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microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health., (© 2024. The Author(s).)

Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds to thousands of metabolites in biological systems. One major challenge in MSI is the annotation of m / z values, which is substantially complicated by background ions introduced throughout the chemicals and equipment used during experimental procedures. Among many factors, the formation of adducts with sodium or potassium ions, or in case of matrix-assisted laser desorption ionization (MALDI)-MSI, the presence of abundant matrix clusters strongly increases total m / z peak counts. Currently, there is a limitation to identify the chemistry of the many unknown peaks to interpret their biological function. We took advantage of the co-localization of adducts with their parent ions and the accuracy of high mass resolution to estimate adduct abundance in 20 datasets from different vendors of mass spectrometers. Metabolites ranging from lipids to amines and amino acids form matrix adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix like [M + (DHB-H 2 O) + H] + and [M + DHB + Na] + . Current data analyses neglect those matrix adducts and overestimate total metabolite numbers, thereby expanding the number of unidentified peaks. Our study demonstrates that MALDI-MSI data are strongly influenced by adduct formation across different sample types and vendor platforms and reveals a major influence of so far unrecognized metabolite-matrix adducts on total peak counts (up to one third). We developed a software package, mass 2 adduct , for the community for an automated putative assignment and quantification of metabolite-matrix adducts enabling users to ultimately focus on the biologically relevant portion of the MSI data.

Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defence molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric-pressure matrix-assisted laser desorption/ionization mass spectrometry to image host-microbe symbioses and their metabolic interactions. The metaFISH pipeline aligns and integrates metabolite and fluorescent images at the micrometre scale to provide a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts and variation in metabolic phenotypes within a single symbiont 16S rRNA phylotype, and enabled the discovery of specialized metabolites from the host-microbe interface. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ and is a powerful tool for microbiologists across fields.

Molecular genetic methods can distinguish divergent evolutionary lineages in what previously appeared to be single species, but it is not always clear what functional differences exist between such cryptic species. We used a metabolomic approach to profile biochemical phenotype (metabotype) differences between two putative cryptic species of the earthworm Lumbricus rubellus. There were no straightforward metabolite biomarkers of lineage, i.e. no metabolites that were always at higher concentration in one lineage. Multivariate methods, however, identified a small number of metabolites that together helped distinguish the lineages, including uncommon metabolites such as Nε-trimethyllysine, which is not usually found at high concentrations. This approach could be useful for characterizing functional trait differences, especially as it is applicable to essentially any species group, irrespective of its genome sequencing status., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.

Systems metabolomics, the identification and quantification of cellular metabolites and their integration with genomics and proteomics data, promises valuable functional insights into cellular biology. However, technical constraints, sample complexity issues and the lack of suitable complementary quantitative data sets prevented accomplishing such studies in the past. Here, we present an integrative metabolomics study of the genome-reduced bacterium Mycoplasma pneumoniae. We experimentally analysed its metabolome using a cross-platform approach. We explain intracellular metabolite homeostasis by quantitatively integrating our results with the cellular inventory of proteins, DNA and other macromolecules, as well as with available building blocks from the growth medium. We calculated in vivo catalytic parameters of glycolytic enzymes, making use of measured reaction velocities, as well as enzyme and metabolite pool sizes. A quantitative, inter-species comparison of absolute and relative metabolite abundances indicated that metabolic pathways are regulated as functional units, thereby simplifying adaptive responses. Our analysis demonstrates the potential for new scientific insight by integrating different types of large-scale experimental data from a single biological source.

One-dimensional (1)H NMR spectra are widely used for metabolic profiling. Such data sets often contain hundreds or thousands of spectra, which typically have variation in their sample chemistry, which leads to chemical shift variation "positional noise". This is a severe problem for metabolite quantification and data analysis, as peak integrals do not necessarily correspond across all spectra in a set. Various alignment algorithms have been developed to address this problem, but different studies have taken different approaches to evaluating the performance of NMR alignment routines and can be subjective or rely on an arbitrary cutoff. Furthermore, most alignment approaches completely fail to deal with peaks that overlap. We adopt the simple and robust method of ordering spectra with respect to an internally varying peak and use this to compare different alignment algorithms. Furthermore, we use the information from this procedure to help improve a Bayesian approach to automated peak deconvolution by restricting the prior probability distribution of the peak position in a model-free manner and compare the performance to manual peak deconvolution and to binning. This combination of spectral ordering and compound deconvolution improved the quality of the data for quantitative metabolomics.

