The underappreciated diversity of bile acid modifications.

The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.

Two-Dimensional Liquid Chromatography Tandem Mass Spectrometry Untangles the Deep Metabolome of Marine Dissolved Organic Matter.

Dissolved organic matter (DOM) is an ultracomplex mixture that plays a central role in global biogeochemical cycles. Despite its importance, DOM remains poorly understood at the molecular level. Over the last decades, significant efforts have been made to decipher the chemical composition of DOM by high-resolution mass spectrometry (HR-MS) and liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS). Yet, the complexity and high degree of nonresolved isomers still hamper the full structural analysis of DOM. To address this challenge, we developed an offline two-dimensional (2D) LC approach using two reversed-phase dimensions with orthogonal pH levels, followed by MS/MS data acquisition and molecular networking. 2D-LC-MS/MS reduced the complexity of DOM, enhancing the quality of MS/MS spectra and increasing spectral annotation rates. Applying our approach to analyze coastal-surface DOM from Southern California (USA) and open-ocean DOM from the central North Pacific (Hawaii), we annotated in total more than 600 structures via MS/MS spectrum matching, which was up to 90% more than that in iterative 1D LC-MS/MS analysis with the same total run time. Our data offer unprecedented insights into the molecular composition of marine DOM and highlight the potential of 2D-LC-MS/MS approaches to decipher the chemical composition of ultracomplex samples.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform.

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

Tattoo Pigment Identification in Inks and Skin Biopsies of Adverse Reactions by Complementary Elemental and Molecular Bioimaging with Mass Spectral Library Matching.

Tattooing has become increasingly popular throughout society. Despite the recognized issue of adverse reactions in tattoos, regulations remain challenging with limited data available and a missing positive list. The diverse chemical properties of mostly insoluble inorganic and organic pigments pose an outstanding analytical challenge, which typically requires extensive sample preparation. Here, we present a multimodal bioimaging approach combining micro X-ray fluorescence (µXRF) and laser desorption ionization-mass spectrometry (LDI-MS) to detect the elemental and molecular composition in the same sample. The pigment structures directly absorb the laser energy, eliminating the need for matrix application. A computational data processing workflow clusters spatially resolved LDI-MS scans to merge redundant information into consensus spectra, which are then matched against new open mass spectral libraries of tattoo pigments. When applied to 13 tattoo inks and 68 skin samples from skin biopsies in adverse tattoo reactions, characteristic signal patterns of isotopes, ion adducts, and in-source fragments in LDI-MS1 scans yielded confident compound annotations across various pigment classes. Combined with µXRF, pigment annotations were achieved for all skin samples with 14 unique structures and 2 inorganic pigments, emphasizing the applicability to larger studies. The tattoo-specific spectral libraries and further information are available on the tattoo-analysis.github.io website.

Feature-based molecular networking for identification of organic micropollutants including metabolites by non-target analysis applied to riverbank filtration.

Due to growing concern about organic micropollutants and their transformation products (TP) in surface and drinking water, reliable identification of unknowns is required. Here, we demonstrate how non-target liquid chromatography (LC)-high-resolution tandem mass spectrometry (MS/MS) and the feature-based molecular networking (FBMN) workflow provide insight into water samples from four riverbank filtration sites with different redox conditions. First, FBMN prioritized and connected drinking water relevant and seasonally dependent compounds based on a modification-aware MS/MS cosine similarity. Within the resulting molecular networks, forty-three compounds were annotated. Here, carbamazepine, sartans, and their respective TP were investigated exemplarily. With chromatographic information and spectral similarity, four additional TP (dealkylated valsartan, dealkylated irbesartan, two oxygenated irbesartan isomers) and olmesartan were identified and partly verified with an authentic standard. In this study, sartans and TP were investigated and grouped regarding their removal behavior under different redox conditions and seasons for the first time. Antihypertensives were grouped into compounds being well removed during riverbank filtration, those primarily removed under anoxic conditions, and rather persistent compounds. Observed seasonal variations were mainly limited to varying river water concentrations. FBMN is a powerful tool for identifying previously unknown or unexpected compounds and their TP in water samples by non-target analysis.

