DeltaMS: a tool to track isotopologues in GC- and LC-MS data

Chemical tools for unpicking plant specialised metabolic pathways

Elucidating the biochemical pathways of specialised metabolites in plants is key to enable or improve their sustainable biotechnological production. Chemical tools can greatly facilitate the discovery of biosynthetic genes and enzymes. Here, we summarise transdisciplinary approaches where methods from chemistry and chemical biology helped to overcome key challenges of pathway elucidation. Based on recent examples, we describe how state-of-the-art isotope labelling experiments can guide the selection of biosynthetic gene candidates, how affinity-based probes enable the identification of novel enzymes, how semisynthesis can improve the availability of elusive pathway intermediates, and how biomimetic reactions provide a better understanding of inherent chemical reactivity. We anticipate that a wider application of such chemical methods will accelerate the pace of pathway elucidation in plants.

Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics

In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host- and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants consumed a single serving of broccoli (n = 16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites. Mass spectrometry analysis indicated 133 metabolites in broccoli sprouts and 139 metabolites in the alfalfa sprouts were labeled with deuterium isotopes. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5,600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13 deuterium-incorporated metabolomic features were detected in urine representing 8 urine metabolites. Plasma was analyzed among collard green consumers and 11 labeled features were detected representing 5 plasma metabolites. These deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-O-glucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated. This work presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

HDPairFinder: A data processing platform for hydrogen/deuterium isotopic labeling-based nontargeted analysis of trace-level amino-containing chemicals in environmental water

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Microbial degradation and assimilation of veratric acid in oxic and anoxic groundwaters

Microbial communities are key players in groundwater ecosystems. In this dark environment, heterotrophic microbes rely on biomass produced by the activity of lithoautotrophs or on the degradation of organic matter seeping from the surface. Most studies on bacterial diversity in groundwater habitats are based on 16S gene sequencing and full genome reconstructions showing potential metabolic pathways used in these habitats. However, molecular-based studies do not allow for the assessment of population dynamics over time or the assimilation of specific compounds and their biochemical transformation by microbial communities. Therefore, in this study, we combined DNA-, phospholipid fatty acid-, and metabolomic-stable isotope probing to target and identify heterotrophic bacteria in the groundwater setting of the Hainich Critical Zone Exploratory (CZE), focusing on 2 aquifers with different physico-chemical conditions (oxic and anoxic). We incubated groundwater from 4 different wells using either 13C-labeled veratric acid (a lignin-derived compound) (single labeling) or a combination of 13CO2 and D-labeled veratric acid (dual labeling). Our results show that heterotrophic activities dominate all groundwater sites. We identified bacteria with the potential to break down veratric acid (Sphingobium or Microbacterium). We observed differences in heterotrophic activities between the oxic and anoxic aquifers, indicating local adaptations of bacterial populations. The dual labeling experiments suggested that the serine pathway is an important carbon assimilation pathway and that organic matter was an important source of hydrogen in the newly produced lipids. These experiments also yielded different labeled taxa compared to the single labeling experiments, showing that there exists a complex interaction network in the groundwater habitats.

Intracellular Acetyl CoA Potentiates the Therapeutic Efficacy of Antitumor CD8+ T Cells

Effector CD8+ T cells rely primarily on glucose metabolism to meet their biosynthetic and functional needs. However, nutritional limitations in the tumor microenvironment can cause T-cell hyporesponsiveness. Therefore, T cells must acquire metabolic traits enabling sustained effector function at the tumor site to elicit a robust antitumor immune response. Here, we report that IL12-stimulated CD8+ T cells have elevated intracellular acetyl CoA levels and can maintain IFNγ levels in nutrient-deprived, tumor-conditioned media (TCM). Pharmacological and metabolic analyses demonstrated an active glucose-citrate-acetyl CoA circuit in IL12-stimulated CD8+ T cells supporting an intracellular pool of acetyl CoA in an ATP-citrate lyase (ACLY)-dependent manner. Intracellular acetyl CoA levels enhanced histone acetylation, lipid synthesis, and IFNγ production, improving the metabolic and functional fitness of CD8+ T cells in tumors. Pharmacological inhibition or genetic knockdown of ACLY severely impaired IFNγ production and viability of CD8+ T cells in nutrient-restricted conditions. Furthermore, CD8+ T cells cultured in high pyruvate-containing media in vitro acquired critical metabolic features of IL12-stimulated CD8+ T cells and displayed improved antitumor potential upon adoptive transfer in murine lymphoma and melanoma models. Overall, this study delineates the metabolic configuration of CD8+ T cells required for stable effector function in tumors and presents an affordable approach to promote the efficacy of CD8+ T cells for adoptive T-cell therapy.

