Environmental Cheminformatics: Case study of Thirdhand Smoke in House Dust

Environmental Cheminformatics: Case Study of Thirdhand Smoke in House Dust

Keynote: https://rfmf-mpf-2020.sciencesconf.org/

Emma L. SCHYMANSKI¹; Sonia TORRES²; Noelia RAMIREZ²

¹ Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg

² University Rovira y Virgili, Tarragona, Spain

Introduction

The environment and the chemicals to which we are exposed is incredibly complex. Household dust samples contain a summary of household exposure to various chemicals, suitable for exploration using high resolution mass spectrometry (HR-MS). Target and non-target HR-MS analysis for third hand smoke (THS) was performed on 75 dust samples (33 smoker, 42 non-smoker households) in Tarragona, Spain, focusing on tobacco-specific nitrosamines, which have been associated with higher health risk [1].

Technological and methodological innovation

Primarily open access/source metabolomics and environmental workflows for HR-MS data analysis were used to interrogate the non-target data. A list of substances in THS was compiled for suspect screening, while an XCMS-based [2] workflow was used to prioritize non-target features of interest that were significantly different between smoker and non-smoker samples. Data was extracted using RMassBank [3], features were annotated using MassBank and MetFrag [4], searching in PubChem and CompTox.

Results and impact

This case study will be used to demonstrate the potential (pros and cons) of various environmental cheminformatics approaches to interpret non-target data, and highlight challenges still facing the field, such as limited coverage of mass spectral libraries [5]. A number of other European and world-wide initiatives will be covered to demonstrate the power of HR-MS and “environmental cheminformatics” to reveal information about the exposome and move to “near real-time” analysis.

References

[1] Ramírez N. et al. 2014. Environment International. 71, 139-147

[2] Smith, C.A. et al. 2006. Analytical Chemistry. 78, 779-787

[3] Stravs, M.A. et al. 2013. Journal of Mass Spectrometry. 48, 89-99

[4] Ruttkies, C. et al. 2016. Journal of Cheminformatics. 8, 3

[5] Frainay C. et al. 2018. Metabolites. 8, 51