Expanding the Kendrick Mass Plot Toolbox in MZmine 2 to Enable Rapid Polymer Characterization in Liquid Chromatography-Mass Spectrometry Data Sets.

Technological advances in mass spectrometry (MS) toward more accurate and faster data acquisition result in highly informative but also more complex data sets. Especially the hyphenation of liquid chromatography (LC) and MS yields large data files containing a high amount of compound specific information. Using electrospray-ionization for compounds such as polymers enables highly sensitive detection, yet results in very complex spectra, containing multiply charged ions and adducts. Recent years have seen the development of novel or updated data mining strategies to reduce the MS spectra complexity and to ultimately simplify the data analysis workflow. Among other techniques, the Kendrick mass defect analysis, which graphically highlights compounds containing a given repeating unit, has been revitalized with applications in multiple fields of study, such as lipids and polymers. Especially for the latter, various data mining concepts have been developed, which extend regular Kendrick mass defect analysis to multiply charged ion series. The aim of this work is to collect and subsequently implement these concepts in one of the most popular open-source MS data mining software, i.e., MZmine 2, to make them rapidly available for different MS based measurement techniques and various vendor formats, with a special focus on hyphenated techniques such as LC-MS. In combination with already existing data mining modules, an example data set was processed and simplified, enabling an ever faster evaluation and polymer characterization.

Fast Online Separation and Identification of Electrochemically Generated Isomeric Oxidation Products by Trapped Ion Mobility-Mass Spectrometry.

Trapped ion mobility spectrometry (TIMS) is presented as a new and rapid method to distinguish between electrochemically generated isomeric oxidation products. Separation was performed online directly after generation and ionization of the analytes, thus providing the opportunity to detect even short-lived and reactive transformation products. The same setup enables structure elucidation based on TIMS aligned fragmentation experiments. Due to the high resolution, TIMS was able to distinguish between two isomeric transformation products of the model compound metoprolol, which only differ in the position of the hydroxylation taking place in the benzylic and aromatic positions, respectively. Using this method, the analysis time is at least five times shorter compared to conventional chromatography approaches. Consequently, TIMS may arise as a powerful tool in electrochemical metabolism studies.

Identification of potential human urinary biomarkers for tomato juice intake by mass spectrometry-based metabolomics.

PURPOSE: Dietary biomarkers allow the accurate and objective determination of the dietary intake of humans and can thus be valuable for investigating the relation between consumption of foods and biochemical as well as physiological responses. The objective of this study was the identification of potential urinary biomarkers for consumption of tomato juice. METHODS: In the course of a dietary intervention study, the human urine metabolome of a study cohort was compared between a tomato-free diet and after intake of tomato juice by application of an LC-HRMS-based metabolomics approach. The data acquisition was achieved using an orbitrap mass spectrometer, followed by multistage data processing and univariate as well as multivariate statistical analysis to identify discriminating features. RESULTS: Statistical analysis revealed several unique features detectable after tomato juice intake. The most discriminating markers were putatively identified as hydroxylated and sulfonated metabolites of esculeogenin B, aglycone of the steroidal glycoalkaloid esculeoside B recently found in tomato juice. Furthermore, the ß-carboline alkaloids tangutorid E and F and glucuronidated derivatives thereof were identified in urine. CONCLUSIONS: Steroidal glycoalkaloids in tomato juice are cleaved after ingestion, and hydroxylated and sulfonated metabolites of their aglycones might serve as urinary biomarkers for tomato juice intake. Similarly, ß-carboline alkaloids and glucuronidated derivatives were identified as potential urinary biomarkers. Both the aglycones of the steroidal alkaloids and the ß-carboline alkaloids might exhibit biological activities worth investigating.

Lipid Species Annotation at Double Bond Position Level with Custom Databases by Extension of the MZmine 2 Open-Source Software Package.

