Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses

Metabolic Profiling of PGPR-Treated Tomato Plants Reveal Priming-Related Adaptations of Secondary Metabolites and Aromatic Amino Acids

Plant growth–promoting rhizobacteria (PGPR) are beneficial microbes in the rhizosphere that can directly or indirectly stimulate plant growth. In addition, some can prime plants for enhanced defense against a broad range of pathogens and insect herbivores. In this study, four PGPR strains (Pseudomonas fluorescens N04, P. koreensis N19, Paenibacillus alvei T19, and Lysinibacillus sphaericus T22) were used to induce priming in Solanum lycopersicum (cv. Moneymaker) plants. Plants were inoculated with each of the four PGPRs, and plant tissues (roots, stems, and leaves) were harvested at 24 h and 48 h post-inoculation. Methanol-extracted metabolites were analyzed by ultra-high performance liquid chromatography mass spectrometry (UHPLC-MS). Chemometric methods were applied to mine the data and characterize the differential metabolic profiles induced by the PGPR. The results revealed that all four strains induced defense-related metabolic reprogramming in the plants, characterized by dynamic changes to the metabolomes involving hydroxycinnamates, benzoates, flavonoids, and glycoalkaloids. In addition, targeted analysis of aromatic amino acids indicated differential quantitative increases or decreases over a two-day period in response to the four PGPR strains. The metabolic alterations point to an altered or preconditioned state that renders the plants primed for enhanced defense responses. The results contribute to ongoing efforts in investigating and unraveling the biochemical processes that define the PGPR priming phenomenon.

Metabolomics Profiles of Smokers from Two Ethnic Groups with Differing Lung Cancer Risk

African American (AA) smokers are at a higher risk of developing lung cancer compared to whites. The variations in the metabolism of nicotine and tobacco-derived carcinogens in these groups were reported previously with the levels of nicotine metabolites and carcinogen-derived metabolites measured using targeted approaches. While useful, these targeted strategies are not able to detect global metabolic changes for use in predicting the detrimental effects of tobacco use and ultimately lung cancer susceptibility among smokers. To address this limitation, we have performed global untargeted metabolomics profiling in urine of AA and white smokers to characterize the pattern of metabolites, identify differentially regulated pathways, and correlate these profiles with the observed variations in lung cancer risk between these two populations. Urine samples from AA (n = 30) and white (n = 30) smokers were used for metabolomics analysis acquired in both positive and negative electrospray ionization modes. LC-MS data were uploaded onto the cloud-based XCMS online (http://xcmsonline.scripps.edu) platform for retention time correction, alignment, feature detection, annotation, statistical analysis, data visualization, and automated systems biology pathway analysis. The latter identified global differences in the metabolic pathways in the two groups including the metabolism of carbohydrates, amino acids, nucleotides, fatty acids, and nicotine. Significant differences in the nicotine degradation pathway (cotinine glucuronidation) in the two groups were observed and confirmed using a targeted LC-MS/MS approach. These results are consistent with previous studies demonstrating AA smokers with lower glucuronidation capacity compared to whites. Furthermore, the d-glucuronate degradation pathway was found to be significantly different between the two populations, with lower amounts of the putative metabolites detected in AA compared to whites. We hypothesize that the differential regulation of the d-glucuronate degradation pathway is a consequence of the variations in the glucuronidation capacity observed in the two groups. Other pathways including the metabolism of amino acids, nucleic acids, and fatty acids were also identified, however, the biological relevance and implications of these differences across ethnic groups need further investigation. Overall, the applied metabolomics approach revealed global differences in the metabolic networks and endogenous metabolites in AA and whites, which could be used and validated as a new potential panel of biomarkers that could be used to predict lung cancer susceptibility among smokers in population-based studies.

Differences in Cystic Fibrosis-Associated Burkholderia spp. Bacteria Metabolomes after Exposure to the Antibiotic Trimethoprim

The Burkholderia cepacia complex is a group of closely related bacterial species with large genomes that infect immunocompromised individuals and those living with cystic fibrosis. Some of these species are found more frequently and cause more severe disease than others, yet metabolomic differences between these have not been described. Furthermore, our understanding of how these species respond to antibiotics is limited. We investigated the metabolomics differences between three most prevalent Burkholderia spp. associated with cystic fibrosis: B. cenocepacia, B. multivorans, and B. dolosa in the presence and absence of the antibiotic trimethoprim. Using a combination of supervised and unsupervised metabolomics data visualization and analysis tools, we describe the overall differences between strains of the same species and between species. Specifically, we report, for the first time, the role of the pyomelanin pathway in the metabolism of trimethoprim. We also report differences in the detection of known secondary metabolites such as fragin, ornibactin, and N-acylhomoserine lactones and their analogs in closely related strains. Furthermore, we highlight the potential for the discovery of new secondary metabolites in clinical strains of Burkholderia spp. The metabolomics differences described in this study highlight the personalized nature of closely related Burkholderia strains.

Age-dependent characterization of volatile organic compounds and age discrimination in Chinese rice wine using an untargeted GC/MS-based metabolomic approach

An untargeted gas chromatography/mass spectrometry (GC/MS)-based metabolomics by XCMS-Online software combined with partial least squares regression (PLSR) was applied to characterize volatile organic compounds (VOCs) during Chinese rice wine aging and discriminate ages for the first time. Finally, seven different ages between 0 and 15 years were well discriminated by PLSR. Total 104 feature groups were isolated from all optimized candidate peaks, and 94 VOCs (including unknowns) were preliminarily identified as aging markers. Therein, alcohols, sulfides, phenols and their derivatives, small esters and acids exhibited significantly better discrimination of short-aged rice wines. Correspondingly, furans, aromatics, aldehydes, ketones, most esters and acids, discriminated the long-aged samples better. Meanwhile, the potential origins of certain VOCs were also proposed for further research. Overall, this untargeted GC/MS-based metabolomics coupled with PLSR was a feasible tool for a rapidly and globally age-dependent characterization of volatile metabolomic signals in Chinese rice wine and thus for age discrimination.

