Applications of NMR spectroscopy to systems biochemistry

Radioisotope-Based Protocol for Determination of Central Carbon Metabolism in T Cells

T lymphocytes are the major components of the adaptive immune system. It's been known that T cells are able to engage a diverse range of metabolic programs to meet the metabolic demands during their life cycle from early development, activation to functional differentiation. Central carbon metabolic pathways provide energy, reducing power, and biosynthetic precursors to support T cell homeostasis, proliferation, and immune functions. As such, quantitative or semiquantitative analysis of central carbon metabolic flux activities offers mechanistic details, as well as insights into the regulation of metabolic pathways and the impact of changing metabolic programs on T cell life cycle. Global profiling of cellular metabolites by mass spectrometry-based metabolomics and metabolic flux analysis (MFA) using radioactive and nonradioactive/stable isotope approaches are powerful tools for determination of central carbon metabolic pathway activity. Here, we describe in detail the procedure for the radioisotope-based approach of analyzing central carbon metabolic fluxes in T cells.

Sensitive NMR method for detecting carbohydrate influx into competing chemocatalytic pathways

Mechanistic pathway studies in sustainable chemistry can be accelerated and have increased information content through the indirect detection of isotope-tracking experiments upon reduction of quaternary carbon sites.

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Histone acetylation landscape in S. cerevisiae nhp6ab mutants reflects altered glucose metabolism

BACKGROUND

The execution of many genetic programs, influenced by environmental conditions, is epigenetically controlled. Thus, small molecules of the intermediate metabolism being precursors of most of nutrition-deriving epigenetic modifications, sense the cell surrounding environment.

METHODS

Here we describe histone H4K16 acetylation distribution in S. cerevisiae nhp6ab mutant, using ChIP-seq analysis; its transcription profile by RNA-seq and its metabolic features by studying the metabolome. We then intersected these three -omic approaches to unveil common crosspoints (if any).

RESULTS

In the nhp6ab mutant, the glucose metabolism is switched to pathways leading to Acetyl-CoA synthesis. These enhanced pathways could lead to histone hyperacetylation altering RNA transcription, particularly of those metabolic genes that maintain high Acetyl-CoA availability.

CONCLUSIONS

Thus, the absence of chromatin regulators like Nhp6 A and B, interferes with a regulative circular mechanism where histone modification, transcription and metabolism influence each other and contribute to clarify the more general phenomenon in which gene regulation feeds metabolic alterations on epigenetic basis.

GENERAL SIGNIFICANCE

This study allowed us to identify, in these two factors, a common element of regulation in metabolism and chromatin acetylation state that could represent a powerful tool to find out relationships existing between metabolism and gene expression in more complex systems.

Inhibition of Anaplerotic Glutaminolysis Underlies Selenite Toxicity in Human Lung Cancer

Large clinical trials and model systems studies suggest that the chemical form of selenium dictates chemopreventive and chemotherapeutic efficacy. Selenite induces excess ROS production, which mediates autophagy and eventual cell death in non-small cell lung cancer adenocarcinoma A549 cells. As the mechanisms underlying these phenotypic effects are unclear, the clinical relevance of selenite for cancer therapy remains to be determined. The authors' previous stable isotope-resolved metabolomics and gene expression analysis showed that selenite disrupts glycolysis, the Krebs cycle, and polyamine metabolism in A549 cells, potentially through perturbed glutaminolysis, a vital anaplerotic process for proliferation of many cancer cells. Herein, the role of the glutaminolytic enzyme glutaminase 1 (GLS1) in selenite's toxicity in A549 cells and in patient-derived lung cancer tissues is investigated. Using [¹³ C₆ ]-glucose and [¹³ C₅ ,¹⁵ N₂ ]-glutamine tracers, selenite's action on metabolic networks is determined. Selenite inhibits glutaminolysis and glutathione synthesis by suppressing GLS1 expression, and blocks the Krebs cycle, but transiently activates pyruvate carboxylase activity. Glutamate supplementation partially rescues these anti-proliferative and oxidative stress activities. Similar metabolic perturbations and necrosis are observed in selenite-treated human patients' cancerous lung tissues ex vivo. The results support the hypothesis that GLS1 suppression mediates part of the anti-cancer activity of selenite both in vitro and ex vivo.

NMR-based newborn urine screening for optimized detection of inherited errors of metabolism

Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized ¹H- NMR-based analysis.

Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from 13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

Metabolomics of osteoarthritis: emerging novel markers and their potential clinical utility

OA is a multifactorial and progressive disease with no cure yet. Substantial efforts have been made and several biochemical and genetic markers have been reported, but neither alone nor in combination is adequate to identify early OA changes or determine disease progression with sufficient predictive values. Recent advances in metabolomics and its application to the study of OA have led to elucidation of involvement of several metabolic pathways and new specific metabolic markers for OA. Some of these metabolic pathways affect amino acid metabolism, including branched chain amino acids and arginine, and phospholipid metabolism involving conversion of phosphatidylcholine to lysophosphatidylcholine. These metabolic markers appear to be clinically actionable and may potentially improve the clinical management of OA patients. In this article, we review the recent studies of metabolomics of OA, discuss those novel metabolic markers and their potential clinical utility, and indicate future research directions in the field.

