Exposure to genotoxic compounds alters in vitro cellular VOC excretion.

Genotoxic carcinogens significantly damage cells and tissues by targeting macromolecules such as proteins and DNA, but their mechanisms of action and effects on human health are diverse. Consequently, determining the amount of exposure to a carcinogen and its cellular effects is essential, yet difficult. The aim of this manuscript was to investigate the potential of detecting alterations in volatile organic compounds (VOCs) profiles in the in vitro headspace of pulmonary cells after exposure to the genotoxic carcinogens cisplatin and benzo[a]pyrene using two different sampling set-ups. A prototype set-up was used for the cisplatin exposure, whereas a modified set-up was utilized for the benzo[a]pyrene exposure. Both carcinogens were added to the cell medium for 24 h. The headspace in the culture flask was sampled to measure the VOC content using gas chromatography-time-of-flight-mass spectrometry. Eight cisplatin-specific VOCs and six benzo[a]pyrene-specific VOCs were discriminatory between treated and non-treated cells. Since the in vivo biological effects of both genotoxic compounds are well-defined, the origin of the identified VOCs could potentially be traced back to common cellular processes including cell cycle pathways, DNA damage and repair. These results indicate that exposing lung cells to genotoxins alters headspace VOC profiles, suggesting that it might be possible to monitor VOC changes in vivo to study drug efficacy or exposure to different pollutants. In conclusion, this study emphasizes the innovative potential of in vitro VOCs experiments to determine their in vivo applicability and discover their endogenous origin.

Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography-mass spectrometry.

The identification of specific volatile organic compounds (VOCs) produced by microorganisms may assist in developing a fast and accurate methodology for the determination of pulmonary bacterial infections in exhaled air. As a first step, pulmonary bacteria were cultured and their headspace analyzed for the total amount of excreted VOCs to select those compounds which are exclusively associated with specific microorganisms. Development of a rapid, noninvasive methodology for identification of bacterial species may improve diagnostics and antibiotic therapy, ultimately leading to controlling the antibiotic resistance problem. Two hundred bacterial headspace samples from four different microorganisms (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumoniae) were analyzed by gas chromatography-mass spectrometry to detect a wide array of VOCs. Statistical analysis of these volatiles enabled the characterization of specific VOC profiles indicative for each microorganism. Differences in VOC abundance between the bacterial types were determined using ANalysis of VAriance-principal component analysis (ANOVA-PCA). These differences were visualized with PCA. Cross validation was applied to validate the results. We identified a large number of different compounds in the various headspaces, thus demonstrating a highly significant difference in VOC occurrence of bacterial cultures compared to the medium and between the cultures themselves. Additionally, a separation between a methicillin-resistant and a methicillin-sensitive isolate of S. aureus could be made due to significant differences between compounds. ANOVA-PCA analysis showed that 25 VOCs were differently profiled across the various microorganisms, whereas a PCA score plot enabled the visualization of these clear differences between the bacterial types. We demonstrated that identification of the studied microorganisms, including an antibiotic susceptible and resistant S. aureus substrain, is possible based on a selected number of compounds measured in the headspace of these cultures. These in vitro results may translate into a breath analysis approach that has the potential to be used as a diagnostic tool in medical microbiology.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis.

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

Pathways for ligand activated nuclear receptors to unravel the genomic responses induced by hepatotoxicants.

The liver is a vital organ in vertebrates that can be subject to disease, among others due to exposure to toxic xenobiotic compounds. A group of transcription factors named ligand activated nuclear receptors (LANR) influence and regulate important liver functions, and can be activated by many xenobiotic compounds, which thereby can cause hepatotoxicity. Systematic analysis of the gene pathways regulated by LANR using modern 'omics technologies is important for investigating modes-of-action of hepatotoxicants. So far, these pathways are not publicly available in a format that allows these studies. We used PathVisio to build liver-specific LANR pathways, both for rats and humans. Since many LANR pathways are linked to each other, we also merged them into a meta-pathway. The pathways are in a GPML-format that enables pathway statistics and visualisations, and will be made available to the public through WikiPathways. We demonstrate the performance of these novel pathways in evaluating transcriptomic studies from the Japanese toxicogenomics project database (Open TG-GATEs). We show that the new pathways can be used to accurately analyse and visualize the effects of prototypical hepatotoxicants in important liver processes, and thus to evaluate the possible mode-of-actions of hepatotoxic xenobiotic compounds by assessing which LANRs are possible targets.