Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms....Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.展开更多
Hyperspectral imaging was applied to classify the damaged wheat kernels and healthy kernels.The spectral information was extracted from damaged wheat kernels and healthy kernels samples.The effective wavelengths were ...Hyperspectral imaging was applied to classify the damaged wheat kernels and healthy kernels.The spectral information was extracted from damaged wheat kernels and healthy kernels samples.The effective wavelengths were obtained from spectral of 865-1711 nm by X-loadings of principal component analysis(PCA)and successive projection algorithm(SPA)method,respectively.Partial least square method(PLS)and least square-support vector machine(LS-SVM)were then used to build classification models on full spectral data and effective wavelengths dataset,respectively.The results showed that the classification accuracy of every LS-SVM model was the best,being 100%.While the accuracy of the PLS model was slightly lower,still over 97%.The confusion matrix showed that several damaged wheat kernels samples were misclassified as healthy samples,while all healthy samples were correctly classified.The overall results indicated that hyperspectral imaging could be used for discriminating the damaged wheat kernels and could provide a reference for detecting other grain kernels grading degrees.Further,this study can provide a research basis for the development of online or portable detectors on grain damaged kernels recognition,which will be beneficial for grain grading or post-harvest quality processing of other grains.展开更多
The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat...The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels.Seventy-nine samples from 11 breeds of wheat kernels were collected.The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value.After comparing the prediction models of principal components regression(PCR)and partial least squares regression(PLSR)with various pretreatment methods,PLSR preprocessed by zero mean normalization(z score)function of MATLAB was found to obtain better prediction results than other regression models.Based on 10 latent variables of PLSR,the radial basis function(RBF)neural network was applied to improve the prediction,in which the coefficients of determination(R2)were greater than 0.92 for both the calibration set and validation set,while the corresponding RMSE values were 0.3496 and 0.4005,respectively.Therefore,hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels’protein content.展开更多
对农作物品种正确分类是作物分类学的重要内容,为考察X-ray成像技术对小麦品种分类研究的有效性,基于软X-ray成像仪采集的3品种(Kama,Rosa and Canadian)每个品种70个籽粒,共210个籽粒样本的X-ray扫描图像,并针对其7个形态几何特征(面...对农作物品种正确分类是作物分类学的重要内容,为考察X-ray成像技术对小麦品种分类研究的有效性,基于软X-ray成像仪采集的3品种(Kama,Rosa and Canadian)每个品种70个籽粒,共210个籽粒样本的X-ray扫描图像,并针对其7个形态几何特征(面积、周长、紧致度、籽粒长度、宽度、偏斜度、种子腹沟长度),提出了一种使用Kernel-ICA的方法先对特征进行优化,再进行小麦品种的聚类与识别的方法,并与K-means、C-means 2种聚类方法以及基于工神经网络(ANN)和支持向量机(SVM)2种识别方法的分类结果进行比较,结果发现:分类正确率从高到低分别为:Kernel-ICA、SVM、C-means、K-means、BP-ANN,分类正确率分别为:91.9%、90.5%、89.5%、87.1%、86.9%。研究提出的Kernel-ICA的方法,聚类优化和识别能力较强,对软X-ray成像的小麦品种进行分类,已基本上满足农艺上对小麦品种分类需要,对农作物种质资源鉴别和作物品种分类研究具有积极意义。展开更多
Despite recent advances in crop metabolomics,the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown.Here,we performed widely tar-geted metabolit...Despite recent advances in crop metabolomics,the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown.Here,we performed widely tar-geted metabolite profiling of kernels from three developmental stages(grain-filling kernels[FKs],mature kernels[MKs],and germinating kernels[GKs])using a population of 159 recombinant inbred lines.We de-tected 625 annotated metabolites and mapped 3173,3143,and 2644 metabolite quantitative trait loci(mQTLs)in FKs,MKs,and GKs,respectively.Only 52 mQTLs were mapped at all three stages,indicating the high stage specificity of the wheat kernel metabolome.Four candidate genes were functionally vali-dated by in vitro enzymatic reactions and/or transgenic approaches in wheat,three of which mediated the tricin metabolic pathway.Metaboliteflux efficiencies within the tricin pathway were evaluated,and su-perior candidate haplotypes were identified,comprehensively delineating the tricin metabolism pathway in wheat.Finally,additional wheat metabolic pathways were re-constructed by updating them to incorporate the 177 candidate genes identified in this study.Our work provides new information on variations in the wheat kernel metabolome and important molecular resources for improvement of wheat nutritional quality.展开更多
基金supported by the Young Top-notch Talent Cultivation Program of Hubei Province,the Natural Science Foundation for Distinguished Young Scientists of Hubei Province(2021CFA058)the First-Class Discipline Construction Funds of College of Plant Science and Technology,Huazhong Agricultural University(2023ZKPY005).
文摘Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.
