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Clarke ED, Ferguson JJ, Stanford J, Collins CE. Dietary Assessment and Metabolomic Methodologies in Human Feeding Studies: A Scoping Review. Adv Nutr 2023; 14:1453-1465. [PMID: 37604308 PMCID: PMC10721540 DOI: 10.1016/j.advnut.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 05/01/2023] [Accepted: 08/16/2023] [Indexed: 08/23/2023] Open
Abstract
Dietary metabolomics is a relatively objective approach to identifying new biomarkers of dietary intake and for use alongside traditional methods. However, methods used across dietary feeding studies vary, thus making it challenging to compare results. The objective of this study was to synthesize methodological components of controlled human feeding studies designed to quantify the diet-related metabolome in biospecimens, including plasma, serum, and urine after dietary interventions. Six electronic databases were searched. Included studies were as follows: 1) conducted in healthy adults; 2) intervention studies; 3) feeding studies focusing on dietary patterns; and 4) measured the dietary metabolome. From 12,425 texts, 50 met all inclusion criteria. Interventions were primarily crossover (n = 25) and parallel randomized controlled trials (n = 22), with between 8 and 395 participants. Seventeen different dietary patterns were tested, with the most common being the "High versus Low-Glycemic Index/Load" pattern (n = 11) and "Typical Country Intake" (n = 11); with 32 providing all or the majority (90%) of food, 16 providing some food, and 2 providing no food. Metabolites were identified in urine (n = 31) and plasma/serum (n = 30). Metabolites were quantified using liquid chromatography, mass spectroscopy (n = 31) and used untargeted metabolomics (n = 37). There was extensive variability in the methods used in controlled human feeding studies examining the metabolome, including dietary patterns tested, biospecimen sample collection, and metabolomic analysis techniques. To improve the comparability and reproducibility of controlled human feeding studies examining the metabolome, it is important to provide detailed information about the dietary interventions being tested, including information about included or restricted foods, food groups, and meal plans provided. Strategies to control for individual variability, such as a crossover study design, statistical adjustment methods, dietary-controlled run-in periods, or providing standardized meals or test foods throughout the study should also be considered. The protocol for this review has been registered at Open Science Framework (https://doi.org/10.17605/OSF.IO/DAHGS).
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Affiliation(s)
- Erin D Clarke
- School of Health Sciences, College of Health Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Jessica Ja Ferguson
- School of Health Sciences, College of Health Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Jordan Stanford
- School of Health Sciences, College of Health Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Clare E Collins
- School of Health Sciences, College of Health Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia.
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2
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Kajtazi A, Russo G, Wicht K, Eghbali H, Lynen F. Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction. Chemosphere 2023; 337:139361. [PMID: 37392796 DOI: 10.1016/j.chemosphere.2023.139361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/06/2023] [Accepted: 06/26/2023] [Indexed: 07/03/2023]
Abstract
Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.
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Affiliation(s)
- Ardiana Kajtazi
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Giacomo Russo
- School of Applied Sciences, Sighthill Campus, Edinburgh Napier University, 9 Sighthill Ct, EH11 4BN, Edinburgh, United Kingdom
| | - Kristina Wicht
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Hamed Eghbali
- Packaging and Specialty Plastics R&D, Dow Benelux B.V., Terneuzen, 4530 AA, the Netherlands
| | - Frédéric Lynen
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
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Chen B, Wang C, Fu Z, Yu H, Liu E, Gao X, Li J, Han L. RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems. J Chromatogr A 2023; 1707:464304. [PMID: 37611386 DOI: 10.1016/j.chroma.2023.464304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 08/15/2023] [Indexed: 08/25/2023]
Abstract
Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.
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Affiliation(s)
- Biying Chen
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China
| | - Chenxi Wang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China
| | - Zhifei Fu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China
| | - Haiyang Yu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China
| | - Erwei Liu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China
| | - Xiumei Gao
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China
| | - Jie Li
- Tianjin Key Laboratory of Clinical Multi-omics, Airport Economy Zone, Tianjin, China.
| | - Lifeng Han
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, PR China.
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Tang S, Zhang P, Gao M, Xiao Q, Li Z, Dong H, Tian Y, Xu F, Zhang Y. A chemical derivatization-based pseudotargeted LC-MS/MS method for high coverage determination of dipeptides. Anal Chim Acta 2023; 1274:341570. [PMID: 37455081 DOI: 10.1016/j.aca.2023.341570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/04/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Abstract
Dipeptides (DPs) have attracted more and more attention in many research fields due to their important biological functions and promising roles as disease biomarkers. However, the determination of DPs in biological samples is very challenging owing to the limited availability of commercial standards, high structure diversity, distinct physical and chemical characteristics, wide concentration range, and the extensive existence of isomers. In this study, a pseudotargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method coupled with chemical derivatization for the simultaneous analysis of 400 DPs and their constructing amino acids (AAs) in biospecimens is established. Dansyl chloride (Dns-Cl) chemical derivatization was introduced to provide characteristic MS fragments for annotation and improve the chromatographic separation of DP isomers. A retention time (RT) prediction model was constructed using 83 standards (63 DPs and 20 AAs) based on their quantitative structural retention relationship (QSRR) after the Dns-Cl labeling, which largely facilitated the annotation of the DPs without standards. Finally, we applied this method to investigate the profile change of DPs in a cisplatin-induced acute kidney injury (AKI) rat model. The established workflow provides a platform to profile DPs and expand our understanding of these little-studied metabolites.
