Development of a practical metabolite identification technique for non-targeted metabolomics

Buyang Huanwu Decoction Alleviates Atherosclerosis by Regulating gut Microbiome and Metabolites in Apolipoprotein E-deficient Mice fed with High-fat Diet

ABSTRACT

The traditional Chinese herbal prescription Buyang Huanwu decoction (BHD), effectively treats atherosclerosis. However, the mechanism of BHD in atherosclerosis remains unclear. We aimed to determine whether BHD could alleviate atherosclerosis by altering the microbiome-associated metabolic changes in atherosclerotic mice. An atherosclerotic model was established in apolipoprotein E-deficient mice fed high-fat diet, and BHD was administered through gavage for 12 weeks at 8.4 g/kg/d and 16.8 g/kg/d. The atherosclerotic plaque size, composition, serum lipid profile, and inflammatory cytokines, were assessed. Mechanistically, metabolomic and microbiota profiles were analyzed by liquid chromatography-mass spectrometry and 16S rRNA gene sequencing, respectively. Furthermore, intestinal microbiota and atherosclerosis-related metabolic parameters were correlated using Spearman analysis. Atherosclerotic mice treated with BHD exhibited reduced plaque area, aortic lumen occlusion, and lipid accumulation in the aortic root. Nine perturbed serum metabolites were significantly restored along with the relative abundance of microbiota at the family and genus levels but not at the phylum level. Gut microbiome improvement was strongly negatively correlated with improved metabolite levels. BHD treatment effectively slows the progression of atherosclerosis by regulating altered intestinal microbiota and perturbed metabolites.

Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

Comparison analysis of metabolite profiling in seeds and bark of Ulmus parvifolia, a Chinese medicine species

Ulmus parvifolia (U. parvifolia) is a Chinese medicine plant whose bark and leaves are used in the treatment of some diseases such as inflammation, diarrhea and fever. However, metabolic signatures of seeds have not been studied. The seeds and bark of U. parvifolia collected at the seed ripening stage were used for metabolite profiling analysis through the untargeted metabolomics approach. A total of 2,578 and 2,207 metabolites, while 503 and 132 unique metabolites were identified in seeds and bark, respectively. Additionally, 574 differential metabolites (DEMs) were detected in the two different organs of U. parvifolia, which were grouped into 52 classes. Most kinds of metabolites classed into prenol lipids class. The relative content of flavonoids class was the highest. DEMs contained some bioactive compounds (e.g., flavonoids, terpene glycosides, triterpenoids, sesquiterpenoids) with antioxidant, anti-inflammatory, and anti-cancer activities. Most kinds of flavonoids and sesquiterpenes were up-regulated in seeds. There were more varieties of terpene glycosides and triterpenoids showing up-regulated in bark. The pathway enrichment was performed, while flavonoid biosynthesis, flavone and flavonol biosynthesis were worthy of attention. This study identified DEMs with pharmaceutical value between seeds and bark during seed maturation and offered a molecular basis for alternative or complementary use of seeds and bark of U. parvifolia as a Chinese medicinal material.

Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks

Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation of exact peak-picked or "labeled" chromatograms since the exact start and end points were often unclear in overlapping peaks in real chromatograms. We present a design of a fake chromatogram generator, along with a method for deep learning of peak-picking neural networks. Fake chromatograms were generated by generation of fake peaks, random sampling of peak positions from feature distributions, and merging with real blank sample chromatograms. Information on the exact start and end points, as labeled on the fake chromatograms, were effective for training and evaluating peak-picking neural networks. The peak-picking neural networks constructed herein outperformed conventional peak-picking software and showed comparable performance with that of experienced operators for processing the widely targeted metabolome data. Results of this study indicate that generation of fake chromatograms would be crucial for developing peak-picking neural networks and a key technology for further improvement of peak picking neural networks.