NMR characterization and evaluation of antibacterial and antiobiofilm activity of organic extracts from stationary phase batch cultures of five marine microalgae (Dunaliella sp., D. salina, Chaetoceros calcitrans, C. gracilis and Tisochrysis lutea)

The chemical composition of five marine microalgae (Dunaliella sp., Dunaliella salina, Chaetoceros calcitrans, Chaetoceros gracilis and Tisochrysis lutea) was investigated through nuclear magnetic resonance (NMR) spectroscopic study of the soluble material obtained by sequential extraction with hexane, ethyl acetate (AcOEt) and methanol of biomass from stationary phase cultures. Hexane extracted the major lipids present in the microalgae during the stationary phase of growth, which correspond to storage lipids. Triacylglycerols (TGs) were the only storage lipids produced by Dunaliella and Chaetoceros. In contrast, T. lutea predominantly stored polyunsaturated long-chain alkenones, with sterols also detected as minor components of the hexane extract. The molecular structure of brassicasterol was determined in T. lutea and the presence of squalene in this sample was also unequivocally detected. Monogalactosyldiacylglycerols (MGDGs) and pigments were concentrated in the AcOEt extracts. C. calcitrans and D. salina constituted an exception due to the high amount of TGs and glycerol produced, respectively, by these two strains. Chlorophylls a and b and β-carotene were the major pigments synthesized by Dunaliella and chlorophyll a and fucoxanthin were the only pigments detected in Chaetoceros and T. lutea. Information concerning the acyl chains present in TGs and MGDGs as well as the positional distribution of acyl chains on the glycerol moiety was obtained by NMR analysis of hexane and AcOEt extracts, with results consistent with those expected for the genera studied. Fatty acid composition of TGs in the two Dunaliella strains was different, with polyunsaturated acyl chains almost absent in the storage lipids produced by D. salina. Except in C. calcitrans, the polar nature of soluble compounds was inferred through the relative extraction yield using methanol as the extraction solvent. Glycerol was the major component of this fraction for the Dunaliella strains. In T. lutea 1,4/2,5-cyclohexanetetrol (CHT) and dimethylsulfoniopropionate (DMSP) preponderated. CHT was also the major polyol present in the Chaetoceros strains in which DMSP was not detected, but prominent signals of 2,3-dihydroxypropane-1-sulfonate (DHSP) were observed in the 1H NMR spectra of methanolic extracts. The presence of DHSP confirms the production of this metabolite by diatoms. In addition, several other minor compounds (digalactosyldiacyglycerols (DGDGs), sulphoquinovosyldiacylglycerols (SQDGs), amino acids, carbohydrates, scyllo-inositol, mannitol, lactic acid and homarine) were also identified in the methanolic extracts. The antibacterial and antibiofilm activities of the extracts were tested. The AcOEt extract from C. gracilis showed a moderate antibiofilm activity.

Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis

Cellular behavior is controlled by the interplay of diverse biomolecules. Most experimental methods, however, can monitor only a single molecule class or reaction type at a time. We developed an in vitro Nuclear Magnetic Resonance spectroscopy (NMR) approach, which permitted dynamic quantification of an entire “heterotypic” network – simultaneously monitoring three distinct molecule classes (metabolites, proteins, RNA) and all elementary reaction types (bimolecular interactions, catalysis, unimolecular changes). Focusing on an 8-reaction co-transcriptional RNA folding network, in a single sample we recorded over 35 time-points with over 170 observables each, and accurately determined 5 core reaction constants in multiplex. This reconstruction revealed unexpected cross-talk between the different reactions. We further observed dynamic phase-separation in a system of five distinct RNA binding domains in the course of the RNA transcription reaction. Our Systems NMR approach provides a deeper understanding of biological network dynamics by combining the dynamic resolution of biochemical assays and the multiplexing ability of “omics”.

Modular transmission line probes for microfluidic nuclear magnetic resonance spectroscopy and imaging

Microfluidic NMR spectroscopy can probe chemical and bio-chemical processes non-invasively in a tightly controlled environment. We present a dual-channel modular probe assembly for high efficiency microfluidic NMR spectroscopy and imaging. It is compatible with a wide range of microfluidic devices, without constraining the fluidic design. It collects NMR signals from a designated sample volume on the device with high sensitivity and resolution. Modular design allows adapting the detector geometry to different experimental conditions with minimal cost, by using the same probe base. The complete probe can be built from easily available parts. The probe body mainly consists of prefabricated aluminium profiles, while the probe circuit and detector are made from printed circuit boards. We demonstrate a double resonance HX probe with a limit of detection of 1.4 nmol s^-1/2 for protons at 600 MHz, resolution of 3.35 Hz, and excellent B₁ homogeneity. We have successfully acquired ¹H-¹³C and ¹H-¹⁵N heteronuclear correlation spectra (HSQC), including a ¹H-¹⁵N HSQC spectrum of 1 mM ¹⁵N labeled ubiquitin in 2.5 μl of sample volume.

