Gas Chromatography Mass Spectrometry (GC-MS) Quantification of Metabolites in Stool Using 13C Labelled Compounds

Distinguish Esophageal Cancer Cells through VOCs Induced by Methionine Regulation

Detection of exhaled volatile organic compounds (VOCs) is promising for noninvasive screening of esophageal cancer (EC). Cellular VOC analysis can be used to investigate potential biomarkers. Considering the crucial role of methionine (Met) during cancer development, exploring associated abnormal metabolic phenotypes becomes imperative. In this work, we employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to investigate the volatile metabolic profiles of EC cells (KYSE150) and normal esophageal epithelial cells (HEECs) under a Met regulation strategy. Using untargeted approaches, we analyzed the metabolic VOCs of the two cell types and explored the differential VOCs between them. Subsequently, we utilized targeted approaches to analyze the differential VOCs in both cell types under gradient Met culture conditions. The results revealed that there were five/six differential VOCs between cells under Met-containing/Met-free culture conditions. And the difference in levels of two characteristic VOCs (1-butanol and ethyl 2-methylbutyrate) between the two cell types intensified with the increase of the Met concentration. Notably, this is the first report on VOC analysis of EC cells and the first to consider the effect of Met on volatile metabolic profiles. The present work indicates that EC cells can be distinguished through VOCs induced by Met regulation, which holds promise for providing novel insights into diagnostic strategies.

A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2-putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors' intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.

The role of gut microbiota (GM) and GM-related metabolites in diabetes and obesity. A review of analytical methods used to measure GM-related metabolites in fecal samples with a focus on metabolites' derivatization step

Disruption of gut microbiota (GM) composition is increasingly related to the pathogenesis of various metabolic diseases. Additionally, GM is responsible for the production and transformation of metabolites involved in the development of metabolic disorders, such as obesity and type 2 diabetes mellitus (T2DM). The current state of knowledge regarding the composition of GM and GM-related metabolites in relation to the progress and development of obesity and T2DM is presented in this review. To understand the relationships between GM-related metabolites and the development of metabolic disorders, their accurate qualitative and quantitative measurement in biological samples is needed. Feces represent a valuable biological matrix which composition may reflect the health status of the lower gastrointestinal tract and the whole organism. Mass spectrometry (MS), mainly in combination with gas chromatography (GC) or liquid chromatography (LC), is commonly used to measure fecal metabolites. However, profiling metabolites in such a complex matrix as feces is challenging from both analytical chemistry and biochemistry standpoints. Chemical derivatization is one of the most effective methods used to overcome these problems. In this review, we provide a comprehensive summary of the derivatization methods of GM-related metabolites prior to GC-MS or LC-MS analysis, which have been published in the last five years (2015-2020). Additionally, analytical methods used for the analysis of GM-related metabolites without the derivatization step are also presented.

Assessment of gut microbiota fecal metabolites by chromatographic targeted approaches

Gut microbiota, the specific microbial community of the gastrointestinal tract, by means of the production of microbial metabolites provides the host with several functions affecting metabolic and immunological homeostasis. Insights into the intricate relationships between gut microbiota and the host require not only the understanding of its structure and function but also the measurement of effector molecules acting along the gut microbiota axis. This article reviews the literature on targeted chromatographic approaches in analysis of gut microbiota specific metabolites in feces as the most accessible biological matrix which can directly probe the connection between intestinal bacteria and the (patho)physiology of the holobiont. Together with a discussion on sample collection and preparation, the chromatographic methods targeted to determination of some classes of microbiota-derived metabolites (e.g., short-chain fatty acids, bile acids, low molecular masses amines and polyamines, vitamins, neurotransmitters and related compounds) are discussed and their main characteristics, summarized in Tables.

Sniffing out resistance - Rapid identification of urinary tract infection-causing bacteria and their antibiotic susceptibility using volatile metabolite profiles

Antibiotic resistance is set to be an unprecedented threat to modern medicine. 'Sniffing' bacteria potentially offers a rapid way to determine susceptibility. A successful proof-of-principle study is described, using thermal desorption-gas chromatography-mass spectrometry (TDGCMS) to 'smell' cephalexin and ciprofloxacin resistant and sensitive Urinary Tract Infection (UTI)-causing bacteria. 578 peaks at unique retention times were detected from 86 chromatograms of 18 bacterial isolates (E. coli, K. pneumoniae and P. aeruginosa). The isolates were grown with and without the presence of antibiotic. Chi-square analysis found 9 compounds that differed significantly between cephalexin sensitive and resistant isolates, and 22 compounds that differed significantly between ciprofloxacin sensitive and resistant isolates, at p ≤ 0.05. When antibiotic was added to the media, more differences were found in the cephalexin group, attributed to lysis, but not in the ciprofloxacin group. Further work with large sample sizes will potentially enable the development of diagnostic algorithms using presence/absence of particular compounds of interest.