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Calabrese V, Brunet TA, Degli-Esposti D, Chaumot A, Geffard O, Salvador A, Clément Y, Ayciriex S. Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum. Anal Bioanal Chem 2024:10.1007/s00216-024-05232-w. [PMID: 38492024 DOI: 10.1007/s00216-024-05232-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/18/2024]
Abstract
The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.
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Affiliation(s)
- Valentina Calabrese
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France.
| | - Thomas Alexandre Brunet
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | | | - Arnaud Chaumot
- Laboratoire d'écotoxicologie, INRAE, UR RiverLy, 69625, Villeurbanne, France
| | - Olivier Geffard
- Laboratoire d'écotoxicologie, INRAE, UR RiverLy, 69625, Villeurbanne, France
| | - Arnaud Salvador
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | - Yohann Clément
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | - Sophie Ayciriex
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France.
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Jang Y, Moon JH, Jeon BK, Park HJ, Lee HJ, Lee DY. Comprehensive Evaluation System for Post-Metabolic Activity of Potential Thyroid-Disrupting Chemicals. J Microbiol Biotechnol 2023; 33:1351-1360. [PMID: 37415082 PMCID: PMC10619556 DOI: 10.4014/jmb.2301.01036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 07/08/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) are compounds that disturb hormonal homeostasis by binding to receptors. EDCs are metabolized through hepatic enzymes, causing altered transcriptional activities of hormone receptors, and thus necessitating the exploration of the potential endocrine-disrupting activities of EDC-derived metabolites. Accordingly, we have developed an integrative workflow for evaluating the post-metabolic activity of potential hazardous compounds. The system facilitates the identification of metabolites that exert hormonal disruption through the integrative application of an MS/MS similarity network and predictive biotransformation based on known hepatic enzymatic reactions. As proof-of-concept, the transcriptional activities of 13 chemicals were evaluated by applying the in vitro metabolic module (S9 fraction). Identified among the tested chemicals were three thyroid hormone receptor (THR) agonistic compounds that showed increased transcriptional activities after phase I+II reactions (T3, 309.1 ± 17.3%; DITPA, 30.7 ± 1.8%; GC-1, 160.6 ± 8.6% to the corresponding parents). The metabolic profiles of these three compounds showed common biotransformation patterns, particularly in the phase II reactions (glucuronide conjugation, sulfation, GSH conjugation, and amino acid conjugation). Data-dependent exploration based on molecular network analysis of T3 profiles revealed that lipids and lipid-like molecules were the most enriched biotransformants. The subsequent subnetwork analysis proposed 14 additional features, including T4 in addition to 9 metabolized compounds that were annotated by prediction system based on possible hepatic enzymatic reaction. The other 10 THR agonistic negative compounds showed unique biotransformation patterns according to structural commonality, which corresponded to previous in vivo studies. Our evaluation system demonstrated highly predictive and accurate performance in determining the potential thyroid-disrupting activity of EDC-derived metabolites and for proposing novel biotransformants.
