scholarly journals Mechanism of Xinfeng Capsule on Adjuvant-Induced Arthritis via Analysis of Urinary Metabolomic Profiles

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hui Jiang ◽  
Jian Liu ◽  
Ting Wang ◽  
Jia-rong Gao ◽  
Yue Sun ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Treated Group ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Adjuvant Induced Arthritis ◽  
Xinfeng Capsule ◽  
Latent Structures ◽  
Potential Biomarkers ◽  
Tof Ms

We aimed to explore the potential effects of Xinfeng capsule (XFC) on urine metabolic profiling in adjuvant-induced arthritis (AA) rats by using gas chromatography time-of-flight mass spectrometry (GC-TOF/MS). GC-TOF/MS technology was combined with multivariate statistical approaches, such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA). These methods were used to distinguish the healthy group, untreated group, and XFC treated group and elucidate potential biomarkers. Nine potential biomarkers such as hippuric acid, adenine, and L-dopa were identified as potential biomarkers, indicating that purine metabolism, fat metabolism, amino acid metabolism, and energy metabolism were disturbed in AA rats. This study demonstrated that XFC is efficacious for RA and explained its potential metabolomics mechanism.

scholarly journals Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4146
Author(s):  
José Enrique Herbert-Pucheta ◽  
José Daniel Lozada-Ramírez ◽  
Ana E. Ortega-Regules ◽  
Luis Ricardo Hernández ◽  
Cecilia Anaya de Parrodi
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Field Gradient ◽  
Multivariate Statistical Analysis ◽  
Data Matrix ◽  
Cabernet Sauvignon ◽  
Gradient Echo ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

scholarly journals Tissue-based metabolomics profiling reveals metabolic signatures and major metabolic pathways of gastric cancer

2021 ◽  
Author(s):  
Yaqin Wang ◽  
Wenchao Chen ◽  
Kun Li ◽  
Gang Wu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Predictive Ability ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pentose Phosphate ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Dodecanoic Acid ◽  
Orthogonal Projections ◽  
Latent Structures

Abstract Purpose This study was aimed to screen differential metabolites between gastric cancer (GC) and paracancerous (PC) tissues and find new biomarkers of GC. Methods GC (n = 28) and matched PC (n = 28) tissues were collected and LC-MS/MS analyses were performed to detect metabolites of GC and PC tissues in positive and negative models. Principal component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis (OPLS-DA) were conducted to describe distribution of origin data and general separation and estimate the robustness and the predictive ability of our mode. Differential metabolites were screened based on criterion of variables with p value < 0.05 and VIP (variable importance in the projection) > 1.0. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic power of differential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to search for metabolite pathways and MetaboAnalyst was used for pathway enrichment analysis. Results Several metabolites were significantly changed in GC group compared with PC group. Thirteen metabolites with high VIP were chose and among which 1-methylnicotinamide, dodecanoic acid and sphinganine possessed high AUC values (AUC > 0.8) indicating an excellent discriminatory ability on GC. Pathways such as pentose phosphate pathway and histidine metabolism were focused based on differential metabolites demonstrating their effects on progress of GC. Conclusions In conclusion, we investigated the tissue-based metabolomics profile of GC and several differential metabolites and signaling pathways were focused. Further study is needed to verify those results.

scholarly journals Metabolomics profiling of Polygoni Multiflori Radix and Polygoni Multiflori Radix Preparata extracts using UPLC-Q/TOF-MS

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhaoyan Zhang ◽  
Liang Yang ◽  
Xiaoyan Huang ◽  
Yue Gao
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Alpha Linolenic Acid ◽  
Multivariate Statistical ◽  
Biochemical Indicators ◽  
Flight Mass Spectrometry ◽  
Underlying Mechanisms ◽  
Tof Ms

