Nuclear magnetic resonance in metabolomics

To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. CHAPTER Nuclear magnetic resonance in metabolomics c0005 5 Abdul-Hamid Emwas1, Kacper Szczepski2, Benjamin Gabriel Poulson2, Ryan McKay3, Leonardo Tenori4, Edoardo Saccenti5, Joanna Lachowicz6 and Mariusz Jaremko2 1 Core Labs, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 2 Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 3 Department of Chemistry, University of Alberta, Edmonton, AB, Canada 4 Department of Chemistry and Magnetic Resonance Center (CERM), University of Florence, Florence, Italy 5 Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands 6 Department of Medical Sciences and Public Health, Università di Cagliari, Cittadella Universitaria, Monserrato, Italy s0010 p0010 Introduction Nuclear magnetic resonance (NMR) spectroscopy is a versatile analytical tool that has been used for decades to identify, quantitate, and structurally elucidate molecules. Traditionally, as a requirement of any chemistry department, NMR branched out into biophysical problems in the late 80s and early 90s with peptide/ protein structure/function studies (Alsiary et al., 2020; Cavanagh et al., 1995; Chu et al., 2010; Marion, 2013; Wüthrich, 1986). NMR has several unique advantages over related methods, including nondestructive and cumulative scanning, high reproducibility, and being a nonbiased method. NMR spectroscopy can examine a molecule at its atomic level, providing a potent tool to distinguish composition a priori, kinetics, energetics, and otherwise difficult to elucidate structural isomers (Dhahri et al., 2020). In contrast to other analytical tools commonly used in metabolomics studies such as GC
MS (gas chromatography
mass spectrometry) (Emwas, Al-Talla et al., 2015; Zhang et al., 2017) and liquid chromatography (LC)
MS (Raji et al., 2013; Wu et al., 2021; Zhang et al., 2017), NMR does not require structural and/or chemical manipulation, nor extra steps for sample preparation or metabolite isolation prior to measurement such as ionization and chemical derivatization (Emwas, 2015; Emwas, Salek et al., 2013). NMR is inherently quantitative as each signal intensity is directly proportional to the atomic concentration of the originating resonance in the mixture or sample (Emwas et al., Metabolomics Perspectives. DOI: https://doi.org/10.1016/B978-0-323-85062-9.00005-2 © 2022 Elsevier Inc. All rights reserved. Troisi-MP-1633011 978-0-323-85062-9 151 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 152 p0015 p0020 p0025 p0030 CHAPTER 5 Nuclear magnetic resonance in metabolomics 2016). Beside its major advantages (i.e., nondestructive and minimal sample preparation requirements), 1D-NMR, especially 1H NMR, is a relatively fast method with a single sample acquisition taking typically seconds to minutes, therefore hundreds of samples can be analyzed in a single day. It has been our experience that careful, consistent sample thawing and preparation is the practical bottle neck, not the instrument access time. The inclusion of cryogenically cooled NMR probes and the related practical signal to noise increases of 3
4-fold (corresponding to a 9
16-fold decrease in experiment time) have made NMR data acquisition even faster. Thus NMR is particularly useful in comparative studies that could involve a high number of samples, for example, hepatitis studies involving hundreds of thousands of volunteers donating multiple biofluid samples on a daily basis (Duarte et al., 2014; Sarfaraz et al., 2016; Wang et al., 2014; Zheng, Chen et al., 2017). NMR applications are not limited to samples in the liquid state; solid-state NMR spectroscopy is also a well-established field (Alahmari et al., 2018; Apperley et al., 2012; Ashbrook et al., 2018; Chisca et al., 2015; Mroue et al., 2010; Renault et al., 2010; Separovic & Sani, 2020). Recently, tissue samples including human, animal, plant and marine tissues have been studied using HRMAS (magic angle spinning) NMR approaches (Bunescu et al., 2010; Heude et al., 2015; Kaebisch et al., 2017; Rocha et al., 2010; Taglienti et al., 2020). One of the most important advantages of NMR-based metabolomics is that liquid samples can be detected in mild/neutral conditions without the need for sample alterations such as chemical modification or cleavage, pressure/vacuum, or high-temperature conditions, and with only minimal preparation steps (e.g., typically an addition of a small amount of deuterated “lock” solvent and an internal reference standard with or without buffering capabilities) (Abdul Jameel et al., 2021; Harris et al., 2007; Kijewska et al., 2021; Markley et al., 1998; Mercier et al., 2011; Sheedy et al., 2010). The inclusion of multidimensional NMR experiments, typically utilized to provide atomic 3D coordination/neighbor/distance information on each atom, can provide additional powerful insights (albeit at the cost of extra instrument time). Taking advantage of the fact that NMR measurements are nondestructive, one can record (and re-record) many NMR experiments on the same sample over different periods of time, thus providing an influential platform for enhanced signal to noise (i.e., adding experimental time together) and/or kinetic studies where the spectra are recorded during the course of a reaction period. This provides “real time” measurement and monitoring of sample status (e.g., stability), chemical reactions, and molecule interactions (e.g., protein/ligand binding), to name a few. As stated, one of the most important advantages of NMR-based metabolomics is that samples can be detected in neutral or mild conditions usually without the need for mechanical manipulation (e.g., sonication, heating/cooling, chemical reaction/modification). This particular advantage allows NMR-based metabolomics studies to monitor metabolic flux for some micro-organisms, such as Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy bacteria and cell lines, and the corresponding metabolite, leading to a new metabolomics field called fluxomics (Winter & Krömer, 2013). s0015 p0035 s0020 p0040 p0045 p0050 p0055 s0025 p0060 Nuclear magnetic resonance spectroscopy NMR spectra can selectively observe isotopes of spin active (Keeler, 2011) single atoms, that is, 1D NMR experiments with the most common being 1H, 13C, 31P and 15N NMR. Popularity is due to natural abundance and magnetic susceptibility increasing signal intensity. Moreover, inductive magnetic correlation between two types of atoms can be routinely investigated through multidimensional experiments, for example, 2D NMR. While requiring much more instrument time [despite recent time-saving advances (Aljuhani et al., 2019; Cui, Zhu et al., 2019; Guennec et al., 2014; Qiu et al., 2019)], multidimensional experiments can resolve spectral overlap and/or atomic ambiguity (Cui, Zhu et al., 2019; Féraud et al., 2020; Le Guennec et al., 2015; Mattar et al., 2004). In this section, the most common metabolomics-relevant 1D NMR as well as 2D NMR experiments will be reviewed. 1D nuclear magnetic resonance 1D NMR is concerned with determining the resonant frequencies of a single type of NMR-active nucleus (i.e., 1H, 13C, 15N, etc.) that depend on the chemical environment around the nuclei. In short, 1D NMR has properties that enable researchers to determine: 1.the type of molecule involved (i.e., aromatics, aliphatics, amino acids, etc.) in an experiment; 2.how the atoms are connected to each other. These two properties of 1D NMR form the backbone for metabolomics research, and we proceed to describe the use and application of 1D NMR experiments to metabolomics according to the most common types of nuclei involved in metabolomics studies. The nuclei discussed (in order) are 1H, 13C, 15N, 31P, and 19 F. 1D 1H nuclear magnetic resonance spectroscopy Simple one-dimensional 1H NMR spectroscopy is the most common approach in metabolomics studies and consists of an excitation “pulse” (e.g., micro-seconds) with an acquisition period (seconds). If the experiment is to be repeated (e.g., to improve signal to noise), then an interscan delay (seconds) is required to reestablish equilibrium prior to the subsequent scan. The short experimental time (e.g., a few minutes) with minimal sample preparation offers a high-throughput method appropriate for metabolomics studies seeking statistical confirmation. Troisi-MP-1633011 978-0-323-85062-9 00005 153 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 154 CHAPTER 5 Nuclear magnetic resonance in metabolomics Studies in metabolomics generally fall into one of three categories (Sahoo et al., 2020): o0010 o0015 o0020 p0080 p0085 s0030 p0090 1. metabolic profiling 2. metabolic fingerprinting 3. metabonomics, that is, a quantitative study over time of the metabolic response to stimuli (Holmes et al., 2008) It is important to clarify our definition of metabonomics as the literature has been somewhat confusing, with “metabolomics” and “metabonomics” often used interchangeably and/or for slightly different interpretations. Regardless, NMR spectroscopy can easily be applied to metabolomic studies falling into any one of these three categories due to the ease of application (Fan & Lane, 2016; Kanwal et al., 2020), versatility (Agrawal, 2020), reproducibility, and important nondestructive nature (Viola et al., 2006) allowing a sample to be studied and analyzed numerous times. Sample stability becomes the limiting factor (Sykes, 2007). NMR spectroscopy (often advantageously coupled with MS) remains a method of choice, even when considering the benefits that the sole use of MS may provide. Indeed, NMR and MS are often combined in metabolomics studies (Abd Ghafar et al., 2020; Laserna et al., 2020; Nizioł et al., 2020; Tayyari et al., 2013; Vassilev et al., 2020) for their mutually complementary benefits in both identifying and analyzing metabolites (Bhinderwala, Wase et al., 2018). However, in this book chapter we will focus on the role of NMR in metabolomics and we will discuss the roles of NMR-detectable nuclei (1D and 2D experiments) in the field of Metabolomics. Strengths and weaknesses of some nuclei for 1D NMR spectroscopy are listed in Table 5.1. Examples of each of the nuclei and their respective metabolomics studies are listed in Table 5.2. 1 H 1D nuclear magnetic resonance in metabolomic studies A basic 1D 1H NMR experiment (i.e., delay-solvent suppression-excitation pulse-acquire) is by far the most commonly used NMR experiment in metabolomics (Chandra et al., 2021). This type of experiment can be applied to any NMR active nuclei (Gallo et al., 2019; Zhang et al., 2020). The 1H nucleus has the highest relative receptivity (Sanders & Hunter, 1993) (aside from the 3H nucleus, which has an extremely low natural abundance therefore making absolute receptivity impractical) and the highest natural abundance at 99.99%. These properties make obtaining a 1H NMR spectrum of any liquid sample (e.g., urine, blood plasma) relatively rapid, and simple to acquire, process, and analyze. A typical 1D 1H NMR experiment usually takes only a few minutes with less frequent cases taking hours. Hydrogen atoms in the spectrometer will induce resonances (i.e., peaks) at distinct frequencies based on their different magnetic/chemical environments (e.g., aliphatic, aromatic, hydrophilic, distance to functional groups, exchangeable/exposed neighbors, etc.). Furthermore, the signal intensity (integrated area) is directly proportional to the quantity of nuclei in a molecule, and Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. t0010 Table 5.1 Strengths and weaknesses of chosen nuclei for 1D NMR spectroscopy metabolomics. Nuclei Strengths Weaknesses 1 Rapid acquisition Narrow chemical shift window (0
