1
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Agongo J, Grady SF, Cho K, Patti GJ, Bythell BJ, Arnatt CK, Edwards JL. Discovery and Identification of Three Homocysteine Metabolites by Chemical Derivatization and Mass Spectrometry Fragmentation. Anal Chem 2024. [PMID: 38976774 DOI: 10.1021/acs.analchem.4c01706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Discovery and identification of a new endogenous metabolite are typically hindered by requirements of large sample volumes and multistage purifications to guide synthesis of the standard. Presented here is a metabolomics platform that uses chemical tagging and tandem mass spectrometry to determine structure, direct synthesis, and confirm identity. Three new homocysteine metabolites are reported: N-succinyl homocysteine, 2-methyl-1,3-thiazinane-4-carboxylic acid (MTCA), and homolanthinone.
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Affiliation(s)
- Julius Agongo
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63103, United States
| | - Scott F Grady
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63103, United States
| | - Kevin Cho
- Department of Chemistry, Medicine, and Center for Mass Spectrometry and Metabolic Tracing, Washington University in St. Louis, 1 Brookings Drive, St. Louis, Missouri 63130, United States
| | - Gary J Patti
- Department of Chemistry, Medicine, and Center for Mass Spectrometry and Metabolic Tracing, Washington University in St. Louis, 1 Brookings Drive, St. Louis, Missouri 63130, United States
| | - Benjamin J Bythell
- Department of Chemistry and Biochemistry, Ohio University, 307 Chemistry Building, Athens, Ohio 45701, United States
| | - Christopher K Arnatt
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63103, United States
| | - James L Edwards
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63103, United States
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Hemmer S, Manier SK, Wagmann L, Meyer MR. Impact of four different extraction methods and three different reconstitution solvents on the untargeted metabolomics analysis of human and rat urine samples. J Chromatogr A 2024; 1725:464930. [PMID: 38696889 DOI: 10.1016/j.chroma.2024.464930] [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: 03/01/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 05/04/2024]
Abstract
Unsuitable sample preparation may result in loss of important analytes and consequently affect the outcome of untargeted metabolomics. Due to species differences, different sample preparations may be required within the same biological matrix. The study aimed to compare the in-house sample preparation method for urine with methods from literature and to investigate the transferability of sample preparation from human urine to rat urine. A total of 12 different conditions for protein precipitation were tested, combining four different extraction solvents and three different reconstitution solvents using an untargeted liquid-chromatography high resolution mass spectrometry (LC-HRMS) metabolomics analysis. Evaluation was done based on the impact on feature count, their detectability, as well as the reproducibility of selected compounds. Results showed that a combination of methanol as extraction and acetonitrile/water (75/25) as reconstitution solvent provided improved results at least regarding the total feature count. Additionally, it was found that a higher amount of methanol was most suitable for extraction of rat urine among the tested conditions. In comparison, human urine requires significantly less volume of extraction solvent. Overall, it is recommended to systematically optimize both, the extraction method, and the reconstitution solvent for the used biofluid and the individual analytical settings.
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Affiliation(s)
- Selina Hemmer
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany
| | - Sascha K Manier
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany
| | - Lea Wagmann
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany
| | - Markus R Meyer
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany.
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Anderson BG, Raskind A, Hissong R, Dougherty MK, McGill SK, Gulati AS, Theriot CM, Kennedy RT, Evans CR. Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation. J Proteome Res 2024. [PMID: 38752739 DOI: 10.1021/acs.jproteome.4c00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.
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Affiliation(s)
- Brady G Anderson
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Michigan Compound Identification Development Core, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Alexander Raskind
- Michigan Compound Identification Development Core, University of Michigan, Ann Arbor, Michigan 48109, United States
- Biomedical Research Core Facilities, University of Michigan, Ann Arbor Michigan 48109, United States
| | - Rylan Hissong
- Michigan Compound Identification Development Core, University of Michigan, Ann Arbor, Michigan 48109, United States
- Biomedical Research Core Facilities, University of Michigan, Ann Arbor Michigan 48109, United States
| | - Michael K Dougherty
- Department of Medicine, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Sarah K McGill
- Department of Medicine, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Ajay S Gulati
- Department of Medicine, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Casey M Theriot
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Robert T Kennedy
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Michigan Compound Identification Development Core, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Pharmacology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles R Evans
- Michigan Compound Identification Development Core, University of Michigan, Ann Arbor, Michigan 48109, United States
- Biomedical Research Core Facilities, University of Michigan, Ann Arbor Michigan 48109, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor Michigan 48109, United States
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4
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Hemmer S, Manier SK, Wagmann L, Meyer MR. Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma. Metabolomics 2024; 20:49. [PMID: 38689195 PMCID: PMC11061011 DOI: 10.1007/s11306-024-02115-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 04/20/2024] [Indexed: 05/02/2024]
Abstract
INTRODUCTION Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose. OBJECTIVES The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices. METHODS Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others. RESULTS All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column. CONCLUSION The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.
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Affiliation(s)
- Selina Hemmer
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany
| | - Sascha K Manier
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany
| | - Lea Wagmann
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany
| | - Markus R Meyer
- Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, Homburg, Germany.
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5
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España Amórtegui JC, Ekroth S, Pekar H, Guerrero Dallos JA. A green-footprint approach for parallel multiclass analysis of contaminants in roasted coffee via LC-HRMS. Anal Bioanal Chem 2024; 416:1541-1560. [PMID: 38349534 PMCID: PMC10899293 DOI: 10.1007/s00216-024-05157-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/15/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
The development and validation of a simple, comprehensive, and environment-friendly procedure to determine pesticide residues, naturally occurring and processing contaminants in roasted coffee is presented. A solid-liquid extraction of pesticides and mycotoxins with ethyl acetate and the concurrent partition of acrylamide to an aqueous phase follows a parallel analytical strategy that requires a single analytical portion to determine contaminants that are typically analyzed by dedicated single residue methods. The partition rules the lipids out of the aqueous extract before an "in-tube" dispersive solid phase microextraction (dSPME) for acrylamide retention. This is followed by the elution with buffer prior to injection. This extract is independently introduced into the system front end followed by the injection of the compounds from the organic phase, yet all spotted in the same run. A novel liquid chromatography high-resolution mass spectrometry (LC-HRMS) method setup enables the quantification of 186 compounds at 10 µg/kg, 226 at 5 µg/kg, and the acrylamide at 200 µg/kg for a total of 414 molecules, with acceptable recoveries (70-120%) and precision (RSD < 20%) making this strategy significantly faster and cost-effective than the dedicated single residue methods. Even though the presence of chlorpyrifos, acrylamide, and ochratoxin A was confirmed on samples of different origins, the findings were below the limit of quantification. During the storage of raw coffee, no proof of masking of OTA was found; however, condensation with glucose was evidenced during thermal processing experiments with sucrose by using stable isotope labeling (SIL). No detected conjugates were found in roasted nor in commercial sugar-added torrefacto samples, an industrial processing usually carried out above the decomposition temperature of the disaccharide.
