Methylclostebol Metabolism Discovery by Untargeted Analysis.

Comparative analysis of targeted metabolomics: Dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

Elucidating the biological characteristics of overweight populations based on urine Raman spectroscopy and bioinformatics analysis.

This study aimed to characterize the metabolic composition of four types of commercially available chicken breeds [village chicken, colored broiler (Hubbard), broiler (Cobb), and spent layers (Dekalb)] by 1H NMR coupling and discriminate them using multivariate analysis. Five chickens were collected for each chicken breed based on the marketing age from the respective commercial farms. The orthogonal partial least squares discriminant analysis (OPLS‐DA) results showed an obvious separation of local village chickens from the other breeds based on the metabolites present in their serum and meat (pectoralis major). The cumulative values of Q2, R2X, and R2Y of the OPLS‐DA model for chicken serum were 0.722, 0.877, and 0.841. For the pectoralis major muscle, the cumulative values of Q2, R2X, and R2Y of the OPLS‐DA model were reported as 0.684, 0.781, and 0.786, respectively. The quality of both OPLS‐DA models was accepted by the cumulative values of Q2 ≥ 0.5 and R2 ≥ 0.65. The 1H NMR result with multivariate analysis has successfully distinguished local village chicken from the other three commercial chicken breeds based on serum and pectoralis major muscle. Nonetheless, colored broiler (Hubbard) was not distinguished from broiler (Cobb) and spent layers (Dekalb) in serum and pectoralis major, respectively. The OPLS‐DA assessment in this study identified 19 and 15 potential metabolites for discriminating different chicken breeds in serum and pectoralis major muscle, respectively. Some of the prominent metabolites identified include amino acids (betaine, glycine, glutamine, guanidoacetate, phenylalanine, and valine), nucleotides (IMP and NAD+), organic acids (lactate, malate, and succinate), peptide (anserine), and sugar alcohol (myo‐inositol).

생약의 원산지를 판별하는 논리적인 일련의 기준을 개발한다면, 현재 유통되는 한약을 좀 더 과학적으로 관리 할 수 있을 것이다. 이러한 노력은 전통적인 한약 산업 발전에 기여할 것이라고 사료된다. 산수유의 원산지 판별법을 개발하기 위해, 본 연구에서는 우선 국산 산수유와 중국산 산수유를 각각 수증기 증류하고 이 때 얻은 휘발성분을 GC/MS를 이용하여 분석하였다. NIST mass spectral library의 데이터베이스로부터 정성분석한 결과를 바탕으로 데이터를 범주화(binning)하여 변수를 얻고, 이에 대하여 PCA, OPLS-DA 등 다변량 통계 분석을 수행함으로써 신속, 정확하게 국산 산수유와 중국산 산수유의 산지를 판별할 수 있는 산지 판별모델을 확립하였다. 산지 판별모델 개발을 위해서 학습집합(n=53)을 분석하여 산지 판별모델을 수립한 후, 검증집합(n=12)을 산지 판별모델에 적용함으로써 그 타당성을 확인하였다. 더불어 1-ethylbutyl-hydroperoxide, nonadecane, butylated hydroxytoluene, 5β,7βH,10α-Eudesm-11-en-1α-ol, 7,9-bis (2-methyl-2-propanyl)-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione, 그리고 2-decyldodecyl-benzene 등 6개의 마커성분을 선정할 수 있었다. 최근에 NMR을 활용한 산수유 원산지 판별에 대한 보고는 있었으나, GC/MS를 기반으로 한 대사체학 연구기법을 이용하여 산지판별 모델을 제시하는 것은 최초의 보고로서 그 의미가 크다. 본 연구결과를 활용하여 한약의 원산지 판별모델 확립과 산수유 원산지의 과학적인 관리에 적용할 수 있으리라 사료된다. It is believed that traditional Korean medicines can be managed more scientifically through the development of logical criteria to verify their region of cultivation, and that this could contribute to the advancement of the traditional herbal medicine industry. This study attempted to determine such criteria for Sansuyu. The volatile compounds were obtained from 20 samples of domestic Corni fructus (Sansuyu) and 45 samples of Chinese Sansuyu by steam distillation. The metabolites were identified in the NIST Mass Spectral Library via the obtained gas chromatography/mass spectrometer (GC/MS) data of 53 training samples. Data binning at 0.2 min intervals was performed to normalize the number of variables used in the statistical analysis. Multivariate statistical analyses, such as principle component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using the SIMCA-P software package. Significant variables with a variable importance in the projection (VIP) score higher than 1.0 were obtained from OPLS-DA, and variables that resulted in a p-value of less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Finally, among the 11 variables extracted, 1-ethylbutyl-hydroperoxide (9.089 min), nonadecane (20.170 min), butylated hydroxytoluene (25.319 min), 5β,7βH,10α-eudesm-11-en-1α-ol (25.921 min), 7,9-bis(2-methyl-2-propanyl)-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione (34.257 min), and 2-decyldodecyl-benzene (54.717 min) were selected as markers to indicate the origin of Sansuyu. The statistical model developed was suitable for the determination of the geographical origin of Sansuyu. The cultivation areas of four Korean and eight Chinese Sansuyu samples were predicted via the established OPLS-DA model, and it was confirmed that 11 of the 12 samples were accurately classified.