Identification of characteristic heroin metabolites in urine based on data-mining technology and multivariate statistics analysis combined with a targeted verification approach for distinguishing heroin abusers

A common phenomenon shows that ingestion of opium poppy shell-containing drugs can result in a "false-positive" urinalysis test result for mandatory or workplace heroin abuse screening. Owing to the short detection window (8 h in urine) of the characteristic heroin metabolite 6-monoacetylmorphine (6-MAM) confirmation or exclusion of heroin abusers still presents major challenges for toxicologists. In this work, we developed an ultra-performance liquid chromatography-time-of-flight mass spectrometry method (UPLC-TOF-MS) with online data acquisition and multiple post-data-mining technologies combined with a multivariate statistical and batch validation analysis workflow to assess the characteristic urine metabolites of heroin abusers. Based on the proposed methods, 28 characteristic metabolites were structurally identified, and their fragmentation patterns and metabolite pathways were also summarized. Correlation analysis was used to investigate the internal relationship and similarities among the identified metabolites, and seven representative metabolites were selected as "Target-metabolites". Multi-batch urine of samples of heroin abusers were certified based on the UPLC-MS/MS method for further validation of the practicability of using this method for routine analysis. Overall, the target-metabolites can be utilized as assistant "biomarkers" in workplace or mandatory drug screenings. This approach encourages further studies on the development of the "false-positive" identification system.

Metabolite Profiling and Classification of Developing Styrax tonkinensis Kernels

Background: Styrax tonkinensis is an economic tree species with high timber, medicine, oil, and ornamental value. Its seed, containing a particularly high oil content, are widely studied for their biodiesel properties by nutritional components and oil body ultrastructure. However, their comprehensive biochemical compositions have not been studied. Methods: During S. tonkinensis kernel development, we collected samples from four time points for metabolite profiling and classification through gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. Results: A total of 187 and 1556 metabolites were obtained, respectively. All of the metabolites were grouped into 19 and 21 classes by their chemical properties and into 8 clusters based on their change trends, respectively. Among all the metabolites, carboxylic acids and derivatives, flavonoids, fatty acyls, glycerophospholipids, organooxygen compounds, prenol lipids, and steroids and steroid derivatives were the main components. Alanine, glutamine, tryptophan, tyrosine and valine were the five most abundant amino acids. Palmitic acid, stearic acid, oleic acid and linoleic acid were the four major free fatty acids. Flavans, flavonoid glycosides and o-methylated flavonoids were the three major flavonoids. The differential metabolites distributions between different time points were identified. A pathway enrichment was performed, which was mainly focused on three groups, amino acids metabolism, carbon flow from sucrose to lipid and secondary metabolites biosynthesis. Conclusions: It’s the first time to analyze the metabolite fingerprinting for developing S. tonkinensis kernels and identify varied kinds of flavonoids. We performed metabolite profiling, classification and pathway enrichment to assess the comprehensive biochemical compositions. Our results described the change in major metabolites and main metabolic processes during S. tonkinensis kernel development and provided a variety of bases for seed applications as biofuel or medicine.

Nontarget analysis: A new tool for the evaluation of wastewater processes

Strategies to determine the removal efficiency of micropollutants in wastewater treatment plants (WWTPs) are widely discussed. Especially the evaluation of the potential benefit of further advanced treatment steps such as an additional tertiary treatment based on ozonation or activated carbon have come into focus. Such evaluation strategies are often based on the removal behavior of known micropollutants via target or suspected analysis. The utilization of nontarget analysis is considered to lead to a more comprehensive picture as also unknown or not expected micropollutants are analyzed. Here, the results of an evaluation via target and nontarget analysis were compared for biological treatment (BT) processes of eleven full-scale WWTPs and three different post-treatments (PTs): one sand filter (SF) and two granular activated carbon (GAC) filters. The similarity of the determined removals from target and nontarget analysis of the BTs increased significantly by excluding easily degradable "features" from the nontarget evaluation. A similar ranking of the removal trends for the BTs could also be achieved by comparing this new subset of nontarget features with a set of nine readily to moderately biodegradable micropollutants. This observation suggests that a performance ranking of BTs based either on target or nontarget analysis is plausible. In contrast to the BTs, the evaluation of the three PTs revealed that the difference of feature removal between SF and the two GACs was small, but large for the target analytes with substantially higher removal effciencies for the GACs compared to the SF. In addition to the removal behavior, the nontarget analysis provided further information about the number and quantity of transformation products (TPs) in the effluent from the BTs. For all BTs more than half (55-67%) of the features detected in the effluent were not found in the influent. A comparable proportion of TPs was also detected after GAC and sand filtration due to their microbial activities.