In recent years, proprietary and open-source bioinformatics software tools have been developed for the identification of lipids in complex biological samples based on high-resolution mass spectrometry data. These existent software tools often rely on publicly available lipid databases, such as LIPID MAPS, which, in some cases, only contain a limited number of lipid species for a specific lipid class. Other software solutions implement their own lipid species databases, which are often confined regarding implemented lipid classes, such as phospholipids. To address these drawbacks, we provide an extension of the widely used open-source metabolomics software MZmine 2, which enables the annotation of detected chromatographic features as lipid species. The extension is designed for straightforward generation of a custom database for selected lipid classes. Furthermore, each lipid's sum formula of the created database can be rapidly modified to search for derivatization products, oxidation products, in-source fragments, or adducts. The versatility will be exemplified by a liquid chromatography-high resolution mass spectrometry data set with postcolumn Paternò-Büchi derivatization. The derivatization reaction was performed to pinpoint the double bond positions in diacylglyceryltrimethylhomoserine lipid species in a lipid extract of a green algae ( Chlamydomonas reinhardtii) sample. The developed Lipid Search module extension of MZmine 2 supports the identification of lipids as far as double bond position level.

Three-dimensional Kendrick mass plots as a tool for graphical lipid identification.

RATIONALE: The rising field of lipidomics strongly relies on the identification of lipids in complex matrices. Recent technical advances regarding liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) enable the mapping of the lipidome of an organism with short data acquisition times. However, interpretation and evaluation of resulting multidimensional datasets are challenging and this is still the bottleneck regarding overall analysis times. METHODS: A novel adaption of Kendrick mass plot analysis is presented for a rapid and accurate analysis of lipids in complex matrices. Separation of lipids by their respective head group was achieved via hydrophilic interaction liquid chromatography (HILIC) coupled to HRMS. The resulting LC/HRMS datasets are processed to a list of chromatographically separated features by applying an optimized MZmine 2 workflow. All features are plotted in a three-dimensional Kendrick mass plot, which allows a fast identification of present lipid classes, based on equidistant features with fitting retention times and the same Kendrick mass defect. Suspected lipid classes are used for exact mass database matching to annotate features. A second three-dimensional Kendrick mass plot of annotated features of a single lipid class helps to reveal potential database mismatches, resulting in a curated list of identified lipid species. RESULTS: The use of the novel adaption of the Kendrick mass plot has accelerated the identification of the relevant lipid species in the green alga Chlamydomonas reinhardtii. A total of 106 species were identified within the lipid classes: phosphatidylserine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol, and sulfoquinovosyldiacylglycerol. CONCLUSIONS: This work shows how the addition of chromatographic information, i.e. the retention time, to a classical two-dimensional Kendrick mass plot enables rapid and accurate analysis of LC/HRMS datasets, exemplified on a green alga (C. reinhardtii) sample. Three-dimensional Kendrick mass plots have improved lipid class identification and fast spotting of falsely annotated lipid species.

A Fungal N-Dimethylallyltryptophan Metabolite from Fusarium fujikuroi.

The range of secondary metabolites (SMs) produced by the rice pathogen Fusarium fujikuroi is quite broad. Several polyketides, nonribosomal peptides and terpenes have been identified. However, no products of dimethylallyltryptophan synthases (DMATSs) have been elucidated, although two putative DMATS genes are present in the F. fujikuroi genome. In this study, the in vivo product derived from one of the DMATSs (DMATS1, FFUJ_09179) was identified with the help of the software MZmine 2. Detailed structure elucidation showed that this metabolite is a reversely N-prenylated tryptophan with a rare form of prenylation. Further identified products probably resulted from side reactions of DMATS1. The genes adjacent to DMATS1 were analyzed; this showed no influence on the biosynthesis of the product.

Seroprevalence of Mycobacterium avium subsp. paratuberculosis in Swiss dairy herds and risk factors for a positive herd status and within-herd prevalence.