Abstract Background The side effects caused by Polygoni Multiflori Radix (PMR) and Polygoni Multiflori Radix Praeparata (PMRP) have often appeared globally. There is no research on the changes of endogenous metabolites among PMR- and PMRP-treated rats. The aim of this study was to evaluate the varying metabolomic effects between PMR- and PMRP-treated rats. We tried to discover relevant differences in biomarkers and endogenous metabolic pathways. Methods Hematoxylin and eosin staining and immunohistochemistry staining were performed to find pathological changes. Biochemical indicators were also measured, one-way analysis of variance with Dunnett’s multiple comparison test was used for biochemical indicators comparison among various groups. Metabolomics analysis based on ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-Q/TOF-MS) was performed to find the changes in metabolic biomarkers. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) were applied to reveal group clustering trend, evaluate and maximize the discrimination between the two groups. MetaboAnalyst 4.0 was performed to find and confirm the pathways. Results PMR extracts exhibited slight hepatotoxic effects on the liver by increasing aspartate and alanine aminotransferase levels. Twenty-nine metabolites were identified as biomarkers, belonging to five pathways, including alpha-linolenic acid metabolism, taurine and hypotaurine metabolism, glycerophospholipid metabolism, arginine and proline metabolism, and primary bile acid biosynthesis. Conclusion This study provided a comprehensive description of metabolomic changes between PMR- and PMRP-treated rats. The underlying mechanisms require further research.

scholarly journals Analysis of Metabolite Profiling in Human Endothelial Cells after Plasma Jet Treatment

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yanjie Yang ◽  
Dehui Xu ◽  
Ning Ning ◽  
Yujing Xu
Keyword(s):  
Endothelial Cells ◽  
Plasma Treatment ◽  
Metabolite Profiling ◽  
Atmospheric Plasma ◽  
Human Endothelial Cells ◽  
Metabolic Pathway Analysis ◽  
Orthogonal Projections ◽  
Blood Capillaries ◽  
Latent Structures ◽  
Tof Ms

Cold atmospheric plasma (CAP) is a novel technology, which has been widely applied in biomedicine, especially in wound healing, dermatological treatment, hemostasis, and cancer treatment. In most cases, CAP treatment will interact with innumerable blood capillaries. Therefore, it is important and necessary to understand the effects of CAP treatment on endothelial cell metabolism. In this study, the metabolite profiling of plasma treatment on endothelial cells was measured by gas chromatography tandem time-of-flight mass spectrometry (GC-TOF-MS). We found that 695 signals (metabolites) were detected by GC-TOF-MS and then evaluated using orthogonal projections to latent structures discriminant analysis (OPLS-DA). All the differential metabolites were listed, and proline and xanthosine were the two of the most downregulated metabolites by plasma treatment. By comprehensive metabolic pathway analysis with the KEGG pathway, we showed that alanine, aspartate, glutamate, and purine metabolism pathways were the most significantly suppressed after gas plasma treatment in human endothelial cells. Our finding gives an overall picture of the metabolic pathways affected by plasma treatment in endothelial cells.

scholarly journals Classification of Wines from Five Spanish Origin Denominations by Aromatic Compound Analysis

2010 ◽  
Vol 93 (6) ◽  
pp. 1916-1922 ◽  
Author(s):  
Cecilia Sáenz ◽  
Trinidad Cedráenzn ◽  
Susana Cabredo
Keyword(s):  
Discriminant Analysis ◽  
Solid Phase ◽  
Principal Component ◽  
Decanoic Acid ◽  
Multivariate Statistical ◽  
Ethyl Hexanoate ◽  
La Mancha ◽  
Diethyl Succinate ◽  
Ethyl Octanoate

Abstract Wine is a complex matrix in which aroma compounds play an important role in the characterization of the flavor pattern of a given wine. Twelve volatile compounds were determined in 244 samples of Spanish red wines from different denominations of origin: Rioja, Navarra, Valdepeas, La Mancha, and Cariena. The samples were analyzed by GC using headspace solid-phase microextraction. The concentration (mg/mL) intervals obtained were 3-methyl-butyl acetate (3.9 to 116), 3-methyl-1-butanol (93 to 724), ethyl hexanoate (0.8 to 39), 1-hexanol (0.3 to 6.7), ethyl octanoate (1.4 to 41), diethyl succinate (0.2 to 13), 2-phenyl ethyl acetate (0 to 5.3), hexanoic acid (0 to 8.3), geraniol (0 to 3.0), 2-phenylethanol (1.5 to 56), octanoic acid (0 to 20), and decanoic acid (0 to 3.3). Wines were classified by multivariate statistical methods: principal component analysis, and lineal discriminant analysis. A correct differentiation among wines according to their origin was obtained by lineal discriminant analysis.

scholarly journals Significant Changes in Metabolic Profiles after Intervention with Selenium and Coenzyme Q10 in an Elderly Population

