1
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Chaleckis R, Ito Y, Wasada H, Wheelock CE, Oishi H, Tomizawa M, Kamijima M. Fungicide Metabolite MS2 Spectral Libraries for Comprehensive Human Biomonitoring. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:18247-18256. [PMID: 39101478 DOI: 10.1021/acs.jafc.4c02339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
Fungicides undergo rapid metabolism and are excreted in the urine. There are few methods for screening these ubiquitous compounds, which have a high potential for human exposure. High-resolution mass spectrometry (HRMS) is a suitable technique to assess fungicide exposures; however, there is a lack of spectral libraries for fungicide annotation and in particular for downstream metabolites. We created spectral libraries for 32 fungicides for suspect screening. Fungicide standards were administered to mice, and 24-h urine was analyzed using hydrophilic interaction and reversed-phase chromatography coupled to hybrid quadrupole-orbitrap mass spectrometry. Suspect metabolite MS2 spectra for library creation were selected based on the ratio of exposed-to-control mouse urine. MS2 libraries were applied to urine collected from female university students (n = 73). Several tetraconazole and tebuconazole metabolites were detected in 3% (2/73) of the samples. The creation of comprehensive suspect screening MS2 libraries is a useful tool to detect fungicide exposure for human biomonitoring.
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Affiliation(s)
- Romanas Chaleckis
- Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
| | - Yuki Ito
- Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
| | - Hitomi Wasada
- Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm 171 77, Sweden
| | - Hisashi Oishi
- Department of Comparative and Experimental Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
| | - Motohiro Tomizawa
- Department of Chemistry, Faculty of Life Sciences, Tokyo University of Agriculture, Setagaya, Tokyo 156-8502, Japan
| | - Michihiro Kamijima
- Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
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2
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Carlo MJ, Nanney ALM, Patrick AL. Energy-Resolved In-Source Collison-Induced Dissociation for Isomer Discrimination. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39016059 DOI: 10.1021/jasms.4c00118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
While mass spectrometry remains a gold-standard tool for analyte detection, characterization, and quantitation, isomer differentiation is often a challenge. Tandem mass spectrometry is a common approach to increase the selectivity of mass spectrometry and energy-resolved measurements can provide further improvements. However, not all mass spectrometers, especially those that are very compact and affordable, are amenable to such experiments. For instance, single-stage mass spectrometers with soft ionization provide no dissociation information and quadrupole ion trap instruments with resonant excitation do not necessarily provide as informative of energy-resolved curves, for instance when extensive sequential dissociation is responsible for much of the "fingerprint". In-source collision-induced dissociation (IS-CID) is one approach to overcoming these barriers to exploit the analytical selectivity of energy-resolved CID without the need for additional instrumentation; this approach could broaden the reach of these selectivity gains to additional user bases (e.g., educational settings, field portable devices). Here, we specifically investigate energy-resolved IS-CID with the goal of (1) comparing between energy-resolved appearance curves measured with true tandem mass spectrometry on a quadrupole time-of-flight instrument and those obtained using IS-CID, (2) evaluating the approach as a means of differentiating isomers/isobar sets, especially those with similar dissociation patterns, and (3) exploring additional analytical considerations relevant to method development and implementation. This proof-of-concept work establishes the analytical potential of this approach, opening doors for future method development for specific applications.
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Affiliation(s)
- Matthew J Carlo
- Department of Chemistry, Mississippi State University, Mississippi State, Mississippi 39762, United States
| | - Andie L M Nanney
- Department of Chemistry, Mississippi State University, Mississippi State, Mississippi 39762, United States
| | - Amanda L Patrick
- Department of Chemistry, Mississippi State University, Mississippi State, Mississippi 39762, United States
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3
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Asakawa D, Saikusa K, Yoshida H, Han Z, Chen LC. Tuning the Internal Energy of Ions Produced by Atmospheric and High-Pressure Electrospray by Modulating the Gas Throughput into the First Vacuum Stage. Anal Chem 2024; 96:10748-10755. [PMID: 38877968 DOI: 10.1021/acs.analchem.4c01813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
In a high-pressure environment, electrospray ionization (ESI) can be achieved without discharge between the emitter and the counter electrode, thus enabling the generation of gas-phase ions from liquid with high surface tension, such as pure water, which requires a high onset voltage for stable ESI. In this study, the ion dissociation during the transferring of ions/charged droplets from a superatmospheric pressure environment to vacuum has been systematically investigated using benzyl ammonium thermometer ions. The ion source pressure did not affect the internal energy distribution of ions, whereas the gas throughput into the first vacuum stage clearly influences the internal energy distribution of the ions. The increase in the gas throughput increased the density of molecules/atoms presented in ion transfer/focusing electrodes located in the first vacuum stage. As a result, the mean free path of ions in the first vacuum stage decreases, and the energy of ions decreases by decreasing the kinetic energy involved in each collision between ions and residue gas. The gas throughput into the first vacuum stage is found to describe the internal energy distribution of ions associated with the local conditions more quantitatively instead of using the measured pressure of the vacuum stage, which is different from the effective local pressure. This study also demonstrated the controlled dissociation of ions using the ion transfer settings of the instrument in combination with ion inlet tubes of different sizes.
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Affiliation(s)
- Daiki Asakawa
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8568, Japan
| | - Kazumi Saikusa
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8563, Japan
| | - Hajime Yoshida
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8563, Japan
| | - Zhongbao Han
- University of Yamanashi, Kofu, Yamanashi 400-0016, Japan
| | - Lee Chuin Chen
- University of Yamanashi, Kofu, Yamanashi 400-0016, Japan
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4
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Kostyukevich Y, Osipenko S, Borisova L, Kireev A. In-Electrospray source Hydrogen/Deuterium exchange coupled to multistage fragmentation for the investigation of the protonation and fragmentation pathways of gas phase ions. JOURNAL OF MASS SPECTROMETRY : JMS 2024; 59:e5032. [PMID: 38736146 DOI: 10.1002/jms.5032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 05/14/2024]
Abstract
Identification of molecules in complex natural matrices relies on matching the fragmentation spectra of ions under investigation and the spectra acquired for the corresponding analytical standards. Currently, there are many databases of experimentally measured tandem mass spectrometry spectra (such as NIST, MzCloud, and Metlin), and considerable progress has been made in the development of software for predicting tandem mass spectrometry fragments in silico using combinatorial, machine learning, and quantum chemistry approaches (such as MetFrag, CFM-ID, and QCxMS). However, the electrospray ionization molecules can be ionized at different sites (protonated or deprotonated), and the fragmentation spectra of such ions are different. Here, we are using the combination of the in-ESI source hydrogen/deuterium exchange reaction and MSn fragmentation for the investigation of the fragmentation pathways for different protomers of organic molecules. It is shown that the distribution of the deuterium in the fragment ions reflects the presence of different protomers. For several molecules, the distribution of deuterium was traced up to the MS5 level of fragmentation revealing many unusual and unexpected effects. For example, we investigated the loss of HF from the ciprofloxacin and norfloxacin ions and observed that for ions protonated at -COOH group, the eliminating hydrogen always comes from -NH group. When ions are protonated at another site, the elimination of hydrogen with a probability of 30% occurs from the -NH group, and with a probability of 70%, it originates from other sites on the molecule. Such effects were not described previously. Quantum chemical simulation was used for the verification of the protonated structures and simulation of the corresponding fragmentation spectra.
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Affiliation(s)
| | - Sergey Osipenko
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Albert Kireev
- Skolkovo Institute of Science and Technology, Moscow, Russia
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5
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Bazzano C, de Felicio R, Alves LFG, Costa JH, Ortega R, Vieira BD, Morais-Urano RP, Furtado LC, Ferreira ELF, Gubiani JR, Berlinck RGS, Costa-Lotufo LV, Telles GP, B. B. Trivella D. NP 3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics. Anal Chem 2024; 96:7460-7469. [PMID: 38702053 PMCID: PMC11099897 DOI: 10.1021/acs.analchem.3c05829] [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] [Received: 12/20/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 05/06/2024]
Abstract
Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.