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Affiliation(s)
- Yurim Jang
- Interdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul 08826, Republic of Korea
| | - Ji Hyun Moon
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, Republic of Korea
| | - Byung Kwan Jeon
- Department of Bio and Fermentation Convergence Technology, Kookmin University, Seoul 02707, Republic of Korea
| | - Ho Jin Park
- Department of Food Science and Biotechnology, Chung-Ang University, Anseong 17546, Republic of Korea
| | - Hong Jin Lee
- Department of Food Science and Biotechnology, Chung-Ang University, Anseong 17546, Republic of Korea
| | - Do Yup Lee
- Interdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul 08826, Republic of Korea
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, Republic of Korea
- Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Sciences, CALS, Seoul National University, Seoul 08826, Republic of Korea
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3
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Zhang H, Zhang R, Su Y, Zheng J, Li H, Han Z, Kong Y, Liu H, Zhang Z, Sai C. Anti-cervical cancer mechanism of bioactive compounds from Alangium platanifolium based on the 'compound-target-disease' network. Heliyon 2023; 9:e20747. [PMID: 37860565 PMCID: PMC10582369 DOI: 10.1016/j.heliyon.2023.e20747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/01/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023] Open
Abstract
In this study, we analyzed the chemical compositions of Alangium platanifolium (Sieb. et Zucc.) Harms (AP) using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) non-targeted plant metabolomics integration MolNetEnhancer strategy. A total of 75 compounds, including flavonoids, alkaloids, terpenes, C21 steroids, among others, were identified by comparing accurate mass-to-charge ratios, MS2 cleavage fragments, retention times, and MolNetenhancer-integrated analytical data, and the cleavage rules of the characteristic compounds were analyzed. A total of 125 potential cervical cancer (CC) therapeutic targets were obtained through Gene Expression Omnibus (GEO) data mining, differential analysis, and database screening. Hub targets were obtained by constructing protein-protein interaction (PPI) networks and CytoNCA topology analysis, including SRC, STAT3, TP53, PIK3R1, MAPK3, and PIK3CA. According to Gene ontology (GO) analysis, AP was primarily against CC by influencing gland development, oxidative stress processes, serine/threonine kinase, and tyrosine kinase activity. Enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the PI3K/AKT and MAPK signaling pathways play a crucial role in AP treatment for CC. The compound-target-pathway (C-T-P) network revealed that quercetin, methylprednisolone, and caudatin may play key roles in the treatment of CC. The results of molecular docking revealed that the core compound could bind significantly to the core target. In this study, the compounds in AP were systematically analyzed qualitatively, and the core components, core targets, and mechanisms of action of AP in the treatment of CC were screened through a combination of network pharmacology tools. Providing a scientific reference for the therapeutic material basis and quality control of AP.
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Affiliation(s)
- Hao Zhang
- College of Pharmacy, Weifang Medical University, Weifang, 261053, China
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Ruiming Zhang
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Yuefen Su
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Jingrou Zheng
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Hui Li
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Zhichao Han
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
- College of Agriculture, Yanbian University, Yanji, 133002, China
| | - Yunzhen Kong
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Han Liu
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Zhen Zhang
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
| | - Chunmei Sai
- College of Pharmacy, Jining Medical University, Rizhao, 276826, China
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4
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Sheng Y, Xue Y, Wang J, Liu S, Jiang Y. Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms. Anal Bioanal Chem 2023:10.1007/s00216-023-04708-5. [PMID: 37119358 DOI: 10.1007/s00216-023-04708-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/01/2023]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexpected from large MS data. Molecular networking is a good tool for exploring, visualizing, and organizing MS/MS spectra, and moreover, it employs shifted peak match to calculate spectral similarity, making it capable of identifying adulteration that is not included in the library. The key of molecular networking is spectral similarity algorithms, and therefore, in this study, we compared the performance of four cutting-edge similarity algorithms, modified cosine similarity (shifted peak match), entropy similarity, and two deep-learning-based algorithms, MS2DeepScore and Spec2Vec, in building molecular networking for identification of adulteration that is not included in the library. We conducted an analysis of excluded-query-compound on all MS/MS spectra in test library and performed a large-scale false discovery rate estimation to investigate whether the spectral similarity calculated by each algorithm could represent the actual structural similarity well. The obtained results demonstrated Spec2Vec exhibited good performance in both detection capability and false discovery rate. Further comprehensive evaluation of the performance of Spec2Vec in the identification of adulteration that is not included in the library or is unexpected in different matrices and in application to real samples proved the approach studied here is a promising and powerful tool for adulterant inspection and improved the capability of analyzing large MS data.
