A framework for tracer-based metabolism in mammalian cells by NMR

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Metabolic Analysis of Intracellular Pathogenic Bacteria Using NMR

Pathogen proliferation and virulence depend on available nutrients, and these vary when the pathogen moves from outside of the host cell (extracellular) to the inside of the host cell (intracellular). Nuclear Magnetic Resonance (NMR) is a versatile analytical method, which lends itself for metabolic studies. In this chapter, we describe how 1H NMR can be combined with a cellular infection model to study the metabolic crosstalk between a bacterial pathogen and its host both in the extracellular and intracellular compartments. Central carbon metabolism is highlighted by using glucose labeled with the stable isotope 13C.

Low-field NMR with multilayer Halbach magnet and NMR selective excitation

This study introduces a low-field NMR spectrometer (LF-NMR) featuring a multilayer Halbach magnet supported by a combined mechanical and electrical shimming system. This setup offers improved field homogeneity and sensitivity compared to spectrometers relying on typical Halbach and dipole magnets. The multilayer Halbach magnet was designed and assembled using three nested cylindrical magnets, with an additional inner Halbach layer that can be rotated for mechanical shimming. The coils and shim-kernel of the electrical shimming system were constructed and coated with layers of zirconia, thermal epoxy, and silver-paste resin to facilitate passive heat dissipation and ensure mechanical and thermal stability. Furthermore, the 7-channel shim coils were divided into two parts connected in parallel, resulting in a reduction of joule heating temperatures from 96.2 to 32.6 °C. Without the shimming system, the Halbach magnet exhibits a field inhomogeneity of approximately 140 ppm over the sample volume. The probehead was designed to incorporate a solenoidal mini coil, integrated into a single planar board. This design choice aimed to enhance sensitivity, minimize [Formula: see text] inhomogeneity, and reduce impedance discrepancies, transmission loss, and signal reflections. Consequently, the resulting linewidth of water within a 3 mm length and 2.4 mm inner diameter sample volume was 4.5 Hz. To demonstrate the effectiveness of spectral editing in LF-NMR applications at 29.934 MHz, we selectively excited hydroxyl and/or methyl protons in neat acetic acid using optimal control pulses calculated through the Krotov algorithm.

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.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Tracer-Based Metabolic Analysis by NMR in Intact Perfused Human Liver Tissue

Human metabolic liver disease is dramatically increasing globally and presents an urgent clinical unmet need. Rodent models of non-alcoholic fatty liver disease (NAFLD) are available, but they fail to fully recreate the metabolic and cellular features of human disease. Thus, it is imperative to understand the metabolic interplay in human cells in the context of disease. We have applied nuclear magnetic resonance (NMR) spectroscopy approaches to enable the detection of numerous metabolites in human cells and within intact tissue in a single measurement. In this chapter, we describe the challenges of using isolated human hepatocytes vs perfused human liver tissue for metabolic tracer experiments and how experimental parameters can be refined to interrogate signals from intact tissue and cells.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Isolated murine skeletal muscles utilize pyruvate over glucose for oxidation

INTRODUCTION

Fuel sources for skeletal muscle tissue include carbohydrates and fatty acids, and utilization depends upon fiber type, workload, and substrate availability. The use of isotopically labeled substrate tracers combined with nuclear magnetic resonance (NMR) enables a deeper examination of not only utilization of substrates by a given tissue, but also their contribution to tricarboxylic acid (TCA) cycle intermediates.

OBJECTIVES

The goal of this study was to determine the differential utilization of substrates in isolated murine skeletal muscle, and to evaluate how isopotomer anlaysis provided insight into skeletal muscle metabolism.

METHODS

Isolated C57BL/6 mouse hind limb muscles were incubated in oxygenated solution containing uniformly labeled 13C6 glucose, 13C3 pyruvate, or 13C2 acetate at room temperature. Isotopomer analysis of 13C labeled glutamate was performed on pooled extracts of isolated soleus and extensor digitorum longus (EDL) muscles.