Introduction: Bovine paratuberculosis (PTB) is a chronic enteric disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). Control of PTB is important given its negative economic consequences and the potential zoonotic role of MAP in Crohn's disease in humans. Methods: To determine the seroprevalence of MAP in Swiss dairy herds and to identify risk factors associated with seropositive herd status and high within-herd seroprevalence, 10,063 serum samples collected from cattle over 12 months of age in 171 Swiss dairy farms were analyzed using a commercial ELISA test. Eight herds were excluded due to non-interpretable ELISA results. Risk factors associated with seropositive herd status and high within-herd seroprevalence were investigated with regression models using results from a questionnaire on management practices possibly associated with the introduction or spread of MAP in the remaining 163 herds. Univariable logistic regression was performed, carrying forward for multivariable regression analysis when p < 0.2. Results: The calculated between-herd true seroprevalence was 3.6% (95% CI, 0.96-8.4%). Due to the low within-herd seroprevalence, it was not possible to calculate the true seroprevalence at animal level; the apparent within-herd seroprevalence ranged from 2.3 to 5.5% with a median of 3.6% in nine positive farms. Herd size (p = 0.037) and the common grazing of lactating cows with cows from other herds (p = 0.014) were associated with seropositive herd status, while heifers sharing alpine pasture with dairy cattle from other herds were associated with a decreased probability of the herd to test seropositive (p = 0.042). Reliable identification of significant risk factors associated with MAP spread and high seroprevalence of PTB within seropositive herds was not possible due to low observed seroprevalence within herds and low sensitivity of the ELISA test. Discussion: These results highlight the limitation of serology for MAP diagnosis in small herds with low infection prevalence.

Native metabolomics for mass spectrometry-based siderophore discovery.

Microorganisms, plants, and animals alike have specialized acquisition pathways for obtaining metals, with microorganisms and plants biosynthesizing and secreting small molecule natural products called siderophores and metallophores with high affinities and specificities for iron or other non-iron metals, respectively. This chapter details a novel approach to discovering metal-binding molecules, including siderophores and metallophores, from complex samples ranging from microbial supernatants to biological tissue to environmental samples. This approach, called Native Metabolomics, is a mass spectrometry method in which pH adjustment and metal infusion post-liquid chromatography are interfaced with ion identity molecular networking (IIMN). This rule-based data analysis workflow that enables the identification of metal-binding species based on defined mass (m/z) offsets with the same chromatographic profiles and retention times. Ion identity molecular networking connects compounds that are structurally similar by their fragmentation pattern and species that are ion adducts of the same compound by chromatographic shape correlations. This approach has previously revealed new insights into metal binding metabolites, including that yersiniabactin can act as a biological zincophore (in addition to its known role as a siderophore), that the recently elucidated lepotchelin natural products are cyanobacterial metallophores, and that antioxidants in traditional medicine bind iron. Native metabolomics can be conducted on any liquid chromatography-mass spectrometry system to explore the binding of any metal or multiple metals simultaneously, underscoring the potential for this method to become an essential strategy for elucidating biological metal-binding molecules.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies.

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis.

Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, n = 33; Sebumeter score > 120, n = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, Staphylococcus epidermidis.IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of Staphylococcus epidermidis, one of the key players of the human skin microbiome.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

plantMASST - Community-driven chemotaxonomic digitization of plants.

Understanding the distribution of hundreds of thousands of plant metabolites across the plant kingdom presents a challenge. To address this, we curated publicly available LC-MS/MS data from 19,075 plant extracts and developed the plantMASST reference database encompassing 246 botanical families, 1,469 genera, and 2,793 species. This taxonomically focused database facilitates the exploration of plant-derived molecules using tandem mass spectrometry (MS/MS) spectra. This tool will aid in drug discovery, biosynthesis, (chemo)taxonomy, and the evolutionary ecology of herbivore interactions.

Empirically establishing drug exposure records directly from untargeted metabolomics data.

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.