Comparative analysis of salt responsive gene regulatory networks in rice and Arabidopsis

Wang, Rui; Cheng, Yanhao; Ke, Xiaojuan; Zhang, Xiaofan; Zhang, Hongsheng; Huang, Ji

doi:10.1016/j.compbiolchem.2019.107188

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…Under Fe deficiency, this helix-loop-helix (HLH) transcription factor positively regulates OsIRO2 expression via directly binding to its promotor. Through global bioinformatics analysis, the HLH transcription factor was found to participate in salt responsive gene regulatory networks ( Wang et al, 2020 ). Overexpression of OsIRO2 results in an elevation of Fe uptake and translocation in rice ( Ogo et al, 2007 ).…”

Section: Resultsmentioning

confidence: 99%

Genome-Wide Association Analysis for Salt–Induced Phenotypic and Physiologic Responses in Rice at Seedling and Reproductive Stages

Gang

Zhao

et al. 2022

Front. Plant Sci.

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Salinity is one of the main adverse environmental factors severely inhibiting rice growth and decreasing grain productivity. Developing rice varieties with salt tolerance (ST) is one of the most economical approaches to cope with salinity stress. In this study, the salt tolerance of 220 rice accessions from rice diversity panel l (RDP1), representing five subpopulations, were evaluated based on 16 ST indices at both seedling and reproductive stages under salt stress. An apparent inconsistency was found for ST between the two stages. Through a gene-based/tightly linked genome-wide association study with 201,332 single nucleotide polymorphisms (SNPs) located within genes and their flanking regions were used, a total of 214 SNPs related to 251 genes, significantly associated with 16 ST-related indices, were detected at both stages. Eighty-two SNPs with low frequency favorable (LFF) alleles in the population were proposed to hold high breeding potential in improving rice ST. Fifty-four rice accessions collectively containing all these LFF alleles were identified as donors of these alleles. Through the integration of meta-quantitative trait locus (QTL) for ST and the response patterns of differential expression genes to salt stress, thirty-eight candidate genes were suggested to be involved in the regulation of rice ST. In total, the present study provides valuable information for further characterizing ST-related genes and for breeding ST varieties across whole developmental stages through marker-assisted selection (MAS).

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Section: Resultsmentioning

confidence: 99%

Genome-Wide Association Analysis for Salt–Induced Phenotypic and Physiologic Responses in Rice at Seedling and Reproductive Stages

Gang

Zhao

et al. 2022

Front. Plant Sci.

