PyINETA: Open-source platform for INADEQUATE-JRES integration in NMR metabolomics.

Annotating compounds with high confidence is a critical element in metabolomics. 13 C-detection NMR experiment INADEQUATE (incredible natural abundance double-quantum transfer experiment) stands out as a powerful tool for structural elucidation, whereas this valuable experiment is not often included in metabolomics studies. This is partly due to the lack of community platform that provides structural information based INADEQUATE. Also, it is often the case that a single study uses various NMR experiments synergistically to improve the quality of information or balance total NMR experiment time, but there is no public platform that can integrate the outputs of INADEQUATE and other NMR experiments either. Here, we introduce PyINETA, Python-based INADEQUATE network analysis. PyINETA is an open-source platform that provides structural information of molecules using INADEQUATE, conducts database search, and integrates information of INADEQUATE and a complementary NMR experiment 13 C J -resolved experiment ( 13 C-JRES). Those steps are carried out automatically, and PyINETA keeps track of all the pipeline parameters and outputs, ensuring the transparency of annotation in metabolomics. Our evaluation of PyINETA using a model mouse study showed that our pipeline successfully integrated INADEQUATE and 13 C-JRES. The results showed that 13 C-labeled amino acids that were fed to mice were transferred to different tissues, and, also, they were transformed to other metabolites. The distribution of those compounds was tissue-specific, showing enrichment of particular metabolites in liver, spleen, pancreas, muscle, or lung. The value of PyINETA was not limited to those known compounds; PyINETA also provided fragment information for unknown compounds. PyINETA is available on NMRbox.

SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Functional annotation and importance of marine bacterial transporters of plankton exometabolites.

Metabolite exchange within marine microbial communities transfers carbon and other major elements through global cycles and forms the basis of microbial interactions. Yet lack of gene annotations and concern about the quality of existing ones remain major impediments to revealing currencies of carbon flux. We employed an arrayed mutant library of the marine bacterium Ruegeria pomeroyi DSS-3 to experimentally annotate substrates of organic compound transporter systems, using mutant growth and compound drawdown analyses to link transporters to their cognate substrates. Mutant experiments verified substrates for thirteen R. pomeroyi transporters. Four were previously hypothesized based on gene expression data (taurine, glucose/xylose, isethionate, and cadaverine/putrescine/spermidine); five were previously hypothesized based on homology to experimentally annotated transporters in other bacteria (citrate, glycerol, N-acetylglucosamine, fumarate/malate/succinate, and dimethylsulfoniopropionate); and four had no previous annotations (thymidine, carnitine, cysteate, and 3-hydroxybutyrate). These bring the total number of experimentally-verified organic carbon influx transporters to 18 of 126 in the R. pomeroyi genome. In a longitudinal study of a coastal phytoplankton bloom, expression patterns of the experimentally annotated transporters linked them to different stages of the bloom, and also led to the hypothesis that citrate and 3-hydroxybutyrate were among the most highly available bacterial substrates. Improved functional annotation of the gatekeepers of organic carbon uptake is critical for deciphering carbon flux and fate in microbial ecosystems.

13C NMR metabolomics: J-resolved STOCSY meets INADEQUATE.

Robust annotation of metabolites is a challenging task in metabolomics. Among available applications, 13C NMR experiment INADEQUATE determines direct 13C-13C connectivity unambiguously, offering indispensable information on molecular structure. Despite its great utility, it is not always practical to collect INADEQUATE data on every sample in a large metabolomics study because of its relatively long experiment time. Here, we propose an alternative approach that maintains the quality of information but saves experiment time. In this approach, individual samples in a study are first screened by 13C homonuclear J-resolved experiment (JRES). Next, JRES data are processed by statistical total correlation spectroscopy (STOCSY) to extract peaks that behave similarly among samples. Finally, INADEQUATE is collected on one internal pooled sample to select STOCSY peaks that originate from the same compound. We tested this concept using the 13C-labeled endometabolome of a model marine diatom strain incubated under various settings, intending to cover a range of metabolites produced under different external conditions. This scheme was able to extract known diatom metabolites proline, 2,3-dihydroxypropane-1-sulfonate (DHPS), ß-1,3-glucan, choline, and glutamate. This pipeline also detected unknown compounds with structural information, which is valuable in metabolomics where a priori knowledge of metabolites is not always available. The ability of this scheme was seen even in sugar regions, which are usually challenging in 1H NMR due to severe peak overlap. JRES and INADEQUATE were highly complementary; INADEQUATE provided directly-bonded 13C networks, whereas JRES linked INADEQUATE networks within the same compound but broken by nitrogen or sulfur atoms, highlighting the advantage of this integrated approach.

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 chromatography-mass 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.

Growth-stage-related shifts in diatom 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.

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.

Ecological drivers of bacterial community assembly in synthetic phycospheres.

In the nutrient-rich region surrounding marine phytoplankton cells, heterotrophic bacterioplankton transform a major fraction of recently fixed carbon through the uptake and catabolism of phytoplankton metabolites. We sought to understand the rules by which marine bacterial communities assemble in these nutrient-enhanced phycospheres, specifically addressing the role of host resources in driving community coalescence. Synthetic systems with varying combinations of known exometabolites of marine phytoplankton were inoculated with seawater bacterial assemblages, and communities were transferred daily to mimic the average duration of natural phycospheres. We found that bacterial community assembly was predictable from linear combinations of the taxa maintained on each individual metabolite in the mixture, weighted for the growth each supported. Deviations from this simple additive resource model were observed but also attributed to resource-based factors via enhanced bacterial growth when host metabolites were available concurrently. The ability of photosynthetic hosts to shape bacterial associates through excreted metabolites represents a mechanism by which microbiomes with beneficial effects on host growth could be recruited. In the surface ocean, resource-based assembly of host-associated communities may underpin the evolution and maintenance of microbial interactions and determine the fate of a substantial portion of Earth's primary production.

Bacterial Substrate Transformation Tracked by Stable-Isotope-Guided NMR Metabolomics: Application in a Natural Aquatic Microbial Community.

The transformation of organic substrates by heterotrophic bacteria in aquatic environments constitutes one of the key processes in global material cycles. The development of procedures that would enable us to track the wide range of organic compounds transformed by aquatic bacteria would greatly improve our understanding of material cycles. In this study, we examined the applicability of nuclear magnetic resonance spectroscopy coupled with stable-isotope labeling to the investigation of metabolite transformation in a natural aquatic bacterial community. The addition of a model substrate (13C6-glucose) to a coastal seawater sample and subsequent incubation resulted in the detection of >200 peaks and the assignment of 22 metabolites from various chemical classes, including amino acids, dipeptides, organic acids, nucleosides, nucleobases, and amino alcohols, which had been identified as transformed from the 13C6-glucose. Additional experiments revealed large variability in metabolite transformation and the key compounds, showing the bacterial accumulation of glutamate over the incubation period, and that of 3-hydroxybutyrate with increasing concentrations of 13C6-glucose added. These results suggest the potential ability of our approach to track substrate transformation in aquatic bacterial communities. Further applications of this procedure may provide substantial insights into the metabolite dynamics in aquatic environments.