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Affiliation(s)
| | - Susanne Ekroth
- Science Department, Swedish Food Agency, Uppsala, Sweden
| | - Heidi Pekar
- Science Department, Swedish Food Agency, Uppsala, Sweden
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Qian Y, Chen Z, Wang J, Peng M, Zhang S, Yan X, Han X, Ou X, Sun J, Li S, Chen K. H/D Exchange Coupled with 2H-labeled Stable Isotope-Assisted Metabolomics Discover Transformation Products of Contaminants of Emerging Concern. Anal Chem 2023; 95:12541-12549. [PMID: 37574906 DOI: 10.1021/acs.analchem.3c02833] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Stable isotope-assisted metabolomics (SIAM) is a powerful tool for discovering transformation products (TPs) of contaminants. Nevertheless, the high cost or lack of isotope-labeled analytes limits its application. In-house H/D (hydrogen/deuterium) exchange reactions enable direct 2H labeling to target analytes with favorable reaction conditions, providing intuitive and easy-to-handle approaches for environmentally relevant laboratories to obtain cost-effective 2H-labeled contaminants of emerging concern (CECs). We first combined the use of in-house H/D exchange and 2H-SIAM to discover potential TPs of 6PPD (N-1,3-dimethylbutyl-N'-phenyl-p-phenylenediamine), providing a new strategy for finding TPs of CECs. 6PPD-d9 was obtained by in-house H/D exchange with favorable reaction conditions, and the impurities were carefully studied. Incomplete deuteride, for instance, 6PPD-d8 in this study, constitutes a major part of the impurities. Nevertheless, it has few adverse effects on the 2H-SIAM pipeline in discovering TPs of 6PPD. The 2H-SIAM pipeline annotated 9 TPs of 6PPD, and commercial standards further confirmed the annotated 6PPDQ (2-anilino-5-(4-methylpentan-2-ylamino)cyclohexa-2,5-diene-1,4-dione) and PPPD (N-phenyl-p-phenylenediamine). Additionally, a possible new formation mechanism for 6PPDQ was proposed, highlighting the performance of the strategy. In summary, this study highlighted a new strategy for discovering the TPs of CECs and broadening the application of SIAM in environmental studies.
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Affiliation(s)
- Yiguang Qian
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, P. R. China
| | - Ziyu Chen
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
| | - Jiahui Wang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
| | - Man Peng
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
| | - Shenghua Zhang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
| | - Xiaoyu Yan
- Department of Chemistry, Renmin University of China, Beijing 100872, P. R. China
| | - Xiaole Han
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
| | - Xiaohui Ou
- Ecological and Environmental Monitoring Centre, Guangxi Zhuang Autonomous Region, Nanning 530028, P. R. China
| | - Jie Sun
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
| | - Siyue Li
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, P. R. China
| | - Ke Chen
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P. R. China
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Li S, Siddiqa A, Thapa M, Chi Y, Zheng S. Trackable and scalable LC-MS metabolomics data processing using asari. Nat Commun 2023; 14:4113. [PMID: 37433854 DOI: 10.1038/s41467-023-39889-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 06/30/2023] [Indexed: 07/13/2023] Open
Abstract
Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.
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Affiliation(s)
- Shuzhao Li
- Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA.
- University of Connecticut School of Medicine, Farmington, CT, USA.
| | - Amnah Siddiqa
- Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA
| | - Maheshwor Thapa
- Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA
| | - Yuanye Chi
- Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA
| | - Shujian Zheng
- Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA
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Favilli L, Griffith CM, Schymanski EL, Linster CL. High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics. Anal Bioanal Chem 2023:10.1007/s00216-023-04724-5. [PMID: 37212869 DOI: 10.1007/s00216-023-04724-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/24/2023] [Accepted: 04/28/2023] [Indexed: 05/23/2023]
Abstract
Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS2 database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.
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Affiliation(s)
- Lorenzo Favilli
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg.
| | - Corey M Griffith
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg
| | - Carole L Linster
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg
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9
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Schippers P, Rasheed S, Park YM, Risch T, Wagmann L, Hemmer S, Manier SK, Müller R, Herrmann J, Meyer MR. Evaluation of extraction methods for untargeted metabolomic studies for future applications in zebrafish larvae infection models. Sci Rep 2023; 13:7489. [PMID: 37161044 PMCID: PMC10170104 DOI: 10.1038/s41598-023-34593-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/04/2023] [Indexed: 05/11/2023] Open
Abstract
Sample preparation in untargeted metabolomics should allow reproducible extractions of as many molecules as possible. Thus, optimizing sample preparation is crucial. This study compared six different extraction procedures to find the most suitable for extracting zebrafish larvae in the context of an infection model. Two one-phase extractions employing methanol (I) and a single miscible phase of methanol/acetonitrile/water (II) and two two-phase methods using phase separation between chloroform and methanol/water combinations (III and IV) were tested. Additional bead homogenization was used for methods III and IV (III_B and IV_B). Nine internal standards and 59 molecules of interest (MoInt) related to mycobacterial infection were used for method evaluation. Two-phase methods (III and IV) led to a lower feature count, higher peak areas of MoInt, especially amino acids, and higher coefficients of variation in comparison to one-phase extractions. Adding bead homogenization increased feature count, peak areas, and CVs. Extraction I showed higher peak areas and lower CVs than extraction II, thus being the most suited one-phase method. Extraction III and IV showed similar results, with III being easier to execute and less prone to imprecisions. Thus, for future applications in zebrafish larvae metabolomics and infection models, extractions I and III might be chosen.
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Affiliation(s)
- Philip Schippers
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarland University, Saarbrücken, Germany
| | - Sari Rasheed
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarland University, Saarbrücken, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover, Braunschweig, Germany
| | - Yu Mi Park
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarland University, Saarbrücken, Germany
| | - Timo Risch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarland University, Saarbrücken, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover, Braunschweig, Germany
| | - Lea Wagmann
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany
| | - Selina Hemmer
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany
| | - Sascha K Manier
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarland University, Saarbrücken, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover, Braunschweig, Germany
| | - Jennifer Herrmann
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarland University, Saarbrücken, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover, Braunschweig, Germany
| | - Markus R Meyer
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany.
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10
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Girel S, Guillarme D, Fekete S, Rudaz S, González-Ruiz V. Investigation of several chromatographic approaches for untargeted profiling of central carbon metabolism. J Chromatogr A 2023; 1697:463994. [PMID: 37086708 DOI: 10.1016/j.chroma.2023.463994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/24/2023]
Abstract
Monitoring the central carbon metabolism (CCM) network using liquid chromatography/mass spectrometry (LC-MS) analysis is hampered by the diverse chemical nature of its analytes, which are extremely difficult to analyze using single chromatographic conditions. Furthermore, CCM-related compounds present non-specific adsorption on metal surfaces, causing detrimental chromatographic effects and sensitivity loss. In this study, polar reversed-phase, mixed-mode (MMC), and zwitterionic hydrophilic interaction chromatography (HILIC) featuring low-adsorption hardware were investigated towards untargeted analysis of biological samples with a focus on energy metabolism-related analytes. Best results were achieved with sulfoalkylbetaine HILIC with different supports, where polymeric option featured the highest coverage and inert hybrid silica facilitated best throughput and kinetic performance at a cost of less selectivity for small carboxylic acids. MMC demonstrated excellent performance for strongly anionic analytes such as multiresidue phosphates. The obtained experimental data also suggested that an additional hydrophilic modulation might be necessary to facilitate better resolution of carboxylic acids in zHILIC mode, as found during the application of the developed method to study the effect of two different mutations on the energy metabolism of S. aureus.
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Affiliation(s)
- Sergey Girel
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland.
| | - Davy Guillarme
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland
| | - Szabolcs Fekete
- Waters Corporation, located in CMU-Rue Michel-Servet 1, Geneva, Switzerland
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Víctor González-Ruiz
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
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11
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El Abiead Y, Bueschl C, Panzenboeck L, Wang M, Doppler M, Seidl B, Zanghellini J, Dorrestein PC, Koellensperger G. Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets. Anal Chim Acta 2022; 1229:340352. [PMID: 36156231 DOI: 10.1016/j.aca.2022.340352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 08/08/2022] [Accepted: 09/01/2022] [Indexed: 11/30/2022]
Abstract
Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS are quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5%-4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.
