Ethylene induces combinatorial effects of histone H3 acetylation in gene expression in Arabidopsis

Proc. Natl. Acad. Sci. U.S.A.

et al. 2017

Self Cite

133

Ethylene gas is essential for developmental processes and stress responses in plants. Although the membrane-bound protein EIN2 is critical for ethylene signaling, the mechanism by which the ethylene signal is transduced remains largely unknown. Here we show the levels of H3K14Ac and H3K23Ac are correlated with the levels of EIN2 protein and demonstrate EIN2 C terminus (EIN2-C) is sufficient to rescue the levels of H3K14/23Ac of ein2-5 at the target loci, using CRISPR/dCas9-EIN2-C. Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and ChIP-reChIP-seq analyses revealed that EIN2-C associates with histone partially through an interaction with EIN2 nuclear-associated protein1 (ENAP1), which preferentially binds to the genome regions that are associated with actively expressed genes both with and without ethylene treatments. Specifically, in the presence of ethylene, ENAP1-binding regions are more accessible upon the interaction with EIN2, and more EIN3 proteins bind to the loci where ENAP1 is enriched for a quick response. Together, these results reveal EIN2-C is the key factor regulating H3K14Ac and H3K23Ac in response to ethylene and uncover a unique mechanism by which ENAP1 interacts with chromatin, potentially preserving the open chromatin regions in the absence of ethylene; in the presence of ethylene, EIN2 interacts with ENAP1, elevating the levels of H3K14Ac and H3K23Ac, promoting more EIN3 binding to the targets shared with ENAP1 and resulting in a rapid transcriptional regulation.T he plant hormone ethylene is essential for a myriad of physiological and developmental processes. It is important in responses to stress, such as drought, cold, flooding, and pathogen infection (1, 2), and modulates stem cell division (3). The common aquatic ancestor of plants possessed the ethylene signaling pathway and the mechanism has been elucidated by analysis of Arabidopsis (4). Ethylene is perceived by a family of receptors bound to the membrane of the endoplasmic reticulum (ER) that are similar in sequence and structure to bacterial two-component histidine kinases (5-9). Each ethylene receptor has an N-terminal transmembrane domain, and the receptors form dimers that bind ethylene via a copper cofactor RAN1 (10, 11). Signaling from one of the receptors, ETR1, induces its physical association with the ER-localized protein RTE1 (12). The ethylene receptors function redundantly to negatively regulate ethylene responses (5) via a downstream Raf-like protein kinase called CTR1 (13,14).In the absence of ethylene, both the ethylene receptors and CTR1 are active, and CTR1 is associated with the ER membrane through a direct interaction with ETR1 (13). The CTR1 downstream factor, EIN2, is localized to the ER membrane, where it interacts with ETR1 (15). The protein stability of EIN2 is regulated by two F-box proteins, ETP1 and ETP2, which mediated its degradation by the ubiquitin-proteasome pathway (16). In the absence of ethylene, the CTR1-mediated phosphorylation at the C-terminal end of EIN2 (EIN2-C) leads to ...

mentioning

confidence: 99%

EIN2 mediates direct regulation of histone acetylation in the ethylene response

Zhang

Proc. Natl. Acad. Sci. U.S.A.

et al. 2017

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133

“…Despite our observations one has to keep in mind that CNVs may not be the only cause of differences in BCO2 gene expression between the Qingjiao Ma and Guangxi Huang chicken breeds. There may be other factors that can be responsible for the reduced expression of the BCO2 gene expression in the Guangxi Huang chicken breed, such as DNA methylation [28][29][30], histone modi cation [31][32][33] and UTR function and thus translational modi cation [34,35]. Additional experiments need to be performed to elucidate the speci c difference in BCO2 expression in the skin of Qingjiao Ma and Guangxi Huang chicken breeds without differences in gene expression in any other tissue.…”

Section: Discussionmentioning

confidence: 99%

Variations in BCO2 Coding Sequence Causing a Difference in Carotenoid Concentration in the Skin of Chickens

Gan

Luo

et al. 2020

Preprint

Background: Carotenoid consumption decreases the risk of cancer, osteoporosis, or neurodegenerative diseases through interrupting the formation of free radicals. Carotenoid-pigmented egg yolk, skin, legs, beak, comb, and feathers in broilers give consumers the impression that they are healthier than none-pigmented ones. Deposition of carotenoids in chicken skin makes the skin color turn from white into yellow. The enzyme β-carotene oxygenase 2 (BCO2) plays a key role during the degradation process of carotenoids in skin. How does the BCO2 affects the skin color of chicken, and whether it is the key factor resulted in the phenotypic difference between yellow- and white- skin chickens are still unclear. Three female chickens of the Guangxi Huang and Qingjiao Ma breed were slaughtered and used for carotenoid concentration and BCO2 expression level analysis. The carotenoid concentration in chicken skin was determined by HPLC. A total of 1054 DNA samples from seven different chicken breeds were genotyped and allele frequencies analysis.Results: In the present study, we measured the concentration of carotenoids in chicken skin showed that the carotenoid concentration in chicken with a yellow skin was significantly higher than that with white skin chicken. Then we analyzed the expression profile of BCO2 in different tissues and observed significant differences in gene expression in the back skin between yellow- and white- skin chicken. Scanning the SNPs in BCO2 gene, it revealed a G/A mutation in exon 6, which was confirmed by PCR-RFLP. Copy number variation (CNV) was also detected; the copy number in white yellow skin chicken was almost 2-fold of that than in yellow skin chicken. These results implied that the G/A mutation and CNV in chicken skin may play a key role in regulating the carotenoids degradation. Conclusions: Generally, reduced expression of the BCO2 gene due to lower CVNs may be responsible for the deposition of carotenoids and as a consequence a yellow skin color in yellow skin chicken. One SNP c.890A>G was found to be associated with the chicken skin color and may be used as a genetic marker in breeding for yellow skin in chicken in the future.

“…Raw ChIP-seq data associated to histone H3K9Ac, H3K14Ac and H3K23Ac from Arabidopsis Col-0 and ein2–5 etiolated seedlings treated with air or 4 h ethylene gas were downloaded from NCBI GEO under GSE77396 [ 8 ] and GSE93875 [ 34 ]. The raw reads were mapped to the Arabidopsis genome (TAIR10) [ 35 ] and uniquely mapped sequencing reads were generated using bowtie software (version 1.1.2) [ 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

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

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.