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Adding complex expert knowledge into chemical database and transforming surfactants in wastewater

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The increasing popularity of high mass accuracy non-target mass spectrometry methods has yielded extensive identification efforts based on chemical compound databases. Candidate structures are often retrieved with either exact mass or molecular formula from large resources such as PubChem, ChemSpider or the EPA CompTox Chemistry Dashboard. Additional data (e.g. fragmentation, physicochemical properties, reference and data source information) is then used to select potential candidates, depending on the experimental context. However, these strategies require the presence of substances of interest in these compound databases, which is often not the case as no database can be fully inclusive. A prominent example with clear data gaps are surfactants, used in many products in our daily lives, yet often absent as discrete structures in compound databases. Linear alkylbenzene sulfonates (LAS) are a common, high use and high priority surfactant class that have highly complex transformation behaviour in wastewater. Despite extensive reports in the environmental literature, few of the LAS and none of the related transformation products were reported in any compound databases during an investigation into Swiss wastewater effluents, despite these forming the most intense signals. The LAS surfactant class will be used to demonstrate how the coupling of environmental observations with high resolution mass spectrometry and detailed literature data (expert knowledge) on the transformation of these species can be used to progressively “fill the gaps” in compound databases. The LAS and their transformation products have been added to the CompTox Chemistry Dashboard (https://comptox.epa.gov/) using a combination of “representative structures” and “related structures” starting from the structural information contained in the literature. By adding this information into a centralized open resource, future environmental investigations can now profit from the expert knowledge previously scattered throughout the literature. Note: This abstract does not reflect US EPA policy.

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Adding complex expert knowledge into chemical database and transforming surfactants in wastewater