RESULTS

Pyruvate and acetate were more avidly consumed than glucose with resultant increases in glutamate labeling in both muscle groups. Glucose incubation resulted in glutamate labeling, but with high anaplerotic flux in contrast to the labeling by pyruvate. Muscle fiber type distinctions were evident by differences in lactate enrichment and extent of substrate oxidation.

CONCLUSION

Isotope tracing experiments in isolated muscles reveal that pyruvate and acetate are avidly oxidized by isolated soleus and EDL muscles, whereas glucose labeling of glutamate is accompanied by high anaplerotic flux. We believe our results may set the stage for future examination of metabolic signatures of skeletal muscles from pre-clinical models of aging, type-2 diabetes and neuromuscular disease.

Crosstalk between AML and stromal cells triggers acetate secretion through the metabolic rewiring of stromal cells

Acute myeloid leukaemia (AML) cells interact and modulate components of their surrounding microenvironment into their own benefit. Stromal cells have been shown to support AML survival and progression through various mechanisms. Nonetheless, whether AML cells could establish beneficial metabolic interactions with stromal cells is underexplored. By using a combination of human AML cell lines and AML patient samples together with mouse stromal cells and a MLL-AF9 mouse model, here we identify a novel metabolic crosstalk between AML and stromal cells where AML cells prompt stromal cells to secrete acetate for their own consumption to feed the tricarboxylic acid cycle (TCA) and lipid biosynthesis. By performing transcriptome analysis and tracer-based metabolic NMR analysis, we observe that stromal cells present a higher rate of glycolysis when co-cultured with AML cells. We also find that acetate in stromal cells is derived from pyruvate via chemical conversion under the influence of reactive oxygen species (ROS) following ROS transfer from AML to stromal cells via gap junctions. Overall, we present a unique metabolic communication between AML and stromal cells and propose two different molecular targets, ACSS2 and gap junctions, that could potentially be exploited for adjuvant therapy.

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.

Multidimensional engineered metasurface for ultrafast terahertz switching at frequency-agile channels

The ability to actively manipulate free-space optical signals by using tunable metasurfaces is extremely appealing for many device applications. However, integrating photoactive semiconductors into terahertz metamaterials still suffers from a limited functionality. The ultrafast switching in picosecond timescale can only be operated at a single frequency channel. In the hybrid metasurface proposed here, we experimentally demonstrate a dual-optically tunable metaphotonic device for ultrafast terahertz switching at frequency-agile channels. Picosecond ultrafast photoswitching with a 100% modulation depth is realized at a controllable operational frequency of either 0.55 THz or 0.86 THz. The broadband frequency agility and ultrafast amplitude modulation are independently controlled by continuous wave light and femtosecond laser pulse, respectively. The frequency-selective, temporally tunable, and multidimensionally-driven features can empower active metamaterials in advanced multiplexing of information, dual-channel wireless communication, and several other related fields.

Untargeted 2D NMR Metabolomics of [13C-methyl]Methionine-Labeled Tumor Models Reveals the Non-DNA Methylome and Provides Clues to Methyl Metabolism Shift during Tumor Progression

For more than a decade, DNA and histone methylations have been the focus of extensive work, although their relationship with methyl group metabolism was overlooked. Recently, it has emerged that epigenetic methylations are influenced by methyl donor nutrient availability, cellular levels of S-adenosyl-methionine (SAM), and cytoplasmic methyltransferase activities. SAM-dependent methyltransferases methylate a wide range of targets, from small molecules to proteins and nucleic acids. However, few investigations of the global methylome of tumors have been performed. Here, untargeted NMR metabolomics of two mouse tumor models labeled with [¹³C-methyl]methionine were used to search for the NMR-visible set of cellular methyl acceptors denoted the global methylome. Tumor models were B16 melanoma cell cultures and B16 melanoma tumors, which may be considered as two stages of B16 tumor development. Based on 2D ¹H-¹³C NMR spectra and orthogonal partial least squares discriminant analysis of spectra, our study revealed markedly different global methylomes for melanoma models. The methylome of B16 melanoma cell cultures was dominated by histone methylations, whereas that of B16 melanoma tumors was dominated by cytoplasmic small-molecule methylations. Overall, the technique gave access to the non-DNA methylome. Comparison of tumor models also exhibiting differential expression of aerobic glycolysis provided clues to a methyl metabolism shift during tumor progression.