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.

Investigation of secondary metabolism in the industrial butanol hyper-producer Clostridium saccharoperbutylacetonicum N1-4

Clostridium saccharoperbutylacetonicum N1-4 (Csa) is a historically significant anaerobic bacterium which can perform saccharolytic fermentations to produce acetone, butanol, and ethanol (ABE). Recent genomic analyses have highlighted this organism's potential to produce polyketide and nonribosomal peptide secondary metabolites, but little is known regarding the identity and function of these metabolites. This study provides a detailed bioinformatic analysis of seven biosynthetic gene clusters (BGCs) present in the Csa genome that are predicted to produce polyketides/nonribosomal peptides. An RNA-seq-based untargeted transcriptomic approach revealed that five of seven BGCs were expressed during ABE fermentation. Additional characterization of a highly expressed nonribosomal peptide synthetase gene led to the discovery of its associated metabolite and its biosynthetic pathway. Transcriptomic analysis suggested an association of this nonribosomal peptide synthetase gene with butanol tolerance, which was supported by butanol challenge assays.

Chromatin Analysis of Metabolic Gene Clusters in Plants

Plant metabolic gene clusters consist of neighboring genes that are involved in the biosynthesis of secondary or specialized metabolites. The genes within clusters are typically co-regulated, share a common set of chromatin marks, and code for the biosynthesis enzymes of a single metabolic pathway. Here, we describe three essential protocols for the basic analysis of metabolic gene clusters at transcription, histone modification, and metabolite level. The protocols are specified to clusters in the Arabidopsis thaliana genome and are transferable to other plant species.

Gastric bypass surgery in a rat model alters the community structure and functional composition of the intestinal microbiota independently of weight loss

BACKGROUND

Roux-en-Y gastric bypass (RYGB) surgery is a last-resort treatment to induce substantial and sustained weight loss in cases of severe obesity. This anatomical rearrangement affects the intestinal microbiota, but so far, little information is available on how it interferes with microbial functionality and microbial-host interactions independently of weight loss.

METHODS

A rat model was employed where the RYGB-surgery cohort is compared to sham-operated controls which were kept at a matched body weight by food restriction. We investigated the microbial taxonomy and functional activity using 16S rRNA amplicon gene sequencing, metaproteomics, and metabolomics on samples collected from theileum, the cecum, and the colon, and separately analysed the lumen and mucus-associated microbiota.

RESULTS

Altered gut architecture in RYGB increased the relative occurrence of Actinobacteria, especially Bifidobacteriaceae and Proteobacteria, while in general, Firmicutes were decreased although Streptococcaceae and Clostridium perfringens were observed at relative higher abundances independent of weight loss. A decrease of conjugated and secondary bile acids was observed in the RYGB-gut lumen. The arginine biosynthesis pathway in the microbiota was altered, as indicated by the changes in the abundance of upstream metabolites and enzymes, resulting in lower levels of arginine and higher levels of aspartate in the colon after RYGB.

CONCLUSION

The anatomical rearrangement in RYGB affects microbiota composition and functionality as well as changes in amino acid and bile acid metabolism independently of weight loss. The shift in the taxonomic structure of the microbiota after RYGB may be mediated by the resulting change in the composition of the bile acid pool in the gut and by changes in the composition of nutrients in the gut. Video abstract.

Gas Chromatography-Mass Spectrometry Profiling of Volatile Compounds Reveals Metabolic Changes in a Non-Aflatoxigenic Aspergillus flavus Induced by 5-Azacytidine

Aspergillus flavus is one of the most opportunistic pathogens invading many important oilseed crops and foodstuffs with such toxic secondary metabolites as aflatoxin (AF) and Cyclopiazonic acid. We previously used the DNA methylation inhibitor 5-azacytidine to treat with an AF-producing A. flavus A133 strain, and isolated a mutant (NT) of A. flavus, which displayed impaired abilities of AF biosynthesis and fungal development. In this study, gas chromatography–mass spectrometry (GC-MS) analysis was used to reveal the metabolic changes between these two strains. A total of 1181 volatiles were identified in these two strains, among which 490 volatiles were found in these two strains in vitro and 332 volatiles were found in vivo. The NT mutant was found to produce decreasing volatile compounds, among which most of the fatty acid-derived volatiles were significantly downregulated in the NT mutant compared to the A133 strain, which are important precursors for AF biosynthesis. Two antioxidants and most of the amino acids derived volatiles were found significantly upregulated in the NT mutant. Overall, our results reveal the difference of metabolic profiles in two different A. flavus isolates, which may provide valuable information for controlling infections of this fungal pathogen.

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Traveling Wave Ion Mobility-Mass Spectrometry to Enhance the Detection of Low Abundance Features in Untargeted Lipidomics

Ion mobility has become a valuable tool in mass spectrometry-based lipidomics workflows, thanks to its ability to separate ions in the gas phase based on their size and conformation. Over the last years, it was demonstrated that the comparative analysis of ion mobility data has the potential to highlight the presence of low abundance species in untargeted lipidomics. The present chapter illustrates the background of the topic and guides the reader from the sample preparation to the data analysis of an untargeted lipidomics experiment performed using ion mobility.

Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics

In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.

UV-B Induced Flavonoids Contribute to Reduced Biotrophic Disease Susceptibility in Lettuce Seedlings

Biotrophic disease is one of the largest causes of decreased yield in agriculture. While exposure to ultraviolet B (UV-B) light (280-320 nm) has been previously observed to reduce plant susceptibility to disease, there is still a paucity of information regarding underlying biological mechanisms. In addition, recent advances in UV-LED technology raise the prospect of UV light treatments in agriculture which are practical and efficient. Here, we characterized the capability of UV-B LED pre-treatments to reduce susceptibility of a range of lettuce (Lactuca sativa) cultivars to downy mildew disease caused by the obligate biotroph Bremia lactucae. Innate cultivar susceptibility level did not seem to influence the benefit of a UV-B induced disease reduction with similar reductions as a percentage of the control observed (54-62% decrease in conidia count) across all susceptible cultivars. UV-B-induced reductions to conidia counts were sufficient to significantly reduce the infectivity of the diseased plant. Secondary infections caused by UV-B pre-treated plants exhibited yet further (67%) reduced disease severity. UV-B-induced flavonoids may in part mediate this reduced disease severity phenotype, as B. lactucae conidia counts of lettuce plants negatively correlated with flavonoid levels in a UV-B-dependent manner (r = -0.81). Liquid chromatography-mass spectrometry (LC-MS) was used to identify metabolic features which contribute to this correlation and, of these, quercetin 3-O-(6"-O-malonyl)-b-D-glucoside had the strongest negative correlation with B. lactucae conidia count (r = -0.68). When quercetin 3-O-(6"-O-malonyl)-b-D-glucoside was directly infiltrated into lettuce leaves, with those leaves subsequently infected, the B. lactucae conidia count was reduced (25-39%) in two susceptible lettuce cultivars. We conclude that UV-B induced phenolics, in particular quercetin flavonoids, may act as phytoanticipins to limit the establishment of biotrophic pathogens thus delaying or reducing their sporulation as measured by conidia count. These findings highlight the opportunity for UV-B morphogenesis to be exploited through the application of UV-LED technology, as part of the development of next-generation, sustainable disease control tools.

A Pipeline for Metabolic Pathway Reconstruction in Plant Orphan Species

In the era of high-throughput biology, it is necessary to develop a simple pipeline for metabolic pathway reconstruction in plant orphan species. However, obtaining a global picture of the plant metabolism may be challenging, especially in nonmodel species. Moreover, the use of bioinformatics tools and statistical analyses is required. This chapter describes how to use different software and online tools for the reconstruction of metabolic pathways of plant species using existing pathway knowledge. In particular, Quercus ilex omics data is employed to develop the present pipeline.

Evaluation of freely available software tools for untargeted quantification of 13C isotopic enrichment in cellular metabolome from HR-LC/MS data

¹³C Metabolic Flux Analysis (¹³C-MFA) involves the quantification of isotopic enrichment in cellular metabolites and fitting the resultant data to the metabolic network model of the organism. Coverage and resolution of the resultant flux map depends on the total number of metabolites and fragments in which ¹³C enrichment can be quantified accurately. Experimental techniques for tracking ¹³C enrichment are evolving rapidly and large volumes of data are now routinely generated through the use of Liquid Chromatography coupled with High-Resolution Mass Spectrometry (HR-LC/MS). Therefore, the current manuscript is focused on the challenges in high-throughput analyses of such large datasets. Current ¹³C-MFA studies often have to rely on the targeted quantification of a small subset of metabolites, thereby leaving a large fraction of the data unexplored. A number of public domain software tools have been reported in recent years for the untargeted quantitation of isotopic enrichment. However, the suitability of their application across diverse datasets has not been investigated. Here, we test the software tools X¹³CMS, DynaMet, geoRge, and HiResTEC with three diverse datasets. The tools provided a global, untargeted view of ¹³C enrichment in metabolites in all three datasets and a much-needed automation in data analysis. Some inconsistencies were observed in results obtained from the different tools, which could be partially ascribed to the lack of baseline separation and potential mass conflicts. After removing the false positives manually, isotopic enrichment could be quantified reliably in a large repertoire of metabolites. Of the software tools explored, geoRge and HiResTEC consistently performed well for the untargeted analysis of all datasets tested.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice

Metabolite profiles obtained from parental seedlings can predict yield heterosis of rice hybrids with correctly dissected population structure across different growth conditions.

Improvement of the breeding efficiencies of heterotic crops adaptive to different conditions can mitigate the food shortage crisis due to overpopulation and climate change. To date, diverse molecular markers have been used to guide field phenotypic selection, whereas accurate predictions of complex heterotic traits are rarely reported. Here, we present a practical metabolome-based strategy for predicting yield heterosis in rice. The dissection of population structure based on untargeted metabolite profiles as the initial critical step in multivariate modeling performed better than the screening of predictive variables. Then the assessment of each predictive variable’s contribution to predictive models according to all latent factors was more precise than the conventional first one. Metabolites belonging to specific pathways were closely associated with yield heterosis, and the up-regulation of galactose metabolism promoted robust yield heterosis in hybrids under different growth conditions. Our study demonstrates that metabolome-based predictive models with correctly dissected population structure and screened predictive variables can facilitate accurate predictions of yield heterosis and have great potential for establishing molecular marker–based precision breeding programs.

