Expanding the Coverage of the Metabolome with Nitrogen-Based NMR

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Quantitative Analysis of the Human Semen Phosphorometabolome by 31P-NMR

Phosphorus-containing metabolites occupy a prominent position in cell pathways. The phosphorometabolomic approach in human sperm samples will deliver valuable information as new male fertility biomarkers could emerge. This study analyzed, by 31P-NMR, seminal plasma and whole semen from asthenozoospermic and normozoospermic samples (71% vs. 27% and 45% vs. 17%, total and progressive sperm motility, respectively), and also ejaculates from healthy donors. At least 16 phosphorus-containing metabolites involved in central energy metabolism and phospholipid, nucleotide, and nicotinamide metabolic pathways were assigned and different abundances between the samples with distinct sperm quality was detected. Specifically, higher levels of phosphocholine, glucose-1-phosphate, and to a lesser degree, acetyl phosphate were found in the asthenozoospermic seminal plasma. Notably, the phosphorometabolites implicated in lipid metabolism were highlighted in the seminal plasma, while those associated with carbohydrate metabolism were more abundant in the spermatozoa. Higher levels of phosphocholine, glucose-1-phosphate, and acetyl phosphate in the seminal plasma with poor quality suggest their crucial role in supporting sperm motility through energy metabolic pathways. In the seminal plasma, phosphorometabolites related to lipid metabolism were prominent; however, spermatozoa metabolism is more dependent on carbohydrate-related energy pathways. Understanding the presence and function of sperm phosphorylated metabolites will enhance our knowledge of the metabolic profile of healthy human sperm, improving assessment and differential diagnosis.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Application of 15N-Edited 1H-13C Correlation NMR Spectroscopy─Toward Fragment-Based Metabolite Identification and Screening via HCN Constructs

Many key building blocks of life contain nitrogen moieties. Despite the prevalence of nitrogen-containing metabolites in nature, 15N nuclei are seldom used in NMR-based metabolite assignment due to their low natural abundance and lack of comprehensive chemical shift databases. However, with advancements in isotope labeling strategies, 13C and 15N enriched metabolites are becoming more common in metabolomic studies. Simple multidimensional nuclear magnetic resonance (NMR) experiments that correlate 1H and 15N via single bond 1JNH or multiple bond 2-3JNH couplings using heteronuclear single quantum coherence (HSQC) or heteronuclear multiple bond coherence are well established and routinely applied for structure elucidation. However, a 1H-15N correlation spectrum of a metabolite mixture can be difficult to deconvolute, due to the lack of a 15N specific database. In order to bridge this gap, we present here a broadband 15N-edited 1H-13C HSQC NMR experiment that targets metabolites containing 15N moieties. Through this approach, nitrogen-containing metabolites, such as amino acids, nucleotide bases, and nucleosides, are identified based on their 13C, 1H, and 15N chemical shift information. This approach was tested and validated using a [15N, 13C] enriched Daphnia magna (water flea) metabolite extract, where the number of clearly resolved 15N-containing peaks increased from only 11 in a standard HSQC to 51 in the 15N-edited HSQC, and the number of obscured peaks decreased from 59 to just 7. The approach complements the current repertoire of NMR techniques for mixture deconvolution and holds considerable potential for targeted metabolite NMR in 15N, 13C enriched systems.

Exploring the potential application of alternative nuclei in NMR based metabolomics

INTRODUCTION

Nuclear magnetic resonance (NMR) is widely used in metabolomics but it focusses on ¹H over other NMR-active nuclei.

OBJECTIVES

To evaluate the potential of alternative NMR-sensitive nuclei to generate useful metabolomic data.

METHOD

Proton, carbon, phosphorus, and nitrogen-based NMR metabolomics was undertaken on extracts from mint and European honey bee tissue.

RESULTS

Carbon NMR provided useful information but required larger sample sizes. Phosphorus produced overlapping peaks in one dimensional (1D) analysis but showed potential in 2D experiments. ¹⁵N NMR was found to not be sensitive enough for general metabolomic work.

CONCLUSIONS

Alternative NMR active nuclei are useful for metabolomics.

Leveraging the HMBC to Facilitate Metabolite Identification

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

Phosphorus NMR and Its Application to Metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

The acid response network of Staphylococcus aureus

Staphylococcus aureus colonizes or causes infection in a multitude of niches within a mammalian host. Many of these niches are acidic, yet specific pH resistance mechanisms that facilitate survival have not been thoroughly investigated. This review discusses recent studies documenting known acid resistance mechanisms in S. aureus and other staphylococcal species. However, studies that clearly define the regulation of the acid resistance regulon and potential interactions with weak organic acids in specific niches of the host including the skin and gut are yet to be defined.

Nitrogen Metabolism in Cancer and Immunity

As one of the fundamental requirements for cell growth and proliferation, nitrogen acquisition and utilization must be tightly regulated. Nitrogen can be generated from amino acids (AAs) and utilized for biosynthetic processes through transamination and deamination reactions. Importantly, limitations of nitrogen availability in cells can disrupt the synthesis of proteins, nucleic acids, and other important nitrogen-containing compounds. Rewiring cellular metabolism to support anabolic processes is a feature common to both cancer and proliferating immune cells. In this review, we discuss how nitrogen is utilized in biosynthetic pathways and highlight different metabolic and oncogenic programs that alter the flow of nitrogen to sustain biomass production and growth, an important emerging feature of cancer and immune cell proliferation.