A Metabolomic Investigation of Eugenol on Colorectal Cancer Cell Line HT-29 by Modifying the Expression of APC, p53, and KRAS Genes

Colorectal cancer is one of the most lethal cancers with a high mortality rate. Chemotherapy results in drug resistance in some cases; hence, herbal medicines are sometimes used in adjunct with it. Eugenol has been reported to have anti-inflammatory, antioxidant, and anticancer properties. Metabolomics is a study of metabolic changes within an organism using high-throughput technology. The purpose of this research was to investigate the anticancer effects of eugenol and variations in p53, KRAS, and APC gene expression and metabolic changes associated with the abovementioned gene expressions using ¹HNMR spectroscopy. The MTT method was used to determine cell viability and its IC50 detected. After treating HT-29 cells with IC50 concentration of eugenol, RNA was extracted and cDNA was obtained from them and the expression of p53, KRAS, and APC genes was measured using the qRT-PCR technique. Metabolites were extracted using the chloroform-ethanol method, lyophilized, and sent for ¹HNMR spectroscopy using the 1D-NOESY protocol. Chemometrics analysis such as PLS-DA was performed, and differentiated metabolites were identified using the Human Metabolome Database. Integrated metabolic analysis using the metabolites and gene expression was performed by the MetaboAnalyst website. The observed IC50 for eugenol was 500 μM, and the relative expression of APC and p53 genes in the treated cells increased compared to the control group, and the expression of KRAS oncogene gene decreased significantly. The crucial changes in convergent metabolic phenotype with genes were identified. The results indicate that eugenol exhibits its antitumor properties by targeting a specific biochemical pathway in the cell's metabolome profile due to changes in genes involved in colon cancer.

Targeting Asparagine and Serine Metabolism in Germinal Centre-Derived B Cells Non-Hodgkin Lymphomas (B-NHL)

BL and DLBCL are subtypes of B-cell lymphomas that arise from germinal centre B lymphocytes. Differentiation between BL and DLBCL is critical and can be challenging, as these two types of cancer share the same morphological, immunophenotypic, and genetic characteristics. In this study, we have examined metabolism in BL and DLBCL lymphomas and found distinctive differences in serine metabolism. We show that BL cells consume significantly more extracellular asparagine than DLBCL cells. Using a tracer-based approach, we find that asparagine regulates the serine uptake and serine synthesis in BL and DLBCL cells. Calculation of Differentially Expressed Genes (DEGs) from RNAseq datasets of BL and DLBCL patients show that BL cancers express the genes involved in serine synthesis at a higher level than DLBCL. Remarkably, combined use of an inhibitor of serine biosynthesis pathway and an anticancer drug asparaginase increases the sensitivity of BL cells to extracellular asparagine deprivation without inducing a change in the sensitivity of DLBCL cells to asparaginase. In summary, our study unravels metabolic differences between BL and DLBCL with diagnostic potential which may also open new avenues for treatment.

Aspergillus fumigatus Acetate Utilization Impacts Virulence Traits and Pathogenicity

