Untargeted metabolomics unravels distinct gut microbial metabolites derived from plant-based and animal-origin proteins using in vitro modeling.

The popularity of plant-based meat alternatives (PBMAs) has sparked a contentious debate about their influence on intestinal homeostasis compared to traditional animal-based meats. This study aims to explore the changes in gut microbial metabolites (GMMs) induced by the gut microbiota on different digested patties: beef meat and pea-protein PBMA. After digesting in vitro, untargeted metabolomics revealed 32 annotated metabolites, such as carnitine and acylcarnitines correlated with beef meat, and 45 annotated metabolites, like triterpenoids and lignans, linked to our PBMA. Secondly, (un)targeted approaches highlighted differences in GMM patterns during colonic fermentations. Our findings underscore significant differences in amino acids and their derivatives. Beef protein fermentation resulted in higher production of methyl-histidine, gamma-glutamyl amino acids, indoles, isobutyric and isovaleric acids. In contrast, PBMAs exhibit a significant release of N-acyl amino acids and unique dipeptides, like phenylalanine-arginine. This research offers valuable insights into how PBMAs and animal-based proteins differently modulate intestinal microenvironments.

Identification and quantification of flavor compounds in smoked tuna fish based on GC-Orbitrap volatolomics approach.

Cold smoking enhances the appeal of fish products, offering consumers a smooth texture and a delicate smoky flavor. This study aims to explore variations in the volatile profile from different exposure times during cold smoking processing (light, moderate, and full-cure) in tune samples. An innovative untargeted analytical approach, headspace solid-phase microextraction combined with gas chromatography and a hybrid quadrupole-orbitrap mass analyzer, was employed to identify 86 volatiles associated with the cold smoking process. Most of these compounds, including phenols, furan derivates, aldehydes, cyclic ketones, and different aromatic species, were found to contribute to the smoke odor. The development of a QuEChERS-based extraction and clean-up method facilitated the quantification of 25 relevant smoky markers across all smoking degrees, revealing significant concentration differences after 15 h of smoking. This research sheds light on the dynamics of cold smoking impact and its on the flavor profile and safety quality of processed fish products.

Effects of structural characteristics of (un)conjugated steroid metabolites in their collision cross section value.

In this work, the collision cross section (CCS) value of 103 steroids (including unconjugated metabolites and phase II metabolites conjugated with sulfate and glucuronide groups) was determined by liquid chromatography coupled to traveling wave ion mobility spectrometry (LC-TWIMS). A time of flight (QTOF) mass analyzer was used to perform the analytes determination at high-resolution mass spectrometry. An electrospray ionization source (ESI) was used to generate [M+H]+, [M + NH4]+ and/or [M - H]- ions. High reproducibility was observed for the CCS determination in both urine and standard solutions, obtaining RSD lower than 0.3% and 0.5% in all cases respectively. CCS determination in matrix was in accordance with the CCS measured in standards solution showing deviations below 2%. In general, CCS values were directly correlated with the ion mass and allowed differentiating between glucuronides, sulfates and free steroids although differences among steroids of the same group were less significant. However, more specific information was obtained for phase II metabolites observing differences in the CCS value of isomeric pairs concerning the conjugation position or the α/ß configuration, which could be useful in the structural elucidation of new steroid metabolites in the anti-doping field. Finally, the potential of IMS reducing interferences from the sample matrix was also tested for the analysis of a glucuronide metabolite of bolasterone (5ß-androstan-7α,17α-dimethyl-3α,17ß-diol-3-glucuronide) in urine samples.

LC-IMS-HRMS for identification of biomarkers in untargeted metabolomics: The effects of pterostilbene and resveratrol consumption in liver steatosis, animal model.

Untargeted metabolomics with the combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) was applied to investigate the impact of resveratrol and pterostilbene supplementation on the metabolic fingerprint of the Wistar rats liver with induced liver steatosis. RP-LC and HILIC in both ionisation modes were employed to analyse the liver samples (n = 40) from Wistar rats fed with a high-fat and high-fructose diet, supplemented or not with resveratrol and pterostilbene. After univariate and multivariate statistical analysis, 34 metabolites were highlighted in the different diets and elucidated. Despite the structural similarity, different alterations in liver metabolism were observed by the supplementations. Resveratrol treatment was characterised by the alteration in metabolism of 17 lysophospholipids, while pterostilbene affected some vitamins and derivatives, among others. IMS has demonstrated great potential in the elucidation process thanks to the additional structural descriptor the CCS (Å2), providing more confidence in the identification.

Untargeted metabolomics approach using UHPLC-IMS-QTOF MS for surface body samples to identify low-volatility chemosignals related to maternal care in mice.