Brain metabonomics study of the antidepressant-like effect of Xiaoyaosan on the CUMS-depression rats by 1H NMR analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Xiaoyaosan (XYS), a famous TCM prescription with a long history of clinical use for relieving a wide variety of depression symptoms, consists of Radix Bupleuri (Bupleurum chinense DC.), Radix Angelicae Sinensis (Angelica sinensis (Oliv.) Diels), Radix PaeoniaeAlba (Paeonia lactiflora Pall.), Rhizoma Atractylodis Macrocepha lae (Atractylodes macrocephala Koidz.), Poria (Poria cocos (Schw.)Wolf), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Herba Menthae Haplocalycis (Mentha haplocalyx Briq.), and Rhizoma Zin-giberis Recens (Zingiber officinale Rosc.).

AIM OF THE STUDY

We aimed to characterize the diversity and variation of two kinds metabolites of brain, i.e. aqueous and lipophilic metabolites, gaining comprehensive insights into the metabolic processes of depression-like behavior, and to reveal the mechanisms of antidepressant-like effects of XYS.

MATERIALS AND METHODS

We first established a CUMS (Chronic Unpredictable Mild Stress)-induced depression-like behavior model. We then extracted both aqueous and lipophilic metabolites of rat brains by a two-phase extraction method, which were subsequently characterized by two differential sequences of ¹H nuclear magnetic resonance (NMR). Multivariate analysis including Principal Components Analysis (PCA) and Orthogonal Partial Least Squares-Discriminate Analysis (OPLS-DA) was applied.

RESULTS

Metabolic profiling by PCA indicated that XYS significantly reversed the metabolic perturbation caused by CUMS. OPLS-DA showed a total of 15 metabolites including 6 lipophilic and 9 aqueous metabolites was screened as potential biomarkers involved in CUMS-induced depression-like behavior. On top of this, five pathways including (1)D-glutamine and D-glutamate metabolism, (2) valine, leucine and isoleucine biosynthesis, (3) alanine, aspartate and glutamate metabolism, (4) taurine and hypotaurine metabolism and (5) arginine and proline metabolism were recognized as the most influenced pathways associated with the process of CUMS-induced depression-like behavior. Notably, XYS significantly reversed abnormality of 5 aqueous and 4 lipophilic metabolites to normal, suggesting that XYS synergistically mediated abnormalities of multiple pathways (1), (3), (4) and (5).

CONCLUSIONS

It is the first report to investigate the antidepressant-like effects and underlying mechanisms of XYS from the perspective of brain metabolites. In a broad sense, this study brings novel and valuable insights to evaluate the efficacy of traditional Chinese medicine (TCM), to interpret mechanisms, and to provide the theoretical basis for further research on therapeutic mechanisms in clinical practice.

Combined In-Cell NMR and Simulation Approach to Probe Redox-Dependent Pathway Control

Dynamic response of intracellular reaction cascades to changing environments is a hallmark of living systems. As metabolism is complex, mechanistic models have gained popularity for describing the dynamic response of cellular metabolism and for identifying target genes for engineering. At the same time, the detailed tracking of transient metabolism in living cells on the subminute time scale has become amenable using dynamic nuclear polarization-enhanced ¹³C NMR. Here, we suggest an approach combining in-cell NMR spectroscopy with perturbation experiments and modeling to obtain evidence that the bottlenecks of yeast glycolysis depend on intracellular redox state. In pre-steady-state glycolysis, pathway bottlenecks shift from downstream to upstream reactions within a few seconds, consistent with a rapid decline in the NAD⁺/NADH ratio. Simulations using mechanistic models reproduce the experimentally observed response and help identify unforeseen biochemical events. Remaining inaccuracies in the computational models can be identified experimentally. The combined use of rapid injection NMR spectroscopy and in silico simulations provides a promising method for characterizing cellular reactions with increasing mechanistic detail.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI- and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

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.

tcaSIM: A Simulation Program for Optimal Design of 13C Tracer Experiments for Analysis of Metabolic Flux by NMR and Mass Spectroscopy

Increasingly sophisticated instrumentation for chemical separations and identification has facilitated rapid advancements in our understanding of the metabolome. Since many analyses are performed using either mass spectroscopy (MS) or nuclear magnetic resonance (NMR) spectroscopy, the spin ½ stable ¹³C isotope is now widely used as a metabolic tracer. There is strong interest in quantitative analysis of metabolic flux through pathways in vivo, particularly in human patients. Although instrumentation advances and scientific interests in metabolism are increasing in parallel, a practical and rational design of a ¹³C tracer study can be challenging. Prior to planning the details of a tracer experiment, is it important to consider whether the analytical results will be sensitive to flux through the pathways of interest. Here, we briefly summarize the various approaches that have been used to design carbon tracer experiments, outline the sources of complexity, and illustrate the use of a software tool, tcaSIM, to aid in the experimental design of both MS and NMR data in complex systems including patients.