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Affiliation(s)
- Shaoran Tang
- China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing, 210009, PR China; Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Pei Zhang
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Meiyu Gao
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Qinwen Xiao
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Zhaoqian Li
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Haijuan Dong
- The Public Laboratory Platform, China Pharmaceutical University, Nanjing, 210009, PR China
| | - Yuan Tian
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Fengguo Xu
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China.
| | - Yuxin Zhang
- China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing, 210009, PR China.
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Wei Y, Sun Y, Jia S, Yan P, Xiong C, Qi M, Wang C, Du Z, Jiang H. Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1226:123776. [PMID: 37311272 DOI: 10.1016/j.jchromb.2023.123776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023]
Abstract
Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.
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Affiliation(s)
- Yinyu Wei
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yi Sun
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shuailong Jia
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China
| | - Pan Yan
- Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410028, China
| | - Chaomei Xiong
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Meiling Qi
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenxi Wang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhifeng Du
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Hongliang Jiang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
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6
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Yang G, Lang Y. Extract identification and evaluation of the cytotoxic activity of Polygala fallax Hemsl in Heilongjiang ethnic medicine against tumors. Technol Health Care 2023; 31:565-575. [PMID: 37066951 DOI: 10.3233/thc-236050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
BACKGROUND Heilongjiang Province is a frontier province with distinctive characteristics, fertile land and rich products. OBJECTIVE This study provides a new method for qualitatively studying flavonoids in traditional Chinese medicine and a new auxiliary means for identifying flavonoid isomers. METHODS The flavonoids in Polygala fallax Hemsl were identified by ultra-performance liquid chromatography-photo-diode array (PDA)-quadrupole-electro- static field orbitrap mass spectrometry tandem by UV Spectrum, primary and secondary high-resolution mass spectrometry (MS1/MS2) cleavage of fragments combined with databases, mass spectrometry cleavage patterns and literature. RESULTS The established QSRR model was used to verify the flavonoids identified from the Polygala fallax Hemsl. CONCLUSION The structure of multiple Polygala fallax Hemsl has been identified using various spectral methods. The tumor cytotoxic activity of the isolated compounds was evaluated. This paper is of great significance for further elucidating the pharmacodynamic substance basis and further developing and utilizing Polygala fallax Hemsl.
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Affiliation(s)
- Guang Yang
- Business Economics Research Institute, Harbin University of Commerce, Harbin, Heilongjiang, China
| | - Yan Lang
- Department of Rehabilitation Therapy, Wuyi University, Nanping, Fujian, China
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Yang Y, Yang L, Zheng M, Cao D, Liu G. Data acquisition methods for non-targeted screening in environmental analysis. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Feng YL, Baesu A. Influence of data acquisition modes and data analysis approaches on non-targeted analysis of phthalate metabolites in human urine. Anal Bioanal Chem 2023; 415:303-316. [PMID: 36346455 PMCID: PMC9823047 DOI: 10.1007/s00216-022-04407-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/12/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022]
Abstract
Humans are often exposed to phthalates and their alternatives, on account of their widespread use in PVC as plasticizers, which are associated with harmful human effects. While targeted biomonitoring provides quantitative information for exposure assessment, only a small portion of phthalate metabolites has been targeted. This results in a knowledge gap in human exposure to other unknown phthalate compounds and their metabolites. Although the non-targeted analysis (NTA) approach is capable of screening a broad spectrum of chemicals, there is a lack of harmonized workflow in NTA to generate reproducible data within and between different laboratories. The objective of this study was to compare two different NTA data acquisition modes, the data-dependent (DDA) and independent (DIA) acquisition (DDA), as well as two data analysis approaches, based on diagnostic ions and Compound Discoverer software for the prioritization of candidate precursors and identification of unknown compounds in human urine. Liquid chromatography coupled to high-resolution mass spectrometry was used for sample analysis. The combination of three-diagnostic-ion extraction and DDA data acquisition was able to improve data filtering and data analysis for prioritizing phthalate metabolites. With DIA, 25 molecular features were identified in human urine, while 32 molecular features were identified in the same urine samples using DDA data. The number of molecular features identified with level 1 confidence was 11 and 9 using DIA and DDA data, respectively. The study demonstrated that besides sample preparation, the impact of data acquisition must be taken into account when developing a NTA method and a consistent protocol for evaluating such an impact is necessary.