Simultaneous identification and characterization of amanita toxins using liquid chromatography-photodiode array detection-ion trap and time-of-flight mass spectrometry and its applications

Rapid and accurate identification of multiple toxins for clinical diagnosis and treatment of mushroom poisoning cases is still a challenge, especially with the lack of authentic references. In this study, we developed an effective method for simultaneous identification of amanita peptide toxins by liquid chromatography coupled with photodiode array detection and ion trap time-of-flight mass spectrometry. The accuracy and selectivity of the methodology were validated through similar multiple fragmentation patterns and characteristic ions of standard α- and β-amanitin. The developed method could successfully separate and identify major toxic constituents in Amanita mushrooms. Two amatoxins and three phallotoxins were confirmed in a single run through their fragmentation patterns and characteristic ions, which can be used as diagnostic fragment ions to identify mushroom toxins in complex samples. Furthermore, the performance of the developed method was verified by using real biological samples, including plasma and urine samples collected from rats after intraperitoneal administration of toxins. Thus, the development methodology could be crucial for the accurate detection of mushroom toxins without standard references.

Metabolite fingerprinting of pennycress (Thlaspi arvense L.) embryos to assess active pathways during oil synthesis

Pennycress (Thlaspi arvense L.), a plant naturalized to North America, accumulates high levels of erucic acid in its seeds, which makes it a promising biodiesel and industrial crop. The main carbon sinks in pennycress embryos were found to be proteins, fatty acids, and cell wall, which respectively represented 38.5, 33.2, and 27.0% of the biomass at 21 days after pollination. Erucic acid reached a maximum of 36% of the total fatty acids. Together these results indicate that total oil and erucic acid contents could be increased to boost the economic competitiveness of this crop. Understanding the biochemical basis of oil synthesis in pennycress embryos is therefore timely and relevant to guide future breeding and/or metabolic engineering efforts. For this purpose, a combination of metabolomics approaches was conducted to assess the active biochemical pathways during oil synthesis. First, gas chromatography-mass spectrometry (GC-MS) profiling of intracellular metabolites highlighted three main families of compounds: organic acids, amino acids, and sugars/sugar alcohols. Secondly, these intermediates were quantified in developing pennycress embryos by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring mode. Finally, partitional clustering analysis grouped the intracellular metabolites that shared a similar pattern of accumulation over time into eight clusters. This study underlined that: (i) sucrose might be stored rather than cleaved into hexoses; (ii) glucose and glutamine would be the main sources of carbon and nitrogen, respectively; and (iii) glycolysis, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and the Calvin cycle were active in developing pennycress embryos.

Metabolite identification strategy of non-targeted metabolomics and its application for the identification of components in Chinese multicomponent medicine Abelmoschus manihot L

Identification of multicomponent in traditional Chinese medicine (TCM) is complex and time-consuming. The inspection of the full-scan mass chromatograms was usually performed manually, which is labor-intensive. It is difficult to distinguish low response signals from complex chemical background. Furthermore, this process is typically based on earlier knowledge of the chemical composition of TCM, and those molecules that have not been characterized earlier were thus ignored. In this paper, a strategy using UPLC-MS combined with pattern recognition analysis was developed to simplify and quicken the identification of multicomponent in Abelmoschus manihot (L.) Medik. First, complex signals obtained by UPLC-MS were processed using automated data mining algorithm and further processed with multivariate chemometric methods. Multicomponent in Abelmoschus manihot L. can be clearly displayed in S- and VIP-plot. Using this method, 320 peaks which present in Abelmoschus manihot L. were detected. In the next step, accurate mass spectra of the characteristic markers acquired by QTOF MS were used to estimate their elemental formulae and enable structure identification. By searching in METLIN database, 41 components were tentatively identified in Abelmoschus manihot L. Our results showed that UPLC-MS based-pattern recognition analysis approach can be used to quickly identify TCM multicomponent and for standardization of herbal preparations.

Method for the Compound Annotation of Conjugates in Nontargeted Metabolomics Using Accurate Mass Spectrometry, Multistage Product Ion Spectra and Compound Database Searching

Owing to biotransformation, xenobiotics are often found in conjugated form in biological samples such as urine and plasma. Liquid chromatography coupled with accurate mass spectrometry with multistage collision-induced dissociation provides spectral information concerning these metabolites in complex materials. Unfortunately, compound databases typically do not contain a sufficient number of records for such conjugates. We report here on the development of a novel protocol, referred to as ChemProphet, to annotate compounds, including conjugates, using compound databases such as PubChem and ChemSpider. The annotation of conjugates involves three steps: 1. Recognition of the type and number of conjugates in the sample; 2. Compound search and annotation of the deconjugated form; and 3. In silico evaluation of the candidate conjugate. ChemProphet assigns a spectrum to each candidate by automatically exploring the substructures corresponding to the observed product ion spectrum. When finished, it annotates the candidates assigning a rank for each candidate based on the calculated score that ranks its relative likelihood. We assessed our protocol by annotating a benchmark dataset by including the product ion spectra for 102 compounds, annotating the commercially available standard for quercetin 3-glucuronide, and by conducting a model experiment using urine from mice that had been administered a green tea extract. The results show that by using the ChemProphet approach, it is possible to annotate not only the deconjugated molecules but also the conjugated molecules using an automatic interpretation method based on deconjugation that involves multistage collision-induced dissociation and in silico calculated conjugation.

