Continuous in vivo Metabolism by NMR

Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa, a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems

Diel investments in metabolite production and consumption in a model microbial system

Organic carbon transfer between surface ocean photosynthetic and heterotrophic microbes is a central but poorly understood process in the global carbon cycle. In a model community in which diatom extracellular release of organic molecules sustained growth of a co-cultured bacterium, we determined quantitative changes in the diatom endometabolome and the bacterial uptake transcriptome over two diel cycles. Of the nuclear magnetic resonance (NMR) peaks in the diatom endometabolites, 38% had diel patterns with noon or mid-afternoon maxima; the remaining either increased (36%) or decreased (26%) through time. Of the genes in the bacterial uptake transcriptome, 94% had a diel pattern with a noon maximum; the remaining decreased over time (6%). Eight diatom endometabolites identified with high confidence were matched to the bacterial genes mediating their utilization. Modeling of these coupled inventories with only diffusion-based phytoplankton extracellular release could not reproduce all the patterns. Addition of active release mechanisms for physiological balance and bacterial recognition significantly improved model performance. Estimates of phytoplankton extracellular release range from only a few percent to nearly half of annual net primary production. Improved understanding of the factors that influence metabolite release and consumption by surface ocean microbes will better constrain this globally significant carbon flux

The future of NMR-based metabolomics

The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications

Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ferrer-González, F. X., Widner, B., Holderman, N. R., Glushka, J., Edison, A. S., Kujawinski, E. B., & Moran, M. A. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME Journal, (2020), doi:10.1038/s41396-020-00811-y.The communities of bacteria that assemble around marine microphytoplankton are predictably dominated by Rhodobacterales, Flavobacteriales, and families within the Gammaproteobacteria. Yet whether this consistent ecological pattern reflects the result of resource-based niche partitioning or resource competition requires better knowledge of the metabolites linking microbial autotrophs and heterotrophs in the surface ocean. We characterized molecules targeted for uptake by three heterotrophic bacteria individually co-cultured with a marine diatom using two strategies that vetted the exometabolite pool for biological relevance by means of bacterial activity assays: expression of diagnostic genes and net drawdown of exometabolites, the latter detected with mass spectrometry and nuclear magnetic resonance using novel sample preparation approaches. Of the more than 36 organic molecules with evidence of bacterial uptake, 53% contained nitrogen (including nucleosides and amino acids), 11% were organic sulfur compounds (including dihydroxypropanesulfonate and dimethysulfoniopropionate), and 28% were components of polysaccharides (including chrysolaminarin, chitin, and alginate). Overlap in phytoplankton-derived metabolite use by bacteria in the absence of competition was low, and only guanosine, proline, and N-acetyl-d-glucosamine were predicted to be used by all three. Exometabolite uptake pattern points to a key role for ecological resource partitioning in the assembly marine bacterial communities transforming recent photosynthate.This work was supported by grants from the Gordon and Betty Moore Foundation (5503) and the National Science Foundation (IOS-1656311) to MAM, ASE, and EBK, and by the Simons Foundation grant 542391 to MAM within the Principles of Microbial Ecosystems (PriME) Collaborative

Ascaroside Expression in Caenorhabditis elegans Is Strongly Dependent on Diet and Developmental Stage