This study was focused on distinguishing of genetically modified organism (Monsanto 89788 variety) and conventional soybeans by employing high-resolution mass spectrometry (HRMS) techniques for non-target screening of sample extracts. Two hyphenated instrumental platforms represented by (i) ultrahigh-performance liquid chromatography (U-HPLC) coupled to quadrupole/time of flight and (ii) ambient mass spectrometry with direct analysis in real time (DART) ion source-coupled OrbitrapMS were used. The statistical processing of generated data (metabolomic fingerprints) was performed by multivariate data analysis; principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) showed that both employed techniques enabled correct classification of genetically modified and conventional soybeans. In addition, some phosphatidylcholines and sugars were identified as the most significant markers.

Metabolomic studies of Aldama spp and other Asteraceae species to identify dual inhibitors of cyclooxygenase-1 and 5-lipoxygenase

BackgroundMetabolite profiling of biofluids has an emergent role in exploring the contribution of host-microbiome interactions to the treatment response of immune-mediated arthritis.[1]ObjectivesAs part of the FLORA trial (exploring the impact...

Metabolomics approaches provide a vast array of analytical datasets, which require a comprehensive analytical, statistical, and biochemical workflow to reveal changes in metabolic profiles. The biological interpretation of mass spectrometric metabolomics results is still obstructed by the reliable identification of the metabolites as well as annotation and/or classification. In this work, the whole Lemna minor (common duckweed) was extracted using various solvents and analyzed utilizing polarity-extended liquid chromatography (reversed-phase liquid chromatography (RPLC)-hydrophilic interaction liquid chromatography (HILIC)) connected to two time-of-flight (TOF) mass spectrometer types, individually. This study (introduces and) discusses three relevant topics for the untargeted workflow: (1) A comparison study of metabolome samples was performed with an untargeted data handling workflow in two different labs with two different mass spectrometers using the same plant material type. (2) A statistical procedure was observed prioritizing significant detected features (dependent and independent of the mass spectrometer using the predictive methodology Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). (3) Relevant features were transferred to a prioritization tool (the FOR-IDENT platform (FI)) and were compared with the implemented compound database PLANT-IDENT (PI). This compound database is filled with relevant compounds of the Lemnaceae, Poaceae, Brassicaceae, and Nymphaceae families according to analytical criteria such as retention time (polarity and LogD (pH 7)) and accurate mass (empirical formula). Thus, an untargeted analysis was performed using the new tool as a prioritization and identification source for a hidden-target screening strategy. Consequently, forty-two compounds (amino acids, vitamins, flavonoids) could be recognized and subsequently validated in Lemna metabolic profile using reference standards. The class of flavonoids includes free aglycons and their glycosides. Further, according to our knowledge, the validated flavonoids robinetin and norwogonin were for the first time identified in the Lemna minor extracts.

