Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data

MS and NMR Analysis of Isotopically Labeled Chloramination Disinfection Byproducts: Hyperlinks and Chemical Reactions

FT-ICR MS and NMR analysis of an isotopically labeled complex mixture of water disinfection byproducts formed by chloramine disinfection of model phenolic acids is described. A new molecular formula assignment procedure using the CoreMS Python library able to assign isotopically enriched formulas is proposed. Statistical analysis of the assigned formulas showed that the number of compounds, the diversity of the mixture, and the chlorine count increase during the chloramination reaction. The complex reaction mixture was investigated as a network of reactions using PageRank and Reverse PageRank algorithms. Independent of the MS signal intensities, the PageRank algorithm calculates the formulas with the highest probability at convergence of the reaction; these were chlorinated and nitrated derivatives of the starting materials. The Reverse PageRank revealed that the most probable chemical transformations in the complex mixture were chlorination and decarboxylation. These agree with the data obtained from INADEQUATE NMR spectra and literature data, indicating that this approach could be applied to gain insight into reactions pathways taking place in complex mixtures without any prior knowledge.

Depicting the genetic and metabolic panorama of chemical diversity in the tea plant

As a frequently consumed beverage worldwide, tea is rich in naturally important bioactive metabolites. Combining genetic, metabolomic and biochemical methodologies, here, we present a comprehensive study to dissect the chemical diversity in tea plant. A total of 2837 metabolites were identified at high-resolution with 1098 of them being structurally annotated and 63 of them were structurally identified. Metabolite-based genome-wide association mapping identified 6199 and 7823 metabolic quantitative trait loci (mQTL) for 971 and 1254 compounds in young leaves (YL) and the third leaves (TL), respectively. The major mQTL (i.e., P < 1.05 × 10-5, and phenotypic variation explained (PVE) > 25%) were further interrogated. Through extensive annotation of the tea metabolome as well as network-based analysis, this study broadens the understanding of tea metabolism and lays a solid foundation for revealing the natural variations in the chemical composition of the tea plant. Interestingly, we found that galloylations, rather than hydroxylations or glycosylations, were the largest class of conversions within the tea metabolome. The prevalence of galloylations in tea is unusual, as hydroxylations and glycosylations are typically the most prominent conversions of plant specialized metabolism. The biosynthetic pathway of flavonoids, which are one of the most featured metabolites in tea plant, was further refined with the identified metabolites. And we demonstrated the further mining and interpretation of our GWAS results by verifying two identified mQTL (including functional candidate genes CsUGTa, CsUGTb, and CsCCoAOMT) and completing the flavonoid biosynthetic pathway of the tea plant.

APEX: an Annotation Propagation Workflow through Multiple Experimental Networks to Improve the Annotation of New Metabolite Classes in Caenorhabditis elegans

Spectral similarity networks, also known as molecular networks, are crucial in non-targeted metabolomics to aid identification of unknowns aiming to establish a potential structural relation between different metabolite features. However, too extensive differences in compound structures can lead to separate clusters, complicating annotation. To address this challenge, we developed an automated Annotation Propagation through multiple EXperimental Networks (APEX) workflow, which integrates spectral similarity networks with mass difference networks and homologous series. The incorporation of multiple network tools improved annotation quality, as evidenced by high matching rates of the molecular formula derived by SIRIUS. The selection of manual annotations as the Seed Nodes Set (SNS) significantly influenced APEX annotations, with a higher number of seed nodes enhancing the annotation process. We applied APEX to different Caenorhabditis elegans metabolomics data sets as a proof-of-principle for the effective and comprehensive annotation of glycerophospho N-acyl ethanolamides (GPNAEs) and their glyco-variants. Furthermore, we demonstrated the workflow's applicability to two other, well-described metabolite classes in C. elegans, specifically ascarosides and modular glycosides (MOGLs), using an additional publicly available data set. In summary, the APEX workflow presents a powerful approach for metabolite annotation and identification by leveraging multiple experimental networks. By refining the SNS selection and integrating diverse networks, APEX holds promise for comprehensive annotation in metabolomics research, enabling a deeper understanding of the metabolome.