Aspergillus fumigatus is a major opportunistic fungal pathogen of immunocompromised and immunocompetent hosts. To successfully establish an infection, A. fumigatus needs to use host carbon sources, such as acetate, present in the body fluids and peripheral tissues. However, utilization of acetate as a carbon source by fungi in the context of infection has not been investigated. This work shows that acetate is metabolized via different pathways in A. fumigatus and that acetate utilization is under the regulatory control of a transcription factor (TF), FacB. A. fumigatus acetate utilization is subject to carbon catabolite repression (CCR), although this is only partially dependent on the TF and main regulator of CCR CreA. The available extracellular carbon source, in this case glucose and acetate, significantly affected A. fumigatus virulence traits such as secondary metabolite secretion and cell wall composition, with the latter having consequences for resistance to oxidative stress, antifungal drugs, and human neutrophil-mediated killing. Furthermore, deletion of facB significantly impaired the in vivo virulence of A. fumigatus in both insect and mammalian models of invasive aspergillosis. This is the first report on acetate utilization in A. fumigatus, and this work further highlights the importance of available host-specific carbon sources in shaping fungal virulence traits and subsequent disease outcome, and a potential target for the development of antifungal strategies. IMPORTANCE Aspergillus fumigatus is an opportunistic fungal pathogen in humans. During infection, A. fumigatus is predicted to use host carbon sources, such as acetate, present in body fluids and peripheral tissues, to sustain growth and promote colonization and invasion. This work shows that A. fumigatus metabolizes acetate via different pathways, a process that is dependent on the transcription factor FacB. Furthermore, the type and concentration of the extracellular available carbon source were determined to shape A. fumigatus virulence determinants such as secondary metabolite secretion and cell wall composition. Subsequently, interactions with immune cells are altered in a carbon source-specific manner. FacB is required for A. fumigatus in vivo virulence in both insect and mammalian models of invasive aspergillosis. This is the first report that characterizes acetate utilization in A. fumigatus and highlights the importance of available host-specific carbon sources in shaping virulence traits and potentially subsequent disease outcome.

Alcoholic hepatitis and metabolic disturbance in female mice: a more tractable model than Nrf2-/- animals

Alcoholic hepatitis (AH) is the dramatic acute presentation of alcoholic liver disease, with a 15% mortality rate within 28 days in severe cases. Research into AH has been hampered by the lack of effective and reproducible murine models that can be operated under different regulatory frameworks internationally. The liquid Lieber-deCarli (LdC) diet has been used as a means of ad libitum delivery of alcohol but without any additional insult, and is associated with relatively mild liver injury. The transcription factor nuclear factor-erythroid 2-related factor 2 (Nrf2) protects against oxidative stress, and mice deficient in this molecule are suggested to be more sensitive to alcohol-induced injury. We have established a novel model of AH in mice and compared the nature of liver injury in C57/BL6 wild-type (WT) versus Nrf2-/- mice. Our data showed that both WT and Nrf2-/- mice demonstrate robust weight loss, and an increase in serum transaminase, steatosis and hepatic inflammation when exposed to diet and ethanol. This is accompanied by an increase in peripheral blood and hepatic myeloid cell populations, fibrogenic response and compensatory hepatocyte regeneration. We also noted characteristic disturbances in hepatic carbohydrate and lipid metabolism. Importantly, use of Nrf2-/- mice did not increase hepatic injury responses in our hands, and female WT mice exhibited a more-reproducible response. Thus, we have demonstrated that this simple murine model of AH can be used to induce an injury that recreates many of the key human features of AH - without the need for challenging surgical procedures to administer ethanol. This will be valuable for understanding of the pathogenesis of AH, for testing new therapeutic treatments or devising metabolic approaches to manage patients whilst in medical care.This article has an associated First Person interview with the joint first authors of the paper.

Ketohexokinase inhibition improves NASH by reducing fructose-induced steatosis and fibrogenesis

Background & Aims

Increasing evidence highlights dietary fructose as a major driver of non-alcoholic fatty liver disease (NAFLD) pathogenesis, the majority of which is cleared on first pass through the hepatic circulation by enzymatic phosphorylation to fructose-1-phosphate via the ketohexokinase (KHK) enzyme. Without a current approved therapy, disease management emphasises lifestyle interventions, but few patients adhere to such strategies. New targeted therapies are urgently required.

Methods

We have used a unique combination of human liver specimens, a murine dietary model of NAFLD and human multicellular co-culture systems to understand the hepatocellular consequences of fructose administration. We have also performed a detailed nuclear magnetic resonance-based metabolic tracing of the fate of isotopically labelled fructose upon administration to the human liver.

