DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans-Pseudomonas aeruginosa infection model

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Microbiota independent effects of oligosaccharides on Caco-2 cells -A semi-targeted metabolomics approach using DI-FT-ICR-MS coupled with pathway enrichment analysis

Milk oligosaccharides (MOS) and galactooligosaccharides (GOS) are associated with many benefits, including anti-microbial effects and immune-modulating properties. However, the cellular mechanisms of these are largely unknown. In this study, the effects of enriched GOS and MOS mixtures from caprine and bovine milk consisting mainly 6'-galactosyllactose, 3'-sialyllactose, and 6'-sialyllactose on Caco-2 cells were investigated, and the treatment-specific metabolomes were described. In the control, the cells were treated with a sugar mix consisting of one-third each of glucose, galactose and lactose. A local metabolomics workflow with pathway enrichment was established, which specifically addresses DI-FT-ICR-MS analyses and includes adaptations in terms of measurement technology and sample matrices. By including quality parameters, especially the isotope pattern, we increased the precision of annotation. The independence from online tools, the fast adaptability to changes in databases, and the specific adjustment to the measurement technology and biomaterial used, proved to be a great advantage. For the first time it was possible to find 71 active pathways in a Caco-2 cell experiment. These pathways were assigned to 12 main categories, with amino acid metabolism and carbohydrate metabolism being the most dominant categories in terms of the number of metabolites and metabolic pathways. Treatment of Caco-2 cells with high GOS and glucose contents resulted in significant effects on several metabolic pathways, whereas the MOS containing treatments resulted only for individual metabolites in significant changes. An effect based on bovine or caprine origin alone could not be observed. Thus, it was shown that MOS and GOS containing treatments can exert microbiome-independent effects on the metabolome of Caco-2 cells.

N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry-based metabolomics

Chromatographic retention time information is valuable, orthogonal information to MS and MS/MS data that can be used in metabolite identification. However, while comparison of MS data between different instruments is possible to a certain degree, retention times (RTs) can vary extensively, even when nominally the same phase system is used. Different factors such as column dead volumes, system extra column volume, and gradient dwell volume can influence absolute retention times. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for deviations in experimental conditions. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics to the same extent as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinium sulfonates (NAPS). These reference standards ionize in both positive and negative ionization modes and are UV-active. We demonstrate the NAPS can be used for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry (RP-LC–MS)–based metabolomics. Having measured >500 metabolite standards and varying flow rate and column dimension, we show that conversion of RT to retention indices (RI) substantially improves comparability of retention information and enables to use of RI for metabolite annotation and identification.

Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation

Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.

Quo Vadis Caenorhabditis elegans Metabolomics-A Review of Current Methods and Applications to Explore Metabolism in the Nematode

Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.

Synergizing the potential of bacterial genomics and metabolomics to find novel antibiotics

Antibiotic development based on natural products has faced a long lasting decline since the 1970s, while both the speed and the extent of antimicrobial resistance (AMR) development have been severely underestimated. The discovery of antimicrobial natural products of bacterial and fungal origin featuring new chemistry and previously unknown mode of actions is increasingly challenged by rediscovery issues. Natural products that are abundantly produced by the corresponding wild type organisms often featuring strong UV signals have been extensively characterized, especially the ones produced by extensively screened microbial genera such as streptomycetes. Purely synthetic chemistry approaches aiming to replace the declining supply from natural products as starting materials to develop novel antibiotics largely failed to provide significant numbers of antibiotic drug leads. To cope with this fundamental issue, microbial natural products science is being transformed from a 'grind-and-find' study to an integrated approach based on bacterial genomics and metabolomics. Novel technologies in instrumental analytics are increasingly employed to lower detection limits and expand the space of detectable substance classes, while broadening the scope of accessible and potentially bioactive natural products. Furthermore, the almost exponential increase in publicly available bacterial genome data has shown that the biosynthetic potential of the investigated strains by far exceeds the amount of detected metabolites. This can be judged by the discrepancy between the number of biosynthetic gene clusters (BGC) encoded in the genome of each microbial strain and the number of secondary metabolites actually detected, even when considering the increased sensitivity provided by novel analytical instrumentation. In silico annotation tools for biosynthetic gene cluster classification and analysis allow fast prioritization in BGC-to-compound workflows, which is highly important to be able to process the enormous underlying data volumes. BGC prioritization is currently accompanied by novel molecular biology-based approaches to access the so-called orphan BGCs not yet correlated with a secondary metabolite. Integration of metabolomics, in silico genomics and molecular biology approaches into the mainstream of natural product research will critically influence future success and impact the natural product field in pharmaceutical, nutritional and agrochemical applications and especially in anti-infective research.