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter

Dissolved organic matter (DOM) is a complex mixture of thousands of natural molecules that undergo constant transformation in the environment, such as sunlight induced photochemical reactions. Despite molecular level resolution from ultrahigh resolution mass spectrometry (UHRMS), trends of mass peak intensities are currently the only way to follow photochemically induced molecular changes in DOM. Many real-world relationships and temporal processes can be intuitively modeled using graph data structures (networks). Graphs enhance the potential and value of AI applications by adding context and interconnections allowing the uncovering of hidden or unknown relationships in data sets. We use a temporal graph model and link prediction to identify transformations of DOM molecules in a photo-oxidation experiment. Our link prediction algorithm simultaneously considers educt removal and product formation for molecules linked by predefined transformation units (oxidation, decarboxylation, etc.). The transformations are further weighted by the extent of intensity change and clustered on the graph structure to identify groups of similar reactivity. The temporal graph is capable of identifying relevant molecules subject to similar reactions and enabling to study their time course. Our approach overcomes previous data evaluation limitations for mechanistic studies of DOM and leverages the potential of temporal graphs to study DOM reactivity by UHRMS.

Rethinking Aerobic Respiration in the Hyporheic Zone under Variation in Carbon and Nitrogen Stoichiometry

Hyporheic zones (HZs)─zones of groundwater-surface water mixing─are hotspots for dissolved organic matter (DOM) and nutrient cycling that can disproportionately impact aquatic ecosystem functions. However, the mechanisms affecting DOM metabolism through space and time in HZs remain poorly understood. To resolve this gap, we investigate a recently proposed theory describing trade-offs between carbon (C) and nitrogen (N) limitations as a key regulator of HZ metabolism. We propose that throughout the extent of the HZ, a single process like aerobic respiration (AR) can be limited by both DOM thermodynamics and N content due to highly variable C/N ratios over short distances (centimeter scale). To investigate this theory, we used a large flume, continuous optode measurements of dissolved oxygen (DO), and spatially and temporally resolved molecular analysis of DOM. Carbon and N limitations were inferred from changes in the elemental stoichiometric ratio. We show sequential, depth-stratified relationships of DO with DOM thermodynamics and organic N that change across centimeter scales. In the shallow HZ with low C/N, DO was associated with the thermodynamics of DOM, while deeper in the HZ with higher C/N, DO was associated with inferred biochemical reactions involving organic N. Collectively, our results suggest that there are multiple competing processes that limit AR in the HZ. Resolving this spatiotemporal variation could improve predictions from mechanistic models, either via more highly resolved grid cells or by representing AR colimitation by DOM thermodynamics and organic N.

Concealed by darkness: Combination of NMR and HRMS reveal the molecular nature of dissolved organic matter in fractured-rock groundwater and connected surface waters

Detailed molecular composition of solid phase extracted dissolved organic matter (SPEDOM) collected from fractured-rock groundwater was compared to connected surface river water at two different watersheds in the unconfined chalk aquifer of Champagne in France using full scan ultrahigh resolution electrospray and photoionization Fourier transform ion cyclotron mass spectrometry (FT-ICR MS), Orbitrap tandem MS (MS/MS) and 1H magnetic resonance spectroscopy (NMR). 1H NMR spectroscopy indicated that groundwater SPEDOM carried a higher contribution of aliphatic compounds while surface river waters SPEDOM were enriched in carboxyl-rich alicyclic molecules (CRAM), acetate derivatives and oxygenated units. Furthermore, we show here that use of photoionization (APPI(+)) in aquifer studies is key, ionizing about eight times more compounds than ESI in surface river water samples, specifically targeting the dissolved organic nitrogen pool, accounting for more than 50% of the total molecular space, as well as a non-polar, more aromatic fraction; with little overlap with compounds detected by ESI(-) FT-ICR MS. On the other hand, groundwater SPEDOM samples did not show similar selectivity as less molecular diversity was observed in APPI compared to ESI. Mass-difference transformation networks (MDiNs) applied to ESI(-) and APPI(+) FT-ICR MS datasets provided an overview of the biogeochemical relationships within the aquifer, revealing chemical diversity and microbial/abiotic reactions. Finally, the combination of ESI(-) FT-ICR MS and detailed Orbitrap MS/MS analysis revealed a pool of polar, anthropogenic sulfur-containing surfactants in the groundwaters, likely originating from agricultural runoff. Overall, our study shows that in this aquifer, groundwater SPEDOM contains a significantly reduced pool of organic compounds compared to surface river waters, possibly related to a combination of lack of sunlight and adsorption of high O/C formulas to mineral surfaces.