Efficient labeling of organic molecules using 13C elemental carbon: universal access to 13C2-labeled synthetic building blocks, polymers and pharmaceuticals

Synthetic methodology enabled by ¹³C-elemental carbon is reported. Calcium carbide Ca¹³C₂ was applied to introduce a universal ¹³C₂ unit in the synthesis of labeled alkynes, O,S,N-vinyl derivatives, labeled polymers and ¹³C₂-pyridazine drug core.

Existing and Emerging Metabolomic Tools for ALS Research

Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.

NMR Spectroscopy-Based Metabolic Profiling of Biospecimens

Metabolomics refers to study of metabolites in biospecimens such as blood serum, tissues, and urine. Nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; mass spectrometry coupled with liquid chromatography) are most frequently employed to analyze complex biological/clinical samples. NMR is a relatively insensitive tool compared to UPLC-MS/MS but offers straightforward quantification and identification and easy sample processing. One-dimensional ¹ H NMR spectroscopy is inherently quantitative and can be readily used for metabolite quantification without individual metabolite standards. Two-dimensional spectroscopy is most commonly used for identification of metabolites but can also be used quantitatively. Although NMR experiments are unbiased regarding the chemical nature of the analyte, it is crucial to adhere to the proper metabolite extraction protocol for optimum results. Selection and implementation of appropriate NMR pulse programs are also important. Finally, employment of the correct metabolite quantification strategy is crucial as well. In this unit, step-by-step guidance for running an NMR metabolomics experiment from typical biospecimens is presented. The unit describes an optimized metabolite extraction protocol, followed by implementation of NMR experiments and quantification strategies using the so-called "targeted profiling" technique. This approach relies on an underlying basis set of metabolite spectra acquired under similar conditions. Some strategies for statistical analysis of the data are also presented. Overall, this set of protocols should serve as a guide for anyone who wishes to enter the world of NMR-based metabolomics analysis. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Metabolite extraction from different biospecimens Basic Protocol 2: Preparation of dried upper fraction for NMR analysis Alternate Protocol: Preparation of urine samples for NMR analysis Basic Protocol 3: NMR experiments Basic Protocol 4: Spectral processing and quantification of metabolites Basic Protocol 5: Statistical analysis of the data.

Extractive Ratio Analysis NMR Spectroscopy for Metabolite Identification in Complex Biological Mixtures

The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method [ Wei et al. Anal. Chem. 2011 , 83 , 7616 - 7623 ] and exploits the simplified NMR spectra provided by the extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially, and the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method by extracting carboxylic acid containing metabolites from human urine, one of the highly complex biological mixtures encountered in the metabolomics field. E-RANSY performs better than STOCSY and the original RANSY method and offers new avenues to identify unknown metabolites in complex biological mixtures.

Real-Time Pure Shift HSQC NMR for Untargeted Metabolomics

Sensitivity and resolution are key considerations for NMR applications in general and for metabolomics in particular, where complex mixtures containing hundreds of metabolites over a large range of concentrations are commonly encountered. There is a strong demand for advanced methods that can provide maximal information in the shortest possible time frame. Here, we present the optimization and application of the recently introduced 2D real-time BIRD 1H-13C HSQC experiment for NMR-based metabolomics of aqueous samples at 13C natural abundance. For mouse urine samples, it is demonstrated how this real-time pure shift sensitivity-improved heteronuclear single quantum correlation method provides broadband homonuclear decoupling along the proton detection dimension and thereby significantly improves spectral resolution in regions that are affected by spectral overlap. Moreover, the collapse of the scalar multiplet structure of cross-peaks leads to a sensitivity gain of about 40-50% over a traditional 2D HSQC-SI experiment. The experiment works well over a range of magnetic field strengths and is particularly useful when resonance overlap in crowded regions of the HSQC spectra hampers accurate metabolite identification and quantitation.

Urease is an essential component of the acid response network of Staphylococcus aureus and is required for a persistent murine kidney infection

Staphylococcus aureus causes acute and chronic infections resulting in significant morbidity. Urease, an enzyme that generates NH₃ and CO₂ from urea, is key to pH homeostasis in bacterial pathogens under acidic stress and nitrogen limitation. However, the function of urease in S. aureus niche colonization and nitrogen metabolism has not been extensively studied. We discovered that urease is essential for pH homeostasis and viability in urea-rich environments under weak acid stress. The regulation of urease transcription by CcpA, Agr, and CodY was identified in this study, implying a complex network that controls urease expression in response to changes in metabolic flux. In addition, it was determined that the endogenous urea derived from arginine is not a significant contributor to the intracellular nitrogen pool in non-acidic conditions. Furthermore, we found that during a murine chronic renal infection, urease facilitates S. aureus persistence by promoting bacterial fitness in the low-pH, urea-rich kidney. Overall, our study establishes that urease in S. aureus is not only a primary component of the acid response network but also an important factor required for persistent murine renal infections.

Urease has been reported to be crucial to bacteria in environmental adaptation, virulence, and defense against host immunity. Although the function of urease in S. aureus is not clear, recent evidence suggests that urease is important for acid resistance in various niches. Our study deciphered a function of S. aureus urease both in laboratory conditions and during host colonization. Furthermore, we uncovered the major components of the regulatory system that fine-tunes the expression of urease. Collectively, this study established the dual function of urease which serves as a significant part of the S. aureus acid response while also serving as an enzyme required for persistent kidney infections and potential subsequent staphylococcal metastasis.

NMR Metabolomics Protocols for Drug Discovery

Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is "fragile" and may be negatively impacted by improper sample collection, storage, and extraction procedures. Similarly, biologically irrelevant conclusions may result from incorrect data collection, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.