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Affiliation(s)
- Yasin El Abiead
- Department of Analytical Chemistry, University of Vienna, 1090, Vienna, Austria.
| | - Christoph Bueschl
- Department of Analytical Chemistry, University of Vienna, 1090, Vienna, Austria; Institute of Bioanalytics and Agro-Metabolomics, Department of Agrobiotechnology IFA-Tulln, University of Natural Resources and Life Sciences, Vienna, 3430, Tulln, Austria
| | - Lisa Panzenboeck
- Department of Analytical Chemistry, University of Vienna, 1090, Vienna, Austria
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave, Riverside, CA, 92521, USA
| | - Maria Doppler
- Institute of Bioanalytics and Agro-Metabolomics, Department of Agrobiotechnology IFA-Tulln, University of Natural Resources and Life Sciences, Vienna, 3430, Tulln, Austria; Core Facility Bioactive Molecules: Screening and Analysis, University of Natural Resources and Life Sciences, Vienna, 3430, Tulln, Austria
| | - Bernhard Seidl
- Institute of Bioanalytics and Agro-Metabolomics, Department of Agrobiotechnology IFA-Tulln, University of Natural Resources and Life Sciences, Vienna, 3430, Tulln, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, 1090, Vienna, Austria
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA; Department of Pharmacology, School of Medicine, University of California San Diego, La Jolla, CA, USA; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA; Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Gunda Koellensperger
- Department of Analytical Chemistry, University of Vienna, 1090, Vienna, Austria.
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12
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Chen K, Xiang Y, Yan X, Li Z, Qin R, Sun J. Global Tracking of Transformation Products of Environmental Contaminants by 2H-Labeled Stable Isotope-Assisted Metabolomics. Anal Chem 2022; 94:7255-7263. [PMID: 35510918 DOI: 10.1021/acs.analchem.2c00500] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Stable isotope-assisted metabolomics (SIAM) enables global tracking of isotopic labels in nontargeted metabolomics in living organisms. However, its application in tracking transformation products (TPs, as metabolites of contaminants) of environmental contaminants is still a challenge due to limits in methodology, unmatured algorithms, and the high cost of 13C-labeled contaminants. Therefore, we developed a 2H-SIAM pipeline coupled with a highly flexible algorithm 2H-SIAM(1.0) (https://github.com/kechen1984/2H-SIAM), facilitating tracking TPs of contaminants in the environmental matrix. A detailed discussion illustrates the theory, behavior, and prospect of 2H-SIAM. We demonstrate that the proposed 2H-SIAM pipeline has unique advantages over 13C-SIAM, for example, cost-effective 2H-labeled contaminants, easy synthesis of 2H-labeled emerging contaminants, and providing more structural information. A pyrene soil degradation study further confirmed its high performance. It efficiently discarded 99% of noise signals and extracted 52 features from the nontargeted high resolution mass spectrometry (HRMS) data. Among them, 13 features were annotated as TPs of pyrene with identification confidence from Level 2a to Level 5, and 5 TPs were reported for the first time. In conclusion, the proposed 2H-SIAM pipeline is powerful in tracking potential TPs of environmental contaminants with unique advantages.
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Affiliation(s)
- Ke Chen
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P.R. China
| | - Yuhui Xiang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P.R. China
| | - Xiaoyu Yan
- Department of Chemistry, Renmin University of China, Beijing 100872, P.R. China
| | - Zhenghui Li
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan, Hubei 430074, P.R. China
| | - Rui Qin
- College of Life Sciences, South-Central Minzu University, Wuhan 430068, P.R. China
| | - Jie Sun
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P.R. China
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13
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Nelson AB, Chow LS, Hughey CC, Crawford PA, Puchalska P. Artifactual FA dimers mimic FAHFA signals in untargeted metabolomics pipelines. J Lipid Res 2022; 63:100201. [PMID: 35315332 PMCID: PMC9034316 DOI: 10.1016/j.jlr.2022.100201] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 12/01/2022] Open
Abstract
FA esters of hydroxy FAs (FAHFAs) are lipokines with extensive structural and regional isomeric diversity that impact multiple physiological functions, including insulin sensitivity and glucose homeostasis. Because of their low molar abundance, FAHFAs are typically quantified using highly sensitive LC-MS/MS methods. Numerous relevant MS databases house in silico-spectra that allow identification and speciation of FAHFAs. These provisional chemical feature assignments provide a useful starting point but could lead to misidentification. To address this possibility, we analyzed human serum with a commonly applied high-resolution LC-MS untargeted metabolomics platform. We found that many chemical features are putatively assigned to the FAHFA lipid class based on exact mass and fragmentation patterns matching spectral databases. Careful validation using authentic standards revealed that many investigated signals provisionally assigned as FAHFAs are in fact FA dimers formed in the LC-MS pipeline. These isobaric FA dimers differ structurally only by the presence of an olefinic bond. Furthermore, stable isotope-labeled oleic acid spiked into human serum at subphysiological concentrations showed concentration-dependent formation of a diverse repertoire of FA dimers that analytically mimicked FAHFAs. Conversely, validated FAHFA species did not form spontaneously in the LC-MS pipeline. Together, these findings underscore that FAHFAs are endogenous lipid species. However, nonbiological FA dimers forming in the setting of high concentrations of FFAs can be misidentified as FAHFAs. Based on these results, we assembled a FA dimer database to identify nonbiological FA dimers in untargeted metabolomics datasets.
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Affiliation(s)
- Alisa B Nelson
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Lisa S Chow
- Division of Diabetes, Endocrinology and Metabolism; Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Curtis C Hughey
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Peter A Crawford
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA; Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, MN, USA; Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA.
| | - Patrycja Puchalska
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
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14
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Dodds JN, Wang L, Patti GJ, Baker ES. Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses. Anal Chem 2022; 94:2527-2535. [PMID: 35089687 PMCID: PMC8934380 DOI: 10.1021/acs.analchem.1c04430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-13C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their 12C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.
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Affiliation(s)
| | | | - Gary J. Patti
- Departments of Chemistry and Medicine, Siteman Cancer Center, Center for Metabolomics and Isotope Tracing, Washington University, St. Louis, Missouri 63130, United States
| | - Erin S. Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
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15
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Giera M, Yanes O, Siuzdak G. Metabolite discovery: Biochemistry's scientific driver. Cell Metab 2022; 34:21-34. [PMID: 34986335 PMCID: PMC10131248 DOI: 10.1016/j.cmet.2021.11.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/26/2021] [Accepted: 11/09/2021] [Indexed: 01/19/2023]
Abstract
Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.
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Affiliation(s)
- Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, Leiden 2333 ZA, the Netherlands
| | - Oscar Yanes
- Universitat Rovira i Virgili, Department of Electronic Engineering, IISPV, Tarragona, Spain; CIBER on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
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16
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Koelmel JP, Xie H, Price EJ, Lin EZ, Manz KE, Stelben P, Paige MK, Papazian S, Okeme J, Jones DP, Barupal D, Bowden JA, Rostkowski P, Pennell KD, Nikiforov V, Wang T, Hu X, Lai Y, Miller GW, Walker DI, Martin JW, Godri Pollitt KJ. An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry. EXPOSOME 2022; 2:osac007. [PMID: 36483216 PMCID: PMC9719826 DOI: 10.1093/exposome/osac007] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/28/2022] [Accepted: 08/03/2022] [Indexed: 04/16/2023]
Abstract
Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.