Nontarget Discovery of Antimicrobial Transformation Products in Wastewater Based on Molecular Networks

Antimicrobial transformation products (ATPs) in the environment have raised extensive concerns in recent years due to their potential health risks. However, only a few ATPs have been investigated, and most of the transformation pathways of antimicrobials have not been completely elucidated. In this study, we developed a nontarget screening strategy based on molecular networks to detect and identify ATPs in pharmaceutical wastewater. We identified 52 antimicrobials and 49 transformation products (TPs) with a confidence level of three or above. Thirty of the TPs had not been previously reported in the environment. We assessed whether TPs could be classified as persistent, mobile, and toxic (PMT) substances based on recent European criteria for industrial substances. Owing to poor experimental data, definitive PMT classifications could not be established for novel ATPs. PMT assessment based on structurally predictive physicochemical properties revealed that 47 TPs were potential PMT substances. These results provide evidence that novel ATPs should be the focus of future research.

Formulae Differences Commence a Database for Interlaboratory Studies of Natural Organic Matter

Direct comparison of high-resolution mass spectrometry (HRMS) data acquired with different instrumentation or parameters remains problematic as the derived lists of molecular species via HRMS, even for the same sample, appear distinct. This inconsistency is caused by inherent inaccuracies associated with instrumental limitations and sample conditions. Hence, experimental data may not reflect a corresponding sample. We propose a method that classifies HRMS data based on the differences in the number of elements between each pair of molecular formulae within the formulae list to preserve the essence of the given sample. The novel metric, formulae difference chains expected length (FDCEL), allowed for comparing and classifying samples measured by different instruments. We also demonstrate a web application and a prototype for a uniform database for HRMS data serving as a benchmark for future biogeochemical and environmental applications. FDCEL metric was successfully employed for both spectrum quality control and examination of samples of various nature.

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

BACKGROUND

Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking.

RESULTS

Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses.

CONCLUSION

Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User's guide of MetaboDirect are freely available through ( https://github.com/Coayala/MetaboDirect ) and ( https://metabodirect.readthedocs.io/en/latest/ ), respectively. Video Abstract.

Network Analysis Reveals Spatial Clustering and Annotation of Complex Chemical Spaces: Application to Astrochemistry

How are molecules linked to each other in complex systems? In a proof-of-concept study, we have developed the method mol2net (https://zenodo.org/record/7025094) to generate and analyze the molecular network of complex astrochemical data (from high-resolution Orbitrap MS¹ analysis of H₂O:CH₃OH:NH₃ interstellar ice analogs) in a data-driven and unsupervised manner, without any prior knowledge about chemical reactions. The molecular network is clustered according to the initial NH₃ content and unlocked HCN, NH₃, and H₂O as spatially resolved key transformations. In comparison with the PubChem database, four subsets were annotated: (i) saturated C-backbone molecules without N, (ii) saturated N-backbone molecules, (iii) unsaturated C-backbone molecules without N, and (iv) unsaturated N-backbone molecules. These findings were validated with previous results (e.g., identifying the two major graph components as previously described N-poor and N-rich molecular groups) but with additional information about subclustering, key transformations, and molecular structures, and thus, the structural characterization of large complex organic molecules in interstellar ice analogs has been significantly refined.

Using Potential Molecular Transformation To Understand the Molecular Trade-Offs in Soil Dissolved Organic Matter

Understanding the chemical composition and molecular transformation in soil dissolved organic matter (DOM) is important to the global carbon cycle. To address this issue, ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) was applied to investigate DOM molecules in 36 paddy soils collected from subtropical China. All the detected 7576 unique molecules were divided into seven compound groups, and nine trade-off relationships between different compound groups were revealed based on principal component analysis and Pearson's correlation. An optimized method was developed to evaluate all potential molecular transformations in DOM samples. The concept of thermodynamics was introduced to evaluate the identified molecular transformations and classify them as thermodynamically favorable (TFP) and thermodynamically limited (TLP) processes. Here, we first tried to understand the molecular trade-offs by using the potential molecular transformations. All the nine trade-offs could be explained by molecular transformations. Six trade-offs had bases of biochemical reactions, and the trade-off-related direct transformations could explain the content variations of carbohydrate-like, condensed aromatic-like, tannin-like, and lignin-like compounds in TLP. More reasonable explanations existed in the TLP rather than TFP, which demonstrated the critical role of external energy in the molecular transformation of soil DOM.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition

Dissolved organic matter (DOM) is a large and complex mixture of molecules that fuels microbial metabolism and regulates biogeochemical cycles. Individual DOM molecules have unique functional traits, but how their assemblages vary deterministically under global change remains poorly understood. Here, we examine DOM and associated bacteria in 300 aquatic microcosms deployed on mountainsides that span contrasting temperatures and nutrient gradients. Based on molecular trait dimensions of reactivity and activity, we partition the DOM composition into labile-active, recalcitrant-active, recalcitrant-inactive, and labile-inactive fractions and quantify the relative influences of deterministic and stochastic processes governing the assembly of each. At both subtropical and subarctic study sites, the assembly of labile or recalcitrant molecules in active fractions is primarily governed by deterministic processes, while stochastic processes are more important for the assembly of molecules within inactive fractions. Surprisingly, the importance of deterministic selection increases with global change gradients for recalcitrant molecules in both active and inactive fractions, and this trend is paralleled by changes in the deterministic assembly of microbial communities and environmental filtering, respectively. Together, our results highlight the shift in focus from potential reactivity to realized activity and indicate that active and inactive fractions of DOM assemblages are structured by contrasting processes, and their recalcitrant components are consistently sensitive to global change. Our study partitions the DOM molecular composition across functional traits and links DOM with microbes via a shared ecological framework of assembly processes. This integrated approach opens new avenues to understand the assembly and turnover of organic carbon in a changing world.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO₂ ) and methane (CH₄ ). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO₂ , and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO₂ and CH₄  fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH₄ contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO₂ release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks under continued ecosystem transition.

Dynamics of organic matter molecular composition under aerobic decomposition and their response to the nitrogen addition in grassland soils

Grassland soils store a substantial proportion of the global soil carbon (C) stock. The transformation of C in grassland soils with respect to chemical composition and persistence strongly regulate the predicted terrestrial-atmosphere C flux in global C biogeochemical cycling models. In addition, increasing atmospheric nitrogen (N) deposition alters C chemistry in grassland soils. However, there remains controversy about the importance of mineralogical versus biochemical preservation of soil C, as well as uncertainty regarding how grassland soil C chemistry responds to elevated N. This study used grassland soils with diverse soil organic matter (SOM) chemistries in an 8-month aerobic incubation experiment to evaluate whether the chemical composition of SOM converged across sites over time, and how SOM persistence responded to the N addition. This study demonstrates that over the course of incubation, the richness of labile compounds decreased in soils with less ferrihydrite content, whereas labile compounds were more persistent in ferrihydrite rich soils. In contrast, we found that the richness of more complex compounds increased over the incubation in most sites, independent of soil mineralogy. Moreover, we demonstrate the extent to which the diverse chemical composition of SOM converged among sites in response to microbial decomposition. N fertilization decreased soil respiration and inhibited the convergence of molecular composition across ecosystems by altering N demand for microbial metabolism and chemical interactions between minerals and organic molecules. This study provides original evidence that the decomposition and metabolism of labile organic molecules were largely regulated by soil mineralogy (physicochemical preservation), while the metabolism of more complex organic molecules was controlled by substrate complexity (biochemical preservation) independent to mineral-organic interactions. This study advanced our understanding of the dynamic biogeochemical cycling of C by unveiling that N addition dampened C respiration and diminished the convergence of SOM chemistry across diverse grassland ecosystems.

Ecological theory applied to environmental metabolomes reveals compositional divergence despite conserved molecular properties

Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed 'meta-metabolome ecology.' Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering and variations in putative biochemical transformations. We refer to this phenomenon as 'thermodynamic redundancy,' which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable concepts, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics.

Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets

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-H₂O) + 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, mass2adduct, 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.

Quantifying Positional Isomers (QPI) by Top-Down Mass Spectrometry

Proteomics has exposed a plethora of posttranslational modifications, but demonstrating functional relevance requires new approaches. Top-down proteomics of intact proteins has the potential to fully characterize protein modifications in terms of amount, site(s), and the order in which they are deposited on the protein; information that so far has been elusive to extract by shotgun proteomics. Data acquisition and analysis of intact multimodified proteins have however been a major challenge, in particular for positional isomers that carry the same number of modifications at different sites. Solutions were previously proposed to extract this information from fragmentation spectra, but these have so far mainly been limited to peptides and have entailed a large degree of manual interpretation. Here, we apply high-resolution Orbitrap fusion top-down analyses in combination with bioinformatics approaches to attempt to characterize multiple modified proteins and quantify positional isomers. Automated covalent fragment ion type definition, detection of mass precision and accuracy, and extensive use of replicate spectra increase sequence coverage and drive down false fragment assignments from 10% to 1.5%. Such improved performance in fragment assignment is key to localize and quantify modifications from fragment spectra. The method is tested by investigating positional isomers of Ubiquitin mixed in known concentrations, which results in quantification of high ratios at very low standard errors of the mean (<5%), as well as with synthetic phosphorylated peptides. Application to multiphosphorylated Bora provides an estimation of the so far unknown stoichiometry of the known set of phosphosites and uncovers new sites from hyperphosphorylated Bora.