Biomolecules ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 553
Author(s):  
Alehagen ◽  
Johansson ◽  
Aaseth ◽  
Alexander ◽  
Surowiec ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Coenzyme Q10 ◽  
Metabolic Profiling ◽  
Demographic Data ◽  
Principal Component ◽  
Elderly Persons ◽  
Double Blind ◽  
Orthogonal Projections ◽  
Healthy Elderly ◽  
Latent Structures

Selenium and coenzyme Q10 (SeQ10) are important for normal cellular function. Low selenium intake leads to increased cardiovascular mortality. Intervention with these substances with healthy elderly persons over a period of four years in a double-blind, randomised placebo-controlled prospective study showed reduced cardiovascular mortality, increased cardiac function, and a lower level of NT-proBNP. Therefore, we wanted to evaluate changes in biochemical pathways as a result of the intervention with SeQ10 using metabolic profiling. From a population of 443 healthy elderly individuals that were given 200 µg selenium and 200 mg coenzyme Q10, or placebo daily for four years, we selected nine males on active intervention and nine males on placebo for metabolic profiling in the main study. To confirm the results, two validation studies (study 1 n = 60 males, study 2 n = 37 males) were conducted. Principal component analyses were used on clinical and demographic data to select representative sets of samples for analysis and to divide the samples into batches for analysis. Gas chromatography time-of-flight mass spectrometry-based metabolomics was applied. The metabolite data were evaluated using univariate and multivariate approaches, mainly T-tests and orthogonal projections to latent structures (OPLS) analyses. Out of 95 identified metabolites, 19 were significantly decreased due to the intervention after 18 months of intervention. Significant changes could be seen in the pentose phosphate, the mevalonate, the beta-oxidation and the xanthine oxidase pathways. The intervention also resulted in changes in the urea cycle, and increases in the levels of the precursors to neurotransmitters of the brain. This adds information to previous published results reporting decreased oxidative stress and inflammation. This is the first-time metabolic profiling has been applied to elucidate the mechanisms behind an intervention with SeQ10. The study is small and should be regarded as hypothesis-generating; however, the results are interesting and, therefore, further research in the area is needed. This study was registered at Clinicaltrials.gov, with the identifier NCT01443780

Raman Spectroscopic Discrimination of Cell Response to Chemical and Physical Inactivation

2007 ◽  
Vol 61 (8) ◽  
pp. 812-823 ◽  
Author(s):  
Maria Fernanda Escoriza ◽  
Jeanne M. Van Briesen ◽  
Shona Stewart ◽  
John Maier
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Species Differentiation ◽  
Multivariate Statistical ◽  
Classification Rate ◽  
Dna And Rna ◽  
Spectral Bands ◽  
Viable Cells ◽  
Standard Plate

Raman spectroscopy was applied to study Escherichia coli and Staphylococcus epidermidis cells that were inactivated by different chemicals and stress conditions including starvation and high temperature. E. coli cells exposed to starvation conditions over several days lost viability at the same rate that spectral bands assigned to DNA and RNA bases decreased in intensity. Band intensities correlate with standard plate counts with R2 = 0.99 and R2 = 0.97, respectively. Principal components analysis and discriminant analysis multivariate statistical techniques were used to evaluate the spectral data collected. Significant changes were observed in the spectra of treated cells in comparison with their respective controls (samples without treatment). As a result, there was a significant differentiation between viable and non-viable cells (treated and non-treated cells) in the first and second principal component plots for all the treatments. Discriminant analysis was used along with PCA to estimate a classification rate based on viability status of the cells. Non-viable cells were differentiated from viable cells with classification rates that ranged between 60 and 90% for specific treatments (i.e., EDTA-treated cells versus control cells). The classification rate obtained considering all the treatments (non-viable cells) and controls (viable cells) at the same time for each of the species studied was 86%. The classification rate based on species differentiation when all the spectra (viable and non-viable) were used was 87%. These results suggest that Raman spectroscopy is a powerful tool that can be used to evaluate viability and to study metabolic changes in microorganisms. It is a robust method for bacterial identification even when high spectral variations are introduced.

scholarly journals Monitoring the Modifications of the Vitreous Humor Metabolite Profile after Death: An Animal Model

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Francesca Rosa ◽  
Paola Scano ◽  
Antonio Noto ◽  
Matteo Nioi ◽  
Roberta Sanna ◽  
...  
Keyword(s):  
Principal Component ◽  
Metabolite Profile ◽  
Vitreous Humor ◽  
Multivariate Statistical ◽  
H Nmr ◽  
Multivariate Statistical Approach ◽  
Compositional Changes ◽  
Latent Structures ◽  
Metabolomic Approach