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Affiliation(s)
- Cristina
F. Bazzano
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
- Institute
of Computing, University of Campinas (UNICAMP), Campinas 13083-852, State of São Paulo, Brazil
| | - Rafael de Felicio
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
| | - Luiz Fernando Giolo Alves
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
| | - Jonas Henrique Costa
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
| | - Raquel Ortega
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
- Institute
of Biology, University of Campinas (UNICAMP), Campinas 13083-852, State of São Paulo, Brazil
| | - Bruna Domingues Vieira
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
| | - Raquel Peres Morais-Urano
- Instituto
de Química de São Carlos, Universidade de São Paulo, CP 780, São Carlos CEP 13560-970, State of São Paulo, Brazil
| | - Luciana Costa Furtado
- Department
of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-000, State of São Paulo, Brazil
| | - Everton L. F. Ferreira
- Instituto
de Química de São Carlos, Universidade de São Paulo, CP 780, São Carlos CEP 13560-970, State of São Paulo, Brazil
| | - Juliana R. Gubiani
- Instituto
de Química de São Carlos, Universidade de São Paulo, CP 780, São Carlos CEP 13560-970, State of São Paulo, Brazil
| | - Roberto G. S. Berlinck
- Instituto
de Química de São Carlos, Universidade de São Paulo, CP 780, São Carlos CEP 13560-970, State of São Paulo, Brazil
| | - Leticia V. Costa-Lotufo
- Department
of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-000, State of São Paulo, Brazil
| | - Guilherme P. Telles
- Institute
of Computing, University of Campinas (UNICAMP), Campinas 13083-852, State of São Paulo, Brazil
| | - Daniela B. B. Trivella
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-970, State of São Paulo, Brazil
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6
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Asakawa D, Yamamoto R, Hanari N, Saikusa K. Differences in the internal energies of ions in electrospray ionization mass spectrometers equipped with capillary-skimmer and capillary-RF lens interfaces. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:6150-6158. [PMID: 37830393 DOI: 10.1039/d3ay01450a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Small metabolites are commonly analyzed using electrospray ionization mass spectrometry (ESI-MS). Although the protonated form of a compound of interest is typically the target ion in ESI-MS, the protonated forms of small metabolites occasionally undergo fragmentation during ion transmission from ambient conditions to vacuum conditions, hindering the unambiguous identification of analyte molecules. To estimate the fragmentation efficiency during ESI processes, the internal energy distribution of the ions (P(E)) must be evaluated. The common approach for the P(E) evaluation is the survival yield method, which uses thermometer ions. In this study, the P(E) of ions produced by an ESI source in a commercial triple quadrupole mass spectrometer equipped with a capillary-skimmer and capillary-RF lens interfaces was evaluated using benzyl ammonium thermometer ions. Furthermore, this study proposes the use of 3-(aminomethyl)indole and related compounds, which have the lowest Eapp values among the reported thermometer ions, to obtain P(E) values of the ions more accurately. Results showed that P(E) strongly depends on whether a capillary-skimmer interface or capillary-RF lens interface was used for ion transport to the vacuum. ESI-MS with a capillary-skimmer interface provided a considerably lower and narrower P(E) of ions than that with a capillary-RF lens interface, thereby producing intact protonated molecules without significant fragmentation of most small metabolites. However, ESI-MS equipped with capillary-RF lens interfaces provided a higher efficiency of ion transmission than ESI-MS equipped with a capillary-skimmer interface, allowing for highly sensitive analysis of metabolites.
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Affiliation(s)
- Daiki Asakawa
- National Institute of Advanced Industrial Science and Technology (AIST), National Metrology Institute of Japan (NMIJ), Tsukuba, Japan.
| | | | - Nobuyasu Hanari
- National Institute of Advanced Industrial Science and Technology (AIST), National Metrology Institute of Japan (NMIJ), Tsukuba, Japan.
| | - Kazumi Saikusa
- National Institute of Advanced Industrial Science and Technology (AIST), National Metrology Institute of Japan (NMIJ), Tsukuba, Japan.
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7
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Zhao T, Xing S, Yu H, Huan T. De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics. Anal Chem 2023; 95:13018-13028. [PMID: 37603462 DOI: 10.1021/acs.analchem.3c00736] [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: 08/23/2023]
Abstract
The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.
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Affiliation(s)
- Tingting Zhao
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
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8
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Xie J, Guo Y, Ma Y, Jiang H, Zhang L, Mao L, Zhu L, Zheng Y, Liu X. Spontaneous In-Source Fragmentation Reaction Mechanism and Highly Sensitive Analysis of Dicofol by Electrospray Ionization Mass Spectrometry. Molecules 2023; 28:molecules28093765. [PMID: 37175171 PMCID: PMC10180504 DOI: 10.3390/molecules28093765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Although dicofol has been widely banned all over the world as a kind of organochlorine contaminant, it still exists in the environment. This study developed a high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS) detection technique for dicofol, an environmental pollutant, for the first time using in-source fragmentation. The results confirmed that m/z 251 was the only precursor ion of dicofol after in-source fragmentation, and m/z 139 and m/z 111 were reasonable product ions. The main factors triggering the in-source fragmentation were the H+ content and solution conductivity when dicofol entered the mass spectrometer. Density functional theory can be used to analyze and interpret the mechanism of dicofol fragmentation reaction in ESI source. Dicofol reduced the molecular energy from 8.8 ± 0.05 kcal/mol to 1.0 ± 0.05 kcal/mol, indicating that the internal energy release from high to low was the key driving force of in-source fragmentation. A method based on HPLC-MS/MS was developed to analyze dicofol residues in environmental water. The LOQ was 0.1 μg/L, which was better than the previous GC or GC-MS methods. This study not only proposed an HPLC-MS/MS analysis method for dicofol for the first time but also explained the in-source fragmentation mechanism of compounds in ESI source, which has positive significance for the study of compounds with unconventional mass spectrometry behavior in the field of organic pollutant analysis and metabonomics.
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Affiliation(s)
- Jun Xie
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Yage Guo
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yongqiang Ma
- Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Hongyun Jiang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lan Zhang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Liangang Mao
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lizhen Zhu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yongquan Zheng
- Shandong Engineering Research Center for Environment-Friendly Agricultural Pest Management, College of Plant Health and Medicine, Qingdao Agricultural University, Qingdao 266109, China
| | - Xingang Liu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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9
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Seyler LM, Kraus EA, McLean C, Spear JR, Templeton AS, Schrenk MO. An untargeted exometabolomics approach to characterize dissolved organic matter in groundwater of the Samail Ophiolite. Front Microbiol 2023; 14:1093372. [PMID: 36970670 PMCID: PMC10033605 DOI: 10.3389/fmicb.2023.1093372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/23/2023] [Indexed: 03/11/2023] Open
Abstract
The process of serpentinization supports life on Earth and gives rise to the habitability of other worlds in our Solar System. While numerous studies have provided clues to the survival strategies of microbial communities in serpentinizing environments on the modern Earth, characterizing microbial activity in such environments remains challenging due to low biomass and extreme conditions. Here, we used an untargeted metabolomics approach to characterize dissolved organic matter in groundwater in the Samail Ophiolite, the largest and best characterized example of actively serpentinizing uplifted ocean crust and mantle. We found that dissolved organic matter composition is strongly correlated with both fluid type and microbial community composition, and that the fluids that were most influenced by serpentinization contained the greatest number of unique compounds, none of which could be identified using the current metabolite databases. Using metabolomics in conjunction with metagenomic data, we detected numerous products and intermediates of microbial metabolic processes and identified potential biosignatures of microbial activity, including pigments, porphyrins, quinones, fatty acids, and metabolites involved in methanogenesis. Metabolomics techniques like the ones used in this study may be used to further our understanding of life in serpentinizing environments, and aid in the identification of biosignatures that can be used to search for life in serpentinizing systems on other worlds.
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Affiliation(s)
- Lauren M. Seyler
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, United States
- Biology Program, School of Natural Sciences and Mathematics, Stockton University, Galloway, NJ, United States
- Blue Marble Space Institute of Science, Seattle, WA, United States
- *Correspondence: Lauren M. Seyler,
| | - Emily A. Kraus
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, United States
- Department of Environmental Engineering, University of Colorado, Boulder, Boulder, CO, United States
| | - Craig McLean
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John R. Spear
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, United States
| | - Alexis S. Templeton
- Department of Geological Sciences, University of Colorado, Boulder, Boulder, CO, United States
| | - Matthew O. Schrenk
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, United States
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, United States
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10
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Wang XC, Zhang JN, Zhao JJ, Guo XM, Li SF, Zheng QX, Liu PP, Lu P, Fu HY, Yu YJ, She Y. AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification. Anal Chem 2023; 95:638-649. [PMID: 36599407 DOI: 10.1021/acs.analchem.2c01795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.