UCP2 Overexpression Redirects Glucose into Anabolic Metabolic Pathways

Uncoupling protein 2 (UCP2) is often upregulated in cancer cells. The UCP2 upregulation is positively correlated with enhanced proliferation, tumorigenesis, and metabolic alterations, thus suggesting that UCP2 upregulation can play a key role in sensing metabolic changes to promote tumorigenesis. To determine the global metabolic impact of UCP2 upregulation, ¹³ C₆ glucose as a source molecule is used to "trace" the metabolic fate of carbon atoms derived from glucose. UCP2 overexpression in skin epidermal cells enhances the incorporation of ¹³ C label to pyruvate, tricarboxylic acid cycle intermediates, nucleotides, and amino acids, suggesting that UCP2 upregulation reprograms cellular metabolism toward macromolecule synthesis. To the best of our knowledge, this is the first study to bring to light the overall metabolic differences caused by UCP2 upregulation.

Stable Isotope-Resolved Metabolomics by NMR

Stable isotopes enable the tracing of atoms from precursors to products via metabolic transformations in situ and in crude extracts. NMR has some unique capabilities that are especially suited for metabolic analysis, including atom position-resolved isotope analysis and isotope editing. In this chapter we describe NMR-based analysis of positional isotopomer distribution using metabolic tracers.

Focusing on “the important” through targeted NMR experiments: an example of selective13C–12C bond detection in complex mixtures

Here, a targeted NMR experiment is introduced which selectively detects the formation of¹³C–¹²C bonds in mixtures.

Rapid, Reproducible, Quantifiable NMR Metabolomics: Methanol and Methanol: Chloroform Precipitation for Removal of Macromolecules in Serum and Whole Blood

Background: Though blood is an excellent biofluid for metabolomics, proteins and lipids present in blood can interfere with 1d-¹H NMR spectra and disrupt quantification of metabolites. Here, we present effective macromolecule removal strategies for serum and whole blood (WB) samples. Methods: A variety of macromolecule removal strategies were compared in both WB and serum, along with tests of ultrafiltration alone and in combination with precipitation methods. Results: In healthy human serum, methanol:chloroform:water extraction with ultrafiltration was compared to methanol precipitation with and without ultrafiltration. Methods were tested in healthy pooled human serum, and in serum from patients with sepsis. Effects of long-term storage at −80 °C were tested to explore the impact of macromolecule removal strategy on serum from different conditions. In WB a variety of extraction strategies were tested in two types of WB (from pigs and baboons) to examine the impact of macromolecule removal strategies on different samples. Conclusions: In healthy human serum methanol precipitation of serum with ultrafiltration was superior, but was similar in recovery and variance to methanol:chloroform:water extraction with ultrafiltration in pooled serum from patients with sepsis. In WB, high quality, quantifiable spectra were obtained with the use of a methanol: chloroform precipitation.

Interplay between hypoxia and androgen controls a metabolic switch conferring resistance to androgen/AR-targeted therapy

Despite recent advances, the efficacy of androgen/androgen receptor (AR)-targeted therapy remains  limited for many patients with metastatic prostate cancer. This is in part because prostate cancers adaptively switch to the androgen/AR-independent pathway for survival and growth, thereby conferring therapy resistance. Tumor hypoxia is considered as a major cause of treatment resistance. However, the exact mechanism is largely unclear. Here we report that chronic-androgen deprivation therapy (ADT) in the condition of hypoxia induces adaptive androgen/AR-independence, and therefore confers resistance to androgen/AR-targeted therapy, e.g., enzalutamide. Mechanistically, this is mediated by glucose-6-phosphate isomerase (GPI), which is transcriptionally repressed by AR in hypoxia, but restored and increased by AR inhibition. In turn, GPI maintains glucose metabolism and energy homeostasis in hypoxia by redirecting the glucose flux from androgen/AR-dependent pentose phosphate pathway (PPP) to hypoxia-induced glycolysis pathway, thereby reducing the growth inhibitory effect of enzalutamide. Inhibiting GPI overcomes the therapy resistance in hypoxia in vitro and increases enzalutamide efficacy in vivo.

NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism

There is considerable interest in defining metabolic reprogramming in human diseases, which is recognized as a hallmark of human cancer. Although radiotracers have a long history in specific metabolic studies, stable isotope-enriched precursors coupled with modern high resolution mass spectrometry and NMR spectroscopy have enabled systematic mapping of metabolic networks and fluxes in cells, tissues and living organisms including humans. These analytical platforms are high in information content, are complementary and cross-validating in terms of compound identification, quantification, and isotope labeling pattern analysis of a large number of metabolites simultaneously. Furthermore, new developments in chemoselective derivatization and in vivo spectroscopy enable tracking of labile/low abundance metabolites and metabolic kinetics in real-time. Here we review developments in Stable Isotope Resolved Metabolomics (SIRM) and recent applications in cancer metabolism using a wide variety of stable isotope tracers that probe both broad and specific aspects of cancer metabolism required for proliferation and survival.