We applied a metabolomic approach to monitor the modifications occurring in goat vitreous humor (VH) metabolite composition at different times (0, 6, 12, 18, and 24 hours) after death. The1H-NMR analysis of the VH samples was performed for the simultaneous determination of several metabolites (i.e., the metabolite profile) representative of the VHstatusat different times. Spectral data were analyzed by Principal Component Analysis (PCA) and by Orthogonal Projection to Latent Structures (OPLS) regression technique. PCA and OPLS suggested that different spectral regions were involved in time-related changes. The major time-related compositional changes, here detected, were the increase of lactate, hypoxanthine, alanine, total glutathione, choline/phosphocholine, creatine, andmyo-inositol and the decrease of glucose and 3-hydroxybutyrate. We attempted a speculative interpretation of the biological mechanisms underlying these changes. These results show that multivariate statistical approach, based on1H NMR metabolite profiling, is a powerful tool for detecting ongoing differences in VH composition and may be applied to investigate several physiological and pathological conditions.

201 USING THE TRANSCRIPTOME SIGNATURE TO PREDICT PREGNANCY SUCCESS IN BEEF COWS AT 6 DAYS AFTER ARTIFICIAL INSEMINATION

2015 ◽  
Vol 27 (1) ◽  
pp. 191
Author(s):  
S. Scolari ◽  
G. Pugliesi ◽  
S. C. S. Andrade ◽  
F. D'Alexandri ◽  
G. Gasparin ◽  
...  
Keyword(s):  
Pregnancy Outcome ◽  
Bos Indicus ◽  
Principal Component ◽  
Beef Cows ◽  
Endometrial Biopsy ◽  
Functional Enrichment ◽  
Marker Genes ◽  
High Fertility ◽  
Orthogonal Projections ◽  
Latent Structures

Pregnancy success is critical to the profitability of cattle operations. Attempts to reduce high rates of early embryonic loss mainly focus on the critical phase of embryo recognition by maternal tissue. However, the molecular events driving the uterine tissue toward a favourable stage, facilitating the maternal receptivity, are poorly understood. This study aimed to characterise the endometrial transcriptome profiles of pregnant versus nonpregnant beef cows during early pregnancy and attempted to define a potential set of marker genes that can be valuable for predicting pregnancy outcome. Therefore, pluriparous, cyclic Nellore (Bos indicus) cows were synchronized (n = 51) and artificially inseminated (n = 36) at detected oestrus using semen from a single high-fertility bull. Six days after AI (Day 6), jugular blood samples and an endometrial biopsy from the uterine horn contralateral to the ovary containing the CL were collected. Pregnancy diagnosis was performed by ultrasonography on Days 22 and 30. Based on pregnancy outcome, samples were retrospectively allocated to the following groups: pregnant (P; n = 6) and nonpregnant (NP; n = 5). Both groups had similar plasma progesterone concentrations on Day 6 (less than 1 ng mL–1 between lowest and greatest concentrations). Endometrial biopsies were submitted to RNA-Seq analysis in an Illumina single flow cell line (Illumina Inc., San Diego, CA). The 272 685 768 million filtered reads were mapped to the Bos taurus UMD3.1 reference genome and 14 654 genes were effectively analysed for differential expression between groups. Transcriptome data showed that 216 genes are differently expressed when comparing P v. NP endometrial tissue (adjusted P < 0.1). More specifically, 36 genes showed a significantly up-regulated expression for pregnant cows and 180 are up-regulated for non-pregant cows. Functional enrichment and pathway analyses revealed enriched expression of genes associated with extracellular matrix remodelling in NP cows and nucleotide binding, microsome, and vesicular fraction in P cows. From the 40 top-ranked genes, six that were down- and three that were up-regulated in pregnant cows were further analysed by qRT–PCR in an additional 26 cows. Subsequent quantitative expression data were evaluated using multivariate statistics. Both principal component analysis (PCA; R2 = 0.82 and Q2 = 0.40) and orthogonal projections to latent structures analysis (OPLS), using pregnancy as the dependent variable (R2Y = 0.95 and Q2 = 0.86), efficiently separated P from NP animals. In conclusion, this study characterizes a unique set of genes, expressed in the endometrium as early as 6 days after AI, that indicate a receptive state leading to pregnancy success. Furthermore, expression of such genes can be used as potential markers to efficiently predict pregnancy success.

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