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Samanipour S, Barron LP, van Herwerden D, Praetorius A, Thomas KV, O’Brien JW. Exploring the Chemical Space of the Exposome: How Far Have We Gone? JACS AU 2024; 4:2412-2425. [PMID: 39055136 PMCID: PMC11267556 DOI: 10.1021/jacsau.4c00220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024]
Abstract
Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.
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Affiliation(s)
- Saer Samanipour
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- UvA
Data Science Center, University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Cornwall Street, Woolloongabba, Queensland 4102, Australia
| | - Leon Patrick Barron
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- MRC
Centre for Environment and Health, Environmental Research Group, School
of Public Health, Faculty of Medicine, Imperial
College London, London W12 0BZ, United Kingdom
| | - Denice van Herwerden
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
| | - Antonia Praetorius
- Institute
for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
| | - Kevin V. Thomas
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Cornwall Street, Woolloongabba, Queensland 4102, Australia
| | - Jake William O’Brien
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Cornwall Street, Woolloongabba, Queensland 4102, Australia
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2
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Huang J, Ye L, Wang J, Deng Y, Du B, Liu W, Su G. A new approach to monitoring typical organophosphorus compounds (OPs) in environmental media: From database building to suspect screening. ENVIRONMENT INTERNATIONAL 2024; 189:108802. [PMID: 38875816 DOI: 10.1016/j.envint.2024.108802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/07/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024]
Abstract
Organophosphorus compounds (OPs) are widely used as flame retardants (FRs) and plasticizers, yet strategies for comprehensively screening of suspect OPs in environmental samples are still lacking. In this work, a neoteric, robust, and general suspect screening technique was developed to identify novel chemical exposures by use of ultra-high performance liquid chromatography-Q Exactive hybrid quadrupole-Orbitrap high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). We firstly established a suspect chemical database which had 7,922 OPs with 4,686 molecular formulas, and then conducted suspect screening in n = 50 indoor dust samples, n = 76 sediment samples, and n = 111 water samples. By use of scoring criteria such as retention time prediction models, we successfully confirmed five compounds by comparison with their authentic standards, and prioritized three OPs candidates including a nitrogen/fluorine-containing compound, that is dimethyl {1H-indol-3-yl[3-(trifluoromethyl)anilino]methyl} phosphonate (DMITFMAMP). Given that the biodegradation half-life values in water (t1/2,w) of DMITFMAMP calculated by EPI Suite is 180 d, it is considered to be potentially persistent. This strategy shows promising potential in environmental pollution assessment, and can be expected to be widely used in future research.
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Affiliation(s)
- Jianan Huang
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Industry and Information Technology, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Langjie Ye
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Industry and Information Technology, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Jun Wang
- Guangdong Provincial Academy of Environmental Science, Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangzhou 510045, China
| | - Yirong Deng
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Industry and Information Technology, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Guangdong Provincial Academy of Environmental Science, Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangzhou 510045, China
| | - Bing Du
- State Environmental Protection Key Laboratory of Dioxin Pollution Control, National Research Center for Environmental Analysis and Measurement, No.1 Yuhuinanlu, Chaoyang District, Beijing 100029, China.
| | - Wei Liu
- State Key Laboratory for Environmental Protection of Water Ecological Health in the Middle and Lower Reaches of the Yangtze River, Jiangsu Provincial Academy of Environmental Science, Nanjing 210036, China
| | - Guanyong Su
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Industry and Information Technology, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
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3
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Tirapelle M, Chia DN, Duanmu F, Besenhard MO, Mazzei L, Sorensen E. In-silico method development and optimization of on-line comprehensive two-dimensional liquid chromatography via a shortcut model. J Chromatogr A 2024; 1721:464818. [PMID: 38564929 DOI: 10.1016/j.chroma.2024.464818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Comprehensive two-dimensional liquid chromatography (LCxLC) represents a valuable alternative to conventional single column, or one-dimensional, liquid chromatography (1D-LC) for resolving multiple components in a complex mixture in a short time. However, developing LCxLC methods with trial-and-error experiments is challenging and time-consuming, which is why the technique is not dominant despite its significant potential. This work presents a novel shortcut model to in-silico predicting retention time and peak width within an RPLCxRPLC separation system (i.e., LCxLC systems that use reversed-phase columns (RPLC) in both separation dimensions). Our computationally effective model uses the hydrophobic-subtraction model (HSM) to predict retention and considers limitations due to the sample volume, undersampling and the maximum pressure drop. The shortcut model is used in a two-step strategy for sample-dependent optimization of RPLCxRPLC separation systems. In the first step, the Kendall's correlation coefficient of all possible combinations of available columns is evaluated, and the best column pair is selected accordingly. In the second step, the optimal values of design variables, flow rate, pH and sample loop volume, are obtained via multi-objective stochastic optimization. The strategy is applied to method development for the separation of 8, 12 and 16 component mixtures. It is shown that the proposed strategy provides an easy way to accelerate method development for full-comprehensive 2D-LC systems as it does not require any experimental campaign and an entire optimization run can take less than two minutes.
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Affiliation(s)
- Monica Tirapelle
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - Dian Ning Chia
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - Fanyi Duanmu
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - Maximilian O Besenhard
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - Luca Mazzei
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - Eva Sorensen
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
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4
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Song D, Tang T, Wang R, Liu H, Xie D, Zhao B, Dang Z, Lu G. Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123763. [PMID: 38492749 DOI: 10.1016/j.envpol.2024.123763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/26/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.
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Affiliation(s)
- Dehao Song
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Ting Tang
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China.
| | - Rui Wang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - He Liu
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Danping Xie
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Bo Zhao
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Zhi Dang
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China
| | - Guining Lu
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China
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5
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Charest N, Lowe CN, Ramsland C, Meyer B, Samano V, Williams AJ. Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis. Anal Bioanal Chem 2024; 416:2565-2579. [PMID: 38530399 PMCID: PMC11228616 DOI: 10.1007/s00216-024-05229-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 03/28/2024]
Abstract
Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.
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Affiliation(s)
- Nathaniel Charest
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.
| | - Charles N Lowe
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
| | | | - Brian Meyer
- Senior Environmental Employment Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
| | - Vicente Samano
- Senior Environmental Employment Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
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6
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Parinet J, Makni Y, Diallo T, Guérin T. Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry. Talanta 2024; 267:125214. [PMID: 37734288 DOI: 10.1016/j.talanta.2023.125214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 09/23/2023]
Abstract
The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.
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Affiliation(s)
- Julien Parinet
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France.
| | - Yassine Makni
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France
| | - Thierno Diallo
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France
| | - Thierry Guérin
- ANSES, Strategy and Programmes Department, 94701, Maisons-Alfort, France
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7
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Hu H, Tekin V, Hu B, Yaghoobi M, Khan A, Ghosh AK, Panda SK, Huang H, Luyten W. Metabolic profiling of Chimonanthus grammatus via UHPLC-HRMS-MS with computer-assisted structure elucidation and its antimicrobial activity. FRONTIERS IN PLANT SCIENCE 2023; 14:1138913. [PMID: 37229132 PMCID: PMC10205022 DOI: 10.3389/fpls.2023.1138913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/27/2023] [Indexed: 05/27/2023]
Abstract
Chimonanthus grammatus is used as Hakka traditional herb to treat cold, flu, etc. So far, the phytochemistry and antimicrobial compounds have not been well investigated. In this study, the orbitrap-ion trap MS was used to characterize its metabolites, combined with a computer-assisted structure elucidation method, and the antimicrobial activities were assessed by a broth dilution method against 21 human pathogens, as well as the bioassay-guided purification work to clarify its main antimicrobial compounds. A total of 83 compounds were identified with their fragmentation patterns, including terpenoids, coumarins, flavonoids, organic acids, alkaloids, and others. The plant extracts can strongly inhibit the growth of three Gram-positive and four Gram-negative bacteria, and nine active compounds were bioassay-guided isolated, including homalomenol C, jasmonic acid, isofraxidin, quercitrin, stigmasta-7,22-diene-3β,5α,6α-triol, quercetin, 4-hydroxy-1,10-secocadin-5-ene-1,10-dione, kaempferol, and E-4-(4,8-dimethylnona-3,7-dienyl)furan-2(5H)-one. Among them, isofraxidin, kaempferol, and quercitrin showed significant activity against planktonic Staphylococcus aureus (IC50 = 13.51, 18.08 and 15.86 µg/ml). Moreover, their antibiofilm activities of S. aureus (BIC50 = 15.43, 17.31, 18.86 µg/ml; BEC50 = 45.86, ≥62.50, and 57.62 µg/ml) are higher than ciprofloxacin. The results demonstrated that the isolated antimicrobial compounds played the key role of this herb in combating microbes and provided benefits for its development and quality control, and the computer-assisted structure elucidation method was a powerful tool for chemical analysis, especially for distinguishing isomers with similar structures, which can be used for other complex samples.
