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Article: Transcriptomic Analysis Reveals the Correlation between End-of-Day Far Red Light and Chilling Stress in Setaria viridis

Sun, Shilei / Liu, Qingjia / Dai, Xiuru / Wang, Xianglan

Genes. 2022 Aug. 31, v. 13, no. 9

2022

Abstract: Low temperature and end-of-day far-red (EOD-FR) light signaling are two key factors limiting plant production and geographical location worldwide. However, the transcriptional dynamics of EOD-FR light conditions during chilling stress remain poorly ... ...

Abstract	Low temperature and end-of-day far-red (EOD-FR) light signaling are two key factors limiting plant production and geographical location worldwide. However, the transcriptional dynamics of EOD-FR light conditions during chilling stress remain poorly understood. Here, we performed a comparative RNA-Seq-based approach to identify differentially expressed genes (DEGs) related to EOD-FR and chilling stress in Setaria viridis. A total of 7911, 324, and 13431 DEGs that responded to low temperature, EOD-FR and these two stresses were detected, respectively. Further DEGs analysis revealed that EOD-FR may enhance cold tolerance in plants by regulating the expression of genes related to cold tolerance. The result of weighted gene coexpression network analysis (WGCNA) using 13431 nonredundant DEGs exhibited 15 different gene network modules. Interestingly, a CO-like transcription factor named BBX2 was highly expressed under EOD-FR or chilling conditions. Furthermore, we could detect more expression levels when EOD-FR and chilling stress co-existed. Our dataset provides a valuable resource for the regulatory network involved in EOD-FR signaling and chilling tolerance in C₄ plants.
Keywords	Setaria viridis ; cold tolerance ; data collection ; far-red light ; gene expression regulation ; genes ; temperature ; transcription (genetics) ; transcription factors ; transcriptomics
Language	English
Dates of publication	2022-0831
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527218-4
ISSN	2073-4425
ISSN	2073-4425
DOI	10.3390/genes13091565
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Transcriptomic Analysis Reveals the Correlation between End-of-Day Far Red Light and Chilling Stress in

Sun, Shilei / Liu, Qingjia / Dai, Xiuru / Wang, Xianglan

Genes

2022 Volume 13, Issue 9

Abstract	Low temperature and end-of-day far-red (EOD-FR) light signaling are two key factors limiting plant production and geographical location worldwide. However, the transcriptional dynamics of EOD-FR light conditions during chilling stress remain poorly understood. Here, we performed a comparative RNA-Seq-based approach to identify differentially expressed genes (DEGs) related to EOD-FR and chilling stress in
MeSH term(s)	Gene Expression Profiling ; Light ; Setaria Plant/genetics ; Setaria Plant/metabolism ; Transcription Factors/genetics ; Transcription Factors/metabolism ; Transcriptome/genetics
Chemical Substances	Transcription Factors
Language	English
Publishing date	2022-08-31
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes13091565
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PGD: a machine learning-based photosynthetic-related gene detection approach.

Wang, Yunchuan / Dai, Xiuru / Fu, Daohong / Li, Pinghua / Du, Baijuan

BMC bioinformatics

2022 Volume 23, Issue 1, Page(s) 183

Abstract: Background: The primary determinant of crop yield is photosynthetic capacity, which is under the control of photosynthesis-related genes. Therefore, the mining of genes involved in photosynthesis is important for the study of photosynthesis. MapMan ... ...

Abstract	Background: The primary determinant of crop yield is photosynthetic capacity, which is under the control of photosynthesis-related genes. Therefore, the mining of genes involved in photosynthesis is important for the study of photosynthesis. MapMan Mercator 4 is a powerful annotation tool for assigning genes into proper functional categories; however, in maize, the functions of approximately 22.15% (9520) of genes remain unclear and are labeled "not assigned", which may include photosynthesis-related genes that have not yet been identified. The fast-increasing usage of the machine learning approach in solving biological problems provides us with a new chance to identify novel photosynthetic genes from functional "not assigned" genes in maize. Results: In this study, we proved the ensemble learning model using a voting eliminates the preferences of single machine learning models. Based on this evaluation, we implemented an ensemble based ML(Machine Learning) methods using a majority voting scheme and observed that including RNA-seq data from multiple photosynthetic mutants rather than only a single mutant could increase prediction accuracy. And we call this approach "A Machine Learning-based Photosynthetic-related Gene Detection approach (PGD)". Finally, we predicted 716 photosynthesis-related genes from the "not assigned" category of maize MapMan annotation. The protein localization prediction (TargetP) and expression trends of these genes from maize leaf sections indicated that the prediction was reliable and robust. And we put this approach online base on google colab. Conclusions: This study reveals a new approach for mining novel genes related to a specific functional category and provides candidate genes for researchers to experimentally define their biological functions.
MeSH term(s)	Female ; Humans ; Machine Learning ; Photosynthesis/genetics ; Plant Leaves/metabolism ; Pregnancy ; Preimplantation Diagnosis ; Zea mays/genetics
Language	English
Publishing date	2022-05-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-022-04722-x
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Article: Chromatin and regulatory differentiation between bundle sheath and mesophyll cells in maize

