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Cyanobacteria are important primary producers of organic matter in diverse environments on a global scale. While mechanisms of CO2 fixation are well understood, the distribution of the flow of fixed organic carbon within individual cells and complex microbial communities is less well characterized. To obtain a general overview of metabolism, we describe the use of deuterium oxide (D2O) to measure deuterium incorporation into the intracellular metabolites of two physiologically diverse cyanobacteria: a terrestrial filamentous strain (Microcoleus vaginatus PCC 9802) and a euryhaline unicellular strain (Synechococcus sp. PCC 7002). D2O was added to the growth medium during different phases of the diel cycle. Incorporation of deuterium into metabolites at nonlabile positions, an indicator of metabolite turnover, was assessed using liquid chromatography mass spectrometry. Expectedly, large differences in turnover among metabolites were observed. Some metabolites, such as fatty acids, did not show significant t...

Desert biological soil crusts (BSCs) are cyanobacteria-dominated surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes, and fueling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modeling of BSCs, we developed a manually curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, gas chromatography–mass spectroscopy (GC–MS) and liquid chromatography–mass spectroscopy (LC–MS) were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between model predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady-state modeling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events 4 times longer than light wetting events.

Rapid genetic and phenotypic adaptation of the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough to salt stress was observed during experimental evolution. In order to identify key metabolites important for salt tolerance, a clone, ES10-5, which was isolated from population ES10 and allowed to experimentally evolve under salt stress for 5,000 generations, was analyzed and compared to clone ES9-11, which was isolated from population ES9 and had evolved under the same conditions for 1,200 generations. These two clones were chosen because they represented the best-adapted clones among six independently evolved populations. ES10-5 acquired new mutations in genes potentially involved in salt tolerance, in addition to the preexisting mutations and different mutations in the same genes as in ES9-11. Most basal abundance changes of metabolites and phospholipid fatty acids (PLFAs) were lower in ES10-5 than ES9-11, but an increase of glutamate and branched PLFA i17:1ω9c under high-salinity conditions was persistent. ES9-11 had decreased cell motility compared to the ancestor; in contrast, ES10-5 showed higher cell motility under both nonstress and high-salinity conditions. Both genotypes displayed better growth energy efficiencies than the ancestor under nonstress or high-salinity conditions. Consistently, ES10-5 did not display most of the basal transcriptional changes observed in ES9-11, but it showed increased expression of genes involved in glutamate biosynthesis, cation efflux, and energy metabolism under high salinity. These results demonstrated the role of glutamate as a key osmolyte and i17:1ω9c as the major PLFA for salt tolerance in D. vulgaris. The mechanistic changes in evolved genotypes suggested that growth energy efficiency might be a key factor for selection., IMPORTANCE High salinity (e.g., elevated NaCl) is a stressor that affects many organisms. Salt tolerance, a complex trait involving multiple cellular pathways, is attractive for experimental evolutionary studies. Desulfovibrio vulgaris Hildenborough is a model sulfate-reducing bacterium (SRB) that is important in biogeochemical cycling of sulfur, carbon, and nitrogen, potentially for bio-corrosion, and for bioremediation of toxic heavy metals and radionuclides. The coexistence of SRB and high salinity in natural habitats and heavy metal-contaminated field sites laid the foundation for the study of salt adaptation of D. vulgaris Hildenborough with experimental evolution. Here, we analyzed a clone that evolved under salt stress for 5,000 generations and compared it to a clone evolved under the same condition for 1,200 generations. The results indicated the key roles of glutamate for osmoprotection and of i17:1ω9c for increasing membrane fluidity during salt adaptation. The findings provide valuable insights about the salt adaptation mechanism changes during long-term experimental evolution.

Desert biological soil crusts (BSCs) are cyanobacteria-dominated, surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes and fuelling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modelling of BSCs, we developed a manually-curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, GC/MS and LC/MS were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between model predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady state modelling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events four times longer than light wetting events.

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are. Assessment of the quality of raw data processing in untargeted metabolomics. Five published untargeted metabolomics studies, were reanalyzed. Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study. Incomplete raw data processing shows unexplored potential of current and legacy data.