SIGNIFICANCE

IL12-mediated metabolic reprogramming increases intracellular acetyl CoA to promote the effector function of CD8+ T cells in nutrient-depleted tumor microenvironments, revealing strategies to potentiate the antitumor efficacy of T cells.

Elucidation of xenoestrogen metabolism by non-targeted, stable isotope-assisted mass spectrometry in breast cancer cells

Environmental exposure to xenoestrogens, i.e., chemicals that imitate the hormone 17β-estradiol, has the potential to influence hormone homeostasis and action. Detailed knowledge of xenobiotic biotransformation processes in cell models is key when transferring knowledge learned from in vitro models to in vivo relevance. This study elucidated the metabolism of two naturally-occurring phyto- and mycoestrogens; namely genistein and zearalenone, in an estrogen receptor positive breast cancer cell line (MCF-7) with the aid of stable isotope-assisted metabolomics and the bioinformatic tool MetExtract II. Metabolism was studied in a time course experiment after 2 h, 6 h and 24 h incubation. Twelve and six biotransformation products of zearalenone and genistein were detected, respectively, clearly demonstrating the abundant xenobiotic biotransformation capability of the cells. Zearalenone underwent extensive phase-I metabolism resulting in α-zearalenol (α-ZEL), a molecule known to possess a significantly higher estrogenicity, and several phase-II metabolites (sulfo- and glycoconjugates) of the native compound and the major phase I metabolite α-ZEL. Moreover, potential adducts of zearalenone with a vitamin and several hydroxylated metabolites were annotated. Genistein metabolism resulted in sulfation, combined sulfation and hydroxylation, acetylation, glucuronidation and unexpectedly adduct formation with pentose- and hexose sugars. Kinetics of metabolite formation and subsequent excretion into the extracellular medium revealed a time-dependent increase in most biotransformation products. The untargeted elucidation of biotransformation products formed during cell culture experiments enables an improved and more meaningful interpretation of toxicological assays and has the potential to identify unexpected or unknown metabolites.

Chemical labeling strategies for small molecule natural product detection and isolation

Covering: Up to 2020.It is widely accepted that small molecule natural products (NPs) evolved to carry out a particular ecological function and that these finely-tuned molecules can sometimes be appropriated for the treatment of disease in humans. Unfortunately, for the natural products chemist, NPs did not evolve to possess favorable physicochemical properties needed for HPLC-MS analysis. The process known as derivatization, whereby an NP in a complex mixture is decorated with a nonnatural moiety using a derivatizing agent (DA), arose from this sad state of affairs. Here, NPs are freed from the limitations of natural functionality and endowed, usually with some degree of chemoselectivity, with additional structural features that make HPLC-MS analysis more informative. DAs that selectively label amines, carboxylic acids, alcohols, phenols, thiols, ketones, and aldehydes, terminal alkynes, electrophiles, conjugated alkenes, and isocyanides have been developed and will be discussed here in detail. Although usually employed for targeted metabolomics, chemical labeling strategies have been effectively applied to uncharacterized NP extracts and may play an increasing role in the detection and isolation of certain classes of NPs in the future.

Predictive Modeling for Metabolomics Data

In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.

Rhizobactin B is the preferred siderophore by a novel Pseudomonas isolate to obtain iron from dissolved organic matter in peatlands

Bacteria often release diverse iron-chelating compounds called siderophores to scavenge iron from the environment for many essential biological processes. In peatlands, where the biogeochemical cycle of iron and dissolved organic matter (DOM) are coupled, bacterial iron acquisition can be challenging even at high total iron concentrations. We found that the bacterium Pseudomonas sp. FEN, isolated from an Fe-rich peatland in the Northern Bavarian Fichtelgebirge (Germany), released an unprecedented siderophore for its genus. High-resolution mass spectrometry (HR-MS) using metal isotope-coded profiling (MICP), MS/MS experiments, and nuclear magnetic resonance spectroscopy (NMR) identified the amino polycarboxylic acid rhizobactin and a novel derivative at even higher amounts, which was named rhizobactin B. Interestingly, pyoverdine-like siderophores, typical for this genus, were not detected. With peat water extract (PWE), studies revealed that rhizobactin B could acquire Fe complexed by DOM, potentially through a TonB-dependent transporter, implying a higher Fe binding constant of rhizobactin B than DOM. The further uptake of Fe-rhizobactin B by Pseudomonas sp. FEN suggested its role as a siderophore. Rhizobactin B can complex several other metals, including Al, Cu, Mo, and Zn. The study demonstrates that the utilization of rhizobactin B can increase the Fe availability for Pseudomonas sp. FEN through ligand exchange with Fe-DOM, which has implications for the biogeochemical cycling of Fe in this peatland.