The present study is focused on the determination of low-volatile chemosignals excreted or secreted by mouse pups in their early days of life involved in maternal care induction in mice adult females. Untargeted metabolomics was employed to differentiate between samples collected with swabs from facial and anogenital area from neonatal mouse pups receiving maternal care (first two weeks of life) and the elder mouse pups in the weaning period (4th week old). The sample extracts were analysed by ultra-high pressure liquid chromatography (UHPLC) coupled to ion mobility separation (IMS) in combination with high resolution mass spectrometry (HRMS). After data processing with Progenesis QI and multivariate statistical analysis, five markers present in the first two weeks of mouse pups life and putatively involved in materno-filial chemical communication were tentatively identified: arginine, urocanic acid, erythro-sphingosine (d17:1), sphingosine (d18:1) and sphinganine. The four-dimensional data and the tools associated to the additional structural descriptor obtained by IMS separation were of great help in the compound identification. The results demonstrated the great potential of UHPLC-IMS-HRMS based untargeted metabolomics to identity putative pheromones in mammals.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines.

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models.

The number of open access databases containing experimental and predicted collision cross section (CCS) values is rising and leads to their increased use for compound identification. However, the reproducibility of reference values with different instrumental designs and the comparison between predicted and experimental CCS values is still under evaluation. This study compared experimental CCS values of 56 small molecules (Contaminants of Emerging Concern) acquired by both drift tube (DT) and travelling wave (TW) ion mobility mass spectrometry (IM-MS). The TWIM-MS included two instrumental designs (Synapt G2 and VION). The experimental TWCCSN2 values obtained by the TWIM-MS systems showed absolute percent errors (APEs) < 2% in comparison to experimental DTIMS data, indicating a good correlation between the datasets. Furthermore, TWCCSN2 values of [M - H]- ions presented the lowest APEs. An influence of the compound class on APEs was observed. The applicability of prediction models based on artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), both built using TWIM-MS data, was investigated for the first time for the prediction of DTCCSN2 values. For [M+H]+ and [M - H]- ions, the 95th percentile confidence intervals of observed APEs were comparable to values reported for both models indicating a good applicability for DTIMS predictions. For the prediction of DTCCSN2 values of [M+Na]+ ions, the MARS based model provided the best results with 73.9% of the ions showing APEs below the threshold reported for [M+Na]+. Finally, recommendations for database transfer and applications of prediction models for future DTIMS studies are made.

Novel sampling strategy for alive animal volatolome extraction combined with GC-MS based untargeted metabolomics: Identifying mouse pup pheromones.

In this study, we identify 11 mouse pup volatiles putatively involved in maternal care induction in adult females. For this purpose, we have adapted the dynamic headspace methodology to extract the volatolome of whole alive animals. Untargeted metabolomic methodology was used to compare the volatolome of neonatal (4-6 days) with elder pups until the age of weaning (21-23 days old). Pup volatolome was analyzed by gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) using automated thermal desorption for sample introduction. After data processing and multivariate statistical analysis, comparison with NIST spectral library allowed identifying compounds secreted preferentially by neonatal pups: di(propylen glycol) methyl ether, 4-nonenal, di(ethylene glycol) monobutyl ether, 2-phenoxyethanol, isomethyl ionone, tridecanal, 1,3-diethylbenzene, 1,2,4,5-tetramethylbenzene, 2-ethyl-p-xylene and tri(propylene glycol) methyl ether. Palmitic acid was enriched in the volatolome of fourth week youngsters compared to neonatal pups. The results demonstrated the great potential of the new sampling procedure combined with GC-MS based untargeted volatolomics to identify volatile pheromones in mammals.

The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment.

Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.

Ultra-Performance Liquid Chromatography-Ion Mobility Separation-Quadruple Time-of-Flight MS (UHPLC-IMS-QTOF MS) Metabolomics for Short-Term Biomarker Discovery of Orange Intake: A Randomized, Controlled Crossover Study.

A major problem with dietary assessments is their subjective nature. Untargeted metabolomics and new technologies can shed light on this issue and provide a more complete picture of dietary intake by measuring the profile of metabolites in biological samples. Oranges are one of the most consumed fruits in the world, and therefore one of the most studied for their properties. The aim of this work was the application of untargeted metabolomics approach with the novel combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) and study the advantages that this technique can bring to the area of dietary biomarker discovery, with the specific case of biomarkers associated with orange consumption (Citrus reticulata) in plasma samples taken during an acute intervention study (consisting of a randomized, controlled crossover trial in healthy individuals). A total of six markers of acute orange consumption, including betonicines and conjugated flavonoids, were identified with the experimental data and previous literature, demonstrating the advantages of ion mobility in the identification of dietary biomarkers and the benefits that an additional structural descriptor, as the collision cross section value (CCS), can provide in this area.

