Soil water deficit and salt stress are major limiting factors of plant growth and agricultural productivity. The primary root is the first organ to perceive the stress signals for drought and salt stress. In this stud...Soil water deficit and salt stress are major limiting factors of plant growth and agricultural productivity. The primary root is the first organ to perceive the stress signals for drought and salt stress. In this study, maize plant subjected to drought, salt and combined stresses displayed a significantly reduced primary root length relative to the control plants. GC-MS was used to determine changes in the metabolites of the primary root of maize in response to salt, drought and combined stresses. A total of 86 metabolites were measured, including 29 amino acids and amines, 21 organic acids, four fatty acids, six phosphoric acids, 10 sugars, 10 polyols, and six others. Among these, 53 metabolites with a significant change under different stresses were identified in the primary root, and the content of most metabolites showed down-accumulation. A total of four and 18 metabolites showed significant up-and down-accumulation to all three treatments, respectively. The levels of several compatible solutes, including sugars and polyols, were increased to help maintain the osmotic balance. The levels of metabolites involved in the TCA cycle, including citric acid, ketoglutaric acid, fumaric acid, and malic acid, were reduced in the primary root. The contents of metabolites in the shikimate pathway, such as quinic acid and shikimic acid, were significantly decreased. This study reveals the complex metabolic responses of the primary root to combined drought and salt stresses and extends our understanding of the mechanisms involved in root responses to abiotic tolerance in maize.展开更多
Several typical supervised clustering methods such as Gaussian mixture model-based supervised clustering （GMM）, k- nearest-neighbor （KNN）, binary support vector machines （SVMs） and multiclass support vector mach...Several typical supervised clustering methods such as Gaussian mixture model-based supervised clustering （GMM）, k- nearest-neighbor （KNN）, binary support vector machines （SVMs） and multiclass support vector machines （MC-SVMs） were employed to classify the computer simulation data and two real microarray expression datasets. False positive, false negative, true positive, true negative, clustering accuracy and Matthews＇ correlation coefficient （MCC） were compared among these methods. The results are as follows： （1） In classifying thousands of gene expression data, the performances of two GMM methods have the maximal clustering accuracy and the least overall FP＋FN error numbers on the basis of the assumption that the whole set of microarray data are a finite mixture of multivariate Gaussian distributions. Furthermore, when the number of training sample is very small, the clustering accuracy of GMM-Ⅱ method has superiority over GMM- Ⅰ method. （2） In general, the superior classification performance of the MC-SVMs are more robust and more practical, which are less sensitive to the curse of dimensionality, and not only next to GMM method in clustering accuracy to thousands of gene expression data, but also more robust to a small number of high-dimensional gene expression samples than other techniques. （3） Of the MC-SVMs, OVO and DAGSVM perform better on the large sample sizes, whereas five MC-SVMs methods have very similar performance on moderate sample sizes. In other cases, OVR, WW and CS yield better results when sample sizes are small. So, it is recommended that at least two candidate methods, choosing on the basis of the real data features and experimental conditions, should be performed and compared to obtain better clustering result.展开更多
基金supported by grants from the National Key Technology Research and Development Program of Ministry of Science and Technology of China (2016YFD0100303)the National Natural Science Foundation of China (31972487, 31902101 and 31801028)+2 种基金the Key Technology Research and Development Program of Jiangsu, China (BE2018325)the Natural Science Foundation of Jiangsu Province, China (BK20180920)the project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (PAPD)。
文摘Soil water deficit and salt stress are major limiting factors of plant growth and agricultural productivity. The primary root is the first organ to perceive the stress signals for drought and salt stress. In this study, maize plant subjected to drought, salt and combined stresses displayed a significantly reduced primary root length relative to the control plants. GC-MS was used to determine changes in the metabolites of the primary root of maize in response to salt, drought and combined stresses. A total of 86 metabolites were measured, including 29 amino acids and amines, 21 organic acids, four fatty acids, six phosphoric acids, 10 sugars, 10 polyols, and six others. Among these, 53 metabolites with a significant change under different stresses were identified in the primary root, and the content of most metabolites showed down-accumulation. A total of four and 18 metabolites showed significant up-and down-accumulation to all three treatments, respectively. The levels of several compatible solutes, including sugars and polyols, were increased to help maintain the osmotic balance. The levels of metabolites involved in the TCA cycle, including citric acid, ketoglutaric acid, fumaric acid, and malic acid, were reduced in the primary root. The contents of metabolites in the shikimate pathway, such as quinic acid and shikimic acid, were significantly decreased. This study reveals the complex metabolic responses of the primary root to combined drought and salt stresses and extends our understanding of the mechanisms involved in root responses to abiotic tolerance in maize.
基金This research was supported by the National Natural Science Foundation of China(30370758)Program for New Century Excellent Talents in Universities(NCET)of Ministry of Education to Dr.Xu Chenwu(NCET-05-0502).
文摘Several typical supervised clustering methods such as Gaussian mixture model-based supervised clustering （GMM）, k- nearest-neighbor （KNN）, binary support vector machines （SVMs） and multiclass support vector machines （MC-SVMs） were employed to classify the computer simulation data and two real microarray expression datasets. False positive, false negative, true positive, true negative, clustering accuracy and Matthews＇ correlation coefficient （MCC） were compared among these methods. The results are as follows： （1） In classifying thousands of gene expression data, the performances of two GMM methods have the maximal clustering accuracy and the least overall FP＋FN error numbers on the basis of the assumption that the whole set of microarray data are a finite mixture of multivariate Gaussian distributions. Furthermore, when the number of training sample is very small, the clustering accuracy of GMM-Ⅱ method has superiority over GMM- Ⅰ method. （2） In general, the superior classification performance of the MC-SVMs are more robust and more practical, which are less sensitive to the curse of dimensionality, and not only next to GMM method in clustering accuracy to thousands of gene expression data, but also more robust to a small number of high-dimensional gene expression samples than other techniques. （3） Of the MC-SVMs, OVO and DAGSVM perform better on the large sample sizes, whereas five MC-SVMs methods have very similar performance on moderate sample sizes. In other cases, OVR, WW and CS yield better results when sample sizes are small. So, it is recommended that at least two candidate methods, choosing on the basis of the real data features and experimental conditions, should be performed and compared to obtain better clustering result.