  1. 1. 1 Adding Complex Expert KnowledgeAdding Complex Expert Knowledge into Chemical Databases:into Chemical Databases: Transforming Surfactants in WastewaterTransforming Surfactants in Wastewater Emma Schymanski Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg. Email: emma.schymanski@uni.lu Juliane Hollender (Eawag, Dübendorf, Switzerland) Chris Grulke (NCCT, US EPA, Research Triangle Park, NC, USA) Antony J. Williams (NCCT, US EPA, Research Triangle Park, NC, USA) The views expressed in this presentation are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.
  2. 2. 2 Why Add Complex Expert Knowledge to Databases? (l) Schymanski et al. 2014, ES&T, 48: 1811-1818. DOI: 10.1021/es4044374. (r) Source: N. Zamboni Unknowns and High Resolution Mass Spectrometry o Over 60 % of HR-MS peaks are potentially relevant but unknown
  3. 3. 3 Why Add Complex Expert Knowledge to Databases? Schymanski et al. 2014, ES&T, DOI: 10.1021/es4044374; M. Loos & H Singer, 2017. J. Cheminf. DOI: 10.1186/s13321-017-0197-z Homologous Series and UVCBs in our samples …. S OO OH CH3 CH3 m n C9H19 O O S O O OHm
  4. 4. 4 Why Add Complex Expert Knowledge to Databases? Homologue screening plots from Swiss Wastewater (Schymanski et al 2014, left) and Novi Sad (right) o Complex mixtures (UVCBs) are a huge and very challenging part of the puzzle
  5. 5. 5 Swiss Wastewater: Top 30 Peaks (ESI-) Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 Artificial Sweeteners Diclofenac Pictures: www.coca-cola-com; www.rivella.ch; www.voltargengel.com
  6. 6. 6 Grouping Isotopes and Adducts Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 0 3000 6000 9000 12000 15000 positive 2% 27% 100% Noise/Blank Targets Non-targets 0 3000 6000 9000 12000 15000 positivenegative 1% 30% 100%
  7. 7. 7 Swiss Wastewater: Top 30 Peaks (ESI-) Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 S OO O - O S O - O CH2 m/z = 79.96 m/z = 183.01 Picture: www.momsteam.com
  8. 8. 8 Surfactant Screening From Literature Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 Literature sources o Formulas, masses (ions), retention times and intensities o Spectra of selected compounds (different instruments) Gonzalez et al. Rapid Comm. Mass Spec. 2008, 22: 1445-54 Lara-Martin et al. EST. 2010, 44: 1670-1676
  9. 9. 9 Surfactant Screening From Literature Adding literature spectra to www.massbank.eu/MassBank/ Enable access and comparison Lara-Martin et al. EST. 2010, 44: 1670-1676 39 literature spectra (so far) Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374
  10. 10. 10 Supporting Evidence for Homologues M. Loos & H Singer, 2017. J. Cheminf. DOI: 10.1186/s13321-017-0197-z & Schymanski et al. 2014, ES&T DOI: 10.1021/es4044374 S OO OH CH3 CH3 m n DATS S OO OH O OH CH3 ()n ()m SPAC S OO OH O OHCH3 ()n ()m STAC http://www.envihomolog.eawag.ch/
  11. 11. 11 Supporting Evidence for Homologues Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374
  12. 12. 12 Supporting Evidence for Homologues Stravs et al. (2013), J. Mass Spectrom, 48(1):89-99. DOI: 10.1002/jms.3131 OHSO O CH3 O OH m n SPA-9C m+n=6 Formulas: http://sourceforge.net/projects/genform/ Meringer et al, 2011, MATCH 65, 259-290 Data: Schymanski et al. 2014, ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 Chromatography and MS/MS Annotation Literature: LIT00034,35 Sample: ETS00002 Standard: ETS00016,17,19,20 https://github.com/MassBank/RMassBank/
  13. 13. 13 Swiss Wastewater: Top 30 Peaks (ESI-) Schymanski et al. (2014), ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 Acesulfame Diclofenac Cyclamate Saccharin C10DATS C10SPAC SPA5C C15DATS STA6C C9DATS SPA2DC S OO OH O OH CH3 S OO OH CH3 CH3 ()n ()m SPAC DATS ()n ()m
  14. 14. 14 NORMAN Suspect Exchange o http://www.norman-network.com/?q=node/236 Schymanski, Aalizadeh et al. in prep.
  15. 15. 15 Eawag Surfactant List Schymanski et al 2014, ES&T, 48: 1811-1818. DOI: 10.1021/es4044374
  16. 16. 16 Using Generic Structures for Screening/Linking o https://github.com/schymane/RChemMass/
  17. 17. 17 Eawag Surfactant List (after many late nights…) Schymanski et al 2014, ES&T, 48: 1811-1818. DOI: 10.1021/es4044374 https://comptox.epa.gov/dashboard/chemical_lists/eawagsurf
  18. 18. 18 Eawag Surfactant List in CompTox Dashboard Schymanski, Grulke, Williams et al, in prep. & Williams et al. 2017 J. Cheminformatics 9:61 DOI: 10.1186/s13321-017-0247-6 https://comptox.epa.gov/dashboard/chemical_lists/eawagsurf CDK Depict
  19. 19. 19 Cross-Linking with Lists in CompTox Dashboard
  20. 20. 20 Structures now accessible for Computational MS Schymanski, Grulke, Williams et al, in prep. & Williams et al. 2017 J. Cheminformatics 9:61 DOI: 10.1186/s13321-017-0247-6 https://comptox.epa.gov/dashboard/chemical_lists/eawagsurf
  21. 21. 21 Acknowledgements emma.schymanski@uni.lu Further Information: www.massbank.eu http://www.norman-network.com/?q=node/236 https://github.com/MassBank/RMassBank/ https://github.com/schymane/RChemMass/ https://comptox.epa.gov/dashboard/ https://wwwen.uni.lu/lcsb/ .eu 2.32.3 EU Grant 603437
  22. 22. 22
  23. 23. 23 European (World-)Wide Exchange of Suspects Schymanski et al. 2015, ABC, DOI: 10.1007/s00216-015-8681-7 NORMAN Suspect List Exchange: http://www.norman-network.com/?q=node/236 Schymanski et al. 2015, ABC, DOI: 10.1007/s00216-015-8681-7 Tentatively Identified Spectra: http://goo.gl/0t7jGp Hits in GNPS MassIVE datasets: TPs in skin: http://goo.gl/NmO4tx Surfactants: http://goo.gl/7sY9Pf
  24. 24. 24 2015: European Non-target Screening Trial Schymanski et al, 2014, ES&T. DOI: 10.1021/es5002105 & Schymanski et al. 2015, ABC, DOI: 10.1007/s00216-015-8681-7 Peak picking Non-target HR-MS(/MS) Acquisition Target Screening Suspect Screening Non-target Screening Start Level 1 Confirmed Structure by reference standard Level 2 Probable Structure by library/diagnostic evidence Start Level 3 Tentative Candidate(s) suspect, substructure, class Level 4 Unequivocal Molecular Formula insufficient structural evidence Start Level 5 Mass of Interest multiple detection, trends, … “downgrading” with contradictory evidence Increasing identification confidence Target list Suspect list Peak picking or XICs
  25. 25. 25 Eawag Surfactant List in CompTox Dashboard CDK Depict https://www.slideshare.net/AntonyWilliams/ markush-enumeration-to-manage-mesh-and-manipulate-substances-of-unknown-or-variable-composition
  26. 26. 26 Target, Suspect and Non-Target Screening KNOWNS SUSPECTS No Prior Knowledge HPLC separation and HR-MS/MS TARGET ANALYSIS SUSPECT SCREENING NON-TARGET SCREENING Targets found Suspects found Masses of interest (Molecular formula) DATABASE SEARCH STRUCTURE GENERATION Confirmation and quantification of compounds present Candidate selection (retention time, MS/MS, calculated properties) Sampling extraction (SPE) HPLC separation HR-MS/MS Time, Effort & Number of Compounds…. SUSPECTS SPECTRUM SEARCH Spectral match

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