Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.7
3.4
Journals
Metabolites
Special Issues
Volatile Metabolites’ New Frontier for Metabolomics
share announcement

Volatile Metabolites’ New Frontier for Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (30 October 2020) | Viewed by 16901

Special Issue Editor

Dr. Agnieszka M. Smolinska

E-Mail Website
Guest Editor
1. Department Pharmacology and Toxicology, Maastricht University, 6229ER Maastricht, The Netherlands
2. NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, 6229ER Maastricht, The Netherlands
Interests: volatile metabolite; exhaled breath; fecal headspace analysis; machine learning; gut microbiome
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Volatile metabolites, i.e., volatile organic compounds (VOCs), are becoming new frontiers in the metabolomics field. With the development of new technologies, the field of VOCs has grown over the last few years. The application of VOCs in breath and feces but also in in vitro studies has demonstrated the valuable potential of volatile metabolites in understanding and discovering the biomedical field. Thus far, volatile metabolites have become an essential part of exploring the emerging biomedical and medical fields. This Special Issue of Metabolites on “Volatile Metabolites’ New Frontier for Metabolomics” will be committed to volatile metabolites analyzed in various biofluids such as breath, blood, urine, feces for monitoring or various diseases, treatment or diet in cell and bacteria culture, and animal and human subjects. The Special Issue will be covering various topics, including but not limited to biomedical/medical application of volatile metabolites, novel approaches for sampling and analyzing the volatile metabolites, machine learning approaches, and statistical modeling of volatile metabolites. Manuscripts dealing with other pertinent challenging issues are also highly desired.

Dr. Agnieszka Smolinska
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Volatile metabolites
  • Breath, feces, urine, blood, cell, and bacteria culture
  • Machine learning and data mining for volatile metabolites
  • Biomedical application of volatile metabolites
  • Biomarker discovery
  • Analytical approaches
  • Sensor analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1815 KiB  
Article
Optimizing Secondary Electrospray Ionization High-Resolution Mass Spectrometry (SESI-HRMS) for the Analysis of Volatile Fatty Acids from Gut Microbiome
by Jisun H. J. Lee and Jiangjiang Zhu
Metabolites 2020, 10(9), 351; https://doi.org/10.3390/metabo10090351 - 28 Aug 2020
Cited by 20 | Viewed by 3669
Abstract
Gut microbiota plays essential roles in maintaining gut homeostasis. The composition of gut microbes and their metabolites are altered in response to diet and remedial agents such as antibiotics. However, little is known about the effect of antibiotics on the gut microbiota and [...] Read more.
Gut microbiota plays essential roles in maintaining gut homeostasis. The composition of gut microbes and their metabolites are altered in response to diet and remedial agents such as antibiotics. However, little is known about the effect of antibiotics on the gut microbiota and their volatile metabolites. In this study, we evaluated the impact of a moderate level of ampicillin treatment on volatile fatty acids (VFAs) of gut microbial cultures using an optimized real-time secondary electrospray ionization coupled with high-resolution mass spectrometry (SESI-HRMS). To evaluate the ionization efficiency, different types of electrospray solvents and concentrations of formic acid as an additive (0.01, 0.05, and 0.1%, v/v) were tested using VFAs standard mixture (C2–C7). As a result, the maximum SESI-HRMS signals of all studied m/z values were observed from water with 0.01% formic acid than those from the aqueous methanolic solutions. Optimal temperatures of sample inlet and ion chamber were set at 130 °C and 85 °C, respectively. SESI spray pressure at 0.5 bar generated the maximum intensity than other tested values. The optimized SESI-HRMS was then used for the analysis of VFAs in gut microbial cultures. We detected that the significantly elevated C4 and C7 VFAs in the headspace of gut microbial cultures six hours after ampicillin treatment (1 mg/L). In conclusion, our results suggested that the optimized SESI-HRMS method can be suitable for the analysis of VFAs from gut microbes in a rapid, sensitive, and non-invasive manner. Full article
(This article belongs to the Special Issue Volatile Metabolites’ New Frontier for Metabolomics)
►▼ Show Figures

Figure 1

18 pages, 457 KiB  
Article
Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons
by Michalis Koureas, Paraskevi Kirgou, Grigoris Amoutzias, Christos Hadjichristodoulou, Konstantinos Gourgoulianis and Andreas Tsakalof
Metabolites 2020, 10(8), 317; https://doi.org/10.3390/metabo10080317 - 3 Aug 2020
Cited by 73 | Viewed by 6207
Abstract
The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological [...] Read more.
The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis. Full article
(This article belongs to the Special Issue Volatile Metabolites’ New Frontier for Metabolomics)
►▼ Show Figures

Figure 1

20 pages, 3293 KiB  
Article
Deciphering the Metabolic Pathways of Pitaya Peel after Postharvest Red Light Irradiation
by Qixian Wu, Huijun Gao, Zhengke Zhang, Taotao Li, Hongxia Qu, Yueming Jiang and Ze Yun
Metabolites 2020, 10(3), 108; https://doi.org/10.3390/metabo10030108 - 14 Mar 2020
Cited by 19 | Viewed by 3206
Abstract
Red light irradiation can effectively prolong the shelf-life of many fruit. However, little is known about red light-induced metabolite and enzyme activities. In this study, pitaya fruit was treated with 100 Lux red light for 24 h. Red light irradiation significantly attenuated the [...] Read more.
Red light irradiation can effectively prolong the shelf-life of many fruit. However, little is known about red light-induced metabolite and enzyme activities. In this study, pitaya fruit was treated with 100 Lux red light for 24 h. Red light irradiation significantly attenuated the variation trend of senescence traits, such as the decrease of total soluble solid (TSS) and TSS/acidity (titratable acidity, TA) ratio, the increase of TA, and respiratory rate. In addition, the reactive oxygen species (ROS) related characters, primary metabolites profiling, and volatile compounds profiling were determined. A total of 71 primary metabolites and 67 volatile compounds were detected and successfully identified by using gas chromatography mass spectrometry (GC-MS). Red light irradiation enhanced glycolysis, tricarboxylic acid (TCA) cycle, aldehydes metabolism, and antioxidant enzymes activities at early stage of postharvest storage, leading to the reduction of H2O2, soluble sugars, organic acids, and C-6 and C-7 aldehydes. At a later stage of postharvest storage, a larger number of resistance-related metabolites and enzyme activities were induced in red light-treated pitaya peel, such as superoxide dismutase (SOD), ascorbate peroxidase (APX), 1,1-diphenyl-2-picryl-hydrazyl (DPPH) radical-scavenging, reducing power, fatty acids, and volatile aroma. Full article
(This article belongs to the Special Issue Volatile Metabolites’ New Frontier for Metabolomics)
►▼ Show Figures

Figure 1

10 pages, 1119 KiB  
Article
Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
by Thijs T. Wingelaar, Paul Brinkman, Rianne de Vries, Pieter-Jan A.M. van Ooij, Rigo Hoencamp, Anke-Hilse Maitland-van der Zee, Markus W. Hollmann and Rob A. van Hulst
Metabolites 2019, 9(12), 286; https://doi.org/10.3390/metabo9120286 - 22 Nov 2019
Cited by 5 | Viewed by 3135
Abstract
Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be [...] Read more.
Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS. Full article
(This article belongs to the Special Issue Volatile Metabolites’ New Frontier for Metabolomics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop