Dataset Information

Transcript Profiling and Gene Identification Involved in the Ethylene Signal Transduction Pathways of Creeping Bentgrass (Agrostis stolonifera) during ISR Response Induced by Butanediol.

ABSTRACT: Creeping bentgrass (Agrostis stolonifera) is the preferred green lawn grass, with excellent turf characteristics but poor disease resistance. At present, the mechanisms of disease resistance in creeping bentgrass are poorly understood, especially the ethylene signal transduction pathway under the induced systemic resistance (ISR) response. In this study, butanediol (BDO), as a new type of disease-resistance compound, was applied to creeping bentgrass seedlings to induce the ISR response. Then, we measured ethylene production and related enzyme activities. Additionally, transcript profiling and gene identification were performed in association to ethylene signal transduction pathways. The changes of ethylene production and related enzyme 1-aminocyclopropane-1-carboxylic acid oxidase (ACO) and 1-aminocyclopropane-1-carboxylic acid synthases (ACS) activities showed significant difference at 24 h after Rhizoctonia solani inoculation among five treatments of various BDO concentrations. After 100 µmol L-1 BDO treatment, ethylene production and related enzyme activities reached their peak levels. Additionally, 208,672 unigenes of creeping bentgrass were obtained by de novo assembly. In total, 15,903 annotated unigenes were grouped into 33 canonical pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. Among those, 1803 unigenes were classified as 'signal transduction'. There were 6766 differentially expressed genes (DEGs) among B24 (inoculated-rhizobacteria in MS medium with 100 µmol L-1 BDO for 24 h), NB24, B72 and NB24 (no rhizobacteria in MS medium with 100 µmol L-1 BDO for 24 h) libraries, and 4,639 DEGs between B24 and B72 (inoculated-rhizobacteria in MS medium with 100 µmol L-1 BDO for 72 h) libraries, with 4489 DEGs in all three libraries. As suggested by the RT-PCR assay, the expression levels of ethylene-responsive and defense-related genes were variable among treated samples during the BDO-induced ISR responses. The expression levels of EIN, ERF, NPR1, PR3 and PR4 genes increased and reached their peaks in the first 24 h after R. solani infection in the BDO-induced ISR reaction compared with NB24 treatments. This results is consistent with the changes of important ethylene biosynthetic enzymes and ethylene concentrations during the BDO-induced ISR responses. We further found the intermediate substances for the signaling pathway, and the relationships between the expression levels of BDO-induced ISR disease-resistance genes and those of the response genes for ethylene signal pathway. Our findings present a genetic basis for systemic resistance of creeping bentgrass through transcriptomic analysis and our study provides a theoretical and practical basis for the improvement of turfgrass disease resistance and quality.

SUBMITTER: Jiang HY

PROVIDER: S-EPMC6017539 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Transcript Profiling and Gene Identification Involved in the Ethylene Signal Transduction Pathways of Creeping Bentgrass (Agrostis stolonifera) during ISR Response Induced by Butanediol.

Jiang Han-Yu HY Zhang Jin-Lin JL Yang Jiang-Wei JW Ma Hui-Ling HL

Molecules (Basel, Switzerland) 20180320 3

Creeping bentgrass (<i>Agrostis stolonifera</i>) is the preferred green lawn grass, with excellent turf characteristics but poor disease resistance. At present, the mechanisms of disease resistance in creeping bentgrass are poorly understood, especially the ethylene signal transduction pathway under the induced systemic resistance (ISR) response. In this study, butanediol (BDO), as a new type of disease-resistance compound, was applied to creeping bentgrass seedlings to induce the ISR response. ...[more]

PMID: 29558428

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Project description:BackgroundTurfgrass species are agriculturally and economically important perennial crops. Turfgrass species are highly susceptible to a wide range of fungal pathogens. Dollar spot and brown patch, two important diseases caused by fungal pathogens Sclerotinia homoecarpa and Rhizoctonia solani, respectively, are among the most severe turfgrass diseases. Currently, turf fungal disease control mainly relies on fungicide treatments, which raises many concerns for human health and the environment. Antimicrobial peptides found in various organisms play an important role in innate immune response.Methodology/principal findingsThe antimicrobial peptide - Penaeidin4-1 (Pen4-1) from the shrimp, Litopenaeus setiferus has been reported to possess in vitro antifungal and antibacterial activities against various economically important fungal and bacterial pathogens. In this study, we have studied the feasibility of using this novel peptide for engineering enhanced disease resistance into creeping bentgrass plants (Agrostis stolonifera L., cv. Penn A-4). Two DNA constructs were prepared containing either the coding sequence of a single peptide, Pen4-1 or the DNA sequence coding for the transit signal peptide of the secreted tobacco AP24 protein translationally fused to the Pen4-1 coding sequence. A maize ubiquitin promoter was used in both constructs to drive gene expression. Transgenic turfgrass plants containing different DNA constructs were generated by Agrobacterium-mediated transformation and analyzed for transgene insertion and expression. In replicated in vitro and in vivo experiments under controlled environments, transgenic plants exhibited significantly enhanced resistance to dollar spot and brown patch, the two major fungal diseases in turfgrass. The targeting of Pen4-1 to endoplasmic reticulum by the transit peptide of AP24 protein did not significantly impact disease resistance in transgenic plants.Conclusion/significanceOur results demonstrate the effectiveness of Pen4-1 in a perennial species against fungal pathogens and suggest a potential strategy for engineering broad-spectrum fungal disease resistance in crop species.

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

Transcript Profiling and Gene Identification Involved in the Ethylene Signal Transduction Pathways of Creeping Bentgrass (Agrostis stolonifera) during ISR Response Induced by Butanediol.

Publications

Transcript Profiling and Gene Identification Involved in the Ethylene Signal Transduction Pathways of Creeping Bentgrass (Agrostis stolonifera) during ISR Response Induced by Butanediol.

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