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“…Experimentally verified PPIs were collected between rice and M. grisea ( Pellegrini et al., 1999 ; Jia et al., 2000 ; Krogh et al., 2001 ; Ng et al., 2003 ; Salwinski et al., 2004 ; Quevillon et al., 2005 ; Shoemaker and Panchenko, 2007 ; Wang et al., 2007 ; Najafabadi and Salavati, 2008 ; Parker et al., 2008 ; Ribot et al., 2008 ; Kumar and Nanduri, 2010 ; Mukhtar et al., 2011 ; Li et al., 2012 ; Maetschke et al., 2012 ; Mentlak et al., 2012 ; Park et al., 2012 ; Schleker et al., 2012 ; Simonsen et al., 2012 ; Meyer et al., 2013 ; Mosca et al., 2014 ; Rao et al., 2014 ; Sahu et al., 2014 ; Tully et al., 2014 ; Nourani et al., 2015 ; Li et al., 2016 ; Singh et al., 2016 ; Klopfenstein et al., 2018 ; Savojardo et al., 2018 ; Karan et al., 2019 ; Ma et al., 2019 ; Sahu et al., 2019 ; Loaiza et al., 2020 ; Lu et al., 2020 ; Singh et al., 2020 ; Wang et al., 2020 ; Rapposelli et al., 2021 ; Kumar et al., 2022 ; Mishra et al., 2022 ; Wu et al., 2015 ) from an exhaustive literature survey and used an independent dataset Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Until now, few numbers have been reported of experimental PPIs between O. sativa and M. grisea that are inadequate to explore the pathogenic molecular mechanism ( Pellegrini et al., 1999 ; Jia et al. 2000 ; Krogh et al., 2001 ; Ng et al., 2003 ; Salwinski et al., 2004 ; Quevillon et al., 2005 ; Shoemaker and Panchenko, 2007 ; Wang et al., 2007 ; Najafabadi and Salavati, 2008 ; Parker et al., 2008 ; Ribot et al., 2008 ; Kumar and Nanduri, 2010 ; Mukhtar et al., 2011 ; Li et al., 2012 ; Maetschke et al., 2012 ; Mentlak et al., 2012 ; Park et al., 2012 ; Schleker et al., 2012 ; Simonsen et al., 2012 ; Meyer et al., 2013 ; Mosca et al., 2014 ; Rao et al., 2014 ; Sahu et al., 2014 ; Tully et al., 2014 ; Nourani et al., 2015 ; Li et al., 2016 ; Singh et al., 2016 ; Klopfenstein et al., 2018 ; Savojardo et al., 2018 ; Karan et al., 2019 ; Ma et al., 2019 ; Sahu et al., 2019 ; Loaiza et al., 2020 ; Lu et al., 2020 ; Singh et al., 2020 ; Wang et al., 2020 ; Rapposelli et al., 2021 ; Kumar et al., 2022 ; Mishra et al., 2022 ; Wu et al., 2015 ). Therefore, the computational approach is seen as an alternative method for the large-scale identification of PPIs.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of these algorithms are based on data mining, which uses information from existing PPIs to predict new interactions ( Jaeger et al., 2008 ). Among these computational methods, the interolog and domain-based methods ( Wu et al., 2006 ; Shoemaker and Panchenko 2007 ; Wang et al., 2007 ; Simonsen et al., 2012 ; Tully et al., 2014 ; Wu et al., 2015 ; Singh et al., 2016 ; Singh et al., 2020 ; Wang et al., 2020 ; Wuchty, (2011) ) are extensively used methodologies for the prediction of PPIs. The potential PPIs between Homo sapiens ( H. sapiens ) and Plasmodium falciparum ( P. falciparum ) are predicted previously ( Dyer et al., 2007 ) using domain information of the host–pathogen system.…”

Section: Introductionmentioning

confidence: 99%

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Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

et al. 2023

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IntroductionPlant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.MethodsIn this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.Results and DiscussionA total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

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“…Under Fe deficiency, this HLH transcription factor positively regulates OsIRO2 expression via directly binding to its promotor. Through global bioinformatics analysis, the HLH transcription factor was found to participate in salt responsive gene regulatory networks (Wang et al, 2020). Overexpression of OsIRO2 results in an elevation of Fe uptake and translocation in rice (Ogo et al, 2007).…”

Section: Candidate Genes Associated With St-related Indicesmentioning

confidence: 99%

Genome-wide association analysis for rice salt tolerance at seedling and reproductive stages

Gang¹,

Hu²,

Zhao³

et al. 2021

Preprint

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Background Salinity is one of the main adverse environmental factors severely inhibiting rice growth and decreasing grain productivity. Developing rice varieties with salt tolerance (ST) is one of the most economical approaches to cope with salinity stress. However, the valuable resources for rice breeding towards ST remain to be identified. In this study, the salt tolerance of 220 rice accessions from RDP1, representing five subpopulations, were evaluated at both seedling and reproductive stages. Results An apparent inconsistency was found for ST between the two stages. Through a gene-based / tightly linked genome-wide association study, a total of 214 single nucleotide polymorphisms (SNPs) related to 251 genes, significantly associated with 16 ST-related indices, were detected at both stages. Eighty-two SNPs with low frequency favorable (LFF) alleles in the population were proposed to hold high breeding potential in improving rice ST. Fifty-four rice accessions collectively containing all these LFF alleles were identified as donors of these alleles. Through the integration of meta-QTL for ST and the response patterns of differential expression genes to salt stress, thirty-one candidate genes were suggested to be involved in the regulation of rice ST. Conclusions In total, the present study provides valuable information for further characterizing ST-related genes and for breeding ST varieties across whole developmental stages through marker-assisted selection (MAS).

show abstract

Comparative analysis of salt responsive gene regulatory networks in rice and Arabidopsis

Cited by 7 publications

References 36 publications

Genome-Wide Association Analysis for Salt–Induced Phenotypic and Physiologic Responses in Rice at Seedling and Reproductive Stages

Genome-Wide Association Analysis for Salt–Induced Phenotypic and Physiologic Responses in Rice at Seedling and Reproductive Stages

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

Genome-wide association analysis for rice salt tolerance at seedling and reproductive stages

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