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Affiliation(s)
- Jeremy P Koelmel
- Department of Environmental Health Science, Yale School of Public Health, New Haven, CT, USA
| | - Hongyu Xie
- Department of Environmental Science, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Elliott J Price
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Elizabeth Z Lin
- Department of Environmental Health Science, Yale School of Public Health, New Haven, CT, USA
| | | | - Paul Stelben
- Department of Environmental Health Science, Yale School of Public Health, New Haven, CT, USA
| | - Matthew K Paige
- Department of Environmental Health Science, Yale School of Public Health, New Haven, CT, USA
| | - Stefano Papazian
- Department of Environmental Science, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
- National Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Joseph Okeme
- Department of Environmental Health Science, Yale School of Public Health, New Haven, CT, USA
| | - Dean P Jones
- School of Medicine, Department of Medicine, Emory University, Atlanta, GA, USA
| | - Dinesh Barupal
- Icahn School of Medicine at Mount Sinai, Department of Environmental Medicine and Public Health, New York, NY, USA
| | - John A Bowden
- Department of Physiological Sciences, Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, USA
- Department of Chemistry, University of Florida, Gainesville, FL, USA
| | | | - Kurt D Pennell
- School of Engineering, Brown University, Providence, RI, USA
| | | | - Thanh Wang
- MTM Research Centre, Örebro University, Örebro, Sweden
| | - Xin Hu
- School of Medicine, Department of Medicine, Emory University, Atlanta, GA, USA
| | - Yunjia Lai
- Mailman School of Public Health, Department of Environmental Health Sciences, Columbia University, New York, NY, USA
| | - Gary W Miller
- Mailman School of Public Health, Department of Environmental Health Sciences, Columbia University, New York, NY, USA
| | | | - Jonathan W Martin
- Department of Environmental Science, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
- National Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Krystal J Godri Pollitt
- To whom correspondence should be addressed: (Krystal J. Godri Pollitt) and (Douglas I. Walker)
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17
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Harris MB, Lesani M, Liu Z, McCall LI. Molecular networking in infectious disease models. Methods Enzymol 2022; 663:341-375. [PMID: 35168796 PMCID: PMC10040239 DOI: 10.1016/bs.mie.2021.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Small molecule metabolites are the product of many enzymatic reactions. Metabolomics thus opens a window into enzyme activity and function, integrating effects at the post-translational, proteome, transcriptome and genome level. In addition, small molecules can themselves regulate enzyme activity, expression and function both via substrate availability mechanisms and through allosteric regulation. Metabolites are therefore at the nexus of infectious diseases, regulating nutrient availability to the pathogen, immune responses, tropism, and host disease tolerance and resilience. Analysis of metabolomics data is however complex, particularly in terms of metabolite annotation. An emerging valuable approach to extend metabolite annotations beyond existing compound libraries and to identify infection-induced chemical changes is molecular networking. In this chapter, we discuss the applications of molecular networking in the context of infectious diseases specifically, with a focus on considerations relevant to these biological systems.
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Affiliation(s)
- Morgan B Harris
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, United States; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, United States
| | - Mahbobeh Lesani
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, United States; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, United States
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, United States; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, United States
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, United States; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, United States; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, United States.
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18
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Griffith CM, Walvekar AS, Linster CL. Approaches for completing metabolic networks through metabolite damage and repair discovery. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:None. [PMID: 34957344 PMCID: PMC8669784 DOI: 10.1016/j.coisb.2021.100379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated "metabolite repair enzymes." Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.
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Affiliation(s)
| | | | - Carole L. Linster
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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19
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Chen L, Lu W, Wang L, Xing X, Chen Z, Teng X, Zeng X, Muscarella AD, Shen Y, Cowan A, McReynolds MR, Kennedy BJ, Lato AM, Campagna SR, Singh M, Rabinowitz JD. Metabolite discovery through global annotation of untargeted metabolomics data. Nat Methods 2021; 18:1377-1385. [PMID: 34711973 PMCID: PMC8733904 DOI: 10.1038/s41592-021-01303-3] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 09/16/2021] [Indexed: 11/08/2022]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
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Affiliation(s)
- Li Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Wenyun Lu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Lin Wang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Xi Xing
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ziyang Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Xin Teng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Xianfeng Zeng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Antonio D Muscarella
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yihui Shen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexis Cowan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Melanie R McReynolds
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Brandon J Kennedy
- Lotus Separations, LLC, Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ashley M Lato
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Shawn R Campagna
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Joshua D Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA.
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20
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HERMES: a molecular-formula-oriented method to target the metabolome. Nat Methods 2021; 18:1370-1376. [PMID: 34725482 PMCID: PMC9284938 DOI: 10.1038/s41592-021-01307-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 09/22/2021] [Indexed: 01/14/2023]
Abstract
Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization.
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21
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Abstract
Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the "dark matter" within microbiomes that maintain healthy humans and environments.
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22
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Ly R, Ly N, Sasaki K, Suzuki M, Kami K, Ohashi Y, Britz-McKibbin P. Nontargeted Serum Lipid Profiling of Nonalcoholic Steatohepatitis by Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry: A Multiplexed Separation Platform for Resolving Ionic Lipids. J Proteome Res 2021; 21:768-777. [PMID: 34676758 DOI: 10.1021/acs.jproteome.1c00682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
New methods are needed for global lipid profiling due to the complex chemical structures and diverse physicochemical properties of lipids. Herein we introduce a robust data workflow to unambiguously select lipid features from serum ether extracts by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). An iterative three-stage screening strategy is developed for nontargeted lipid analyses when using multiplexed electrophoretic separations coupled to an Orbitrap mass analyzer under negative ion mode. This approach enables the credentialing of 270 serum lipid features annotated based on their accurate mass and relative migration time, including 128 ionic lipids reliably measured (median CV ≈ 13%) in most serum samples (>75%) from nonalcoholic steatohepatitis (NASH) patients (n = 85). A mobility map is introduced to classify charged lipid classes over a wide polarity range with selectivity complementary to chromatographic separations, including lysophosphatidic acids, phosphatidylcholines, phosphatidylinositols, phosphatidylethanolamines, and nonesterified fatty acids (NEFAs). Serum lipidome profiles were also used to differentiate high- from low-risk NASH patients using a k-means clustering algorithm, where elevated circulating NEFAs (e.g., palmitic acid) were associated with increased glucose intolerance, more severe liver fibrosis, and greater disease burden. MSI-NACE-MS greatly expands the metabolome coverage of conventional aqueous-based CE-MS protocols and is a promising platform for large-scale lipidomic studies.
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Affiliation(s)
- Ritchie Ly
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1, Canada
| | - Nicholas Ly
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1, Canada
| | - Kazunori Sasaki
- Human Metabolome Technologies, Inc., Tsuruoka, Yamagata 997-0052, Japan
| | - Makoto Suzuki
- Human Metabolome Technologies, Inc., Tsuruoka, Yamagata 997-0052, Japan
| | - Kenjiro Kami
- Human Metabolome Technologies, Inc., Tsuruoka, Yamagata 997-0052, Japan
| | - Yoshiaki Ohashi
- Human Metabolome Technologies, Inc., Tsuruoka, Yamagata 997-0052, Japan
| | - Philip Britz-McKibbin
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1, Canada
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23
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Tötsch K, Fjeldsted JC, Stow SM, Schmitz OJ, Meckelmann SW. Effect of Sampling Rate and Data Pretreatment for Targeted and Nontargeted Analysis by Means of Liquid Chromatography Coupled to Drift Time Ion Mobility Quadruple Time-of-Flight Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2592-2603. [PMID: 34515480 DOI: 10.1021/jasms.1c00217] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Ion mobility as an additional separation dimension can help to resolve and annotate metabolite and lipid biomarkers and provides important information about the components in a sample. Identifying relevant information in the resulting data is challenging because of the complexity of the data and data evaluation strategies for both targeted or nontargeted workflows. Frequently, feature analysis is used as a first step to search for differences between samples in discovery workflows. However, follow-up experimentation often leads to more targeted data extraction methods. In both cases, optimizing data sets for data extraction can make an important contribution to the overall results. In this work, we evaluate the effect of experimental conditions including acquisition sampling rate and data pretreatment on lipid standards and lipid extracts as examples of complex biological samples analyzed by liquid chromatography coupled to drift time ion mobility quadrupole time-of-flight mass spectrometry. The results show that a reduction of both peak variation and background noise can be achieved by optimizing the sampling rate. The use of data pretreatment including data smoothing, intensity thresholding, and spike removal also play an important role in improving detection and annotation of analytes from complex biological samples, whereas nonoptimal data sampling rates and preprocessing can lead to adverse effects including the loss or alternation of small, or closely eluting, low-abundant peaks.
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Affiliation(s)
- Kristina Tötsch
- Applied Analytical Chemistry, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
- Teaching and Research Center for Separation, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
| | - John C Fjeldsted
- Agilent Technologies, Santa Clara, California 95051, United States
| | - Sarah M Stow
- Agilent Technologies, Santa Clara, California 95051, United States
| | - Oliver J Schmitz
- Applied Analytical Chemistry, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
- Teaching and Research Center for Separation, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
| | - Sven W Meckelmann
- Applied Analytical Chemistry, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
- Teaching and Research Center for Separation, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
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24
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Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 2021; 93:7763-7773. [PMID: 34029068 PMCID: PMC8465926 DOI: 10.1021/acs.analchem.0c04850] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - David M Reif
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
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25
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Cho K, Schwaiger-Haber M, Naser FJ, Stancliffe E, Sindelar M, Patti GJ. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics. Anal Chim Acta 2021; 1149:338210. [PMID: 33551064 PMCID: PMC8189644 DOI: 10.1016/j.aca.2021.338210] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/16/2020] [Accepted: 01/05/2021] [Indexed: 12/22/2022]
Abstract
When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.
