Metabolomics Provides Novel Insights into the Potential Toxicity Associated with Heated Tobacco Products, Electronic Cigarettes, and Tobacco Cigarettes on Human Bronchial Epithelial BEAS-2B Cells.

Smoking is an established risk factor for various pathologies including lung cancer. Electronic cigarettes (e-cigs) and heated tobacco products (HTPs) have appeared on the market in recent years, but their safety or, conversely, their toxicity has not yet been demonstrated. This study aimed to compare the metabolome of human lung epithelial cells exposed to emissions of e-cigs, HTPs, or 3R4F cigarettes in order to highlight potential early markers of toxicity. BEAS-2B cells were cultured at the air-liquid interface and exposed to short-term emissions from e-cigs set up at low or medium power, HTPs, or 3R4F cigarettes. Untargeted metabolomic analyses were performed using liquid chromatography coupled with mass spectrometry. Compared to unexposed cells, both 3R4F cigarette and HTP emissions affected the profiles of exogenous compounds, one of which is carcinogenic, as well as those of endogenous metabolites from various pathways including oxidative stress, energy metabolism, and lipid metabolism. However, these effects were observed at lower doses for cigarettes (2 and 4 puffs) than for HTPs (60 and 120 puffs). No difference was observed after e-cig exposure, regardless of the power conditions. These results suggest a lower acute toxicity of e-cig emissions compared to cigarettes and HTPs in BEAS-2B cells. The pathways deregulated by HTP emissions are also described to be altered in respiratory diseases, emphasizing that the toxicity of HTPs should not be underestimated.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics.

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R2 score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

P-glycoprotein (ABCB1) and breast cancer resistance protein (ABCG2) localize in the microvessels forming the blood-tumor barrier in ependymomas.

Ependymomas are neuroepithelial tumors that arise from the ependymal layer bordering the cerebral ventricles and spinal canal. Intracranial ependymoma represents a major encephalic tumor in children, while spinal ependymoma develops more frequently in adults. To understand the pharmacoresistance that characterizes this tumoral entity, we analyzed the level of expression and localization of three major efflux transport proteins with a multidrug resistance function, P-glycoprotein, multidrug resistance-related protein 1 (MRP1) and breast cancer resistance protein (BCRP), in a series of 25 ependymomas from both children and adults. Real-time-PCR analysis showed that all three genes were expressed in all tumors, with no apparent correlation between the level of expression and either age or tumor grade. The MRP1 transcript was expressed at a significantly higher level in spinal tumors than in intracranial tumors. The expression of the proteins corresponding to these genes was confirmed by Western blot analysis. In an immunohistochemical study, P-glycoprotein and BCRP were shown to be associated with the tumoral vessels, where they presented a luminal localization, a prerequisite for their efflux drug activity into the blood. These data indicate that a biochemical, transporter-dependent blood-tumor barrier may exist in ependymomas, which may reduce the tumoral bioavailability of lipophilic and amphiphilic anticancer drugs.

Gene expression profiling in brain following acute systemic hypertonicity: novel genes possibly involved in osmoadaptation.

In brain osmoprotective genes known to be involved in cellular osmoadaptation to hypertonicity, as well as the related transcription factor tonicity-responsive enhancer binding protein (TonEBP) are only expressed in some cell subsets. In the search for other genes possibly involved in osmoadaptation of brain cells we have analyzed, through microarray, the transcriptional profile of forebrain from rats subjected to 45 min, 90 min, and 6 h systemic hypertonicity. Microarray data were validated by quantitative real-time PCR. Around 23 000 genes gave a reliable hybridization signal. The number of genes showing a higher expression increased from around 15 (45 min) up to nearly 200 (6 h). Among about 30 immediate early genes (IEGs) encoding transcription factors, only Atf3, Verge, and Klf4 showed a rapid increased expression. TonEBP-mRNA tissue level and TonEBP-mRNA labeling in neurons remained unchanged whereas TonEBP labeling was rapidly increased in neurons. Sodium-dependent neutral amino acid transporter-2 (SNAT2) encoded by gene Slc38a2 showed a delayed increased expression. The rapid tonicity-induced activation of Atf3, Verge, and Klf4 may regulate genes involved in osmoadaptation. Nfat5 encoding TonEBP is not an IEG and the early tonicity-induced expression of TonEBP in neurons may result from translational activation. Increased expression of sodium-dependent neutral amino-acid transporter 2 may lead to the cellular accumulation of amino acids for adaptation to hypertonicity.