Dai, Xiuru / Tu, Xiaoyu / Du, Baijuan / Dong, Pengfei / Sun, Shilei / Wang, Xianglan / Sun, Jing / Li, Gang / Lu, Tiegang / Zhong, Silin / Li, Pinghua

plant journal. 2022 Feb., v. 109, no. 3

2022

Abstract: C₄ plants partition photosynthesis enzymes between the bundle sheath (BS) and the mesophyll (M) cells for the better delivery of CO₂ to RuBisCO and to reduce photorespiration. To better understand how C₄ photosynthesis is regulated at the transcriptional ...

Abstract	C₄ plants partition photosynthesis enzymes between the bundle sheath (BS) and the mesophyll (M) cells for the better delivery of CO₂ to RuBisCO and to reduce photorespiration. To better understand how C₄ photosynthesis is regulated at the transcriptional level, we performed RNA‐seq, ATAC‐seq, ChIP‐seq and Bisulfite‐seq (BS‐seq) on BS and M cells isolated from maize leaves. By integrating differentially expressed genes with chromatin features, we found that chromatin accessibility coordinates with epigenetic features, especially H3K27me3 modification and CHH methylation, to regulate cell type‐preferentially enriched gene expression. Not only the chromatin‐accessible regions (ACRs) proximal to the genes (pACRs) but also the distal ACRs (dACRs) are determinants of cell type‐preferentially enriched expression. We further identified cell type‐preferentially enriched motifs, e.g. AAAG for BS cells and TGACC/T for M cells, and determined their corresponding transcription factors: DOFs and WRKYs. The complex interaction between cis and trans factors in the preferential expression of C₄ genes was also observed. Interestingly, cell type‐preferentially enriched gene expression can be fine‐tuned by the coordination of multiple chromatin features. Such coordination may be critical in ensuring the cell type‐specific function of key C₄ genes. Based on the observed cell type‐preferentially enriched expression pattern and coordinated chromatin features, we predicted a set of functionally unknown genes, e.g. Zm00001d042050 and Zm00001d040659, to be potential key C₄ genes. Our findings provide deep insight into the architectures associated with C₄ gene expression and could serve as a valuable resource to further identify the regulatory mechanisms present in C₄ species.
Keywords	carbon dioxide ; chromatin ; chromatin immunoprecipitation ; corn ; epigenetics ; gene expression ; gene expression regulation ; mesophyll ; methylation ; photorespiration ; ribulose-bisphosphate carboxylase ; sequence analysis ; transcription (genetics)
Language	English
Dates of publication	2022-02
Size	p. 675-692.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	1088037-9
ISSN	1365-313X ; 0960-7412
ISSN (online)	1365-313X
ISSN	0960-7412
DOI	10.1111/tpj.15586
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Author Correction: Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors.

Tu, Xiaoyu / Mejía-Guerra, María Katherine / Valdes Franco, Jose A / Tzeng, David / Chu, Po-Yu / Shen, Wei / Wei, Yingying / Dai, Xiuru / Li, Pinghua / Buckler, Edward S / Zhong, Silin

Nature communications

2023 Volume 14, Issue 1, Page(s) 1586

Language	English
Publishing date	2023-03-22
Publishing country	England
Document type	Published Erratum
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-37423-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Non‐homology‐based prediction of gene functions in maize (Zea mays ssp. mays)

Dai, Xiuru / Xu, Zheng / Liang, Zhikai / Tu, Xiaoyu / Zhong, Silin / Schnable, James C. / Li, Pinghua

The plant genome. 2020 July, v. 13, no. 2

2020

Abstract: Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have ... ...

Abstract	Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non‐homology gene features. Among the eight supervised classification algorithms evaluated, random‐forest‐based prediction consistently provided the most accurate gene function prediction. Non‐homology‐based functional annotation provides complementary strengths to homology‐based annotation, with higher average performance in Biological Process GO terms, the domain where homology‐based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology‐based functional annotation is highest. GO prediction models trained with homology‐based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non‐homology‐based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology‐based functional annotations.
Keywords	corn ; genes ; prediction
Language	English
Dates of publication	2020-07
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	2375444-8
ISSN	1940-3372 ; 0011-183X
ISSN (online)	1940-3372
ISSN	0011-183X
DOI	10.1002/tpg2.20015
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Article ; Online: Predicting transcriptional responses to cold stress across plant species.