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Affiliation(s)
- Haibo Hu
- National Engineering Research Center for Modernization of Traditional Chinese Medicine - Hakka Medical Resources Branch, School of Pharmacy, Gannan Medical University, Ganzhou, China
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
| | - Volkan Tekin
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
| | - Bin Hu
- National Engineering Research Center for Modernization of Traditional Chinese Medicine - Hakka Medical Resources Branch, School of Pharmacy, Gannan Medical University, Ganzhou, China
| | - Mahdi Yaghoobi
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
- Department of Phytochemistry, Medicinal Plants and Drug Research Institute, Shahid Beheshti University, Tehran, Iran
| | - Ajmal Khan
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
- Leishmania Diagnostic & Drug Delivery Research Laboratory, University of Peshawar, Peshawar, Pakistan
| | - Alokesh Kumar Ghosh
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna, Bangladesh
| | - Sujogya Kumar Panda
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
- Center of Environment Climate Change and Public Health, Utkal University, Vani Vihar, Bhubaneswar, Odisha, India
| | - Hao Huang
- National Engineering Research Center for Modernization of Traditional Chinese Medicine - Hakka Medical Resources Branch, School of Pharmacy, Gannan Medical University, Ganzhou, China
| | - Walter Luyten
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
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Liakh I, Harshkova D, Hrouzek P, Bišová K, Aksmann A, Wielgomas B. Green alga Chlamydomonas reinhardtii can effectively remove diclofenac from the water environment - A new perspective on biotransformation. JOURNAL OF HAZARDOUS MATERIALS 2023; 455:131570. [PMID: 37163898 DOI: 10.1016/j.jhazmat.2023.131570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/19/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023]
Abstract
The use of unicellular algae to remove xenobiotics (including drugs) from wastewaters is one of the rapidly developing areas of environmental protection. Numerous data indicate that for efficient phycoremediation three processes are important, i.e. biosorption, bioaccumulation, and biotransformation. Although biosorption and bioaccumulation do not raise any serious doubts, biotransformation is more problematic since its products can be potentially more toxic than the parent compounds posing a threat to organisms living in a given environment, including organisms that made this transformation. Thus, two questions need to be answered before the proper algae strain is chosen for phycoremediation, namely what metabolites are produced during biotransformation, and how resistant is the analyzed strain to a mixture of parent compound and metabolites that appear over the course of culture? In this work, we evaluated the remediation potential of the model green alga Chlamydomonas reinhardtii in relation to non-steroidal anti-inflammatory drugs (NSAIDs), as exemplified by diclofenac. To achieve this, we analysed the susceptibility of C. reinhardtii to diclofenac as well as its capability to biosorption, bioaccumulation, and biotransformation of the drug. We have found that even at a relatively high concentration of diclofenac the algae maintained their vitality and were able to remove (37.7%) DCF from the environment. A wide range of phase I and II metabolites of diclofenac (38 transformation products) was discovered, with many of them characteristic rather for animal and bacterial biochemical pathways than for plant metabolism. Due to such a large number of detected products, 18 of which were not previously reported, the proposed scheme of diclofenac transformation by C. reinhardtii not only significantly contributes to broadening the knowledge in this field, but also allows to suggest possible pathways of degradation of xenobiotics with a similar structure. It is worth pointing out that a decrease in the level of diclofenac in the media observed in this study cannot be fully explained by biotransformation (8.4%). The mass balance analysis indicates that other processes (total 22%), such as biosorption, a non-extractable residue formation, or complete decomposition in metabolic cycles can be involved in the diclofenac disappearance, and those findings open the prospects of further research.
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Affiliation(s)
- Ivan Liakh
- Department of Toxicology, Faculty of Pharmacy, Medical University of Gdansk, Gdansk, Poland
| | - Darya Harshkova
- Department of Plant Physiology and Biotechnology, Faculty of Biology, University of Gdansk, Gdansk, Poland
| | - Pavel Hrouzek
- Laboratory of Algal Biotechnology, Centre Algatech, Institute of Microbiology of the Czech Academy of Sciences, Třeboň, Czech Republic
| | - Kateřina Bišová
- Laboratory of Cell Cycles of Algae, Centre Algatech, Institute of Microbiology of the Czech Academy of Sciences, Třeboň, Czech Republic
| | - Anna Aksmann
- Department of Plant Physiology and Biotechnology, Faculty of Biology, University of Gdansk, Gdansk, Poland.
| | - Bartosz Wielgomas
- Department of Toxicology, Faculty of Pharmacy, Medical University of Gdansk, Gdansk, Poland.
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9
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Brookhart A, Arora M, McCullagh M, Wilson ID, Plumb RS, Vissers JP, Tanna N. Understanding mobile phase buffer composition and chemical structure effects on electrospray ionization mass spectrometry response. J Chromatogr A 2023; 1696:463966. [PMID: 37054638 DOI: 10.1016/j.chroma.2023.463966] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/15/2023]
Abstract
Mobile phase selection is of critical importance in liquid chromatography - mass spectrometry (LC-MS) based studies, since it affects retention, chromatographic selectivity, ionization, limits of detection and quantification, and linear dynamic range. Generalized LC-MS mobile phase selection criteria, suitable for a broad class of chemical compounds, do not exist thus far. Here we have performed a large-scale qualitative assessment of the effect of solvent composition used for reversed-phase LC separations on electrospray ionization (ESI) response for 240 small molecular weight drugs, representing various chemical compound classes. Of these 240 analytes 224 were detectable using ESI. The main chemical structural features affecting ESI response were found to all be surface area or surface charge-related. Mobile phase composition was found to be less differentiating, although for some compounds a pH effect was noted. Unsurprisingly, chemical structure was found to be the dominant factor for ESI response for the majority of the investigated analytes, representing about 85% of the replicating detectable complement of the sample data set. A weak correlation between ESI response and structure complexity was observed. Solvents based on isopropanol, and those containing phosphoric or di- and trifluoracetic acids, performed relatively poorly in terms of chromatographic or ESI response, whilst the best performing 'generic' LC solvents were based on methanol, acetonitrile using formic acid and ammonium acetate as buffer components, consistent with current practice in many laboratories.
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Affiliation(s)
- Allison Brookhart
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, MA
| | - Mahika Arora
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA
| | | | - Ian D Wilson
- Computational & Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, United Kingdom
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10
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Boelrijk J, van Herwerden D, Ensing B, Forré P, Samanipour S. Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. J Cheminform 2023; 15:28. [PMID: 36829215 PMCID: PMC9960388 DOI: 10.1186/s13321-023-00699-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.
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Affiliation(s)
- Jim Boelrijk
- AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands. .,Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands.
| | - Denice van Herwerden
- grid.7177.60000000084992262Van’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, The Netherlands
| | - Bernd Ensing
- grid.7177.60000000084992262AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands ,Computational Chemistry Group, Van’t Hoff Institute for Molecular Sciences (HIMS), Amsterdam, The Netherlands
| | - Patrick Forré
- grid.7177.60000000084992262AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands ,grid.7177.60000000084992262Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands
| | - Saer Samanipour
- Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, The Netherlands. .,UvA Data Science Center, University of Amsterdam, Amsterdam, The Netherlands. .,Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Australia.
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11
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Wambaugh JF, Rager JE. Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:783-793. [PMID: 36347934 PMCID: PMC9742338 DOI: 10.1038/s41370-022-00492-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/21/2022] [Accepted: 10/21/2022] [Indexed: 05/10/2023]
Abstract
Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.
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Affiliation(s)
- John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, USA.