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Affiliation(s)
- Xing-Cai Wang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Juan-Juan Zhao
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Xiao-Meng Guo
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Shu-Fang Li
- Institute of Quality Standard and Testing Technology for Agro-products, Henan Academy of Agricultural Science, Zhengzhou 450002, China
| | - Qing-Xia Zheng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Ping-Ping Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Peng Lu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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11
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Dong Y, Shachaf N, Feldberg L, Rogachev I, Heinig U, Aharoni A. PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging. Anal Chem 2023; 95:1652-1662. [PMID: 36594613 PMCID: PMC9850408 DOI: 10.1021/acs.analchem.2c04778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in "pseudo-MS/MS spectra" that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification.
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Affiliation(s)
- Yonghui Dong
- Department
of Plant Sciences, Weizmann Institute of
Science, Rehovot7610001, Israel,Department
of Life Sciences Core Facilities, Weizmann
Institute of Science, Rehovot7610001, Israel
| | - Nir Shachaf
- Department
of Plant Sciences, Weizmann Institute of
Science, Rehovot7610001, Israel
| | - Liron Feldberg
- Department
of Analytical Chemistry, Israel Institute
for Biological Research, Ness Ziona7410001, Israel
| | - Ilana Rogachev
- Department
of Plant Sciences, Weizmann Institute of
Science, Rehovot7610001, Israel
| | - Uwe Heinig
- Department
of Plant Sciences, Weizmann Institute of
Science, Rehovot7610001, Israel,Department
of Life Sciences Core Facilities, Weizmann
Institute of Science, Rehovot7610001, Israel
| | - Asaph Aharoni
- Department
of Plant Sciences, Weizmann Institute of
Science, Rehovot7610001, Israel,. Phone: +972 544 784259
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12
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NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures. Cells 2022; 11:cells11213526. [DOI: 10.3390/cells11213526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/29/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.
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13
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Li L, Mac Aogáin M, Xu T, Jaggi TK, Chan LLY, Qu J, Wei L, Liao S, Cheng HS, Keir HR, Dicker AJ, Tan KS, De Yun W, Koh MS, Ong TH, Lim AYH, Abisheganaden JA, Low TB, Hassan TM, Long X, Wark PAB, Oliver B, Drautz-Moses DI, Schuster SC, Tan NS, Fang M, Chalmers JD, Chotirmall SH. Neisseria species as pathobionts in bronchiectasis. Cell Host Microbe 2022; 30:1311-1327.e8. [PMID: 36108613 DOI: 10.1016/j.chom.2022.08.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 05/30/2022] [Accepted: 07/18/2022] [Indexed: 02/07/2023]
Abstract
Neisseria species are frequently identified in the bronchiectasis microbiome, but they are regarded as respiratory commensals. Using a combination of human cohorts, next-generation sequencing, systems biology, and animal models, we show that bronchiectasis bacteriomes defined by the presence of Neisseria spp. associate with poor clinical outcomes, including exacerbations. Neisseria subflava cultivated from bronchiectasis patients promotes the loss of epithelial integrity and inflammation in primary epithelial cells. In vivo animal models of Neisseria subflava infection and metabolipidome analysis highlight immunoinflammatory functional gene clusters and provide evidence for pulmonary inflammation. The murine metabolipidomic data were validated with human Neisseria-dominant bronchiectasis samples and compared with disease in which Pseudomonas-, an established bronchiectasis pathogen, is dominant. Metagenomic surveillance of Neisseria across various respiratory disorders reveals broader importance, and the assessment of the home environment in bronchiectasis implies potential environmental sources of exposure. Thus, we identify Neisseria species as pathobionts in bronchiectasis, allowing for improved risk stratification in this high-risk group.
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Affiliation(s)
- Liang Li
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Micheál Mac Aogáin
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Biochemical Genetics Laboratory, Department of Biochemistry, St. James's Hospital, Dublin, Ireland; Clinical Biochemistry Unit, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Tengfei Xu
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore; College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PRC
| | - Tavleen Kaur Jaggi
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Louisa L Y Chan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jing Qu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lan Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shumin Liao
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hong Sheng Cheng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Holly R Keir
- University of Dundee, Ninewells Hospital, Medical School, Dundee, Scotland
| | - Alison J Dicker
- University of Dundee, Ninewells Hospital, Medical School, Dundee, Scotland
| | - Kai Sen Tan
- Department of Otolaryngology, Infectious Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wang De Yun
- Department of Otolaryngology, Infectious Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Mariko Siyue Koh
- Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore
| | - Thun How Ong
- Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore
| | - Albert Yick Hou Lim
- Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - John A Abisheganaden
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Teck Boon Low
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore
| | | | - Xiang Long
- Department of Respiratory Medicine and Critical Care, Peking University Shenzhen Hospital, Shenzhen, China
| | - Peter A B Wark
- Priority Research Centre for Healthy Lungs, Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia; Department of Respiratory and Sleep Medicine, John Hunter Hospital, New Lambton Heights, NSW, Australia
| | - Brian Oliver
- Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia; School of Life Sciences, University of Technology Sydney, Sydney, NSW, Australia
| | - Daniela I Drautz-Moses
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore, Singapore
| | - Stephan C Schuster
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore, Singapore
| | - Nguan Soon Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Mingliang Fang
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore; Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - James D Chalmers
- University of Dundee, Ninewells Hospital, Medical School, Dundee, Scotland
| | - Sanjay H Chotirmall
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
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14
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Asakawa D, Todoroki K, Mizuno H. Fragmentation of Protonated Histamine and Histidine by Electrospray Ionization In-Source Collision-Induced Dissociation. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1716-1722. [PMID: 35973087 DOI: 10.1021/jasms.2c00140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electrospray ionization (ESI) generally produces intact gas-phase ions without extensive fragmentation; however, for histamine and histidine, ESI provides fragment ions through in-source collision-induced dissociation (CID). In this study, we investigated the fragmentation of these compounds both experimentally and using density functional theory calculations. We found that histamine undergoes protonation with subsequent NH3 loss by ESI in-source CID. The corresponding fragmentation mainly produces bicyclo and spiro compounds. In contrast, the ESI in-source CID of protonated histidine preferentially results in H2O loss rather than NH3 loss. However, the corresponding fragment ion is not observed in the ESI mass spectrum of histidine, because it undergoes further CO loss within 100 ps. Consequently, protonated histidine produces a fragment ion arising from a 46 Da loss, which corresponds to the masses of H2O and CO, by ESI in-source CID. The fragment ion yields of histamine and histidine produced by ESI in-source CID are then estimated from the dissociation rate constant and internal energy of the analyte ion, respectively. The dissociation rate constant and internal energy of the analyte ion were determined by double-hybrid density functional theory calculations and the survival yield method using benzylpyridinium thermometer ions, respectively. Because intense fragment ion signals are present in the ESI mass spectrum, the analysis of the fragment ions produced by ESI in-source CID facilitates the identification of metabolites originating from aromatic amino acids, such as histamine.