N-acyl taurines are endogenous lipid messengers that improve glucose homeostasis

Fatty acid amide hydrolase (FAAH) degrades 2 major classes of bioactive fatty acid amides, the N-acylethanolamines (NAEs) and N-acyl taurines (NATs), in central and peripheral tissues. A functional polymorphism in the human FAAH gene is linked to obesity and mice lacking FAAH show altered metabolic states, but whether these phenotypes are caused by elevations in NAEs or NATs is unknown. To overcome the problem of concurrent elevation of NAEs and NATs caused by genetic or pharmacological disruption of FAAH in vivo, we developed an engineered mouse model harboring a single-amino acid substitution in FAAH (S268D) that selectively disrupts NAT, but not NAE, hydrolytic activity. The FAAH-S268D mice accordingly show substantial elevations in NATs without alterations in NAE content, a unique metabolic profile that correlates with heightened insulin sensitivity and GLP-1 secretion. We also show that N-oleoyl taurine (C18:1 NAT), the most abundant NAT in human plasma, decreases food intake, improves glucose tolerance, and stimulates GPR119-dependent GLP-1 and glucagon secretion in mice. Together, these data suggest that NATs act as a class of lipid messengers that improve postprandial glucose regulation and may have potential as investigational metabolites to modify metabolic disease.

Untargeted Metabolite Profiling for Screening Bioactive Compounds in Digestate of Manure under Anaerobic Digestion

Untargeted metabolite profiling was performed on chicken manure (CHM), swine manure (SM), cattle manure (CM), and their respective digestate by XCMS coupled with MetaboAnalyst programs. Through global chemical profiling, the chemical characteristics of different digestates and types of manure were displayed during the anaerobic digestion (AD) process. As the feed for AD, CM had less easily-degradable organics, SM contained the least O-alkyls and anomerics of carbohydrates, and CHM exhibited relatively lower bio-stability. The derived metabolite pathways of different manure during the AD process were identified by MetaboAnalyst. Twelve, 8, and 5 metabolic pathways were affected by the AD process in CHM, SM, and CM, respectively. Furthermore, bioactive compounds of digestate were detected, such as amino acids (L-arginine, L-ornithine, L-cysteine, and L-aspartate), hormones (L-adrenaline, 19-hydroxy androstenedione, and estrone), alkaloids (tryptamine and N-methyltyramine), and vitamin B5, in different types of manure and their digestates. The combination of XCMS and MetaboAnalyst programs can be an effective strategy for metabolite profiling of manure and its anaerobic digestate under different situations.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material^®s (SRM^®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Cytokinin Detection during the Dictyostelium discoideum Life Cycle: Profiles Are Dynamic and Affect Cell Growth and Spore Germination

Cytokinins (CKs) are a family of evolutionarily conserved growth regulating hormones. While CKs are well-characterized in plant systems, these N6-substituted adenine derivatives are found in a variety of organisms beyond plants, including bacteria, fungi, mammals, and the social amoeba, Dictyostelium discoideum. Within Dictyostelium, CKs have only been studied in the late developmental stages of the life cycle, where they promote spore encapsulation and dormancy. In this study, we used ultra high-performance liquid chromatography-positive electrospray ionization-high resolution tandem mass spectrometry (UHPLC-(ESI+)-HRMS/MS) to profile CKs during the Dictyostelium life cycle: growth, aggregation, mound, slug, fruiting body, and germination. Comprehensive profiling revealed that Dictyostelium produces 6 CK forms (cis-Zeatin (cZ), discadenine (DA), N6-isopentenyladenine (iP), N6-isopentenyladenine-9-riboside (iPR), N6-isopentenyladenine-9-riboside-5' phosphate (iPRP), and 2-methylthio-N6-isopentenyladenine (2MeSiP)) in varying abundance across the sampled life cycle stages, thus laying the foundation for the CK biosynthesis pathway to be defined in this organism. Interestingly, iP-type CKs were the most dominant CK analytes detected during growth and aggregation. Exogenous treatment of AX3 cells with various CK types revealed that iP was the only CK to promote the proliferation of cells in culture. In support of previous studies, metabolomics data revealed that DA is one of the most significantly upregulated small molecules during Dictyostelium development, and our data indicates that total CK levels are highest during germination. While much remains to be explored in Dictyostelium, this research offers new insight into the nature of CK biosynthesis, secretion, and function during Dictyostelium growth, development, and spore germination.

A Bioinformatics Guide to Plant Microbiome Analysis

Recent evidence for intimate relationship of plants with their microbiota shows that plants host individual and diverse microbial communities that are essential for their survival. Understanding their relatedness using genome-based and high-throughput techniques remains a hot topic in microbiome research. Molecular analysis of the plant holobiont necessitates the application of specific sampling and preparatory steps that also consider sources of unwanted information, such as soil, co-amplified plant organelles, human DNA, and other contaminations. Here, we review state-of-the-art and present practical guidelines regarding experimental and computational aspects to be considered in molecular plant-microbiome studies. We discuss sequencing and "omics" techniques with a focus on the requirements needed to adapt these methods to individual research approaches. The choice of primers and sequence databases is of utmost importance for amplicon sequencing, while the assembly and binning of shotgun metagenomic sequences is crucial to obtain quality data. We discuss specific bioinformatic workflows to overcome the limitation of genome database resources and for covering large eukaryotic genomes such as fungi. In transcriptomics, it is necessary to account for the separation of host mRNA or dual-RNAseq data. Metaproteomics approaches provide a snapshot of the protein abundances within a plant tissue which requires the knowledge of complete and well-annotated plant genomes, as well as microbial genomes. Metabolomics offers a powerful tool to detect and quantify small molecules and molecular changes at the plant-bacteria interface if the necessary requirements with regard to (secondary) metabolite databases are considered. We highlight data integration and complementarity which should help to widen our understanding of the interactions among individual players of the plant holobiont in the future.