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Julia E Rager
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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12
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Rocco K, Margoum C, Richard L, Coquery M. Enhanced database creation with in silico workflows for suspect screening of unknown tebuconazole transformation products in environmental samples by UHPLC-HRMS. JOURNAL OF HAZARDOUS MATERIALS 2022; 440:129706. [PMID: 35961075 DOI: 10.1016/j.jhazmat.2022.129706] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/12/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
The search and identification of organic contaminants in agricultural watersheds has become a crucial effort to better characterize watershed contamination by pesticides. The past decade has brought a more holistic view of watershed contamination via the deployment of powerful analytical strategies such as non-target and suspect screening analysis that can search more contaminants and their transformation products. However, suspect screening analysis remains broadly confined to known molecules, primarily due to the lack of analytical standards and suspect databases for unknowns such as pesticide transformation products. Here we developed a novel workflow by cross-comparing the results of various in silico prediction tools against literature data to create an enhanced database for suspect screening of pesticide transformation products. This workflow was applied on tebuconazole, used here as a model pesticide, and resulted in a suspect screening database counting 291 transformation products. The chromatographic retention times and tandem mass spectra were predicted for each of these compounds using 6 models based on multilinear regression and more complex machine-learning algorithms. This comprehensive approach to the investigation and identification of tebuconazole transformation products was retrospectively applied on environmental samples and found 6 transformation products identified for the first time in river water samples.
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Affiliation(s)
- Kevin Rocco
- INRAE, UR RiverLy, 69625 Villeurbanne, France.
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13
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Evaluation of Retention Range of Extractables Under Linear Gradient Conditions for Reversed-Phase Chromatographic Considerations and Requirements in Extractables Analytical Methods for Chemical Characterization of Medical Devices. Chromatographia 2022. [DOI: 10.1007/s10337-022-04185-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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14
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Milman BL, Zhurkovich IK. Present-Day Practice of Non-Target Chemical Analysis. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s1061934822050070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract
We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.
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15
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Ribar D, Rijavec T, Kralj Cigić I. An exploration into the use of Hansen solubility parameters for modelling reversed-phase chromatographic separations. J Anal Sci Technol 2022. [DOI: 10.1186/s40543-022-00322-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractThe suitability of Hansen solubility parameters as descriptors for modelling analyte retention during reversed-phase chromatographic experiments was investigated. A novel theoretical model using Hansen solubility parameters as the basis for a complete mathematical derivation of the model was developed. The theoretical model also includes the cavitation volumes of the analytes, which were calculated using ab initio density functional theory methods. A set of three homologous phthalates was used for experimental data collection and subsequent model construction. The training error and the generalization error of the model were additionally evaluated using a range of chemically diverse analytes. Statistical evaluation of the results revealed that the model is suitable for analyte retention prediction but is limited to the analytes used in the model construction. Therefore, the resulting theoretical model cannot be easily generalized. A retention anomaly attributed to the column temperature and mobile phase composition was experimentally observed and mathematically investigated.
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16
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Minkus S, Bieber S, Letzel T. Spotlight on mass spectrometric non-target screening analysis: Advanced data processing methods recently communicated for extracting, prioritizing and quantifying features. ANALYTICAL SCIENCE ADVANCES 2022; 3:103-112. [PMID: 38715638 PMCID: PMC10989605 DOI: 10.1002/ansa.202200001] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 06/13/2024]
Abstract
Non-target screening of trace organic compounds complements routine monitoring of water bodies. So-called features need to be extracted from the raw data that preferably represent a chemical compound. Relevant features need to be prioritized and further be interpreted, for instance by identifying them. Finally, quantitative data is required to assess the risks of a detected compound. This review presents recent and noteworthy contributions to the processing of non-target screening (NTS) data, prioritization of features as well as (semi-) quantitative methods that do not require analytical standards. The focus lies on environmental water samples measured by liquid chromatography, electrospray ionization and high-resolution mass spectrometry. Examples for fully-integrated data processing workflows are given with options for parameter optimization and choosing between different feature extraction algorithms to increase feature coverage. The regions of interest-multivariate curve resolution method is reviewed which combines a data compression alternative with chemometric feature extraction. Furthermore, prioritization strategies based on a confined chemical space for annotation, guidance by targeted analysis and signal intensity are presented. Exploiting the retention time (RT) as diagnostic evidence for NTS investigations is highlighted by discussing RT indexing and prediction using quantitative structure-retention relationship models. Finally, a seminal technology for quantitative NTS is discussed without the need for analytical standards based on predicting ionization efficiencies.
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Affiliation(s)
- Susanne Minkus
- AFIN‐TS GmbHAugsburgGermany
- Technical University of Munich (Chair of Urban Water Systems Engineering)MunichGermany
| | | | - Thomas Letzel
- AFIN‐TS GmbHAugsburgGermany
- Technical University of Munich (Chair of Urban Water Systems Engineering)MunichGermany
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17
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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18
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Deep learning for retention time prediction in reversed-phase liquid chromatography. J Chromatogr A 2021; 1664:462792. [PMID: 34999303 DOI: 10.1016/j.chroma.2021.462792] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 01/16/2023]
Abstract
Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. Lots of approaches were developed for retention time prediction in liquid chromatography for a different number of molecules considering various molecular properties and machine learning algorithms. The recently built large retention time data set of standard compounds from the Metabolite and Chemical Entity Database (METLIN) allows researchers to create a model that can be used for retention time prediction of small molecules with wide varieties of structures and physicochemical properties. The ability to predict retention times using the largest data set was studied for different architectures of deep learning models that were trained on molecular fingerprints, and SMILES (string representation of a molecule) represented as one-hot matrices. The best result was achieved with a one-dimensional convolutional neural network (1D CNN) that uses SMILES as an input. The proposed model reached the mean absolute error and the median absolute error equal to 34.7 and 18.7 s, respectively, which outperformed the results previously obtained for this data set. The pre-trained 1D CNN on the METLIN SMRT data set was transferred on five other data sets to evaluate the generalization ability.
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19
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Parinet J. Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks. Heliyon 2021; 7:e08563. [PMID: 34950792 PMCID: PMC8671870 DOI: 10.1016/j.heliyon.2021.e08563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/26/2021] [Accepted: 12/03/2021] [Indexed: 11/29/2022] Open
Abstract
To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.
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Affiliation(s)
- Julien Parinet
- Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France
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20
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Suspect and non-target screening of chemicals in clothing textiles by reversed-phase liquid chromatography/hybrid quadrupole-Orbitrap mass spectrometry. Anal Bioanal Chem 2021; 414:1403-1413. [PMID: 34786606 PMCID: PMC8724091 DOI: 10.1007/s00216-021-03766-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 11/27/2022]
Abstract
The global manufacturing of clothing is usually composed of multistep processes, which include a large number of chemicals. However, there is generally no information regarding the chemical content remaining in the finished clothes. Clothes in close and prolonged skin contact may thus be a significant source of daily human exposure to hazardous compounds depending on their ability to migrate from the textiles and be absorbed by the skin. In the present study, twenty-four imported garments on the Swedish market were investigated with respect to their content of organic compounds, using a screening workflow. Reversed-phase liquid chromatography coupled to electrospray ionization/high-resolution mass spectrometry was used for both suspect and non-target screening. The most frequently detected compound was benzothiazole followed by quinoline. Nitroanilines with suspected mutagenic and possible skin sensitization properties, and quinoline, a carcinogenic compound, were among the compounds occurring at the highest concentrations. In some garments, the level of quinoline was estimated to be close to or higher than 50,000 ng/g, the limit set by the REACH regulation. Other detected compounds were acridine, benzotriazoles, benzothiazoles, phthalates, nitrophenols, and organophosphates. Several of the identified compounds have logP and molecular weight values enabling skin uptake. This pilot study indicates which chemicals and compound classes should be prioritized for future quantitative surveys and control of the chemical content in clothing as well as research on skin transfer, skin absorption, and systemic exposure. The results also show that the current control and prevention from chemicals in imported garments on the Swedish market is insufficient.
