Discovery and validation of biomarkers for Zhongning goji berries using liquid chromatography mass spectrometry
01 Jan 2020-Journal of Chromatography A-
TL;DR: A nontargeted metabolomics approach based on ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was used to find the differential composition between ZNG and NZNG and showed that two sets of combinative biomarkers to distinguish ZNG from NZNG with good sensitivity and specificity.
Abstract: Daodi medicinal material (DMM), which is traditional Chinese herbal medicine that has been used for long periods and have gained credibility in clinical practice, is part of the Chinese culture. However, Zhongning Goji berries (ZNG), a DMM, are illegally adulterated in the market by adding non Zhongning goji berries (NZNG). Consequently, the development of biomarker(s) is necessary for proper identification of ZNG and NZNG. In this study, a nontargeted metabolomics approach based on ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was used to find the differential composition between ZNG and NZNG. Using a combination of single-factor and multivariate statistical analyses, seven compounds with significant differences were discovered and identified, one of which was an unreported compound (a glycoside of pyrrolidine alkaloid). These compounds could be used as single biomarkers for receiver operating characteristic (ROC) analysis. In particular, the binary logistic regression result showed that two sets of combinative biomarkers to distinguish ZNG from NZNG with good sensitivity and specificity. Moreover, there was a significant positive correlation between the two combinative biomarkers and the glycoside of pyrrolidine alkaloid. The results of this study provide new ideas on the developments of ZNG identification, authenticity control and against adulteration in the Chinese circulation market.
TL;DR: Optimal analytical methods for metabolic profiling in the fruits of three Solanaceae species, reported here for the first time to the authors' knowledge, revealed compounds discriminating the Lycium species were more abundant in Lycium chinense, whereas Lycium barbarum accumulated more lycibarbarphenylpropanoids A-B, coumaric acid, fructose and glucose.
Abstract: Metabolic profile is a key component of fruit quality, which is a challenge to study due to great compound diversity, especially in species with high nutritional value. This study presents optimized analytical methods for metabolic profiling in the fruits of three Solanaceae species: Lycium barbarum, Lycium chinense and Solanumlycopersicum. It includes the most important chemical classes involved in nutrition and taste, i.e., carotenoids, phenolic compounds and primary compounds. Emphasis has been placed on the systematic achievement of good extraction yields, sample stability, and high response linearity using common LC-ESI-TQ-MS and GC-EI-MS apparatuses. A set of 13 carotenoids, 46 phenolic compounds and 67 primary compounds were profiled in fruit samples. Chemometrics revealed metabolic markers discriminating Lycium and Solanum fruits but also Lycium barbarum and Lycium chinense fruits and the effect of the crop environment. Typical tomato markers were found to be lycopene, carotene, glutamate and GABA, while lycibarbarphenylpropanoids and zeaxanthin esters characterized goji (Lycium spp.) fruits. Among the compounds discriminating the Lycium species, reported here for the first time to our knowledge, chlorogenic acids, asparagine and quinic acid were more abundant in Lycium chinense, whereas Lycium barbarum accumulated more lycibarbarphenylpropanoids A-B, coumaric acid, fructose and glucose.
Cites background from "Discovery and validation of biomark..."
..., who found biomarkers of geographic origin of goji berries, but also pesticide traces and putative markers of medicinal material adulteration....
01 Jan 2021
TL;DR: In this article, the authors proposed a method to understand the full genetic diversity and pathogenicity of leprosy and tuberculosis using the conventional genomic and proteomic approaches, which can assist the clinicians in making a judgment.
Abstract: Tuberculosis (TB) and leprosy (caused by mycobacterial pathogens) are two age-old infections, which we are facing even today. India is a major contributor to the global burden of leprosy and tuberculosis, which adversely affects the diverse communities as well as having a prevalence in different parts of the country. Timely diagnostics and effective treatment are very challenging, and the emergence of drug resistance has further complicated the management of these mycobacterial diseases. Various lineages of these mycobacterial pathogens show varying phenotypes in terms of clinical presentations and treatment outcomes. Altogether these factors make it further difficult to understand the full genetic diversity and pathogenicity of these pathogens using the conventional genomic and proteomic approaches. However, thanks to the recent technological advances in the genomics and proteomics field, many of these constraints have been suitably addressed. While it is relatively simpler to produce the omics data in a high-throughput manner, the bottleneck now is the pace to assimilate this large data into some useful information to reach a relevant, meaningful conclusion in a timely manner to assist the clinician in making a judgment.