Investigation on the consumption of synthetic cannabinoids among teenagers by the analysis of herbal blends and urine samples.

The use of synthetic cannabinoids (SCs), which escape conventional detection systems, may be a good alternative to elude routine drug analysis for cannabis. The detection of these drugs in urine is unusual due to their complete and fast metabolism, therefore requiring alternative strategies. In this work, an investigation has been made on SCs consumption by minors (less than 18 years old) in juvenile offenders' centres. 667 urine samples (from 127 minors) were collected after their permits with stay at home. We also studied the SCs from 7 herbal blends available at the smartshop frequented by the minors. Both, urine and herbal blends, were analysed by liquid chromatography coupled to high resolution mass spectrometry. The analysis of urine confirmed the absence of more than 200 SCs investigated. Thus, the focus was made on metabolites reported for those SCs identified in the herbal blends collected from the smart-shop. The major metabolites of XLR-11 and UR-144 (N-pentanoic acid and N-(5-hydroxypentyl)) were found in several urine samples. Apart from the main metabolites included in the initial searching, a thorough investigation of more metabolites for these SCs was additionally performed, including MS/MS experiments for the tentative identification of compounds detected in the urine samples. The 16 samples positive to the XLR-11 metabolites were assigned to 6 minors, only 2 of which had recognized consumption. On the basis of the results obtained, preventive and therapeutic interventions must be implemented to reduce the consumption of psychoactive substances and to improve the risk-perception of these substances by minors.

Variación en el patrón de consumo de cannabinoides sintéticos de una paciente a lo largo de 2018 / Variation in the pattern of synthetic cannabinoid use by a female patient during 2018

No disponible

Disruption of gut integrity and permeability contributes to enteritis in a fish-parasite model: a story told from serum metabolomics.

BACKGROUND: In the animal production sector, enteritis is responsible for serious economic losses, and intestinal parasitism is a major stress factor leading to malnutrition and lowered performance and animal production efficiency. The effect of enteric parasites on the gut function of teleost fish, which represent the most ancient bony vertebrates, is far from being understood. The intestinal myxozoan parasite Enteromyxum leei dwells between gut epithelial cells and causes severe enteritis in gilthead sea bream (Sparus aurata), anorexia, cachexia, growth impairment, reduced marketability and increased mortality. METHODS: This study aimed to outline the gut failure in this fish-parasite model using a multifaceted approach and to find and validate non-lethal serum markers of gut barrier dysfunction. Intestinal integrity was studied in parasitized and non-parasitized fish by immunohistochemistry with specific markers for cellular adhesion (E-cadherin) and tight junctions (Tjp1 and Cldn3) and by functional studies of permeability (oral administration of FITC-dextran) and electrophysiology (Ussing chambers). Serum samples from parasitized and non-parasitized fish were analyzed using non-targeted metabolomics and some significantly altered metabolites were selected to be validated using commercial kits. RESULTS: The immunodetection of Tjp1 and Cldn3 was significantly lower in the intestine of parasitized fish, while no strong differences were found in E-cadherin. Parasitized fish showed a significant increase in paracellular uptake measured by FITC-dextran detection in serum. Electrophysiology showed a decrease in transepithelial resistance in infected animals, which showed a diarrheic profile. Serum metabolomics revealed 3702 ions, from which the differential expression of 20 identified compounds significantly separated control from infected groups in multivariate analyses. Of these compounds, serum inosine (decreased) and creatine (increased) were identified as relevant and validated with commercial kits. CONCLUSIONS: The results demonstrate the disruption of tight junctions and the loss of gut barrier function, a metabolomic profile of absorption dysfunction and anorexia, which further outline the pathophysiological effects of E. leei.

What about the herb? A new metabolomics approach for synthetic cannabinoid drug testing.