WebSpecmine: A Website for Metabolomics Data Analysis and Mining

Metabolomics data analysis is an important task in biomedical research. The available tools do not provide a wide variety of methods and data types, nor ways to store and share data and results generated. Thus, we have developed WebSpecmine to overcome the aforementioned limitations. WebSpecmine is a web-based application designed to perform the analysis of metabolomics data based on spectroscopic and chromatographic techniques (NMR, Infrared, UV-visible, and Raman, and LC/GC-MS) and compound concentrations. Users, even those not possessing programming skills, can access several analysis methods including univariate, unsupervised and supervised multivariate statistical analysis, as well as metabolite identification and pathway analysis, also being able to create accounts to store their data and results, either privately or publicly. The tool's implementation is based in the R project, including its shiny web-based framework. Webspecmine is freely available, supporting all major browsers. We provide abundant documentation, including tutorials and a user guide with case studies.

Metabolomic Markers for the Early Selection of Coffea canephora Plants with Desirable Cup Quality Traits

Genetic improvement of coffee plants represents a great challenge for breeders. Conventional breeding takes a too long time for responding timely to market demands, climatic variations and new biological threads. The correlation of genetic markers with the plant phenotype and final product quality is usually poor. Additionally, the creation and use of genetically modified organisms (GMOs) are often legally restricted and rejected by customers that demand natural products. Therefore, we developed a non-targeted metabolomics approach to accelerate conventional breeding. Our main idea was to identify highly heritable metabolites in Coffea canephora seedlings, which are linked to coffee cup quality. We employed a maternal half-sibs approach to estimate the metabolites heritability in open-pollinated plants in both leaves and fruits at an early plant development stage. We evaluated the cup quality of roasted beans and correlated highly heritable metabolites with sensory quality traits of the coffee beverage. Our results provide new insights about the heritability of metabolites of C. canephora plants. Furthermore, we found strong correlations between highly heritable metabolites and sensory traits of coffee beverage. We revealed metabolites that serve as predictive metabolite markers at an early development stage of coffee plants. Informed decisions can be made on plants of six months old, compared to 3.5 to 5 years using conventional selection methods. The metabolome-wide association study (MWAS) drastically accelerates the selection of C. canephora plants with desirable characteristics and represents a novel approach for the focused breeding of crops.

Elucidating the interaction of kansui and licorice by comparative plasma/tissue metabolomics and a heatmap with relative fold change

Although compatibility is highly advocated in traditional Chinese medicine (TCM), inappropriate combination of some herbs may reduce the therapeutic action and even produce toxic effects. Kansui and licorice, one of TCM "Eighteen Incompatible Medicaments", are the most representative cases of improper herbal combination, which may still be applied simultaneously under given conditions. However, the potential mechanism of their compatibility and incompatibility is unclear. In the present study, two different ratios of kansui and licorice, representing their compatibility and incompatibility respectively, were designed to elucidate their interaction by comparative plasma/tissue metabolomics and a heatmap with relative fold change. As a result, glycocholic acid, prostaglandin F2a, dihydroceramide and sphinganine were screened out as the principal alternative biomarkers of compatibility group; sphinganine, dihydroceramide, arachidonic acid, leukotriene B4, acetoacetic acid and linoleic acid were those of incompatibility group. Based on the values of biomarkers in each tissue, the liver was identified as the compatible target organ, while the heart, liver, and kidney were the incompatible target organs. Furthermore, important pathways for compatibility and incompatibility were also constructed. These results help us to better understand and utilize the two herbs, and the study was the first to reveal some innate characters of herbs related to TCM "Eighteen Incompatible Medicaments".

A fiber-deprived diet disturbs the fine-scale spatial architecture of the murine colon microbiome

Compartmentalization of the gut microbiota is thought to be important to system function, but the extent of spatial organization in the gut ecosystem remains poorly understood. Here, we profile the murine colonic microbiota along longitudinal and lateral axes using laser capture microdissection. We found fine-scale spatial structuring of the microbiota marked by gradients in composition and diversity along the length of the colon. Privation of fiber reduces the diversity of the microbiota and disrupts longitudinal and lateral gradients in microbiota composition. Both mucus-adjacent and luminal communities are influenced by the absence of dietary fiber, with the loss of a characteristic distal colon microbiota and a reduction in the mucosa-adjacent community, concomitant with depletion of the mucus layer. These results indicate that diet has not only global but also local effects on the composition of the gut microbiota, which may affect function and resilience differently depending on location.

Mass Spectrometry Fingerprints of Small-Molecule Metabolites in Biofluids: Building a Spectral Library of Recurrent Spectra for Urine Analysis

A large fraction of ions observed in electrospray liquid chromatography-mass spectrometry (LC-ESI-MS) experiments of biological samples remain unidentified. One of the main reasons for this is that spectral libraries of pure compounds fail to account for the complexity of the metabolite profiling of complex materials. Recently, the NIST Mass Spectrometry Data Center has been developing a novel type of searchable mass spectral library that includes all recurrent unidentified spectra found in the sample profile. These libraries, in conjunction with the NIST tandem mass spectral library, allow analysts to explore most of the chemical space accessible to LC-MS analysis. In this work, we demonstrate how these libraries can provide a reliable fingerprint of the material by applying them to a variety of urine samples, including an extremely altered urine from cancer patients undergoing total body irradiation. The same workflow is applicable to any other biological fluid. The selected class of acylcarnitines is examined in detail, and derived libraries and related software are freely available. They are intended to serve as online resources for continuing community review and improvement.

