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

Environment-specific virocell metabolic reprogramming

Viruses impact microbial systems through killing hosts, horizontal gene transfer, and altering cellular metabolism, consequently impacting nutrient cycles. A virus-infected cell, a "virocell," is distinct from its uninfected sister cell as the virus commandeers cellular machinery to produce viruses rather than replicate cells. Problematically, virocell responses to the nutrient-limited conditions that abound in nature are poorly understood. Here we used a systems biology approach to investigate virocell metabolic reprogramming under nutrient limitation. Using transcriptomics, proteomics, lipidomics, and endo- and exo-metabolomics, we assessed how low phosphate (low-P) conditions impacted virocells of a marine Pseudoalteromonas host when independently infected by two unrelated phages (HP1 and HS2). With the combined stresses of infection and nutrient limitation, a set of nested responses were observed. First, low-P imposed common cellular responses on all cells (virocells and uninfected cells), including activating the canonical P-stress response, and decreasing transcription, translation, and extracellular organic matter consumption. Second, low-P imposed infection-specific responses (for both virocells), including enhancing nitrogen assimilation and fatty acid degradation, and decreasing extracellular lipid relative abundance. Third, low-P suggested virocell-specific strategies. Specifically, HS2-virocells regulated gene expression by increasing transcription and ribosomal protein production, whereas HP1-virocells accumulated host proteins, decreased extracellular peptide relative abundance, and invested in broader energy and resource acquisition. These results suggest that although environmental conditions shape metabolism in common ways regardless of infection, virocell-specific strategies exist to support viral replication during nutrient limitation, and a framework now exists for identifying metabolic strategies of nutrient-limited virocells in nature.

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.

PP2C phosphatases Ptc1 and Ptc2 dephosphorylate PGK1 to regulate autophagy and aflatoxin synthesis in the pathogenic fungus Aspergillus flavus

IMPORTANCE

Aspergillus flavus is a model filamentous fungus that can produce aflatoxins when it infects agricultural crops. This study evaluated the protein phosphatase 2C (PP2C) family as a potential drug target with important physiological functions and pathological significance in A. flavus. We found that two redundant PP2C phosphatases, Ptc1 and Ptc2, regulate conidia development, aflatoxin synthesis, autophagic vesicle formation, and seed infection. The target protein phosphoglycerate kinase 1 (PGK1) that interacts with Ptc1 and Ptc2 is essential to regulate metabolism and the autophagy process. Furthermore, Ptc1 and Ptc2 regulate the phosphorylation level of PGK1 S203, which is important for influencing aflatoxin synthesis. Our results provide a potential target for interdicting the toxicity of A. flavus.

Molecular-Scale Investigation on the Formation of Brown Carbon Aerosol via Iron-Phenolic Compound Reactions in the Dark

Brown carbon (BrC) is one of the most mysterious aerosol components responsible for global warming and air pollution. Iron (Fe)-induced catalytic oxidation of ubiquitous phenolic compounds has been considered as a potential pathway for BrC formation in the dark. However, the reaction mechanism and product composition are still poorly understood. Herein, 13 phenolic precursors were employed to react with Fe under environmentally relevant conditions. Using Fourier transform ion cyclotron resonance mass spectrometry, a total of 764 unique molecular formulas were identified, and over 85% of them can be found in atmospheric aerosols. In particular, products derived from precursors with catechol-, guaiacol-, and syringol-like-based structures can be distinguished by their optical and molecular characteristics, indicating the structure-dependent formation of BrC from phenolic precursors. Multiple pieces of evidence indicate that under acidic conditions, the contribution of either autoxidation or oxygen-induced free radical oxidation to BrC formation is extremely limited. Ligand-to-Fe charge transfer and subsequent phenoxy radical coupling reactions were the main mechanism for the formation of polymerization products with high molecular diversity, and the efficiency of BrC generation was linearly correlated with the ionization potential of phenolic precursors. The present study uncovered how chemically diverse BrC products were formed by the Fe-phenolic compound reactions at the molecular level and also provide a new paradigm for the study of the atmospheric aerosol formation mechanism.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

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.

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.