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Affiliation(s)
- Kevin Cho
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Fuad J Naser
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Miriam Sindelar
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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26
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Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology. Curr Environ Health Rep 2021; 8:34-41. [PMID: 33569731 DOI: 10.1007/s40572-021-00306-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE OF REVIEW The exposome is a recently coined concept that comprises the totality of nongenetic factors that affect human health. It is recognized as a major conceptual advancement in environmental epidemiology, and there is increased demand for technologies that capture the spatial, temporal, and chemical variability of exposures across individuals (i.e., "exposomic sensors"). We review a selection of these tools, highlighting their strengths and limitations with regard to epidemiological research. RECENT FINDINGS Wearable passive samplers are emerging as promising exposomic sensors for individuals. In conjunction with targeted and untargeted assays, these sensors enable the measurement of complex multipollutant mixtures, which can include both known and previously unknown environmental contaminants. Because of their minimally burdensome and noninvasive nature, they are deployable among sensitive populations, such as seniors, pregnant women, and children. The integration of exposomic data captured by these sensors with other omic data (e.g., transcriptomic and metabolomic) presents exciting opportunities for investigating disease risk factors. For example, the linkage of exposomic sensor data with other omic data may indicate perturbation by multipollutant mixtures at multiple physiological levels, which would strengthen evidence of their effects and potentially indicate targets for interventions. However, there remain considerable theoretical and methodological challenges that must be overcome to realize the potential promise of omic integration. Through continued investment and improvement in exposomic sensor technologies, it may be possible to refine their application and reduce their outstanding limitations to advance the fields of exposure science and epidemiology.
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27
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Telu KH, Marupaka R, Andriamaharavo NR, Simón-Manso Y, Liang Y, Mirokhin YA, Bukhari TH, Preston RJ, Kashi L, Kelman Z, Stein SE. Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components. Biotechnol Bioeng 2021; 118:1491-1510. [PMID: 33404064 PMCID: PMC8048470 DOI: 10.1002/bit.27661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 12/02/2020] [Accepted: 12/13/2020] [Indexed: 02/02/2023]
Abstract
This paper reports the first implementation of a new type of mass spectral library for the analysis of Chinese hamster ovary (CHO) cell metabolites that allows users to quickly identify most compounds in any complex metabolite sample. We also describe an annotation methodology developed to filter out artifacts and low‐quality spectra from recurrent unidentified spectra of metabolites. CHO cells are commonly used to produce biological therapeutics. Metabolic profiles of CHO cells and media can be used to monitor process variability and look for markers that discriminate between batches of product. We have created a comprehensive library of both identified and unidentified metabolites derived from CHO cells that can be used in conjunction with tandem mass spectrometry to identify metabolites. In addition, we present a workflow that can be used for assigning confidence to a NIST MS/MS Library search match based on prior probability of general utility. The goal of our work is to annotate and identify (when possible), all liquid chromatography‐mass spectrometry generated metabolite ions as well as create automatable library building and identification pipelines for use by others in the field.
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Affiliation(s)
- Kelly H Telu
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Ramesh Marupaka
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Nirina R Andriamaharavo
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Yamil Simón-Manso
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Yuxue Liang
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Yuri A Mirokhin
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Tallat H Bukhari
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Renae J Preston
- Biomolecular Labeling Laboratory, Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, Maryland, USA
| | - Lila Kashi
- Biomolecular Labeling Laboratory, Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, Maryland, USA
| | - Zvi Kelman
- Biomolecular Labeling Laboratory, Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, Maryland, USA
| | - Stephen E Stein
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
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28
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Dodds JN, Alexander NLM, Kirkwood KI, Foster MR, Hopkins ZR, Knappe DRU, Baker ES. From Pesticides to Per- and Polyfluoroalkyl Substances: An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics. Anal Chem 2021; 93:641-656. [PMID: 33136371 PMCID: PMC7855838 DOI: 10.1021/acs.analchem.0c04359] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Nancy Lee M Alexander
- Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27607, United States
| | - Kaylie I Kirkwood
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - MaKayla R Foster
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Zachary R Hopkins
- Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27607, United States
| | - Detlef R U Knappe
- Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27607, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
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29
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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30
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Domenick TM, Gill EL, Vedam-Mai V, Yost RA. Mass Spectrometry-Based Cellular Metabolomics: Current Approaches, Applications, and Future Directions. Anal Chem 2020; 93:546-566. [PMID: 33146525 DOI: 10.1021/acs.analchem.0c04363] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Taylor M Domenick
- Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200, United States
| | - Emily L Gill
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104-4283, United States.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104-4283, United States
| | - Vinata Vedam-Mai
- Department of Neurology, University of Florida, Gainesville, Florida 32610, United States
| | - Richard A Yost
- Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200, United States
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31
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Harshman SW, Strayer KE, Davidson CN, Pitsch RL, Narayanan L, Scott AM, Schaeublin NM, Wiens TL, Phelps MS, O'Connor ML, Mackowski NS, Barrett KN, Leyh SM, Eckerle JJ, Strang AJ, Martin JA. Rate normalization for sweat metabolomics biomarker discovery. Talanta 2020; 223:121797. [PMID: 33303130 DOI: 10.1016/j.talanta.2020.121797] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/15/2020] [Accepted: 10/17/2020] [Indexed: 02/07/2023]
Abstract
As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ -0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.
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Affiliation(s)
- Sean W Harshman
- UES Inc., Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright- Patterson AFB, OH, 45433, USA.
| | - Kraig E Strayer
- UES Inc., Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright- Patterson AFB, OH, 45433, USA
| | - Christina N Davidson
- Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright-Patterson AFB, OH, 45433, USA
| | - Rhonda L Pitsch
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, Wright- Patterson AFB, OH, 45433, USA
| | - Latha Narayanan
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, Wright- Patterson AFB, OH, 45433, USA
| | - Alexander M Scott
- Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright-Patterson AFB, OH, 45433, USA
| | - Nicole M Schaeublin
- UES Inc., Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright- Patterson AFB, OH, 45433, USA
| | - Taylor L Wiens
- Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright-Patterson AFB, OH, 45433, USA
| | - Mandy S Phelps
- UES Inc., Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright- Patterson AFB, OH, 45433, USA
| | - Maegan L O'Connor
- InfoSciTex Corp., Air Force Research Laboratory, 711th Human Performance Wing/RHBCN, Wright-Patterson AFB, OH, 45433, USA
| | - Nicholas S Mackowski
- InfoSciTex Corp., Air Force Research Laboratory, 711th Human Performance Wing/RHBCN, Wright-Patterson AFB, OH, 45433, USA
| | - Kristyn N Barrett
- InfoSciTex Corp., Air Force Research Laboratory, 711th Human Performance Wing/RHBCN, Wright-Patterson AFB, OH, 45433, USA
| | - Samantha M Leyh
- Oak Ridge Institute of Science & Education, Air Force Research Laboratory, 711th Human Performance Wing/RHBCN, Wright-Patterson AFB, OH, 45433, USA
| | - Jason J Eckerle
- InfoSciTex Corp., Air Force Research Laboratory, 711th Human Performance Wing/RHBCN, Wright-Patterson AFB, OH, 45433, USA
| | - Adam J Strang
- Air Force Research Laboratory, 711th Human Performance Wing/RHBCN, 2510 Fifth Street, Area B, Building 840, Wright-Patterson AFB, OH, 45433, USA
| | - Jennifer A Martin
- Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright-Patterson AFB, OH, 45433, USA
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32
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Ćeranić A, Bueschl C, Doppler M, Parich A, Xu K, Lemmens M, Buerstmayr H, Schuhmacher R. Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics. Metabolites 2020; 10:metabo10110434. [PMID: 33121096 PMCID: PMC7692853 DOI: 10.3390/metabo10110434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/21/2020] [Accepted: 10/22/2020] [Indexed: 12/28/2022] Open
Abstract
Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly 13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.