Synthetic cannabinoids (SCs) are consumed as legal alternative to cannabis and often allow passing drug-screening tests. Their rapid transience on the drug scene, combined with their mostly unknown metabolic profiles, creates a scenario with constantly moving analytical targets, making their monitoring and identification challenging. The development of fast screening strategies for SCs, not directly focused on their chemical structure, as an alternative to the commonly applied target acquisition methods, would be highly appreciated in forensic and public health laboratories. An innovative untargeted metabolomics approach, focused on herbal components commonly used for 'spice' products, was applied. Saliva samples of healthy volunteers were collected at pre-dose and after smoking herbal components and analysed by high-resolution mass spectrometry. The data obtained, combined with appropriate statistical analysis, allowed to highlight and elucidate two markers (scopoletin and N,N-bis(2-hydroxyethyl)dodecylamine), which ratio permitted to differentiate herbal smokers from non-smokers. The proposed strategy will allow discriminating potential positives, on the basis of the analysis of two markers identified in the herbal blends. This work is presented as a step forward in SC drug testing, promoting a smart first-line screening approach, which will allow reducing the number of samples to be further investigated by more sophisticated HRMS methods. Graphical abstract The development of an alternative, generic screening methods of synthetic cannabinoids, not directly based on the chemical structure, in order to provide fast response on its potential consumption.

Assessment of protected designation of origin for Colombian coffees based on HRMS-based metabolomics.

An untargeted metabolomics approach based on HRMS has been applied to Colombian green coffee to develop a discrimination model to highlight the most differential compounds. For this purpose, 41 green coffee samples of different genotypes collected from 5 regions were analysed. Samples were extracted with aqueous and organic solvents to cover a wide range of compounds. Sample extracts were randomly injected and data were pre-processed with XCMS software. PCA was used to verify quality control samples behaviour, and PLS-DA and DD-SIMCA were employed to create models for discrimination using VIP variable selection method. Thirteen different compounds correctly separate green coffee samples according to their origin, several related to the quality and health benefits of coffee. Model validation was achieved using both cross-validation and an additional set with coffee samples from different harvest year. The results reveal that UHPLC-(Q)ToF MS-based metabolomics is a suitable tool to develop food origin discrimination strategies.

Untargeted metabolomics approach for unraveling robust biomarkers of nutritional status in fasted gilthead sea bream (Sparus aurata).

A metabolomic study has been performed to identify sensitive and robust biomarkers of malnutrition in farmed fish, using gilthead sea bream (Sparus aurata) as a model. The metabolomic fingerprinting of serum from fasted fish was assessed by means of ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. More than 15,000 different m/z ions were detected and Partial Least Squares-Discriminant analysis allowed a clear differentiation between the two experimental groups (fed and 10-day fasted fish) with more than 90% of total variance explained by the two first components. The most significant metabolites (up to 45) were elucidated on the basis of their tandem mass spectra with a broad representation of amino acids, oligopeptides, urea cycle metabolites, L-carnitine-related metabolites, glutathione-related metabolites, fatty acids, lysophosphatidic acids, phosphatidylcholines as well as biotin- and noradrenaline-related metabolites. This untargeted approach highlighted important adaptive responses in energy and oxidative metabolism, contributing to identify robust and nutritionally-regulated biomarkers of health and metabolic condition that will serve to assess the welfare status of farmed fish.

Potential of gas chromatography-atmospheric pressure chemical ionization-tandem mass spectrometry for screening and quantification of hexabromocyclododecane.

A fast method for the screening and quantification of hexabromocyclododecane (sum of all isomers) by gas chromatography using a triple quadrupole mass spectrometer with atmospheric pressure chemical ionization (GC-APCI-QqQ) is proposed. This novel procedure makes use of the soft atmospheric pressure chemical ionization source, which results in less fragmentation of the analyte than by conventional electron impact (EI) and chemical ionization (CI) sources, favoring the formation of the [M - Br](+) ion and, thus, enhancing sensitivity and selectivity. Detection was based on the consecutive loses of HBr from the [M - Br](+) ion to form the specific [M - H5Br6](+) and [M - H4Br5](+) ions, which were selected as quantitation (Q) and qualification (q) transitions, respectively. Parameters affecting ionization and MS/MS detection were studied. Method performance was also evaluated; calibration curves were found linear from 1 pg/µL to 100 pg/µL for the total HBCD concentration; instrumental detection limit was estimated to be 0.10 pg/µL; repeatability and reproducibility, expressed as relative standard deviation, were better than 7% in both cases. The application to different real samples [polyurethane foam disks (PUFs), food, and marine samples] pointed out a rapid way to identify and allow quantification of this compound together with a number of polybrominated diphenyl ethers (BDE congeners 28, 47, 66, 85, 99, 100, 153, 154, 183, 184, 191, 196, 197, and 209) and two other novel brominated flame retardants [i.e., decabromodiphenyl ethane (DBDPE) and 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE)] because of their presence in the same fraction when performing the usual sample treatment.

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis.

The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (tR) and mass spectral comparisons. Chromatographic tR prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for tR prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound tR dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85% of all compounds to within 2min of their measured tR for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95% prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.