Plasma Lipid Profiling Identifies Biomarkers of Cerebral Microvascular Disease

Brain-specific sphingolipids (SLs) may serve as effective biomarkers of white matter hyperintensities (WMH). Here, we investigate the efficacy of SLs as a novel fluid-based biomarker to identify WMH reflective of chronic ischemia. Patients presenting to our stroke center for evaluation of acute neurological deficits were enrolled in the Advanced Serum Profiling in Recent Stroke (ASPIRE) study. From this cohort of 202 individuals, 58 patients who underwent an MRI and did not have a clinical stroke event were included in this study. Plasma samples were collected at the time of MRI, and targeted SL profiling was performed by HPLC/tandem mass spectrometry. T₂ FLAIR imaging was evaluated for WMH and scored according to the Fazekas scoring (FS) method and manually quantified. Twenty two SLs were definitively identified, consisting of ceramide (Cer) and sphingomyelin (SM) species. Of these, two sphingolipids, SM 38:1 and Cer 34:1, significantly correlated with high FS (r = 0.287, p = 0.029, and r = 0.356, p = 0.006, respectively) and were used in subsequent analysis. SM 38:1 (OR 1.129, 95% CI 1.032, 1.236, p = 0.008) and Cer 34:1 (OR 1.118, 95% CI 1.031, 1.212, p = 0.007), accurately differentiated between FS 0–2 vs. 2.5–6 in regression analysis. A combined lipid score demonstrated fair discrimination in ROC analysis (AUC = 0.729, p = 0.003) and was cross-validated using leave-one-out analysis. Plasma levels of brain-specific SLs may serve as effective biomarkers of subacute white matter disease.

Taro-corms mucilage-alginate microspheres for the sustained release of pregabalin: In vitro &in vivo evaluation

Taro corms mucilage (TCM)-alginate microspheres had been prepared using TCM and alginate as blend and coated form in various ratios through inotropic gelation approach. The prepared microspheres have been of sphere-formed having coarse surface with average particle size within the range 498 μm ± 0.17 to 715 μm ± 0.34. The drug entrapment efficiency was 74.33 ± 0.04% to 89.63 ± 0.01% and swelling of microspheres followed the pattern (blended >coated >plain). FTIR research showed that there had been no interactions among pregabalin and polymers used; these microspheres were further characterized by DSC and XRD. The in vitro drug release followed sustained release (Korsmeyer-Peppas model) pattern (R₂ = 0.9552-0.9906) and value of n > 1 showed that drug released by means of anomalous (non-Fickian) diffusion. The in vivo research established that there were highly significant difference with p < 0.001 within the pharmacokinetic parameters (C_max, t^½, AUC_0-∞, K_e), while pregabalin microspheres in comparison to pure drug. Therefore, it is concluded that blended microspheres has greater bioavailability for pregabalin with sustained release effect. This evolved that TCM has been proved to be emerging potential pharmaceutical excipient for sustained release drug delivery systems.

Using diphenyleneiodonium to induce a viable but non-culturable phenotype in Mycobacterium tuberculosis and its metabolomics analysis

Depletion of oxygen levels is a well-accepted model for induction of non-replicating, persistent states in mycobacteria. Increasing the stress levels in mycobacterium bacilli facilitates their entry into a non-cultivable, dormant state. In this study, it was shown that diphenyleneiodonium, an inhibitor of NADH oxidase, induced a viable, but non-culturable state in mycobacteria, having similar features to dormant bacilli, like loss of acid-fastness, upregulation of stress-regulated genes and decreased superoxide levels as compared to actively growing bacilli. Comprehensive, untargeted metabolic profiling also confirmed a decrease in biogenesis of amino acids, NAD, unsaturated fatty acids and nucleotides. Additionally, an increase in the level of lactate, fumarate, succinate and pentose phosphate pathways along with increased mycothiol and sulfate metabolites, similar to dormant bacilli, was observed in the granuloma. These non-cultivable bacilli were resuscitated by supplementation of fetal bovine serum, regaining their culturability in liquid as well as on agar medium. This study focused on the effect of diphenyleneiodonium treatment in causing mycobacteria to rapidly transition from an active state into a viable, but non-cultivable state, and comparing their characteristics with dormant phenotypes.

Opposing reactions in coenzyme A metabolism sensitize Mycobacterium tuberculosis to enzyme inhibition

Mycobacterium tuberculosis (Mtb) is the leading infectious cause of death in humans. Synthesis of lipids critical for Mtb's cell wall and virulence depends on phosphopantetheinyl transferase (PptT), an enzyme that transfers 4'-phosphopantetheine (Ppt) from coenzyme A (CoA) to diverse acyl carrier proteins. We identified a compound that kills Mtb by binding and partially inhibiting PptT. Killing of Mtb by the compound is potentiated by another enzyme encoded in the same operon, Ppt hydrolase (PptH), that undoes the PptT reaction. Thus, loss-of-function mutants of PptH displayed antimicrobial resistance. Our PptT-inhibitor cocrystal structure may aid further development of antimycobacterial agents against this long-sought target. The opposing reactions of PptT and PptH uncover a regulatory pathway in CoA physiology.

