Dataset Information

Overexpression of Arabidopsis thaliana brassinosteroid-related acyltransferase 1 gene induces brassinosteroid-deficient phenotypes in creeping bentgrass.

ABSTRACT: Brassinosteroids (BRs) are naturally occurring steroidal hormones that play diverse roles in various processes during plant growth and development. Thus, genetic manipulation of endogenous BR levels might offer a way of improving the agronomic traits of crops, including plant architecture and stress tolerance. In this study, we produced transgenic creeping bentgrass (Agrostis stolonifera L.) overexpressing a BR-inactivating enzyme, Arabidopsis thaliana BR-related acyltransferase 1 (AtBAT1), which is known to catalyze the conversion of BR intermediates to inactive acylated conjugates. After putative transgenic plants were selected using herbicide resistance assay, genomic integration of the AtBAT1 gene was confirmed by genomic PCR and Southern blot analysis, and transgene expression was validated by northern blot analysis. The transgenic creeping bentgrass plants exhibited BR-deficient phenotypes, including reduced plant height with shortened internodes (i.e., semi-dwarf), reduced leaf growth rates with short, wide, and thick architecture, high chlorophyll contents, decreased numbers of vascular bundles, and large lamina joint bending angles (i.e., erect leaves). Subsequent analyses showed that the transgenic plants had significantly reduced amounts of endogenous BR intermediates, including typhasterol, 6-deoxocastasterone, and castasterone. Moreover, the AtBAT1 transgenic plants displayed drought tolerance as well as delayed senescence. Therefore, the results of the present study demonstrate that overexpression of an Arabidopsis BR-inactivating enzyme can reduce the endogenous levels of BRs in creeping bentgrass resulting in BR-deficient phenotypes, indicating that the AtBAT1 gene from a dicot plant is also functional in the monocot crop.

SUBMITTER: Han YJ

PROVIDER: S-EPMC5662239 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Overexpression of Arabidopsis thaliana brassinosteroid-related acyltransferase 1 gene induces brassinosteroid-deficient phenotypes in creeping bentgrass.

Han Yun-Jeong YJ Kim Young Soon YS Hwang Ok-Jin OJ Roh Jeehee J Ganguly Keya K Kim Seong-Ki SK Hwang Ildoo I Kim Jeong-Il JI

PloS one 20171030 10

Brassinosteroids (BRs) are naturally occurring steroidal hormones that play diverse roles in various processes during plant growth and development. Thus, genetic manipulation of endogenous BR levels might offer a way of improving the agronomic traits of crops, including plant architecture and stress tolerance. In this study, we produced transgenic creeping bentgrass (Agrostis stolonifera L.) overexpressing a BR-inactivating enzyme, Arabidopsis thaliana BR-related acyltransferase 1 (AtBAT1), whic ...[more]

PMID: 29084267

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Project description:Brassinosteroids (BRs) are signaling molecules that play essential roles in the spatial regulation of plant growth and development. In contrast to other plant hormones BRs act locally, close to the sites of their synthesis, and thus homeostatic mechanisms must operate at the cellular level to equilibrate BR concentrations. Whilst it is recognized that levels of bioactive BRs are likely adjusted by controlling the relative rates of biosynthesis and by catabolism, few factors, which participate in these regulatory events, have as yet been identified. Previously we have shown that the UDP-glycosyltransferase UGT73C5 of Arabidopsis thaliana catalyzes 23-O-glucosylation of BRs and that glucosylation renders BRs inactive. This study identifies the closest homologue of UGT73C5, UGT73C6, as an enzyme that is also able to glucosylate BRs in planta.In a candidate gene approach, in which homologues of UGT73C5 were screened for their potential to induce BR deficiency when over-expressed in plants, UGT73C6 was identified as an enzyme that can glucosylate the BRs CS and BL at their 23-O-positions in planta. GUS reporter analysis indicates that UGT73C6 shows over-lapping, but also distinct expression patterns with UGT73C5 and YFP reporter data suggests that at the cellular level, both UGTs localize to the cytoplasm and to the nucleus. A liquid chromatography high-resolution mass spectrometry method for BR metabolite analysis was developed and applied to determine the kinetics of formation and the catabolic fate of BR-23-O-glucosides in wild type and UGT73C5 and UGT73C6 over-expression lines. This approach identified novel BR catabolites, which are considered to be BR-malonylglucosides, and provided first evidence indicating that glucosylation protects BRs from cellular removal. The physiological significance of BR glucosylation, and the possible role of UGT73C6 as a regulatory factor in this process are discussed in light of the results presented.The present study generates essential knowledge and molecular and biochemical tools, that will allow for the verification of a potential physiological role of UGT73C6 in BR glucosylation and will facilitate the investigation of the functional significance of BR glucoside formation in plants.

Project description:Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 29%, and N fertilization rates of 0 to 17.5 kg ha-1 applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. The highest coefficient of determination (R2) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R 2 = 0.01). However, the machine-learning models created were not able to accurately predict the clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.

Dataset Information

Overexpression of Arabidopsis thaliana brassinosteroid-related acyltransferase 1 gene induces brassinosteroid-deficient phenotypes in creeping bentgrass.

Publications

Overexpression of Arabidopsis thaliana brassinosteroid-related acyltransferase 1 gene induces brassinosteroid-deficient phenotypes in creeping bentgrass.

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