基金This work was supported by the National Natural Science Foundation of China(Grant No.31671632No.31701325)Green Farming and Mechanical Innovation Team of Fruit Harvesting under Soil.
文摘Hyperspectral imaging was applied to classify the damaged wheat kernels and healthy kernels.The spectral information was extracted from damaged wheat kernels and healthy kernels samples.The effective wavelengths were obtained from spectral of 865-1711 nm by X-loadings of principal component analysis(PCA)and successive projection algorithm(SPA)method,respectively.Partial least square method(PLS)and least square-support vector machine(LS-SVM)were then used to build classification models on full spectral data and effective wavelengths dataset,respectively.The results showed that the classification accuracy of every LS-SVM model was the best,being 100%.While the accuracy of the PLS model was slightly lower,still over 97%.The confusion matrix showed that several damaged wheat kernels samples were misclassified as healthy samples,while all healthy samples were correctly classified.The overall results indicated that hyperspectral imaging could be used for discriminating the damaged wheat kernels and could provide a reference for detecting other grain kernels grading degrees.Further,this study can provide a research basis for the development of online or portable detectors on grain damaged kernels recognition,which will be beneficial for grain grading or post-harvest quality processing of other grains.
基金National Natural Science Foundation of China(31501228,61473235,41301450)Natural Science Foundation of Shaanxi Province(2015JM3110)+3 种基金Fundamental Research Funds for the Central Universities(Z109021561,QN2013062,2452015381)Scientific Research Foundation for Doctor,Northwest A&F University(2012BSJJ027)Comprehensive Innovation Technology Project of Shaanxi Province(2015KTZDNY01-06)Special Talent Fund of Shaanxi Province(Z111021303).
文摘The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels.Seventy-nine samples from 11 breeds of wheat kernels were collected.The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value.After comparing the prediction models of principal components regression(PCR)and partial least squares regression(PLSR)with various pretreatment methods,PLSR preprocessed by zero mean normalization(z score)function of MATLAB was found to obtain better prediction results than other regression models.Based on 10 latent variables of PLSR,the radial basis function(RBF)neural network was applied to improve the prediction,in which the coefficients of determination(R2)were greater than 0.92 for both the calibration set and validation set,while the corresponding RMSE values were 0.3496 and 0.4005,respectively.Therefore,hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels’protein content.
文摘对农作物品种正确分类是作物分类学的重要内容,为考察X-ray成像技术对小麦品种分类研究的有效性,基于软X-ray成像仪采集的3品种(Kama,Rosa and Canadian)每个品种70个籽粒,共210个籽粒样本的X-ray扫描图像,并针对其7个形态几何特征(面积、周长、紧致度、籽粒长度、宽度、偏斜度、种子腹沟长度),提出了一种使用Kernel-ICA的方法先对特征进行优化,再进行小麦品种的聚类与识别的方法,并与K-means、C-means 2种聚类方法以及基于工神经网络(ANN)和支持向量机(SVM)2种识别方法的分类结果进行比较,结果发现:分类正确率从高到低分别为:Kernel-ICA、SVM、C-means、K-means、BP-ANN,分类正确率分别为:91.9%、90.5%、89.5%、87.1%、86.9%。研究提出的Kernel-ICA的方法,聚类优化和识别能力较强,对软X-ray成像的小麦品种进行分类,已基本上满足农艺上对小麦品种分类需要,对农作物种质资源鉴别和作物品种分类研究具有积极意义。
基金supported by the National Major Program of China (2023ZD0406903)the Natural Science Foundation for Distinguished Young Scientists of Hubei Province (2021CFA058)+2 种基金the Young Topnotch Talent Cultivation Program of Hubei Provincethe National Natural Science Foundation of China (32001541)the China Postdoctoral Science Foundation (2021T140246).
文摘Despite recent advances in crop metabolomics,the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown.Here,we performed widely tar-geted metabolite profiling of kernels from three developmental stages(grain-filling kernels[FKs],mature kernels[MKs],and germinating kernels[GKs])using a population of 159 recombinant inbred lines.We de-tected 625 annotated metabolites and mapped 3173,3143,and 2644 metabolite quantitative trait loci(mQTLs)in FKs,MKs,and GKs,respectively.Only 52 mQTLs were mapped at all three stages,indicating the high stage specificity of the wheat kernel metabolome.Four candidate genes were functionally vali-dated by in vitro enzymatic reactions and/or transgenic approaches in wheat,three of which mediated the tricin metabolic pathway.Metaboliteflux efficiencies within the tricin pathway were evaluated,and su-perior candidate haplotypes were identified,comprehensively delineating the tricin metabolism pathway in wheat.Finally,additional wheat metabolic pathways were re-constructed by updating them to incorporate the 177 candidate genes identified in this study.Our work provides new information on variations in the wheat kernel metabolome and important molecular resources for improvement of wheat nutritional quality.