10 ppm) Low resolution may result in spectral overlap H Ability to identify many (approximately 50
200) metabolites (with or without the aid of software/databases) Signal intensity is directly proportional to metabolite concentration, and the number of nuclei in the sample Ease of analysis (if there is sufficient resolution and resolving power) 13 15 C N Well-defined peaks (narrow line widths) Ideal for untargeted analysis Broad chemical shift dispersion (B200 ppm) High resolution, less spectral overlap More stable to pH and other sample conditions [58] Minimal amount of homonuclear coupling (i.e., 13C-13C) at natural abundance (B1.1%) Ideal metabolic tracer [59
61] Directly measures the backbone structures of metabolites [62] Broad chemical shift dispersion (B100 ppm) Present in a number of important metabolites Expands the coverage of the metabolome [63] 31 P Highly abundant (nearly 100%) Relatively high sensitivity Powerful metabolic imaging tool 19 F High natural abundance (100%) High sensitivity Troisi-MP-1633011 Solvent suppression may be needed, and may obfuscate metabolite signal May not provide sufficient information to determine complete atom connectivity Low natural abundance (B1.1%) Low sensitivity Experiment may take several hours Long relaxation delays (on the NMR timescale) Low natural abundance (0.37%) Low sensitivity Long experimental time More sample preparation required for biological samples (i.e., proteins) Small chemical shift window Peak overlap Very sensitive to environmental conditions (pH, T, solvent, etc.) Not many 31P reference spectra available 31 P containing compounds are not very stable Structurally similar to other metabolites, making compound identification difficult Not many reference spectra currently available Not many metabolites contain 19F 978-0-323-85062-9 00005 Table 5.2 Applications of 1D NMR techniques in metabolomics. Nucleus Purpose(s) of Study NMR Methods Results Paper 1 To determine the metabolic differences between three Quinoa Ecotypes found in Ecuador before and after treatment by either washing, cooking, and/or germination 1 Regardless of the Quinoa ecotype, germination (as opposed to washing or cooking treatments) caused the greatest increase of metabolites Lalaleo et al. (2020) 1. To identify potential biomarkers for aflatoxin B1 ingestion in dairy cows 2. 2. To evaluate the effect of adding clay and/ or yeast fermentation products on identified biomarkers 1 Ogunade and Jiang (2019) To identify and monitor metabolites specific to breast cancer patients 1 To monitor the effects of treatment with VSL #3 on children with non-alcoholic fatty liver disease (NAFLD) in order to identify non-invasive biomarkers 1 To evaluate the effects of Algerian date (Deglet) seeds on the metabolome of LPS-IFN-γ-induced RAW 264.7 cells 1 To determine a (more) complete biochemical signature of autism spectrum disorders (ASD) 1 Of 15 total metabolites (acetic acid, 12 amino acids, mannose, and ethanol) identified through biomarker analysis, ethanol was most influenced by the study's conditions (control, toxin, toxin with clay, and control with yeast fermentation product), and was therefore chosen as the candidate biomarker of aflatoxin B1 ingestion in dairy cows that had not ingested a sequestering agent 36 metabolites in both groups were identified, with creatine, glycine, serine, dimethylamine, trimethylamine N-oxide, α-hydroxyisobutyrate, mannitol, glutamine, cis-aconitate, and trigonelline showing the highest levels of sensitivity and specificity for differentiating breast cancer patients from healthy individuals VSL#3 treatment-dependent urinary metabolites involved in amino-acid metabolism, nucleic acid degradation, and creatine metabolism of children with NAFLD may be considered non-invasive and effective biomarkers to evaluate their response to treatment. Treating RAW 264.7 cells with Deglet seed interfere with the energy and amino acid metabolism; Deglet seeds could serve as food with anti-inflammatory properties The differences between the metabolic profiles of the urine of ASD patients and normal, healthy individuals may serve as strong indicators for the diagnosis of ASD. H H NMR Troisi-MP-1633011 H NMR H NMR HSQC 978-0-323-85062-9 H NMR COSY TOCSY H-13C HSQC 1H-13C HMBC 1 00005 H NMR H-NMR 1H
13C HSQC Silva et al. (2019) Miccheli et al. (2015) Abdul-Hamid et al. (2019) NadalDesbarats et al. (2014) To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. t0015 1 H NMR 1H
13C HSQC H
13C HMBC To discriminate between the metabolic profiles of serum, plasma, and plasma subtypes 1 To analyze the metabolic responses (in serum) to food intake according to three different diets: 1 978-0-323-85062-9 00005 Tasic et al. (2019) H NMR 1H-13C HSQC 1H-1H TOCSY Blood serum metabolomics can be used to differentiate patients with schizophrenia and bipolar disorder (although there are many similarities between the two), and from healthy control individuals Several metabolites were identified that distinguish serum, plasma, and plasma subtypes from each other. Correction for inter-individual variation was necessary to identify the distinguishing metabolites. Changes in concentration of postprandial metabolites (in serum) can be linked to food intake. 1 1 H NMR Metabolomics has the potential to diagnose Alzheimer's disease in its early stages of development. Yilmaz et al. (2017) 1 H NMR 1H-1H COSY 1H-1H TOCSY 1H-13C HSQC 1. Differences in the metabolic profiles among the subjects clearly distinguished healthy controls from breast cancer patients. 2. Relationship identified between key metabolites (glucose, lactate, glutamate, lysine, alanine, pyruvate, NAG, and some lipids) and the high expression of 1,4,5 triphosphate receptor in breast cancer patients. Singh et al. (2017) 1 H NMR COSY TOCSY NOESY 1H
13C HSQC 1 H-13C HMBC Combining Trichoderma with compost fertilizer may increase phosphate uptake in plants with the necessary phosphate nutrients. Vinci et al. (2018) 1 Several characteristic metabolites were identified in animal urine samples that were not present in human urine samples, some of which could be used Lee et al. (2019) 1 H NMR COSY TOSCY H-13C HSQC UPLC-MS 1 Kaluarachchi et al. (2018) Radjursoga et al. (2018) 1. Vegan 2. Lacto ovo-egetarian 3. 3.Omnivore To assess the use of 1H NMR Metabolomics in distinguishing the metabolites (of saliva) of healthy controls, mild cognitive impairment sufferers, and Alzheimer's disease patients. To elucidate the relationship between inositol 1,4,5 triphosphate receptor and metabolic processes. To examine the reaction (growth, nutrient uptake) of Zea mays plants upon inoculation with Trichoderma and treatment (or lack therefore) of different types of fertilizers. To see if 1H-NMR based metabolomics can distinguish animal urine samples (dog, cat, horse, monkey, etc.) from human urine samples. H NMR H NMR (Continued) To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Troisi-MP-1633011 To find biomarkers that distinguish the diagnosis of schizophrenia from bipolar disorder Nucleus 13 C Purpose(s) of Study NMR Methods Troisi-MP-1633011 978-0-323-85062-9 00005 To see if uranium inhibits renal gluconeogenesis in humans and mice. To test the ability of metabolomics in discriminating between five drugs found in citrus fruits. 13 To assess if 13C-MRS of hyperpolarized [1-13C] pyruvate can differentiate between responding and resistant BRAFV600E melanoma cells and xenografts. To investigate aspartate metabolism in hepatocellular carcinoma. To monitor the effect of daily administration of low amounts of 3-iodothyronamine (a metabolite) to obese mice. HP-13C-MRS (1D 13C NMR) EPR To isolate and identify secondary metabolites from the oil-derived fungus Aspergillus isolated from the rhizospheric soil of Phoenix dactylifera (Date palm tree). 1 H NMR 13C, DEPT-135 NMR COSY 1H, 13C-HMBC 1H,13CHSQC To develop and test analysis methods to improve quantification of pyruvate to lactate, a key indicator of cancer cell metabolism. 13 To see if biological metabolites hyperpolarized via dissolution dynamic nuclear polarization (d-DNP) and cross polarization (CP) yield readable NMR spectra. 13 C NMR 1 H NMR 13C NMR 1 H NMR 13C NMR 1 H NMR 13C NMR C - NMR C NMR 1H-13C HSQC Results as biomarkers to distinguish animal urine samples from human urine samples. Naturally occurring uranium inhibits lactate metabolism in humans and mice. 13 C based metabolomics may be a useful method to classify and distinguish drugs found in citrus fruits. The hyperpolarized lactate/pyruvate ratio may be an early indicator of response to vemurafenib (or other BRAF inhibitors) in melanoma. Changes in aspartate metabolism are characteristic of hepatocellular carcinoma. Subchronic effects of 3-iodothyronamine administration may include lipolysis and protein breakdown, 3-iodothyronamine may have a lasting effect on weight maintenance in mice. One novel compound (1-(4-hydroxy-2,6-dimethoxy3,5-dimethylphenyl)-2-methyl-1-butanone), and four secondary metabolites (citricin, dihydrocitrinone, 2, 3, 4-trimethyl-5, 7-dihydroxy-2, 3dihydrobenzofuran, and oricinol) were identified, with the novel compound showing strong antimicrobial activity against Staphylococcus aureus, and significant growth inhibition against Candida albicans and Candida parapsilosis. NMR analysis of hyperpolarized carbon-13 pyruvate, coupled with dynamic imaging and kinetic modeling, provides quantitative assessments of prostate cancer metabolism. d-DNP combined with CP can enhance the 13C signal of biological metabolites in less time than a standard 1D 13C NMR or 1H-13C HSQC spectrum with comparable (or even enhanced) quality of spectra obtained from standard experiments. Paper Renault et al. (2010) Tsujimoto et al. (2018) Acciardo et al. (2020) Darpolor et al. (2014) Haviland et al. (2013) Orfali and Perveen (2019) Larson et al. (2018) Dumez et al. (2015) To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Table 5.2 Applications of 1D NMR techniques in metabolomics. Continued 978-0-323-85062-9 N 1 H NMR 13C NMR To synthesize 1-13C-phosphoenolpyruvate-d2, a precursor for parahydrogen-induced polarization (PHIP) of 1-13C- phospholactate-d2. To explore methods (chemical derivatization 1 13C NMR techniques) to improve characterization of bodily fluids such as urine and serum. 13 00005 PHIP hyperpolarized glucose molecules may serve as biomarkers for tumor progression. Reineri et al. (2010) 13 Aqueous solutions of hyperpolarized metabolites have moderate levels of toxicity. Cavallari et al. (2020) 13 PHIP-SAH can be used to hyperpolarize acetate in a quick and cost-effective manner. This hyperpolarized acetate can then be converted into pyruvate, a common metabolite used for in vivo metabolic profiling of cancer cells. The NMR signal of 13C nuclear polarization of 1-13Cphospholactate-d2 was enhanced by greater than 3 3 107 fold. Chemical derivatization of human bodily fluids (urine and serum) coupled with 13C labeled compounds and 13C based NMR techniques are sufficient to derive high quality NMR spectra of said human bodily fluids. The assimilation of NH41 in A. incana root nodules primarily occurs through the GS-GOGAT (glutamine synthetase (GS) and glutamate synthase (GOGAT)) pathway. SABRE-SHEATH effectively hyperpolarizes pyridine and nicotinamide (vitamin B3 amide), 15N containing metabolites. Reineri et al. (2015) 15 N NMR signal of imidazole-15N2 was enhanced B2000-fold, and imidazole-15N2 could be used for in vivo pH sensing. Shchepin et al. (2016) C NMR C NMR C NMR 1 H NMR 13C NMR 1H-13C HSQC To investigate the primary nitrogen metabolism of the N2-fixing root nodule symbiosis Alnus incana (L.)