Collagen prolyl 4-hydroxylase 1 is essential for HIF-1α stabilization and TNBC chemoresistance

Collagen prolyl 4-hydroxylase (P4H) expression and collagen hydroxylation in cancer cells are necessary for breast cancer progression. Here, we show that P4H alpha 1 subunit (P4HA1) protein expression is induced in triple-negative breast cancer (TNBC) and HER2 positive breast cancer. By modulating alpha ketoglutarate (α-KG) and succinate levels P4HA1 expression reduces proline hydroxylation on hypoxia-inducible factor (HIF) 1α, enhancing its stability in cancer cells. Activation of the P4HA/HIF-1 axis enhances cancer cell stemness, accompanied by decreased oxidative phosphorylation and reactive oxygen species (ROS) levels. Inhibition of P4HA1 sensitizes TNBC to the chemotherapeutic agent docetaxel and doxorubicin in xenografts and patient-derived models. We also show that increased P4HA1 expression correlates with short relapse-free survival in TNBC patients who received chemotherapy. These results suggest that P4HA1 promotes chemoresistance by modulating HIF-1-dependent cancer cell stemness. Targeting collagen P4H is a promising strategy to inhibit tumor progression and sensitize TNBC to chemotherapeutic agents.

Hyperactivation of HIF-1α is crucial in progression of triple-negative breast cancer, but how HIF-1α stability is maintained in a hypoxia-independent manner is unclear. Here, the authors show collagen prolyl-4-hydroylase 1 stabilises HIF-1α and is involved in chemoresistance in TNBC.

An Overview of Metabolic Phenotyping in Blood Pressure Research

PURPOSE OF THE REVIEW

This review presents the analytical techniques, processing and analytical steps used in metabolomics phenotyping studies, as well as the main results from epidemiological studies on the associations between metabolites and high blood pressure.

RECENT FINDINGS

A variety of metabolomic approaches have been applied to a range of epidemiological studies to uncover the pathophysiology of high blood pressure. Several pathways have been suggested in relation to blood pressure including the possible role of the gut microflora, inflammatory, oxidative stress, and lipid pathways. Metabolic changes have also been identified associated with blood pressure lowering effects of diets high in fruits and vegetables and low in meat intake. However, the current body of literature on metabolic profiling and blood pressure is still in its infancy, not fully consistent and requires careful interpretation. Metabolic phenotyping is a promising approach to uncover metabolic pathways associated with high blood pressure and throw light into the complex pathophysiology of hypertension.

Stable Isotope-Resolved Metabolomics Shows Metabolic Resistance to Anti-Cancer Selenite in 3D Spheroids versus 2D Cell Cultures

Conventional two-dimensional (2D) cell cultures are grown on rigid plastic substrates with unrealistic concentration gradients of O₂, nutrients, and treatment agents. More importantly, 2D cultures lack cell–cell and cell–extracellular matrix (ECM) interactions, which are critical for regulating cell behavior and functions. There are several three-dimensional (3D) cell culture systems such as Matrigel, hydrogels, micropatterned plates, and hanging drop that overcome these drawbacks but they suffer from technical challenges including long spheroid formation times, difficult handling for high throughput assays, and/or matrix contamination for metabolic studies. Magnetic 3D bioprinting (M3DB) can circumvent these issues by utilizing nanoparticles that enable spheroid formation and growth via magnetizing cells. M3DB spheroids have been shown to emulate tissue and tumor microenvironments while exhibiting higher resistance to toxic agents than their 2D counterparts. It is, however, unclear if and how such 3D systems impact cellular metabolic networks, which may determine altered toxic responses in cells. We employed a Stable Isotope-Resolved Metabolomics (SIRM) approach with ¹³C₆-glucose as tracer to map central metabolic networks both in 2D cells and M3DB spheroids formed from lung (A549) and pancreatic (PANC1) adenocarcinoma cells without or with an anti-cancer agent (sodium selenite). We found that the extent of ¹³C-label incorporation into metabolites of glycolysis, the Krebs cycle, the pentose phosphate pathway, and purine/pyrimidine nucleotide synthesis was largely comparable between 2D and M3DB culture systems for both cell lines. The exceptions were the reduced capacity for de novo synthesis of pyrimidine and sugar nucleotides in M3DB than 2D cultures of A549 and PANC1 cells as well as the presence of gluconeogenic activity in M3DB spheroids of PANC1 cells but not in the 2D counterpart. More strikingly, selenite induced much less perturbation of these pathways in the spheroids relative to the 2D counterparts in both cell lines, which is consistent with the corresponding lesser effects on morphology and growth. Thus, the increased resistance of cancer cell spheroids to selenite may be linked to the reduced capacity of selenite to perturb these metabolic pathways necessary for growth and survival.

Lipidomic signature of serum from the rats exposed to alcohol for one year

Alcohol abuse and its related diseases are the major risk factors for human health. Although the mechanism of alcohol-related disorders has been widely investigated, serum metabolites associated with long-term alcohol intake have not been well explored. In this study, we aimed to investigate the profiles of serum metabolites and lipid species of rats chronically exposed to alcohol, which may be involved in the pathogenesis of alcohol-associated disease. An ¹H NMR-based metabolomics and Q-TOF/MS-based lipidomics approach were applied to investigate the profile of serum metabolites and lipid species of rats administrated daily with alcohol (12% vol/vol, 10 ml/kg per day, i.g.) for one year continuously. The rats administered with sterile water (10 ml/kg per day, i.g.) were used as control. We found that alcohol affected mostly the lipid species rather than small molecule metabolites in the serum of both female and male rats. Among the modified lipids, glycerophospholipid, sphingolipid and glycerolipids metabolism pathways were profoundly altered. The prominent changes in lipid profiles included diacylglycerol (DG), lysophosphatidylcholine (LysoPC), phosphatidic acid (PA), phosphatidylcholine (PC), phosphatidylethanolamine (PE) and triacylglycerol (TG). Moreover, fatty-acyl profile of lipids and total degree of unsaturation of fatty acid were also significantly altered by alcohol. The modified lipidomic profile may help to understand the pathogenesis of alcohol-associated diseases and also be of value for clinical evaluation of alcohol abuse, alcohol-associated disease diagnosis.

