore, DNA and histone methylation data has been shown to be powerful for ML-based model for prediction of gene expression in many systems, including lung cancer cells. Therefore, it is promising that ML-based methods could help to identify the DEGs that are not identified by traditional RNA-seq method.ResultsWe identified the top 23 most informative features through assessing the performance of three different feature selection algorithms combined with five different classification methods on training and testing data sets. By comprehensive comparison, we found that the model based on InfoGain feature selection and Logistic Regression classification is powerful for DEGs prediction. Moreover, the power and performance of ML-based prediction was validated by the prediction on ethylene regulated gene expression and the following qRT-PCR.ConclusionsOur study shows that the combination of ML-based method with RNA-seq greatly improves the sensitivity of DEGs identification.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4932-2) contains supplementary material, which is available to authorized users.

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Phosphorylation of CBP20 Links MicroRNA to Root Growth in the Ethylene Response

Cited by 36 publications

References 61 publications

Histone Deacetylases SRT1 and SRT2 Interact with ENAP1 to Mediate Ethylene-Induced Transcriptional Repression

Histone Deacetylases SRT1 and SRT2 Interact with ENAP1 to Mediate Ethylene-Induced Transcriptional Repression

Monitoring of Plant Protein Post-translational Modifications Using Targeted Proteomics

RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes

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