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21
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Parinet J. Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets. CHEMOSPHERE 2021; 275:130036. [PMID: 33676277 DOI: 10.1016/j.chemosphere.2021.130036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring of pesticides, their metabolites and transformation products. The development of quantitative structure-retention relationship models require selecting the most adequate and best set of molecular descriptors and the best machine-learning algorithm. Here, we used seven molecular descriptor sets extracted from four well-known studies and applied them to roughly 800 pesticides and their chromatographic reversed-phase retention times. We used and optimized five different machine-learning algorithms with these descriptor sets to carry out predictions. Our results show that a support-vector machine regression algorithm with only eight molecular descriptors gave the best compromise between the number of molecular descriptors, processing time and model complexity to optimize prediction performance for this specific gradient LC method.
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Affiliation(s)
- Julien Parinet
- Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France.
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22
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Yu Y, Yao C, Guo DA. Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry. Acta Pharm Sin B 2021; 11:1469-1492. [PMID: 34221863 PMCID: PMC8245813 DOI: 10.1016/j.apsb.2021.02.017] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/02/2021] [Accepted: 02/22/2021] [Indexed: 12/21/2022] Open
Abstract
Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.
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Key Words
- BS, background subtraction
- CCS, collision cross section
- CE, collision energy
- CID, collision-induced dissociation
- DDA, data-dependent acquisition
- DE, dynamic exclusion
- DIA, data-independent acquisition
- DIF, diagnostic ion filtering
- DM, database matching
- Data acquisition
- Data post-processing
- EL, exclusion list
- EMS, enhanced mass spectrum
- EPI, enhanced product ion
- FS, full scan
- HCD, high-energy C-trap dissociation
- IDA, information dependent acquisition
- IM, ion mobility
- IPF, isotope pattern filtering
- ISCID, in-source collision-induced dissociation
- LC, liquid chromatography
- LTQ-Orbitrap, linear ion-trap/orbitrap
- Liquid chromatography−mass spectrometry
- MDF, mass defect filtering
- MIM, multiple ion monitoring
- MN, molecular networking
- MRM, multiple reaction monitoring
- MS, mass spectrometry
- MTSF, mass spectral trees similarity filter
- NL, neutral loss
- NLF, neutral loss filtering
- NLS, neutral loss scan
- NRF, nitrogen rule filtering
- PCA, principal component analysis
- PIL, precursor ion list
- PIS, precursor ion scan
- PLS-DA, partial least square-discriminant analysis
- Q-TRAP, hybrid triple quadrupole-linear ion trap
- QSRR, quantitative structure retention relationship
- QqQ, triple quadrupole
- Qualitative analysis
- RT, retention time
- SA, statistical analysis
- TCM, traditional Chinese medicine
- Traditional Chinese medicine
- UHPLC, ultra-high performance liquid chromatography
- cMRM, conventional multiple reaction monitoring
- sMRM, scheduled multiple reaction monitoring
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Affiliation(s)
- Yang Yu
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Changliang Yao
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - De-an Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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23
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Bride E, Heinisch S, Bonnefille B, Guillemain C, Margoum C. Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling. JOURNAL OF HAZARDOUS MATERIALS 2021; 409:124652. [PMID: 33277075 DOI: 10.1016/j.jhazmat.2020.124652] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 10/02/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.
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Affiliation(s)
- Eloi Bride
- INRAE, UR RiverLy, F-69625 Villeurbanne, France
| | - Sabine Heinisch
- Université de Lyon, Institut des Sciences Analytiques, UMR 5280, CNRS, F-69100 Villeurbanne, France
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24
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González-Gaya B, Lopez-Herguedas N, Bilbao D, Mijangos L, Iker AM, Etxebarria N, Irazola M, Prieto A, Olivares M, Zuloaga O. Suspect and non-target screening: the last frontier in environmental analysis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:1876-1904. [PMID: 33913946 DOI: 10.1039/d1ay00111f] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Suspect and non-target screening (SNTS) techniques are arising as new analytical strategies useful to disentangle the environmental occurrence of the thousands of exogenous chemicals present in our ecosystems. The unbiased discovery of the wide number of substances present over environmental analysis needs to find a consensus with powerful technical and computational requirements, as well as with the time-consuming unequivocal identification of discovered analytes. Within these boundaries, the potential applications of SNTS include the studies of environmental pollution in aquatic, atmospheric, solid and biological samples, the assessment of new compounds, transformation products and metabolites, contaminant prioritization, bioremediation or soil/water treatment evaluation, and retrospective data analysis, among many others. In this review, we evaluate the state of the art of SNTS techniques going over the normalized workflow from sampling and sample treatment to instrumental analysis, data processing and a brief review of the more recent applications of SNTS in environmental occurrence and exposure to xenobiotics. The main issues related to harmonization and knowledge gaps are critically evaluated and the challenges of their implementation are assessed in order to ensure a proper use of these promising techniques in the near future.
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Affiliation(s)
- B González-Gaya
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940 Leioa, Basque Country, Spain.
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25
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Low DY, Micheau P, Koistinen VM, Hanhineva K, Abrankó L, Rodriguez-Mateos A, da Silva AB, van Poucke C, Almeida C, Andres-Lacueva C, Rai DK, Capanoglu E, Tomás Barberán FA, Mattivi F, Schmidt G, Gürdeniz G, Valentová K, Bresciani L, Petrásková L, Dragsted LO, Philo M, Ulaszewska M, Mena P, González-Domínguez R, Garcia-Villalba R, Kamiloglu S, de Pascual-Teresa S, Durand S, Wiczkowski W, Bronze MR, Stanstrup J, Manach C. Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. Food Chem 2021; 357:129757. [PMID: 33872868 DOI: 10.1016/j.foodchem.2021.129757] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/29/2021] [Accepted: 04/06/2021] [Indexed: 11/18/2022]
Abstract
Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29-103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03-0.76 min and interval width of 0.33-8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.
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Affiliation(s)
- Dorrain Yanwen Low
- Université Clermont Auvergne, INRAE, UNH, F-63000 Clermont Ferrand, France.
