Oligosaccharide production and signaling correlate with delayed flowering in an Arabidopsis genotype grown and selected in high [CO2]

Since industrialization began, atmospheric CO2 ([CO2]) has increased from 270 to 415 ppm and is projected to reach 800-1000 ppm this century. Some Arabidopsis thaliana (Arabidopsis) genotypes delayed flowering in elevated [CO2] relative to current [CO2], while others showed no change or accelerations. To predict genotype-specific flowering behaviors, we must understand the mechanisms driving flowering response to rising [CO2]. [CO2] changes alter photosynthesis and carbohydrates in plants. Plants sense carbohydrate levels, and exogenous carbohydrate application influences flowering time and flowering transcript levels. We asked how organismal changes in carbohydrates and transcription correlate with changes in flowering time under elevated [CO2]. We used a genotype (SG) of Arabidopsis that was selected for high fitness at elevated [CO2] (700 ppm). SG delays flowering under elevated [CO2] (700 ppm) relative to current [CO2] (400 ppm). We compared SG to a closely related control genotype (CG) that shows no [CO2]-induced flowering change. We compared metabolomic and transcriptomic profiles in these genotypes at current and elevated [CO2] to assess correlations with flowering in these conditions. While both genotypes altered carbohydrates in response to elevated [CO2], SG had higher levels of sucrose than CG and showed a stronger increase in glucose and fructose in elevated [CO2]. Both genotypes demonstrated transcriptional changes, with CG increasing genes related to fructose 1,6-bisphosphate breakdown, amino acid synthesis, and secondary metabolites; and SG decreasing genes related to starch and sugar metabolism, but increasing genes involved in oligosaccharide production and sugar modifications. Genes associated with flowering regulation within the photoperiod, vernalization, and meristem identity pathways were altered in these genotypes. Elevated [CO2] may alter carbohydrates to influence transcription in both genotypes and delayed flowering in SG. Changes in the oligosaccharide pool may contribute to delayed flowering in SG. This work extends the literature exploring genotypic-specific flowering responses to elevated [CO2].

CRAGE enables rapid activation of biosynthetic gene clusters in undomesticated bacteria

It is generally believed that exchange of secondary metabolite biosynthetic gene clusters (BGCs) among closely related bacteria is an important driver of BGC evolution and diversification. Applying this idea may help researchers efficiently connect many BGCs to their products and characterize the products' roles in various environments. However, existing genetic tools support only a small fraction of these efforts. Here, we present the development of chassis-independent recombinase-assisted genome engineering (CRAGE), which enables single-step integration of large, complex BGC constructs directly into the chromosomes of diverse bacteria with high accuracy and efficiency. To demonstrate the efficacy of CRAGE, we expressed three known and six previously identified but experimentally elusive non-ribosomal peptide synthetase (NRPS) and NRPS-polyketide synthase (PKS) hybrid BGCs from Photorhabdus luminescens in 25 diverse γ-Proteobacteria species. Successful activation of six BGCs identified 22 products for which diversity and yield were greater when the BGCs were expressed in strains closely related to the native strain than when they were expressed in either native or more distantly related strains. Activation of these BGCs demonstrates the feasibility of exploiting their underlying catalytic activity and plasticity, and provides evidence that systematic approaches based on CRAGE will be useful for discovering and identifying previously uncharacterized metabolites.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

Comprehensive Metabolite Identification Strategy Using Multiple Two-Dimensional NMR Spectra of a Complex Mixture Implemented in the COLMARm Web Server

Identification of metabolites in complex mixtures represents a key step in metabolomics. A new strategy is introduced, which is implemented in a new public web server, COLMARm, that permits the coanalysis of up to three two-dimensional (2D) NMR spectra, namely, ¹³C-¹H HSQC (heteronuclear single quantum coherence spectroscopy), ¹H-¹H TOCSY (total correlation spectroscopy), and ¹³C-¹H HSQC-TOCSY, for the comprehensive, accurate, and efficient performance of this task. The highly versatile and interactive nature of COLMARm permits its application to a wide range of metabolomics samples independent of the magnetic field. Database query is performed using the HSQC spectrum, and the top metabolite hits are then validated against the TOCSY-type experiment(s) by superimposing the expected cross-peaks on the mixture spectrum. In this way the user can directly accept or reject candidate metabolites by taking advantage of the complementary spectral information offered by these experiments and their different sensitivities. The power of COLMARm is demonstrated for a human serum sample uncovering the existence of 14 metabolites that hitherto were not identified by NMR.

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.

Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics

PURPOSE OF REVIEW

This review describes some of the advances made over the past year in NMR-based metabolomics for the elucidation of known and unknown compounds, including new ways of how to combine this information with high-resolution mass spectrometry.

RECENT FINDINGS

A new method allows the back-calculation of mass spectra from NMR spectra that have been queried against databases improving the accuracy of the identified compounds by validation and consistency analysis. For the de-novo characterization of unknown compounds, an algorithm has been introduced that predicts all viable NMR spectra from accurate masses allowing, by comparison with experimental NMR data, the determination of the structures of new metabolites in complex mixtures.

SUMMARY

Recent advances in NMR and mass spectrometry-based metabolomics and their synergistic use promises to significantly improve metabolomics sample characterization both in terms of identification and quantitation, and accelerate metabolite discovery.

Use of Charged Nanoparticles in NMR-Based Metabolomics for Spectral Simplification and Improved Metabolite Identification

Metabolomics aims at a complete characterization of all metabolites in biological samples in terms of both their identities and concentrations. Because changes of metabolites and their concentrations are a direct reflection of cellular activity, it allows for a better understanding of cellular processes and function to be obtained. Although NMR spectroscopy is routinely applied to complex biological mixtures without purification, overlapping NMR peaks often pose a challenge for the comprehensive and accurate identification of the underlying metabolites. To address this problem, we present a novel nanoparticle-based strategy that differentiates between metabolites based on their electric charge. By adding electrically charged silica nanoparticles to the solution NMR sample, metabolites of opposite charge bind to the nanoparticles and their NMR signals are weakened or entirely suppressed due to peak broadening caused by the slow rotational tumbling of the nanometer-sized nanoparticles. Comparison of the edited with the original spectrum significantly facilitates analysis and reduces ambiguities in the identification of metabolites. This method makes NMR directly sensitive to the detection of molecular charges at constant pH, as demonstrated here both for model mixtures and human urine. The simplicity of the approach should make it useful for a wide range of metabolomics applications.

NMR/MS Translator for the Enhanced Simultaneous Analysis of Metabolomics Mixtures by NMR Spectroscopy and Mass Spectrometry: Application to Human Urine

A novel metabolite identification strategy is presented for the combined NMR/MS analysis of complex metabolite mixtures. The approach first identifies metabolite candidates from 1D or 2D NMR spectra by NMR database query, which is followed by the determination of the masses (m/z) of their possible ions, adducts, fragments, and characteristic isotope distributions. The expected m/z ratios are then compared with the MS(1) spectrum for the direct assignment of those signals of the mass spectrum that contain information about the same metabolites as the NMR spectra. In this way, the mass spectrum can be assigned with very high confidence, and it provides at the same time validation of the NMR-derived metabolites. The method was first demonstrated on a model mixture, and it was then applied to human urine collected from a pool of healthy individuals. A number of metabolites could be detected that had not been reported previously, further extending the list of known urine metabolites. The new analysis approach, which is termed NMR/MS Translator, is fully automated and takes only a few seconds on a computer workstation. NMR/MS Translator synergistically uses the power of NMR and MS, enhancing the accuracy and efficiency of the identification of those metabolites compiled in databases.

Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures

A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to Escherichia coli extract, where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides, and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures, overcoming the need of experimental MS and NMR metabolite databases.

Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra

A new metabolomics database and query algorithm for the analysis of ¹³C–¹H HSQC spectra is introduced, which unifies NMR spectroscopic information on 555 metabolites from both the Biological Magnetic Resonance Data Bank (BMRB) and Human Metabolome Database (HMDB). The new database, termed Complex Mixture Analysis by NMR (COLMAR) ¹³C–¹H HSQC database, can be queried via an interactive, easy to use web interface at http://spin.ccic.ohio-state.edu/index.php/hsqc/index. Our new HSQC database separately treats slowly exchanging isomers that belong to the same metabolite, which permits improved query in cases where lowly populated isomers are below the HSQC detection limit. The performance of our new database and query web server compares favorably with the one of existing web servers, especially for spectra of samples of high complexity, including metabolite mixtures from the model organisms Drosophila melanogaster and Escherichia coli. For such samples, our web server has on average a 37% higher accuracy (true positive rate) and a 82% lower false positive rate, which makes it a useful tool for the rapid and accurate identification of metabolites from ¹³C–¹H HSQC spectra at natural abundance. This information can be combined and validated with NMR data from 2D TOCSY-type spectra that provide connectivity information not present in HSQC spectra.

