scholarly journals Nontarget Metabolomics of Grape Seed Metabolites Produced by Various Scion–Rootstock Combinations

2020 ◽  
Vol 145 (4) ◽  
pp. 247-256
Author(s):  
Zhijun Zhang ◽  
Huaifeng Liu ◽  
Junli Sun ◽  
Songlin Yu ◽  
Wang He ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Metabolic Pathways ◽  
Tricarboxylic Acid Cycle ◽  
Principal Component ◽  
Grape Seed ◽  
Grape Seeds ◽  
And Control

The use of resistant rootstocks is an inevitable trend in the development and production of grapes (Vitis sp.). The present study analyzed differences in the metabolites in grape seeds of different rootstock combinations (1103P, 5C, SO4, 3309C, 140R, and control) grafted onto ‘Cabernet Sauvignon’ (CS) wine grape (Vitis vinifera) scions (control, CS/CS, self-rooted grafting vines) using liquid chromatography–mass spectrometry (LC-MS) and nontargeted metabolomic techniques. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal-partial least squares discriminant analysis identified 30 significant metabolites and 22 metabolic pathways in the seeds of CS that differed significantly from the control seeds. This study revealed that rootstocks influence metabolite concentrations and metabolic pathways (alanine–aspartate–glutamate pathway, arginine-proline pathway, and the tricarboxylic acid cycle) in the scion onto which they are grafted. The rootstocks increased the concentration of delphinidin-3-(6-acetylglucoside), peonidin 3-(6-p-coumarylglucoside), L-threonine, and D-tartaric in CS seeds. Appropriate rootstock combinations can be used to improve the quality of grape seeds by changing the concentrations of amino acids, organic acids, polyphenols, and vitamin B. This study provides a theoretical basis for selecting grape rootstocks and provides important insights for improving the quality of commercial products derived from grape seeds.

scholarly journals Human Melanoma-Cell Metabolic Profiling: Identification of Novel Biomarkers Indicating Metastasis

2020 ◽  
Vol 21 (7) ◽  
pp. 2436 ◽  
Author(s):  
Mariangela Kosmopoulou ◽  
Aikaterini F. Giannopoulou ◽  
Aikaterini Iliou ◽  
Dimitra Benaki ◽  
Aristeidis Panagiotakis ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
High Resolution ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cell Lines ◽  
High Performance ◽  
Principal Component ◽  
Metastatic Tumor ◽  
Human Melanoma ◽  
Melanoma Metastasis

Melanoma is the most aggressive type of skin cancer, leading to metabolic rewiring and enhancement of metastatic transformation. Efforts to improve its early and accurate diagnosis are largely based on preclinical models and especially cell lines. Hence, we herein present a combinational Nuclear Magnetic Resonance (NMR)- and Ultra High Performance Liquid Chromatography-High-Resolution Tandem Mass Spectrometry (UHPLC-HRMS/MS)-mediated untargeted metabolomic profiling of melanoma cells, to landscape metabolic alterations likely controlling metastasis. The cell lines WM115 and WM2664, which belong to the same patient, were examined, with WM115 being derived from a primary, pre-metastatic, tumor and WM2664 clonally expanded from lymph-node metastases. Metabolite samples were analyzed using NMR and UHPLC-HRMS. Multivariate statistical analysis of high resolution NMR and MS (positive and negative ionization) results was performed by Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), while metastasis-related biomarkers were determined on the basis of VIP lists, S-plots and Student’s t-tests. Receiver Operating Characteristic (ROC) curves of NMR and MS data revealed significantly differentiated metabolite profiles for each cell line, with WM115 being mainly characterized by upregulated levels of phosphocholine, choline, guanosine and inosine. Interestingly, WM2664 showed notably increased contents of hypoxanthine, myo-inositol, glutamic acid, organic acids, purines, pyrimidines, AMP, ADP, ATP and UDP(s), thus indicating the critical roles of purine, pyrimidine and amino acid metabolism during human melanoma metastasis.

scholarly journals Harvest year effects on Apulian EVOOs evaluated by1H NMR based metabolomics

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2740 ◽  
Author(s):  
Chiara R. Girelli ◽  
Laura Del Coco ◽  
Paride Papadia ◽  
Sandra A. De Pascali ◽  
Francesco P. Fanizzi
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Metabolic Profile ◽  
Principal Component ◽  
Summer Rainfall ◽  
Weather Conditions ◽  
Genetic Identification ◽  
Mahalanobis Distances ◽  
Olive Trees

