Dataset Information

Transcriptome analysis of creeping bentgrass exposed to drought stress and polyamine treatment.

ABSTRACT: Creeping bentgrass is an important cool-season turfgrass species sensitive to drought. Treatment with polyamines (PAs) has been shown to improve drought tolerance; however, the mechanism is not yet fully understood. Therefore, this study aimed to evaluate transcriptome changes of creeping bentgrass in response to drought and exogenous spermidine (Spd) application using RNA sequencing (RNA-Seq). The high-quality sequences were assembled and 18,682 out of 49,190 (38%) were detected as coding sequences. A total of 22% and 19% of genes were found to be either up- or down-regulated due to drought while 20% and 34% genes were either up- or down- regulated in response to Spd application under drought conditions, respectively. Gene ontology (GO) and enrichment analysis were used to interpret the biological processes of transcripts and relative transcript abundance. Enriched or differentially expressed transcripts due to drought stress and/or Spd application were primarily associated with energy metabolism, transport, antioxidants, photosynthesis, signaling, stress defense, and cellular response to water deprivation. This research is the first to provide transcriptome data for creeping bentgrass under an abiotic stress using RNA-Seq analysis. Differentially expressed transcripts identified here could be further investigated for use as molecular markers or for functional analysis in responses to drought and Spd.

SUBMITTER: Ma Y

PROVIDER: S-EPMC5406032 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Transcriptome analysis of creeping bentgrass exposed to drought stress and polyamine treatment.

Ma Yingmei Y Shukla Vijaya V Merewitz Emily B EB

PloS one 20170426 4

Creeping bentgrass is an important cool-season turfgrass species sensitive to drought. Treatment with polyamines (PAs) has been shown to improve drought tolerance; however, the mechanism is not yet fully understood. Therefore, this study aimed to evaluate transcriptome changes of creeping bentgrass in response to drought and exogenous spermidine (Spd) application using RNA sequencing (RNA-Seq). The high-quality sequences were assembled and 18,682 out of 49,190 (38%) were detected as coding seque ...[more]

PMID: 28445484

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Project description:BACKGROUND:Chitosan (CTS), a natural polysaccharide, exhibits multiple functions of stress adaptation regulation in plants. However, effects and mechanism of CTS on alleviating salt stress damage are still not fully understood. Objectives of this study were to investigate the function of CTS on improving salt tolerance associated with metabolic balance, polyamine (PAs) accumulation, and Na+ transport in creeping bentgrass (Agrostis stolonifera). RESULTS:CTS pretreatment significantly alleviated declines in relative water content, photosynthesis, photochemical efficiency, and water use efficiency in leaves under salt stress. Exogenous CTS increased endogenous PAs accumulation, antioxidant enzyme (SOD, POD, and CAT) activities, and sucrose accumulation and metabolism through the activation of sucrose synthase and pyruvate kinase activities, and inhibition of invertase activity. The CTS also improved total amino acids, glutamic acid, and ?-aminobutyric acid (GABA) accumulation. In addition, CTS-pretreated plants exhibited significantly higher Na+ content in roots and lower Na+ accumulation in leaves then untreated plants in response to salt stress. However, CTS had no significant effects on K+/Na+ ratio. Importantly, CTS enhanced salt overly sensitive (SOS) pathways and also up-regulated the expression of AsHKT1 and genes (AsNHX4, AsNHX5, and AsNHX6) encoding Na+/H+ exchangers under salt stress. CONCLUSIONS:The application of CTS increased antioxidant enzyme activities, thereby reducing oxidative damage to roots and leaves. CTS-induced increases in sucrose and GABA accumulation and metabolism played important roles in osmotic adjustment and energy metabolism during salt stress. The CTS also enhanced SOS pathway associated with Na+ excretion from cytosol into rhizosphere, increased AsHKT1 expression inhibiting Na+ transport to the photosynthetic tissues, and also up-regulated the expression of AsNHX4, AsNHX5, and AsNHX6 promoting the capacity of Na+ compartmentalization in roots and leaves under salt stress. In addition, CTS-induced PAs accumulation could be an important regulatory mechanism contributing to enhanced salt tolerance. These findings reveal new functions of CTS on regulating Na+ transport, enhancing sugars and amino acids metabolism for osmotic adjustment and energy supply, and increasing PAs accumulation when creeping bentgrass responds to salt stress.

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

Transcriptome analysis of creeping bentgrass exposed to drought stress and polyamine treatment.

Publications

Transcriptome analysis of creeping bentgrass exposed to drought stress and polyamine treatment.

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