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Affiliation(s)
- Asja Ćeranić
- Department of Agrobiotechnology, Institute of Bioanalytics and Agro-Metabolomics, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (A.Ć.); (C.B.); (M.D.); (A.P.); (K.X.)
| | - Christoph Bueschl
- Department of Agrobiotechnology, Institute of Bioanalytics and Agro-Metabolomics, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (A.Ć.); (C.B.); (M.D.); (A.P.); (K.X.)
| | - Maria Doppler
- Department of Agrobiotechnology, Institute of Bioanalytics and Agro-Metabolomics, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (A.Ć.); (C.B.); (M.D.); (A.P.); (K.X.)
| | - Alexandra Parich
- Department of Agrobiotechnology, Institute of Bioanalytics and Agro-Metabolomics, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (A.Ć.); (C.B.); (M.D.); (A.P.); (K.X.)
| | - Kangkang Xu
- Department of Agrobiotechnology, Institute of Bioanalytics and Agro-Metabolomics, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (A.Ć.); (C.B.); (M.D.); (A.P.); (K.X.)
| | - Marc Lemmens
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (M.L.); (H.B.)
| | - Hermann Buerstmayr
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (M.L.); (H.B.)
| | - Rainer Schuhmacher
- Department of Agrobiotechnology, Institute of Bioanalytics and Agro-Metabolomics, IFA-Tulln, University of Natural Resources and Life Sciences Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Upper Austria, Austria; (A.Ć.); (C.B.); (M.D.); (A.P.); (K.X.)
- Correspondence: ; Tel.: +43-1-47654-97307
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Peterson LA, Balbo S, Fujioka N, Hatsukami DK, Hecht SS, Murphy SE, Stepanov I, Tretyakova NY, Turesky RJ, Villalta PW. Applying Tobacco, Environmental, and Dietary-Related Biomarkers to Understand Cancer Etiology and Evaluate Prevention Strategies. Cancer Epidemiol Biomarkers Prev 2020; 29:1904-1919. [PMID: 32051197 PMCID: PMC7423750 DOI: 10.1158/1055-9965.epi-19-1356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/18/2019] [Accepted: 01/27/2020] [Indexed: 01/20/2023] Open
Abstract
Many human cancers are caused by environmental and lifestyle factors. Biomarkers of exposure and risk developed by our team have provided critical data on internal exposure to toxic and genotoxic chemicals and their connection to cancer in humans. This review highlights our research using biomarkers to identify key factors influencing cancer risk as well as their application to assess the effectiveness of exposure intervention and chemoprevention protocols. The use of these biomarkers to understand individual susceptibility to the harmful effects of tobacco products is a powerful example of the value of this type of research and has provided key data confirming the link between tobacco smoke exposure and cancer risk. Furthermore, this information has led to policy changes that have reduced tobacco use and consequently, the tobacco-related cancer burden. Recent technological advances in mass spectrometry led to the ability to detect DNA damage in human tissues as well as the development of adductomic approaches. These new methods allowed for the detection of DNA adducts in tissues from patients with cancer, providing key evidence that exposure to carcinogens leads to DNA damage in the target tissue. These advances will provide valuable insights into the etiologic causes of cancer that are not tobacco-related.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."
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Affiliation(s)
- Lisa A Peterson
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, Minnesota.
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Silvia Balbo
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, Minnesota
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Naomi Fujioka
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, Minnesota
| | - Dorothy K Hatsukami
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Stephen S Hecht
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota
| | - Sharon E Murphy
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Irina Stepanov
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, Minnesota
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Natalia Y Tretyakova
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
- Department of Medicinal Chemistry, University of Minnesota, Minneapolis, Minnesota
| | - Robert J Turesky
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
- Department of Medicinal Chemistry, University of Minnesota, Minneapolis, Minnesota
| | - Peter W Villalta
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
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Lu W, Xing X, Wang L, Chen L, Zhang S, McReynolds MR, Rabinowitz JD. Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity. Anal Chem 2020; 92:11573-11581. [PMID: 32614575 PMCID: PMC7484094 DOI: 10.1021/acs.analchem.0c00985] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts, and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a buffer modification workflow (BMW) in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3-acetate buffer and in solvent with the buffer modified with 15NH3-formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Overall, it identified 26% of ∼27 000 liver LC-MS features as putative metabolites, of which ∼2600 showed HMDB or KEGG database formula match. This workflow is well suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.
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Affiliation(s)
- Wenyun Lu
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Xi Xing
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Lin Wang
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Li Chen
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Sisi Zhang
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Melanie R McReynolds
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Joshua D Rabinowitz
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
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Depke T, Thöming JG, Kordes A, Häussler S, Brönstrup M. Untargeted LC-MS Metabolomics Differentiates Between Virulent and Avirulent Clinical Strains of Pseudomonas aeruginosa. Biomolecules 2020; 10:biom10071041. [PMID: 32668735 PMCID: PMC7407980 DOI: 10.3390/biom10071041] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/27/2020] [Accepted: 07/07/2020] [Indexed: 01/02/2023] Open
Abstract
Pseudomonas aeruginosa is a facultative pathogen that can cause, inter alia, acute or chronic pneumonia in predisposed individuals. The gram-negative bacterium displays considerable genomic and phenotypic diversity that is also shaped by small molecule secondary metabolites. The discrimination of virulence phenotypes is highly relevant to the diagnosis and prognosis of P. aeruginosa infections. In order to discover small molecule metabolites that distinguish different virulence phenotypes of P. aeruginosa, 35 clinical strains were cultivated under standard conditions, characterized in terms of virulence and biofilm phenotype, and their metabolomes were investigated by untargeted liquid chromatography-mass spectrometry. The data was both mined for individual candidate markers as well as used to construct statistical models to infer the virulence phenotype from metabolomics data. We found that clinical strains that differed in their virulence and biofilm phenotype also had pronounced divergence in their metabolomes, as underlined by 332 features that were significantly differentially abundant with fold changes greater than 1.5 in both directions. Important virulence-associated secondary metabolites like rhamnolipids, alkyl quinolones or phenazines were found to be strongly upregulated in virulent strains. In contrast, we observed little change in primary metabolism. A hitherto novel cationic metabolite with a sum formula of C12H15N2 could be identified as a candidate biomarker. A random forest model was able to classify strains according to their virulence and biofilm phenotype with an area under the Receiver Operation Characteristics curve of 0.84. These findings demonstrate that untargeted metabolomics is a valuable tool to characterize P. aeruginosa virulence, and to explore interrelations between clinically important phenotypic traits and the bacterial metabolome.
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Affiliation(s)
- Tobias Depke
- Department of Chemical Biology, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany;
| | - Janne Gesine Thöming
- Institute of Molecular Bacteriology, Twincore, Centre for Clinical and Experimental Infection Research, 30625 Hannover, Germany; (J.G.T.); (A.K.); (S.H.)
| | - Adrian Kordes
- Institute of Molecular Bacteriology, Twincore, Centre for Clinical and Experimental Infection Research, 30625 Hannover, Germany; (J.G.T.); (A.K.); (S.H.)
| | - Susanne Häussler
- Institute of Molecular Bacteriology, Twincore, Centre for Clinical and Experimental Infection Research, 30625 Hannover, Germany; (J.G.T.); (A.K.); (S.H.)