TL;DR: In this article , a UPLC-MS/MS-based, widely targeted metabolomics approach was adapted to compare the chemical composition of Chinese yam and loessial soil.
Abstract: Chinese yam (Dioscorea opposita Thunb. cv. Tiegun), a type of homologous medicinal plant, mainly grows in sandy soil (SCY) and loessial soil (LCY). However, the effects of the soil on the metabolites in SCY and LCY remain unclear. Herein, this study aims to comprehensively elucidate the metabolites in SCY and LCY. A UPLC-MS/MS-based, widely targeted metabolomics approach was adapted to compare the chemical composition of SCY and LCY. A total of 988 metabolites were detected, including 443 primary metabolites, 510 secondary metabolites, and 35 other compounds. Notably, 177 differential metabolites (classified into 12 categories) were identified between SCY and LCY; among them, 85.9% (152 differential metabolites) were upregulated in LCY. LCY significantly increased the contents of primary metabolites such as 38 lipids and 6 nucleotides and derivatives, as well as some secondary metabolites such as 36 flavonoids, 28 phenolic acids, 13 alkaloids, and 6 tannins. The results indicate that loessial soil can improve the nutritional and medicinal value of D. opposita.
TL;DR: New database visualization tools and new data content have been added or enhanced to the HMDB, which includes better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps.
Abstract: The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40,000 (a 600% increase). This enormous expansion is a result of the inclusion of both 'detected' metabolites (those with measured concentrations or experimental confirmation of their existence) and 'expected' metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).
TL;DR: This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAn analyst 3.0), followed by eight detailed protocols.
Abstract: MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc.
TL;DR: A detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts is described.
Abstract: Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 samples.
TL;DR: A practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites is described, representing a robust method for untargeted metabolic screening of tissue samples.
Abstract: Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ∼48 h: 1 h for system setup, 8–10 h for sample preparation, 34 h for UPLC-MS analysis and 2–3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.
TL;DR: It has been demonstrated that wild grown species generally contain more phenolics than cultivated ones, and this information is interesting for nutritionists as well as berry growers and breeders who can promote the cultivation of species and new cultivars with higher phenolic content.
Abstract: Sugars, organic acids, and total phenolic content in fruit of 25 wild and cultivated berry species were identified and quantified with high-performance liquid chromatograph. The composition of sugars, organic acids, and total phenolic compounds in various species of Vaccinium, Rubus, Ribes, and Fragaria genus was evaluated. Additonally, total phenolics of less known berry species of the Morus, Amelanchier, Sorbus, Sambucus, Rosa, Lycium, Actinidia, and Aronia genus were determined in wild growing as well as in cultivated fruits. Significant differences in the concentration of sugars and organic acids were detected among the berry species. Glucose and fructose were the most abundant sugars in berry fruits and the major organic acids were malic and citric acid. However, in kiwi fruit, sucrose represented as much as 71.9% of total sugars. Sorbitol has been detected and quantified in chokeberry, rowanberry, and eastern shadbush fruit. The highest content of total analyzed sugars was determined in rowanberry fruit, followed by dog rose, eastern shadbush, hardy kiwifruit, American cranberry, chokeberry, and jostaberry fruit. Rowanberry stands out as the fruit with the highest content of total analyzed organic acids, followed by jostaberry, lingonberry, red gooseberry, hardy kiwifruit, and black currant. The berries of white gooseberry, black currant, red currant, and white currant had the lowest sugar/organic acid ratio and were thus perceptively the sourest species analyzed. On the other hand, the species with highest sugar/organic acid ratio were goji berry, eastern shadbush, black mulberry, and wild grown blackberry. The highest amounts of total phenols were quantified in chokeberry fruit. Wild strawberry, raspberry, and blackberry had 2- to 5-fold more total phenolics compared to cultivated plants. Practical Application: The fruit of analyzed berry species contained different levels of sugars, organic acids, and total phenolics. Moreover, it has been demonstrated that wild grown species generally contain more phenolics than cultivated ones. This information is interesting for nutritionists as well as berry growers and breeders who can promote the cultivation of species and new cultivars with higher phenolic content.