Phillyrin is an effective inhibitor of quorum sensing with potential as an anti-Pseudomonas aeruginosa infection therapy

In the present study, we evaluated the antibacterial and anti-quorum sensing qualities of phillyrin. The minimum inhibitory concentration (MIC) of phillyrin with regard to Pseudomonas aeruginosa is 0.5 mg/ml. The production of virulence factors—such as rhamnolipid (>78.69%), pyocyanin (>85.94%), and elastase (>89.95%)—that affect the pathogenicity of the P. aeruginosa strain PAO1 apparently declined in the presence of 0.25 mg/ml phillyrin. Biofilm formation decreased by 84.48%. In a Caenorhabditis elegans–Pseudomonas aeruginosa infection model, diseased worms lived longer (63.33%) in a phillyrin-containing medium than in a drug-free medium, and the drug did not directly kill the pathogen. Therefore, the present work suggests that phillyrin has potential as an antimicrobial agent for the control of infectious pathogens.

Bio- and Chemoinformatics Approaches for Metabolomics Data Analysis

Metabolomics data analysis includes several repetitive tasks, including data sorting, calculation of exact masses or other physicochemical properties, or searching for identifiers in different databases. Several of these tasks can be automated using command line tools or short scripts in different scripting languages like Perl, Python, or R. This chapter presents simple solutions and short scripts written in R that can be used for the interaction with specific web services or for the calculation of physicochemical properties or molecular formulae.

Chemical messages from an ancient buried bottle: metabolomics for wine archeochemistry

Restoration works in the old Clunisian Saint-Vivant monastery in Burgundy revealed an unidentified wine bottle (SV1) dating between 1772 and 1860. Chemical evidence for SV1 origin and nature are presented here using non-targeted Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and Nuclear Magnetic Resonance analyses. The SV1 chemical diversity was compared to red wines (Pinot Noir) from the Romanée Saint Vivant appellation and from six different vintages spanning from 1915 to 2009. The close metabolomic signature between SV1 and Romanée Saint Vivant wines spoke in favor of a filiation between these wines, in particular considering the Pinot noir grape variety. A further statistical comparison with up to 77 Pinot noir wines from Burgundy and vintages from nearly all the 20th century, confirmed that SV1 must have been made more than one hundred years ago. The increasing number of detected high masses and of nitrogen containing compounds with the ageing of the wine was in accordance with known ageing mechanisms. Besides, resveratrol was shown here to be preserved for more than one hundred years in wine. For the first time, the age of an old unknown wine along with its grape variety have been assessed through non-targeted metabolomic analyses.

For the first time, non-targeted metabolomics analyses were employed to assess the age of an unlabeled old wine together with its grape variety. Roullier-Gall at the Technische Universitat Munchen and coworkers from both Germany and France identified the metabolomics of unknown wine SV1 using high resolution FT-ICR Mass Spectrometry in combination with NMR. By comparing the metabolic signature of SV1 with the reference wines, it was found the SV1 wine was more than 100 years old and its grape variety was close to the Pinot Noir from the Romanée Saint Vivant appellation. Moreover, this was the first time that resveratrol was observed in wines of more than one hundred years. This archeochemical method can serve as an alternative approach to the commonly used genetic-based techniques on the wine authentication.

Digging into the low molecular weight peptidome with the OligoNet web server

Bioactive peptides play critical roles in regulating many biological processes. Recently, natural short peptides biomarkers are drawing significant attention and are considered as “hidden treasure” of drug candidates. High resolution and high mass accuracy provided by mass spectrometry (MS)-based untargeted metabolomics would enable the rapid detection and wide coverage of the low-molecular-weight peptidome. However, translating unknown masses (<1 500 Da) into putative peptides is often limited due to the lack of automatic data processing tools and to the limit of peptide databases. The web server OligoNet responds to this challenge by attempting to decompose each individual mass into a combination of amino acids out of metabolomics datasets. It provides an additional network-based data interpretation named “Peptide degradation network” (PDN), which unravels interesting relations between annotated peptides and generates potential functional patterns. The ab initio PDN built from yeast metabolic profiling data shows a great similarity with well-known metabolic networks, and could aid biological interpretation. OligoNet allows also an easy evaluation and interpretation of annotated peptides in systems biology, and is freely accessible at https://daniellyz200608105.shinyapps.io/OligoNet/.