In-Vivo NMR Spectroscopy: A Powerful and Complimentary Tool for Understanding Environmental Toxicity

Part review, part perspective, this article examines the applications and potential of in-vivo Nuclear Magnetic Resonance (NMR) for understanding environmental toxicity. In-vivo NMR can be applied in high field NMR spectrometers using either magic angle spinning based approaches, or flow systems. Solution-state NMR in combination with a flow system provides a low stress approach to monitor dissolved metabolites, while magic angle spinning NMR allows the detection of all components (solutions, gels and solids), albeit with additional stress caused by the rapid sample spinning. With in-vivo NMR it is possible to use the same organisms for control and exposure studies (controls are the same organisms prior to exposure inside the NMR). As such individual variability can be reduced while continual data collection over time provides the temporal resolution required to discern complex interconnected response pathways. When multidimensional NMR is combined with isotopic labelling, a wide range of metabolites can be identified in-vivo providing a unique window into the living metabolome that is highly complementary to more traditional metabolomics studies employing extracts, tissues, or biofluids.

Multiomics tools for the diagnosis and treatment of rare neurological disease

Conventional workup of rare neurological disease is frequently hampered by diagnostic delay or lack of diagnosis. While biomarkers have been established for many neurometabolic disorders, improved methods are required for diagnosis of previously unidentified or underreported causes of rare neurological disease. This would result in a higher diagnostic yield and increased patient numbers required for interventional studies. Recent studies using next-generation sequencing and metabolomics have led to identification of novel disease-causing genes and biomarkers. This combined approach can assist in overcoming challenges associated with analyzing and interpreting the large amount of data obtained from each technique. In particular, metabolomics can support the pathogenicity of sequence variants in genes encoding enzymes or transporters involved in metabolic pathways. Moreover, metabolomics can show the broader perturbation caused by inborn errors of metabolism and identify a metabolic fingerprint of metabolic disorders. As such, using "omics" has great potential to meet the current needs for improved diagnosis and elucidation of rare neurological disease.

Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome

Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.

Promises and pitfalls of untargeted metabolomics

Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi-quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC-MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre-analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post-analytical (data processing).

Funaria hygrometrica Hedw. elevated tolerance to D2O: its use for the production of highly deuterated metabolites

The method introduced here to grow F. hygrometrica in high concentrations of D ₂ O is an excellent alternative to produce highly deuterated metabolites with broad applications in metabolic studies. Our mass spectrometry experiments strongly indicate the successful incorporation of deuterium into organic compounds. Deuterated metabolites are useful tracers for metabolic studies, yet their wide utilization in research is limited by the multi-step total synthesis required to produce them in the laboratory. Alternatively, deuterated metabolites can be obtained from organisms grown in D₂O or deuterated nutrients. This approach also has limitations as D₂O in high concentrations negatively affects the survival of most organisms. Here we report the moss Funaria hygrometrica as an unusual high tolerant to D₂O in liquid culture. We found that this moss is able to grow in up to 90% D₂O, a condition lethal for many eukaryotes. Mass spectrometric analyses of F. hygrometrica extracts showed a strong deuteration pattern. The ability to tolerate high concentrations of D₂O together with the development of a rich molecular toolbox makes F. hygrometrica an ideal system for the production of valuable deuterated metabolites.

Stable isotope compounds - production, detection, and application

Stable isotopes are used in wide fields of application from natural tracers in biology, geology and archeology through studies of metabolic fluxes to their application as tracers in quantitative proteomics and structural biology. We review the use of stable isotopes of biogenic elements (H, C, N, O, S, Mg, Se) with the emphasis on hydrogen and its heavy isotope deuterium. We will discuss the limitations of enriching various compounds in stable isotopes when produced in living organisms. Finally, we overview methods for measuring stable isotopes, focusing on methods for detection in single cells in situ and their exploitation in modern biotechnologies.

Study of Methanol Extracts from Different Parts of Peganum harmala L. Using 1H-NMR Plant Metabolomics

A nuclear magnetic resonance- (NMR-) based metabolomics method was used to identify differential metabolites of methanol extracts obtained from six parts of Peganum harmala L. (P. harmala), namely, the root, stem, leaf, flower, testa, and seed. Two multivariate statistical analysis methods, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), were combined to clearly distinguish among the P. harmala samples from the six different parts. Eleven differential components were screened by the PLS-DA loading plot, and the relative contents were calculated by univariate analysis of variance. Chemometric results showed significant differences in the metabolites of the different parts of P. harmala. The seeds contained large amounts of harmaline, harmine, and vasicine compared to other organs. The acetic acid, proline, lysine, and sucrose contents of the roots were significantly higher than those of the other parts. In the testa, the vasicine, asparagine, choline, and 4-hydroxyisoleucine contents were clearly dominant. The obtained data revealed the distribution characteristics of the metabolomes of the different P. harmala parts and provided fundamental knowledge for the rational development of its medicinal parts.

