Global Isotope Metabolomics Reveals Adaptive Strategies for Nitrogen Assimilation

Central carbon metabolism exhibits unique characteristics during the handling of fungal patterns by monocyte-derived dendritic cells

Monocyte-derived dendritic cells (MDDCs) are key players in the defense against fungal infection because of their outstanding capacity for non-opsonic phagocytosis and phenotypic plasticity. Accordingly, MDDCs rewire metabolism to meet the energetic demands for microbial killing and biomass synthesis required to restore homeostasis. It has been commonplace considering the metabolic reprogramming a mimicry of the Warburg effect observed in tumor cells. However, this may be an oversimplification since the offshoots of glycolysis and the tricarboxylic acid (TCA) cycle are connected in central carbon metabolism. Zymosan, the external wall of Saccharomyces cerevisiae, contains β-glucan and α-mannan chains that engage the C-type lectin receptors dectin-1/2 and Toll-like receptors. This makes it an optimal fungal surrogate for experimental research. Using real-time bioenergetic assays and [U-13C]glucose labeling, central hubs connected to cytokine expression were identified. The pentose phosphate pathway (PPP) exhibited a more relevant capacity to yield ribose-5-phosphate than reducing equivalents of NADPH, as judged from the high levels of isotopologues showing 13C-labeling in the ribose moiety and the limited contribution of the oxidative arm of the PPP to the production of ROS by NADPH oxidases (NOX). The finding of 13C-label in the purine ring and in glutathione unveiled the contribution of serine-derived glycine to purine ring and glutathione synthesis. Serine synthesis also supported the TCA cycle. Zymosan exhausted NAD+ and ATP, consistent with intracellular consumption and/or extracellular export. Poly-ADP-ribosylated proteins detected in the nuclear fractions of MDDCs did not show major changes upon zymosan stimulation, which suggests its dependence on constitutive Fe(II)/2-oxoglutarate-dependent demethylation of 5-methylcytosine by TET translocases and/or demethylation of histone H3 lysine 27 by JMJD demethylases rather than on NOX activities. These results disclose a unique pattern of central carbon metabolism following fungal challenge, characterized by the leverage of glycolysis offshoots and an extensive recycling of NAD+ and poly(ADP-ribose).

de la Fuente M, Simarro M. The mitochondrial succinate dehydrogenase complex controls the STAT3-IL-10 pathway in inflammatory macrophages

The functions of macrophages are tightly regulated by their metabolic state. However, the role of the mitochondrial electron transport chain (ETC) in macrophage functions remains understudied. Here, we provide evidence that the succinate dehydrogenase (SDH)/complex II (CII) is required for respiration and plays a role in controlling effector responses in macrophages. We find that the absence of the catalytic subunits Sdha and Sdhb in macrophages impairs their ability to effectively stabilize HIF-1α and produce the pro-inflammatory cytokine IL-1β in response to LPS stimulation. We also arrive at the novel result that both subunits are essential for the LPS-driven production of IL-10, a potent negative feedback regulator of the macrophage inflammatory response. This phenomenon is explained by the fact that the absence of Sdha and Sdhb leads to the inhibition of Stat3 tyrosine phosphorylation, caused partially by the excessive accumulation of mitochondrial reactive oxygen species (mitoROS) in the knockout cells.

Fungal Patterns Induce Cytokine Expression through Fluxes of Metabolic Intermediates That Support Glycolysis and Oxidative Phosphorylation

Cytokine expression is fine-tuned by metabolic intermediates, which makes research on immunometabolism suitable to yield drugs with a wider prospect of application than the biological therapies that block proinflammatory cytokines. Switch from oxidative phosphorylation (OXPHOS) to glycolysis has been considered a characteristic feature of activated immune cells. However, some stimuli might enhance both routes concomitantly. The connection between the tricarboxylic acid cycle and cytokine expression was scrutinized in human monocyte-derived dendritic cells stimulated with the fungal surrogate zymosan. Results showed that nucleocytosolic citrate and ATP-citrate lyase activity drove IL1B, IL10, and IL23A expression by yielding acetyl-CoA and oxaloacetate, with the latter one supporting glycolysis and OXPHOS by maintaining cytosolic NAD⁺ and mitochondrial NADH levels through mitochondrial shuttles. Succinate dehydrogenase showed a subunit-specific ability to modulate IL23A and IL10 expression. Succinate dehydrogenase A subunit activity supported cytokine expression through the control of the 2-oxoglutarate/succinate ratio, whereas C and D subunits underpinned cytokine expression by conveying electron flux from complex II to complex III of the electron transport chain. Fatty acids may also fuel the tricarboxylic acid cycle and influence cytokine expression. Overall, these results show that fungal patterns support cytokine expression through a strong boost of glycolysis and OXPHOS supported by the use of pyruvate, citrate, and succinate, along with the compartmentalized NAD(H) redox state maintained by mitochondrial shuttles.

Metabolite discovery: Biochemistry's scientific driver

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21^st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.

Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics

To uncover metal toxicity targets and defense mechanisms of the facultative anaerobe Pantoea sp. strain MT58 (MT58), we used a multiomic strategy combining two global techniques, random bar code transposon site sequencing (RB-TnSeq) and activity-based metabolomics. MT58 is a metal-tolerant Oak Ridge Reservation (ORR) environmental isolate that was enriched in the presence of metals at concentrations measured in contaminated groundwater at an ORR nuclear waste site. The effects of three chemically different metals found at elevated concentrations in the ORR contaminated environment were investigated: the cation Al³⁺, the oxyanion CrO₄^2-, and the oxycation UO₂²⁺. Both global techniques were applied using all three metals under both aerobic and anaerobic conditions to elucidate metal interactions mediated through the activity of metabolites and key genes/proteins. These revealed that Al³⁺ binds intracellular arginine, CrO₄^2- enters the cell through sulfate transporters and oxidizes intracellular reduced thiols, and membrane-bound lipopolysaccharides protect the cell from UO₂²⁺ toxicity. In addition, the Tol outer membrane system contributed to the protection of cellular integrity from the toxic effects of all three metals. Likewise, we found evidence of regulation of lipid content in membranes under metal stress. Individually, RB-TnSeq and metabolomics are powerful tools to explore the impact various stresses have on biological systems. Here, we show that together they can be used synergistically to identify the molecular actors and mechanisms of these pertubations to an organism, furthering our understanding of how living systems interact with their environment. IMPORTANCE Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In this study, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism, Pantoea sp. strain MT58, and metals contaminating a site where the organism can be located. A number of novel metal-microbe interactions were uncovered, including Al³⁺ toxicity targeting arginine synthesis, which could lead to a deeper understanding of the impact Al³⁺ contamination has on microbial communities as well as its impact on higher-level organisms, including plants for whom Al³⁺ contamination is an issue. Using multiomic approaches like the one described here is a way to further our understanding of microbial interactions and their impacts on the environment overall.

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

METLIN: A Tandem Mass Spectral Library of Standards

Untargeted mass spectrometry metabolomics studies rely on accurate databases for the identification of metabolic features. Leveraging unique fragmentation patterns as well as characteristic dissociation routes allows for structural information to be gained for specific metabolites and molecular classes, respectively. Here we describe the evolution of METLIN as a resource for small molecule analysis as well as the tools (e.g., Fragment Similarity Search and Neutral Loss Search) used to query the database and their workflows for the identification of molecular entities. Additionally, we will discuss the functionalities of isoMETLIN, a database of isotopic metabolites, and the latest addition to the METLIN family, METLIN-MRM, which facilitates the analysis of quantitative mass spectrometry data generated with triple quadrupole instrumentation.

Tricarboxylic Acid Cycle Activity and Remodeling of Glycerophosphocholine Lipids Support Cytokine Induction in Response to Fungal Patterns

Increased glycolysis parallels immune cell activation, but the role of pyruvate remains largely unexplored. We found that stimulation of dendritic cells with the fungal surrogate zymosan causes decreases of pyruvate, citrate, itaconate, and α-ketoglutarate, while increasing oxaloacetate, succinate, lactate, oxygen consumption, and pyruvate dehydrogenase activity. Expression of IL10 and IL23A (the gene encoding the p19 chain of IL-23) depended on pyruvate dehydrogenase activity. Mechanistically, pyruvate reinforced histone H3 acetylation, and acetate rescued the effect of mitochondrial pyruvate carrier inhibition, most likely because it is a substrate of the acetyl-CoA producing enzyme ACSS2. Mice lacking the receptor of the lipid mediator platelet-activating factor (PAF; 1-O-hexadecyl-2-acetyl-sn-glycero-3-phosphocholine) showed reduced production of IL-10 and IL-23 that is explained by the requirement of acetyl-CoA for PAF biosynthesis and its ensuing autocrine function. Acetyl-CoA therefore intertwines fatty acid remodeling of glycerophospholipids and energetic metabolism during cytokine induction.

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

The Chemistry, Biology, and Modulation of Ammonium Nitrification in Soil

Approximately two percent of the world's energy is consumed in the production of ammonia from hydrogen and nitrogen gas. Ammonia is used as a fertilizer ingredient for agriculture and distributed in the environment on an enormous scale to promote crop growth in intensive farming. Only 30-50 % of the nitrogen applied is assimilated by crop plants; the remaining 50-70 % goes into biological processes such as nitrification by microbial metabolism in the soil. This leads to an imbalance in the global nitrogen cycle and higher nitrous oxide emissions (a potent and significant greenhouse gas) as well as contamination of ground and surface waters by nitrate from the nitrogen-fertilized farmland. This Review gives a critical overview of the current knowledge of soil microbes involved in the chemistry of ammonia nitrification, the structures and mechanisms of the enzymes involved, and phytochemicals capable of inhibiting ammonia nitrification.

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

METLIN: A Technology Platform for Identifying Knowns and Unknowns

METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN's high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool's capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/ z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN's MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.