| | - Pierre Micheau
- Université Clermont Auvergne, INRAE, UNH, F-63000 Clermont Ferrand, France
| | - Ville Mikael Koistinen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, FI-70211 Kuopio, Finland; Department of Biochemistry, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, FI-70211 Kuopio, Finland; Department of Biochemistry, University of Turku, FI-20014 Turun yliopisto, Finland; Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - László Abrankó
- Department of Applied Chemistry, Faculty of Food Science, Szent István Egyetem, 29-43 Villanyi Street, 1118 Budapest, Hungary
| | - Ana Rodriguez-Mateos
- Department of Nutritional Sciences, School of Life Course Sciences, King's College London, SE1 9NH London, United Kingdom
| | - Andreia Bento da Silva
- Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal; Faculty of Pharmacy, University of Lisbon, Avenida Professor Gama Pinto, 1649-003 Lisbon, Portugal
| | - Christof van Poucke
- Technology and Food Science Department, Flanders Research Institute for Agriculture Fisheries and Food (ILVO), Brusselsesteenweg 370, B-9090 Melle, Belgium
| | - Conceição Almeida
- Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - Cristina Andres-Lacueva
- Nutrition, Food Science and Gastronomy Department, Pharmacy Faculty, University of Barcelona, Av Joan XXlll, 08028 Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Dilip K Rai
- Department of Food BioSciences, Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland
| | - Esra Capanoglu
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey
| | - Francisco A Tomás Barberán
- Department of Food Science and Technology, CEBAS-CSIC, Campus Universitario de Espinardo, edf 25, 30100 Murcia, Spain; Department of Biotechnology, College of Science, Taif University, Taif 26571, Saudi Arabia
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Metabolomics Unit, Research and Innovation Centre, Fondazione Edmund Mach, 38010 San Michele all'Adige, Italy; Department of Cellular, Computational and Integrative Biology, CIBIO, University of Trento, 38123 Trento, Italy
| | - Gesine Schmidt
- Department of Food and Health, Nofima, Norwegian Institute of Food, Fisheries and Aquaculture Research, PB 210, NO-1433 Ås, Norway
| | - Gözde Gürdeniz
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg DK-1985, Denmark
| | - Kateřina Valentová
- Laboratory of Biotransformation, Institute of Microbiology of the CAS, Vídeňská 1083, CZ-142 20 Prague, Czechia
| | - Letizia Bresciani
- Human Nutrition Unit, Department of Veterinary Science, University of Parma, Via Volturno, 39, 43125 Parma PR, Italy
| | - Lucie Petrásková
- Laboratory of Biotransformation, Institute of Microbiology of the CAS, Vídeňská 1083, CZ-142 20 Prague, Czechia
| | - Lars Ove Dragsted
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg DK-1985, Denmark
| | - Mark Philo
- Quadram Institute Biosciences, Norwich Research Park NR4 7 UQ, United Kingdom
| | - Marynka Ulaszewska
- Department of Food Quality and Nutrition, Metabolomics Unit, Research and Innovation Centre, Fondazione Edmund Mach, 38010 San Michele all'Adige, Italy
| | - Pedro Mena
- Human Nutrition Unit, Department of Food and Drug, University of Pharma, Via Volturno, 39, 43125 Parma PR, Italy
| | - Raúl González-Domínguez
- Nutrition, Food Science and Gastronomy Department, Pharmacy Faculty, University of Barcelona, Av Joan XXlll, 08028 Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Rocío Garcia-Villalba
- Department of Food Science and Technology, CEBAS-CSIC, Campus Universitario de Espinardo, edf 25, 30100 Murcia, Spain
| | - Senem Kamiloglu
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey; Science and Technology Application and Research Center (BITUAM), Bursa Uludag University, 16059 Gorukle, Bursa, Turkey
| | - Sonia de Pascual-Teresa
- Department of Metabolism and Nutrition, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC), Jose Antonio Novais 10, 28040 Madrid, Spain
| | - Stéphanie Durand
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, F-63000 Clermont Ferrand, France
| | - Wieslaw Wiczkowski
- Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Tuwima 10, 10-748 Olsztyn, Poland
| | - Maria Rosário Bronze
- Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal; Instituto de Biologia Experimental Tecnológica, Av. da República, Quinta do Marquês, Edificio iBET/ITQB, 2780-157 Oeiras, Portugal; Research Institute for Medicines, Faculty of Pharmacy, University of Lisbon, Avenida Professor Gama Pinto, 1649-003 Lisbon, Portugal
| | - Jan Stanstrup
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg DK-1985, Denmark
| | - Claudine Manach
- Université Clermont Auvergne, INRAE, UNH, F-63000 Clermont Ferrand, France
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26
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Data processing strategies for non-targeted analysis of foods using liquid chromatography/high-resolution mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116188] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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27
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Davis JJ, Foster SW, Grinias JP. Low-cost and open-source strategies for chemical separations. J Chromatogr A 2021; 1638:461820. [PMID: 33453654 PMCID: PMC7870555 DOI: 10.1016/j.chroma.2020.461820] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/18/2022]
Abstract
In recent years, a trend toward utilizing open access resources for laboratory research has begun. Open-source design strategies for scientific hardware rely upon the use of widely available parts, especially those that can be directly printed using additive manufacturing techniques and electronic components that can be connected to low-cost microcontrollers. Open-source software eliminates the need for expensive commercial licenses and provides the opportunity to design programs for specific needs. In this review, the impact of the "open-source movement" within the field of chemical separations is described, primarily through a comprehensive look at research in this area over the past five years. Topics that are covered include general laboratory equipment, sample preparation techniques, separations-based analysis, detection strategies, electronic system control, and software for data processing. Remaining hurdles and possible opportunities for further adoption of open-source approaches in the context of these separations-related topics are also discussed.
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Affiliation(s)
- Joshua J Davis
- Department of Chemistry & Biochemistry, Rowan University, Glassboro, NJ 08028, United States
| | - Samuel W Foster
- Department of Chemistry & Biochemistry, Rowan University, Glassboro, NJ 08028, United States
| | - James P Grinias
- Department of Chemistry & Biochemistry, Rowan University, Glassboro, NJ 08028, United States.
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Chaker J, Gilles E, Léger T, Jégou B, David A. From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening. Anal Chem 2020; 93:1792-1800. [DOI: 10.1021/acs.analchem.0c04660] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Erwann Gilles
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Thibaut Léger
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
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29
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Knolhoff AM, Premo JH, Fisher CM. A Proposed Quality Control Standard Mixture and Its Uses for Evaluating Nontargeted and Suspect Screening LC/HR-MS Method Performance. Anal Chem 2020; 93:1596-1603. [PMID: 33274925 DOI: 10.1021/acs.analchem.0c04036] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Nontargeted (NTA) and suspect screening analyses (SSA) aim to detect and identify unknown compounds of interest from a given sample. The complexity and diversity of NTA and SSA methodologies necessitate the use of a comprehensive quality control standard mixture to determine if methods are fit for purpose, but to our knowledge, such a standard has not been developed that can be used by multiple disciplines, nor is one readily available. This work describes the development and analysis of a proposed nontargeted standard/quality control mixture for NTA and SSA applications using liquid chromatography/electrospray ionization-high resolution-mass spectrometry. Considerations in its development included achieving diversity of compounds with respect to elemental composition, molecular weight, retention time, and ionization in positive and/or negative ion modes, which resulted in the inclusion of 89 compounds. The utility of the standard mixture was applied on our own NTA and SSA workflows where sample preparation efficiency and potential sources of error due to instrumental and data processing methods were evaluated. Some areas in need of improvement were identified, such as hydrophilic compound detection and molecular formula generation for compounds containing fluorine. However, our overall methodology was found to be fit for purpose and we were able to establish thresholds to increase reliability and throughput of reported results.
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Affiliation(s)
- Ann M Knolhoff
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, Maryland 20740, United States
| | - Jacob H Premo
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, Maryland 20740, United States
| | - Christine M Fisher
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, Maryland 20740, United States
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30
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An assessment of quality assurance/quality control efforts in high resolution mass spectrometry non-target workflows for analysis of environmental samples. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.116063] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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31
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Darmawan MA, Muhammad BZ, Harahap AFP, Ramadhan MYA, Sahlan M, Haryuni, Supriyadi T, Abd-Aziz S, Gozan M. Reduction of the acidity and peroxide numbers of tengkawang butter ( Shorea stenoptera) using thermal and acid activated bentonites. Heliyon 2020; 6:e05742. [PMID: 33364505 PMCID: PMC7750560 DOI: 10.1016/j.heliyon.2020.e05742] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/21/2020] [Accepted: 12/11/2020] [Indexed: 11/21/2022] Open
Abstract
Tengkawang fat (Shorea stenoptera), from an indigenous plant of the Kalimantan forest, has excellent potential as an alternative source of vegetable fat because it has a high level of fatty acids composition. Activated natural bentonite can be used as a bleaching agent to improve the quality of tengkawang fat. This research aims to reduce the acidity, peroxide number values and identify the physicochemical properties (fatty acid composition, nutrients, and thermal) of tengkawang butter. Initially, tengkawang samples from Nanga Yen and Sintang were pre-treated using the degumming process with 1% phosphoric acid and the neutralization process with a 1 M NaOH 10% w/w solution. The results show that the acidity (mg NaOH/g) of the tengkawang fat samples was reduced from 11.00 to 3.36 when using bentonite activated at 200 °C. The bentonite activated with 0.5 M HCl reduced the acidity to 3.61. The peroxide number (meq O2/kg) of the tengkawang fat samples was reduced from 9.45 to 4.84 and 3.47 by bleaching with thermal-activated and acid-activated bentonites, respectively. Peroxide value correlates with β-carotene content. The smaller of the β-carotene content, the smaller the peroxide value. The acidity, peroxide number, and iodine number values from tengkawang fat after treatment adhere to the SNI 2903: 2016 standard. The main content of fatty acids in tengkawang fat is palmitic acid, stearic acid, and oleic acid. These results show that both products are suitable for the food industry in terms of the acid and peroxide numbers. The application of this research results will assist local people in increasing the economic value of the product from tengkawang plant, which is an indigenous plant from Kalimantan.