Determination of online quenching efficiency for an automated cellular microfluidic metabolomic platform using mass spectrometry based ATP degradation analysis

As microfluidic cell culture progresses, the need for robust and reproducible intracellular analyses grows. In particular, intracellular metabolites are subject to perturbation and degradation during the lysing process. The reliability of intracellular metabolomic analysis in microfluidic devices depends on the preservation of metabolite integrity during sample preparation and storage. Described here is a novel automated microfluidic system exhibiting the necessary rapid cellular lysis and quenching of enzymatic activity. Quenching efficiency was assessed using a novel ratiometric MALDI-MS-based assay of exogenous isotopic adenosine triphosphate (ATP) hydrolysis to isotopic adenosine diphosphate (ADP) as a marker of metabolite degradation. The lysis system of the microfluidic device was enhanced using a Peltier cooler to chill the lysate and quench aberrant enzymatic activity. Parameter optimization (flow rate, collection time, and temperature control) improved the endogenous and exogenous ADP/ATP ratios by 44.9% and 39.8% respectively consistent with traditional quenching techniques. The effects of chilling/quenching on metabolism were evaluated resulting in over 500 significant features compared to non-chilled from untargeted capillary LC-MS metabolomic analyses. These include increased levels of tryptophan, histidine, and pyruvate as well as decreased levels in UDP-N-acetylglucosamine. The results illustrate the need for both rapid lysis and quenching in microfluidic cell culture platforms. Graphical abstract.

BLANKA: an Algorithm for Blank Subtraction in Mass Spectrometry of Complex Biological Samples

Multispecies microbiome systems are known to be closely linked to human, animal, and plant life processes. The growing field of metabolomics presents the opportunity to detect changes in overall metabolomic profiles of microbial species interactions. These metabolomic changes provide insight into function of metabolites as they correlate to different species presence and the observed phenotypic changes, but detection of subtle changes is often difficult in samples with complex backgrounds. Natural environments such as soil and food contain many molecules that convolute mass spectrometry-based analyses, and identification of microbial metabolites amongst environmental metabolites is an informatics problem we begin to address here. Our microbes are grown on solid or liquid cheese curd media. This medium, which is necessary for microbial growth, contains high amounts of salts, lipids, and casein breakdown products which make statistical analyses using LC-MS/MS data difficult due to the high background from the media. We have developed a simple algorithm to carry out background subtraction from microbes grown on solid or liquid cheese curd media to aid in our ability to conduct statistical analyses so that we may prioritize metabolites for further structure elucidation. Graphical Abstract .

Skeletal Muscle Lipidomics as a New Tool to Determine Altered Lipid Homeostasis in Fish Exposed to Urban and Industrial Wastewaters

This work applies ultrahigh performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) to characterize for the first time the lipidome of the skeletal muscle of two fish species (Barbus meridionalis, Squalius laietanus) collected in a Mediterranean River affected by urban and industrial wastewater outflows. The untargeted analysis allowed a clear separation of the lipidome of fish from polluted and reference sites; phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), and their lyso and ether-linked forms were among the distinctive features. The targeted analysis consistently detected a decrease in PC-plasmalogens (36:4, 36:6, 38:6) and highly unsaturated PCs (36:5, 36:6, 38:6, 40:6, 40:7) and an increase in plasmanyl-PCs (36:5, 38:5), lyso-PCs (16:1, 18:1, 22:4) and cholesteryl esters (CEs) (16:0, 18:0, 20:4) in fish from polluted sites. These lipid profiles were indicative of oxidative stress and dysregulation of cholesterol homeostasis in fish from polluted sites. This methodology represents a promising tool for the development of novel noninvasive diagnostic methods based on muscle tissue biopsies to assess the effects of water pollution in wildlife.

Chemoselective probe for detailed analysis of ketones and aldehydes produced by gut microbiota in human samples

New strategies are required for the discovery of unknown bioactive molecules produced by gut microbiota in the human host. Herein, we utilize a chemoselective probe immobilized to magnetic beads for analysis of carbonyls in human fecal samples. We identified 112 metabolites due to femtomole analysis and an increased mass spectrometric sensitivity by up to six orders of magnitude.

Comprehensive metabolic profile analysis of the root bark of different species of tree peonies (Paeonia Sect. Moutan)

Tree peonies (Paeonia Sect. Moutan) are well-known for their medicinal and ornamental uses but most wild species in the Moutan section are endangered. The comprehensive metabolomics evaluation of tree peonies is essential to distinguish different species and to identify undescribed compounds, thereby elucidating the diversity of their metabolites and discovering potential active ingredients. In this study, the metabolome variations of root barks of nine species and their varieties collected from one botanical garden after years of localization were systematically investigated. A digital database of specialized metabolites was established to improve feature identification or annotation and various bio- and cheminformatics tools were employed to analyse and visualize the profiled metabolomic data. As a result, 384 compounds were identified or annotated, including various monoterpene glycosides, flavonoids, phenols, terpenoids and steroids, tannins, stilbenes and others. All samples were clearly divided into two subsections: Vaginatae and Delavayanae. The distribution and abundance of metabolites were also analysed and discussed in order to find potential biomarkers in different wild tree peonies.

Targeted and Untargeted Metabolomics as an Enhanced Tool for the Detection of Pomegranate Juice Adulteration

Pomegranate juice is one of the most popular fruit juices, is well-known as a "superfood", and plays an important role in healthy diets. Due to its constantly growing demand and high value, pomegranate juice is often targeted for adulteration, especially with cheaper substitutes such as apple and red grape juice. In the present study, the potential of applying a metabolomics approach to trace pomegranate juice adulteration was investigated. A novel methodology based on high-resolution mass spectrometric analysis was developed using targeted and untargeted screening strategies to discover potential biomarkers for the reliable detection of pomegranate juice adulteration from apple and red grape juice. Robust classification and prediction models were built with the use of unsupervised and supervised techniques (principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)), which were able to distinguish pomegranate juice adulteration to a level down to 1%. Characteristic m/z markers were detected, indicating pomegranate juice adulteration, and several marker compounds were identified. The results obtained from this study clearly demonstrate that Mass Spectrometry (MS)-based metabolomics have the potential to be used as a reliable screening tool for the rapid determination of food adulteration.