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Affiliation(s)
- Yanghao Sheng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Ying Xue
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Jue Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Yueping Jiang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
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5
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Du K, Zhai C, Li X, Gang H, Gao X. Feature-Based Molecular Networking Facilitates the Comprehensive Identification of Differential Metabolites in Diabetic Cognitive Dysfunction Rats. Metabolites 2023; 13:metabo13040538. [PMID: 37110195 PMCID: PMC10142102 DOI: 10.3390/metabo13040538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/18/2023] [Accepted: 03/31/2023] [Indexed: 04/29/2023] Open
Abstract
Cognitive dysfunction is a frequent complication of type 2 diabetes mellitus (T2DM), usually accompanied by metabolic disorders. However, the metabolic changes in diabetic cognitive dysfunction (DCD) patients, especially compared to T2DM groups, are not fully understood. Due to the subtle differences in metabolic alterations between DCD groups and T2DM groups, the comprehensive detection of the untargeted metabolic profiles of hippocampus and urine samples of rats was conducted by LC-MS, considering the different ionization modes and polarities of the examined compounds, and feature-based molecular networking (FBMN) was performed to help identify differential metabolites from a comprehensive perspective in this study. In addition, an association analysis of the differential metabolites in hippocampus and urine was conducted by the O2PLS model. Finally, a total of 71 hippocampal tissue differential metabolites and 179 urine differential metabolites were identified. The pathway enrichment results showed that glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism, glycerol phospholipid metabolism, TCA cycle, and arginine biosynthesis in the hippocampus of DCD animals were changed. Seven metabolites (AUC > 0.9) in urine appeared as key differential metabolites that might reflect metabolic changes in the target tissue of DCD rats. This study showed that FBMN facilitated the comprehensive identification of differential metabolites in DCD rats. The differential metabolites may suggest an underlying DCD and be considered as potential biomarkers for DCD. Large samples and clinical experiments are needed for the subsequent elucidation of the possible mechanisms leading to these alterations and the verification of potential biomarkers.
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Affiliation(s)
- Ke Du
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing102488, China
| | - Chuanjia Zhai
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing102488, China
| | - Xuejiao Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing102488, China
| | - Hongchuan Gang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing102488, China
| | - Xiaoyan Gao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing102488, China
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6
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Li W, Mei S, Zhou H, Salman Farid M, Hu T, Wu T. Metabolite fingerprinting of the ripening process in Pixian douban using a feature-based molecular network and metabolomics analysis. Food Chem 2023; 418:135940. [PMID: 36965392 DOI: 10.1016/j.foodchem.2023.135940] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/27/2023]
Abstract
The unique flavor of Pixian douban (PXDB) is widely acknowledged to be associated with its maturation process. However, there is limited knowledge about the non-volatile metabolites that contribute to this flavor. To bridge this gap, this study employed a metabolomics approach and a feature-based molecular network (FBMN) analysis to investigate the non-volatile metabolite fingerprints of PXDB during its two-year maturation process. Specifically, the FBMN tool was utilized to annotate the flavonoid, amide derivatives, and lipid components of PXDB for the first time. Subsequently, the MolNetEnhancer tool was employed to complement the FBMN annotation and identify eight substructural components. Finally, metabolomics analysis was carried out to identify 45 key metabolites involved in flavor formation across 10 major metabolic pathways (p < 0.05). Overall, the findings of this study have significantly expanded our understanding of the non-volatile metabolite fingerprinting and flavor formation mechanisms.
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Affiliation(s)
- Weili Li
- Food Microbiology Key Laboratory of Sichuan Province, Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Xihua University, No.999 Guangchang Road, Chengdu 610039, China
| | - Sen Mei
- Food Microbiology Key Laboratory of Sichuan Province, Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Xihua University, No.999 Guangchang Road, Chengdu 610039, China
| | - Huanzhen Zhou
- Food Microbiology Key Laboratory of Sichuan Province, Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Xihua University, No.999 Guangchang Road, Chengdu 610039, China
| | - Muhammad Salman Farid
- College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Tao Hu
- Sichuan Teway Food Group Co., Ltd., No. 333, Tengfei 1st Road, Xihangangangang Street, Chengdu 610207, China
| | - Tao Wu
- Food Microbiology Key Laboratory of Sichuan Province, Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Xihua University, No.999 Guangchang Road, Chengdu 610039, China.