Stable Isotope-Resolved Analysis with Quantitative Dissolution Dynamic Nuclear Polarization

Metabolite profiles and their isotopomer distributions can be studied noninvasively in complex mixtures with NMR. The advent of dissolution Dynamic Nuclear Polarization (dDNP) and isotope enrichment add sensitivity and resolution to such metabolic studies. Metabolic pathways and networks can be mapped and quantified if protocols that control and exploit the ex situ signal enhancement are created. We present a sample preparation method, including cell incubation, extraction and signal enhancement, to obtain reproducible and quantitative dDNP (qdDNP) NMR-based stable isotope-resolved analysis. We further illustrate how qdDNP was applied to gain metabolic insights into the phenotype of aggressive cancer cells.

Robust and transferable quantification of NMR spectral quality using IROC analysis

Non-Fourier methods are increasingly utilized in NMR spectroscopy because of their ability to handle nonuniformly-sampled data. However, non-Fourier methods present unique challenges due to their nonlinearity, which can produce nonrandom noise and render conventional metrics for spectral quality such as signal-to-noise ratio unreliable. The lack of robust and transferable metrics (i.e. applicable to methods exhibiting different nonlinearities) has hampered comparison of non-Fourier methods and nonuniform sampling schemes, preventing the identification of best practices. We describe a novel method, in situ receiver operating characteristic analysis (IROC), for characterizing spectral quality based on the Receiver Operating Characteristic curve. IROC utilizes synthetic signals added to empirical data as "ground truth", and provides several robust scalar-valued metrics for spectral quality. This approach avoids problems posed by nonlinear spectral estimates, and provides a versatile quantitative means of characterizing many aspects of spectral quality. We demonstrate applications to parameter optimization in Fourier and non-Fourier spectral estimation, critical comparison of different methods for spectrum analysis, and optimization of nonuniform sampling schemes. The approach will accelerate the discovery of optimal approaches to nonuniform sampling experiment design and non-Fourier spectrum analysis for multidimensional NMR.

NMR Metabolomics for Stem Cell type discrimination

Cell metabolism is a key determinant factor for the pluripotency and fate commitment of Stem Cells (SCs) during development, ageing, pathological onset and progression. We derived and cultured selected subpopulations of rodent fetal, postnatal, adult Neural SCs (NSCs) and postnatal glial progenitors, Olfactory Ensheathing Cells (OECs), respectively from the subventricular zone (SVZ) and the olfactory bulb (OB). Cell lysates were analyzed by proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy leading to metabolites identification and quantitation. Subsequent multivariate analysis of NMR data by Principal Component Analysis (PCA), and Partial Least Square Discriminant Analysis (PLS-DA) allowed data reduction and cluster analysis. This strategy ensures the definition of specific features in the metabolic content of phenotypically similar SCs sharing a common developmental origin. The metabolic fingerprints for selective metabolites or for the whole spectra demonstrated enhanced peculiarities among cell types. The key result of our work is a neat divergence between OECs and the remaining NSC cells. We also show that statistically significant differences for selective metabolites characterizes NSCs of different ages. Finally, the retrived metabolome in cell cultures correlates to the physiological SC features, thus allowing an integrated bioengineering approach for biologic fingerprints able to dissect the (neural) SC molecular specificities.

Plant metabolism as studied by NMR spectroscopy

The study of plant metabolism impacts a broad range of domains such as plant cultural practices, plant breeding, human or animal nutrition, phytochemistry and green biotechnologies. Plant metabolites are extremely diverse in terms of structure or compound families as well as concentrations. This review attempts to illustrate how NMR spectroscopy, with its broad variety of experimental approaches, has contributed widely to the study of plant primary or specialized metabolism in very diverse ways. The review presents recent developments of one-dimensional and multi-dimensional NMR methods to study various aspects of plant metabolism. Through recent examples, it highlights how NMR has proved to be an invaluable tool for the global characterization of sample composition within metabolomic studies, and shows some examples of use for targeted phytochemistry, with a special focus on compound identification and quantitation. In such cases, NMR approaches are often used to provide snapshots of the plant sample composition. The review also covers dynamic aspects of metabolism, with a description of NMR techniques to measure metabolic fluxes - in most cases after stable isotope labelling. It is mainly intended for NMR specialists who would be interested to learn more about the potential of their favourite technique in plant sciences and about specific details of NMR approaches in this field. Therefore, as a practical guide, a paragraph on the specific precautions that should be taken for sample preparation is also included. In addition, since the quality of NMR metabolic studies is highly dependent on approaches to data processing and data sharing, a specific part is dedicated to these aspects. The review concludes with perspectives on the emerging methods that could change significantly the role of NMR in the field of plant metabolism by boosting its sensitivity. The review is illustrated throughout with examples of studies selected to represent diverse applications of liquid-state or HR-MAS NMR.