IntroductionUntargeted metabolomics is a powerful tool for biological discoveries. Significant advances in computational approaches to analyzing the complex raw data have been made, yet it is not clear how exhaustive and reliable are the data analysis results.ObjectivesAssessment of the quality of data analysis results in untargeted metabolomics.MethodsFive published untargeted metabolomics studies acquired using instruments from different manufacturers were reanalyzed.ResultsOmissions of at least 50 relevant compounds from original results as well as examples of representative mistakes are reported for each study.ConclusionIncomplete data analysis shows unexplored potential of current and legacy data.

Cyanobacteria are important primary producers of organic matter in diverse environments on a global scale. While mechanisms of CO

© 2016 Bouskill, Wood, Baran, Hao, Ye, Bowen, Lim, Nico, Holman, Gilbert, Silver, Northen and Brodie. Climate model projections for tropical regions show clear perturbation of precipitation patterns leading to increased frequency and severity of drought in some regions. Previous work has shown declining soil moisture to be a strong driver of changes in microbial trait distribution, however, the feedback of any shift in functional potential on ecosystem properties related to carbon cycling are poorly understood. Here we show that drought-induced changes in microbial functional diversity and activity shape, and are in turn shaped by, the composition of dissolved and soil-associated carbon. We also demonstrate that a shift in microbial functional traits that favor the production of hygroscopic compounds alter the efflux of carbon dioxide following soil rewetting. Under drought the composition of the dissolved organic carbon pool changed in a manner consistent with a microbial metabolic response. We hypothesize that this microbial ecophysiological response to changing soil moisture elevates the intracellular carbon demand stimulating extracellular enzyme production, that prompts the observed decline in more complex carbon compounds (e.g., cellulose and lignin). Furthermore, a metabolic response to drought appeared to condition (biologically and physically) the soil, notably through the production of polysaccharides, particularly in experimental plots that had been pre-exposed to a short-term drought. This hysteretic response, in addition to an observed drought-related decline in phosphorus concentration, may have been responsible for a comparatively modest CO2efflux following wet-up in drought plots relative to control plots.

Metabolite profiling using mass spectrometry provides an attractive approach for the interrogation of cellular metabolic capabilities. Untargeted metabolite profiling has the potential to identify numerous novel metabolites; however, de novo identification of metabolites from spectral features remains a challenge. Here we present an integrated workflow for metabolite identification using uniform stable isotope labeling. Metabolite profiling of cell and growth media extracts of unlabeled control, (15)N, and (13)C-labeled cultures of the cyanobacterium, Synechococcus sp. PCC 7002 was performed using normal phase liquid chromatography coupled to mass spectrometry (LC-MS). Visualization of three-way comparisons of raw data sets highlighted characteristic labeling patterns for metabolites of biological origin allowing exhaustive identification of corresponding spectral features. Additionally, unambiguous assignment of chemical formulas was greatly facilitated by the use of stable isotope labeling. Chemical formulas of metabolites responsible for redundant spectral features were determined and fragmentation (MS/MS) spectra for these metabolites were collected. Analysis of acquired MS/MS spectra against spectral database records led to the identification of a number of metabolites absent not only from the reconstructed draft metabolic network of Synechococcus sp. PCC 7002 but not included in databases of metabolism (MetaCyc or KEGG).

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.

Alga-derived lipids represent an attractive potential source of biofuels. However, lipid accumulation in algae is a stress response tightly coupled to growth arrest, thereby imposing a major limitation on productivity. To identify transcriptional regulators of lipid accumulation, we performed an integrative chromatin signature and transcriptomic analysis to decipher the regulation of lipid biosynthesis in the alga Chlamydomonas reinhardtii. Genome-wide histone modification profiling revealed remarkable differences in functional chromatin states between the algae and higher eukaryotes and uncovered regulatory components at the core of lipid accumulation pathways. We identified the transcription factor, PSR1, as a pivotal switch that triggers cytosolic lipid accumulation. Dissection of the PSR1-induced lipid profiles corroborates its role in coordinating multiple lipid-inducing stress responses. The comprehensive maps of functional chromatin signatures in a major clade of eukaryotic life and the discovery of a transcriptional regulator of algal lipid metabolism will facilitate targeted engineering strategies to mediate high lipid production in microalgae.

