Metabolites — Open Access Journal

Assessment and treatment of postoperative pain can be challenging as objective examination techniques to detect and quantify pain are lacking. We aimed to investigate changes of exhaled volatile organic compounds (VOCs) in patients with postoperative pain before and after treatment with opioid analgesics. In an observational study in 20 postoperative patients, we monitored for postoperative pain, hemodynamic parameters, and catecholamines before and during treatment. VOCs in the patients were determined by direct real-time proton transfer reaction time-of-flight mass spectrometry prior (0 min) and after piritramide application (15 min as well as 30 min). Cardiovascular variables changed and norepinephrine levels decreased during treatment. The VOCs acetonitrile (<0.001), acetaldehyde (p = 0.002), benzopyran (p = 0.004), benzene (p < 0.001), hexenal (p = < 0.001), 1-butanethiol (p = 0.004), methanethiol (p < 0.001), ethanol (p = 0.003), and propanol (p = < 0.001) changed significantly over time. Patients with Numeric Rating Scale (NRS) < 4 showed a significantly lower concentration of hexenal compared to patients with NRS > 4 at the time points 15 min (45.0 vs. 385.3 ncps, p = 0.047) and 30 min (38.3 vs. 334.6 ncps, p = 0.039). Breath analysis can provide additional information for noninvasive monitoring for analgesic treatment in postoperative patients. Full article

Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs. Full article

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data. Full article

The detection of salivary molecules associated with pathological and physiological alterations has encouraged the search of novel and non-invasive diagnostic biomarkers for oral health evaluation. While genomic, transcriptomic, and proteomic profiles of human saliva have been reported, its metabolic composition is a topic of research: metabolites in submandibular/sublingual saliva have never been analyzed systematically. In this study, samples of whole, parotid, and submandibular/sublingual saliva from 20 healthy donors, without dental or periodontal diseases, were examined by nuclear magnetic resonance. We identified metabolites which are differently distributed within the three saliva subtypes (54 in whole, 49 in parotid, and 36 in submandibular/sublingual saliva). Principal component analysis revealed a distinct cluster for whole saliva and a partial overlap for parotid and submandibular/sublingual metabolites. We found exclusive metabolites for each subtype: 2-hydroxy-3-methylvalerate, 3-methyl-glutarate, 3-phenylpropionate, 4-hydroxyphenylacetate, 4-hydroxyphenyllactate, galactose, and isocaproate in whole saliva; caprylate and glycolate in submandibular/sublingual saliva; arginine in parotid saliva. Salivary metabolites were classified into standard and non-proteinogenic amino acids and amines; simple carbohydrates; organic acids; bacterial-derived metabolites. The identification of a salivary gland-specific metabolic composition in healthy people provides the basis to invigorate the search for salivary biomarkers associated with oral and systemic diseases. Full article

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

Early growth is associated with future metabolic risk; however, little is known of the underlying biological pathways. In this prospective study of 249 boys and 227 girls, we sought to identify sex-specific metabolite profiles that mark the relationship between age and magnitude of the infancy body mass index (BMI) peak, and the childhood BMI rebound with a metabolic syndrome z-score (MetS z-score) during early adolescence (median age 12.8 years). Thirteen consensus metabolite networks were generated between male and female adolescents using weighted correlation network analysis. In girls, none of the networks were related to BMI milestones after false discovery rate (FDR) correction at 5%. In boys, age and/or magnitude of BMI at rebound were associated with three metabolite eigenvector (ME) networks comprising androgen hormones (ME7), lysophospholipids (ME8), and diacylglycerols (ME11) after FDR correction. These networks were also associated with MetS z-score in boys after accounting for age and race/ethnicity: ME7 (1.43 [95% CI: 0.52, 2.34] units higher MetS z-score per 1 unit of ME7), ME8 (−1.01 [95% CI: −1.96, −0.07]), and ME11 (2.88 [95% CI: 2.06, 3.70]). These findings suggest that alterations in sex steroid hormone and lipid metabolism are involved in the relationship of early growth with future metabolic risk in males. Full article