Phenolics and flavonoids in honey are considered as the main phytonutrients which not only act as natural antioxidants, but can also be used as floral markers for honey identification. In this study, the chemical profiles of phenolics and flavonoids, antioxidant competences including total phenolic content, DPPH and ABTS assays and discrimination using chemometric analysis of various Chinese monofloral honeys from six botanical origins (acacia, Vitex, linden, rapeseed, Astragalus and Codonopsis) were examined. A reproducible and sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was optimized and validated for the simultaneous determination of 38 phenolics, flavonoids and abscisic acid in honey. Formononetin, ononin, calycosin and calycosin-7-O-β-d-glucoside were identified and quantified in honeys for the first time. Principal component analysis (PCA) showed obvious differences among the honey samples in three-dimensional space accounting for 72.63% of the total variance. Hierarchical cluster analysis (HCA) also revealed that the botanical origins of honey samples correlated with their phenolic and flavonoid contents. Partial least squares-discriminant analysis (PLS-DA) classification was performed to derive a model with high prediction ability. Orthogonal partial least squares-discriminant analysis (OPLS-DA) model was employed to identify markers specific to a particular honey type. The results indicated that Chinese honeys contained various and discriminative phenolics and flavonoids, as well as antioxidant competence from different botanical origins, which was an alternative approach to honey identification and nutritional evaluation.

The study investigates a consistent technique for tracing the cashmere breeds and their geographical origins using mineral element fingerprinting from Inner Mongolia, China. 237 cashmere samples were collected from three different regions (Ordos, Chifeng and Alxa League) and four breeds (Albas, Mingan, Hanshan, Alxa white cashmere goats). Concentrations of 21 mineral elements were quantified by inductively coupled plasma mass spectrometry (ICP-MS) and assessed using multivariate statistical methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA). Among them, six elements (Mg, Mn, As, Sr, Ce and U; with Ce in regions and Sr in breeds, while the other five are common) discriminated significant regional and breed-specific differences (P < 0.05). Their concentrations ranged from 189.25-639.49 µg g-1, 7.48-16.24 µg g-1, 19.03-66.73 µg/100 g, 311.26-888.30 µg/100 g, 56.03-113.92 µg/100 g and 1.64-9.87 µg/100 g respectively across the regions with highly enriched Alxa samples. These six mineral elements served as key identifiers for traceability with high variable importance in projection (VIP > 1.0). The OPLS-DA model achieved excellent classification accuracy for both origin (R 2 = 0.97) and breeds (R 2 = 0.787). This novel study presents an integrated ICP-MS and OPLS-DA approach for authenticating geographical origins and breeds, providing a robust analytical basis for preventing fraud, certifying products and ensuring supply chain transparency in the luxury textile sector.

The ultra-high performance liquid chromatography( UPLC) characteristic fingerprints of Angelica dahurica and A. dahurica var. formosana were established. The compounds corresponding to common peaks were identified by ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry( UPLC-Q-TOF-MS/MS). The results were combined with chemometrics and quantitative analysis of multi-components with a single-marker method(QAMS) to study the quality control of A. dahurica and A. dahurica var. formosana. The separation was performed on a Titank C_(18) column(2. 1 mm × 150 mm, 1. 8 μm)with a mobile phase of acetonitrile-0. 2% formic acid at a flow rate of 0. 3 m L·min~(-1). The column temperature was 35 ℃ and the injection volume was 1. 2 μL. Seven batches of A. dahurica and 11 batches of A. dahurica var. formosana were injected and analyzed. The UPLC characteristic fingerprints of A. dahurica and A. dahurica var. formosana were established according to the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine( version 2012), and 19 and 20 characteristic peaks were matched respectively. The common peaks were identified by reference substance comparison and UPLC-Q-TOF-MS/MS. Cluster analysis(CA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA)were performed to analyze the chemical pattern recognition of A. dahurica and A. dahurica var. formosana. The results of CA and PCA could distinguish Angelicae Dahuricae Radix from different producing areas, and the differential quality markers of A. dahurica and A. dahurica var. formosana were obtained by OPLS-DA. With imperatorin as the internal reference, the relative correction factors of oxypeucedanin hydrate, byakangelicin, bergapten, isopimpinellin, oxypeucedanin, and isoimperatorin were 1. 310, 1. 069, 0. 729, 0. 633, 0. 753, and 1. 010, respectively. There was no significant difference between the QAMS and external standard method(ESM)results of each component, indicating that the QAMS established with imperatorin as the internal reference was accurate and reliable. The characteristic fingerprints, chemometrics, and QAMS established in this study can quickly and efficiently control the quality of A. dahurica and A. dahurica var. formosana.