Sulfonolipids as novel metabolite markers of Alistipes and Odoribacter affected by high-fat diets

The gut microbiota generates a huge pool of unknown metabolites, and their identification and characterization is a key challenge in metabolomics. However, there are still gaps on the studies of gut microbiota and their chemical structures. In this investigation, an unusual class of bacterial sulfonolipids (SLs) is detected in mouse cecum, which was originally found in environmental microbes. We have performed a detailed molecular level characterization of this class of lipids by combining high-resolution mass spectrometry and liquid chromatography analysis. Eighteen SLs that differ in their capnoid and fatty acid chain compositions were identified. The SL called “sulfobacin B” was isolated, characterized, and was significantly increased in mice fed with high-fat diets. To reveal bacterial producers of SLs, metagenome analysis was acquired and only two bacterial genera, i.e., Alistipes and Odoribacter, were revealed to be responsible for their production. This knowledge enables explaining a part of the molecular complexity introduced by microbes to the mammalian gastrointestinal tract and can be used as chemotaxonomic evidence in gut microbiota.

Foodomics and Food Safety: Where We Are

The power of foodomics as a discipline that is now broadly used for quality assurance of food products and adulteration identification, as well as for determining the safety of food, is presented. Concerning sample preparation and application, maintenance of highly sophisticated instruments for both high-performance and high-throughput techniques, and analysis and data interpretation, special attention has to be paid to the development of skilled analysts. The obtained data shall be integrated under a strong bioinformatics environment. Modern mass spectrometry is an extremely powerful analytical tool since it can provide direct qualitative and quantitative information about a molecule of interest from only a minute amount of sample. Quality of this information is influenced by the sample preparation procedure, the type of mass spectrometer used and the analyst's skills. Technical advances are bringing new instruments of increased sensitivity, resolution and speed to the market. Other methods presented here give additional information and can be used as complementary tools to mass spectrometry or for validation of obtained results. Genomics and transcriptomics, as well as affinity-based methods, still have a broad use in food analysis. Serious drawbacks of some of them, especially the affinity-based methods, are the cross-reactivity between similar molecules and the influence of complex food matrices. However, these techniques can be used for pre-screening in order to reduce the large number of samples. Great progress has been made in the application of bioinformatics in foodomics. These developments enabled processing of large amounts of generated data for both identification and quantification, and for corresponding modeling.

Characterization of poplar metabotypes via mass difference enrichment analysis

Instrumentation technology for metabolomics has advanced drastically in recent years in terms of sensitivity and specificity. Despite these technical advances, data analytical strategies are still in their infancy in comparison with other 'omics'. Plants are known to possess an immense diversity of secondary metabolites. Typically, more than 70% of metabolomics data are not amenable to systems biological interpretation because of poor database coverage. Here, we propose a new general strategy for mass-spectrometry-based metabolomics that incorporates all exact mass features with known sum formulas into the evaluation and interpretation of metabolomics studies. We extend the use of mass differences, commonly used for feature annotation, by redefining them as variables that reflect the remaining 'omic' domains. The strategy uses exact mass difference network analyses exemplified for the metabolomic description of two grey poplar (Populus × canescens) genotypes that differ in their capability to emit isoprene. This strategy established a direct connection between the metabotype and the non-isoprene-emitting phenotype, as mass differences pertaining to prenylation reactions were over-represented in non-isoprene-emitting poplars. Not only was the analysis of mass differences able to grasp the known chemical biology of poplar, but it also improved the interpretability of yet unknown biochemical relationships.

Insights into host-pathogen interactions from state-of-the-art animal models of respiratory Pseudomonas aeruginosa infections

Pseudomonas aeruginosa is an important opportunistic pathogen that can cause acute respiratory infections in immunocompetent patients or chronic infections in immunocompromised individuals and in patients with cystic fibrosis. When acquiring the chronic infection state, bacteria are encapsulated within biofilm structures enabling them to withstand diverse environmental assaults, including immune reactions and antimicrobial therapy. Understanding the molecular interactions within the bacteria, as well as with the host or other bacteria, is essential for developing innovative treatment strategies. Such knowledge might be accumulated in vitro. However, it is ultimately necessary to confirm these findings in vivo. In the present Review, we describe state-of-the-art in vivo models that allow studying P. aeruginosa infections in molecular detail. The portrayed mammalian models exclusively focus on respiratory infections. The data obtained by alternative animal models which lack lung tissue, often provide molecular insights that are easily transferable to mammals. Importantly, these surrogate in vivo systems reveal complex molecular interactions of P. aeruginosa with the host. Herein, we also provide a critical assessment of the advantages and disadvantages of such models.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics

BACKGROUND

Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve.

RESULTS

ICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained.