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Affiliation(s)
- Daiki Asakawa
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568, Japan
| | - Kenichiro Todoroki
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8526, Japan
| | - Hajime Mizuno
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8526, Japan
- Faculty of Pharmacy, Meijo University, Nagoya, Aichi 468-8502, Japan
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15
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Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
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Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
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16
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Guijas C, To A, Montenegro-Burke JR, Domingo-Almenara X, Alipio-Gloria Z, Kok BP, Saez E, Alvarez NH, Johnson KA, Siuzdak G. Drug-Initiated Activity Metabolomics Identifies Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation. Metabolites 2022; 12:749. [PMID: 36005620 PMCID: PMC9415469 DOI: 10.3390/metabo12080749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/21/2022] Open
Abstract
Worldwide, obesity rates have doubled since the 1980s and in the USA alone, almost 40% of adults are obese, which is closely associated with a myriad of metabolic diseases such as type 2 diabetes and arteriosclerosis. Obesity is derived from an imbalance between energy intake and consumption, therefore balancing energy homeostasis is an attractive target for metabolic diseases. One therapeutic approach consists of increasing the number of brown-like adipocytes in the white adipose tissue (WAT). Whereas WAT stores excess energy, brown adipose tissue (BAT) can dissipate this energy overload in the form of heat, increasing energy expenditure and thus inhibiting metabolic diseases. To facilitate BAT production a high-throughput screening approach was developed on previously known drugs using human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes. The screening allowed us to discover that zafirlukast, an FDA-approved small molecule drug commonly used to treat asthma, was able to differentiate adipocyte precursors and white-biased adipocytes into functional brown adipocytes. However, zafirlukast is toxic to human cells at higher dosages. Drug-Initiated Activity Metabolomics (DIAM) was used to investigate zafirlukast as a BAT inducer, and the endogenous metabolite myristoylglycine was then discovered to mimic the browning properties of zafirlukast without impacting cell viability. Myristoylglycine was found to be bio-synthesized upon zafirlukast treatment and was unique in inducing brown adipocyte differentiation, raising the possibility of using endogenous metabolites and bypassing the exogenous drugs to potentially alleviate disease, in this case, obesity and other related metabolic diseases.
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Affiliation(s)
- Carlos Guijas
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA 92037, USA
| | - Andrew To
- California Institute for Biomedical Research (Calibr), Scripps Research, La Jolla, CA 92037, USA
| | - J. Rafael Montenegro-Burke
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA 92037, USA
- Department of Molecular Genetics, Donnelly Center, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Xavier Domingo-Almenara
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA 92037, USA
- Computational Metabolomics for Systems Biology Lab, Omics Sciences Unit, Eurecat—Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Zaida Alipio-Gloria
- California Institute for Biomedical Research (Calibr), Scripps Research, La Jolla, CA 92037, USA
| | - Bernard P. Kok
- Department of Molecular Medicine, Scripps Research, La Jolla, CA 92037, USA
| | - Enrique Saez
- Department of Molecular Medicine, Scripps Research, La Jolla, CA 92037, USA
| | - Nicole H. Alvarez
- California Institute for Biomedical Research (Calibr), Scripps Research, La Jolla, CA 92037, USA
| | - Kristen A. Johnson
- California Institute for Biomedical Research (Calibr), Scripps Research, La Jolla, CA 92037, USA
| | - Gary Siuzdak
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA 92037, USA
- Departments of Chemistry, Molecular, and Computational Biology, Scripps Research, La Jolla, CA 92037, USA
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17
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Asakawa D, Saikusa K. Characterization of the Internal Energy of Ions Produced by Electrospray Ionization Using Substituted Benzyl Ammonium Thermometer Ions. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1548-1554. [PMID: 35853154 DOI: 10.1021/jasms.2c00116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We propose the use of substituted benzyl ammonium species as thermometer ions to characterize the internal energy distribution of the ions produced by electrospray ionization (ESI). Crucially, we found that the activation of the benzyl ammonium species preferentially provided a benzyl cation via N-Cα bond cleavage. In addition, calculations at the CCSD(T)/cc-PVTZ//M06-2X-D3/6-311++G(d,p) level of theory revealed that the threshold energies of fragmentation of the tested model ions ranged from 86 to 192 kJ mol-1, significantly lower than those of conventional 4-substituted benzylpyridinium thermometer ions. Thus, the substituted benzyl ammoniums are suitable for the characterization of the ESI process under typical experimental conditions. Further, the internal energies of the ions were found to depend on the radiofrequency voltage of the ion funnel, which is used to increase the transport efficiency of the ions from atmospheric to vacuum conditions. Our reported method will aid the determination of the optimum ion-funnel radiofrequency voltage for the analysis of small molecules such as metabolites. Furthermore, benzyl ammoniums are commercially available, which will facilitate the routine and widespread measurement of the internal energy distributions of ions.
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Affiliation(s)
- Daiki Asakawa
- Research Institute for Measurement and Analytical Instrumentation, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
| | - Kazumi Saikusa
- Research Institute for Material and Chemical Measurement, National Institute of Advanced Industrial Science and Technology (AIST), National Metrology Institute of Japan (NMIJ), Tsukuba Central 3, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8563, Japan
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18
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Guijas C, Horton LE, Hoang L, Domingo-Almenara X, Billings EM, Ware BC, Sullivan B, Siuzdak G. Microbial Metabolite 3-Indolepropionic Acid Mediates Immunosuppression. Metabolites 2022; 12:metabo12070645. [PMID: 35888769 PMCID: PMC9317520 DOI: 10.3390/metabo12070645] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 06/27/2022] [Accepted: 07/06/2022] [Indexed: 01/07/2023] Open
Abstract
The microbial-derived metabolite, 3-indolepropionic acid (3-IPA), has been intensely studied since its origins were discovered in 2009; however, 3-IPA's role in immunosuppression has had limited attention. Untargeted metabolomic analyses of T-cell exhaustion and immunosuppression, represented by dysfunctional under-responsive CD8+ T cells, reveal a potential role of 3-IPA in these responses. T-cell exhaustion was examined via infection of two genetically related mouse strains, DBA/1J and DBA/2J, with lymphocytic choriomeningitis virus (LCMV) Clone 13 (Cl13). The different mouse strains produced disparate outcomes driven by their T-cell responses. Infected DBA/2J presented with exhausted T cells and persistent infection, and DBA/1J mice died one week after infection from cytotoxic T lymphocytes (CTLs)-mediated pulmonary failure. Metabolomics revealed over 70 metabolites were altered between the DBA/1J and DBA/2J models over the course of the infection, most of them in mice with a fatal outcome. Cognitive-driven prioritization combined with statistical significance and fold change were used to prioritize the metabolites. 3-IPA, a tryptophan-derived metabolite, was identified as a high-priority candidate for testing. To test its activity 3-IPA was added to the drinking water of the mouse models during LCMV Cl13 infection, with the results showing that 3-IPA allowed the mice to survive longer. This negative immune-modulation effect might be of interest for the modulation of CTL responses in events such as autoimmune diseases, type I diabetes or even COVID-19. Moreover, 3-IPA's bacterial origin raises the possibility of targeting the microbiome to enhance CTL responses in diseases such as cancer and chronic infection.
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Affiliation(s)
- Carlos Guijas
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
| | - Lucy E. Horton
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (L.E.H.); (B.C.W.)
| | - Linh Hoang
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
| | - Xavier Domingo-Almenara
- Computational Metabolomics for Systems Biology Lab, Omics Sciences Unit, Eurecat—Technology Centre of Catalonia, 08005 Barcelona, Catalonia, Spain;
| | - Elizabeth M. Billings
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
| | - Brian C. Ware
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (L.E.H.); (B.C.W.)
| | - Brian Sullivan
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (L.E.H.); (B.C.W.)
- Correspondence: (B.S.); (G.S.); Tel.: +1-858-784-9425 (G.S.)
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
- Departments of Chemistry, Molecular, and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
- Correspondence: (B.S.); (G.S.); Tel.: +1-858-784-9425 (G.S.)
