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A potential pathway leading to ketones production by HepG2 cells involves alcohol dehydrogenases ADHs. ADHs are very abundant in liver and play a major role in hepatic ethanol metabolism [ 22 , 23 , 25 , 46 ]. They are also capable of metabolizing longer-chain and cyclic alcohols, however, primary alcohols seem to be their preferred substrates [ 22 , 23 ]. Although secondary alcohols were shown to be rather poor substrates for ADHs [ 23 ], catalysis of ketones production from secondary alcohols has been evidenced in the literature e.

Moreover, the total alcohol dehydrogenases activity is significantly higher in liver cancer tissues than in healthy ones, significantly exceeding the activity of ALDHs [ 45 , 48 ]. Thus, all observed ketones can originate from the respective secondary alcohols. The source of the secondary alcohols remains unclear. Perhaps the applied medium contained small amounts of long-chain secondary alcohols. For example, 3-heptanone was found to be a product of valproic acid metabolism [ 3 ] and 4-heptanone was shown to originate from 2-ethylhexanoic acid [ 49 ]. The potential substrates for this metabolic pathway could in turn be metabolites of the respective branched-chain primary alcohols e.

However, it is not clear if these substrates were present in the applied medium. The second most dominant chemical class amongst the released species were volatile sulphur compounds VSCs with DMS as the most abundant analyte. The production of VSCs in humans is ascribed to the metabolization of the sulfur-containing amino acids methionine and cysteine in the transamination pathway [ 50 ]. In liver thiol S-methyltransferase forms methyl thioethers via the methylation of thiols [ 50 — 52 ].

For instance, DMS is formed via the methylation of methyl mercaptane [ 50 ]. Whereas, human lung cells were reported to release some ketones e. This difference could be ascribed to the expression of liver-specific enzymes. Amongst hydrocarbons HCs , only 2-heptene was found to be produced by HepG2. This finding clearly distinguishes HepG2 cells from lung cancer cells liberating numerous unsaturated and branched hydrocarbons [ 12 , 15 ]. Nevertheless, this chemical class is of particular interest as some HCs have been proposed as non-invasive markers of numerous diseases in the human organism [ 6 — 8 ] and their origin is still not clear.

Interestingly, n-propyl acetate found to be emitted by HepG2 is also released by normal lung cells but not by cancer ones [ 12 ]. The present study aimed at the identification and quantification of volatile organic compounds emitted or metabolized by HepG2 hepatocellular carcinoma cells.

For this purpose gas chromatography with mass spectrometric detection coupled with head-space needle trap extraction HS-NTD as pre-concentration technique was applied. Nine species were found to be consumed and further 12 released by HepG2 cells. The emission and uptake of the aforementioned species may be explained by the activity of enzymes that are particularly abundant in human liver and additionally highly expressed in cancer cells. Thus, aldehydes were probably oxidized by aldehyde dehydrogenases to carboxylic acids, ketones were presumably the products of branched, or secondary alcohols metabolism and thioethers release could be an expression of thiol S-methyltransferase activity.

Several limitation of the study can be indicated. Firstly, the study involved transformed hepatocytes, which may exhibit an altered metabolism as compared to the normal ones [ 26 , 45 ]. Next, no additional liver-resident cells were included in the present study e.

Thus, their contribution to the production and metabolism of VOCs in the liver remains to be established. Moreover, in vivo metabolic pathways may be regulated by numerous factors e. The initial background levels of VOCs were not strictly equal during cultivations.

This results from the fact, that before each experiment fresh medium was prepared and purged, frequently from components originating from different production lots. These initial VOC levels could also be affected by small fluctuations of purging conditions e. Consequently, the production and consumption rates of VOCs under study did not depend exclusively on the number of cells and their metabolism. In summary, the analysis of volatile organic compounds has the potential to identify and monitor enzyme activities.

This feature may be helpful for the detection and analysis of cancers, which may carry mutations in metabolic enzymes [ 26 , 45 , 53 ]. Gaseous multi-compound calibration mixtures were prepared from pure liquid substances. Gaseous calibration mixtures were produced by means of a GasLab calibration mixtures generator Breitfuss Messtechnik, Germany.

However, for the goals of this study, pure substances were additionally diluted — with distilled water prior to evaporation in order to reduce the resulting concentration levels. The calibration mixtures were sampled and analyzed using identical conditions as in the case of head-space measurements of cell cultures and blanks i. The epithelial hepatocellular carcinoma cell line HepG2 was used during the in vitro experiments. The cells were grown in Dulbecco's Modified Eagle high glucose 4.