Soils are arguably the most microbially diverse ecosystems. Physicochemical properties have been associated with the maintenance of this diversity. Yet, the role of microbial substrate specialization is largely unexplored since substrate utilization studies have focused on simple substrates, not the complex mixtures representative of the soil environment. Here we examine the exometabolite composition of desert biological soil crusts (biocrusts) and the substrate preferences of seven biocrust isolates. The biocrust's main primary producer releases a diverse array of metabolites, and isolates of physically associated taxa use unique subsets of the complex metabolite pool. Individual isolates use only 13−26% of available metabolites, with only 2 out of 470 used by all and 40% not used by any. An extension of this approach to a mesophilic soil environment also reveals high levels of microbial substrate specialization. These results suggest that exometabolite niche partitioning may be an important factor in the maintenance of microbial diversity., Production and consumption of metabolites by soil microorganisms are important for nutrient cycling and maintenance of microbial diversity. Here, Baran et al. study metabolite uptake and release by desert soil microorganisms, showing that coexisting microbes can have divergent substrate preferences.

Metabolomics is an emerging tool that can be used to gain insights into cellular and physiological responses. Here we present a metabolome differential display method based on capillary electrophoresis time-of-flight mass spectrometry to profile liver metabolites following acetaminophen-induced hepatotoxicity. We globally detected 1,859 peaks in mouse liver extracts and highlighted multiple changes in metabolite levels, including an activation of the ophthalmate biosynthesis pathway. We confirmed that ophthalmate was synthesized from 2-aminobutyrate through consecutive reactions with gamma-glutamylcysteine and glutathione synthetase. Changes in ophthalmate level in mouse serum and liver extracts were closely correlated and ophthalmate levels increased significantly in conjunction with glutathione consumption. Overall, our results provide a broad picture of hepatic metabolite changes following acetaminophen treatment. In addition, we specifically found that serum ophthalmate is a sensitive indicator of hepatic GSH depletion, and may be a new biomarker for oxidative stress. Our method can thus pinpoint specific metabolite changes and provide insights into the perturbation of metabolic pathways on a large scale and serve as a powerful new tool for discovering low molecular weight biomarkers.

Two isoenzymes of xyloglucan endo-transglycosylase (XET, EC 2.4.1.207) were identified in nasturtium (Tropaeolum majus L.), so far. One is located in seeds (sXET) and is expressed during germination. The other enzyme (eXET) is confined to epicotyls and other growing regions. In this work, we examined catalytic properties of the two XETs and tried to find a correlation with their presumed functions. The two enzymes had similar isoelectric points at about pH 6.5 but had different pH-activity profiles and differed in Km values for xyloglucan-derived oligosaccharides (XGOS) as acceptor substrates. Moreover, they showed clearly distinct preferences in selecting the site of attack on xyloglucan (XG) molecules. While sXET selected the site of cleavage on XG molecules stochastically along the length of their polyglucose main chain and preferred low-molecular mass (MM) XGOS as glycosyl acceptors, eXET attacked the substrate molecule predominantly near the reducing end and showed no preference as to the size of XGOS acceptors. These properties corroborate well with the proposed functions of the two isoenzymes: the sXET plays a role in degrading XG reserves in seeds during germination, whereas the eXET is engaged in cell wall rearrangement and integration of new XG molecules into the preexisting cell wall structure during growth.