Frankia. 15 N NMR 31P NMR To demonstrate the use of SABRE-SHEATH (signal amplification by reversible exchange in shield enables alignment transfer to heteronuclei) to enhance the signal of 15N molecules. To observe the effects of using SABRE-SHEATH (reversible exchange in shield enables alignment transfer to heteronuclei) to hyperpolarize imidazole-15N2. 15 N NMR 15 N NMR Shchepin et al. (2014) Shanaiah et al. (2007) Lundberg and Lundquist (2004) Theis et al. (2015) (Continued) To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Troisi-MP-1633011 15 To find acceptable candidates for in vivo MRI (NMR) imaging to determine glucose cellular uptake using para-hydrogen-induced polarization (PHIP). To investigate the toxicity of using para-hydrogeninduced polarization Side Arm Hydrogenation approach (PHIP-SAH) to hyperpolarize metabolites in order to enable metabolic imaging of prostate cancer cell lines. To develop an economic approach to synthesize hyperpolarized pyruvate. Nucleus Troisi-MP-1633011 978-0-323-85062-9 00005 Purpose(s) of Study NMR Methods Results Paper To explore how using 15N labeled choline affects metabolomics experiments (with MS and NMR) and metabolomics analysis. 1 Tayyari et al. (2013) To determine the metabolic effects of adding methylseleninic acid (an anti-cancer agent) on A549 lung cancer cells. To improve detection of low concentration metabolites containing carboxyl groups via 15N labeling with ethanolamine. To understand how the metabolism of prostate cancer subtypes vary. 1D-1H NMR TOCSY 1H-13CHSQC 1H-15N HSQC Adding 15N labeled choline significantly improves the detection of metabolites containing the carboxyl group (-COOH), using both NMR and MS techniques. Methylseleninic acid inhibits nucleotide turnover, and the incorporation of nucleotides into RNA. 1D 1H NMR COSY 1H-15N HSQC Improved identification and quantification of metabolites containing carboxyl groups. Ye et al. (2009) 1 Significant differences in metabolite patterns were detected between prostate cancer samples and benign tissue samples. Large (59%) differences in phosphorus metabolism between sympatric tree species were observed, indicating that phosphorus metabolism of sympatric trees is species specific. Nucleoside 20 ,30 -cyclic monophosphates contributing to a 20 ppm 31P NMR signal were identified, and fully characterized. Dudka et al. (2020) It is possible to observe a slight change in 31P metabolites in breast cancer patients after the first cycle of neoadjuvant therapy. Following chemical shift changes in only 1H or 31P does not yield enough information to determine pH sensitivity. However, if 31P chemical shift changes are combined (with a 2D experiment), identification of pH sensitive metabolites is easier. Krikken et al. (2019) H NMR 1H-15N 2D HSQC H NMR 31P NMR To investigate the foliar phosphorous metabolism of trees of a French Guiana rainforest. 31 To elucidate the metabolic profiles of Aphanizomenon flos-aquae (AFA) cyanobacteria from Klamath Lake (Oregon state, USA). COSY TOCSY ROESY 31P 1 31 H, P
HMBC 1H,31PHSQC-TOCSY 1H,13C
HSQC 1H,13C - HMBC 31 P-MRSI (NMR) To determine possibility of non-invasive early detection of breast cancer after the first cycle of neoadjuvant therapy. To present an alternative to determining pHsensitive metabolites via NMR. P NMR 1 H NMR 31P NMR 1H,31P Fast-HMQC Fan et al. (2012) GargalloGarriga et al. (2020) Zambon et al. (2019) Koskela et al. (2018) To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Table 5.2 Applications of 1D NMR techniques in metabolomics. Continued F Troisi-MP-1633011 978-0-323-85062-9 1 To trace the metabolism of leniolisib, a PI3K inhibitor, in healthy controls. To understand how much the lens accumulates possibly toxic products of vitamin C degradation. 19 To probe ascorbic acid homeostasis and degradation in diabetes. 19 To monitor the plasma and metabolic stability of BLT-F2 and BLT-S-F6, two tumor targeting drug conjugates. 1 Select applications of 1D NMR techniques in metabolomics. H NMR 13C NMR 31P NMR H,1H COSY 1H,13C HSQC 1 F NMR 19 F NMR F NMR H NMR 13C NMR 19F NMR H2O2 induced stress decreased the amount of amino acids (with the exception of alanine), and increased the amount of lactate, indicating that alanine synthesis de novo is likely connected to lactate release from myotubules. Elimination of leniolisib occurred mostly through internal metabolic processes. Fluoro-dehydroascorbate and ascorbic acid, a fluorine containing metabolite, are taken up into HLE-B3 cells. Diabetes pushes ascorbic acid homeostasis towards a higher oxidative state in liver, kidney, spleen, and plasma, but tends to a lower oxidative state in brain, adrenal glands, and heart. Both 19F NMR probes could be used as metabolic tracing agents in cancer studies. Straadt et al. (2010) Pearson et al. (2019) Satake et al. (2003) Nishikawa et al. (2003) Seitz et al. (2015) 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 19 To study the effects of H2O2 induced stress upon metabolites in C2C112 myotubules. To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 162 p0095 p0100 p0105 p0110 s0035 p0115 CHAPTER 5 Nuclear magnetic resonance in metabolomics therefore can be used to determine the molecular concentration (Cui, Liew et al., 2019; Emwas, Roy et al., 2019). Owing to these properties of qualification and quantitation, a single 1H NMR spectrum can often provide enough information to readily identify and elucidate the proportions of anywhere between 30 and 200 metabolites (Bouatra et al., 2013; Emwas, Roy et al., 2019) depending on the nature of the studied sample (biofluids, species, etc.) (Holmes et al., 2008; Li et al., 2017; Tarachiwin et al., 2007; Wei et al., 2009; Yilmaz et al., 2017). Metabolite identification is usually augmented by tools such as freely available software programs, for example, CAMERA and MetaboMiner (Spicer et al., 2017), commercial software, for example, Chenomx Inc. (Weljie et al., 2006), Mestrelab’s Mnova (Mestrelab Research S.L.—Analytical Chemistry Software, 2021), and CRAFT (KCmurthy, 2013), and/or a database such as the Human Metabolome Database (HMDB) (Wishart et al., 2007, 2009, 2018). These tools facilitate metabolite identification from the raw NMR data and assist in organizing/presenting results. 1D 1H NMR experiments are typically used in untargeted metabolomics studies (Blasco et al., 2014; Emwas, Roy et al., 2019; Flores et al., 2020; Hasanpour et al., 2020; Karaman et al., 2016; Lee et al., 2019; Luke et al., 2020; MadridGambin et al., 2018; Silva et al., 2019; Stringer et al., 2014) as opposed to targeted metabolomic studies. “Untargeted” simply means that all the measurable analytes, including chemical unknowns, undergo a full comprehensive analysis including advanced chemometric techniques (e.g., multivariate analysis). This is necessary to eliminate possible outliers and to make the dataset(s) easier to manage (Roberts et al., 2012). “Targeted” metabolomics refers to the measurement of only defined groups of characterized and annotated metabolites (Roberts et al., 2012). 1D 1H NMR has a marvelous ability to discriminate (i.e., reveal the differences between) metabolic profiles and quantify their respective metabolites. Fig. 5.1, for example, shows a 1D 1H NMR spectrum that distinguishes between major nutrients and metabolites of a culture of Aspergillus fumigatus, according to their individual peak assignments. The abilities of 1D 1H NMR to identify and quantify metabolites are perhaps the single greatest advantages 1D 1H NMR can offer to the field of metabolomics, and arise from the relatively high sensitivity and highly quantifiable nature (Emwas, Roy et al., 2019) of the 1H nucleus. Representative cases that demonstrate the sensitivity and quantifiability of 1D 1H NMR are discussed (see below). Some representative additional studies that use 1D 1H NMR are discussed (see below). Moreover, other studies in which 1D 1H NMR has been involved (with or without additional techniques) are listed in Table 5.2. 1D 1H nuclear magnetic resonance examples Silva et al. (2019) used an untargeted 1H-NMR based metabolomics approach in order to identify and monitor metabolites specific to breast cancer patients. To do Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy f0010 FIGURE 5.1 Standard example of a labeled 1D 1H NMR spectrum for metabolomics research. Copied with permission from Plummer, R., Bodkin, J., Power, D., Pantarat, N., Bubb, W. A., Kuchel, P. W., & Sorrell, T. C. (2007). Effect of caspofungin on metabolite profiles of Aspergillus species determined by nuclear magnetic resonance spectroscopy. Antimicrobial Agents and Chemotherapy, 51 (11), 4077
4084. https://doi.org/10.1128/AAC0.00602-7. p0120 so, they took urine samples from 38 healthy controls (HCs), and 40 breast cancer patients, and analyzed the 1D 1H NMR spectra of the samples. These spectra enabled the rapid identification of 33 metabolites in both study groups. Further multivariate statistical analysis allowed the identification of ten key metabolites (creatine, glycine, serine, dimethylamine, trimethylamine N-oxide, α-hydroxyisobutyrate, mannitol, glutamine, cis-aconitate, and trigonelline), which showed the highest sensitivity levels and specificity between both study groups (HCs and breast cancer patients); these metabolites could therefore be useful in the diagnosis of breast cancer as they successfully discriminate the urinary profiles of breast cancer patients from those of HCs (Silva et al., 2019). This study is an excellent example of the potential of untargeted 1D 1H NMR-based metabolomics. A similar approach was taken by Rocha et al. (2011). In their subsequent study of lung cancer involving blood plasma samples from 85 lung cancer patients (55 males, 30 females) and 78 HCs (38 male, 40 female), the authors successfully identified 36 metabolites. Increased levels of pyruvate, lactate, Troisi-MP-1633011 978-0-323-85062-9 00005 163 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 164 p0125 p0130 p0135 CHAPTER 5 Nuclear magnetic resonance in metabolomics VLDL 1 LDL were observed, along with decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), methanol, and HDL. These changes could be linked to known characteristics of cancer such as increased glycolysis, glutaminolysis, and gluconeogenesis, a suppressed Krebs cycle, and a decreased catabolism of lipids. Blood plasma samples were taken from a diverse population of lung cancer patients {i.e., male and female, varying histopathology, different TNM stages [tumor (T), nodes (N), and metastases (M)]} and HCs (i.e., smokers and nonsmokers, male and female). Despite the variation and diversity among the samples from the lung cancer patients, 1D 1H NMR metabolomics consistently discriminated the metabolic profiles of blood plasma samples of lung cancer patients from those of HCs (Rocha et al., 2011). An earlier study by Jordan et al. (2010) also involved 1D 1H NMR to distinguish lung cancer patients from HCs. Though they took fewer serum samples (total 5 21) from a smaller, less diverse population of subjects [lung cancer patients with either squamous cell carcinoma (SCC) or adenocarcinoma (AC), and HCs] than Rocha et al. (2011), they nonetheless demonstrated the potential of serum NMR metabolomics to discriminate between the two lung cancer types (SCC or AC), as well as between cancer patients and HCs. Although the Jordan study was more limited and less robust than the Rocha study (Rocha et al., 2011), both illustrate the ability of 1D 1H NMR to differentiate the metabolic profiles of diseased patients from those of HCs, and provided preliminary evidence for 1D 1 H NMR-based metabolomics becoming a key method for disease diagnosis. This method may be especially critical in the earlier stages of the disease. 1D 1H NMR has also been used to identify and quantify metabolites specific to patients with psychiatric disorders (Sethi et al., 2017; Tasic et al., 2017; Yilmaz et al., 2017). Samples were taken from over 100 individuals [60 HCs, 50 schizophrenia (SCZ) patients, and 45 patients with bipolar disorder (BD)], with the intent to find metabolites that distinguish the three study groups (HC, SCZ, BD) from each other. 1D 1H NMR spectra allowed Tasic et al. (2019) to identify and quantify metabolites specific to each study group; for example, SCZ patients had a unique presence of isovaleryl carnitine, pantothenate, mannitol, glycine, and GABA, whereas BD patients had 2,3-diphospho-D-glyceric acid, N-acetyl aspartyl-glutamic acid, and monoethyl malonate. SCZ and BD patients both possessed 6-hydroxydopamine, while HC individuals did not. Higher lipid levels were also observed in SCZ and BD as compared to HC individuals (Tasic et al., 2019). Taken together, this approach may support the diagnosis of patients with psychiatric diseases such as SCZ and BD, and the studies lay the foundation for more robust and reliable diagnosis of psychiatric diseases. Despite several advantages and successes of 1D 1H NMR in metabolomics, this technique does have drawbacks. The high sensitivity of the 1H nucleus may cause an “information overload.” The presence of several hundred metabolites in the analyte, each with several NMR signals, may cause severe NMR spectral overlap. Thus NMR is a “double-edged sword” for metabolomics: it provides a Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy p0140 p0145 p0150 p0155 p0160 wealth of information that can enable the identification of many metabolites but, at the same time, cripples the identification of individual molecule types. Fortunately, the effect of spectral overlap can be mitigated and overcome with the use of 2D NMR techniques such as Heteronuclear Single Quantum Coherence (HSQC), Heteronuclear Multiple Bond Correlations (HMBC), and COSY. 2D NMR techniques usually provide enough resolution and resolving power to overcome the problem of peak overlap, and to reveal the connectivity of atoms in greater detail. 