•

ETD fragmentation reveals the presence of positional isomers.

•

For proteins up to 40 kDa these positional isomers can accurately be quantified.

•

For in-vitro phosphorylated BoraNT a wide array of positional isomers is revealed.

•

Use of Fragment ion FDR levels improve the quality of extracted stoichiometries.

Maize specialized metabolome networks reveal organ-preferential mixed glycosides

Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).

Using metacommunity ecology to understand environmental metabolomes

Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

Despite growing interest in environmental metabolomics, we lack conceptual frameworks for considering how metabolites vary across space and time in ecological systems. Here, the authors apply (species) community assembly concepts to metabolomics data, offering a way forward in understanding the assembly of metabolite assemblages.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships

Untargeted metabolomics analysis captures chemical reactions among small molecules. Common mass spectrometry-based metabolomics workflows first identify the small molecules significantly associated with the outcome of interest, then begin exploring their biochemical relationships to understand biological fate or impact. We suggest an alternative by which general chemical relationships including abiotic reactions can be directly retrieved through untargeted high-resolution paired mass distance (PMD) analysis without a priori knowledge of the identities of participating compounds. PMDs calculated from the mass spectrometry data are linked to chemical reactions obtained via data mining of small molecule and reaction databases, i.e. 'PMD-based reactomics'. We demonstrate applications of PMD-based reactomics including PMD network analysis, source appointment of unknown compounds, and biomarker reaction discovery as complements to compound discovery analyses used in traditional untargeted workflows. An R implementation of reactomics analysis and the reaction/PMD databases is available as the pmd package.

Molecular characterization of water extractable Euglena gracilis cellular material composition using asymmetrical flow field-flow fractionation and high-resolution mass spectrometry

Asymmetrical flow field-flow fractionation (AF4) and high-resolution Orbitrap mass spectrometry (HRMS) were used to separate and characterize cellular fractions of the dark- and light-grown Euglena gracilis cellular material. Biological replicates analyzed by HRMS shared 21-73% of commonly detected m/z values. Greater variability in shared features was found in light-grown cellular fractions (p < 0.05), likely due to small variations in growth stage. Significant differences in molecular composition were observed between AF4 cellular fractions, with dark cell fractions showing a propensity towards carbohydrate-like and tannin-like compounds, and higher double-bond equivalent (DBE) and modified aromatic index (AI_mod) were associated with light-grown cell fractions. Fractionation and high-resolution mass spectrometry aided characterization demonstrated the power of the AF4 to selectively cater to certain compounds/cellular entities with distinct compositional classes and double-bond equivalents and aromaticity index characteristics. Graphical abstract.

Mass Spectrometry-Based Untargeted Plant Metabolomics

Metabolomics has grown into one of the major approaches for systems biology studies, in part driven by developments in mass spectrometry (MS), providing high sensitivity and coverage of the metabolome at high throughput. Untargeted metabolomics allows for the investigation of metabolic phenotypes involving several hundreds to thousands of metabolites. In this approach, all signals in a mass chromatogram are processed in an unbiased way, allowing for a deeper investigation of metabolic phenotypes, but also resulting in significantly more complex data processing and post-processing steps. In this article, we discuss all the intricacies involved in extracting and analyzing metabolites by chromatography coupled to MS, as well as the processing and analysis of such datasets. © 2019 The Authors. Basic Protocol 1: Metabolite extraction for LC-MS Alternate Protocol: Methyl tert-butyl ether (MTBE) extraction for multiple mass spectrometry platforms (GC-polar, LC-polar, LC-lipid) Basic Protocol 2: LC-MS analysis Support Protocol 1: GC-MS derivatization and analysis Support Protocol 2: Lipid analysis Basic Protocol 3: LC-MS data processing Basic Protocol 4: Data analysis Basic Protocol 5: Metabolite annotation Support Protocol 3: Molecular networking using MetNet Support Protocol 4: Co-injection of authentic standards.

Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics

High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.

Prioritizing candidate diseases-related metabolites based on literature and functional similarity

Background

As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases.

Results

We construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%.

Conclusion

In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites.