Probing the metabolic phenotype of breast cancer cells by multiple tracer stable isotope resolved metabolomics

Breast cancers vary by their origin and specific set of genetic lesions, which gives rise to distinct phenotypes and differential response to targeted and untargeted chemotherapies. To explore the functional differences of different breast cell types, we performed Stable Isotope Resolved Metabolomics (SIRM) studies of one primary breast (HMEC) and three breast cancer cells (MCF-7, MDAMB-231, and ZR75-1) having distinct genotypes and growth characteristics, using 13C6-glucose, 13C-1+2-glucose, 13C5,15N2-Gln, 13C3-glycerol, and 13C8-octanoate as tracers. These tracers were designed to probe the central energy producing and anabolic pathways (glycolysis, pentose phosphate pathway, Krebs Cycle, glutaminolysis, nucleotide synthesis and lipid turnover). We found that glycolysis was not associated with the rate of breast cancer cell proliferation, glutaminolysis did not support lipid synthesis in primary breast or breast cancer cells, but was a major contributor to pyrimidine ring synthesis in all cell types; anaplerotic pyruvate carboxylation was activated in breast cancer versus primary cells. We also found that glucose metabolism in individual breast cancer cell lines differed between in vitro cultures and tumor xenografts, but not the metabolic distinctions between cell lines, which may reflect the influence of tumor architecture/microenvironment.

NMR-based metabolomics reveals the metabolite profiles of Vibrio parahaemolyticus under ferric iron stimulation

Vibrio parahaemolyticus is a halophilic bacterium endemic to coastal areas, and its pathogenicity has caused widespread seafood poisoning. In our previous research, the protein expression of V. parahaemolyticus in Fe³⁺ medium was determined using isobaric tags for relative and absolute quantitation (iTRAQ). Here, nuclear magnetic resonance (NMR) was used to detect changes in the V. parahaemolyticus metabolome. NMR spectra were obtained using methanol-water extracts of intracellular metabolites from V. parahaemolyticus under various culture conditions, and 62 metabolites were identified, including serine, arginine, alanine, ornithine, tryptophan, glutamine, malate, NAD⁺, NADP⁺, oxypurinol, xanthosine, dCTP, uracil, thymine, hypoxanthine, and betaine. Among these, 21 metabolites were up-regulated after the stimulation of the cells by ferric iron, and 9 metabolites were down-regulated. These metabolites are involved in amino acid and protein synthesis, energy metabolism, DNA and RNA synthesis and osmolality. Based on these results, we conclude that Fe³⁺ influences the metabolite profiles of V. parahaemolyticus.

Achieving High Levels of NMR-Hyperpolarization in Aqueous Media With Minimal Catalyst Contamination Using SABRE

Signal amplification by reversible exchange (SABRE) is shown to allow access to strongly enhanced ¹H NMR signals in a range of substrates in aqueous media. To achieve this outcome, phase‐transfer catalysis is exploited, which leads to less than 1.5×10⁻⁶ mol dm⁻³ of the iridium catalyst in the aqueous phase. These observations reflect a compelling route to produce a saline‐based hyperpolarized bolus in just a few seconds for subsequent in vivo MRI monitoring. The new process has been called catalyst separated hyperpolarization through signal amplification by reversible exchange or CASH‐SABRE. We illustrate this method for the substrates pyrazine, 5‐methylpyrimidine, 4,6‐d₂‐methyl nicotinate, 4,6‐d₂‐nicotinamide and pyridazine achieving ¹H signal gains of approximately 790‐, 340‐, 3000‐, 260‐ and 380‐fold per proton at 9.4 T at the time point at which phase separation is complete.

Exploring cancer metabolism using stable isotope-resolved metabolomics (SIRM)

Metabolic reprogramming is a hallmark of cancer. The changes in metabolism are adaptive to permit proliferation, survival, and eventually metastasis in a harsh environment. Stable isotope-resolved metabolomics (SIRM) is an approach that uses advanced approaches of NMR and mass spectrometry to analyze the fate of individual atoms from stable isotope-enriched precursors to products to deduce metabolic pathways and networks. The approach can be applied to a wide range of biological systems, including human subjects. This review focuses on the applications of SIRM to cancer metabolism and its use in understanding drug actions.

Integrative metabolomics as emerging tool to study autophagy regulation

Recent technological developments in metabolomics research have enabled in-depth characterization of complex metabolite mixtures in a wide range of biological, biomedical, environmental, agricultural, and nutritional research fields. Nuclear magnetic resonance spectroscopy and mass spectrometry are the two main platforms for performing metabolomics studies. Given their broad applicability and the systemic insight into metabolism that can be obtained it is not surprising that metabolomics becomes increasingly popular in basic biological research. In this review, we provide an overview on key metabolites, recent studies, and future opportunities for metabolomics in studying autophagy regulation. Metabolites play a pivotal role in autophagy regulation and are therefore key targets for autophagy research. Given the recent success of metabolomics, it can be expected that metabolomics approaches will contribute significantly to deciphering the complex regulatory mechanisms involved in autophagy in the near future and promote understanding of autophagy and autophagy-related diseases in living cells and organisms.