Every day, humans are exposed to chemicals such as cigarette smoke or pollutants that may trigger molecular changes in their cells. The identification of specific markers of response to this exposure is important to help assess whether a subject has indeed been exposed to particular chemicals or toxicants. For cigarette smoke, they provide an estimate of the accumulated exposure, reflecting both individual puffing behavior and number of conventional cigarettes smoked. In the case of in vivo inhalation studies, it also allows to confirm that the animals have been exposed as expected.
Markers of exposure can be measured in different matrices, blood and urine being the easiest to obtain. Markers of exposure variety ranges from single molecule biomarkers to more complex gene signatures.
The response to cigarette smoke exposure has been monitored by widely used biomarkers such as the levels of “nicotine equivalents" 1 in urine or the metabolites of the tobacco-specific lung carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone 2. However, the use of these biomarkers is associated with several flaws, such as their short half-life and inter-individual differences in metabolism 3,4. While hair and saliva nicotine and cotinine measurements may provide accurate verification of nonsmoking status and provide useful measure of secondhand smoke exposure, they are restricted to a single constituent present in cigarette smoke. Moreover, such biomarkers do not offer insights into the biological mechanisms that are impacted in response to cigarette smoke exposure, a feature advocated by 21st century toxicity testing principles 5.
New technologies, such as whole genome microarrays, have therefore been incorporated into toxicity testing to increase efficiency and to provide a more data-driven approach to exposure response assessment 6.
Whole blood-based gene expression signature classification models were developped to predict smoking exposure status 7,8. Such a smoking signature can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity: it consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA18. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure.
This was also confirmed by crowd-verification and, in addition, other gene signatures and predictive classification models have been identified 9. Overall, these smoking exposure-associated genes, which are part of the core signature, constitute a robust set of blood markers that can be leveraged to monitor and quantify the smoking exposure status.