Meng, Xiaoxi / Liang, Zhikai / Dai, Xiuru / Zhang, Yang / Mahboub, Samira / Ngu, Daniel W / Roston, Rebecca L / Schnable, James C

Proceedings of the National Academy of Sciences of the United States of America

2021 Volume 118, Issue 10

Abstract: Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic ... ...

Abstract	Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification model to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained by using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species provided at least equivalent performance to models trained and tested in a single species and outperformed single-species models in cross-species prediction. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene-expression patterns in related, less-studied species with sequenced genomes.
MeSH term(s)	Cold-Shock Response ; Gene Expression Profiling ; Gene Expression Regulation, Plant ; Models, Genetic ; Poaceae/genetics ; Poaceae/metabolism ; Species Specificity ; Transcription, Genetic
Language	English
Publishing date	2021-02-23
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2026330118
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Article: Transcriptomic Profiling Provides Molecular Insights Into Hydrogen Peroxide-Enhanced

Zhang, Qikun / Dai, Xiuru / Wang, Huanpeng / Wang, Fanhua / Tang, Dongxue / Jiang, Chunyun / Zhang, Xiaoyan / Guo, Wenjing / Lei, Yuanyuan / Ma, Changle / Zhang, Hui / Li, Pinghua / Zhao, Yanxiu / Wang, Zenglan

Frontiers in plant science

2022 Volume 13, Page(s) 866063

Abstract: Salt stress is an important environmental factor limiting plant growth and crop production. Plant adaptation to salt stress can be improved by chemical pretreatment. This study aims to identify whether hydrogen peroxide ( ... ...

Abstract	Salt stress is an important environmental factor limiting plant growth and crop production. Plant adaptation to salt stress can be improved by chemical pretreatment. This study aims to identify whether hydrogen peroxide (H
Language	English
Publishing date	2022-04-06
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.866063
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Article ; Online: Genome-wide identification of R2R3-MYB family genes and gene response to stress in ginger.

Yao, Xiaoyan / Meng, Fei / Wu, Liping / Guo, Xiaohu / Sun, Zongping / Jiang, Weimin / Zhang, Jing / Wu, Jing / Wang, Shuting / Wang, Zhaojian / Su, Xinglong / Dai, Xiuru / Qu, Changqing / Xing, Shihai

The plant genome

2022 Volume 17, Issue 1, Page(s) e20258

Abstract: Ginger (Zingiber officinale Roscoe) is an important plant used worldwide for medicine and food. The R2R3-MYB transcription factor (TF) family has essential roles in plant growth, development, and stresses resistance, and the number of genes in the family ...

Abstract	Ginger (Zingiber officinale Roscoe) is an important plant used worldwide for medicine and food. The R2R3-MYB transcription factor (TF) family has essential roles in plant growth, development, and stresses resistance, and the number of genes in the family varies greatly among different types of plants. However, genome-wide discovery of ZoMYBs and gene responses to stresses have not been reported in ginger. Therefore, genome-wide analysis of R2R3-MYB genes in ginger was conducted in this study. Protein phylogenetic relations and conserved motifs and chromosome localization and duplication, structure, and cis-regulatory elements were analyzed. In addition, the expression patterns of selected genes were analyzed under two different stresses. A total of 299 candidate ZoMYB genes were discovered in ginger. Based on groupings of R2R3-MYB genes in the model plant Arabidopsis thaliana (L.) Heynh., ZoMYBs were divided into eight groups. Genes were distributed across 22 chromosomes at uneven densities. In gene duplication analysis, 120 segmental duplications were identified in the ginger genome. Gene expression patterns of 10 ZoMYBs in leaves of ginger under abscisic acid (ABA) and low-temperature stress treatments were different. The results will help to determine the exact roles of ZoMYBs in anti-stress responses in ginger.
MeSH term(s)	Zingiber officinale/genetics ; Genes, myb ; Phylogeny ; Plant Proteins/metabolism ; Plant Leaves/genetics ; Arabidopsis/genetics
Chemical Substances	Plant Proteins
Language	English
Publishing date	2022-10-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	2375444-8
ISSN	1940-3372 ; 0011-183X
ISSN (online)	1940-3372
ISSN	0011-183X
DOI	10.1002/tpg2.20258
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays).

Dai, Xiuru / Xu, Zheng / Liang, Zhikai / Tu, Xiaoyu / Zhong, Silin / Schnable, James C / Li, Pinghua

The plant genome

2020 Volume 13, Issue 2, Page(s) e20015

Abstract	Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random-forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.
MeSH term(s)	Algorithms ; Chromosome Mapping ; Computational Biology ; Machine Learning ; Zea mays/genetics
Language	English
Publishing date	2020-04-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2375444-8
ISSN	1940-3372 ; 0011-183X
ISSN (online)	1940-3372
ISSN	0011-183X
DOI	10.1002/tpg2.20015
Database	MEDical Literature Analysis and Retrieval System OnLINE

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