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7
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Luo L, Ma W, Liang K, Wang Y, Su J, Liu R, Liu T, Shyh-Chang N. Spatial metabolomics reveals skeletal myofiber subtypes. SCIENCE ADVANCES 2023; 9:eadd0455. [PMID: 36735792 PMCID: PMC10939097 DOI: 10.1126/sciadv.add0455] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 01/04/2023] [Indexed: 06/18/2023]
Abstract
Skeletal muscle myofibers are heterogeneous in their metabolism. However, metabolomic profiling of single myofibers has remained difficult. Mass spectrometry imaging (MSI) is a powerful tool for imaging molecular distributions. In this work, we optimized the workflow of matrix-assisted laser desorption/ionization (MALDI)-based MSI from cryosectioning to metabolomics data analysis to perform high-spatial resolution metabolomic profiling of slow- and fast-twitch myofibers. Combining the advantages of MSI and liquid chromatography-MS (LC-MS), we produced spatial metabolomics results that were more reliable. After the combination of high-spatial resolution MSI and LC-MS metabolomic analysis, we also discovered a new subtype of superfast type 2B myofibers that were enriched for fatty acid oxidative metabolism. Our technological workflow could serve as an engine for metabolomics discoveries, and our approach has the potential to provide critical insights into the metabolic heterogeneity and pathways that underlie fundamental biological processes and disease states.
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Affiliation(s)
- Lanfang Luo
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Wenwu Ma
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
- Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Kun Liang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Yuefan Wang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Jiali Su
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Ruirui Liu
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Taoyan Liu
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Ng Shyh-Chang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
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8
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Cai Y, Zhou Z, Zhu ZJ. Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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9
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Zhang Y, Huang Y, Fan J, Zhang M, Hasan A, Yi Y, Yu R, Zhou X, Ye M, Qiao X. Expanding the Scope of Targeted Metabolomics by One-pot Microscale Synthesis and Tailored Metabolite Profiling: Investigation of Bile Acid–Amino Acid Conjugates. Anal Chem 2022; 94:16596-16603. [DOI: 10.1021/acs.analchem.2c02086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Yang Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuxi Huang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Jingjing Fan
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Meng Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Aobulikasimu Hasan
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yang Yi
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Rong Yu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Xujie Zhou
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology; Key Laboratory of Renal Disease, Ministry of Health of China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Ministry of Education, Beijing 100034, China
| | - Min Ye
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University-Yunnan Baiyao International Medical Research Center, 38 Xueyuan Road, Beijing 100191, China
| | - Xue Qiao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University-Yunnan Baiyao International Medical Research Center, 38 Xueyuan Road, Beijing 100191, China
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10
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Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nat Commun 2022; 13:6656. [PMID: 36333358 PMCID: PMC9636193 DOI: 10.1038/s41467-022-34537-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.
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11
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Renai L, Ulaszewska M, Mattivi F, Bartoletti R, Del Bubba M, van der Hooft JJJ. Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles. Metabolites 2022; 12:metabo12101005. [PMID: 36295906 PMCID: PMC9610267 DOI: 10.3390/metabo12101005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/24/2022] Open
Abstract
Untargeted metabolomics approaches deal with complex data hindering structural information for the comprehensive analysis of unknown metabolite features. We investigated the metabolite discovery capacity and the possible extension of the annotation coverage of the Feature-Based Molecular Networking (FBMN) approach by adding two novel nutritionally-relevant (contextual) mass spectral libraries to the existing public ones, as compared to widely-used open-source annotation protocols. Two contextual mass spectral libraries in positive and negative ionization mode of ~300 reference molecules relevant for plant-based nutrikinetic studies were created and made publicly available through the GNPS platform. The postprandial urinary metabolome analysis within the intervention of Vaccinium supplements was selected as a case study. Following the FBMN approach in combination with the added contextual mass spectral libraries, 67 berry-related and human endogenous metabolites were annotated, achieving a structural annotation coverage comparable to or higher than existing non-commercial annotation workflows. To further exploit the quantitative data obtained within the FBMN environment, the postprandial behavior of the annotated metabolites was analyzed with Pearson product-moment correlation. This simple chemometric tool linked several molecular families with phase II and phase I metabolism. The proposed approach is a powerful strategy to employ in longitudinal studies since it reduces the unknown chemical space by boosting the annotation power to characterize biochemically relevant metabolites in human biofluids.