Untargeted metabolomic profiling of Sphagnum fallax reveals novel antimicrobial metabolites

Sphagnum mosses dominate peatlands by employing harsh ecosystem tactics to prevent vascular plant growth and microbial degradation of these large carbon stores. Knowledge about Sphagnum-produced metabolites, their structure and their function, is important to better understand the mechanisms, underlying this carbon sequestration phenomenon in the face of climate variability. It is currently unclear which compounds are responsible for inhibition of organic matter decomposition and the mechanisms by which this inhibition occurs. Metabolite profiling of Sphagnum fallax was performed using two types of mass spectrometry (MS) systems and 1H nuclear magnetic resonance spectroscopy (1H NMR). Lipidome profiling was performed using LC-MS/MS. A total of 655 metabolites, including one hundred fifty-two lipids, were detected by NMR and LC-MS/MS-329 of which were novel metabolites (31 unknown lipids). Sphagum fallax metabolite profile was composed mainly of acid-like and flavonoid glycoside compounds, that could be acting as potent antimicrobial compounds, allowing Sphagnum to control its environment. Sphagnum fallax metabolite composition comparison against previously known antimicrobial plant metabolites confirmed this trend, with seventeen antimicrobial compounds discovered to be present in Sphagnum fallax, the majority of which were acids and glycosides. Biological activity of these compounds needs to be further tested to confirm antimicrobial qualities. Three fungal metabolites were identified providing insights into fungal colonization that may benefit Sphagnum. Characterizing the metabolite profile of Sphagnum fallax provided a baseline to understand the mechanisms in which Sphagnum fallax acts on its environment, its relation to carbon sequestration in peatlands, and provide key biomarkers to predict peatland C store changes (sequestration, emissions) as climate shifts.

Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1

In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154-294), 294 to 336 d of age (ADG294-336), and 154 to 336 d of age (ADG154-336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154-294, ADG294-336, and ADG154-336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.

Effects of Moxibustion and Moxa Smoke on Behavior Changes and Energy Metabolism in APP/PS1 Mice

OBJECTIVE

To investigate the antiaging effects of moxibustion and moxa smoke on APP/PS1 mice and to illustrate the mechanism of moxibustion improving Alzheimer's disease (AD).

METHODS

36 male APP/PS1 mice were randomly assigned into three groups (n = 12), including a model control group, a moxibustion group, and a moxa smoke group. In addition, 12 C57BL/6 normal mice served as a normal (negative) control group. Mice in the moxibustion group received moxibustion intervention using Guanyuan (RN4) acupoint. Mice in the moxa smoke group received moxa smoke exposure with the same frequency as the moxibustion group. Behavioral tests were implemented in the 9th week, 3 days after the completion of the intervention. Tricarboxylic acid cycle and fatty acid metabolomics assessments of the mice were determined after behavioral tests.

RESULTS

In this study, relative to normal mice, we found that AD mice showed altered tricarboxylic and fatty acid metabolism and showed behavioral changes consistent with the onset of AD. However, both the moxibustion and moxa smoke interventions were able to mitigate these effects to some degree in AD mice.

CONCLUSIONS

The data suggest that tricarboxylic acid cycle and unsaturated fatty acid metabolomics changes may be a target of AD, and the beneficial effects of moxibustion on cognitive behaviors may be mediated by the energy metabolism system.

MetNet: Metabolite Network Prediction from High-Resolution Mass Spectrometry Data in R Aiding Metabolite Annotation

A major bottleneck of mass spectrometric metabolomic analysis is still the rapid detection and annotation of unknown m/ z features across biological matrices. This kind of analysis is especially cumbersome for complex samples with hundreds to thousands of unknown features. Traditionally, the annotation was done manually imposing constraints in reproducibility and automatization. Furthermore, different analysis tools are typically used at different steps which requires parsing of data and changing of environments. We present here MetNet, implemented in the R programming language and available as an open-source package via the Bioconductor project. MetNet, which is compatible with the output of the xcms/CAMERA suite, uses the data-rich output of mass spectrometry metabolomics to putatively link features on their relation to other features in the data set. MetNet uses both structural and quantitative information on metabolomics data for network inference and enables the annotation of unknown analytes.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

Integrative omics analysis in Pandanus odorifer (Forssk.) Kuntze reveals the role of Asparagine synthetase in salinity tolerance