Results

Expression of KHK isoforms is found in multiple human hepatic cell types, although hepatocyte expression predominates. KHK knockout mice show a reduction in serum transaminase, reduced steatosis and altered fibrogenic response on an Amylin diet. Human co-cultures exposed to fructose exhibit steatosis and activation of lipogenic and fibrogenic gene expression, which were reduced by pharmacological inhibition of KHK activity. Analysis of human livers exposed to ¹³C-labelled fructose confirmed that steatosis, and associated effects, resulted from the accumulation of lipogenic precursors (such as glycerol) and enhanced glycolytic activity. All of these were dose-dependently reduced by administration of a KHK inhibitor.

Conclusions

We have provided preclinical evidence using human livers to support the use of KHK inhibition to improve steatosis, fibrosis, and inflammation in the context of NAFLD.

Lay summary

We have used a mouse model, human cells, and liver tissue to test how exposure to fructose can cause the liver to store excess fat and become damaged and scarred. We have then inhibited a key enzyme within the liver that is responsible for fructose metabolism. Our findings show that inhibition of fructose metabolism reduces liver injury and fibrosis in mouse and human livers and thus this may represent a potential route for treating patients with fatty liver disease in the future.

Power of Pure Shift HαCα Correlations: A Way to Characterize Biomolecules under Physiological Conditions

Intrinsically disordered proteins (IDPs) constitute an important class of biomolecules with high flexibility. Atomic-resolution studies for these molecules are essentially limited to NMR spectroscopy, which should be performed under physiological pH and temperature to populate relevant conformational ensembles. In this context, however, fundamental problems arise with established triple resonance NMR experiments: high solvent accessibility of IDPs promotes water exchange, which disfavors classical amide ¹H-detection, while ¹³C-detection suffers from significantly reduced sensitivity. A favorable alternative, the conventional detection of nonexchangeable ¹Hα, so far resulted in broad signals with insufficient resolution and sensitivity. To overcome this, we introduce here a selective Hα,Cα-correlating pure shift detection scheme, the selective Hα,Cα-HSQC (SHACA-HSQC), using extensive hetero- and homonuclear decoupling applicable to aqueous samples (≥90% H₂O) and tested on small molecules and proteins. SHACA-HSQC spectra acquired on IDPs provide uncompromised resolution and sensitivity (up to fivefold increased S/N compared to the standard ¹H,¹³C-HSQC), as shown for resonance distinction and unambiguous assignment on the disordered transactivation domain of the tumor suppressor p53, α-synuclein, and folded ubiquitin. The detection scheme can be implemented in any ¹Hα-detected triple resonance experiment and may also form the basis for the detection of isotope-labeled markers in biological studies or compound libraries.

Discriminating aspects of global metabolism of neonatal cardiomyocytes from wild type and KO-CSRP3 rats using proton magnetic resonance spectroscopy of culture media samples

Knockout of multifunction gene cysteine- and glycine-rich protein 3 (CSRP3) in cardiomyocytes (CMs) of mice leads to heart dilation, severely affecting its functions. In humans, CSRP3 mutations are associated with hypertrophic (HCM) and dilated cardiomyopathy (DCM). The absence of the CSRP3 expression produces unknown effects on in vitro neonatal CMs' metabolism. The metabolome changes in culture media conditioned by CSRP3 knockout (KO-CSRP3), and wild type (WT) neonatal cardiomyocytes were investigated under untreated or after metabolic challenging conditions produced by isoproterenol (ISO) stimulation, by in vitro high-resolution proton magnetic resonance spectroscopy (¹H-MRS)-based metabolomics. Metabolic differences between neonatal KO-CSRP3 and WT rats' CMs were identified. After 72 h of culture, ISO administration was associated with increased CMs' energy requirements and increased levels of threonine, alanine, and 3-hydroxybutyrate in both neonatal KO-CSRP3 and WT CMs conditioned media. When compared with KO-CSRP3, culture media derived from WT cells presented higher lactate concentrations either under basal or ISO-stimulated conditions. The higher activity of ketogenic biochemical pathways met the elevated energy requirements of the contractile cells. Both cells are considered phenotypically indistinguishable in the neonatal period of animal lives, but the observed metabolic stress responses of KO-CSRP3 and WT CMs to ISO were different. KO-CSRP3 CMs produced less lactate than WT CMs in both basal and stimulated conditions. Mainly, ISO-stimulated conditions produced evidence for lactate overload within KO-CSRP3 CMs, while WT CMs succeeded to manage the metabolic stress. Thus, ¹H-MRS-based metabolomics was suitable to identify early inefficient energetic metabolism in neonatal KO-CSRP3 CMs. These results may reflect an apparent lower lactate transport and consumption, in association with protein catabolism.