Iron-organic matter complexes accelerate microbial iron cycling in an iron-rich fen

The accessibility of iron (Fe) species for microbial processes is dependent on solubility and redox state, which are influenced by complexation with dissolved organic matter (DOM) and water-extractable organic matter (WEOM). We evaluated the complexation of these pools of organic matter to soluble Fe(II) and Fe(III) in the slightly acidic Schlöppnerbrunnen fen and subsequent effects on Fe(II) oxidation and Fe(III) reduction. We found the majority of soluble Fe(II) and Fe(III) is complexed to DOM. High-resolution mass spectrometry identified potential complexing partners in peat-derived water extracts (PWE), including compound classes known to function as ligands or electron shuttles, like tannins and sulfur-containing compounds. Furthermore, we observed clear differences in the stability of Fe(II)- and Fe(III)-DOM, with more labile complexes dominating the upper, oxic layers (0-10 cm) and more stable complexes in lower, anoxic layers (15-30 cm). Metal isotope-coded profiling identified a single potential chemical formula (C₄₂H₅₇O₁₃N₉Fe₂) associated with a stable Fe-DOM complex. Fe(III) reduction and Fe(II) oxidation incubations with Geobacter sulfurreducens PCA and Shewanella oneidensis MR-1 or Sideroxydans CL-21, respectively, were used to determine the influence of Fe-DOM complexes on Fe cycling rates. The addition of PWE led to a 2.3-fold increase in Fe(III) reduction rates and 0.5-fold increase in Fe(II) oxidation rates, indicating Fe-DOM complexes greatly influence microbial Fe cycling by potentially serving as electron shuttles. Molecular analyses revealed Fe(III)-reducing and Fe(II)-oxidizing bacteria co-exist across all depths, in approximately equal proportions (representing 0.1-1.0% of the total microbial community), despite observed changes in redox potential. The activity of Fe(III)-reducing bacteria might explain the presence of the detected Fe(II) stabilized via complexation with DOM even under oxic conditions in upper peat layers. Therefore, these Fe(II)-DOM complexes can be recycled by microaerophilic Fe(II)-oxidizers. Taken together, these results suggest Fe-DOM complexation in the fen accelerates microbial-mediated redox processes across the entire redox continuum.

Halogenated anilines as novel natural products from a marine biofilm forming microalga

A microalga produces the toxic halogenated anilines 2,4,6-tribromoaniline, 2,4,6-trichloroaniline and their dibromochloro and bromodichloro derivatives that were considered as compounds of exclusive synthetic origin.

Metallophore profiling of nitrogen-fixingFrankiaspp. to understand metal management in the rhizosphere of actinorhizal plants

Frankiaspp. are widespread nitrogen-fixing and metallophore releasing soil bacteria, which often live in symbiosis with a broad spectrum of hosts.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Nitrosopyridine Probe To Detect Polyketide Natural Products with Conjugated Alkenes: Discovery of Novodaryamide and Nocarditriene

An optimized nitroso-based probe that facilitates the discovery of conjugated alkene-containing natural products in unprocessed extracts was developed. It chemoselectively reacts with conjugated olefins via a nitroso-Diels-Alder cyclization to yield derivatives with a distinct chromophore and an isotopically unique bromine atom that can be rapidly identified using liquid chromatography/mass spectrometry and a bioinformatics tool called MeHaloCoA (Marine Halogenated Compound Analysis). The probe is ideally employed when genome-mining techniques identify strains containing polyketide gene clusters with two or more repeating KS-AT-DH-KR-ACP domain sequences, which are required for the biosynthesis of conjugated alkenes. Comparing the reactivity and spectral properties of five brominated arylnitroso reagents with model compounds spiramycin, bufalin, rapamycin, and rifampicin led to the identification of 5-bromo-2-nitrosopyridine as the most suitable probe structure. The utility of the dienophile probe was then demonstrated in bacterial extracts. Tylactone, novodaryamide and daryamide A, piperazimycin A, and the saccharamonopyrones A and B were cleanly labeled in extracts from their respective bacterial producers, in high regioselectivity but with varying degrees of diastereoselectivity. Further application of the method led to the discovery of a new natural product called nocarditriene, containing an unprecedented epoxy-2,3,4,5-tetrahydropyridine structure, from marine-derived Nocardiopsis strain CNY-503.

Thermodynamic study on 8-hydroxyquinoline-2-carboxylic acid as a chelating agent for iron found in the gut of Noctuid larvae

8-HQA is a good sequestering agent towards Fe²⁺ and Fe³⁺ over a wide pH range.