Pandanus odorifer (Forssk) Kuntze grows naturally along the coastal regions and withstands salt-sprays as well as strong winds. A combination of omics approaches and enzyme activity studies was employed to comprehend the mechanistic basis of high salinity tolerance in P. odorifer. The young seedlings of P. odorifer were exposed to 1 M salt stress for up to three weeks and analyzed using RNAsequencing (RNAseq) and LC-MS. Integrative omics analysis revealed high expression of the Asparagine synthetase (AS) (EC 6.3.5.4) (8.95 fold) and remarkable levels of Asparagine (Asn) (28.5 fold). This indicated that salt stress promoted Asn accumulation in P. odorifer. To understand this further, the Asn biosynthesis pathway was traced out in P. odorifer. It was noticed that seven genes involved in Asn bisynthetic pathway namely glutamine synthetase (GS) (EC 6.3.1.2) glutamate synthase (GOGAT) (EC 1.4.1.14), aspartate kinase (EC 2.7.2.4), pyruvate kinase (EC 2.7.1.40), aspartate aminotransferase (AspAT) (EC 2.6.1.1), phosphoenolpyruvate carboxylase (PEPC) (EC 4.1.1.31) and AS were up-regulated under salt stress. AS transcripts were most abundant thereby showed its highest activity and thus were generating maximal Asn under salt stress. Also, an up-regulated Na+/H+ antiporter (NHX1) facilitated compartmentalization of Na+ into vacuoles, suggesting P. odorifer as salt accumulator species.

Metabolomic investigations in cerebrospinal fluid of Parkinson's disease

The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

Multi 'omics comparison reveals metabolome biochemistry, not microbiome composition or gene expression, corresponds to elevated biogeochemical function in the hyporheic zone

Biogeochemical hotspots are pervasive at terrestrial-aquatic interfaces, particularly within groundwater-surface water mixing zones (hyporheic zones), and they are critical to understanding spatiotemporal variation in biogeochemical cycling. Here, we use multi 'omic comparisons of hotspots to low-activity sediments to gain mechanistic insight into hyporheic zone organic matter processing. We hypothesized that microbiome structure and function, as described by metagenomics and metaproteomics, would distinguish hotspots from low-activity sediments by shifting metabolism towards carbohydrate-utilizing pathways and elucidate discrete mechanisms governing organic matter processing in each location. We also expected these differences to be reflected in the metabolome, whereby hotspot carbon (C) pools and metabolite transformations therein would be enriched in sugar-associated compounds. In contrast to expectations, we found pronounced phenotypic plasticity in the hyporheic zone microbiome that was denoted by similar microbiome structure, functional potential, and expression across sediments with dissimilar metabolic rates. Instead, diverse nitrogenous metabolites and biochemical transformations characterized hotspots. Metabolomes also corresponded more strongly to aerobic metabolism than bulk C or N content only (explaining 67% vs. 42% and 37% of variation respectively), and bulk C and N did not improve statistical models based on metabolome composition alone. These results point to organic nitrogen as a significant regulatory factor influencing hyporheic zone organic matter processing. Based on our findings, we propose incorporating knowledge of metabolic pathways associated with different chemical fractions of C pools into ecosystem models will enhance prediction accuracy.

Propagating annotations of molecular networks using in silico fragmentation

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Influences of organic carbon speciation on hyporheic corridor biogeochemistry and microbial ecology

The hyporheic corridor (HC) encompasses the river–groundwater continuum, where the mixing of groundwater (GW) with river water (RW) in the HC can stimulate biogeochemical activity. Here we propose a novel thermodynamic mechanism underlying this phenomenon and reveal broader impacts on dissolved organic carbon (DOC) and microbial ecology. We show that thermodynamically favorable DOC accumulates in GW despite lower DOC concentration, and that RW contains thermodynamically less-favorable DOC, but at higher concentrations. This indicates that GW DOC is protected from microbial oxidation by low total energy within the DOC pool, whereas RW DOC is protected by lower thermodynamic favorability of carbon species. We propose that GW–RW mixing overcomes these protections and stimulates respiration. Mixing models coupled with geophysical and molecular analyses further reveal tipping points in spatiotemporal dynamics of DOC and indicate important hydrology–biochemistry–microbial feedbacks. Previously unrecognized thermodynamic mechanisms regulated by GW–RW mixing may therefore strongly influence biogeochemical and microbial dynamics in riverine ecosystems.

The mechanisms responsible for stimulating biogeochemical activity in the hyporheic corridor (HC) are poorly understood. Here, the authors find that previously unrecognized thermodynamic mechanisms regulated by groundwater-river water mixing may strongly influence HC biogeochemical and microbial dynamics.

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.