Nuclear magnetic resonance (NMR) spectroscopy is widely used as an analytical platform for metabolomics. Many studies make use of 1D spectra, which have the advantages of relative simplicity and rapid acquisition times. The spectral data can then be analyzed either with a chemometric workflow or by an initial deconvolution or fitting step to generate a list of identified metabolites and associated sample concentrations. Various software tools exist to simplify the fitting process, but at least for 1D spectra, this still requires a degree of skilled operator input. It is of critical importance that we know how much person-to-person variability affects the results, in order to be able to judge between different studies. Here we tested a commercially available software package (Chenomx' NMR Suite) for fitting metabolites to a set of NMR spectra of yeast extracts and compared the output of five different people for both metabolite identification and quantitation. An initial comparison showed good agreement for a restricted set of common metabolites with characteristic well-resolved resonances but wide divergence in the overall identities and number of compounds fitted; refitting according to an agreed set of metabolites and spectral processing approach increased the total number of metabolites fitted but did not dramatically increase the quality of the metabolites that could be fitted without prior knowledge about peak identity. Hence, robust peak assignments are required in advance of manual deconvolution, when the widest range of metabolites is desired. However, very low concentration metabolites still had high coefficients of variation even with shared information on peak assignment. Overall, the effect of the person was less than the experimental group (in this case, sampling method) for almost all of the metabolites.

As a versatile pathogen Staphylococcus aureus can cause various disease patterns, which are influenced by strain specific virulence factor repertoires but also by S. aureus physiological adaptation capacity. Here, we present metabolomic descriptions of S. aureus central metabolic pathways and demonstrate the potential for combined metabolomics- and proteomics-based approaches for the basic research of this important pathogen. This study provides a time-resolved picture of more than 500 proteins and 94 metabolites during the transition from exponential growth to glucose starvation. Under glucose excess, cells exhibited higher levels of proteins involved in glycolysis and protein-synthesis, whereas entry into the stationary phase triggered an increase of enzymes of TCC and gluconeogenesis. These alterations in levels of metabolic enzymes were paralleled by more pronounced changes in the concentrations of associated metabolites, in particular, intermediates of the glycolysis and several amino acids.

Little is known about intracellular metabolite pools in pathogens such as Staphylococcus aureus. We have studied a particular metabolome by means of the presented LC-MS method. By investigating the central carbon metabolism which includes most of the energy transfer molecules like nucleotides, sugar mono- and biphosphates, and cofactors, a conclusion about phenotypes and stress answers in microorganisms is possible. Quantitative metabolite levels of S. aureus grown in complex lysogeny broth and in minimal medium were compared in the wild-type S. aureus strain 8325 and the isogenic eukaryotic-like protein serine/threonine kinase (DeltapknB) and phosphatase (Deltastp) deletion mutants. Detection of several remarkable differences, e.g., in nucleotide metabolism and especially cell wall precursor metabolites, indicates a previously unreported importance of serine/threonine kinase/phosphatase on peptidoglycan and wall teichoic acid biosynthesis. These findings may lead to new insights into the regulation of staphylococcal cell wall metabolism., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

Systems biology studies assume the acquisition of reliable and reproducible data sets. Metabolomics, in particular, requires comprehensive evaluated workflows to enable the analysis of hundreds of different compounds. Therefore, a protocol to elucidate the metabolome of the gram-positive pathogen, Staphylococcus aureus COL strain, grown in a chemically defined medium is introduced here. Different standard operating procedures in the field of metabolome experiments were tested for common pitfalls. These included suitable and fast sampling processes, efficient metabolite extraction, quenching effectiveness (energy charge), and estimation of leakage and recovery of metabolites. Moreover, a cell disruption protocol for S. aureus was developed and optimized for metabolome analyses, for the express purpose of obtaining reproducible data. We used complementary methods (e.g., gas chromatography and/or liquid chromatography coupled with mass spectrometry) to detect the highly chemically diverse groups of metabolites for a global insight into the intracellular metabolism of S. aureus., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Analysis of metabolome samples by gas chromatography/mass spectrometry requires a comprehensive derivatization method to afford quantitative and qualitative information of a complex biological sample. Here we describe an extremely time-effective microwave-assisted protocol for the commonly used methoxyamine and N-methyl-N-trimethylsilylfluoracetamide silylation method of primary metabolites. Our studies show that microwave irradiation can decrease the sample preparation time from approximately 120 min to 6 min without loss of either qualitative or quantitative information for the tested synthetic metabolite mixtures and microbial-derived metabolome samples collected from Bacillus subtilis and Staphylococcus aureus. Comparisons of metabolic fingerprints and selected metabolites show no noticeable differences compared with the commonly used heating block methods., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)