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Affiliation(s)
- Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, AL: 2203 B, 251 Sir Frederick Banting Driveway, Ottawa, ON K1A 0K9 Canada
| | - Anca Baesu
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, AL: 2203 B, 251 Sir Frederick Banting Driveway, Ottawa, ON K1A 0K9 Canada
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Liu M, Xu X, Wang X, Wang H, Mi Y, Gao X, Guo D, Yang W. Enhanced Identification of Ginsenosides Simultaneously from Seven Panax Herbal Extracts by Data-Dependent Acquisition Including a Preferred Precursor Ions List Derived from an In-House Programmed Virtual Library. J Agric Food Chem 2022; 70:13796-13807. [PMID: 36239255 DOI: 10.1021/acs.jafc.2c06781] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Data-dependent acquisition (DDA) is widely utilized for metabolite identification in natural product research and food science, which, however, can suffer from low coverage. A potential solution to improve DDA coverage is to include the precursor ions list (PIL). Here, we aimed to construct a PIL-containing DDA strategy based on an in-house library of ginsenosides (VLG) and identify ginsenosides simultaneously from seven Panax herbal extracts. VLG, combined with mass defect filtering, could efficiently screen the ginsenoside precursors and elaborate the separate PIL involved in DDA for each ginseng extract. Consequently, we could characterize 500 ginsenosides, including 176 ones with unknown masses. Using the Panax ginseng extract, the superiority of this strategy was embodied in targeting more known ginsenoside masses and newly acquiring the MS2 spectra of 13 components. Conclusively, knowledge-based large-scale molecular prediction and PIL-DDA can represent a powerful targeted/untargeted strategy beneficial to novel natural compound discovery.
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Affiliation(s)
- Meiyu Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Yueguang Mi
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiumei Gao
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Dean Guo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China
| | - Wenzhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
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Feng YL, Singh R, Chao A, Li Y. Diagnostic Fragmentation Pathways for Identification of Phthalate Metabolites in Nontargeted Analysis Studies. J Am Soc Mass Spectrom 2022; 33:981-995. [PMID: 35588523 PMCID: PMC9890958 DOI: 10.1021/jasms.2c00052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Phthalates have been studied due to their linkages with adverse developmental effects; however, metabolites of this class of compounds are undercharacterized and are poorly captured by traditional targeted analysis. In this study, we developed a nontargeted analysis approach for identifying and classifying phthalate metabolites based on a comprehensive study of their fragmentation pathways in electrospray ionization (ESI) quadrupole-time-of-flight mass spectrometry (QTOF-MS). This approach identifies molecular features in the data as phthalate metabolites via the detection of three structurally significant fragment ions. Then phthalate metabolites are classified into four types based on the presence of additional fragment ions specific to each type. Cleavage mechanisms for each class of phthalate metabolite are proposed based on fragmentation patterns generated at various collision energies (CE). All of the tested phthalate metabolites including oxidative and nonoxidative metabolites produced a fragment ion at m/z 121.0295, representing the deprotonated benzoate ion [C6H5COO]-. Most tested phthalate metabolites can produce a specific ion at m/z 147.0088, the deprotonated o-phthalic anhydride ion. However, phthalate carboxylate metabolites can only produce the [M-H-R]- ion at m/z 165.0193 and do not produce the fragment at m/z 147.0088. Other phthalate oxidative metabolites (hydroxyl- and oxo-) follow a different fragmentation pathway than nonoxidative metabolites. With this workflow, eight unknown phthalate metabolites were putatively identified in pooled urine, with one identified as a previously unreported metabolite by a combination of the MS/MS spectrum and the predicted retention time. Method detection limits for phthalate metabolites in urine were also estimated.