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19
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Lu Y, Yao G, Wang X, Zhang Y, Zhao J, Yu YJ, Wang H. Chemometric discrimination of the geographical origin of licorice in China by untargeted metabolomics. Food Chem 2022; 380:132235. [DOI: 10.1016/j.foodchem.2022.132235] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 12/13/2022]
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20
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Henniger MT, Wells JE, Hales KE, Lindholm-Perry AK, Freetly HC, Kuehn LA, Schneider LG, McLean KJ, Campagna SR, Christopher CJ, Myer PR. Effects of a Moderate or Aggressive Implant Strategy on the Rumen Microbiome and Metabolome in Steers. FRONTIERS IN ANIMAL SCIENCE 2022. [DOI: 10.3389/fanim.2022.889817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The effects of growth-promoting implants have been well-defined for their ability to impact growth performance in beef cattle. Production-relevant microbes and microbiomes in the rumen have also been associated with growth traits. However, the role of implants on the rumen microbiome has not been determined. The objective of this study was to determine if different doses of implant hormones cause gain-associated ruminal microbial community changes. To assess this, a completely randomized design was used and 336 fall-born steers 450 to 470 days of age from the germplasm evaluation population at the US Meat Animal Research Center (Clay Center, NE) were divided into two treatment groups: 1) a moderate implant strategy (n = 167) of Revalor-IS (80 mg trenbolone acetate and 16 mg estradiol) followed by Revalor-S (120 mg trenbolone acetate and 24 mg estradiol) or 2) an aggressive implant strategy (n = 169) of Revalor-IS followed by Revalor-200 (200 mg trenbolone acetate and 20 mg estradiol). Steers were fed the same diet (57.0% dry-rolled corn, 30% wet distiller’s grains with solubles, 8.0% alfalfa hay, 4.25% vitamin and mineral supplement, and 0.75% urea, on a DM basis). On d 85 after implants administration, rumen contents were collected via orogastric tubing. Samples were sequenced to target and identify bacteria, archaea, and protozoa. Untargeted metabolomics was performed on rumen content using ultra high performance liquid chromatography high resolution mass spectrometry. Production data between implant strategies was analyzed using a mixed model ANOVA (SASv9.4, Cary, NC) followed by separation of least squares means. Microbial diversity between strategies did not differ for archaea or protozoa (P > 0.05). Average daily gain was different (P = 0.01; 1.72 vs 1.66 ± 0.02 kg, aggressive vs moderate, respectively); however, large microbial community shifts were not associated with implant strategy. Two metabolites, N-acetyllysine and N-acetylornithine, were found in greater abundance in the moderate implant strategy (P ≤ 0.04). Understanding associations between the rumen microbiome and implant strategies may allow improvement of growth efficiency in beef cattle.
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21
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Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites. Biomedicines 2022; 10:biomedicines10040879. [PMID: 35453629 PMCID: PMC9024754 DOI: 10.3390/biomedicines10040879] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/04/2022] [Accepted: 04/06/2022] [Indexed: 11/16/2022] Open
Abstract
In gas chromatography–mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.
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22
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Kostyukevich Y, Sosnin S, Osipenko S, Kovaleva O, Rumiantseva L, Kireev A, Zherebker A, Fedorov M, Nikolaev EN. PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways. ACS OMEGA 2022; 7:9710-9719. [PMID: 35350354 PMCID: PMC8945079 DOI: 10.1021/acsomega.1c07272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/28/2022] [Indexed: 05/13/2023]
Abstract
Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or 16O/18O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.
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23
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Graça G, Cai Y, Lau CHE, Vorkas PA, Lewis MR, Want EJ, Herrington D, Ebbels TMD. Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR. Anal Chem 2022; 94:3446-3455. [PMID: 35180347 PMCID: PMC8892435 DOI: 10.1021/acs.analchem.1c03032] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 12/15/2021] [Indexed: 11/29/2022]
Abstract
Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).
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Affiliation(s)
- Gonçalo Graça
- Section
of Bioinformatics, Division of Systems Medicine, Department of Metabolism,
Digestion and Reproduction, Imperial College
London, South Kensington Campus, Sir Alexander Fleming Building, London SW7 2AZ, U.K.
| | - Yuheng Cai
- Section
of Bioinformatics, Division of Systems Medicine, Department of Metabolism,
Digestion and Reproduction, Imperial College
London, South Kensington Campus, Sir Alexander Fleming Building, London SW7 2AZ, U.K.
| | - Chung-Ho E. Lau
- Department
of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, U.K.
| | - Panagiotis A. Vorkas
- Section
of Biomolecular Medicine, Division of Systems Medicine, Department
of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, Sir Alexander Fleming Building, London SW7 2AZ, U.K.
- Institute
of Applied Biosciences, Centre for Research
and Technology Hellas, Thessaloniki 57001, Greece
| | - Matthew R. Lewis
- Section
of Bioanalytical Chemistry and National Phenome Centre, Division of
Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London W12 0NN, U.K.
| | - Elizabeth J. Want
- Section
of Biomolecular Medicine, Division of Systems Medicine, Department
of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, Sir Alexander Fleming Building, London SW7 2AZ, U.K.
| | - David Herrington
- Section on
Cardiovascular Medicine, Wake Forest School
of Medicine, Winston-Salem, North Carolina 27157, United States
| | - Timothy M. D. Ebbels
- Section
of Bioinformatics, Division of Systems Medicine, Department of Metabolism,
Digestion and Reproduction, Imperial College
London, South Kensington Campus, Sir Alexander Fleming Building, London SW7 2AZ, U.K.
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24
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A new platform for untargeted UHPLC-HRMS data analysis to address the time-shift problem. Anal Chim Acta 2022; 1193:339393. [DOI: 10.1016/j.aca.2021.339393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 11/18/2022]
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25
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Mamani-Huanca M, Gradillas A, López-Gonzálvez Á, Barbas C. In-Source Fragmentation for the Identification of Compounds by CE-ESI-TOF in Human Plasma. L-Proline as Case Study. Methods Mol Biol 2022; 2531:185-202. [PMID: 35941486 DOI: 10.1007/978-1-0716-2493-7_12] [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] [Indexed: 06/15/2023]
Abstract
One of the aims of untargeted metabolomics is searching for selective biomarkers of different pathophysiological conditions. Modified amino acids originated from the posttranslational modification of proteins play a key role as potential biomarkers; however, they are very often still classified as unknown after metabolite annotation. We have developed an analytical workflow for the targeted screening of these compounds using CE-ESI-MS. The workflow is based on the in-source fragmentation of molecules that produces diagnostic ions that we have collected in an open-source library. In this chapter, we describe in detail the strategy for the targeted screening of modified amino acids (MAAs), using as an example L-proline and its modified derivatives. We illustrate the strategy with two case studies in human plasma.
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Affiliation(s)
- Maricruz Mamani-Huanca
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
| | - Ana Gradillas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
| | - Ángeles López-Gonzálvez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain.
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26
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Chang CJ, Barr DB, Ryan PB, Panuwet P, Smarr MM, Liu K, Kannan K, Yakimavets V, Tan Y, Ly V, Marsit CJ, Jones DP, Corwin EJ, Dunlop AL, Liang D. Per- and polyfluoroalkyl substance (PFAS) exposure, maternal metabolomic perturbation, and fetal growth in African American women: A meet-in-the-middle approach. ENVIRONMENT INTERNATIONAL 2022; 158:106964. [PMID: 34735953 PMCID: PMC8688254 DOI: 10.1016/j.envint.2021.106964] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND Prenatal exposures to per- and polyfluoroalkyl substances (PFAS) have been linked to reduced fetal growth. However, the detailed molecular mechanisms remain largely unknown. This study aims to investigate biological pathways and intermediate biomarkers underlying the association between serum PFAS and fetal growth using high-resolution metabolomics in a cohort of pregnant African American women in the Atlanta area, Georgia. METHODS Serum perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) measurements and untargeted serum metabolomics profiling were conducted in 313 pregnant African American women at 8-14 weeks gestation. Multiple linear regression models were applied to assess the associations of PFAS with birth weight and small-for-gestational age (SGA) birth. A high-resolution metabolomics workflow including metabolome-wide association study, pathway enrichment analysis, and chemical annotation and confirmation with a meet-in-the-middle approach was performed to characterize the biological pathways and intermediate biomarkers of the PFAS-fetal growth relationship. RESULTS Each log2-unit increase in serum PFNA concentration was significantly associated with higher odds of SGA birth (OR = 1.32, 95% CI 1.07, 1.63); similar but borderline significant associations were found in PFOA (OR = 1.20, 95% CI 0.94, 1.49) with SGA. Among 25,516 metabolic features extracted from the serum samples, we successfully annotated and confirmed 10 overlapping metabolites associated with both PFAS and fetal growth endpoints, including glycine, taurine, uric acid, ferulic acid, 2-hexyl-3-phenyl-2-propenal, unsaturated fatty acid C18:1, androgenic hormone conjugate, parent bile acid, and bile acid-glycine conjugate. Also, we identified 21 overlapping metabolic pathways from pathway enrichment analyses. These overlapping metabolites and pathways were closely related to amino acid, lipid and fatty acid, bile acid, and androgenic hormone metabolism perturbations. CONCLUSION In this cohort of pregnant African American women, higher serum concentrations of PFOA and PFNA were associated with reduced fetal growth. Perturbations of biological pathways involved in amino acid, lipid and fatty acid, bile acid, and androgenic hormone metabolism were associated with PFAS exposures and reduced fetal growth, and uric acid was shown to be a potential intermediate biomarker. Our results provide opportunities for future studies to develop early detection and intervention for PFAS-induced fetal growth restriction.
