Identification of Putative Transmembrane Proteins Involved in Salinity Tolerance in Chenopodium quinoa by Integrating Physiological Data, RNAseq, and SNP Analyses
AuthorsSchmöckel, Sandra M.
Mohamad Razali, Rozaimi
Tester, Mark A.
Jarvis, David Erwin
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Desert Agriculture Initiative
Permanent link to this recordhttp://hdl.handle.net/10754/625182
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AbstractChenopodium quinoa (quinoa) is an emerging crop that produces nutritious grains with the potential to contribute to global food security. Quinoa can also grow on marginal lands, such as soils affected by high salinity. To identify candidate salt tolerance genes in the recently sequenced quinoa genome, we used a multifaceted approach integrating RNAseq analyses with comparative genomics and topology prediction. We identified 219 candidate genes by selecting those that were differentially expressed in response to salinity, were specific to or overrepresented in quinoa relative to other Amaranthaceae species, and had more than one predicted transmembrane domain. To determine whether these genes might underlie variation in salinity tolerance in quinoa and its close relatives, we compared the response to salinity stress in a panel of 21 Chenopodium accessions (14 C. quinoa, 5 C. berlandieri, and 2 C. hircinum). We found large variation in salinity tolerance, with one C. hircinum displaying the highest salinity tolerance. Using genome re-sequencing data from these accessions, we investigated single nucleotide polymorphisms and copy number variation (CNV) in the 219 candidate genes in accessions of contrasting salinity tolerance, and identified 15 genes that could contribute to the differences in salinity tolerance of these Chenopodium accessions.
CitationSchmöckel SM, Lightfoot DJ, Razali R, Tester M, Jarvis DE (2017) Identification of Putative Transmembrane Proteins Involved in Salinity Tolerance in Chenopodium quinoa by Integrating Physiological Data, RNAseq, and SNP Analyses. Frontiers in Plant Science 8. Available: http://dx.doi.org/10.3389/fpls.2017.01023.
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).
PublisherFrontiers Media SA
JournalFrontiers in Plant Science
CollectionsArticles; Biological and Environmental Sciences and Engineering (BESE) Division; Biological and Environmental Sciences and Engineering (BESE) Division; Computational Bioscience Research Center (CBRC); Computational Bioscience Research Center (CBRC); Desert Agriculture Initiative; Desert Agriculture Initiative
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