2D NMR techniques are also used to confirm the chemical shifts assignments of 1D 1H NMR spectra. 2D NMR techniques may be used in addition to 1D 1H NMR experiments (Beckonert et al., 2007; Cao et al., 2020; Feraud et al., 2019) (also see Table 5.2) even if the latter provides enough information to identify the metabolites. Adding information from additional nuclei and/or multidimensional correlation experiments (e.g., 1 H-13C) comes at the price of extended instrument time (Van et al., 2008) because another variable (i.e., and additional nucleus) is involved (Cavanagh et al., 1995; Claridge, 2016). For example, Jiang et al. (2013) studied the metabolic changes occurring in plasma, urine and liver extracts from hamsters fed a high-fat/high-cholesterol diet using a 1D 1H NMR-based approach. Even though over 100 metabolites were identified from just the 1H spectra of the samples, additional 2D NMR experiments (1H,1H-TOCSY and 1H,13C-HSQC) were performed to confirm the assignments (Jiang et al., 2013). In 2010, Jung et al. also used a 2D NMR technique in addition to 1D 1H NMR to determine the geographical origin of beef samples (Jung et al., 2010). From 1H NMR spectra, they identified 25 metabolites. Overlapping signals from a few metabolites were resolved with 2D NMR techniques (1H,1H-TOCSY, 1 13 H, C-HMBC, and 1H,13C-HSQC), and the 2D NMR techniques were also used to validate the metabolites identified from the 1D spectra (Jung et al., 2010). Clearly, using 2D NMR techniques to aid 1D 1H NMR chemical assignment and to resolve peak overlap could be a standard in metabolomics studies (Martineau & Giraudeau, 2019), however 2D NMR techniques are not common because they take longer to perform (hours to days) (Emwas, Alghrably et al., 2019; Emwas, Roy et al., 2019), and take more experience to process and interpret. 1D 1H NMR also suffers from the effects of solvent and/or attempts at suppression. Signals from the solvent may completely cover signals from metabolites. The concentration of 1H in pure water (H2O) is B110 mol L21, which is significantly higher than the concentration of metabolites (nM
mM) in the water solution (for example). As most metabolomics studies are carried out in water (Emwas, Roy et al., 2019), solvent suppression becomes mandatory, either by attempting to deuterate the solvent (i.e., replace H2O with highly pure D2O), and/ or by applying an NMR pulse sequence. Much work has gone into overcoming the effects of solvent suppression and the results can be seen in Giraudeau et al. (2015), McKay (2009), and Mckay (2011). Troisi-MP-1633011 978-0-323-85062-9 00005 165 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 166 s0040 p0165 p0170 p0175 p0180 CHAPTER 5 Nuclear magnetic resonance in metabolomics 1D 13 C nuclear magnetic resonance in metabolomic studies In terms of inherent resolution and reduced spectral overlap, 1D 13C NMR is far superior to 1D 1H NMR. 13C NMR has a much broader observed chemical shift range (B200 ppm) than that of 1H (B10 ppm) (Edison et al., 2019), and therefore offers better resolution than 1D 1H NMR. This leads to higher quality spectra with narrow line width peaks (Emwas, Roy et al., 2019), and facilitates the identification of organic functional groups (i.e., aromatics, aliphatics, and carbonylcontaining compounds). This is especially true for metabolites and most organic molecules containing a carbon backbone. Hence, 1D 13C NMR provides a direct way to measure the primary structures of many metabolites (Clendinen et al., 2014). Furthermore, 1D 13C NMR signals are less sensitive to environmental changes such as pH (Edison et al., 2019), unlike the 1D 1H NMR peak frequencies, which are more sensitive to changes in pH (Dona et al., 2016; Tredwell et al., 2016; Tynkkynen et al., 2009). The lack of 13C to 13C homonuclear coupling at natural abundance also makes 13C spectra easier to interpret (Edison et al., 2019). While these advantages may seem to make 13C spectra an obvious first choice, 1D 13C NMR suffers from inherently low sensitivity. The 13C isotope is 62 times (cube of the gyromagnetic ratio difference) less sensitive than the 1H nucleus, and the natural abundance of 13C is far lower (99.99% for 1H, B1.1% for 13C). The absolute receptivity difference is more than 5717-fold. This inherently low sensitivity of the 13C nucleus, combined with its low natural abundance, significantly impedes its use in metabolomics studies. There are several avenues to overcome this weakness. Metabolites can be isotopically enriched with 13C nuclei; the most straightforward approach to overcoming the low natural abundance (Clendinen et al., 2015). This approach is most beneficial to 2D correlation-based NMR techniques (e.g., INADEQUATE, HSQC, and HMBC) that involve the 13C nucleus (Clendinen et al., 2015; Geier et al., 2019; Lewis et al., 2010; Otto et al., 2015; Pan et al., 2016). Isotopic enrichment (often termed labeling) is usually accomplished by feeding the organism of interest (e.g., bacteria) with 13C-enriched nutrients (e.g., U-13C6 glucose, or fully labeled maximal media) (Malloy et al., 2010). A new indirect and useful method to detect 13C at low and/or natural abundance concentrations involves hyperpolarization or a variant of such. Hyperpolarization implies transferring the nuclear spins of a sensitive nucleus (such as 1H) to a less sensitive nucleus (such as 13C) (Emwas et al., 2008; Hill et al., 2018; Kovtunov et al., 2018; Ludwig et al., 2010), thus increasing the overall sensitivity of the less sensitive nuclei, often by a theoretical factor of 104
105 (Altes & Salerno, 2004), although practical applications rarely achieve this ideal factor. This in turn can reduce a standard 1D 13C NMR experiment from several hours to several minutes, an acquisition time comparable to that of the standard 1D 1H NMR experiment (Emwas, Roy et al., 2019). As mentioned, this Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy p0185 p0190 p0195 theoretical enhancement is rarely fully achieved. Hyperpolarization is, however, still used to enhance the readability of 13C at natural abundance (B1.1%), especially for in vivo metabolic imaging in which the hyperpolarized metabolite and its concentration can be tracked in real-time (Walker & Happer, 1997; Wang et al., 2019). Methods to hyperpolarize 13C metabolites include PHIP (Parahydrogen Induced Polarization), SABRE (Signal Amplification by Reversible Exchange), and DNP (Dynamic Nuclear Polarization). It must be noted that hyperpolarization methods are invasive, and this must be taken into account when considering subsequent sample testing. Applications of each technique are discussed below, and more applications are listed in Table 5.2. Zacharias et al. (2016) employed PHIP to hyperpolarize 13C labeled succinate (SUC) and its derivative diethyl succinate (DES). This allowed the authors to monitor the uptake and cellular conversion (or lack thereof) of SUC and DES in five cancer allograft animal models: breast (4T1), Renal Cell Carcinoma (RENCA), colon (CT26), lymphoma NSO, and lymphoma A20 via 13C Mrs and MRI (both essentially inductive magnetic resonance techniques similar to NMR). They found that RENCA metabolized SUC and DES, while the other cancers did not (Zacharias et al., 2016). In a related study, Zacharias et al. (2012) created hyperpolarized DES-1
13C-2,3-d2 (via the PHIP method) to monitor the Krebs cycle in real-time. Downstream metabolites (malate, succinate, fumarate, and aspartate) of hyperpolarized DES were identified in vivo via high resolution 1D 13 C NMR spectra. Both studies serve as powerful indicators of the large capacity of 13C NMR (with 13C molecules metabolites hyperpolarized via PHIP) to track metabolites related to diseases such as cancer in real-time. This could have enormous applications in the health and pharmaceutical industries. Similar to PHIP, SABRE is another hyperpolarization technique to increase the sensitivity of 13C nuclei. In a novel study involving a low concentration 4methylpyridine [4MP, a metabolite involved in bacteria metabolism (Khasaeva et al., 2016)], Richardson et al. (2018) used the SABRE technique to enhance their 13C NMR signal of 4-methylpyridine (4MP). Without SABRE, the total time required to collect the13C signal of 4MP was 52 hours (4096 cumulative scans). With SABRE, the same experiment took only 15 seconds (i.e., 1 scan). The concentration of 4-MP for both experiments was in the millimolar range, and both experiments were performed on a benchtop NMR spectrometer (BNMR) with a low field strength (Richardson et al., 2018). Clearly, SABRE is a powerful tool to increase 13C sensitivity, and to significantly reduce total experimental time for metabolite measurements. An interesting case study of SABRE comes from the work of Lloyd et al. (2012). Using para-hydrogen as the polarization source, they hyperpolarized quinoline, a metabolite mostly found in plants (Diaz et al., 2015). Of note, the 13C spectrum of the quinoline molecule typically shows good resolution and a large peak distribution. 1D and 2D NMR techniques were performed with the SABRE technique and resulted in high resolution NMR spectra. The time to record these experiments was reduced significantly from several hours to several minutes, and Troisi-MP-1633011 978-0-323-85062-9 00005 167 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 168 p0200 p0205 p0210 p0215 p0220 p0225 CHAPTER 5 Nuclear magnetic resonance in metabolomics it is important to note that all quinolone samples had low concentrations (µM— mM) (Lloyd et al., 2012); this study therefore establishes the mighty potential of hyperpolarization in metabolomics studies. Although PHIP and SABRE are prime hyperpolarization techniques with enormous potential for metabolomics applications, DNP is much more common in the scientific literature than either PHIP or SABRE. Indeed, a number of key metabolites (1
13C-pyruvic acid, 13C-bicarbonate, 1
13C-fumarate, and 5
13C-glutamine) have been successfully hyperpolarized via DNP. These metabolites, however, were already isotopically enriched with 13C (Nikolaou et al., 2015). DNP however still works effectively for 1D 13C NMR experiments at natural 13C abundance. An excellent example of using DNP at natural 13C abundance comes from the work of Dey et al. (2020). They utilized DNP to hyperpolarize plant metabolites of red and green tomato plant extract. This made it possible to collect clean and well-resolved 1D 13C NMR spectra (1 scan each) from both sets of plant extracts (red tomato vs. green tomato). Additional statistical analysis of 13C NMR data was able to distinguish between both sets of plant extracts (Dey et al., 2020). This study effectively showed that DNP worked well to hyperpolarize and enhance the sensitivity of 13C NMR at natural abundance. It also demonstrates that DNP is suitable for distinguishing metabolic profiles of similar species. DEPT (distortions enhancement by polarization transfer) NMR is also a useful technique in NMR metabolomics that shows how other nuclei are coupled to the carbon nucleus. DEPT is used to distinguish between a CH3 group (methyl), a CH2 group (methylene), and a CH group (methine). DEPT has a pulse set at 45, 90, or 135 degrees in three separate experiments. DEPT can increase the sensitivity of 13C by a factor of 4, and is therefore useful in metabolomics studies. For example, Kamal et al. (2012), found four bioactive metabolites from a culture medium of a bacterial strain of a new Pseudomonas sp. that had antimicrobial and biosurfactant activities, and used DEPT-135 (along with other NMR methods) to identify the metabolites as 1-hydroxyphenazine, phenazine-1carboxylic acid, rhamnolipid-1, and rhamnolipid-2. Li et al. (2019) used DEPT as part of their proposal to statistically correlate NMR spectra with LC