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Affiliation(s)
- Che-Jung Chang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Dana Boyd Barr
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - P Barry Ryan
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Parinya Panuwet
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Melissa M Smarr
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ken Liu
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Kurunthachalam Kannan
- Department of Pediatrics and Department of Environmental Medicine, New York University School of Medicine, New York, NY, USA
| | - Volha Yakimavets
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Youran Tan
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - ViLinh Ly
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Dean P Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | | | - Anne L Dunlop
- Woodruff Health Sciences Center, School of Medicine and Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA
| | - Donghai Liang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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27
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Hubert CB, de Carvalho LPS. Metabolomic approaches for enzyme function and pathway discovery in bacteria. Methods Enzymol 2022; 665:29-47. [DOI: 10.1016/bs.mie.2021.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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28
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Wasito H, Causon T, Hann S. Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics. Talanta 2022; 236:122828. [PMID: 34635218 DOI: 10.1016/j.talanta.2021.122828] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 02/07/2023]
Abstract
Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.
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Affiliation(s)
- Hendri Wasito
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria; Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Dr. Soeparno Street, 53122, Purwokerto, Indonesia
| | - Tim Causon
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria.
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29
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Asakawa D, Saikusa K. Fragmentation efficiency of phenethylamines in electrospray ionization source estimated by theoretical chemistry calculation. JOURNAL OF MASS SPECTROMETRY : JMS 2022; 57:e4802. [PMID: 34929756 DOI: 10.1002/jms.4802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/08/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
Abstract
Small molecules with polar functional groups, including substituted phenethylamines, are commonly analyzed by liquid chromatography-mass spectrometry (LC-MS) with electrospray ionization (ESI). Analyte molecules are mostly detected in protonated and cation-adducted forms through positive-ion electrospray ionization-mass spectrometry (ESI-MS). However, the ESI of substituted phenethylamines commonly provides an intense signal of fragment ions by ESI in-source collision-induced dissociation (IS-CID), which hinders the unambiguous identification of phenethylamines. This phenomenon was approximated as a unimolecular dissociation model, and the dissociation efficiency was evaluated by various quantum chemistry calculations to determine the ESI IS-CID efficiency. The calculated results were consistent with the experimental data, when the dissociation threshold energy of phenethylamines was calculated using the post-Hartree-Fock (post-HF) method, CCSD(t)/cc-pVTZ//MP2(full)/6-311++G(d,p). In contrast to post-HF methods, the utilization of density functional theory calculations with a suitable functional is recognized as an accurate and competitive low-cost approach. In particular, ωB97-XD, M06-2X-D3, and recently developed Minnesota functionals, such as M11, MN12-SX, and MN15, provided reliable results, as in the case of the post-HF method. The results obtained by the recently developed double hybrid functionals, DSD-PEBP86-D3(BJ), PBE0-DH, and PBE-QIDH, were also reliable. The consideration of ESI IS-CID can facilitate the identification of analyte molecules because most phenethylamines, except for N-methylated analogs, provide an intense signal in the ESI mass spectrum.
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Affiliation(s)
- Daiki Asakawa
- Research Institute for Measurement and Analytical Instrumentation, National Institute of Advanced Industrial Science and Technology, National Metrology Institute of Japan, Tsukuba, Japan
| | - Kazumi Saikusa
- Research Institute for Material and Chemical Measurement, National Institute of Advanced Industrial Science and Technology, National Metrology Institute of Japan, Tsukuba, Japan
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30
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Unsihuay D, Yin R, Sanchez DM, Yang M, Li Y, Sun X, Dey SK, Laskin J. High-resolution imaging and identification of biomolecules using Nano-DESI coupled to ion mobility spectrometry. Anal Chim Acta 2021; 1186:339085. [PMID: 34756271 DOI: 10.1016/j.aca.2021.339085] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/15/2021] [Accepted: 09/19/2021] [Indexed: 10/20/2022]
Abstract
Simultaneous spatial localization and structural characterization of molecules in complex biological samples currently represents an analytical challenge for mass spectrometry imaging (MSI) techniques. In this study, we describe a novel experimental platform, which substantially expands the capabilities and enhances the depth of chemical information obtained in high spatial resolution MSI experiments performed using nanospray desorption electrospray ionization (nano-DESI). Specifically, we designed and constructed a portable nano-DESI MSI platform and coupled it with a drift tube ion mobility (IM) spectrometer-mass spectrometer. We demonstrate imaging of drift time-separated ions with a high spatial resolution of better than ∼25 μm using uterine tissues on day 4 of pregnancy in mice. Collision cross-section measurements provide unique molecular descriptors of molecules observed in nano-DESI-IM-MSI necessary for their unambiguous identification by comparison with databases. Meanwhile, isomer-specific imaging reveals variations in the isomeric composition across the tissue. Furthermore, IM separation efficiently eliminates isobaric and isomeric interferences originating from solvent peaks, overlapping isotopic peaks of endogenous molecules extracted from the tissue, and products of in-source fragmentation, which is critical to obtaining accurate concentration gradients in the sample using MSI. The structural information provided by the IM separation substantially expands the molecular specificity of high-resolution MSI necessary for unraveling the complexity of biological systems.
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Affiliation(s)
- Daisy Unsihuay
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Ruichuan Yin
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | | | - Manxi Yang
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Yingju Li
- Division of Reproductive Sciences, Cincinnati Children's Hospital Medical Centre and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45229, USA
| | - Xiaofei Sun
- Division of Reproductive Sciences, Cincinnati Children's Hospital Medical Centre and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45229, USA
| | - Sudhansu K Dey
- Division of Reproductive Sciences, Cincinnati Children's Hospital Medical Centre and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45229, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA.
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31
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Xue J, Derks RJE, Hoang L, Giera M, Siuzdak G. Proteomics with Enhanced In-Source Fragmentation/Annotation: Applying XCMS-EISA Informatics and Q-MRM High-Sensitivity Quantification. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2644-2654. [PMID: 34633184 DOI: 10.1021/jasms.1c00188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Enhanced in-source fragmentation/annotation (EISA) has recently been shown to produce fragment ions that match tandem mass spectrometry data across a wide range of small molecules. EISA has been developed to facilitate data-dependent acquisition (DDA), data-independent acquisiton (DIA), and multiple-reaction monitoring (MRM), enabling molecular identifications in untargeted metabolomics and targeted quantitative single-quadrupole MRM (Q-MRM) analyses. Here, EISA has been applied to peptide-based proteomic analysis using optimized in-source fragmentation to generate fragmentation patterns for a mixture of 38 peptides, which were comparable to the b- and y-type fragment ions typically observed in tandem MS experiments. The optimal in-source fragmentation conditions at which high-abundance peptide fragments and precursor ions coexist were compared with automated data-dependent acquisition (DDA) in the same quadrupole time-of-flight (QTOF-MS) mass spectrometer, generating a significantly higher fragment percentage of peptides from both singly and doubly charged b- and y-type fragment (b+, y+, b2+, and y2+) ions. Higher fragment percentages were also observed for these fragment ion series over linear ion trap instrumentation. An XCMS-EISA annotation/deconvolution program was developed, making use of the retention time and peak shape continuity between precursor fragment ions, to perform automated proteomic data analysis on the enhanced in-source fragments. Post-translational modification (PTM) characterization on peptides was demonstrated with EISA, producing fragment ions corresponding to a neutral loss of phosphoric acid with greater intensity than observed with DDA on a QTOF-MS. Moreover, Q-MRM demonstrated the ability to use EISA for peptide quantification. The availability of more sophisticated in-source fragmentation informatics, beyond XCMS-EISA, will further enable EISA for sensitive autonomous identification and Q-MRM quantitative analyses in proteomics.