Incubation of isolated NaCl-washed cell walls from epicotyls of pea ( Pisum sativum ) and nasturtium ( Tropaeolum majus ) with solutions of various oligosaccharides released among others the cell wall marker enzyme xyloglucan endotransglycosylase (XET, EC 2.4.1.207). The greatest release of XET occurred upon incubation of the cell walls with xyloglucan-derived oligosaccharides (XGOS, DP 7-9). Concomitantly, reduced radioactive nonasaccharide 〚 3 H〛-XLLGol (Gal 2 .Xyl 3 .Glc 3 .〚1- 3 H〛-glucitol) was incorporated into the cell walls. Subsequent hydrolysis of the radioactively labelled cell walls with Trichoderma cellulase liberated XGOS-alditols, DP 7-9 as the sole radioactive products indicating that 〚 3 H〛-XLLGol was incorporated into the cell wall xyloglucan by transglycosylation, as an entity. Oligosaccharides of cello-, chito- and/or oligoglucurono-series were much less effective than XGOS but a substantial liberation of XET and other proteins from plant cell walls could be achieved by the nucleophile 0.1 M imidazole. The specific release of the cell wall-associated XET activity by incubation with xyloglucan-derived oligosaccharides and the simultaneous incorporation of the tritiated xyloglucan nonasaccharide en bloc into the cell walls indicates that XET is present in the cell walls in form of a competent glycosyl-enzyme complex which decomposes by transglycosylation of its glycan moiety to added xyloglucan-oligosaccharide acceptors. This finding suggests a new concept for the regulation of activity of cell wall-associated glycanases/transglycosylases: they exist in plant cell walls in a transiently latent state as covalent glycosyl-enzyme complexes and are active only when suitable glycosyl acceptors become available.

Sugarcane is one of the main economic resources of Guadeloupe (France). Cane grown on the island's vertisols shows nitrogen deficiency which is accentuated with each successive ratoon. This deficiency could partially explain the observed decrease in yield. The present study, based on the isotopic N method applied to different ratoons in the field, was aimed at: (i) diagnosing the problem in the crop environment itself; and (ii) quantifying the fertilizer-N balance. The results indicated that decrease in yield and N absorption by the cane was related to ratoon number. The real utilization coefficient for the fertilizer (RUC%) ranged from 6 and 34%, and a high proportion (30–40%) of fertilizer-N was immobilized in the soil (NiS%) after the annual crop cycle. The N absorbed by the cane was essentially derived from the soil. Rainfall at the beginning of (re)growth determined crop development and N supply to the crop. When the water requirements of the crop are satisfied, nitrogen supply and cane yield can be improved in two ways: (i) by increasing the efficiency of the applied N fertilizer (RUC% and NiS%); and (ii) by maintaining the soil's capacity to supply cane with N. This implies maintaining and, if necessary, upgrading the structural state of the vertisols.

International audience; Mass spectrometry-based metabolomics has become a powerful tool for the detection of metabolites in complex biological systems and for the identification of novel metabolites. We previously identified a number of unexpected metabolites in the cyanobacterium Synechococcus sp. PCC 7002, such as histidine betaine, its derivatives and several unusual oligosaccharides. To test for the presence of these compounds and to assess the diversity of small polar metabolites in other cyanobacteria, we profiled cell extracts of nine strains representing much of the morphological and evolutionary diversification of this phylum. Spectral features in raw metabolite profiles obtained by normal phase liquid chromatography coupled to mass spectrometry (MS) were manually curated so that chemical formulae of metabolites could be assigned. For putative identification, retention times and MS/MS spectra were cross-referenced with those of standards or available sprectral library records. Overall, we detected 264 distinct metabolites. These included indeed different betaines, oligosaccharides as well as additional unidentified metabolites with chemical formulae not present in databases of metabolism. Some of these metabolites were detected only in a single strain, but some were present in more than one. Genomic OPEN ACCESS Mar. Drugs 2013, 11 3618 interrogation of the strains revealed that generally, presence of a given metabolite corresponded well with the presence of its biosynthetic genes, if known. Our results show the potential of combining metabolite profiling and genomics for the identification of novel biosynthetic genes.

