scholarly journals Metabolite Profiling of Different Sweet sorghum cultivars Seeds

2019 ◽  
Author(s):  
Yaxing Zhou ◽  
Zhenguo Wang ◽  
Yan Li ◽  
Zhigang Li ◽  
Hui Liu ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Principal Component ◽  
Venn Diagram ◽  
Seed Color ◽  
Benzoic Acid Derivatives ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Comparison Groups ◽  
Different Color ◽  
Diagram Analysis

Abstract Background Sorghum bicolor is one of the most important cereal crops in the world and is widely grown in arid or semi-arid areas. Results This study focused on the metabolic Diversity of three Sweet sorghum cultivars with white, red, and purple seeds to elucidate the factors responsible for the differences in Seed color. We found 651 metabolites were divided into 24 categories including Lipids_Fatty acids, Lipids_Glycerolipids, Flavonoid, Benzoic acid derivatives, Anthocyanins, Nucleotide and its derivates, etc. Through clustering analysis, principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA), different samples were clearly separated. It shows that contained metabolites of three groups are quite different. There are 217 significantly different metabolites between Z27 and HC4 (106 down-regulated, 111up-regulated), 240 between Z6 and HC4 (58 down-regulated, 182 up-regulated), 199 between Z6 and Z27 (54 down-regulated, 135 up-regulated). Venn diagram analysis indicated that 45 the differential metabolites were common to all three comparison groups. Conclusions This study provides new insights into the differences of metabolites between different color seeds and provides theoretical basis for the sufficient utilization of Sweet sorghum cultivars.

scholarly journals Metabolite Profiling of Sorghum Seeds of Different Colors from Different Sweet Sorghum Cultivars Using a Widely Targeted Metabolomics Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yaxing Zhou ◽  
Zhenguo Wang ◽  
Yan Li ◽  
Zhigang Li ◽  
Hui Liu ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Principal Component ◽  
Seed Color ◽  
Targeted Metabolomics ◽  
Phenotypic Differences ◽  
Benzoic Acid Derivatives ◽  
Orthogonal Signal Correction ◽  
Significant Difference ◽  
Comprehensive Comparison ◽  
Widely Targeted Metabolomics

Sweet sorghum (Sorghum bicolor) is one of the most important cereal crops in the world with colorful seeds. To study the diversity and cultivar-specificity of phytochemicals in sweet sorghum seeds, widely targeted metabolomics was used to analyze the metabolic profiles of the white, red, and purple seeds from three sweet sorghum cultivars Z6, Z27, and HC4. We identified 651 metabolites that were divided into 24 categories, including fatty acids, glycerolipids, flavonoids, benzoic acid derivatives, anthocyanins, and nucleotides and its derivatives. Among them, 217 metabolites were selected as significantly differential metabolites which could be related to the seed color by clustering analysis, principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA). A significant difference was shown between the red seed and purple seed samples, Z27 and HC4, in which 106 were downregulated and 111 were upregulated. The result indicated that 240 metabolites were significantly different, which could be related to the purple color with 58 metabolites downregulated and 182 metabolites upregulated. And 199 metabolites might be involved in the red phenotype with 54 downregulated and 135 upregulated. There were 45 metabolites that were common to all three cultivars, while cyanidin O-malonyl-malonyl hexoside, cyanidin O-acetylhexoside, and cyanidin 3-O-glucosyl-malonylglucoside were significantly upregulated red seeds, which could be the basis for the variety of seed colors. Generally, our findings provide a comprehensive comparison of the metabolites between the three phenotypes of S. bicolor and an interpretation of phenotypic differences from the point of metabolomics.

scholarly journals Analysis of Flavonoid Metabolites in Buckwheat Leaves Using UPLC-ESI-MS/MS

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1310 ◽  
Author(s):  
Jing Li ◽  
Pu Yang ◽  
Qinghua Yang ◽  
Xiangwei Gong ◽  
Hongchi Ma ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Ultra Performance Liquid Chromatography ◽  
Tartary Buckwheat ◽  
Biological Functions ◽  
Common Buckwheat ◽  
Flavonoid Content ◽  
Esi Ms ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Flavonoids from plants are particularly important in our diet. Buckwheat is a special crop that is rich in flavonoids. In this study, four important buckwheat varieties, including one tartary buckwheat and three common buckwheat varieties, were selected as experimental materials. The total flavonoid content of leaves from red-flowered common buckwheat was the highest, followed by tartary buckwheat leaves. A total of 182 flavonoid metabolites (including 53 flavone, 37 flavonol, 32 flavone C-glycosides, 24 flavanone, 18 anthocyanins, 7 isoflavone, 6 flavonolignan, and 5 proanthocyanidins) were identified based on Ultra Performance Liquid Chromatography–Electrospray Ionization–Tandem Mass Spectrometry (UPLC-ESI-MS/MS) system. Through clustering analysis, principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA), different samples were clearly separated. Considerable differences were observed in the flavonoid metabolites between tartary buckwheat leaves and common buckwheat leaves, and both displayed unique metabolites with important biological functions. This study provides new insights into the differences of flavonoid metabolites between tartary buckwheat and common buckwheat leaves and provides theoretical basis for the sufficient utilization of buckwheat.