Each Teflon plug was equipped with a rubber septum enabling the insertion of the needle trap devices into the headspace of the bottle and the Teflon tube being the inlet of the zero air stream. To provide proper mixing of the headspace during sampling the inner end of the Teflon tube protruded 15—17 cm from the plug into the headspace volume, whereas the outer end was equipped with a sterile filter.

The bottom area of the bottles approximately cm 2 was coated with Poly-Lysin Sigma Aldrich, USA and the caps were slightly loosened to allow ventilation during proliferation. Such a treatment reduced the medium VOCs abundances approximately by a factor of 5—8. One day after subcultivation the culture media were changed and the bottles were sealed to facilitate the accumulation of species released by the cells and to avoid contamination by the ambient atmosphere. The analyses of the headspace were performed after 24h. Cell viability counts trypan blue exclusion method were performed immediately after the GC-MS analyses.

In total 6 cultivation experiments have been performed. In addition to the experiments involving cells cultures, blank control experiments were performed in parallel. These blank experiments followed the same protocol as mentioned above, however, without the addition of cells into the measurement bottles. An effort was made to always use the same flushed medium in blanks and corresponding cell cultures. The reproducibility of such a protocol was checked by a comparison of head-space levels of compounds under study consumed ones in five cultivation bottles containing the same medium after 24 hours of simulated cultivation.

Volatile compounds were pre-concentrated manually using three-bed side-hole gauge stainless steel needle trap devices NTD PAS Technology, Germany [ 54 — 56 ]. Their re-conditioning was performed directly before sampling at the same temperature, however, with shorter flushing times of 10 minutes. GmbH, Austria. Consequently, no transfer line had to be installed between the headspace sample and the needle trap. To maintain a constant pressure during sampling high purity zero air was continuously introduced into the bottle at a flow equal to the sampling flow.

The identification of compounds was performed in two steps. At first, the peak spectrum was checked against the NIST mass spectral library. Next, the NIST identification was confirmed by comparing the respective retention times with retention times obtained on the basis of standard mixtures prepared from pure compounds. Peak integration was based on extracted ion chromatograms. Int J Mass Spectrometry. Amann A, Smith D: Breath analysis for clinical diagnosis and therapeutic monitoring.

J Breath Res. Clin Chim Acta. Edited by: Amann A. BMC Cancer. Respir Res. Int J Mass Spectrom. Kanoh S, Kobayashi H, Motoyoshi K: Exhaled ethane: an in vivo biomarker of lipid peroxidation in interstitial lung diseases. Clin Chem Lab Med. Cancer Epidemiol. Biomarkers Prev. Cancer Cell Int. Cancer Biomark. Anticancer Res. BMC Microbiol. Cell Biol Toxicol.

Huber W: Basic calculations about the limit of detection and its optimal determination. Accred Qual Assur. Crabb DW, Matsumoto M, Chang D, You M: Overview of the role of alcohol dehydrogenase and aldehyde dehydrogenase and their variants in the genesis of alcohol-related pathology.

Proc Nutr Soc. Klyosov AA: Kinetics and specificity of human liver aldehyde dehydrogenases toward aliphatic, aromatic, and fused polycyclic aldehydes. Molecular cancer research : MCR. Imai T: Human carboxylesterase isozymes: catalytic properties and rational drug design. Drug Metab Pharmacokinet. Chem Biol Interact. Bogaards JJ, Freidig AP, van Bladeren PJ: Prediction of isoprene diepoxide levels in vivo in mouse, rat and man using enzyme kinetic data in vitro and physiologically-based pharmacokinetic modelling. Clin Chem. Sharkey TD: Isoprene synthesis by plants and animals.

Silver GM, Fall R: Enzymatic synthesis of isoprene from dimethylallyl diphosphate in aspen leaf extracts. Plant Physiol. Eur J Heart Fail. Biochem Biophys Res Commun. Physiol Meas.


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J Theor Biol. Cytometry B Clin Cytom. Active-site amino acid sequence explains substrate specificity compared with liver isozymes. J Biol Chem. J Clin Lab Anal. Walker V, Mills GA: Urine 4-heptanone: a beta-oxidation product of 2-ethylhexanoic acid from plasticisers. Tangerman A: Measurement and biological significance of the volatile sulfur compounds hydrogen sulfide, methanethiol and dimethyl sulfide in various biological matrices. Peak at mass of , which corresponds to the C 8 H 8 structure. Peaks at mass 78, 63, 51 and 40 very likely correspond to C 6 H 6, C 5 H3, C 4 H 3 , and C 3 H 3 respectively; these are all common fragmentations seen to come from cyclooctatetraene.