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Affiliation(s)
- Lapo Renai
- Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy
- Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands
- Correspondence: (L.R.); (M.U.); (J.J.J.v.d.H.)
| | - Marynka Ulaszewska
- Metabolomics Unit, Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, San Michele all’Adige, 38098 Trento, Italy
- Correspondence: (L.R.); (M.U.); (J.J.J.v.d.H.)
| | - Fulvio Mattivi
- Metabolomics Unit, Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, San Michele all’Adige, 38098 Trento, Italy
- Department of Cellular, Computational, and Integrative Biology (CIBIO), University of Trento, Via Mach 1, San Michele all’Adige, 38098 Trento, Italy
| | - Riccardo Bartoletti
- Department of Translational Research and New Technologies, University of Pisa, Via Risorgimento 36, 56126 Pisa, Italy
| | - Massimo Del Bubba
- Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy
| | - Justin J. J. van der Hooft
- Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
- Correspondence: (L.R.); (M.U.); (J.J.J.v.d.H.)
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12
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Zhu Y, Zang Q, Luo Z, He J, Zhang R, Abliz Z. An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body. Anal Chem 2022; 94:7286-7294. [PMID: 35548855 DOI: 10.1021/acs.analchem.2c00557] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.
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Affiliation(s)
- Ying Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qingce Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhigang Luo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.,Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), National Ethnic Affairs Commission, Beijing 100081, China.,Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
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13
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Chen L, Chen F, Liu T, Feng F, Guo W, Zhang Y, Feng X, Lin JM, Zhang F. Lipidomics Profiling of HepG2 Cells and Interference by Mycotoxins Based on UPLC-TOF-IMS. Anal Chem 2022; 94:6719-6727. [PMID: 35475631 DOI: 10.1021/acs.analchem.1c05543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Discovering the fungus-infected or mycotoxin-contaminated biomarkers is significant for systems biology since the metabolites in biological samples have significant polarity differences in both stochastic gene expression and microenvironmental change. Here, we aim to establish a comprehensive method for a lipidome by ion mobility mass spectrometry (IMS) merged with chemometrics to accurately find out the more scientific markers of cell interference by mycotoxins and for pathogenesis exploration and drug development. The differences in the abundances of several small molecules found in these metabolites were explored through multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), to further screen biomarkers. Good applicability and predictability were demonstrated by R2(X) and Q2 (R2 = 0.959, Q2 = 0.999). Five compounds with m/z values of 512.502 8, 540.5343, 722.525 8, 787.667 5, and 813.683 0 were selected as markers, and four of them were further confirmed by chemical standards (i.e., MSMS of m/z 813.683 0 covering m/z 86.0978, 125.0008, 184.0745, and 185.0781). In summary, we demonstrated the integration of UPLC-TOF-IMS and the chemometrics approach to elucidate identified biomarkers, which also provides a new way of thinking for covering lipid biomarkers or prognostic indicators for mycotoxins.