Determining the Mode of Action of Antimalarial Drugs Using Time-Resolved LC-MS-Based Metabolite Profiling

Methods for assessing the mode of action of new antimalarial compounds identified in high throughput phenotypic screens are needed to triage and facilitate lead compound development and to anticipate potential resistance mechanisms that might emerge. Here we describe a mass spectrometry-based approach for detecting metabolic changes in asexual erythrocytic stages of Plasmodium falciparum induced by antimalarial compounds. Time-resolved or concentration-resolved measurements are used to discriminate between putative targets of the compound and nonspecific and/or downstream secondary metabolic effects. These protocols can also be coupled with ¹³C-stable-isotope tracing experiments under nonequilibrative (or nonstationary) conditions to measure metabolic dynamics following drug exposure. Time-resolved ¹³C-labeling studies greatly increase confidence in target assignment and provide a more comprehensive understanding of the metabolic perturbations induced by small molecule inhibitors. The protocol provides details on the experimental design, Plasmodium falciparum culture, sample preparation, analytical approaches, and data analysis used in either targeted (pathway focused) or untargeted (all detected metabolites) analysis of drug-induced metabolic perturbations.

A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments

Marker-based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F1 hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F1 hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate-adaptive varieties.

Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics

Recent developments in high-resolution mass spectrometry (HRMS) technology enabled ultrasensitive detection of proteins, peptides, and metabolites in limited amounts of samples, even single cells. However, extraction of trace-abundance signals from complex data sets ( m/ z value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. To bridge this gap, we here developed "Trace", a software framework that incorporates machine learning (ML) to automate feature selection and optimization for the extraction of trace-level signals from HRMS data. The method was validated using primary (raw) and manually curated data sets from single-cell metabolomic studies of the South African clawed frog ( Xenopus laevis) embryo using capillary electrophoresis electrospray ionization HRMS. We demonstrated that Trace combines sensitivity, accuracy, and robustness with high data processing throughput to recognize signals, including those previously identified as metabolites in single-cell capillary electrophoresis HRMS measurements that we conducted over several months. These performance metrics combined with a compatibility with MS data in open-source (mzML) format make Trace an attractive software resource to facilitate data analysis for studies employing ultrasensitive high-resolution MS.

A non-targeted LC-HRMS approach for detecting exposure to illegal veterinary treatments: The case of cephalosporins in commercial laying Hens

Cephalosporins are of particular importance in human medicine and should be reserved for second-line curative treatment in the veterinary field to avoid any emerging antimicrobial resistance. Due to misuse of ceftiofur in the poultry sector in France, it is now recommended to completely stop using cephalosporins in this sector. Methods currently used for the control of veterinary practices are mostly based on liquid chromatography coupled to mass spectrometry in a targeted mode, including parent compounds and any major metabolites. The aim of the present study was to evaluate the relevance of untargeted metabolomic approaches to highlight a possible exposure of laying hens to cephalosporins using a predictive model including selected treatment biomarkers. An experimentation carried out on living animals involved the administration of cefquinome and ceftiofur. Three biological matrices-droppings, eggs and liver-were investigated. Metabolites were extracted and analysed by liquid chromatography coupled to high resolution mass spectrometry in a full scan mode. Metabolites impacted by the treatment were selected by using univariate and multivariate statistical analyses. Predictive models built from the potential biomarkers selected in the "droppings" matrix were validated and able to classify "treated" and "control" hens. PLS-DA and logistic regression models were compared and both models gave satisfactory results in terms of prediction. Results were of less interest for other matrices in which only biomarkers of exposure to cefquinome were detected.

Untargeted Metabolomics Analytical Strategy Based on Liquid Chromatography/Electrospray Ionization Linear Ion Trap Quadrupole/Orbitrap Mass Spectrometry for Discovering New Polyphenol Metabolites in Human Biofluids after Acute Ingestion of Vaccinium myrtillus Berry Supplement

In this work, liquid chromatography, coupled with an electrospray ionization hybrid linear ion trap quadrupole/Orbitrap mass spectrometry, has been used to accurately identify polyphenol metabolites in human serum and urine after acute ingestion of a V. myrtillus berry supplement. The supplement was obtained by cryo-milling of bilberries, which were freeze-dried within 1 week after their harvesting, to maintain the berry native composition. Thirty-six derivatives of benzoic acids, hydroxyhippuric acids, cinnamic acids, phenylpropionic acids, phenylvaleric acids, phenylpentenoic acids and abscisic acid, together with two berry-native anthocyanins, one flavonol metabolite and two catechol derivatives were putatively identified in the investigated biofluids. The annotated compounds included 13 metabolites, among glucuronides and sulphates of phenylvaleric and phenylpentenoic acids, which have been identified for the first time in human biofluids after ingestion of V. myrtillus berries. It should be emphasized that the presence of phenylvaleric and phenylpentenoic acid derivatives is in agreement with their origin from fruit native flavanol monomers and oligomers, which are widely distributed in Vaccinium berries, but usually overlooked in metabolomics studies regarding bilberry. The identification of these compounds confirmed the key-role of untargeted metabolomics approach in the discovery of new metabolites which could be biologically active. Graphical Abstract.