- Department of Molecular Bacteriology, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany
| | - Mark Brönstrup
- Department of Chemical Biology, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany;
- German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, 38124 Braunschweig, Germany
- Correspondence:
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Galvez L, Rusz M, Schwaiger-Haber M, El Abiead Y, Hermann G, Jungwirth U, Berger W, Keppler BK, Jakupec MA, Koellensperger G. Preclinical studies on metal based anticancer drugs as enabled by integrated metallomics and metabolomics. Metallomics 2020; 11:1716-1728. [PMID: 31497817 DOI: 10.1039/c9mt00141g] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Resistance development is a major obstacle for platinum-based chemotherapy, with the anticancer drug oxaliplatin being no exception. Acquired resistance is often associated with altered drug accumulation. In this work we introduce a novel -omics workflow enabling the parallel study of platinum drug uptake and its distribution between nucleus/protein and small molecule fraction along with metabolic changes after different treatment time points. This integrated metallomics/metabolomics approach is facilitated by a tailored sample preparation workflow suitable for preclinical studies on adherent cancer cell models. Inductively coupled plasma mass spectrometry monitors the platinum drug, while the metabolomics tool-set is provided by hydrophilic interaction liquid chromatography combined with high-resolution Orbitrap mass spectrometry. The implemented method covers biochemical key pathways of cancer cell metabolism as shown by a panel of >130 metabolite standards. Furthermore, the addition of yeast-based 13C-enriched internal standards upon extraction enabled a novel targeted/untargeted analysis strategy. In this study we used our method to compare an oxaliplatin sensitive human colon cancer cell line (HCT116) and its corresponding resistant model. In the acquired oxaliplatin resistant cells distinct differences in oxaliplatin accumulation correlated with differences in metabolomic rearrangements. Using this multi-omics approach for platinum-treated samples facilitates the generation of novel hypotheses regarding the susceptibility and resistance towards oxaliplatin.
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Affiliation(s)
- Luis Galvez
- Institute of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Waehringer Strasse 38, 1090 Vienna, Austria.
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Guan S, Armbruster MR, Huang T, Edwards JL, Bythell BJ. Isomeric Differentiation and Acidic Metabolite Identification by Piperidine-Based Tagging, LC–MS/MS, and Understanding of the Dissociation Chemistries. Anal Chem 2020; 92:9305-9311. [DOI: 10.1021/acs.analchem.0c01640] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Shanshan Guan
- Department of Chemistry and Biochemistry, Ohio University, 391 Clippinger Laboratories, Athens, Ohio 45701, United States
- Department of Chemistry and Biochemistry, University of Missouri, 1 University Blvd, St. Louis, Missouri 63121, United States
| | - Michael R. Armbruster
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63102, United States
| | - Tianjiao Huang
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63102, United States
| | - James L. Edwards
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63102, United States
| | - Benjamin J. Bythell
- Department of Chemistry and Biochemistry, Ohio University, 391 Clippinger Laboratories, Athens, Ohio 45701, United States
- Department of Chemistry and Biochemistry, University of Missouri, 1 University Blvd, St. Louis, Missouri 63121, United States
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Abstract
Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.
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Affiliation(s)
- Miriam Sindelar
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J. Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
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Manier SK, Meyer MR. Impact of the used solvent on the reconstitution efficiency of evaporated biosamples for untargeted metabolomics studies. Metabolomics 2020; 16:34. [PMID: 32124055 PMCID: PMC7052028 DOI: 10.1007/s11306-019-1631-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 12/31/2019] [Indexed: 11/28/2022]
Abstract
INTRODUCTION Untargeted metabolomics intends to objectively analyze a wide variety of compounds. Their diverse physicochemical properties make it difficult to choose an appropriate reconstitution solvent after sample evaporation without influencing the chromatography or hamper column sorbent integrity. OBJECTIVES The study aimed to identify the most appropriate reconstitution solvent for blood plasma samples in terms of feature recovery, four endogenous compounds, and one selected internal standard. METHODS We investigated several reconstitution solvent mixtures containing acetonitrile and methanol to resolve human plasma extract and evaluated them concerning the peak areas of tryptophan-d5, glucose, creatinine, palmitic acid, and the phophatidylcholine PC(P-16:0/P-16:0), as well as the total feature count RESULTS: Results indicated that acetonitrile containing 30% methanol was best suited to match all tested criteria at least for human blood plasma samples. CONCLUSION Despite identifying the mixture of acetonitrile and methanol being suitable as solvent for human blood plasma extracts, we recommend to systematically test for an appropriate reconstitution solvent for each analyzed biomatrix.
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Affiliation(s)
- Sascha K Manier
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany
| | - Markus R Meyer
- Department of Experimental and Clinical Toxicology, Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Germany.
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Cable J, Finley L, Tu BP, Patti GJ, Oliver TG, Vardhana S, Mana M, Ericksen R, Khare S, DeBerardinis R, Stockwell BR, Edinger A, Haigis M, Kaelin W. Leveraging insights into cancer metabolism-a symposium report. Ann N Y Acad Sci 2019; 1462:5-13. [PMID: 31792987 DOI: 10.1111/nyas.14274] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 10/23/2019] [Indexed: 12/15/2022]
Abstract
Tumor cells have devised unique metabolic strategies to garner enough nutrients to sustain continuous growth and cell division. Oncogenic mutations may alter metabolic pathways to unlock new sources of energy, and cells take the advantage of various scavenging pathways to ingest material from their environment. These changes in metabolism result in a metabolic profile that, in addition to providing the building blocks for macromolecules, can also influence cell signaling pathways to promote tumor initiation and progression. Understanding what pathways tumor cells use to synthesize the materials necessary to support metabolic growth can pave the way for new cancer therapeutics. Potential strategies include depriving tumors of the materials needed to grow or targeting pathways involved in dependencies that arise by virtue of their altered metabolis.
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Affiliation(s)
| | - Lydia Finley
- Center for Epigenetics Research, Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Benjamin P Tu
- Department of Biochemistry, UT Southwestern Medical Center, Dallas, Texas
| | - Gary J Patti
- Departments of Chemistry and Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Trudy G Oliver
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Santosha Vardhana
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Miyeko Mana
- The David H. Koch Institute for Integrative Cancer Research at the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Russell Ericksen
- Singapore Bioimaging Consortium, Agency for Science, Technology, and Research, Singapore, Singapore
| | - Sanika Khare
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ralph DeBerardinis
- Howard Hughes Medical Institute and Children's Medical Center Research Institute, UT Southwestern Medical Center, Dallas, Texas
| | - Brent R Stockwell
- Department of Biological Sciences and Department of Chemistry, Columbia University, New York, New York
| | - Aimee Edinger
- Department of Developmental and Cell Biology, University of California, Irvine, California
| | - Marcia Haigis
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts
| | - William Kaelin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Howard Hughes Medical Institute, Chevy Chase, Maryland
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Kantz ED, Tiwari S, Watrous JD, Cheng S, Jain M. Deep Neural Networks for Classification of LC-MS Spectral Peaks. Anal Chem 2019; 91:12407-12413. [PMID: 31483992 PMCID: PMC7089603 DOI: 10.1021/acs.analchem.9b02983] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.
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Affiliation(s)
- Edward D. Kantz
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093
- Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA, 92093
| | - Saumya Tiwari
- Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA, 92093
| | - Jeramie D. Watrous
- Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA, 92093
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Mohit Jain
- Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA, 92093
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Baker ES, Patti GJ. Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:2031-2036. [PMID: 31440979 PMCID: PMC7310669 DOI: 10.1007/s13361-019-02295-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 05/04/2023]
Abstract
In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics.
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Affiliation(s)
- Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
| | - Gary J Patti
- Departments of Chemistry and Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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Llufrio EM, Cho K, Patti GJ. Systems-level analysis of isotopic labeling in untargeted metabolomic data by X 13CMS. Nat Protoc 2019; 14:1970-1990. [PMID: 31168088 PMCID: PMC7323898 DOI: 10.1038/s41596-019-0167-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 03/15/2019] [Indexed: 12/18/2022]
Abstract
Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.
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Affiliation(s)
- Elizabeth M Llufrio
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
| | - Kevin Cho
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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Abstract
There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).