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Affiliation(s)
- Jingchuan Xue
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Rico J E Derks
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands
| | - Linh Hoang
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
- Departments of Chemistry, Molecular, and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
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32
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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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33
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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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van Liempd S, Cabrera D, Pilzner C, Kollmus H, Schughart K, Falcón-Pérez JM. Impaired beta-oxidation increases vulnerability to influenza A infection. J Biol Chem 2021; 297:101298. [PMID: 34637789 PMCID: PMC8564733 DOI: 10.1016/j.jbc.2021.101298] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/06/2021] [Accepted: 10/07/2021] [Indexed: 12/20/2022] Open
Abstract
Influenza A virus (IAV) infection casts a significant burden on society. It has particularly high morbidity and mortality rates in patients suffering from metabolic disorders. The aim of this study was to relate metabolic changes with IAV susceptibility using well-characterized inbred mouse models. We compared the highly susceptible DBA/2J (D2) mouse strain for which IAV infection is lethal with the C57BL/6J (B6) strain, which exhibits a moderate course of disease and survives IAV infection. Previous studies showed that D2 has higher insulin and glucose levels and is predisposed to develop diet-induced type 2 diabetes. Using high-resolution liquid chromatography–coupled MS, the plasma metabolomes of individual animals were repeatedly measured up to 30 days postinfection. The biggest metabolic difference between these strains in healthy and infected states was in the levels of malonylcarnitine, which was consistently increased 5-fold in D2. Other interstrain and intrastrain differences in healthy and infected animals were observed for acylcarnitines, glucose, branched-chain amino acids, and oxidized fatty acids. By mapping metabolic changes to canonical pathways, we found that mitochondrial beta-oxidation is likely disturbed in D2 animals. In noninfected D2 mice, this leads to increased glycerolipid production and reduced acylcarnitine production, whereas in infected D2 animals, peroxisomal beta-oxidation becomes strongly increased. From these studies, we conclude that metabolic changes caused by a distortion of mitochondrial and peroxisomal metabolism might impact the innate immune response in D2, leading to high viral titers and mortality.
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Affiliation(s)
| | - Diana Cabrera
- Metabolomics Platform CIC bioGUNE-BRTA, Derio, Spain
| | - Carolin Pilzner
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Heike Kollmus
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany; University of Veterinary Medicine Hannover, Hannover, Germany; Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Juan M Falcón-Pérez
- Metabolomics Platform CIC bioGUNE-BRTA, Derio, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
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35
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Montenegro-Burke JR, Kok BP, Guijas C, Domingo-Almenara X, Moon C, Galmozzi A, Kitamura S, Eckmann L, Saez E, Siuzdak GE, Wolan DW. Metabolomics activity screening of T cell-induced colitis reveals anti-inflammatory metabolites. Sci Signal 2021; 14:eabf6584. [PMID: 34582249 DOI: 10.1126/scisignal.abf6584] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- J Rafael Montenegro-Burke
- Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research Institute; La Jolla, California 92037, USA
| | - Bernard P Kok
- Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research Institute; La Jolla, California 92037, USA
| | - Carlos Guijas
- Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research Institute; La Jolla, California 92037, USA
| | - Xavier Domingo-Almenara
- Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research Institute; La Jolla, California 92037, USA
| | - Clara Moon
- Department of Molecular Medicine, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrea Galmozzi
- Department of Molecular Medicine, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Seiya Kitamura
- Department of Molecular Medicine, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Lars Eckmann
- Department of Medicine, University of California, La Jolla CA 92093, USA
| | - Enrique Saez
- Department of Molecular Medicine, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Gary E Siuzdak
- Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research Institute; La Jolla, California 92037, USA.,Department of Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Dennis W Wolan
- Department of Molecular Medicine, Scripps Research Institute, La Jolla, CA 92037, USA.,Department of Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
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36
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Xue J, Derks RJ, Webb W, Billings EM, Aisporna A, Giera M, Siuzdak G. Single Quadrupole Multiple Fragment Ion Monitoring Quantitative Mass Spectrometry. Anal Chem 2021; 93:10879-10889. [PMID: 34313111 PMCID: PMC8762722 DOI: 10.1021/acs.analchem.1c01246] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Single quadrupole mass spectrometry (MS) with enhanced in-source multiple fragment ion monitoring was designed to perform high sensitivity quantitative mass analyses. Enhanced in-source fragmentation amplifies fragmentation from traditional soft electrospray ionization producing fragment ions that have been found to be identical to those generated in tandem MS. We have combined enhanced in-source fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative analyses. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for these quantitative analyses was comparable to triple quadrupole multiple reaction monitoring (MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labeled standards demonstrated accuracy in the range of 91-110% for the amino acids, 76-129% for the fatty acids, and good precision (coefficient of variation <10%). To enhance specificity, a newly developed correlated ion monitoring algorithm was designed to facilitate these analyses. This algorithm autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of coeluting species based on precursor and fragment ion ratios. Single quadrupole instrumentation can provide MRM level quantitative performance by monitoring/correlating precursor and fragment ions facilitating high sensitivity analysis on existing single quadrupole instrumentation that are generally inexpensive, easy to operate, and technically less complex.
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Affiliation(s)
- Jingchuan Xue
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Rico J.E. Derks
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands
| | - William Webb
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Elizabeth M. Billings
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Aries Aisporna
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
- Departments of Chemistry, Molecular, and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, United States
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37
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An N, Zhu QF, Wang YZ, Xiong CF, Hu YN, Feng YQ. Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes. Anal Chem 2021; 93:11321-11328. [PMID: 34369157 DOI: 10.1021/acs.analchem.1c02673] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSn spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSn spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.
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Affiliation(s)
- Na An
- Department of Chemistry, Wuhan University, Wuhan 430072, PR China
| | - Quan-Fei Zhu
- Department of Chemistry, Wuhan University, Wuhan 430072, PR China
| | - Yan-Zhen Wang
- Department of Chemistry, Wuhan University, Wuhan 430072, PR China
| | - Cai-Feng Xiong
- Department of Chemistry, Wuhan University, Wuhan 430072, PR China
| | - Yu-Ning Hu
- Department of Chemistry, Wuhan University, Wuhan 430072, PR China
| | - Yu-Qi Feng
- Department of Chemistry, Wuhan University, Wuhan 430072, PR China.,Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan 430072, PR China.,School of Health Sciences, Wuhan University, Wuhan 430071, PR China
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38
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Karunaratne E, Hill DW, Pracht P, Gascón JA, Grimme S, Grant DF. High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra. Anal Chem 2021; 93:10688-10696. [PMID: 34288660 PMCID: PMC8404482 DOI: 10.1021/acs.analchem.1c02244] [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: 11/29/2022]
Abstract
The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.
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Affiliation(s)
- Erandika Karunaratne
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Dennis W Hill
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Philipp Pracht
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany
| | - José A Gascón
- Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany
| | - David F Grant
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States
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Guo J, Shen S, Xing S, Yu H, Huan T. ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data. Anal Chem 2021; 93:10243-10250. [PMID: 34270210 DOI: 10.1021/acs.analchem.1c01644] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS2) spectra of their precursor ions, and (3) sharing similar MS2 fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS2 spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).
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Affiliation(s)
- Jian Guo
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Sam Shen
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
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40
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Narayanaswamy P, Teo G, Ow JR, Lau A, Kaldis P, Tate S, Choi H. MetaboKit: a comprehensive data extraction tool for untargeted metabolomics. Mol Omics 2021; 16:436-447. [PMID: 32519713 DOI: 10.1039/d0mo00030b] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.