Peak at mass 44 is a common base peak for many organic compounds, including cyclooctatetraene. Our pilot study provides initial evidence that FAIMS has potential application as an alternative non-invasive test for the initial screening of patients suspected of having coeliac disease.

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This is done via the detection of a unique gas phase bio-odorant fingerprint found in the urine of patients with coeliac disease. IBS tends to be diagnosed in patients with diarrhoea, constipation or abdominal discomfort for which no underlying cause can be ascertained. Therefore, instead of a distinct VOC profile, there is likely to be large patient-to-patient variation, and this is reflected in the data found here. Additionally, data given by the GC-MS has revealed a peak unique for those with coeliac disease — specifically mass spectra that indicate it is likely due to the volatile compound Cyclooctatetraene.

Previous studies have shown production of this compound by various species of fungi for its inhibitory effect on the growth of other microbes [26] , [27]. There have also been a number of studies into volatiles produced from stool samples [28] , without being linked to any particular disease. This could indicate a potential role for these technologies in the monitoring of compliance with a gluten free diet in coeliac patients as currently tTG antibodies have shown inconsistent results when used for this purpose [7] , [8]. Analysis of the VOCs in urine could in the future represent a more effective and real time means of monitoring compliance by patients at home with a portable device or specialised mobile phone application.

The unique chemical fingerprint produced by the different disease states shows the potential of this technology as an initial alternative screening test for coeliac disease. Furthermore it has the potential to aid in the further investigation of individuals with other GI disease in whom the diagnosis is not clear. VOCs are believed to be produced by colonic fermentation: the result of a complex interaction between the colonocyte cells, human faecal flora, mucosal integrity and invading pathogens [19].

These thereafter pass into bodily fluids and as a result, VOCs found in urine, faeces and breath have huge potential as biomarkers to aid in the assessment of gastrointestinal diseases. Any changes found in the pattern of VOCs are reflective of changes and variations within the gastrointestinal environment. This suggests a possible role for gut microflora dysbiosis in the pathophysiology of coeliac disease which has been found in several studies including paediatric coeliac disease [29] , [30] , [31] , [32].

In addition, identification of this chemical was made via the NIST library by forward and reverse matching scores between documented spectra and those found in the sample set. However, further validation of the presence of this chemical is required using appropriate standards. Moreover, it is likely that there are additional bio-markers and we will be able to identify global changes in the total chemical profile.

Future work will attempt to validate the chemicals identified here and to undertake a more thorough characterisation of the urinary headspace. Its advantages include portability, rapid real time and cost effective diagnostic approach. Further validation studies are necessary to confirm its accuracy as well as ability to distinguish between inflammatory and non-inflammatory conditions. Recruitment of patients and specimen collections: NO CB. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.

Materials and Methods 2. Download: PPT. We use the following machine learning classification algorithms [25] : Sparse logistic regression: A version of logistic regression that imposes feature sparsity via an elasticnet prior. This has the effect of removing uninformative features from the analysis, thereby improving the quality of the analysis.

Random Forest classification: An ensemble of decision trees, which leads to highly flexible data modelling. Support Vector Machine: A kernel-based method for separating the data space into separate disease subspaces. Table 2. Table 3. Figure 1.

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Figure 3. Scatter plot of TTG serology vs classification probability for Coeliac cases. Table 5. Predicted probability of coeliac disease and Marsh scores at diagnosis. GC-MS The data from the GC-MS were analysed by observing the retention times of chromatogram peaks, and comparing the corresponding mass spectra found by the instrument to those from a known NIST library of chemical components. Figure 5. Section of coeliac disease sample chromatogram showing unique peak. Figure 6. Section of mass spectrum corresponding to unique GC peaks. Discussion Our pilot study provides initial evidence that FAIMS has potential application as an alternative non-invasive test for the initial screening of patients suspected of having coeliac disease.

Acknowledgments The authors would like to thank everyone that contributed towards this project. References 1. World J Gastroenterol. View Article Google Scholar 3. Am J Gastroenterol. View Article Google Scholar 4. Postgrad Med J. View Article Google Scholar 5. Eur J Gastroenterol Hepatol. View Article Google Scholar 6. A prospective, biopsy-confirmed study with economic analysis. Clin Gastroenterol Hepatol. View Article Google Scholar 7.

View Article Google Scholar 8. Dahle C, Hagman A, Ignatova S, Strom M Antibodies against deamidated gliadin peptides identify adult coeliac disease patients negative for antibodies against endomysium and tissue transglutaminase. Aliment Pharmacol Ther. View Article Google Scholar 9. Clin Chem.

Volatile Biomarkers

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