scholarly journals Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden

Agroteknika ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 67-74
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Cucumis Melo L

Kadar air merupakan salah satu atribut kualitas yang penting pada komoditas hortikultura. Penetapan kadar air buah melon dengan metode konvensional memakan waktu yang lama dan perlu merusak sampel buah. Penelitian ini bertujuan untuk memprediksi kadar air buah melon golden menggunakan teknologi visible-near infrared spectroscopy (Vis-NIRS). Metode koreksi spektra orthogonal signal correction (OSC) diterapkan pada spektra original untuk meningkatkan kehandalan model kalibrasi. Partial least squares regression (PLSR) digunakan sebagai metode pendekatan regresi untuk mengekstraksi data spektra Vis-NIRS. Hasil penelitian membuktikan bahwa Vis-NIRS dapat diandalkan untuk memprediksi kadar air buah melon golden. Metode koreksi spektra OSC mampu memperkecil jumlah principal component (PC) pada spektra original. Linieritas pada model kalibrasi menggunakan spektra OSC tercatat memperoleh nilai tertinggi sebesar 0,92. Ratio of performance to deviation (RPD) pada spektra OSC menampilkan nilai tertinggi pula yaitu 3,63. Model kalibrasi yang diperoleh pada penelitian ini dapat ditransfer ke dalam spektrometer Vis-NIRS untuk prediksi kadar air melon golden secara cepat dan simultan.

scholarly journals Screening for Key Pathways Associated with the Development of Osteoporosis by Bioinformatics Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Yanqing Liu ◽  
Yueqiu Wang ◽  
Yanxia Zhang ◽  
Zhiyong Liu ◽  
Hongfei Xiang ◽  
...  
Keyword(s):  
Rho Gtpases ◽  
Bioinformatics Analysis ◽  
Venn Diagram ◽  
Mitotic Metaphase ◽  
Profile Data ◽  
Functional Interaction ◽  
Pathway Enrichment ◽  
Comparison Groups ◽  
Key Pathways ◽  
Diagram Analysis

Objectives.We aimed to find the key pathways associated with the development of osteoporosis.Methods.We downloaded expression profile data of GSE35959 and analyzed the differentially expressed genes (DEGs) in 3 comparison groups (old_op versus middle, old_op versus old, and old_op versus senescent). KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses were carried out. Besides, Venn diagram analysis and gene functional interaction (FI) network analysis were performed.Results.Totally 520 DEGs, 966 DEGs, and 709 DEGs were obtained in old_op versus middle, old_op versus old, and old_op versus senescent groups, respectively. Lysosome pathway was the significantly enriched pathways enriched by intersection genes. The pathways enriched by subnetwork modules suggested that mitotic metaphase and anaphase and signaling by Rho GTPases in module 1 had more proteins from module.Conclusions.Lysosome pathway, mitotic metaphase and anaphase, and signaling by Rho GTPases may be involved in the development of osteoporosis. Furthermore, Rho GTPases may regulate the balance of bone resorption and bone formation via controlling osteoclast and osteoblast. These 3 pathways may be regarded as the treatment targets for osteoporosis.

scholarly journals Venn diagram analysis overestimates the extent of circadian rhythm reprogramming

FEBS Journal ◽  
2021 ◽  
Author(s):  
Anne Pelikan ◽  
Hanspeter Herzel ◽  
Achim Kramer ◽  
Bharath Ananthasubramaniam
Keyword(s):  
Circadian Rhythm ◽  
Venn Diagram ◽  
Diagram Analysis

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Orthogonal Signal Correction for Voltammetry

2010 ◽  
Vol 22 (5) ◽  
pp. 564-574 ◽  
Author(s):  
Małgorzata Jakubowska
Keyword(s):  
Orthogonal Signal ◽  
Orthogonal Signal Correction ◽  
Signal Correction
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