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Affiliation(s)
| | - Guoshou Teo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Jin Rong Ow
- Institute of Molecular and Cell Biology, A*STAR, Singapore
| | | | - Philipp Kaldis
- Institute of Molecular and Cell Biology, A*STAR, Singapore and Department of Clinical Sciences, Lund University, Sweden
| | | | - Hyungwon Choi
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. and Institute of Molecular and Cell Biology, A*STAR, Singapore
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41
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Zhang S, Lachance BB, Mattson MP, Jia X. Glucose metabolic crosstalk and regulation in brain function and diseases. Prog Neurobiol 2021; 204:102089. [PMID: 34118354 DOI: 10.1016/j.pneurobio.2021.102089] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 04/08/2021] [Accepted: 06/01/2021] [Indexed: 01/11/2023]
Abstract
Brain glucose metabolism, including glycolysis, the pentose phosphate pathway, and glycogen turnover, produces ATP for energetic support and provides the precursors for the synthesis of biological macromolecules. Although glucose metabolism in neurons and astrocytes has been extensively studied, the glucose metabolism of microglia and oligodendrocytes, and their interactions with neurons and astrocytes, remain critical to understand brain function. Brain regions with heterogeneous cell composition and cell-type-specific profiles of glucose metabolism suggest that metabolic networks within the brain are complex. Signal transduction proteins including those in the Wnt, GSK-3β, PI3K-AKT, and AMPK pathways are involved in regulating these networks. Additionally, glycolytic enzymes and metabolites, such as hexokinase 2, acetyl-CoA, and enolase 2, are implicated in the modulation of cellular function, microglial activation, glycation, and acetylation of biomolecules. Given these extensive networks, glucose metabolism dysfunction in the whole brain or specific cell types is strongly associated with neurologic pathology including ischemic brain injury and neurodegenerative disorders. This review characterizes the glucose metabolism networks of the brain based on molecular signaling and cellular and regional interactions, and elucidates glucose metabolism-based mechanisms of neurological diseases and therapeutic approaches that may ameliorate metabolic abnormalities in those diseases.
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Affiliation(s)
- Shuai Zhang
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, 21201, United States
| | - Brittany Bolduc Lachance
- Program in Trauma, Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, 21201, United States
| | - Mark P Mattson
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, United States
| | - Xiaofeng Jia
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, 21201, United States; Department of Orthopedics, University of Maryland School of Medicine, Baltimore, MD, 21201, United States; Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, 21201, United States; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, United States; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, United States.
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42
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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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43
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Cognitive analysis of metabolomics data for systems biology. Nat Protoc 2021; 16:1376-1418. [PMID: 33483720 DOI: 10.1038/s41596-020-00455-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/27/2020] [Indexed: 01/30/2023]
Abstract
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.
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44
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McCarthy RA, Sen Gupta A. Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:24727-24737. [PMID: 33796430 PMCID: PMC8011560 DOI: 10.1109/access.2021.3056955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound. Autonomously identifying target peaks within the GC/MS/MS and associating them with non-target peaks can help improve the analysis of collected samples. Association of peaks refers to classifying peaks as known congeners regardless if the peak is a target or non-target. Uncertainty of peaks fitted and discovered through raw instrument signals from GC/MS/MS data is assessed to create topographical illustrations of target annotated peaks among sample raw instrument signals collected across diverse locations in the Chicago area. The term "annotated peak" is used to assign peaks found at specific retention times as a known congener. Adaptive signal processing techniques are utilized to smooth data and correct baseline drifts as well as detect and separate coeluted (overlapped) peaks in the raw instrument signal to provide key feature extraction. 150 air samples are analyzed for individual polychlorinated biphenyls (PCB) with GC/MS/MS across Chicago, IL. 80% of the data is used for training classification of target PCBs and 20% of the data is evaluated to identify and associate consistently occurring non-target peaks with target PCBs. A random forest classifier is used to associate identified peaks to target PCB peaks. Geographical topographical representations of target PCBs in the raw instrument signal demonstrates how PCBs accumulate and degrade in certain locations.
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Affiliation(s)
- Ryan A McCarthy
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
- Iowa Technology Institute (ITI), The University of Iowa, Iowa City, IA 52242, USA
| | - Ananya Sen Gupta
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
- Iowa Technology Institute (ITI), The University of Iowa, Iowa City, IA 52242, USA
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Metabolomics and In Silico Docking-Directed Discovery of Small-Molecule Enzyme Targets. Anal Chem 2021; 93:3072-3081. [DOI: 10.1021/acs.analchem.0c03684] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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46
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Scossa F, Alseekh S, Fernie AR. Integrating multi-omics data for crop improvement. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153352. [PMID: 33360148 DOI: 10.1016/j.jplph.2020.153352] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/13/2020] [Accepted: 12/14/2020] [Indexed: 05/26/2023]
Abstract
Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.
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Affiliation(s)
- Federico Scossa
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476, Potsdam, Golm, Germany; Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics (CREA-GB), 00178, Rome, Italy.
| | - Saleh Alseekh
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476, Potsdam, Golm, Germany; Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Alisdair R Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476, Potsdam, Golm, Germany; Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria.
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Montenegro-Burke JR, Guijas C, Siuzdak G. METLIN: A Tandem Mass Spectral Library of Standards. Methods Mol Biol 2021; 2104:149-163. [PMID: 31953817 DOI: 10.1007/978-1-0716-0239-3_9] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Untargeted mass spectrometry metabolomics studies rely on accurate databases for the identification of metabolic features. Leveraging unique fragmentation patterns as well as characteristic dissociation routes allows for structural information to be gained for specific metabolites and molecular classes, respectively. Here we describe the evolution of METLIN as a resource for small molecule analysis as well as the tools (e.g., Fragment Similarity Search and Neutral Loss Search) used to query the database and their workflows for the identification of molecular entities. Additionally, we will discuss the functionalities of isoMETLIN, a database of isotopic metabolites, and the latest addition to the METLIN family, METLIN-MRM, which facilitates the analysis of quantitative mass spectrometry data generated with triple quadrupole instrumentation.
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Affiliation(s)
| | - Carlos Guijas
- Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA, USA
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA, USA
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48
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Passero K, Setia-Verma S, McAllister K, Manrai A, Patel C, Hall M. What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2021; 26:309-315. [PMID: 34409132 PMCID: PMC8323787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
The environment plays an important role in mediating human health. In this session we consider research addressing ways to overcome the challenges associated with studying the multifaceted and ever-changing environment. Environmental health research has a need for technological and methodological advances which will further our knowledge of how exposures precipitate complex phenotypes and exacerbate disease.
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Affiliation(s)
- Kristin Passero
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Shefali Setia-Verma
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104
| | - Kimberly McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, P.O. Box 12233 (MD EC-21), Research Triangle Park, NC 27709
| | - Arjun Manrai
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Chirag Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Molly Hall
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
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Desmet S, Brouckaert M, Boerjan W, Morreel K. Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks. Comput Struct Biotechnol J 2020; 19:72-85. [PMID: 33384856 PMCID: PMC7753198 DOI: 10.1016/j.csbj.2020.11.050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 02/06/2023] Open
Abstract
Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.
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Affiliation(s)
- Sandrien Desmet
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Marlies Brouckaert
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Wout Boerjan
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Kris Morreel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
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50
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Mamani-Huanca M, de la Fuente AG, Otero A, Gradillas A, Godzien J, Barbas C, López-Gonzálvez Á. Enhancing confidence of metabolite annotation in Capillary Electrophoresis-Mass Spectrometry untargeted metabolomics with relative migration time and in-source fragmentation. J Chromatogr A 2020; 1635:461758. [PMID: 33302137 DOI: 10.1016/j.chroma.2020.461758] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023]
Abstract
Capillary electrophoresis coupled to mass spectrometry is a power tool in untargeted metabolomics studies to analyze charged and polar compounds. However, identification is a challenge due to the variability of migration times and the lack of MS/MS spectra in CE-TOF-MS, the type of instruments most frequently employed. We present here a CE-MS search platform incorporated in CEU Mass Mediator to annotate metabolites with a confidence level L2. For its the development we analyzed 226 compounds using two fragmentor voltages: 100 and 200 V. The information obtained, such as relative migration times (RMT) and in-source fragments, were incorporated into the platform. In addition, we validated the CE-MS search functionality using different types of biological samples such as plasma samples (human, rat, and rabbit), mouse macrophages, and human urine. The RMT tolerance percentage for the search of metabolites has been determined, establishing 5% for all compounds, except for the compounds migrating in the electro-osmotic flow, for which the tolerance should be of 10%. It has also been demonstrated the robustness of the in-source fragmentation, which makes possible the annotation of compounds by means of their fragmentation pattern. As an example, 3-methylhistidine and 1-methilhistidine, whose RMT are very close, have been annotated. Studies of the fragmentation mechanisms of acyl-L-carnitines have shown that in-source fragmentation follows the general fragmentation rules and is a suitable alternative to MS/MS.
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Affiliation(s)
- Maricruz Mamani-Huanca
- Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Alberto Gil de la Fuente
- Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain; Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Abraham Otero
- Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Joanna Godzien
- Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain; Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Ángeles López-Gonzálvez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain.
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