Background: The ascarosides form a family of small molecules that have been isolated from cultures of the nematode Caenorhabditis elegans. They are often referred to as “dauer pheromones” because most of them induce formation of long-lived and highly stress resistant dauer larvae. More recent studies have shown that ascarosides serve additional functions as social signals and mating pheromones. Thus, ascarosides have multiple functions. Until now, it has been generally assumed that ascarosides are constitutively expressed during nematode development. Methodology/Principal Findings: Cultures of C. elegans were developmentally synchronized on controlled diets. Ascarosides released into the media, as well as stored internally, were quantified by LC/MS. We found that ascaroside biosynthesis and release were strongly dependent on developmental stage and diet. The male attracting pheromone was verified to be a blend of at least four ascarosides, and peak production of the two most potent mating pheromone components, ascr#3 and asc#8 immediately preceded or coincided with the temporal window for mating. The concentration of ascr#2 increased under starvation conditions and peaked during dauer formation, strongly supporting ascr#2 as the main population density signal (dauer pheromone). After dauer formation, ascaroside production largely ceased and dauer larvae did not release any ascarosides. These findings show that both total ascaroside production and the relative proportions of individual ascarosides strongly correlate with these compounds' stage-specific biological functions. Conclusions/Significance: Ascaroside expression changes with development and environmental conditions. This is consistent with multiple functions of these signaling molecules. Knowledge of such differential regulation will make it possible to associate ascaroside production to gene expression profiles (transcript, protein or enzyme activity) and help to determine genetic pathways that control ascaroside biosynthesis. In conjunction with findings from previous studies, our results show that the pheromone system of C. elegans mimics that of insects in many ways, suggesting that pheromone signaling in C. elegans may exhibit functional homology also at the sensory level. In addition, our results provide a strong foundation for future behavioral modeling studies

Growth-stage-related shifts in phytoplankton endometabolome composition set the stage for bacterial heterotrophy

Phytoplankton-derived metabolites fuel a large fraction of heterotrophic bacterial production in the global ocean, yet methodological challenges have limited our understanding of the organic molecules transferred between these microbial groups. In an experimental bloom study consisting of three heterotrophic marine bacteria growing together with the diatom Thalassiosira pseudonana, we concurrently measured diatom endometabolites (i.e., potential exometabolite supply) by nuclear magnetic resonance (NMR) spectroscopy and bacterial gene expression (i.e., potential exometabolite uptake) by metatranscriptomic sequencing. Twenty-two diatom endometabolites were annotated, with nine increasing in internal concentration in the late stage of the bloom, eight decreasing, and five showing no variation through the bloom progression. Some metabolite changes could be linked to shifts in diatom gene expression, as well as to shifts in bacterial community composition and their expression of substrate uptake and catabolism genes. Yet an overall low match indicated that endometabolome concentration was not a good predictor of exometabolite availability, and that complex physiological and ecological interactions underlie metabolite exchange. Six diatom endometabolites accumulated to higher concentrations in the bacterial co-cultures compared to axenic cultures, suggesting a bacterial influence on rates of synthesis or release of glutamate, arginine, leucine, 2,3-dihydroxypropane-1-sulfonate, glucose, and glycerol-3-phosphate. Better understanding of phytoplankton metabolite production, release, and transfer to assembled bacterial communities is key to untangling this nearly invisible yet pivotal step in ocean carbon cycling

NMR and Metabolomics—A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatographymass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples

A blend of small molecules regulates both mating and development in Caenorhabditis elegans

In many organisms, population-density sensing and sexual attraction rely on small-molecule-based signalling systems. In the nematode Caenorhabditis elegans, population density is monitored through specific glycosides of the dideoxysugar ascarylose (the ‘ascarosides’) that promote entry into an alternative larval stage, the non-feeding and highly persistent dauer stage. In addition, adult C. elegans males are attracted to hermaphrodites by a previously unidentified small-molecule signal. Here we show, by means of combinatorial activity-guided fractionation of the C. elegans metabolome, that the mating signal consists of a synergistic blend of three dauer-inducing ascarosides, which we call ascr#2, ascr#3 and ascr#4. This blend of ascarosides acts as a potent male attractant at very low concentrations, whereas at the higher concentrations required for dauer formation the compounds no longer attract males and instead deter hermaphrodites. The ascarosides ascr#2 and ascr#3 carry different, but overlapping, information, as ascr#3 is more potent as a male attractant than ascr#2, whereas ascr#2 is slightly more potent than ascr#3 in promoting dauer formation7. We demonstrate that ascr#2, ascr#3 and ascr#4 are strongly synergistic, and that two types of neuron, the amphid single-ciliated sensory neuron type K (ASK) and the male-specific cephalic companion neuron (CEM), are required for male attraction by ascr#3. On the basis of these results, male attraction and dauer formation in C. elegans appear as alternative behavioural responses to a common set of signalling molecules. The ascaroside signalling system thus connects reproductive and developmental pathways and represents a unique example of structure- and concentration-dependent differential activity of signalling molecules