MS data, facilitating metabolite structure identification. Hyperpolarization techniques do suffer from some limitations, one being that sample or NMR probe deterioration can occur as a result of the irradiation pulses that are required for hyperpolarization techniques to work (Richardson et al., 2018). A brief summary of the advantages and disadvantages of the hyperpolarization techniques presented here (PHIP, SABRE, and DNP) is provided in Table 5.2. More studies involving hyperpolarized 13C metabolites are listed in Table 5.2. For those desiring a deeper level of theory regarding hyperpolarization methods and how they work, they are referred to the following published manuscripts (Barskiy et al., 2017; Halse, 2016; Kovtunov et al., 2018; Meier et al., 2014; Nikolaou et al., 2015). Aside from isotopic enrichment and the application of hyperpolarization techniques, another practical approach to improving 13C sensitivity for 1D 13C NMR Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy experiments involves the use of enhanced NMR equipment. For example, NMR cryoprobe technology can be used in the probe and electronics. This involves reducing the temperature of the equipment down to B20K as a way to reduce electronic noise, and results in a two to fourfold improvement in signal to noise (Emwas, Roy et al., 2019). One can also use higher magnetic field strengths, but 13 C (and other lower gyromagnetic ratio nuclei) is relatively insensitive to the magnetic field strength (Halse, 2016). s0045 p0230 p0235 p0240 p0245 1D 15N nuclear magnetic resonance in metabolomics As with 13C NMR, 15N also has a large chemical shift dispersion (B100 ppm) (Emwas, Roy et al., 2019) allowing for improved resolution and identification of nitrogen-containing compounds. 15N however has a low natural abundance (B0.37%, even lower than that of 13C), and an even lower sensitivity than that of 13 C. Direct observation of 15N nuclei using a standard 1D 15N NMR experiment is difficult and time consuming unless steps are taken to mitigate the physical features. A number of important metabolites, for example, amino acids, alkaloids, purines, pyrimidines, and terpenoids (Ramirez et al., 2019; Song et al., 2020) contain nitrogen, and the ability to study these atoms can provide a more complete picture of the metabolome (Bhinderwala, Lonergan et al., 2018; Kanamori, 2017). Chemically tagging metabolites with an 15N labeled compound is one way to increase the sensitivity, and this has been demonstrated successfully by Tayyari et al. (2013) and Ye et al. (2009). Chemically tagging metabolites with 15N labeled atoms is mostly beneficial to 2D 15N-NMR techniques and is rarely, if ever, applied to 1D 15N NMR due to cost and synthesis/expression considerations. Direct observation of 15N containing metabolites is mostly done with hyperpolarization techniques (similar for 13C), as hyperpolarization can significantly improve the sensitivity and observability of 15N containing metabolites. In fact, hyperpolarization seems to be the method of choice for 1D 15N NMR when it comes to metabolomics. Barskiy et al. (2016), for example, used SABRE-SHEATH (SABRE in SHield Enables Alignment Transfer to Heteronuclei) to directly hyperpolarize the 15N sites of metronidazole, a precursor of two major oxidative products, an acid metabolite and a hydroxy metabolite (Pendland et al., 1994). By using SABRESHEATH, the authors were able to hyperpolarize the 15N nuclei in metronidazole to over 20% in less than one minute, thus increasing the sensitivity of 15N in metronidazole and its observability via 1D 15N NMR at low concentration (50 mM) in a short amount of time (Kanamori, 2017). An increase in metronidazole nitrogen sensitivity could have useful applications such as direct in vivo imaging of mechanisms of action or hypoxia sensing, as metronidazole is an antimicrobial drug against species such as Entamoeba histolytica, Giardia lamblia, and Trichomonas vaginalis (Freeman et al., 1997). A similar study by Shchepin et al. (2019) also examined the effects of hyperpolarizing metronidazole via SABRESHEATH. During the process, they were able to transfer the hyperpolarization of iridium hydrides to a distance of up to six chemical bonds. The polarization level Troisi-MP-1633011 978-0-323-85062-9 00005 169 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 170 p0250 p0255 s0050 p0260 p0265 CHAPTER 5 Nuclear magnetic resonance in metabolomics of metronidazole achieved by Shchepin et al. (B15%) (Shchepin et al., 2019) was smaller than that achieved by Barskiy et al. (B20%) (Barskiy et al., 2016), and yet both groups of researchers achieved their respective levels of metronidazole hyperpolarization in under one minute. These two studies demonstrate the potential of hyperpolarization-based techniques to increase the observability of 1D 15N NMR spectra of 15N containing metabolites, which could also eventually transform 1D 15N NMR into a live, real-time metabolic imaging tool. Hyperpolarization could also enable the molecular imaging of viruses, as demonstrated in the study by Shchepin et al. (Shchepin & Chekmenev, 2014). They measured the T1 relaxation times of hyperpolarized 15N labeled Azidothymidine (AZT), an antiretroviral medication used to prevent and treat HIV/AIDS (Eckhardt et al., 2017). In 2014, they also determined that NMR T1 relaxation times were sufficiently long to allow in vivo imaging of 15N-AZT and its relevant kinetics after injection into an HIV-infected patient. Strictly speaking, Shchepin et al. did not generate 1D 15N NMR spectra of 15N-AZT; nevertheless, the long T1 times measured indicated that the 1D 15N NMR spectra of hyperpolarized 15NAZT would be of higher quality than those of nonpolarized 15N AZT. This study thus points to the potential of hyperpolarized 15N-containing molecules and metabolites in analyzing and monitoring the “metabolism” of viruses or even viruslike particles. Additional examples of hyperpolarized 15N NMR experiments are listed in Table 5.2. We hope the readers agree that hyperpolarization of 15N containing metabolites is valuable, and perhaps crucial for future metabolomics applications. 31 P nuclear magnetic resonance in metabolomic studies 1D 31P NMR has a unique niche for structural elucidation and metabolomics studies (Babgi et al., 2021; Tomah Al-Masri et al., 2012). Phosphate metabolism is quite diverse across different living systems and organs (e.g., liver, muscle tissues, and kidney) (Felsenfeld & Levine, 2015; Gattineni & Friedman, 2015; Moe & Daoud, 2014; Quinn, 2012; Silver et al., 2002; Tebben et al., 2013; Uday et al., 2019) and several phosphorus-containing metabolites [especially those that transport the inorganic phosphate group (Bhinderwala et al., 2020)] are vital intermediaries and regulators of essential biochemical pathways, including glycolysis (Berg et al., 2002), the Krebs cycle (Enderle, 2012), and fatty acid β-oxidation (Bosc et al., 2020). As such, 1D 31P NMR is a powerful method to study metabolic profiles, and to expand the coverage of the metabolome (Bhinderwala et al., 2020). 31 P has a high natural abundance (100%), and a relatively good sensitivity (lower than that of 1H, but higher than those of 13C or 15N), and 31P NMR does not usually require an external supplemental source (Bhinderwala et al., 2020). Despite these advantages, 31P NMR suffers from severe drawbacks (listed in Table 5.1) which have limited its applicability in metabolomics (Emwas, Roy et al., 2019). The 31P nucleus is extremely sensitive to pH changes (Blaive et al., 2000; Zheng, Liu et al., 2017), solvent selection, and the experimental temperature. 31P resonances also have a limited chemical shift which dispersion tends to Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy p0270 p0275 p0280 p0285 be broad and causes obscured peak splitting (Bhinderwala et al., 2020). 1D 31P NMR also experiences signal overlap from phosphorylated compounds (Markley et al., 2017). 1D 31P reference NMR spectra are generally lacking in databases such as the HMDB, which further limits the use of 31P NMR in metabolomics applications (Bhinderwala et al., 2020). However, the scientific literature contains a plethora of 31 P NMR studies applied to metabolomics (Buchli et al., 1994; Cady et al., 1983; Carlbom et al., 2017; Chorao et al., 2010; Gout et al., 2011; Komoroski et al., 2008; Levine et al., 2003; Park & Park, 2001; Qiao et al., 2006; Shah et al., 2014; Sterin et al., 2001; Thebault et al., 2009; Tiret et al., 2016; Tokumaru et al., 2009; Vauclare et al., 2013; Wijnen et al., 2012). Many studies have employed 1D 31P NMR as an in vivo metabolic imaging technique. A couple of examples demonstrating the use of 1D 31P NMR in metabolomics are discussed below. Additional examples of 1D 31P NMR in metabolomics are listed in Table 5.2. Sterin et al. (2001) used 31P spectra to test the effects of antimitotic drugs on breast cancer cell metabolism. Analysis of observations revealed a correlation between the mechanism of action of selected anticancer drugs (i.e., paclitaxel, vincristine, colchicine, nocodazole, methotrexate, and doxorubicin), and observed differences in breast cancer cell metabolism [i.e., hormonal response, estrogen receptors (positive/negative), and metastatic potential]. Specifically, Sterin et al. discovered that the antimicrotubule drugs (paclitaxel, vincristine, colchicine, and nocodazole) increased the amount of intracellular glycerophosphorylcholine (GPC), an intracellular metabolite, whereas the nonantimicrotubule drugs (methotrexate and Adriamycin) did not. These results suggest that the level of intracellular GPC is indicative of cellular microtubule functionality (Sterin et al., 2001), and demonstrate that 31P NMR has the ability and potential to determine the effects of drugs on the target of interest. This could also extend beyond cancer cells to targets such as proteins. Levine et al. (2003) showed that 31P NMR can be extended to other living systems such as fish. They used 1D 31P NMR to assess how adding acetyl-L-carnitine (ALCAR) and myo-inositol influenced the levels of phosphate-containing metabolites in zebrafish. Zebrafish present a unique model for studying high-energy phosphate and membrane phospholipid metabolism in living systems. Addition of ALCAR and myo-inositol decreased the levels of phosphodiesters and inorganic orthophosphate, and increased levels of phosphocreatine in the zebrafish (Levine et al., 2003). This is important because ALCAR and myo-inositol are both antidepressant drugs (Chiechio et al., 2018; Nasca et al., 2013; Nemets et al., 2001), and monitoring the metabolic profiles and effects of antidepressants or any other type of drug via 1D 31P NMR may allow scientists to obtain a more complete picture of how these drugs affect patients. This is especially true of 31P NMR, which has been, and most likely will continue to be, used as an in vivo metabolic imaging tool of living systems. Past scientific work has proven the utility of 1D 31P NMR in in vivo metabolic imaging, and other meaningful applications. However, more work, such as Troisi-MP-1633011 978-0-323-85062-9 00005 171 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 172 CHAPTER 5 Nuclear magnetic resonance in metabolomics the creation of more 1D 31P NMR reference spectra (Bhinderwala et al., 2020), is required to make 1D 31P NMR a standardized tool for metabolomics research. The results of such work will be worthwhile and meaningful for a number of applications, which will most likely have patient benefits (e.g., the health industry will be better able to monitor the effect of a drug on patients with the use of 1D 31 P NMR). s0055 p0290 p0295 p0300 s0060 p0305 p0310 19 F in metabolomic studies 19 F NMR studies are rarer than any of the previously mentioned 1D NMR experiments, but still deserve a mention. 19F has a sensitivity akin to that of 1H, and like 31P, has a natural abundance of 100%. 19F is not common in naturally occurring metabolites, but a number of drugs containing a fluorine atom and their respective metabolisms have been studied in detail (Park et al., 2001). For example, Pawłowski et al. (2019) combined 19F NMR and in silico techniques to study 5-fluorouracil (5-FU) (an anticancer drug containing fluorine) metabolism in yeast under low ATP conditions. Lutz and Hull (1999) used 1D 19 F NMR to study how some of 5-FU’s anabolites [5-fluorouracil (FUra), 5fluorouridine (FUrd), 5-fluoro-20 -deoxyuridine (FdUrd), 5-fluorouridine-50 -monophosphate (FUMP), FdUMP, 5-fluorouridine-50 -diphosphate (FUDP), FUTP and 5-fluorouridine-50 -diphospho(1)-a-D-glucose (FUDPG)] responded to different pH values. These anabolites (anabolic metabolites) are key to fluoropyrimidine chemotherapy in cancer treatment (Lutz & Hull, 1999), and thus an understanding of their behavior under different environmental conditions may prove useful for future treatment options. 1D 19F NMR may find additional applications in environmental studies, as 19F is a convenient way to study the biodegradation of environmental pollutants, and to obtain a quick initial scan of the metabolic profiles of newly isolated organisms (Boersma et al., 2001). Nevertheless, future studies are needed to fully appreciate and expand the use of 1D 19F NMR in metabolomics studies. 2D nuclear magnetic resonance spectroscopy Mono-dimensional (1D) proton (1H) NMR spectra from complex biological samples like blood and urine or plant extracts are crowded with overlapping peaks of hundreds to thousands of molecules, making it difficult, if not impossible, to obtain an accurate peak assignment and disentangle the information content attributable to each molecule. Multidimensional NMR experiments, and particularly two-dimensional (2D NMR), can be used for peak assignment and structural determination of compounds (Dona et al., 2016; Emwas, Roy et al., 2019). The use of 2D experiments is not widespread in metabolomics mostly because the acquisition of a 2D spectrum typically need the repetition of several hundreds of 1D experiments, leading to acquisition times between a few tens of minutes and several hours, depending on the type of experiment (Emwas, Roy et al., 2019; Giraudeau, 2020). This Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy p0315 p0320 p0325 p0330 p0335 makes it impossible to use 2D experiments in a high-throughput setting. However, over the years several approaches have been proposed to accelerate the acquisition of 2D spectra (Giraudeau, 2020; Rouger et al., 2017) including fast repetition techniques (Farjon, 2017), spectral aliasing (Vitorge et al., 2009), nonuniform sampling (Mobli & Hoch, 2014), Hadamard (Kupče & Freeman, 2003), and UF (Frydman et al., 2002) spectroscopy. Several 2D NMR experiments are available and have been used in metabolomics for several applications including molecular identification, structural elucidation, and kinetic or energetic analysis (Chu et al., 2010; Emwas, Al-Talla et al., 2013; Sahoo et al., 2020). 2D NMR experiments can be used to overcome the problem of overlapping resonances by spreading a different type of information about peaks onto a second dimension. Correlation methods exploit chemical shifts from covalently attached neighbors, while other methods exploit other chemo physical properties, like the coupling constant J (which contains information about relative bond distances and angles) or the diffusion time (which is the time need by a molecule to diffuse along the magnetic) (Emwas, Roy et al., 2019). The correlation spectroscopy (COSY) (Alonso et al., 1989) experiment is the simplest of all 2D NMR experiments and provides information on homonuclear correlations between coupled nuclei (1H-1H). It has been widely used for molecular identification and structural elucidation (Hunt et al., 1984; Kono, 2013; Lown & Hanstock, 1985; Macura et al., 1983) and has proven to be particularly useful for metabolomics research (Blasco et al., 2010; Flores-Sanchez et al., 2012; Kim et al., 2010; Le Guennec et al., 2012; Sekiyama et al., 2011). TOCSY (total COSY), also known as the homonuclear Hartmann