Nine hundred extra virgin olive oils (EVOO) were extracted from individual olive trees of four olive cultivars (Coratina, Cima di Mola, Ogliarola, Peranzana), originating from the provinces of Bari and Foggia (Apulia region, Southern Italy) and collected during two consecutive harvesting seasons (2013/14 and 2014/15). Following genetic identification of individual olive trees, a detailed Apulian EVOO NMR database was built using 900 oils samples obtained from 900 cultivar certified single trees. A study on the olive oil lipid profile was carried out by statistical multivariate analysis (Principal Component Analysis, PCA, Partial Least-Squares Discriminant Analysis, PLS-DA, Orthogonal Partial Least-Squares Discriminant Analysis, OPLS-DA). Influence of cultivar and weather conditions, such as the summer rainfall, on the oil metabolic profile have been evaluated. Mahalanobis distances andJ2criterion have been measured to assess the quality of resulting scores clusters for each cultivar in the two harvesting campaigns. The four studied cultivars showed non homogeneous behavior. Notwithstanding the geographical spread and the wide number of samples, Coratina showed a consistent behavior of its metabolic profile in the two considered harvests. Among the other three Peranzana showed the second more consistent behavior, while Cima di Mola and Ogliarola having the biggest change over the two years.

scholarly journals Multivariate Methods Based Soft Measurement for Wine Quality Evaluation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Shen Yin ◽  
Lei Liu ◽  
Xin Gao ◽  
Hamid Reza Karimi
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Multivariate Methods ◽  
Least Squares Regression ◽  
Wine Quality ◽  
Soft Measurement

Soft measurement is a new, developing, and promising industry technology and has been widely used in the industry nowadays. This technology plays a significant role especially in the case where some key variables are difficult to be measured by traditional measurement methods. In this paper, the quality of the wine is evaluated given the wine physicochemical indexes according to multivariate methods based soft measurement. The multivariate methods used in this paper include ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). By comparing the performance of the four methods, the MPLSR prediction model shows superior results than the others. In general, to determine the quality of the wine, experienced wine tasters are hired to taste the wine and make a decision. However, since the physicochemical indexes of wine can to some extent reflect the quality of wine, the multivariate statistical methods based soft measure can help the oenologist in wine evaluation.

An Integrated Strategy to Qualitatively Differentiate Components of Raw and Processed Viticis Fructus Based on NIR, HPLC and UPLC-MS Analysis

Planta Medica ◽  
2018 ◽  
Vol 84 (17) ◽  
pp. 1280-1291 ◽  
Author(s):  
Jiayin Diao ◽  
Can Xu ◽  
Huiting Zheng ◽  
Siyi He ◽  
Shumei Wang
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Principal Component ◽  
Close Correlation ◽  
Chinese Herbs ◽  
Plot Analysis ◽  
Near Infrared Spectra ◽  
Processed Products

AbstractViticis Fructus is a traditional Chinese herbal drug processed by various methods to achieve different clinical purposes. Thermal treatment potentially alters chemical composition, which may impact on effectiveness and toxicity. In order to interpret the constituent discrepancies of raw versus processed (stir-fried) Viticis Fructus, a multivariate detection method (NIR, HPLC, and UPLC-MS) based on metabonomics and chemometrics was developed. Firstly, synergy interval partial least squares and partial least squares-discriminant analysis were employed to screen the distinctive wavebands (4319 – 5459 cm−1) based on preprocessed near-infrared spectra. Then, HPLC with principal component analysis was performed to characterize the distinction. Subsequently, a total of 49 compounds were identified by UPLC-MS, among which 42 compounds were eventually characterized as having a significant change during processing via the semiquantitative volcano plot analysis. Moreover, based on the partial least squares-discriminant analysis, 16 compounds were chosen as characteristic markers that could be in close correlation with the discriminatory near-infrared wavebands. Together, all of these characterization techniques effectively discriminated raw and processed products of Viticis Fructus. In general, our work provides an integrated way of classifying Viticis Fructus, and a strategy to explore discriminatory chemical markers for other traditional Chinese herbs, thus ensuring safety and efficacy for consumers.

scholarly journals Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

2021 ◽  
Vol 11 (9) ◽  
pp. 837
Author(s):  
Paulo V. G. Alabarse ◽  
Jordana M. S. Silva ◽  
Rafaela C. E. Santo ◽  
Marianne S. Oliveira ◽  
Andrelise S. Almeida ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Clinical Score ◽  
Resonance Spectroscopy ◽  
Muscle Loss ◽  
Least Squares Regression ◽  
Collagen Induced Arthritis ◽  
Exact Test ◽  
And Control

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher’s exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 102) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 102) days 55 vs. 65, histamine (VIPs 1.1 × 102) days 55 vs. 65, and L-methionine (VIPs 1.1 × 102) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (−0.177) and body weight- (−0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient’s urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

scholarly journals Multivariate statistical analysis of tropane alkaloids in Anisodus tanguticus (Maxim.) Pascher from different regions to trace geographical origins