Alterations in the human metabolome occur years before clinical manifestation of type 2 diabetes (T2DM). By contrast, there is little knowledge of how metabolite alterations in individuals with diabetes relate to risk of diabetes complications and premature mortality. Metabolite profiling was performed using liquid chromatography-mass spectrometry in 743 participants with T2DM from the population-based prospective cohorts The Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC) and The Malmö Preventive Project (MPP). During follow-up, a total of 175 new-onset cases of cardiovascular disease (CVD) and 298 deaths occurred. Cox regressions were used to relate baseline levels of plasma metabolites to incident CVD and all-cause mortality. A total of 11 metabolites were significantly (false discovery rate (fdr) <0.05) associated with all-cause mortality. Acisoga, acylcarnitine C10:3, dimethylguanidino valerate, homocitrulline, N2,N2-dimethylguanosine, 1-methyladenosine and urobilin were associated with an increased risk, while hippurate, lysine, threonine and tryptophan were associated with a decreased risk. Ten out of 11 metabolites remained significantly associated after adjustments for cardiometabolic risk factors. The associations between metabolite levels and incident CVD were not as strong as for all-cause mortality, although 11 metabolites were nominally significant (p < 0.05). Further examination of the mortality-related metabolites may shed more light on the pathophysiology linking diabetes to premature mortality. Full article

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research. Full article

Glucosinolates are a group of plant secondary metabolites that can be hydrolyzed into a variety of breakdown products such as isothiocyanates, thiocyanates, and nitriles. These breakdown products can facilitate plant defense and function as attractants to natural enemies of insect pests. As part of the diet, some of these compounds have shown cancer-preventing activities, and the levels of these metabolites in the edible parts of the plants are of interest. In this study, we systematically examined variations in glucosinolates, their precursors, and their breakdown products in 12 commonly consumed vegetables of the Brassicaceae family with gas chromatography—quadrupole time-of-flight mass spectrometer (GC-Q-TOF/MS), liquid chromatography–quadrupole time-of-flight mass spectrometer (LC-Q-TOF/MS), and liquid chromatography—triple quadrupole mass spectrometer (LC-QQQ/MS), using both untargeted and targeted approaches. The findings were integrated with data from literature to provide a comprehensive map of pathways for biosynthesis of glucosinolates and isothiocyanates. The levels of precursor glucosinolates are found to correlate well with their downstream breakdown products. Further, the types and abundances of glucosinolates among different genera are significantly different, and these data allow the classification of plants based on morphological taxonomy. Further validation on three genera, which are grown underground, in damp soil, and above ground, suggests that each genus has its specific biosynthetic pathways and that there are variations in some common glucosinolate biosynthesis pathways. Our methods and results provide a good starting point for further investigations into specific aspects of glucosinolate metabolism in the Brassica vegetables. Full article

Heat stress is one of the main threats to dairy cow production; in order to resist heat stress, the animal exhibits a variety of physiological and hormonal responses driven by complex molecular mechanisms. Heat-stressed cows have high insulin activity, decreased non-esterified fatty acids, and increased glucose disposal. Glucose, as one of the important biochemical components of the energetic metabolism, is affected at multiple levels by the reciprocal changes in hormonal secretion and adipose metabolism under the influence of heat stress in dairy cattle. Therefore, alterations in glucose metabolism have negative consequences for the animal’s health, production, and reproduction under heat stress. Lactose is a major sugar of milk which is affected by the reshuffle of the whole-body energetic metabolism during heat stress, contributing towards milk production losses. Glucose homeostasis is maintained in the body by one of the glucose transporters’ family called facilitative glucose transporters (GLUTs encoded by SLC2A genes). Besides the glucose level, the GLUTs expression level is also significantly changed under the influence of heat stress. This review aims to describe the effect of heat stress on systemic glucose metabolism, facilitative glucose transporters, and its consequences on health and milk production. Full article

Chocolate quality is largely due to the presence of polyphenols and especially of flavan-3-ols and their derivatives that contribute to bitterness and astringency. The aim of the present work was to assess the potential of a quantitative polyphenol targeted metabolomics analysis based on mass spectrometry for relating cocoa bean polyphenol composition corresponding chocolate polyphenol composition and sensory properties. One-hundred cocoa bean samples were transformed to chocolates using a standard process, and the latter were attributed to four different groups by sensory analysis. Polyphenols were analyzed by an ultra-high-performance liquid chromatography (UPLC) system hyphenated to a triple quadrupole mass spectrometer. A multiblock method called a Common Component and Specific Weights Analysis (CCSWA) was used to study relationships between the three datasets, i.e., cocoa polyphenols, chocolate polyphenols and sensory profiles. The CCSWA multiblock method coupling sensory and chocolate polyphenols differentiated the four sensory poles. It showed that polyphenolic and sensory data both contained information enabling the sensory poles’ separation, even if they can be also complementary. A large amount of variance in the cocoa bean and corresponding chocolate polyphenols has been linked. The cocoa bean phenolic composition turned out to be a major factor in explaining the sensory pole separation. Full article