Hahn experiment (Braunschweiler & Ernst, 1983) is an extension of the COSY experiment wherein the chemical shift of a given nucleus is correlated with the chemical shift of other nuclei within the total (or near total) spin system of a given compound. Typical applications of 2D-TOCSY are the structural elucidation of carbohydrates and peptides since all protons belonging to the same sugar residue or to a single amino acid will appear correlated (Johnson et al., 1995), or metabolite identification. An example of TOCSY is given in Fig. 5.2A. COSY (like COSY and TOCSY-like spectroscopy) is not limited to homonuclear correlations; therefore, it can also be used for measuring heteronuclear correlations, that is, plotting the chemical shift from the 1H against the chemical shift of other atoms (like 13C or 15N) as in the HSQC and HMBC. In an 1H-13C-HSQC spectrum (Bodenhausen & Ruben, 1980), the chemical shifts of proton and carbon atoms that are directly bonded are mapped, providing only one cross-peak for each H
C coupled pair. Similarly, an 1H-13C-HSQC maps proton and carbon atoms that are directly bonded. HSQC experiments are very useful for resolving and assigning overlapping proton signals, particularly for metabolite signals arising from complex biofluid mixtures (Emwas, Roy et al., 2019). An example of an H-13C-HSQC spectrum of sucrose overlaid onto an Arabidopsis extract is given in Fig. 5.2B. Troisi-MP-1633011 978-0-323-85062-9 00005 173 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 174 f0015 CHAPTER 5 Nuclear magnetic resonance in metabolomics FIGURE 5.2 (A) Example of TOCSY NMR spectrum. Example of TOCSY NMR spectrum obtained from a single mouse urine sample from the 3
2.4 ppm chemical shift region: keys: (1) 2oxoglutarate; (2) citrate; (3) 3-hydroxyphenylpropionate; (4) methylamine and dimethylamine correlation; (5) dimethylamine and trimethylamine correlation. (B) 1H
13C HSQC NMR spectrum of sucrose from the Biological Magnetic Resonance Data Bank (BMRDB) (Ulrich et al., 2007) (red) overlaid onto an aqueous whole-plant extract from Arabidopsis thaliana (blue) (Lewis et al., 2007). (C) 2D HMBC spectrum of a urine sample acquired at 900 MHz. The zoomed zone represents the aromatic region. Aromatic peaks of Hippurate (1), PAG (2), Histidine (3) and mHPPA (4) can be distinguished. (D) Twodimensional representation of STOCSY of 1H NMR spectra obtained from 599 urine samples; numeric key as for panel (A). (A and D) Adapted from Cloarec, O., Dumas, M.-E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., & Holmes, E. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77(5), 1282
1289; (B) Adapted from Emwas, A.-H., Roy, R., McKay, R. T., Tenori, L., Saccenti, E., Gowda, G., Raftery, D., Alahmari, F., Jaremko, L., & Jaremko, M. (2019). NMR spectroscopy for metabolomics research. Metabolites, 9(7), 123; (C) From Bernini, P., Bertini, I., Luchinat, C., Nepi, S., Saccenti, E., Schażfer, H., Schużtz, B., Spraul, M., & Tenori, L. (2009). Individual human phenotypes in metabolic space and time. Journal of Proteome Research, 8(9), 4264
4271. p0340 p0345 Heteronuclear multiple-quantum correlation spectroscopy (HMQC) is similar to HSBQ. It provides correlation, as in HSQC, and the two methods give similar quality results for small to medium-sized molecules, but HSQC is more appropriate for larger molecules (Keeler, 2011). HMBC (Bax & Summers, 1986) is similar to HSQC but the HMBC experiment reveals correlations between nuclei that are separated by two or more Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Nuclear magnetic resonance spectroscopy p0350 p0355 p0360 p0365 p0370 chemical bonds. The utility of 1H-13C HMBC in metabolomics has been shown by the possibility of easily distinguishing some of the aromatic peaks of hippurate, phenylacetylglycine, and histidine in urine samples (Bernini et al., 2009), as shown in Fig. 5.2C. The combined use of HSQC and HMBC has proven to be particularly useful in metabolomics for the identification of new compounds from plant extracts (Liang et al., 2006), spider venom (Taggi et al., 2004), and insects (Dossey et al., 2007). J-resolved (JRES) experiments (Aue et al., 1976) are by far the most used in metabolomics because of their simplicity and short acquisition time (Giraudeau, 2020; Ludwig & Viant, 2010; Mahrous & Farag, 2015). Through JRES experiment, simplified projection of the proton spectrum, in which all peaks from a multiplet appear as a singlet, is obtained by plotting the proton spectrum along one dimension and the coupling constant (J value) of each signal along the second dimension. JRES experiments have been applied to resolve overlapping resonances of metabolites identification in human biofluids such as urine, blood plasma, and cerebral spinal fluid (Foxall et al., 1993; Lutz et al., 1998; Yang et al., 2008). Moreover, JRES spectra connection between neighboring protons can be established, and information about the J value of each signal can be used to distinguish between some isomers such as α and β anomers of sugars and glycosides or cis and trans isomers of olefinic compounds (Mahrous & Farag, 2015). In 2D diffusion-ordered (DOSY) experiments (Stilbs, 1987), a similar approach is used by plotting the proton spectrum along one dimension and the diffusion coefficient related to each NMR signal along the second dimension. Since molecules with different molecular weights have different diffusion coefficients, it is in principle possible to identify them. It is possible to obtain a good degree of separation between compounds that differ substantially in their molecular weights (Mahrous & Farag, 2015). 2D-DOSY have been used in the assignment of the anomeric protons of mono-, di-, or oligosaccharides in apple and grape juices (Gil et al., 2004). Building on the concepts of the TOCSY experiment, an idea has been proposed to exploit the statistical correlation among peaks that can be calculated using multiple mono-dimensional. The Statistical TOSCY (STOCSY) (Cloarec et al., 2005) uses multicollinearity of the intensity variables in a set of 1-D spectra to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This has been used to assign and identify relevant metabolites in a metabolomic study of a model of insulin resistance (Cloarec et al., 2005). An example of STOCSY obtained from a stack of X samples is given in Fig. 5.2D and can be compared with a TOCSY spectrum obtained from one sample, Fig. 5.2C. The method can be expanded to statistical total correlation spectroscopy editing (STOCSY-E) (Sands et al., 2009) and to the hetero-nuclear HET-STOCSY by combining mono-dimensional 31P and 1H NMR signals and used for biomarker detection. In the case of 2D spectra, there is no linear correlation between the molar concentration of any compound and the area under the curve of the corresponding Troisi-MP-1633011 978-0-323-85062-9 00005 175 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 176 p0375 s0065 p0380 p0385 CHAPTER 5 Nuclear magnetic resonance in metabolomics signals because other factors like the resonance specific signal attenuation contribute to the intensity of the cross-peaks and their corresponding integration volumes (Mahrous & Farag, 2015). Several approaches have been proposed to extract quantitative information from 2D spectra (Giraudeau et al., 2007; Lewis et al., 2007; Michel & Akoka, 2004; Parsons et al., 2007; Pathan et al., 2011). However, none of the methods is routinely applied in metabolomics for quantification purposes. Contextually, 2D spectra have seldom been used directly for (multivariate) data analysis since this kind of analysis needs either to transform 2D spectra to 1D spectra or the use of a multivariate approach that can handle three-dimensional data since there is a data matrix (the 2D spectra) for each sample. However, some applications have been proposed for the use of NMR 2D spectra showing the value of using 2D (Bertelli et al., 2010; Brinson et al., 2020; Farag et al., 2014; Sharma et al., 2017) or providing some guidelines (Brinson et al., 2020). High-resolution magic-angle spinning nuclear magnetic resonance spectroscopy High-Resolution Magic-Angle Spinning NMR Spectroscopy (HR-MAS) is a versatile tool that utilizes the MAS to measure suspended solid- or gel-like samples that contain some internal molecular motions (Händel et al., 2003). In MAS NMR [similar to solid-state NMR (ssNMR)], in order to obtain high resolution information from a spectra, the sample has to be positioned in a rotor that rapidly rotates at the fixed angle of 54.736 degrees (the so-called “magic angle”) with respect to the Z-axis of the static magnetic field (Fig. 5.3). This rotation is required to average the anisotropic spin interactions and remove the dipolar coupling, which normally cause line-broadening effects on the spectra, as shown in Fig. 5.4 (Ashbrook et al., 2018; Beckonert et al., 2010; Watts, 2005). For example, tissue samples are usually spun at approximately 4
6 kHz. Under this kind of condition, the acquisition of NMR spectra of the tissue is comparable with solution state NMR (Beckonert et al., 2010). The problem however lies in the preservation of the tissue: the speed of rotation affects the morphology and metabolite profile of the sample (Gogiashvili et al., 2019). For example, two hours of rotation at 3 kHz destroys 15%
19% of adipocytes. The amount of sample used can vary from a standard 65 µL that gives an improved signal to noise ratio, however the required size could be reduced down to 12 µL by using spacers. The use of spacers improves the field homogeneity and the peak line shape due to the compact spherical sample volume (Beckonert et al., 2010). Currently, MAS probes enable metabolomics researchers to measure different types of nuclei such as 1H, 13C, 15N and 31P for human and animal tissue samples (Esmaeili et al., 2014; Levin et al., 2009; McNally et al., 2006). The application of HR-MAS was found to be useful in an “integrative metabolomics” approach when investigating metabolic profiles of different tissue types Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. f0020 FIGURE 5.3 MAS NMR. MAS NMR rotor with an angle of 54.736 degrees with respect to the z axis of the static magnetic field. The rotor is spinning with a frequency of 1 for up to 140 kHz (Lin et al., 2018), which diminishes the line broadening effects. Created with BioRender.com. f0025 FIGURE 5.4 Rotating speed effect on NMR spectral line widths. The effects of different rotating speeds on resulting NMR spectral line widths. The removal of dipolar coupling combined with averaging anisotropic spin interactions results in narrower resonances and therefore higher resolution spectra. Created with BioRender.com. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 178 p0390 p0395 s0070 p0400 CHAPTER 5 Nuclear magnetic resonance in metabolomics and bio fluids (Beckonert et al., 2010). One of the examples is the work done by Chan et al. (2009) in which they analyzed colon mucosae in order to define metabolites that could help to discriminate malignant (colorectal cancer) forms of mucosae from normal mucosae. They utilized HR-MAS NMR (1D NOESY and 1D Carr-Purcell-Meiboom-Gill) and GC-MS to profile tumor specimens and compare them with normal mucosae. The results obtained from these experiments revealed a higher level of both saturated and unsaturated lipids and/or fatty acids in normal mucosae compared to malignant samples, and an increase in levels of lactate and glycine with the decrease of glucose and arachidonic acid levels in tumor specimens (Chan et al., 2009). Another prominent example of HR-MAS in practice is the use of 1H HR-MAS NMR to investigate human bone cancer biology. The work done by Tavel et al. (2016) focused on multiple myeloma—a type of bone marrow cancer. The goal of the study was to gain insight and understand the rules behind the genetic heterogeneity within the same tumor lesion. For that, the authors combined 1H HRMAS, and multivariate analysis, and complemented this with information obtained through histo-morphological observations. Although the differences between lipids in the two morphologically distinct types of the tumor (termed as “oily” and “calcified”) were not significant, the peak at 2.75 ppm that was significantly lower in the calcified tumor samples as compared to the oily ones suggested the existence of a differential lipid composition (Tavel et al., 2016). HR-MAS NMR could also be used in more industry types of applications, for example, to control the process of production. An example of such an approach was taken by Garcı́a-Garcı́a et al. (2018) where the authors investigated the manufacturing process of dry fermented salchichón type sausages and predict their early days of ripening. They collected the samples after 0, 2, 4, 7, 11, and 14 days of drying and then measured them using 1H HR-MAS NMR. The results obtained showed that salchichón production is a three-stage process that includes formulation, fermentation and drying-ripening. Each of the processes could be distinguished by metabolomic profiles related to microbial activity. For example, the beginnings of the fermentation stage were characterized by the increase of signals related to ethanol (signal 9), acetic acid (signal 24) and 2,3-butanediol (signals 7, 8, 89). However, on the 14th day of ripening (the profile of final product), those signals were reduced and an increase in signals of Ile, Leu, Val, α-Ala, Glu, Gln, Met, Thr, Phe, Tyr, and Trp was observed (Garcı́a-Garcı́a et al., 2018). Those signals were signs of proteolysis and were related to the future flavor development of salchichón (Diaz et al., 1997). Pure shift nuclear magnetic resonance Additionally, the proton-proton homonuclear scalar coupling results in splitting of signals into complex multiples, creating in some cases more severely overlapped Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Recent advances p0405 p0410 s0075 s0080 p0415 spectra. When compared to 13C (at the state natural isotopic abundance), 13C NMR spectra tend to be better separated with singlets only present (assuming perfect 1H decoupling during observation). Although the homonuclear 13C