Microorganisms grow under a remarkable range of extreme conditions. Environmental transcriptomic and proteomic studies have highlighted metabolic pathways active in extremophilic communities. However, metabolites directly linked to their physiology are less well defined because metabolomics methods lag behind other omics technologies due to a wide range of experimental complexities often associated with the environmental matrix. We identified key metabolites associated with acidophilic and metal-tolerant microorganisms using stable isotope labeling coupled with untargeted, high-resolution mass spectrometry. We observed >3,500 metabolic features in biofilms growing in pH ~0.9 acid mine drainage solutions containing millimolar concentrations of iron, sulfate, zinc, copper, and arsenic. Stable isotope labeling improved chemical formula prediction by >50% for larger metabolites (>250 atomic mass units), many of which were unrepresented in metabolic databases and may represent novel compounds. Taurine and hydroxyectoine were identified and likely provide protection from osmotic stress in the biofilms. Community genomic, transcriptomic, and proteomic data implicate fungi in taurine metabolism. Leptospirillum group II bacteria decrease production of ectoine and hydroxyectoine as biofilms mature, suggesting that biofilm structure provides some resistance to high metal and proton concentrations. The combination of taurine, ectoine, and hydroxyectoine may also constitute a sulfur, nitrogen, and carbon currency in the communities., IMPORTANCE Microbial communities are central to many critical global processes and yet remain enigmatic largely due to their complex and distributed metabolic interactions. Metabolomics has the possibility of providing mechanistic insights into the function and ecology of microbial communities. However, our limited knowledge of microbial metabolites, the difficulty of identifying metabolites from complex samples, and the inability to link metabolites directly to community members have proven to be major limitations in developing advances in systems interactions. Here, we show that combining stable-isotope-enabled metabolomics with genomics, transcriptomics, and proteomics can illuminate the ecology of microorganisms at the community scale.

The discrepancy between the pace of sequencing and functional characterization of genomes is a major challenge in understanding complex microbial metabolic processes and metabolic interactions in the environment. Here, we identified and validated genes related to the utilization of specific metabolites in bacteria by profiling metabolite utilization in libraries of mutant strains. Untargeted mass spectrometry based metabolomics was used to identify metabolites utilized by Escherichia coli and Shewanella oneidensis MR-1. Targeted high-throughput metabolite profiling of spent media of 8042 individual mutant strains was performed to link utilization to specific genes. Using this approach we identified genes of known function as well as novel transport proteins and enzymes required for the utilization of tested metabolites. Specific examples include two subunits of a predicted ABC transporter encoded by the genes SO1043 and SO1044 required for the utilization of citrulline and a predicted histidase encoded by the gene SO3057 required for the utilization of ergothioneine by S. oneidensis. In vitro assays with purified proteins showed substrate specificity of SO3057 toward ergothioneine and histidine betaine in contrast to substrate specificity of a paralogous histidase SO0098 toward histidine. This generally applicable, high-throughput workflow has the potential both to discover novel metabolic capabilities of microorganisms and to identify the corresponding genes.

The exponential growth in the number of sequenced microorganisms versus the relative slow influx of direct biochemical characterization of microbes is limiting the utility of sequence information. High-throughput experimental approaches to functionally characterize microbial metabolism are urgently needed to leverage genome sequences for example: to understand host-microbe interactions, microbial communities, to utilize microbes for bioenergy, bioremediation, etc. Mass spectrometry based small molecule metabolite analysis is rapidly becoming a method of choice to meet these needs and enables multiple paths to discovering and validating the functional assignments. Approaches range from the targeted in vitro screening of small sets of metabolic transformations to define enzymatic activities to global metabolic profiling (metabolomics) to define metabolic pathways and gain insights into microbial responses to environmental and genetic perturbations. The combination of metabolite profiling with genome-scale models of metabolism and other -omic approaches provides opportunities to expand our understanding of microbial metabolic networks, stress responses, and to identify genes associated with specific enzymatic and regulatory activities.

Background With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods. Results We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites. Conclusion Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.

The search for novel enzymes is an important but difficult task in functional genomics. Here, we present a systematic method based on in vitro assays in combination with metabolite profiling to discover novel enzymatic activities. A complex mixture of metabolites is incubated with purified candidate proteins and the reaction mixture is subsequently profiled by capillary electrophoresis electrospray ionization mass spectrometry (CE-MS). Specific changes in the metabolite composition can directly suggest the presence of an enzymatic activity while subsequent identification of the compounds whose level changed specifically can pinpoint the actual substrate(s) and product(s) of the reaction. We first evaluated the method using several Escherichia coli metabolic enzymes and then applied it to the functional screening of uncharacterized proteins. In this manner, YbhA and YbiV proteins were found to display both phosphotransferase and phosphatase activity toward different sugars/sugar phosphates. Our approach should be broadly applicable and useful for enzyme discovery in any system.

Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

Background Mass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements. Results Of 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae. Conclusions To facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (15N) or carbon (13C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.