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Affiliation(s)
- Lingjue Wang
- Department of Chemistry, Washington University, St. Louis, MO, USA
| | - Fuad J Naser
- Department of Chemistry, Washington University, St. Louis, MO, USA
| | - Jonathan L Spalding
- Department of Chemistry, Washington University, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University, St. Louis, MO, USA.
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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46
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Boulangé CL, Rood IM, Posma JM, Lindon JC, Holmes E, Wetzels JFM, Deegens JKJ, Kaluarachchi MR. NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor. Mol Omics 2019; 15:39-49. [DOI: 10.1039/c8mo00190a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
When using an appropriate data analysis pipeline, protease inhibitor (PI)-containing urine samples are fit-for-purpose for metabolic phenotyping of patients with nephrotic syndrome and proteinuria.
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Affiliation(s)
| | - Ilse M. Rood
- Department of Nephrology
- Radboud University Medical Center
- Nijmegen
- The Netherlands
| | - Joram M. Posma
- Imperial College London
- Division of Computational and Systems Medicine
- Department of Surgery and Cancer
- Faculty of Medicine
- London SW7 2AZ
| | - John C. Lindon
- Metabometrix Ltd
- London SW7 2AZ
- UK
- Imperial College London
- Division of Computational and Systems Medicine
| | - Elaine Holmes
- Metabometrix Ltd
- London SW7 2AZ
- UK
- Imperial College London
- Division of Computational and Systems Medicine
| | - Jack F. M. Wetzels
- Department of Nephrology
- Radboud University Medical Center
- Nijmegen
- The Netherlands
| | - Jeroen K. J. Deegens
- Department of Nephrology
- Radboud University Medical Center
- Nijmegen
- The Netherlands
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Spalding JL, Naser FJ, Mahieu NG, Johnson SL, Patti GJ. Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics. J Proteome Res 2018; 17:3537-3546. [PMID: 30160483 DOI: 10.1021/acs.jproteome.8b00487] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Existing hydrophilic interaction liquid chromatography (HILIC) methods, considered individually, each exhibit poor chromatographic performance for a substantial fraction of polar metabolites. In addition to limiting metabolome coverage, such deficiencies also complicate automated data processing. Here we show that some of these analytical challenges can be addressed for the ZIC-pHILIC, a zwitterionic stationary phase commonly used in metabolomics, with the addition of trace levels of phosphate. Specifically, micromolar phosphate extended metabolome coverage by hundreds of credentialed features, improved peak shapes, and reduced peak-detection errors during informatic processing. Although the addition of high levels of phosphate (millimolar) as a HILIC mobile phase buffer has been explored previously, such concentrations interfere with mass spectrometric (MS) detection. We show that using phosphate as a trace additive at micromolar concentrations improves analysis by electrospray MS, increasing signal for a diverse set of polar standards. Given the small amount of phosphate needed, comparable chromatographic improvements were also achieved by direct addition of phosphate to the sample during reconstitution. Our results suggest that defects in ZIC-pHILIC performance are predominantly driven by electrostatic interactions, which can be modulated by phosphate. These findings constitute both a methodological improvement for untargeted metabolomics and an advance in our understanding of the mechanisms limiting HILIC coverage.
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Affiliation(s)
- Jonathan L Spalding
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States.,Department of Genetics , Washington University in St. Louis , St. Louis , MO 63110 , United States.,Department of Medicine , Washington University in St. Louis , St. Louis , MO 63110 , United States
| | - Fuad J Naser
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States
| | - Nathaniel G Mahieu
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States
| | - Stephen L Johnson
- Department of Genetics , Washington University in St. Louis , St. Louis , MO 63110 , United States
| | - Gary J Patti
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States.,Department of Medicine , Washington University in St. Louis , St. Louis , MO 63110 , United States
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Vuckovic D. Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery. Chem Commun (Camb) 2018; 54:6728-6749. [PMID: 29888773 DOI: 10.1039/c8cc02592d] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.
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Affiliation(s)
- Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec H4B 1R6, Canada.
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49
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Llufrio EM, Wang L, Naser FJ, Patti GJ. Sorting cells alters their redox state and cellular metabolome. Redox Biol 2018; 16:381-387. [PMID: 29627745 PMCID: PMC5952879 DOI: 10.1016/j.redox.2018.03.004] [Citation(s) in RCA: 118] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 03/03/2018] [Accepted: 03/06/2018] [Indexed: 02/08/2023] Open
Abstract
A growing appreciation of the metabolic artifacts of cell culture has generated heightened enthusiasm for performing metabolomics on populations of cells purified from tissues and biofluids. Fluorescence activated cell sorting, or FACS, is a widely used experimental approach to purify specific cell types from complex heterogeneous samples. Here we show that FACS introduces oxidative stress and alters the metabolic state of cells. Compared to unsorted controls, astrocytes subjected to FACS prior to metabolomic analysis showed altered ratios of GSSG to GSH, NADPH to NADP+, and NAD+ to NADH. Additionally, a 50% increase in reactive oxygen species was observed in astrocytes subjected to FACS relative to unsorted controls. At a more comprehensive scale, nearly half of the metabolomic features that we profiled by liquid chromatography/mass spectrometry were changed by at least 1.5-fold in intensity due to cell sorting. Some specific metabolites identified to have significantly altered levels as a result of cell sorting included glycogen, nucleosides, amino acids, central carbon metabolites, and acylcarnitines. Although the addition of fetal bovine serum to the cell-sorting buffer decreased oxidative stress and attenuated changes in metabolite concentrations, fetal bovine serum did not preserve the metabolic state of the cells during FACS. We conclude that, irrespective of buffer components and data-normalization strategies we examined, metabolomic results from sorted cells do not accurately reflect physiological conditions prior to sorting.
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Affiliation(s)
- Elizabeth M Llufrio
- Department of Chemistry, Washington University, St. Louis, MO 63130, United States
| | - Lingjue Wang
- Department of Chemistry, Washington University, St. Louis, MO 63130, United States
| | - Fuad J Naser
- Department of Chemistry, Washington University, St. Louis, MO 63130, United States
| | - Gary J Patti
- Department of Chemistry, Washington University, St. Louis, MO 63130, United States; Department of Medicine, Washington University, School of Medicine, St. Louis, MO 63110, United States.
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Li Z, Lu Y, Guo Y, Cao H, Wang Q, Shui W. Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection. Anal Chim Acta 2018; 1029:50-57. [PMID: 29907290 DOI: 10.1016/j.aca.2018.05.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 04/24/2018] [Accepted: 05/01/2018] [Indexed: 01/22/2023]
Abstract
Data analysis represents a key challenge for untargeted metabolomics studies and it commonly requires extensive processing of more than thousands of metabolite peaks included in raw high-resolution MS data. Although a number of software packages have been developed to facilitate untargeted data processing, they have not been comprehensively scrutinized in the capability of feature detection, quantification and marker selection using a well-defined benchmark sample set. In this study, we acquired a benchmark dataset from standard mixtures consisting of 1100 compounds with specified concentration ratios including 130 compounds with significant variation of concentrations. Five software evaluated here (MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer) showed similar performance in detection of true features derived from compounds in the mixtures. However, significant differences between untargeted metabolomics software were observed in relative quantification of true features in the benchmark dataset. MZmine 2 outperformed the other software in terms of quantification accuracy and it reported the most true discriminating markers together with the fewest false markers. Furthermore, we assessed selection of discriminating markers by different software using both the benchmark dataset and a real-case metabolomics dataset to propose combined usage of two software for increasing confidence of biomarker identification. Our findings from comprehensive evaluation of untargeted metabolomics software would help guide future improvements of these widely used bioinformatics tools and enable users to properly interpret their metabolomics results.
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Affiliation(s)
- Zhucui Li
- University of Chinese Academy of Sciences, Beijing 100049, China; iHuman Institute, ShanghaiTech University, Shanghai 201210, China; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Yan Lu
- University of Chinese Academy of Sciences, Beijing 100049, China; iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Yufeng Guo
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Haijie Cao
- College of Pharmacy, Nankai University, Tianjin 300071, China
| | - Qinhong Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
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