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Affiliation(s)
- Muhammad Arif Darmawan
- Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
| | - Bagas Zaki Muhammad
- Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
| | | | | | - Muhammad Sahlan
- Research Center for Biomedical Engineering, Universitas Indonesia, Depok 16424, Indonesia
| | - Haryuni
- Department of Agronomy, Faculty of Agriculture, Universitas Tunas Pembangunan, Surakarta, Central of Java, Indonesia
| | - Teguh Supriyadi
- Department of Agronomy, Faculty of Agriculture, Universitas Tunas Pembangunan, Surakarta, Central of Java, Indonesia
| | - Suraini Abd-Aziz
- Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Misri Gozan
- Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
- Research Center for Biomedical Engineering, Universitas Indonesia, Depok 16424, Indonesia
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32
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Guo Z, Huang S, Wang J, Feng YL. Recent advances in non-targeted screening analysis using liquid chromatography - high resolution mass spectrometry to explore new biomarkers for human exposure. Talanta 2020; 219:121339. [DOI: 10.1016/j.talanta.2020.121339] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 05/16/2020] [Accepted: 06/09/2020] [Indexed: 12/29/2022]
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33
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Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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34
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McEachran AD, Chao A, Al-Ghoul H, Lowe C, Grulke C, Sobus JR, Williams AJ. Revisiting Five Years of CASMI Contests with EPA Identification Tools. Metabolites 2020; 10:E260. [PMID: 32585902 PMCID: PMC7345619 DOI: 10.3390/metabo10060260] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 06/03/2020] [Accepted: 06/17/2020] [Indexed: 01/02/2023] Open
Abstract
Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency's (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA.
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Affiliation(s)
- Andrew D. McEachran
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (A.C.); (H.A.-G.)
| | - Alex Chao
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (A.C.); (H.A.-G.)
| | - Hussein Al-Ghoul
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (A.C.); (H.A.-G.)
| | - Charles Lowe
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (C.L.); (C.G.); (J.R.S.)
| | - Christopher Grulke
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (C.L.); (C.G.); (J.R.S.)
| | - Jon R. Sobus
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (C.L.); (C.G.); (J.R.S.)
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA; (C.L.); (C.G.); (J.R.S.)
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35
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Chao A, Al-Ghoul H, McEachran AD, Balabin I, Transue T, Cathey T, Grossman JN, Singh RR, Ulrich EM, Williams AJ, Sobus JR. In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples. Anal Bioanal Chem 2020; 412:1303-1315. [PMID: 31965249 PMCID: PMC7021669 DOI: 10.1007/s00216-019-02351-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/27/2019] [Accepted: 12/11/2019] [Indexed: 12/31/2022]
Abstract
High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract.
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Affiliation(s)
- Alex Chao
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.
- Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.
| | - Hussein Al-Ghoul
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
- Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Andrew D McEachran
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
- Agilent Technologies Inc., Santa Clara, CA, 95051, USA
| | - Ilya Balabin
- General Dynamics Information Technology, 79 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Tom Transue
- General Dynamics Information Technology, 79 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Tommy Cathey
- General Dynamics Information Technology, 79 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Jarod N Grossman
- Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
- Agilent Technologies Inc., Santa Clara, CA, 95051, USA
| | - Randolph R Singh
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365, Esch-sur-Alzette, Luxembourg
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.
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36
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Development of a Validated UPLC-MS/MS Method for Analyzing Major Ginseng Saponins from Various Ginseng Species. Molecules 2019; 24:molecules24224065. [PMID: 31717607 PMCID: PMC6891701 DOI: 10.3390/molecules24224065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 11/04/2019] [Accepted: 11/08/2019] [Indexed: 01/06/2023] Open
Abstract
Ginsenosides, which contain one triterpene and one or more sugar moieties, are the major bioactive compounds of ginseng. The aim of this study was to develop and optimize a specific and reliable ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the analysis of twelve different resources of ginseng. The six marker compounds of ginsenoside Rb1, ginsenoside Rb2, ginsenoside Rc, ginsenoside Rd, ginsenoside Re, and ginsenoside Rg1, as well as an internal standard, were separated by a reversed-phase C-18 column with a gradient elution of water and methanol-acetonitrile. The multiple-reaction monitoring (MRM) mode was used to quantify and identify twelve market products. The results demonstrated that not only is the logarithm of its partition coefficient (cLog P; octanol-water partition coefficient) one of the factors, but also the number of sugars, position of sugars, and position of the hydroxyl groups are involved in the complicated separation factors for the analytes in the analytical system. If the amount of ginsenoside Rb1 was higher than 40 mg/g, then the species might be Panax quinquefolius, based on the results of the marker ginsenoside contents of various varieties. In summary, this study provides a rapid and precise analytical method for identifying the various ginsenosides from different species, geographic environments, and cultivation cultures.
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Song Q, Li J, Huo H, Cao Y, Wang Y, Song Y, Tu P. Retention Time and Optimal Collision Energy Advance Structural Annotation Relied on LC–MS/MS: An Application in Metabolite Identification of an Antidementia Agent Namely Echinacoside. Anal Chem 2019; 91:15040-15048. [DOI: 10.1021/acs.analchem.9b03720] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Qingqing Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
- Beijing Key Lab for Quality Evaluation of Chinese Meteria Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Huixia Huo
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yan Cao
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yitao Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa 999078, Macao
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
- Beijing Key Lab for Quality Evaluation of Chinese Meteria Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
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38
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Mansouri K, Cariello NF, Korotcov A, Tkachenko V, Grulke CM, Sprankle CS, Allen D, Casey WM, Kleinstreuer NC, Williams AJ. Open-source QSAR models for pKa prediction using multiple machine learning approaches. J Cheminform 2019; 11:60. [PMID: 33430972 PMCID: PMC6749653 DOI: 10.1186/s13321-019-0384-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 09/03/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. METHODS The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure-activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). RESULTS The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. CONCLUSIONS This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.
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Affiliation(s)
- Kamel Mansouri
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - Neal F. Cariello
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - Alexandru Korotcov
- Science Data Software LLC, 14914 Bradwill Court, Rockville, MD 20850 USA
| | - Valery Tkachenko
- Science Data Software LLC, 14914 Bradwill Court, Rockville, MD 20850 USA
| | - Chris M. Grulke
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Mail Code D143-02, Research Triangle Park, NC 27709 USA
| | - Catherine S. Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - David Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - Warren M. Casey
- National Institute of Environmental Health Sciences, P.O. Box 12233, Mail Stop K2-16, Research Triangle Park, NC 27709 USA
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, P.O. Box 12233, Mail Stop K2-16, Research Triangle Park, NC 27709 USA
| | - Antony J. Williams
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Mail Code D143-02, Research Triangle Park, NC 27709 USA
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Fitch WL, Khojasteh C, Aliagas I, Johnson K. Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification. Drug Metab Lett 2019; 12:93-100. [PMID: 30070179 PMCID: PMC6350196 DOI: 10.2174/1872312812666180802093347] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Revised: 06/13/2018] [Accepted: 07/31/2018] [Indexed: 11/26/2022]
Abstract
Background: There is a continued need for improvements in the efficiency of metabolite structure elucidation. Objective: We propose to take LC Retention Time (RT) into consideration during the process of structure determination. Methods: Herein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a pre-dictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature exam-ples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransfor-mation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding). Results: The CHI system for retention indexing is shown to be practical and simple to implement. A da-tabase of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an addi-tional 17 datasets from the literature. Application of this database is illustrated. Conclusion: In our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.