Abiotic stresses are major factors that negatively affect plant growth and productivity. Plants have developed complex strategies to ensure their survival and reproduction under adverse conditions, activating mechanisms that involve changes at different metabolic levels. In order to select stress-resistant species, research has focused on molecular studies and genetic engineering, showing promising results. In this work, the insertion of the rolD gene from Agrobacterium rhizogenes into Nicotiana langsdorffii plants is investigated, in order to assess the potential of this genetic modification towards mitigating water and heat stresses. Different approaches were combined: a high-throughput metabolomics and ionomics study was performed, together with the determination of important plant phytohormones. The aim was to identify the influence of abiotic stresses on plants and to highlight the effects of the rolD genetic modification on plant stress response. The most relevant compounds for each kind of stress were identified, belonging mainly to the classes of lipids, acyl sugars, glycosides, and amino acid derivatives. Water stress (WS) determined a decrease of elements and secondary metabolites, while amino acids and their derivatives increased, proving to be key molecules in this type of stress. RolD plants exposed to high temperature stress (HS) presented higher dry weight levels than controls, as well as increased amounts of K and adenosine and lower levels of damage-associated metabolites, suggesting the increased resistance of rolD-modified plants toward HS. Full article

Preparative separation of three lycorine type alkaloids from Rhodolirum speciosum (Amaryllidaceae) was successfully carried out using pH-zone-refinement centrifugal partition chromatography (CPC) using the solvent system methyl-tert-butyl ether/acetonitrile/water (4:1:5, v/v/v) in descending mode. Using this system, Alkaloid 1 (165.7 mg, 88.2%, purity), 2 (60.1 mg, 97.7% purity) and 3 (12.3 mg, 84.4% purity) were obtained in one step. For structure elucidation, the pure alkaloids were subjected to spectroscopy analysis using nuclear magnetic resonance experiments (¹H-NMR, ¹³C-NMR) and gas chromatography coupled with mass spectrometry (GC-MS). Alkaloids 1, 2, and 3 were identified as 1-O-acetyl-5,6-dehydrolycorine, 1-O-acetyl-lycorine, and 1,2-O-diacetyl-5,6-dehydrolycorine, respectively. The acetylcholinesterase inhibitory activity of these alkaloids was IC₅₀ 151.1 μg/mL, IC₅₀ 203.5 μg/mL, IC₅₀ 470.0 μg/mL, and IC₅₀ 17.1 μg/mL, respectively. Full article

Geography is one of the key drivers of the significant variation in the etiopathogenic profile and prevalence of type 2 diabetes mellitus (T2DM) and obesity, therefore geographically based data are fundamental for implementing the appropriate interventions. Presently, the selection criteria of T2DM and obesity patients for laparoscopic sleeve gastrectomy (LSG) have not reached a worldwide consensus—highlighting the need for sharing experts’ guidance in the preoperative evaluation, choice of the interventional procedure, perioperative management and patient long-term care. The aim of the current study was to evaluate the impact of LSG on T2DM (T2DM) remission in Romanian obese male patients, based on a multiparametric, prospective investigation. We have conducted a randomized controlled study on 41 obese male participants with the body mass index (BMI) ≥ 30 kg/m², aged 30–65 years, which were randomly divided in two study groups: one receiving conventional treatment and the second undergoing LSG. The clinical and anthropometrical parameters, resting metabolic rate, general biochemical status, adipocytes profile, gastrointestinal hormones levels, proinflammatory, oxidant and antioxidant profiles were determined at three time points: V1 (baseline), V2 (after six months) and V3 (after 12 months). Glycated hemoglobin (HbA1c), blood glucose levels, BMI, weight, visceral fat level, HDL-cholesterol, incretin hormones, proinflammatory and the oxidative stress status were significantly improved in the LSG versus conventional treatment group. This is the first study reporting on the evaluation of metabolic surgery impact on Romanian obese male patients with T2DM. Our results confirm that LSG could contribute to T2DM remission in patients with diabesity, but this beneficial effect seems to be critically influenced by the duration of T2DM rather than by the obesity status. Our results show that, in addition to the parameters included in the prediction algorithm, the proinsulin levels, proinsulin/insulin ratio and the visceral fat percentage could bring added value to the assessment of metabolic status. Full article