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Affiliation(s)
- Lan Chen
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China.,School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Fengming Chen
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Tong Liu
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Feng Feng
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Wei Guo
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Yuan Zhang
- School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Xuesong Feng
- School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Jin-Ming Lin
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Feng Zhang
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
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14
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Carnevale Neto F, Clark TN, Lopes NP, Linington RG. Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Product Mixtures. JOURNAL OF NATURAL PRODUCTS 2022; 85:519-529. [PMID: 35235328 PMCID: PMC11095131 DOI: 10.1021/acs.jnatprod.1c01048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The comprehensive chemical characterization of biological samples remains a central challenge in the field of natural products. Conventional workflows using liquid chromatography (LC)-coupled high-resolution tandem mass spectrometry (MS/MS or MS2) allow the detection of relevant small molecules while providing diagnostic fragment ions for their structural assignment. Still, many natural product extracts are of a molecular complexity that challenges the resolving power of modern LC-MS2 pipelines. In this study, we examined the effect of integrating ion mobility spectrometry (IMS) to our LC-MS2 platform for the characterization of natural product mixtures. IMS provides an additional axis of separation in the gas phase as well as experimental collision cross-sectional (CCS) values. We analyzed a mixture of 20 commercial standards at 2 concentration ranges, either solubilized in solvent or spiked into an actinobacterial extract. Data were acquired in positive ion mode using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) MS2 fragmentation approaches and assessed for both chemical coverage and spectral quality. IMS-DIA identified the largest number of standards in the spiked extract at the lower concentration of standards (17), followed by IMS-DDA (10), DDA (8), and DIA (6). In addition, we examined how these data sets performed in the Global Natural Products Social Molecular Networking (GNPS) platform. Overall, integrating IMS increased both metabolite detection and the quality of MS2 spectra, particularly for samples analyzed in DIA mode.
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Affiliation(s)
- Fausto Carnevale Neto
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
- Núcleo de Pesquisa em Produtos Naturais e Sintéticos (NPPNS), Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, 14040-903 Ribeirão Preto, SP, Brazil
- Northwest Metabolomics Research Center (NW-MRC), Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
| | - Trevor N Clark
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Norberto P Lopes
- Núcleo de Pesquisa em Produtos Naturais e Sintéticos (NPPNS), Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, 14040-903 Ribeirão Preto, SP, Brazil
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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15
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Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils. Molecules 2022; 27:molecules27051588. [PMID: 35268689 PMCID: PMC8912102 DOI: 10.3390/molecules27051588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/20/2022] [Accepted: 02/24/2022] [Indexed: 01/11/2023] Open
Abstract
Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL).
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16
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Yu JS, Nothias LF, Wang M, Kim DH, Dorrestein PC, Kang KB, Yoo HH. Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies. Anal Chem 2022; 94:1456-1464. [DOI: 10.1021/acs.analchem.1c04925] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jun Sang Yu
- Institute of Pharmaceutical Science and Technology and College of Pharmacy, Hanyang University, Ansan 15588, Republic of Korea
| | - Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Dong Hyun Kim
- Department of Pharmacology, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women’s University, Seoul 04310, Republic of Korea
| | - Hye Hyun Yoo
- Institute of Pharmaceutical Science and Technology and College of Pharmacy, Hanyang University, Ansan 15588, Republic of Korea
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17
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Lai J, Huang L, Bao Y, Wang L, Lyu Q, Kuang H, Wang K, Sang X, Yang Q, Shan Q, Cao G. A deep clustering-based mass spectral data visualization strategy for anti-renal fibrotic lead compound identification from natural products. Analyst 2022; 147:4739-4751. [DOI: 10.1039/d2an01185a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We present a deep clustering-based MS data visualization strategy (MCnebula), integrated with the influential open-source automatic MS annotation platform SIRIUS and in vivo and in vitro methods, to screen and validate potential lead compounds from natural products.
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Affiliation(s)
- Jieying Lai
- School of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Lichuang Huang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Yini Bao
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Lu Wang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Qiang Lyu
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Haodan Kuang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Kuilong Wang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xianan Sang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Qiao Yang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Qiyuan Shan
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Gang Cao
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
- School of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China
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