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Affiliation(s)
- William L Fitch
- Department of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, United States
| | - Cyrus Khojasteh
- Department of Drug Metabolism and Pharmacokinetics 1 DNA Way MS 412a, Genentech Inc., South San Francisco, CA 94080, United States
| | - Ignacio Aliagas
- Discovery Chemistry, 1 DNA Way, Genentech Inc., South San Francisco, CA 94080, United States
| | - Kevin Johnson
- Department of Drug Metabolism and Pharmacokinetics 1 DNA Way MS 412a, Genentech Inc., South San Francisco, CA 94080, United States
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Hernández F, Bakker J, Bijlsma L, de Boer J, Botero-Coy AM, Bruinen de Bruin Y, Fischer S, Hollender J, Kasprzyk-Hordern B, Lamoree M, López FJ, Laak TLT, van Leerdam JA, Sancho JV, Schymanski EL, de Voogt P, Hogendoorn EA. The role of analytical chemistry in exposure science: Focus on the aquatic environment. CHEMOSPHERE 2019; 222:564-583. [PMID: 30726704 DOI: 10.1016/j.chemosphere.2019.01.118] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/15/2019] [Accepted: 01/20/2019] [Indexed: 06/09/2023]
Abstract
Exposure science, in its broadest sense, studies the interactions between stressors (chemical, biological, and physical agents) and receptors (e.g. humans and other living organisms, and non-living items like buildings), together with the associated pathways and processes potentially leading to negative effects on human health and the environment. The aquatic environment may contain thousands of compounds, many of them still unknown, that can pose a risk to ecosystems and human health. Due to the unquestionable importance of the aquatic environment, one of the main challenges in the field of exposure science is the comprehensive characterization and evaluation of complex environmental mixtures beyond the classical/priority contaminants to new emerging contaminants. The role of advanced analytical chemistry to identify and quantify potential chemical risks, that might cause adverse effects to the aquatic environment, is essential. In this paper, we present the strategies and tools that analytical chemistry has nowadays, focused on chromatography hyphenated to (high-resolution) mass spectrometry because of its relevance in this field. Key issues, such as the application of effect direct analysis to reduce the complexity of the sample, the investigation of the huge number of transformation/degradation products that may be present in the aquatic environment, the analysis of urban wastewater as a source of valuable information on our lifestyle and substances we consumed and/or are exposed to, or the monitoring of drinking water, are discussed in this article. The trends and perspectives for the next few years are also highlighted, when it is expected that new developments and tools will allow a better knowledge of chemical composition in the aquatic environment. This will help regulatory authorities to protect water bodies and to advance towards improved regulations that enable practical and efficient abatements for environmental and public health protection.
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Affiliation(s)
- F Hernández
- Research Institute for Pesticides and Water (IUPA), University Jaume I, Avda. Sos Baynat S/n, E-12071 Castellón, Spain.
| | - J Bakker
- National Institute for Public Health and the Environment (RIVM), Centre for Safety of Substances and Products, P.O. Box 1, 3720, BA Bilthoven, the Netherlands
| | - L Bijlsma
- Research Institute for Pesticides and Water (IUPA), University Jaume I, Avda. Sos Baynat S/n, E-12071 Castellón, Spain
| | - J de Boer
- Vrije Universiteit, Department Environment & Health, De Boelelaan 1087, 1081, HV Amsterdam, the Netherlands
| | - A M Botero-Coy
- Research Institute for Pesticides and Water (IUPA), University Jaume I, Avda. Sos Baynat S/n, E-12071 Castellón, Spain
| | - Y Bruinen de Bruin
- European Commission Joint Research Centre, Directorate E - Space, Security and Migration, Italy
| | - S Fischer
- Swedish Chemicals Agency (KEMI), P.O. Box 2, SE-172 13, Sundbyberg, Sweden
| | - J Hollender
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600, Dübendorf, Switzerland; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092, Zürich, Switzerland
| | - B Kasprzyk-Hordern
- University of Bath, Department of Chemistry, Faculty of Science, Bath, BA2 7AY, United Kingdom
| | - M Lamoree
- Vrije Universiteit, Department Environment & Health, De Boelelaan 1087, 1081, HV Amsterdam, the Netherlands
| | - F J López
- Research Institute for Pesticides and Water (IUPA), University Jaume I, Avda. Sos Baynat S/n, E-12071 Castellón, Spain
| | - T L Ter Laak
- KWR Watercycle Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430, BB Nieuwegein, the Netherlands
| | - J A van Leerdam
- KWR Watercycle Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430, BB Nieuwegein, the Netherlands
| | - J V Sancho
- Research Institute for Pesticides and Water (IUPA), University Jaume I, Avda. Sos Baynat S/n, E-12071 Castellón, Spain
| | - E L Schymanski
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600, Dübendorf, Switzerland; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - P de Voogt
- KWR Watercycle Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430, BB Nieuwegein, the Netherlands; Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090, GE Amsterdam, the Netherlands
| | - E A Hogendoorn
- National Institute for Public Health and the Environment (RIVM), Centre for Safety of Substances and Products, P.O. Box 1, 3720, BA Bilthoven, the Netherlands
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Geer Wallace MA, Pleil JD, Oliver KD, Whitaker DA, Mentese S, Fent KW, Horn GP. Non-targeted GC/MS analysis of exhaled breath samples: Exploring human biomarkers of exogenous exposure and endogenous response from professional firefighting activity. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2019; 82:244-260. [PMID: 30907277 PMCID: PMC8668041 DOI: 10.1080/15287394.2019.1587901] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
A non-targeted analysis workflow was applied to analyze exhaled breath samples collected from firefighters pre- and post-structural fire suppression. Breath samples from firefighters functioning in attack and search positions were examined for target and non-target compounds in automated thermal desorption-GC/MS (ATD-GC/MS) selected ion monitoring (SIM)/scan mode and reviewed for prominent chemicals. Targeted chemicals included products of combustion such as benzene, toluene, xylenes, and polycyclic aromatic hydrocarbons (PAH) that serve as a standard assessment of exposure. Sixty unique chemical features representative of exogenous chemicals and endogenous compounds, including single-ring aromatics, polynuclear aromatic hydrocarbons, volatile sulfur-containing compounds, aldehydes, alkanes, and alkenes were identified using the non-targeted analysis workflow. Fifty-seven out of 60 non-targeted features changed by at least 50% from pre- to post-fire suppression activity in at least one subject, and 7 non-targeted features were found to exhibit significantly increased or decreased concentrations for all subjects as a group. This study is important for (1) alerting the firefighter community to potential new exposures, (2) expanding the current targeted list of toxicants, and (3) finding biomarkers of response to firefighting activity as reflected by changes in endogenous compounds. Data demonstrate that there are non-targeted compounds in firefighters' breath that are indicative of environmental exposure despite the use of protective gear, and this information may be further utilized to improve the effectiveness of personal protective equipment.
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Affiliation(s)
- M Ariel Geer Wallace
- a Office of Research and Development, National Exposure Research Laboratory , U.S. Environmental Protection Agency , Research Triangle Park , NC , USA
| | - Joachim D Pleil
- a Office of Research and Development, National Exposure Research Laboratory , U.S. Environmental Protection Agency , Research Triangle Park , NC , USA
| | - Karen D Oliver
- a Office of Research and Development, National Exposure Research Laboratory , U.S. Environmental Protection Agency , Research Triangle Park , NC , USA
| | - Donald A Whitaker
- a Office of Research and Development, National Exposure Research Laboratory , U.S. Environmental Protection Agency , Research Triangle Park , NC , USA
| | - Sibel Mentese
- b Department of Environmental Engineering , Çanakkale Onsekiz Mart University , Merkez/Çanakkale , Turkey
| | - Kenneth W Fent
- c Division of Surveillance, Hazard Evaluations and Field Studies , National Institute for Occupational Safety and Health (NIOSH) , Cincinnati , OH , USA
| | - Gavin P Horn
- d Illinois Fire Service Institute , University of Illinois at Urbana-Champaign , Champaign , IL , USA
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Aalizadeh R, Nika MC, Thomaidis NS. Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants. JOURNAL OF HAZARDOUS MATERIALS 2019; 363:277-285. [PMID: 30312924 DOI: 10.1016/j.jhazmat.2018.09.047] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 09/16/2018] [Accepted: 09/17/2018] [Indexed: 05/13/2023]
Abstract
Hydrophilic interaction liquid chromatography (HILIC) and reversed phase LC (RPLC) coupled to high resolution mass spectrometry (HRMS) are widely used for the identification of suspects and unknown compounds in the environment. For the identification of unknowns, apart from mass accuracy and isotopic fitting, retention time (tR) and MS/MS spectra evaluation is required. In this context, a novel comprehensive workflow was developed to study the tR behavior of large groups of emerging contaminants using Quantitative Structure-Retention Relationships (QSRR). 682 compounds were analyzed by HILIC-HRMS in positive Electrospray Ionization mode (ESI). Moreover, an extensive dataset was built for RPLC-HRMS including 1830 and 308 compounds for positive and negative ESI, respectively. Support Vector Machines (SVM) was used to model the tR data. The applicability domains of the models were studied by Monte Carlo Sampling (MCS) methods. The MCS method was also used to calculate the acceptable error windows for the predicted tR from various LC conditions. This paper provides validated models for predicting tR in HILIC/RPLC-HRMS platforms to facilitate identification of new emerging contaminants by suspect and non-target HRMS screening, and were applied for the identification of transformation products (TPs) of emerging contaminants and biocides in wastewater and sludge.