The use of anabolic steroid hormones as growth promoters in feed for farm animals has been banned in the European Union since 1988 on the basis of Council Directive 96/22/EC. However, there is still ongoing monitoring and reporting of positive findings of these banned substances in EU countries. The aim of this work was to investigate the efficacy and discriminatory ability of metabolic fingerprinting after the administration of 17β-testosterone esters to pigs. Plasma and urine samples were chromatographically separated on a Hypersil Gold C18 column. High resolution mass spectrometry metabolomic fingerprints were analysed on a hybrid mass spectrometer Q-Exactive. Three independent multivariate statistical methods, namely principal component analysis, clustre analysis, and orthogonal partial least squares discriminant analysis showed significant differences between the treated and control groups of pigs even 14 days after the administration of the hormonal drug. Plasma samples were also analysed by a conventional quantitative analysis using liquid chromatography with tandem mass spectrometry and a pharmacokinetic curve was constructed based on the results. In this case, no testosterone residue was detected 14 days after the administration. The results clearly showed that a metabolomics approach can be a useful and effective tool for the detection and monitoring of banned anabolic steroids used illegally in pig fattening. Full article

Psychoactive stimulants are a popular drug class which are used recreationally. Over the last decade, large numbers of new psychoactive substances (NPS) have entered the drug market and these pose a worldwide problem to human health. Metabolomics approaches are useful tools for simultaneous detection of endogenous metabolites affected by drug use. They allow identification of pathways or characteristic metabolites, which might support the understanding of pharmacological actions or act as indirect biomarkers of consumption behavior or analytical detectability. Herein, we performed a comparative metabolic profiling of three psychoactive stimulant drugs 3,4-methylenedioxymethamphetamine (MDMA), amphetamine and the NPS mephedrone by liquid chromatography-high resolution mass spectrometry (LC-HRMS) in order to identify common pathways or compounds. Plasma samples were obtained from controlled administration studies to humans. Various metabolites were identified as increased or decreased based on drug intake, mainly belonging to energy metabolism, steroid biosynthesis and amino acids. Linoleic acid and pregnenolone-sulfate changed similarly in response to intake of all drugs. Overall, mephedrone produced a profile more similar to that of amphetamine than MDMA in terms of affected energy metabolism. These data can provide the basis for further in-depth targeted metabolome studies on pharmacological actions and search for biomarkers of drug use. Full article

Intake of probiotic cheese improves the intestinal health of humans and animals. However, metabolic changes in the intestines of dogs in response to the ingestion of probiotic cheese have not been evaluated. Thus, we aimed to determine the metabolic changes in healthy beagle dogs fed queso blanco cheese with added Lactobacillus reuteri KACC 92293 and Bifidobacterium longum KACC 91563 (QCLB) and to identify potential fecal biomarkers to distinguish the metabolic changes based on intake of probiotic cheese through metabolomics approaches. The dogs were randomly divided into three groups and fed a regular diet without any cheese (control), a diet with queso blanco cheese (QC), or one with QCLB for eight weeks. The concentrations of acetic, propionic, and 4-aminobutyric acids were increased in the QCLB group compared to those in the control group. Additionally, higher levels of propionic acid and lower levels of xylose were found in the QCLB group compared to those in the QC group. This is the first report on the identification of metabolic changes in beagle dogs fed queso blanco cheese with added L. reuteri KACC 92293 and B. longum KACC 91563. We also found that metabolomics approaches can be useful for identifying potential fecal markers in dogs fed probiotic cheese. Full article

The mevalonate pathway produces cholesterol and other compounds crucial for numerous cellular processes. It is well known that age and sex modulate this pathway in the liver. Recently, similar effects were also noted in different brain areas, suggesting that alterations of the mevalonate pathway are at the root of marked sex-specific disparities in some neurodevelopmental disorders related to disturbed cholesterol homeostasis. Here, we show how the mevalonate pathway is modulated in a sex-, age- and region-specific manner, and how maternal exposure to exogenous compounds can disturb the regulation of this pathway in the brain, possibly inducing functional alterations. Full article

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field. Full article