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Biotransformation of Monoterpenoids by Microorganisms, Insects, and Mammals
Published in K. Hüsnü Can Başer, Gerhard Buchbauer, Handbook of Essential Oils, 2020
Yoshiaki Noma, Yoshinori Asakawa
The most important metabolites from geraniol (271), nerol (272), and citronellol (258) are summarized in Figure 22.9. In the same year, the biotransformation of these monoterpenes by B. cinerea in model solutions was described by another group (Rapp and Mandery, 1988). Although the major metabolites found were ω-hydroxylation compounds, it is important to note that some new compounds that were not described by the previous group were detected (Figure 22.9). Geraniol (271) was mainly transformed to (2E,5E)-3,7-dimethyl-2,5-octadiene-1,7-diol (318), (E)-3,7-dimethyl-2,7-octadiene-1,6-diol (319), and (2E,6E)-2,6-dimethyl-2,6-octadiene-1,8-diol (300) and nerol (272) to (2Z,5E)-3,7-dimethyl-2,5-octadiene-1,7-diol (314), (Z)-3,7-dimethyl-2,7-octadiene-1,6-diol (315), and (2E,6Z)-2,6-dimethyl-2,6-octadiene-1,8-diol (316). Furthermore, a cyclization product (318) that was not previously described was formed. Finally, citronellol (258) was converted to trans- (312) and cis-rose oxide (313) (a cyclization product not identified by the other group), (E)-3,7-dimethyl-5-octene-1,7-diol (311), 3,7-dimethyl-7-octene-1,6-diol (260), and (E)-2,6-dimethyl-2-octene-1,8-diol (265) (Miyazawa et al., 1996a) (Figure 22.10).
Breathomics and its Application for Disease Diagnosis: A Review of Analytical Techniques and Approaches
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
David J. Beale, Oliver A. H. Jones, Avinash V. Karpe, Ding Y. Oh, Iain R. White, Konstantinos A. Kouremenos, Enzo A. Palombo
Zhao et al. (2014) investigated the application of metabolomics as a diagnostic tool in human lung tissue samples collected from patients diagnosed with PAH and age-matched controls. Using a combination of LC-MS and GC-MS, it was identified that patients diagnosed with PAH showed unbiased metabolomic profiles of disrupted glycolysis, increased tricarboxylic acid (TCA) cycle, and fatty acid metabolites with altered oxidation pathways; indicating increased adenosine triphosphate (ATP) synthesis. It was concluded that these biomarkers could be used for the diagnosis of PAH, however, collecting lung tissue samples is considered invasive. An alternative approach would be to analyze the exhaled breath of patients diagnosed with PAH. Such an approach was undertaken by Cikach et al. (2014), where fasting state breath samples were collected and analyzed by SIFT-MS. It was found that the concentrations of the exhaled ammonia, 2-propanol, acetaldehyde, ammonia, ethanol, pentane, 1-decene, 1-octene, and 2-nonene were significantly different in patients with PAH compared to the control cohort (Cikach et al., 2014), with the concentration of compounds correlating with the severity of PAH. This suggests that differences in the breath metabolic profile can potentially be used to diagnose and classify the severity of PAH. Furthermore, with such observed differences, there is potential for the development of rapid diagnostic breath analyzers that could be used to monitor PAH progression. Such metabolomic approaches and tool development are also being applied to other respiratory diseases, such as acute respiratory distress syndrome (ARDS) (Bos et al., 2014; Stringer et al., 2016), COPD (Santini et al., 2016), lung cancer (Peralbo-Molina et al., 2016), or diseases with a clinically relevant respiratory component including cystic fibrosis (Montuschi et al., 2012; Muhlebach and Sha, 2015) and primary ciliary dyskinesia (Montuschi et al., 2014; Paris et al., 2015).
Cancer breath testing: a patent review
Published in Expert Opinion on Therapeutic Patents, 2018
K. M. Mohibul Kabir, William A. Donald
In US 9528979 B2 [20], by use of GC-MS for the analysis of breath samples from 28 lung cancer patients (25 non-small cell lung cancer and 3 small cell lung cancer) and 11 subjects with benign nodules, a relatively high concentration of 1-octene was correlated with lung cancer [48]. The advantage of finding a small number of biomarkers that have significantly different concentrations in healthy and cancer subjects is that biomarker-specific sensors can be developed (e.g. a QCM sensor with an analyte-sensitive layer that is selective to 1-octene) to reduce the true/false positive rates in cancer screening. Interestingly, the use of nanosensor arrays (described in Section 3) to analyze the VOCs from 53 subjects with malignant lung cancer and 19 subjects with benign nodules resulted in the identification of cancer with a sensitivity and specificity of 86 ± 4% and 96 ± 4%, respectively [48]. Nanosensor array analysis also resulted in the discrimination between 47 small cell and 6 non-small cell lung cancer patients with a sensitivity of 75 ± 4% and specificity of 97 ± 1%. Significantly, subjects in the early (n = 30) and advanced (n = 23) stages of non-small cell lung cancer can be distinguished from each other with a sensitivity and specificity of 86 ± 3% and 88 ± 6%, respectively. These results indicate that the nanosensor array analysis after a low-dose CT scan can avoid delay in cancer treatment because of the high sensitivity and specificity of the method.
The potential of volatile organic compound analysis for pathogen detection and disease monitoring in patients with cystic fibrosis
Published in Expert Review of Respiratory Medicine, 2022
Anton Barucha, Renan M. Mauch, Franziska Duckstein, Carlos Zagoya, Jochen G. Mainz
Robroeks et al. assessed the difference in the VOC pattern in breath samples between children with CF (n = 48) and non-CF healthy controls (n = 57). With a combination of 22 discriminatory VOCs, they could discriminate between pwCF and controls with 100% sensitivity and 100% specificity. Ten most discriminatory VOCs were identified, namely 3,3-dimethylhex-1-ene, 2-buten-1-ol, N-methyl-2-methylpropylamine, C8H16 hydrocarbon (2-octene, 3-octene), Tolualdehyde (o-, m-, or p- isomers), C16 poly-unsaturated hydrocarbon, C12 saturated hydrocarbon, C13 saturated hydrocarbon, Benzothiazole, and long chain alkylbenzene [24].