Special Issue "Volatile Metabolites’ New Frontier for Metabolomics"

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

Deadline for manuscript submissions: 30 October 2020.

Special Issue Editor

Dr. Agnieszka Smolinska
Website
Guest Editor
Department of Pharmacology & Toxicology, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, the Netherlands
Interests: volatile metabolite; exhaled breath; fecal headspace analysis; machine learning; gut microbiome

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 papers will be 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 1600 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

Published Papers (3 papers)

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Research

Open AccessArticle
Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons
Metabolites 2020, 10(8), 317; https://doi.org/10.3390/metabo10080317 - 03 Aug 2020
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)
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Open AccessArticle
Deciphering the Metabolic Pathways of Pitaya Peel after Postharvest Red Light Irradiation
Metabolites 2020, 10(3), 108; https://doi.org/10.3390/metabo10030108 - 14 Mar 2020
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)
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Open AccessArticle
Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
Metabolites 2019, 9(12), 286; https://doi.org/10.3390/metabo9120286 - 22 Nov 2019
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)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Application of GC × GC TOF MS for profiling of volatiles from human urine samples in screening for kidney diseases

 

Tomasz Ligor, Boguslaw Buszewski at al.

 

Abstract: Comprehensive two-dimensional gas chromatography time of  flight mass spectrometry (GC × GC TOF MS) and solid phase  microextraction (SPME) was applied to the analysis of urinary  volatiles from persons with chronic glomerular disease and healthy  control. Different VOC profiles were obtained from patients and control. Over 100 compounds were observed in each sample. The  automatically identified substances were manually verified in order to remove artefacts  contaminants, silicones, column bleeding etc.)  and exogenous compounds. The peak areas of identified substances were  used to build the data matrix for chemometric analyses. Six compounds  were found in elevated amounts in the patients group, i.e.  1-phenylethanol, 3,5,5-trimethyl-3-cyclohexen-1-one, methyl  hexadecanoate, 9-octadecen-1-ol, phenol and  6,10-dimethyl-5,9-undecadien-2-one. The level of these compounds  ranged from 1.7 times higher (methyl hexadecanoate) to 14 times  higher (9-octadecen-1-ol) in the urine of patients than in the control.

 Key words: urine analysis, volatile organic compounds, two dimensional comprehensive gas chromatography, mass spectrometry.

 

 

Title: Untargeted Volatile Molecular Profiling of Human Breast Milk using  HS-SPME-GC×GC-TOFMS for the Detection of Novel Metabolites

 

Lili Kang, Jane Hill et al.

 

Abstract

Volatile compounds present in human breast milk are thought to play an important role in maternal-infant bonding, however, the compound(s) responsible for this apparent “scent signal” have not yet been elucidated. Untargeted metabolomic profiling using highly sensitive analytical instrumentation could aid in the identification of these compounds, with potential implications for infant health and nutritional status.

To characterize the volatile compounds present in human milk using headspace solid-phase microextraction (HS-SPME) coupled to comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS).

Human milk was collected from 43 mothers at their six-week postpartum appointments, and the volatile molecules present in the headspace of these milk samples were concentrated using HS-SPME and analyzed via comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry GC×GC-TOFMS.

506 unique volatile compounds were detected in the headspace of human milk samples, of which 188 (37 %) were common to all mothers, while the remaining 318 were detected in only a subset of samples. One hundred and forty-eight (29 %) compounds could be assigned putative identifications based on mass spectral matching, of which 97 have not previously been detected in human milk.

The use of HS-SPME-GC×GC-TOFMS for the untargeted analysis of volatile compounds in human milk reveals a greater number and wider variety of chemical compounds than previously reported using other analytical techniques, and demonstrates considerable mother-to-mother variability. While the precise identities of the volatile compounds responsible for maternal-infant signaling remain undetermined, the novel compounds reported in this study represent potential candidates.

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