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Affiliation(s)
- Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, AL: 2203 B, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9, Canada
| | - Randolph Singh
- Laboratoire Biogéochimie des Contaminants Organiques, Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Rue de l’Ile d’Yeu, BP 21105, Nantes Cedex 3, 44311, France
| | - Alex Chao
- U.S. Environmental Protection Agency (EPA), Office of Research and Development (ORD), Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA
| | - Yan Li
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, AL: 2203 B, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9, Canada
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Zhang C, Liu M, Xu X, Wu J, Li X, Wang H, Gao X, Guo D, Tian X, Yang W. Application of Large-Scale Molecular Prediction for Creating the Preferred Precursor Ions List to Enhance the Identification of Ginsenosides from the Flower Buds of Panax ginseng. J Agric Food Chem 2022; 70:5932-5944. [PMID: 35503923 DOI: 10.1021/acs.jafc.2c01435] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This work was designed to evaluate the coverage of data-dependent acquisition (DDA) extensively utilized in the untargeted metabolite/component identification in the food sciences and pharmaceutical analysis. Using saponins from the flower buds of Panax ginseng (PGF) as an example, precursor ions list (PIL)-including DDA on a Q-Orbitrap mass spectrometer could enable higher coverage than the other four MS2 acquisition approaches in characterizing PGF ginsenosides. A "Virtual Library of Ginsenoside" containing 13,536 ginsenoside molecules was established by C-language-programmed large-scale molecular prediction, which in combination with mass defect filtering could create a new PIL involving 1859 PGF saponin precursors. We could newly obtain the MS2 spectra of at least 17 components and characterize 36 ginsenosides with unknown masses, among the 164 compounds identified from PGF. Conclusively, a molecular-prediction-oriented PIL in DDA can assist to discover more potentially novel molecules benefiting to the development of functional foods and new drugs.
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Affiliation(s)
- Chunxia Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Meiyu Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Jia Wu
- Shanghai Standard Technology Co., Ltd., 58 Xinhao Road, Shanghai 201314, China
| | - Xue Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiumei Gao
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Dean Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China
| | - Xiaoxuan Tian
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Wenzhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
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Klingberg J, Keen B, Cawley A, Pasin D, Fu S. Developments in high-resolution mass spectrometric analyses of new psychoactive substances. Arch Toxicol 2022. [PMID: 35141767 DOI: 10.1007/s00204-022-03224-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
Abstract
The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.
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Fedorova ES, Matyushin DD, Plyushchenko IV, Stavrianidi AN, Buryak AK. Deep learning for retention time prediction in reversed-phase liquid chromatography. J Chromatogr A 2021; 1664:462792. [PMID: 34999303 DOI: 10.1016/j.chroma.2021.462792] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 01/16/2023]
Abstract
Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. Lots of approaches were developed for retention time prediction in liquid chromatography for a different number of molecules considering various molecular properties and machine learning algorithms. The recently built large retention time data set of standard compounds from the Metabolite and Chemical Entity Database (METLIN) allows researchers to create a model that can be used for retention time prediction of small molecules with wide varieties of structures and physicochemical properties. The ability to predict retention times using the largest data set was studied for different architectures of deep learning models that were trained on molecular fingerprints, and SMILES (string representation of a molecule) represented as one-hot matrices. The best result was achieved with a one-dimensional convolutional neural network (1D CNN) that uses SMILES as an input. The proposed model reached the mean absolute error and the median absolute error equal to 34.7 and 18.7 s, respectively, which outperformed the results previously obtained for this data set. The pre-trained 1D CNN on the METLIN SMRT data set was transferred on five other data sets to evaluate the generalization ability.
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Yan P, Wang L, Li S, Liu X, Sun Y, Tao J, Ouyang H, Zhang J, Du Z, Jiang H. Improved structural annotation of triterpene metabolites of traditional Chinese medicine in vivo based on quantitative structure-retention relationships combined with characteristic ions: Alismatis Rhizoma as an example. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1187:123012. [PMID: 34768050 DOI: 10.1016/j.jchromb.2021.123012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/08/2021] [Accepted: 10/27/2021] [Indexed: 11/19/2022]
Abstract
As a fast, sensitive and selective method, liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) has been used for studying the in vivo metabolism of traditional Chinese medicine (TCM). However, the rapid discovery and characterization of metabolites, especially isomers, remain challenging due to their complexity and low concentration in vivo. This study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a quantitative structure-retention relationship (QSRR) model, and Alismatis Rhizoma (AR) triterpenes were used as an example. This strategy consists of four steps. First, based on an in-house database reported previously, prototypes and metabolites in biosamples were preliminarily identified. Second, the candidate structures of prototype compounds and metabolites were determined by characteristic ions, databases or potential metabolic pathways. Then, a QSRR model was established to predict the retention times of the proposed structure. Finally, the structures of unknown prototypes and metabolites were determined by matching experimental retention times with the predicted values. The QSRR model built by the genetic algorithm-multiple linear regression (GA-MLR) has excellent regression correlation (R2 = 0.9966). Based on this strategy, a total of 118 compounds were identified, including 47 prototypes and 71 metabolites, among which 61 unknown compounds were reasonably characterized. The typical compound identified by this strategy was successfully validated using a triterpene standard. This strategy can improve the annotation confidence of in vivo metabolites of TCM and facilitate further pharmacological research.
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Affiliation(s)
- Pan Yan
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lu Wang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sen Li
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xuechen Liu
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yi Sun
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jianmei Tao
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hui Ouyang
- Jiangxi University of Traditional Chinese Medicine, Nanchang 330000, China
| | - Jianqing Zhang
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Zhifeng Du
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Hongliang Jiang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
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