Identification of metabolic markers in coronary artery disease using an untargeted LC-MS based metabolomic approach

Coronary artery disease (CAD), a complex metabolic disorder, is one of the largest causes of death worldwide. Both environmental and genetic factors contribute to the etiology of this metabolic disease. The gene-environment interaction could lead to modulation of various metabolic pathways resulting in altered levels of various metabolites. Thus, identifying metabolites could aid in deciphering pathways that could be involved in the pathophysiology of the disease. With the advent of high resolution mass spectrometry based methodologies, it is now possible to screen thousands of metabolites in a single snapshot thus, allowing the identification of potential disease metabolite markers. In this work, using an untargeted metabolomic approach, we attempted to identify metabolites that have altered levels in CAD patients. Using reverse phase and HILIC based chromatography followed by mass spectrometry we identified a total of 32 metabolites (2 fold; p<0.05) in plasma whose levels were significantly altered in CAD samples. Further, we have validated the discriminative ability of these metabolites in an independent set of CAD and control samples using multivariate PLS-DA analysis. Interestingly, Lyso PC (18:0), Cortisol, Lyso PC (P-17:0), and glycerophosphocholine were among the top discriminators for CAD which implies involvement of phosphatidylcholine pathway in the pathogenesis of atherosclerosis.

BIOLOGICAL SIGNIFICANCE

Herein, we report that an unbiased metabolomic study has the potential to identify newer markers which are involved in several important biological pathways like lipid metabolism, phosphatidylcholine pathway etc. which in turn are implicated in CAD. These markers could be of potential clinical importance for screening subjects at risk of CAD. This article is part of a Special Issue entitled: Proteomics in India.

An integrated high resolution mass spectrometric data acquisition method for rapid screening of saponins in Panax notoginseng (Sanqi)

The aim of this study was to develop a convenient method without pretreatments for nontarget discovery of interested compounds. The segment and exposure strategy, coupled with two mass spectrometer data acquisition methods was firstly proposed for screening the saponins in extract of Panax notoginseng (Sanqi) via high-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (HPLC-QTOF/MS). By gradually removing certain major or moderate interference compounds, the developed segment and exposure strategy could significantly improve the detection efficiency for trace compounds. Moreover, the newly developed five-point screening approach based on a modified mass defect filter strategy and the visual isotopic ion technique was verified to be efficient and reliable in picking out the interested precursor ions. In total, 234 ginsenosides including 67 potential new ones were characterized or tentatively identified from the extract of Sanqi. Particularly, some unusual compounds containing the branched glycosyl group or new substituted acyl groups were firstly reported. The proposed integrated strategy held a strong promise for analyses of the complex mixtures.

Recent Advances in the Application of Metabolomics to Studies of Biogenic Volatile Organic Compounds (BVOC) Produced by Plant

In many plants, biogenic volatile organic compounds (BVOCs) are produced as specialized metabolites that contribute to the characteristics of each plant. The varieties and composition of BVOCs are chemically diverse by plant species and the circumstances in which the plants grow, and also influenced by herbivory damage and pathogen infection. Plant-produced BVOCs are receptive to many organisms, from microorganisms to human, as both airborne attractants and repellants. In addition, it is known that some BVOCs act as signals to prime a plant for the defense response in plant-to-plant communications. The compositional profiles of BVOCs can, thus, have profound influences in the physiological and ecological aspects of living organisms. Apart from that, some of them are commercially valuable as aroma/flavor compounds for human. Metabolomic technologies have recently revealed new insights in biological systems through metabolic dynamics. Here, the recent advances in metabolomics technologies focusing on plant-produced BVOC analyses are overviewed. Their application markedly improves our knowledge of the role of BVOCs in chemosystematics, ecological influences, and aroma research, as well as being useful to prove the biosynthetic mechanisms of BVOCs.