Unambiguous identification and discovery of bacterial siderophores by direct injection 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry

Under iron-limiting conditions, bacteria produce low molecular mass Fe(iii) binding molecules known as siderophores to sequester the Fe(iii), along with other elements, increasing their bioavailability. Siderophores are thought to influence iron cycling and biogeochemistry in both marine and terrestrial ecosystems and hence the need for rapid, confident characterization of these compounds has increased. In this study, the type of siderophores produced by two marine bacterial species, Synechococcus sp. PCC 7002 and Vibrio cyclitrophicus 1F53, were characterized by use of a newly developed 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometer (FTICR MS) with direct injection electrospray ionization. This technique allowed for the rapid detection of synechobactins from Synechococcus sp. PCC 7002 as well as amphibactins from Vibrio cyclitrophicus 1F53 based on high mass accuracy and resolution allowing for observation of specific Fe isotopes and isotopic fine structure enabling highly confident identification of these siderophores. When combined with molecular network analysis two new amphibactins were discovered and verified by tandem MS. These results show that high-field FTICR MS is a powerful technique that will greatly improve the ability to rapidly identify and discover metal binding species in the environment.

MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification

•

An update to the ab-initio network construction tool MetaNetter has been produced.

•

The tool creates networks of masses from high resolution mass spectrometry data.

•

The new plugin provides both chemical transformation and adduct mapping.

•

Tables mapping adduct and transform counts across samples can be generated.

•

Retention time windows are supported for both adduct and transform network generation.

Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them.

We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships.

We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset.

Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis.

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/.

Transposon Sequencing Uncovers an Essential Regulatory Function of Phosphoribulokinase for Methylotrophy

Methylotrophy is the ability of organisms to grow at the expense of reduced one-carbon compounds, such as methanol or methane. Here, we used transposon sequencing combining hyper-saturated transposon mutagenesis with high-throughput sequencing to define the essential methylotrophy genome of Methylobacterium extorquens PA1, a model methylotroph. To distinguish genomic regions required for growth only on methanol from general required genes, we contrasted growth on methanol with growth on succinate, a non-methylotrophic reference substrate. About 500,000 insertions were mapped for each condition, resulting in a median insertion distance of five base pairs. We identified 147 genes and 76 genes as specific for growth on methanol and succinate, respectively, and a set of 590 genes as required under both growth conditions. For the integration of metabolic functions, we reconstructed a genome-scale metabolic model and performed in silico essentiality analysis. In total, the approach uncovered 95 genes not previously described as crucial for methylotrophy, including genes involved in respiration, carbon metabolism, transport, and regulation. Strikingly, regardless of the absence of the Calvin cycle in the methylotroph, the screen led to the identification of the gene for phosphoribulokinase as essential during growth on methanol, but not during growth on succinate. Genetic experiments in addition to metabolomics and proteomics revealed that phosphoribulokinase serves a key regulatory function. Our data support a model according to which ribulose-1,5-bisphosphate is an essential metabolite that induces a transcriptional regulator driving one-carbon assimilation.

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.

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.

Metabolomics: A Primer

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.

Using network analysis to discern compositional patterns in ultrahigh-resolution mass spectrometry data of dissolved organic matter

RATIONALE

Marine dissolved organic matter (DOM) has long been recognized as a large and dynamic component of the global carbon cycle. Yet, DOM is chemically varied and complex and these attributes present challenges to researchers interested in addressing questions about the role of DOM in global biogeochemical cycles.

METHODS

Organic matter extracts from seawater were analyzed by direct infusion with electrospray ionization into a Fourier transform ion cyclotron resonance mass spectrometer. Network analysis was used to quantify the number of chemical transformations between mass-to-charge values in each sample. The network of chemical transformations was calculated using the MetaNetter plug-in within Cytoscape. The chemical transformations serve as markers for the shared structural characteristics of compounds within complex DOM.

RESULTS

Network analysis revealed that transformations involving selected sulfur-containing moieties and isomers of amino acids were more prevalent in the deep sea than in the surface ocean. Common chemical transformations were not significantly different between the deep sea and surface ocean. Network analysis complements existing computational tools used to analyze ultrahigh-resolution mass spectrometry data.

CONCLUSIONS

This combination of ultrahigh-resolution mass spectrometry with novel computational tools has identified new potential building blocks of organic compounds in the deep sea, including the unexpected importance of dissolved organic sulfur components. The method described here can be readily applied by researchers to analyze heterogeneous and complex DOM. Copyright © 2016 John Wiley & Sons, Ltd.

Computational Metabolomics: A Framework for the Million Metabolome

"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

Challenges of metabolomics in human gut microbiota research

The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.