Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

Teng Yang; Zhen Li; Siwei Chen; Ting Lan; Zhongbing Lu; Longfa Fang; Huan Zhao; Qirun Li; Yinwei Luo; Bo Yang; Jinian Shu

doi:10.1016/j.jhazmat.2024.134151

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

J Hazard Mater. 2024 May 15:470:134151. doi: 10.1016/j.jhazmat.2024.134151. Epub 2024 Mar 27.

Authors

Teng Yang¹, Zhen Li², Siwei Chen¹, Ting Lan³, Zhongbing Lu³, Longfa Fang⁴, Huan Zhao¹, Qirun Li¹, Yinwei Luo⁵, Bo Yang¹, Jinian Shu¹

Affiliations

¹ National Engineering Laboratory for VOCs Pollution Control Material & Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
² National Engineering Laboratory for VOCs Pollution Control Material & Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Binzhou Institute of Technology, Weiqiao-UCAS Science and Technology Park, Binzhou, Shandong Province 256606, China. Electronic address: lizhen20@ucas.ac.cn.
³ College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
⁴ State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems. Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020 China.
⁵ National Engineering Laboratory for VOCs Pollution Control Material & Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Binzhou Institute of Technology, Weiqiao-UCAS Science and Technology Park, Binzhou, Shandong Province 256606, China.

PMID: 38554517
DOI: 10.1016/j.jhazmat.2024.134151

Abstract

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly understood. This study aimed to identify novel exhaled biomarkers in ozone-exposed mice using ultra-sensitive photoinduced associative ionization time-of-flight mass spectrometry and machine learning. Distinct ion peaks for acetonitrile (m/z 42, 60, and 78), butyronitrile (m/z 70, 88, and 106), and hydrogen sulfide (m/z 35) were detected. Integration of tissue characteristics, oxidative stress-related mRNA expression, and exhaled breath condensate free-radical analysis enabled a comprehensive exploration of the relationship between ozone-induced biological responses and potential biomarkers. Under similar exposure levels, C57BL/6 mice exhibited pulmonary injury characterized by significant inflammation, oxidative stress, and cardiac damage. Notably, C57BL/6 mice showed free radical signals, indicating a distinct susceptibility profile. Immunodeficient non-obese diabetic Prkdc^-/-/Il2rg^-/- (NPI) mice exhibited minimal biological responses to pulmonary injury, with little impact on the heart. These findings suggest a divergence in ozone-induced damage pathways in the two mouse types, leading to alterations in exhaled biomarkers. Integrating biomarker discovery with comprehensive biopathological analysis forms a robust foundation for targeted interventions to manage health risks posed by ozone exposure.

Keywords: Exhaled biomarker; Immunodeficiency; Machine learning; Mass spectrometry; Ozone-induced damage.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants / analysis
Air Pollutants / toxicity
Animals
Biomarkers* / analysis
Biomarkers* / metabolism
Breath Tests*
Exhalation
Lung Injury / chemically induced
Lung Injury / metabolism
Machine Learning*
Male
Mass Spectrometry
Mice
Mice, Inbred C57BL*
Oxidative Stress / drug effects
Ozone* / toxicity

Substances

Ozone
Biomarkers
Air Pollutants