Gene regulatory network and its constituent transcription factors that control nitrogen‐deficiency responses in rice

Ueda, Yoshiaki; Ohtsuki, Namie; Kadota, Koji; Tezuka, Ayumi; Nagano, Atsushi J.; Kadowaki, Tomoko; Kim, Yonghyun; Miyao, Mitsue; Yanagisawa, Shuichi

doi:10.1111/nph.16627

Cited by 60 publications

(46 citation statements)

References 103 publications

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“…Our analysis here shows for the first time that a number of genes involved in N-uptake, translocation and amino acid metabolism are regulated by RGA1 in rice, corroborating our earlier findings in Arabidopsis 5,6 for the first time in any crop plant. Both G-protein signaling and N-responsive signaling are separately known to use TFs to regulate the expression of target genes 71,72 . But the role of G-protein signaling in N-responsive transcriptional regulation is not established in any plant so far.…”

Section: Discussionmentioning

confidence: 99%

Heterotrimeric G-protein α subunit (RGA1) regulates tiller development, yield, cell wall, nitrogen response and biotic stress in rice

Pathak

Mandal

Jangam

et al. 2021

Sci Rep

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G-proteins are implicated in plant productivity, but their genome-wide roles in regulating agronomically important traits remain uncharacterized. Transcriptomic analyses of rice G-protein alpha subunit mutant (rga1) revealed 2270 differentially expressed genes (DEGs) including those involved in C/N and lipid metabolism, cell wall, hormones and stress. Many DEGs were associated with root, leaf, culm, inflorescence, panicle, grain yield and heading date. The mutant performed better in total weight of filled grains, ratio of filled to unfilled grains and tillers per plant. Protein–protein interaction (PPI) network analysis using experimentally validated interactors revealed many RGA1-responsive genes involved in tiller development. qPCR validated the differential expression of genes involved in strigolactone-mediated tiller formation and grain development. Further, the mutant growth and biomass were unaffected by submergence indicating its role in submergence response. Transcription factor network analysis revealed the importance of RGA1 in nitrogen signaling with DEGs such as Nin-like, WRKY, NAC, bHLH families, nitrite reductase, glutamine synthetase, OsCIPK23 and urea transporter. Sub-clustering of DEGs-associated PPI network revealed that RGA1 regulates metabolism, stress and gene regulation among others. Predicted rice G-protein networks mapped DEGs and revealed potential effectors. Thus, this study expands the roles of RGA1 to agronomically important traits and reveals their underlying processes.

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Section: Discussionmentioning

confidence: 99%

Heterotrimeric G-protein α subunit (RGA1) regulates tiller development, yield, cell wall, nitrogen response and biotic stress in rice

Pathak

Mandal

Jangam

et al. 2021

Sci Rep

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“…To date, plenty of literatures show that bZIPs regulate plant tolerances to various abiotic stresses, such as low temperature, drought, high salt, nitrogen deficiency, zinc deficiency time (Lilay et al, 2020;Ueda et al, 2020). Besides, there are many reports reveal that flavonoids participate in various stress responses.…”

Section: Discussionmentioning

confidence: 99%

Regulation Mechanisms of Plant Basic Leucine Zippers to Various Abiotic Stresses

Qian

Jiang

et al. 2020

Front. Plant Sci.

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“…We compared ve methods: two clustering-based methods (MBCdeg1 and MBCdeg2) and three conventional methods (edgeR [11], DESeq2 [15], and TCC [14]). The edgeR and DESeq2 packages are widely used [25]. TCC is used as the main alternative to MBCdeg as it utilizes the DEGES normalization algorithm.…”

Section: Analysis Of Simulated Data For a Two-group Comparisonmentioning

confidence: 99%

“…To date, several methods to enable the analysis of RNA-seq data have been developed, including normalization [5][6][7][8][9][10], various R packages [11][12][13][14][15][16], and graphical user interfaces (GUI) [17][18][19]. Research on more e cient and accurate methods to identify DEGs continues, and new ndings continue to be reported by researchers [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Obtaining a ranked gene list for the degree of differential expression (DE) is a starting point for gaining biological insights into the groups being compared [26]. Several analysis methods such as gene ontology and the construction of co-expression networks have been used to examine the biological mechanisms underlying DEGs [25]. Additionally, gene clustering based on the similarity of expression patterns has been widely used to group and classify DEGs [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Differential Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

Osabe

Shimizu

Kadota

2020

Preprint

Self Cite

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Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report a model-based clustering algorithm, MBCluster.Seq, that can be implemented using an R package for DE analysis.Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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Gene regulatory network and its constituent transcription factors that control nitrogen‐deficiency responses in rice

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 60 publications

References 103 publications

Heterotrimeric G-protein α subunit (RGA1) regulates tiller development, yield, cell wall, nitrogen response and biotic stress in rice

Heterotrimeric G-protein α subunit (RGA1) regulates tiller development, yield, cell wall, nitrogen response and biotic stress in rice

Regulation Mechanisms of Plant Basic Leucine Zippers to Various Abiotic Stresses

Differential Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

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