CONCLUSIONS

Correlating the components obtained with prior biological knowledge allows understanding how non-targeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://github.com/daniellyz/MetICA.

Regulatory role of hexosamine biosynthetic pathway on hepatic cancer stem cell marker CD133 under low glucose conditions

Cancer was hypothesized to be driven by cancer stem cells (CSCs), but the metabolic determinants of CSC-like phenotype still remain elusive. Here, we present that hexosamine biosynthetic pathway (HBP) at least in part rescues cancer cell fate with inactivation of glycolysis. Firstly, metabolomic analysis profiled cellular metabolome in CSCs of hepatocellular carcinoma using CD133 cell-surface marker. The metabolic signatures of CD133-positive subpopulation compared to CD133-negative cells highlighted HBP as one of the distinct metabolic pathways, prompting us to uncover the role of HBP in maintenance of CSC-like phenotype. To address this, CSC-like phenotypes and cell survival were investigated in cancer cells under low glucose conditions. As a result, HBP inhibitor azaserine reduced CD133-positive subpopulation and CD133 expression under high glucose condition. Furthermore, treatment of N-Acetylglucosamine in part restores CD133-positive subpopulation when either 2.5 mM glucose in culture media or glycolytic inhibitor 2-deoxy-D-glucose in HCC cell lines was applied, enhancing CD133 expression as well as promoting cancer cell survival. Together, HBP might be a key metabolic determinant in the functions of hepatic CSC marker CD133.

UHPLC-FT-ICR-MS combined with serum pharmacochemistry for bioactive compounds discovery of Zhi-Zi-Da-Huang-decoction against alcohol-induced hepatotoxicity in rats

In this study, a comprehensive strategy based on UHPLC-FT-ICR-MS and serum pharmacochemistry was developed to reveal the bioactive constituents of Zhi-Zi-Da-Huang decoction against alcohol-induced hepatotoxicity in rats after oral administration.

The Role of Dafachronic Acid Signaling in Development and Longevity in Caenorhabditis elegans: Digging Deeper Using Cutting-Edge Analytical Chemistry

Steroid hormones regulate physiological processes in species ranging from plants to humans. A wide range of steroid hormones exist, and their contributions to processes, such as growth, reproduction, development, and aging, is almost always complex. Understanding the biosynthetic pathways that generate steroid hormones and the signaling pathways that mediate their effects is thus of fundamental importance. In this work, we review recent advances in (i) the biological role of steroid hormones in the roundworm Caenorhabditis elegans and (ii) the development of novel methods to facilitate the detection and identification of these molecules. Our current understanding of steroid signaling in this simple organism serves to illustrate the challenges we face moving forward. First, it seems clear that we have not yet identified all of the enzymes responsible for steroid biosynthesis and/or degradation. Second, perturbation of steroid signaling affects a wide range of phenotypes, and subtly different steroid molecules can have distinct effects. Finally, steroid hormone levels are critically important, and minute variations in quantity can profoundly impact a phenotype. Thus, it is imperative that we develop innovative analytical tools and combine them with cutting-edge approaches including comprehensive and highly selective liquid chromatography coupled to mass spectrometry based on new methods such as supercritical fluid chromatography coupled to mass spectrometry (SFC-MS) if we are to obtain a better understanding of the biological functions of steroid signaling.

Changes in Brain Metallome/Metabolome Pattern due to a Single i.v. Injection of Manganese in Rats

Exposure to high concentrations of Manganese (Mn) is known to potentially induce an accumulation in the brain, leading to a Parkinson related disease, called manganism. Versatile mechanisms of Mn-induced brain injury are discussed, with inactivation of mitochondrial defense against oxidative stress being a major one. So far, studies indicate that the main Mn-species entering the brain are low molecular mass (LMM) compounds such as Mn-citrate. Applying a single low dose MnCl₂ injection in rats, we observed alterations in Mn-species pattern within the brain by analysis of aqueous brain extracts by size-exclusion chromatography—inductively coupled plasma mass spectrometry (SEC-ICP-MS). Additionally, electrospray ionization—ion cyclotron resonance-Fourier transform-mass spectrometry (ESI-ICR/FT-MS) measurement of methanolic brain extracts revealed a comprehensive analysis of changes in brain metabolisms after the single MnCl₂ injection. Major alterations were observed for amino acid, fatty acid, glutathione, glucose and purine/pyrimidine metabolism. The power of this metabolomic approach is the broad and detailed overview of affected brain metabolisms. We also correlated results from the metallomic investigations (Mn concentrations and Mn-species in brain) with the findings from metabolomics. This strategy might help to unravel the role of different Mn-species during Mn-induced alterations in brain metabolism.