Customized metabolomics database for the analysis of NMR ¹H-¹H TOCSY and ¹³C-¹H HSQC-TOCSY spectra of complex mixtures

A customized metabolomics NMR database, termed ¹H(¹³C)-TOCCATA, is introduced, which contains complete ¹H and ¹³C chemical shift information on individual spin systems and isomeric states of common metabolites. Since this information directly corresponds to cross sections of 2D ¹H–¹H TOCSY and 2D ¹³C–¹H HSQC-TOCSY spectra, it allows the straightforward and unambiguous identification of metabolites of complex metabolic mixtures at ¹³C natural abundance from these types of experiments. The ¹H(¹³C)-TOCCATA database, which is complementary to the previously introduced TOCCATA database for the analysis of uniformly ¹³C-labeled compounds, currently contains 455 metabolites, and it can be used through a publicly accessible web portal. We demonstrate its performance by applying it to 2D ¹H–¹H TOCSY and 2D ¹³C–¹H HSQC-TOCSY spectra of a cell lysate from E. coli, which yields a substantial improvement over other databases, as well as 1D NMR-based approaches, in the number of compounds that can be correctly identified with high confidence.

Quantitative analysis of metabolic mixtures by two-dimensional 13C constant-time TOCSY NMR spectroscopy

An increasing number of organisms can be fully (13)C-labeled, which has the advantage that their metabolomes can be studied by high-resolution two-dimensional (2D) NMR (13)C-(13)C constant-time (CT) total correlation spectroscopy (TOCSY) experiments. Individual metabolites can be identified via database searching or, in the case of novel compounds, through the reconstruction of their backbone-carbon topology. Determination of quantitative metabolite concentrations is another key task. Because strong peak overlaps in one-dimensional (1D) NMR spectra prevent straightforward quantification through 1D peak integrals, we demonstrate here the direct use of (13)C-(13)C CT-TOCSY spectra for metabolite quantification. This is accomplished through the quantum mechanical treatment of the TOCSY magnetization transfer at short and long-mixing times or by the use of analytical approximations, which are solely based on the knowledge of the carbon-backbone topologies. The methods are demonstrated for carbohydrate and amino acid mixtures.

TOCCATA: a customized carbon total correlation spectroscopy NMR metabolomics database

A customized metabolomics NMR database, TOCCATA, is introduced, which uses (13)C chemical shift information for the reliable identification of metabolites, their spin systems, and isomeric states. TOCCATA, whose information was derived from the BMRB and HMDB databases and the literature, currently contains 463 compounds and 801 spin systems, and it can be used through a publicly accessible web server. TOCCATA allows the identification of metabolites in the submillimolar concentration range from (13)C-(13)C total correlation spectroscopy experiments of complex mixtures, which is demonstrated for an Escherichia coli cell lysate, a carbohydrate mixture, and an amino acid mixture, all of which were uniformly (13)C-labeled.

Carbon backbone topology of the metabolome of a cell

The complex metabolic makeup of a biological system, such as a cell, is a key determinant of its biological state providing unique insights into its function. Here we characterize the metabolome of a cell by a novel homonuclear (13)C 2D NMR approach applied to a nonfractionated uniformly (13)C-enriched lysate of E. coli cells and determine de novo their carbon backbone topologies that constitute the "topolome". A protocol was developed, which first identifies traces in a constant-time (13)C-(13)C TOCSY NMR spectrum that are unique for individual mixture components and then assembles for each trace the corresponding carbon-bond topology network by consensus clustering. This led to the determination of 112 topologies of unique metabolites from a single sample. The topolome is dominated by carbon topologies of carbohydrates (34.8%) and amino acids (45.5%) that can constitute building blocks of more complex structures.

Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.

Higher-Rank Correlation NMR Spectra with Spectral Moment Filtering

Higher-rank correlation spectroscopy is introduced as an alternative to 3D Fourier-transform (FT) NMR spectroscopy for resonance assignment and molecular structure determination. The method combines standard 2D FT spectra that share a common frequency dimension, such as a 2D (13)C-(1)H HSQC and a 2D (1)H-(1)H TOCSY spectrum, and constructs higher-rank correlation spectra with ultra-high spectral resolution. Spectral overlap along a common dimension, in particular the (1)H dimension, is addressed by a spectral filtering method, which identifies mismatches between the 1(st) and 2(nd) moments of cross-peak profiles. The method, which provides a substantial speed-up over traditional 3D FT spectroscopy while effectively suppressing false peaks, is demonstrated for the triple-rank (13)C-(1)H HSQC-TOCSY spectrum of a cyclic decapeptide with different mixing times. Higher-rank correlation spectroscopy is usefully applicable to the analysis of a wide range of NMR spectra of synthetic and natural products.