Urinary metabolic dysregulations associated to thirdhand smoke exposure in mice: Implications for human urine biomonitoring

This dataset contains all raw LC-MS and GC-MS data of urine extracts from mice exposed to thirdhand smoke (THS) used in the paper Urinary metabolic dysregulations associated to thirdhand smoke exposure in mice: Implications for human urine biomonitoring.

Objective: The goal of this study is to characterize the THS-induced molecular alterations in urine using an LC-MS and GC-MS based untargeted metabolomic approaches. We have selected urine as a target matrix because it is a non-invasive valuable diagnostic biofluid, rich in metabolites that reflect dysregulations of all biochemical pathways, which also has been one of the most widely used biofluid to assess human exposure to tobacco smoke.

Experimental groups: Urine extracts were from two mice groups: control group (CTRL, n=5), which was never exposed to THS; and THS exposed group (THS, n=5), which was exposed to THS from weaning (three weeks of age) to 24 weeks weeks without exposure to SHS during the study.

Sample extraction: Metabolites were extracted using a mixture of methanol and Milli-Q water. In the case of GC-MS, samples were pre-treated with urease prior metabolite extraction and dried under nitrogen, lyophilized, and derivatized after extraction. Quality control samples (QCs) were prepared by pooling equal volumes of each extract and analysed every five samples to ensure data quality and reproducibility.

LC-MS analysis: LC-MS urine extracts were analysed using a 1290 UHPLC system coupled to a 6550 quadrupole time of flight (QTOF) mass spectrometer from Agilent Technologies operated in ESI+ mode. Chromatographic separation was conducted on a ACQUITY UPLC HSS T3 C18 column (1.8 µm, 150 × 2.1 mm, Waters). MS/MS was performed in targeted mode, and the instrument was set to acquire over the m/z range 50-1000, with a default iso width of 4 m/z. The collision energy was fixed at 20 V.

GC-MS analysis: GC-MS urine extracts were analysed using a 7890A gas chromatograph coupled to a 7200 QTOF mass spectrometer from Agilent Technologies. The MS was operated in the electron impact ionization mode at 70 eV, with a fixed emission current of 20 µA.

Files are named following this code:

Folders: mzDATA: contains GC-MS and LC-MS raw data in mzXML format; LC-MSMS: contains LC-MS/MS data used for identification of metabolites in mzXML format.

Analytical approaches: GCMS: data from GC-MS urine extracts; LCMS: data from LC-MS urine extracts.

Samples: CTRL: control group; THS: THS exposed group.

Technical information

Sample extraction and data acquisition (single date, range, approximate date): 2016-2017

Data analysis (single date, range, approximate date): 2017-2021

Geographic location of data collection:

Mice exposure: Department of Molecular, Cell and Systems Biology, University of California, Riverside CA 92521, U.S.A.

Sample extraction and data acquisition: Universitat Rovira i Virgili, Departament d’Enginyeria Electrònica, Elèctrica i Automàtica, Tarragona, Spain.

Information about funding sources that supported the collection of the data: This research was funded by the European Union’s Horizon 2020 research and innovation pro-gramme under the Marie Sklodowska-Curie grant agreement No. 660034; the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya through C.M.’s predoctoral grant number 2020 FI_B2 00118; the Spanish Ministry of Science & Innovation through N.R.’s Juan de la Cierva Incorporación grant No. (IJCI- 2015-23158); N.R.’s Miguel Servet contract (CP19/00060) from Instituto de Salud Carlos III, co-financed by Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, “Una manera de hacer Europa”; and the Tobacco-Related Disease Research Program (TRDRP) of the University of California under projects 22RT- 0121 and 23DT-0103.