Stable isotope resolved metabolomics classification of prostate cancer cells using hyperpolarized NMR data

Metabolic fingerprinting is a strong tool for characterization of biological phenotypes. Classification with machine learning is a critical component in the discrimination of molecular determinants. Cellular activity can be traced using stable isotope labelling of metabolites from which information on cellular pathways may be obtained. Nuclear magnetic resonance (NMR) spectroscopy is, due to its ability to trace labelling in specific atom positions, a method of choice for such metabolic activity measurements. In this study, we used hyperpolarization in the form of dissolution Dynamic Nuclear Polarization (dDNP) NMR to measure signal enhanced isotope labelled metabolites reporting on pathway activity from four different prostate cancer cell lines. The spectra have a high signal-to-noise, with less than 30 signals reporting on 10 metabolic reactions. This allows easy extraction and straightforward interpretation of spectral data. Four metabolite signals selected using a Random Forest algorithm allowed a classification with Support Vector Machines between aggressive and indolent cancer cells with 96.9% accuracy, -corresponding to 31 out of 32 samples. This demonstrates that the information contained in the few features measured with dDNP NMR, is sufficient and robust for performing binary classification based on the metabolic activity of cultured prostate cancer cells.

Real-Time NMR Spectroscopy for Studying Metabolism

Current metabolomics approaches utilize cellular metabolite extracts, are destructive, and require high cell numbers. We introduce here an approach that enables the monitoring of cellular metabolism at lower cell numbers by observing the consumption/production of different metabolites over several kinetic data points of up to 48 hours. Our approach does not influence cellular viability, as we optimized the cellular matrix in comparison to other materials used in a variety of in-cell NMR spectroscopy experiments. We are able to monitor real-time metabolism of primary patient cells, which are extremely sensitive to external stress. Measurements are set up in an interleaved manner with short acquisition times (approximately 7 minutes per sample), which allows the monitoring of up to 15 patient samples simultaneously. Further, we implemented our approach for performing tracer-based assays. Our approach will be important not only in the metabolomics fields, but also in individualized diagnostics.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

Quantitative Isotopomer Rates in Real-Time Metabolism of Cells Determined by NMR Methods

Tracer-based metabolism is becoming increasingly important for studying metabolic mechanisms in cells. NMR spectroscopy offers several approaches to measure label incorporation in metabolites, including ¹³ C- and ¹ H-detected spectra. The latter are generally more sensitive, but quantification depends on the proton-carbon ¹ J_CH coupling constant, which varies significantly between different metabolites. It is therefore not possible to have one experiment optimised for all metabolites, and quantification of ¹ H-edited spectra such as HSQCs requires precise knowledge of coupling constants. Increasing interest in tracer-based and metabolic flux analysis requires robust analyses with reasonably small acquisition times. Herein, we compare ¹³ C-filtered and ¹³ C-edited methods for quantification and show the applicability of the methods for real-time NMR spectroscopy of cancer-cell metabolism, in which label incorporations are subject to constant flux. We find an approach using a double filter to be most suitable and sufficiently robust to reliably obtain ¹³ C incorporations from difference spectra. This is demonstrated for JJN3 multiple myeloma cells processing glucose over 24 h. The proposed method is equally well suited for calculating the level of label incorporation in labelled cell extracts in the context of metabolic flux analysis.