Elevated temperatures drive abiotic and biotic degradation of organic matter in a peat bog under oxic conditions

Understanding the effects of elevated temperatures on soil organic matter (SOM) decomposition pathways in northern peatlands is central to predicting their fate under future warming. Peatlands role as carbon (C) sink is dependent on both anoxic conditions and low temperatures that limit SOM decomposition. Previous studies have shown that elevated temperatures due to climate change can disrupt peatland's C balance by enhancing SOM decomposition and increasing CO₂ emissions. However, little is known about how SOM decomposition pathways change at higher temperatures. Here, we used an integrated research approach to investigate the mechanisms behind enhanced CO₂ emissions and SOM decomposition under elevated temperatures of surface peat soil collected from a raised and Sphagnum dominated mid-continental bog (S1 bog) peatland at the Marcel Experimental Forest in Minnesota, USA, incubated under oxic conditions at three different temperatures (4, 21, and 35 °C). Our results indicated that elevated temperatures could destabilize peatland's C pool via a combination of abiotic and biotic processes. In particular, temperature-driven changes in redox conditions can lead to abiotic destabilization of Fe-organic matter (phenol) complexes, previously an underestimated decomposition pathway in peatlands, leading to increased CO₂ production and accumulation of polyphenol-like compounds that could further inhibit extracellular enzyme activities and/or fuel the microbial communities with labile compounds. Further, increased temperatures can alter strategies of microbial communities for nutrient acquisition via changes in the activities of extracellular enzymes by priming SOM decomposition, leading to enhanced CO₂ emission from peatlands. Therefore, coupled biotic and abiotic processes need to be incorporated into process-based climate models to predict the fate of SOM under elevated temperatures and to project the likely impacts of environmental change on northern peatlands and CO₂ emissions.

Integrating multi-omics data for crop improvement

Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.

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.

Staring into the void: demystifying microbial metabolomics

Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a 'choose your own adventure' approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.

Spatial metabolomics of in situ host-microbe interactions at the micrometre scale

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.

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.

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.

Aerobic and Anaerobic Bacterial Mercury Uptake is Driven by Algal Organic Matter Composition and Molecular Weight

The biological mobilization of mercury (Hg) into microbes capable of Hg methylation is one of the limiting steps in the formation of the neurotoxin methylmercury (MeHg). Although algal dissolved organic matter (DOM) has been associated with increased MeHg production, the relationship between bacterial Hg uptake and algal DOM remains unexplored. In this study, we aimed to address how the quantity and quality of DOM, freshly harvested from several algae, affected the bacterial uptake of Hg with the use of a biosensor capable of functioning both aerobically and anaerobically. We combined biosensor measurements with high-resolution mass spectrometry and field-flow fractionation to elucidate how DOM composition and molecular weight influenced microbial Hg uptake. We showed that freshly harvested DOM from Chlorophyte and Euglena mutabilis strongly inhibited aerobic and anaerobic Hg uptake, whereas DOM harvested from Euglena gracilis did not exhibit this same pronounced effect. Once fractionated, we found that amino acids and polyamines, most abundant in Euglena gracilis DOM, were positively correlated to increase Hg uptake, suggesting that these molecules are potentially underappreciated ligands affecting Hg bioavailability. As water quality is affected by eutrophication, algal community assemblages will change, leading to variations in the nature of autochthonous DOM released in aquatic systems. Our results highlight that variations in the emergent properties of DOM originating from varying algal species can have a profound effect on bacterial Hg uptake and thus methylation.

MS/MS-Guided Isolation of Clarinoside, a New Anti-Inflammatory Pentalogin Derivative

Re-investigation of the chemical composition of the annual plant Mitracarpus scaber Zucc. led to the identification of clarinoside, a new pentalogin derivative containing a rare quinovose moiety, and the known compound harounoside. While the planar structure was fully determined using tandem mass spectrometry (MS) and quantum mechanics (QM) calculations, the tridimensional structure was unravelled after isolation and NMR analysis. The absolute configuration was assigned by comparison of experimental and theoretical synchrotron radiation circular dichroism spectra. Both compounds were tested for anti-inflammatory activity, and compound 1 showed the ability to inhibit the production of interleukin-8 (Il-8) with an IC50 value of 9.17 μM.

New tools and resources in metabolomics: 2016-2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.

LC-MS-based plasma metabolomics reveals metabolic variations in ovariectomy-induced osteoporosis in female Wistar rats

Osteoporosis with a reduction in bone mineral density has become one of the most common metabolic bone diseases.