1H NMR and MS based metabolomics study of the therapeutic effect of Cortex Fraxini on hyperuricemic rats

This study investigated the effect of high pressure processing (HPP) on the fatty acids and amino acids content in New Zealand Diamond Shell (Spisula aequilatera), Storm Shell (Mactra murchisoni), and Tua Tua (Paphies donacina) clams. The clam samples were subjected to HPP with varying levels of pressure (100, 200, 300, 400, 500, and 600 MPa) and holding times (5 and 600 s) at 20 °C. Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were deployed to fingerprint the discriminating amino and fatty acids post-HPP processing while considering their inherent biological variation. Aspartic acid (ASP), isoleucine (ILE), leucine (LEU), lysine (LYS), methionine (MET), serine (SER), threonine (THR), and valine (VAL) were identified as discriminating amino acids, while C18:0, C22:1n9, C24:0, and C25:5n3 were identified as discriminating fatty acids. These amino and fatty acids were then subjected to mixed model ANOVA. Mixed model ANOVA was employed to investigate the influence of HPP pressure and holding times on amino acids and fatty acids in New Zealand clams. A significant effect of pressure levels was reported for all three clam species for both amino and fatty acids composition. Additionally, holding time was a significant factor that mainly influenced amino acid content. butnot fatty acids, suggesting that hydrostatic pressure hardly causes hydrolysis of triglycerides. This study demonstrates the applicability of OPLS-DA in identifying the key discriminating chemical components prior to traditional ANOVA analysis. Results from this research indicate that lower pressure and shorter holding time (100 MPa and 5 s) resulted in the least changes in amino and fatty acids content of clams.

Authentication of the production season of Xinyang Maojian green tea using two-dimensional fingerprints coupled with chemometric multivariate calibration and pattern recognition analysis

Glycan heterogeneity on recombinant human erythropoietin (rEPO) product is considered to be one of the critical quality attributes, and similarity tests of glycan heterogeneities are required in the manufacturing process changes and developments of biosimilars. A method for differentiating highly complex and diverse glycosylations is needed to evaluate comparability and biosimilarity among rEPO batches and products manufactured by different processes. The glycan heterogeneities of nine rEPO products (four innovator products and five biosimilar products) were distinguished by multivariate analysis (MVA) using the peak area ratios of each glycan to the total peak area of glycans in mass spectra obtained by liquid chromatography/mass spectrometry (LC/MS) of N-glycans from rEPOs. Principal component analysis (PCA) using glycan profiles obtained by LC/MS proved to be a useful method for differentiating glycan heterogeneities among nine rEPOs. Using PC values as indices, we were able to visualize and digitalize the glycan heterogeneities of each rEPO. The characteristic glycans of each rEPO were also successfully identified by orthogonal partial least-squares discrimination analysis (OPLS-DA), an MVA method, using the glycan profile data. PCA values were useful for evaluating the relative differences among the glycan heterogeneities of rEPOs. The characteristic glycans that contributed to the differentiation were also successfully identified by OPLS-DA. PCA and OPLS-DA based on mass spectrometric data are applicable for distinguishing glycan heterogeneities, which are virtually indistinguishable on rEPO products.