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Maria-Christina Nika
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece.
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43
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Sobus JR, Grossman JN, Chao A, Singh R, Williams AJ, Grulke CM, Richard AM, Newton SR, McEachran AD, Ulrich EM. Using prepared mixtures of ToxCast chemicals to evaluate non-targeted analysis (NTA) method performance. Anal Bioanal Chem 2019; 411:835-851. [PMID: 30612177 DOI: 10.1007/s00216-018-1526-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 11/14/2018] [Accepted: 11/27/2018] [Indexed: 10/27/2022]
Abstract
Non-targeted analysis (NTA) methods are increasingly used to discover contaminants of emerging concern (CECs), but the extent to which these methods can support exposure and health studies remains to be determined. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) was launched in 2016 to address this need. As part of ENTACT, 1269 unique substances from EPA's ToxCast library were combined to make ten synthetic mixtures, with each mixture containing between 95 and 365 substances. As a participant in the trial, we first performed blinded NTA on each mixture using liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS). We then performed an unblinded evaluation to identify limitations of our NTA method. Overall, at least 60% of spiked substances could be observed using selected methods. Discounting spiked isomers, true positive rates from the blinded and unblinded analyses reached a maximum of 46% and 65%, respectively. An overall reproducibility rate of 75% was observed for substances spiked into more than one mixture and observed at least once. Considerable discordance in substance identification was observed when comparing a subset of our results derived from two separate reversed-phase chromatography methods. We conclude that a single NTA method, even when optimized, can likely characterize only a subset of ToxCast substances (and, by extension, other CECs). Rigorous quality control and self-evaluation practices should be required of labs generating NTA data to support exposure and health studies. Accurate and transparent communication of performance results will best enable meaningful interpretations and defensible use of NTA data. Graphical abstract ᅟ.
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Affiliation(s)
- Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.
| | - Jarod N Grossman
- Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.,Agilent Technologies Inc., Santa Clara, CA, 95051, USA
| | - Alex Chao
- Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Randolph Singh
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Christopher M Grulke
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Seth R Newton
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Andrew D McEachran
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
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McEachran AD, Mansouri K, Grulke C, Schymanski EL, Ruttkies C, Williams AJ. "MS-Ready" structures for non-targeted high-resolution mass spectrometry screening studies. J Cheminform 2018; 10:45. [PMID: 30167882 PMCID: PMC6117229 DOI: 10.1186/s13321-018-0299-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 08/21/2018] [Indexed: 02/05/2023] Open
Abstract
Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workflow has been developed to produce structural representations observed using HRMS ("MS-Ready structures") and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA's Chemistry Dashboard ( https://comptox.epa.gov/dashboard/ ). This article describes the workflow for the generation and linking of ~ 700,000 MS-Ready structures (derived from ~ 760,000 original structures) as well as download, search and export capabilities to serve structure identification using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/ .
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Affiliation(s)
- Andrew D. McEachran
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
- Present Address: Integrated Laboratory Systems, Inc., 601 Keystone Dr., Morrisville, NC 27650 USA
| | - Chris Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, 4367 Belvaux, Luxembourg
| | - Christoph Ruttkies
- Department of Stress and Development Biology, Leibniz Institute of Plant Biochemistry (IPB), Weinberg 3, 06120 Halle (Saale), Germany
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
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Peters K, Worrich A, Weinhold A, Alka O, Balcke G, Birkemeyer C, Bruelheide H, Calf OW, Dietz S, Dührkop K, Gaquerel E, Heinig U, Kücklich M, Macel M, Müller C, Poeschl Y, Pohnert G, Ristok C, Rodríguez VM, Ruttkies C, Schuman M, Schweiger R, Shahaf N, Steinbeck C, Tortosa M, Treutler H, Ueberschaar N, Velasco P, Weiß BM, Widdig A, Neumann S, Dam NMV. Current Challenges in Plant Eco-Metabolomics. Int J Mol Sci 2018; 19:E1385. [PMID: 29734799 PMCID: PMC5983679 DOI: 10.3390/ijms19051385] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/24/2018] [Accepted: 04/25/2018] [Indexed: 12/22/2022] Open
Abstract
The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Anja Worrich
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
- UFZ-Helmholtz-Centre for Environmental Research, Department Environmental Microbiology, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
| | - Oliver Alka
- Applied Bioinformatics Group, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany.
| | - Gerd Balcke
- Leibniz Institute of Plant Biochemistry, Cell and Metabolic Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Claudia Birkemeyer
- Institute of Analytical Chemistry, University of Leipzig, Linnéstr. 3, 04103 Leipzig, Germany.
| | - Helge Bruelheide
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany.
| | - Onno W Calf
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Sophie Dietz
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Kai Dührkop
- Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Emmanuel Gaquerel
- Centre for Organismal Studies, Heidelberg University, Im Neuenheimer Feld 360, 69120 Heidelberg, Germany.
| | - Uwe Heinig
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Marlen Kücklich
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Mirka Macel
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Caroline Müller
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Yvonne Poeschl
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Informatics, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany.
| | - Georg Pohnert
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Christian Ristok
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Victor Manuel Rodríguez
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Meredith Schuman
- Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, 07745 Jena, Germany.
| | - Rabea Schweiger
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Nir Shahaf
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Christoph Steinbeck
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Maria Tortosa
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Nico Ueberschaar
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Pablo Velasco
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Brigitte M Weiß
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Anja Widdig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
- Research Group of Primate Kin Selection, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Nicole M van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
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McEachran AD, Hedgespeth ML, Newton SR, McMahen R, Strynar M, Shea D, Nichols EG. Comparison of emerging contaminants in receiving waters downstream of a conventional wastewater treatment plant and a forest-water reuse system. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:12451-12463. [PMID: 29460251 PMCID: PMC6739829 DOI: 10.1007/s11356-018-1505-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 02/06/2018] [Indexed: 05/22/2023]
Abstract
Forest-water reuse (FWR) systems treat municipal, industrial, and agricultural wastewaters via land application to forest soils. Previous studies have shown that both large-scale conventional wastewater treatment plants (WWTPs) and FWR systems do not completely remove many contaminants of emerging concern (CECs) before release of treated wastewater. To better characterize CECs and potential for increased implementation of FWR systems, FWR systems need to be directly compared to conventional WWTPs. In this study, both a quantitative, targeted analysis and a nontargeted analysis were utilized to better understand how CECs release to waterways from an FWR system compared to a conventional treatment system. Quantitatively, greater concentrations and total mass load of CECs was exhibited downstream of the conventional WWTP compared to the FWR. Average summed concentrations of 33 targeted CECs downstream of the conventional system were ~ 1000 ng/L and downstream of the FWR were ~ 30 ng/L. From a nontargeted chemical standpoint, more tentatively identified chemicals were present, and at a greater relative abundance, downstream of the conventional system as well. Frequently occurring contaminants included phthalates, pharmaceuticals, and industrial chemicals. These data indicate that FWR systems represent a sustainable wastewater treatment alternative and that emerging contaminant release to waterways was lower at a FWR system than a conventional WWTP.
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Affiliation(s)
- Andrew D McEachran
- Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, USA.
| | - Melanie L Hedgespeth
- Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, USA
| | - Seth R Newton
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Rebecca McMahen
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Mark Strynar
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Damian Shea
- Department of Biological Sciences, College of Science, North Carolina State University, Raleigh, NC, USA
| | - Elizabeth Guthrie Nichols
- Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, USA
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Mansouri K, Grulke CM, Judson RS, Williams AJ. OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminform 2018. [PMID: 29520515 PMCID: PMC5843579 DOI: 10.1186/s13321-018-0263-1] [Citation(s) in RCA: 257] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The collection of chemical structure information and associated experimental data for quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.![]()
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA. .,Oak Ridge Institute for Science and Education, 1299 Bethel Valley Road, Oak Ridge, TN, 37830, USA. .,ScitoVation LLC, 6 Davis Drive, Research Triangle Park, NC, 27709, USA.
| | - Chris M Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Richard S Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
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