2021 ◽  
Author(s):  
Chen Chen ◽  
Jingjing Li ◽  
Feng Xiong ◽  
Bo Wang ◽  
Yuanming Xiao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quantitative Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Geographical Origin ◽  
Principal Component ◽  
Tropane Alkaloids ◽  
Component Analysis ◽  
Tibet Plateau

Abstract Anisodus tanguticus (Maxim.) Pascher is an important Tibetan folk medicine and the source of tropane alkaloids (TAs) grown in Qinghai-Tibet Plateau. There are marked differences in quality of A. tanguticus from geographic areas. The aim of present research was to establish a method for the quantitative analysis of TAs coupled with chemometrics analysis to trace geographical origins. Qualitative analysis of TAs in A. tanguticus was carried out using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and quantitative analysis of TAs in different plant organs from different geographical origin was achieved. Contents of TAs were subjected to the principal component analysis, and orthogonal partial least-squares discriminant analysis. The contents of the three marker compounds (anisodamine, anisodine and atropine) in the roots and acrial parts of A. tanguticus were positive correlated and varied significantly from different geographical origins. Principal component analysis, and orthogonal partial least-squares discriminant analysis results showed excellent discrimination between different geographical origin of A. tanguticus. This study could provide comprehensive evaluation and further utilization of A. tanguticus resources.

scholarly journals Distinguishing Smilax glabra and Smilax china rhizomes by flow-injection mass spectrometry combined with principal component analysis

2018 ◽  
Vol 68 (1) ◽  
pp. 87-96
Author(s):  
Jian Liang ◽  
Meng Zhou ◽  
Lin-Yu Li ◽  
Ji-Cheng Shu ◽  
Yong-Hong Liang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Flow Injection ◽  
Principal Component ◽  
Component Analysis ◽  
Chemometric Method ◽  
Predictive Study

Abstract Flow-injection mass spectrometry (FIMS) coupled with a chemometric method is proposed in this study to profile and distinguish between rhizomes of Smilax glabra (S. glabra) and Smilax china (S. china). The proposed method employed an electrospray-time-of-flight MS. The MS fingerprints were analyzed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with the aid of SIMCA software. Findings showed that the two kinds of samples perfectly fell into their own classes. Further predictive study showed desirable predictability and the tested samples were successfully and reliably identified. The study demonstrated that the proposed method could serve as a powerful tool for distinguishing between S. glabra and S. china.

scholarly journals Exosome-Enriched Plasma Analysis as a Tool for the Early Detection of Hypertensive Gestations

2021 ◽  
Vol 12 ◽  
Author(s):  
Rodrigo Barbano Weingrill ◽  
Sandra Luft Paladino ◽  
Matheus Leite Ramos Souza ◽  
Eduardo Manoel Pereira ◽  
Aldilane Lays Xavier Marques ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Proton Nuclear Magnetic Resonance ◽  
Hypertensive Disorders Of Pregnancy ◽  
H Nmr ◽  
Hypertensive Disorders

Hypertensive disorders of pregnancy are closely associated with prematurity, stillbirth, and maternal morbidity and mortality. The onset of hypertensive disorders of pregnancy (HDP) is generally noticed after the 20th week of gestation, limiting earlier intervention. The placenta is directly responsible for modulating local and systemic physiology by communicating using mechanisms such as the release of extracellular vesicles, especially exosomes. In this study, we postulated that an analysis of exosome-enriched maternal plasma could provide a more focused and applicable approach for diagnosing HDP earlier in pregnancy. Therefore, the peripheral blood plasma of 24 pregnant women (11 controls, 13 HDP) was collected between 20th and 24th gestational weeks and centrifuged for exosome enrichment. Exosome-enriched plasma samples were analyzed by Raman spectroscopy and by proton nuclear magnetic resonance metabolomics (1H NMR). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the Raman data, from the spectral region of 600–1,800 cm–1, to determine its potential to discriminate between groups. Using principal component analysis, we were able to differentiate the two groups, with 89% of all variances found in the first three principal components. In patients with HDP, most significant differences in Raman bands intensity were found for sphingomyelin, acetyl CoA, methionine, DNA, RNA, phenylalanine, tryptophan, carotenoids, tyrosine, arginine, leucine, amide I and III, and phospholipids. The 1H NMR analysis showed reduced levels of D-glucose, L-proline, L-tyrosine, glycine, and anserine in HDP, while levels of 2-hydroxyvalerate, polyunsaturated fatty acids, and very-low-density lipoprotein (VLDL) were increased. 1H NMR results were able to assign an unknown sample to either the control or HDP groups at a precision of 88.3% using orthogonal partial least squares discriminant analysis and 87% using logistic regression analysis. Our results suggested that an analysis of exosome-enriched plasma could provide an initial assessment of placental function at the maternal-fetal interface and aid HDP diagnosis, prognosis, and treatment, as well as to detect novel, early biomarkers for HDP.

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