13C couplings can be seen in the spectra, they are extremely weak and usually hidden in the baseline noise (Zangger, 2015). While the methods for suppression of heteronuclear J couplings have existed for many decades, development still continues (Kogler et al., 1983; Kövér & Batta, 1987; Rutar, 1984; Schilling et al., 2014). The removal of homonuclear J couplings (i.e., short range through bond 1H-1H) has been more challenging. The goal of pure shift NMR (homonuclear broadband decoupling) is to convert all of the signals into singlets by manipulating the acquired time-domain observations, and exhibit the average evolution only under the effects of chemical shift evolution while excluding J-coupling (Zangger, 2015). To achieve this, a combination of novel NMR pulse sequences with innovative data acquisition and data processing techniques has been developed. Most pure shift NMR spectra are obtained using one of two different classes of experiments: J-refocusing experiments in which evolution under scalar coupling is refocused, or constant-time experiments with a constant amount of evolution (Adams, 2007). Both are based on a somewhat similar approach. First, a portion of free induction decay (FID) signal is collected for each increment of an evolution period. Second, the increments are combined to create an interferogram that can be later transformed as a conventional FID to obtain pure shift spectra (Adams, 2007; Emwas, Roy et al., 2019; Zangger & Sterk, 1997). More technical details about the pulse sequences and different types of pure shift experiments can be found in (Adams, 2007; Castañar, 2017; Zangger, 2015). The pure shift NMR offers a great opportunity for applications in metabolomics. Although the main disadvantage of this method is the general loss of sensitivity, significant advances have been made to overcome this challenge. For example, utilization of pure shift (real-time BIRD) 1H-13C HSQC-SI experiments on the metabolomics studies have been shown to increase the sensitivity of a spectra by about 40%
50% over a traditional 2D HSQC-SI experiment, and improve the chemical shift matching against NMR metabolomics databases (Timári et al., 2019). Another example shows that the use of SAPPHIREPSYCHE experiments combined with Statistical TOtal Correlation SpectroscopY (STOCSY) allows for easier identification of metabolites present in the mixture of Physalis peruviana fruits, and even enabled the identification of a new metabolite that was omitted by standard proton NMR (Lopez et al., 2019). Recent advances Improvements in nuclear magnetic resonance hardware and techniques and additional tools to aid in metabolomics studies Efforts are underway to improve the inherently low sensitivity and limited resolution of NMR magnets. Many metabolomic studies use NMR to identify and Troisi-MP-1633011 978-0-323-85062-9 00005 179 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 180 p0420 p0425 s0085 p0430 p0435 CHAPTER 5 Nuclear magnetic resonance in metabolomics quantify metabolites present at very low concentrations (nM—µM) (Psychogios et al., 2011) also see https://serummetabolome.ca/concentrations). Correctly identifying and quantifying metabolites requires good quality NMR spectra, which may take several hours to a few days to obtain, even with high field NMR magnets (600
950 MHz). High field NMR magnets require routine liquid helium and nitrogen filling, which adds to the cost of running NMR experiments. To acquire the large number of samples expected, for example, in population screening programs or in epidemiological studies, it is necessary to improve the technique to reach an even higher throughput, possibly in the range of hundreds per day or more. A smart approach to deal with this issue was proposed by Mulder et al. (2019). The authors suggest the addition of a small amount of a commercial gadolinium contrast agent solution to urine samples. The resulting effect is a drastic shortening of the relaxation times (T1) of the molecules in solution, thus permitting the reduction of the relaxation delay parameter. An increase in speed of up to fourfold with respect to standard protocols was reported, without appreciable effects on spectral quality. Nuclear magnetic resonance magnets Currently, 950 MHz and 1 GHz NMR magnets are quite commonly available for commercial applications. Intriguingly, 1.2 GHz magnets have been also introduced on the market, opening a new era of ultra-high field applications. Noteworthy, the first 1H NMR spectrum at 1.2 GHz of a urine sample has been recently acquired at CERM (Banci et al., 2019) 1.2 GHz magnets increase the sensitivity and resolution of the experiments, thus potentially allowing the detection of a higher number of metabolites in biofluids (Wishart, 2019). Larger magnets, however, typically cost more to use and maintain, and also take up a significant amount of lab space. As a result, BNMR spectrometers with field strengths of B100 MHz that take up much less space are gaining interest from researchers. BNMR spectrometers have permanent magnets that are maintenance free and do not require dedicated lab space for their use (Blümich, 2019). Because of their permanent magnets, BNMR have lower magnetic fields (60, 90, 100 MHz), and do not offer as high resolution as the larger high magnetic field NMR magnets. However, their low cost and portability are attractive reasons for using them over larger magnets with higher fields, especially for academic laboratories with low budgets (Wishart, 2019). BNMR are also easy for novice NMR spectroscopists to use (Lawson et al., 2020; Romero et al., 2020; van Beek, 2021) and are even used for academic teaching purposes (Riegel & Leskowitz, 2016). Studies with BNMR show that BNMR (low-field magnets) can produce NMR spectra that are comparable to high magnetic fields for biofluid samples of patients with tuberculosis (Izquierdo-Garcia et al., 2019) and type II diabetes (Percival et al., 2019), demonstrating the value of BNMR for disease diagnosis. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Recent advances p0440 p0445 s0090 p0450 p0455 s0095 p0460 The use of BNMR in metabolomics has emerged in recent years. Percival et al. (2019) developed a protocol for the analysis of urine, saliva, and blood serum, all of which are readily available biofluids on a BNMR (60 MHz). The authors successfully detected common diabetic markers, particularly α-glucose (#2.8 mM) and acetone (25 µM) in urine samples (Percival et al., 2019). In addition, Edgar et al. (2021) used a 60 MHz BNMR to perform a multicomponent metabolomics analysis for urine samples of patients with type II diabetes. The authors were able to successfully detect and quantify B15 metabolite biomarkers for type II diabetes, which could be monitored to achieve rapid diagnosis and prognosis of patients (Edgar et al., 2021). It is possible that BNMRs may become more popular for future metabolomics studies than high field magnets, but they may never achieve the full level of resolution and sensitivity of more costly, high-field magnetic fields. Nevertheless, whether or not time and cost are of concern, BNMRs and high-field NMR are of great value and have tremendous potential for metabolomic studies involving the diagnosis and treatment of diseases. Nuclear magnetic resonance probes The probe is essential for NMR experiments as it contains the necessary electronics and hardware to transmit and receive radiofrequency energy into the NMR sample of interest (Emwas, Roy et al., 2019). The probe can be designed in such a way as to accommodate various sizes of NMR tubes, and to have channels for the reading of multiple nuclei (Hong et al., 2018; Webb, 2006). Several changes have been made to probes over the last 20 years that increase the sensitivity of NMR, making it possible in some cases to enhance the signal of NMR-active nuclei with low natural abundance (such as 13C) (Emwas, Roy et al., 2019; Keun, Beckonert et al., 2002). Cryoprobes, for example, can increase the sensitivity of NMR by reducing the level of thermal noise generated by electronic circuits. As a result, the signal to noise ratio can increase by a factor of four, which is useful for NMR-nuclei with low sensitivity such as 13C (Webb, 2006). For example, Keun, Beckonert et al. (2002) demonstrated the use of cryoprobes in obtaining information-rich and high-quality 1D 13C NMR spectra of rat urine. Cryoprobes are considered a major advancement in NMR technology (Kovacs et al., 2005), and will likely play a larger role in future metabolomic studies involving NMR-active nuclei with low sensitivity. Flow probes Flow NMR is a term used to describe various techniques in which the sample is flowing through a tube into the NMR probe and out to a reservoir. Samples are not individually contained and measured in an NMR tube. The field of flow NMR is based on the combination with LC (i.e., LC-NMR), in which the first Troisi-MP-1633011 978-0-323-85062-9 00005 181 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 182 p0465 CHAPTER 5 Nuclear magnetic resonance in metabolomics experimental outlet of an HPLC system is connected directly to the NMR probe, which dates back to the end of the 1970s (Keifer, 2007; Watanabe & Niki, 1978). Currently, the field has evolved with various techniques such as Direct Injection NMR (DI-NMR) (Keifer et al., 2000), Flow Injection Analysis NMR (FIA-NMR) (Keifer, 2003) and Solid-Phase-Extraction NMR (SPE-NMR) (Griffiths & Horton, 1998). The hardware for LC-NMR consists of an HPLC fluid pump, some form of injector loop and valve control for the introduction of the sample into the system, a separating column, and one or more HPLC detectors (e.g., UV detector, conductivity detector), and this material is output to the NMR flow probe (Keifer, 2003). The sample is loaded into the injector loop and manually or automatically injected at the start of the LC-NMR run (Keifer, 2007; 2003). In DI-NMR, the hardware can consist of only a Gilson 215 Liquids Handler and an NMR flow probe (Keifer et al., 2000; Keifer, 2003). The samples (in solution state) are automatically injected into an NMR flow cell and then withdrawn the same way it came in, with rinsing of the flow cell in between the different samples (Keifer et al., 2000; Keifer, 2003, 2007). On the other hand, FIA-NMR is a simplified version of LC-NMR where the chromatographic column and the detectors are removed, leaving only a pump, an injector loop, a valve and a connector between the injector loop and the NMR flow probe (Keifer, 2003; Keifer, 2007). Each of the methods have an NMR flow probe in common. The flow probe has to be adapted for both continuous sample injection and removal, for example, the connection to the HPLC column/system, and be designed to minimalize crosscontamination of samples and the formation of air bubbles in the flow cell (a glass or quartz sample tube with openings at the top and the bottom) (Haner & Keifer, 2007; Keifer, 2007). Two classes of probes exist: probes with the sample tube positioned vertically (like a standard NMR tube) and with saddle-shaped RF coils, and probes with the capillary sample tube positioned horizontally, with a solenoidal RF coil (Haner & Keifer, 2007). Most of those probes are optimized for 1H detection and 2H lock with the option of extra RF channels either single tuned (13C) or double (13C/15N) (Haner & Keifer, 2007). Additionally, since the flow probe is directly connected to the HPLC system, it has to be adapted to the limitations of HPLC. For example, the flow cells usually have B60 µL volumes, which is a typical analytical LC peak (Haner & Keifer, 2007; Keifer, 2007). It is also worth mentioning that cryogenic flow probes became available some time ago, which increased sensitivity and resolution, and enabled faster acquisition of NMR data (Haner & Keifer, 2007; Keifer, 2007). Flow NMR has several complications such as cross-contamination, which can be alleviated by multiple rinses in-between. However, this requires time and increased solvent volumes, leading to the dilution of samples. Solvent suppression also becomes an issue, as the desire is often to scan samples as they flow by negating the ability to actively and slowly reduce solvent signals. Different solvent suppression techniques are therefore required (Altieri et al., 1996; Hoult, 1976; McKay, 2009; Price, 1999). Otherwise sample flow has to be halted while NMR acquisition occurs, which limits the advantages of flow systems. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Recent advances s0100 p0470 p0475 s0105 p0480 Metabolomics databases and nuclear magnetic resonance software programs A plethora of metabolomics databases and NMR software programs (Metabolomics Software and Servers) (Bingol, 2018; Ellinger et al., 2013; Giraudeau, 2020; Halabalaki et al., 2014; Izquierdo-Garcia et al., 2021; Johnson & Lange, 2015; Lipfert et al., 2019; Nagana Gowda & Raftery, 2017; Okazaki & Saito, 2012; Wishart et al., 2009, 2019), both free and licensed, is available to assist researchers with the identification and quantification of metabolites and natural products (Emwas, Roy et al., 2019; Johnson & Lange, 2015). Databases provide reference spectra that assist researchers in identifying the metabolites in their samples (Johnson & Lange, 2015), and/or separating known compounds from unknown compounds (Lai et al., 2018), an especially useful tool for untargeted metabolomics studies (Bingol, 2018). Software programs can be tuned to accelerate (Ellinger et al., 2013; Puchades-Carrasco et al., 2016; Spicer et al., 2017) and automate (Beirnaert et al., 2018; Bingol, 2018; de Brouwer & Stegeman, 2011; Howarth et al., 2020; Johnson & Lange, 2015; Kern et al., 2019) the identification and quantification of metabolites. Since MS is also widely used in metabolomics studies, many databases include NMR and MS reference spectra, while others only contain NMR or MS spectra (Johnson & Lange, 2015). Some databases contain no spectral data at all (Johnson & Lange, 2015). Since the focus of this chapter is the role of NMR in metabolomics, we will limit our discussion to databases that contain NMR spectral data and to software tools that are capable of analyzing NMR spectra of metabolites. We introduce some of these tools below, and briefly describe some improvements that could be made for future applications. Databases for nuclear magnetic resonance-based metabolomics The HMDB, found at https://hmdb.ca/, is probably the most widely used database for metabolomics studies, and is considered the standard metabolic database for human metabolic studies (Emwas, Roy et al., 2019; Wishart et al., 2009). Created in 2007 by David Wishart (Wishart et al., 2007, 2009, 2012), it contains over 100,000 metabolites, with many data fields hyperlinked to additional open-source databases. Since its creation, the number of experimental and predicted NMR spectra in HMDB has increased fourfold. HMDB also contains data about many drugs and drug metabolites, toxins and environmental pollutants, pathway diagrams for human metabolic and disease pathways, food components and food additives in its subdatabases DrugBank, T3DB, SMPDB and FooDB, respectively, all of which extend its utility beyond metabolomics studies into clinical and environmental studies. The number of metabolites for each of these subdatabases extends into the thousands. It is likely that HMDB will become the de facto database for NMR-based metabolomics studies (Emwas, Roy et al., 2019) if it is not already, and Wishart and his colleagues will continue to increase the number of Troisi-MP-1633011 978-0-323-85062-9 00005 183 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 184 p0485 p0490 p0495 CHAPTER 5 Nuclear magnetic resonance in metabolomics metabolites in the database, and their relevant spectral information. Though not nearly as extensive as HMDB, the Biological Magnetic Resonance Bank (BMRB, found at https://bmrb.io/) (Ulrich et al., 2007) contains information regarding where the cataloged compounds (B1000 total) were collected, the solution conditions, how the data (i.e., NMR spectra) were obtained, and the NMR pulse sequences used to obtain the NMR spectra (Markley et al., 2007). It has several types of NMR spectra, including 1H, 13C, DEPT90, DEPT 135, 1H J-resolved, 1 H-13C HSQC, 1H-13C HMBC, 1H-1H TOCSY, and 1H-1H COSY spectra (Johnson & Lange, 2015). The BMRB database is searchable by compound name, structure, 1D and 2D HSQC peak lists, and solvent and field strength. BMRB has several output files that can be easily read by third party NMR software. It also contains a bulk download option, which is extremely convenient for researchers who need to do their work offline. The Madison-Qingdao Metabolomics Consortium Database (MMCD) (found at http://mmcd.nmrfam.wisc.edu/) contains over 20,000 metabolites, with standard 1H, 13C, 1H,1H-TOCSY, 1H,13C-HSQC NMR spectra for 794 of their cataloged compounds. As of 2015, MMCD contained a total of 5,256 NMR spectra. Far from simply holding useful NMR (and MS) data for researchers, MMCD is an efficient and flexible query system that accepts input in the forms of text, molecular structure search, NMR parameters, MS parameters, and/or miscellaneous (i.e., reference, data source, organism, etc.). These search parameters may include information about isotopomer molecular weight, nomenclature, physical properties, empirical and calculated chemical shifts, and/or NMR sample conditions. It is possible to submit queries as a single file or as a batch (Cui et al., 2008). All of these properties make MMCD a flexible and useful tool for metabolomics research, though improvements such as more data entries and more synchronization with other databases would help to increase its already large versatility. The last NMR-metabolomics database we will discuss in detail is the Birmingham Metabolite Library BML-NMR (found at http://www.bml-nmr.org). BML-NMR contains comparatively few metabolites (208 total) compared to the databases discussed above. However, the metabolites it contains are widely detected in metabolomics studies, and its standard solutions were prepared at pH values (6.6, 7.0, 7.4), which are close to physiological pH (7.35
7.45) (Duarte, Jaremko et al., 2020) enabling the effect of pH on the chemical shifts of the NMR signals to be quantified. 1D and 2D J-resolved NMR spectra, quantified peak lists, and metadata that comply with the Metabolomics Standards Initiative (Rubtsov et al., 2007) are included in the database (Ludwig et al., 2012). Though not as comprehensive as other metabolomic databases such as HMDB, the BMLNMR database provides detailed and high-quality data of metabolite standards that serve as a useful starting point for the identification of metabolites, and for filtering the “known” metabolites from the “unknown” metabolites. Despite a plethora of metabolomic databases to aid in the detection, identification, and quantification of metabolites, much more work must be done to “unify” them to enhance their capacity in NMR-based metabolomics research. A crucial Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Recent advances p0500 p0505 s0110 p0510 element would be to have a downloadable, uniform/common file output system (e.g., every file is.xml or.txt) across the multiple databases. Virtually all metabolomic databases have their own unique file output, with some being vendorspecific, and some having no form of downloadable data whatsoever (Johnson & Lange, 2015). The creation of a common output file format (regardless of the databases from which NMR data is taken) would make it convenient for scientists to analyze standard and known metabolites. A second option would be to create a software program that converts the various output files into one, searchable file format (such as a text file). The more pressing need, however, is to increase the amount of available data on metabolites. Currently, only a tiny fraction of the known metabolome has been analyzed, while the rest remaining uncharacterized and named as the “dark matter” of the metabolome (da Silva et al., 2015; Wishart et al., 2018). Most of the databases are limited to NMR spectra involving 1H and 13C nuclei, with comparatively few spectra involving other important nuclei such as 19F, 15N (Emwas, Roy et al., 2019) and 31P (Bhinderwala et al., 2020). Though increasing the amount of data in the NMR metabolomics databases would take a considerable amount of time, the effort would pay off since many important diseases such as cancer are metabolic in nature (Čuperlović-Culf, 2012; Palmnas & Vogel, 2013; Wishart et al., 2016), and the increase in data amount and availability would most likely help in the identification of novel biomarkers (Gebregiworgis & Powers, 2012; Serkova & Niemann, 2006; Smolinska et al., 2012). For those interested in a more in-depth review of metabolomics databases containing NMR spectra, we recommend the following (Ellinger et al., 2013; Halabalaki et al., 2014; Izquierdo-Garcia et al., 2021; Johnson & Lange, 2015; Misra & van der Hooft, 2016; Puchades-Carrasco et al., 2016; Wishart, 2019). Use of software to analyze metabolite nuclear magnetic resonance data In order to correctly identify and quantify metabolites, NMR data are subject to additional analyses, which include pre and post data processing (Ellinger et al., 2013; Emwas et al., 2018; Euceda et al., 2015; Karaman, 2017), normalization (Kohl et al., 2012; Roberts et al., 2014; Zacharias et al., 2018) and statistical methods such as multivariate analysis (Bartel et al., 2013; De Livera et al., 2013; Misra & van der Hooft, 2016; Puchades-Carrasco et al., 2016; Ren et al., 2015; Saccenti et al., 2014). Several NMR software programs are available to aid in the analysis and interpretation of NMR metabolite data. There are both open source and commercially available NMR software for metabolomics studies (Ellinger et al., 2013; Lewis et al., 2009; Puchades-Carrasco et al., 2016), though the availability of and interest in open-source NMR software have increased in recent years (O’Sullivan et al., 2007). Below, we discuss a few of the available NMR software tools, and their uses in metabolomics research. Troisi-MP-1633011 978-0-323-85062-9 00005 185 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 186 p0515 p0520 p0525 CHAPTER 5 Nuclear magnetic resonance in metabolomics The BATMAN (Bayesian AuTomated Metabolite Analyzer for NMR data) program is a freely available, R-based package used to deconvolute, analyze, and quantify the peaks of metabolites in 1H NMR spectra (Hao et al., 2012). BATMAN implements a Bayesian model, followed by a Markov chain Monte Carlo algorithm to automatically quantify metabolites, and then assign the metabolites based on user-defined parameters (e.g., list of metabolites, chemical shift region) (Hao et al., 2012). BATMAN can also account for shifts in the position of peaks commonly observed in NMR spectra (Izquierdo-Garcia et al., 2021). In short, BATMAN automatically assigns the peaks and gives concentration estimates of metabolites in the sample (Hao et al., 2012), thereby saving time that would otherwise be spent assigning and picking peaks manually, which may be especially applicable in discriminating diseased patients from HCs. For example, Padayachee et al. (2019) compared BATMAN with the common binning approach to discriminate between serum samples of lung cancer patients and those of HCs. Though their use of BATMAN was not completely automatic (i.e., they had to spend significant amounts of time using the software to analyze the samples), they found that BATMAN had a fair predicting power for metabolite concentrations at high magnet field strengths (900 MHz) (Padayachee et al., 2019). This example illustrates the clinical relevance of BATMAN for clinical studies and could be used in metabolomic studies to discover new biomarkers for diseases. The BAYESIL program (Web-based) is another software that implements the Bayesian model to automatically identify and quantify metabolites from 1D 1H NMR spectra of ultra-filtered plasma, serum, or cerebrospinal fluid (CSF) (Ravanbakhsh et al., 2015). For BAYESIL to work properly and optimally, a strict protocol (see http://bayesil.ca/spectra_collection) must be followed. BAYESIL automatically performs processing steps, such as Fourier transformation, phasing, solvent filtering, chemical shift referencing, baseline correction and reference line shape convolution. It is the first fully-automatic publicly-accessible system to quantify NMR spectra, and has an identification accuracy similar to and even higher than that of highly trained spectroscopists (Ravanbakhsh et al., 2015). For the less experienced users of the Web-based BAYESIL interface, a protocol is available that details the steps on how to submit NMR data for spectral analysis (Lipfert et al., 2019). BAYESIL is a great stand-alone tool, but it can be combined with other NMR programs such as BATMAN (Mediani et al., 2017) to increase data output and to compare/confirm the identification and quantification of metabolites. The most common and well-known software tool for NMR metabolomics is ChenoMX (http://www.chenomx.com/software/), a commercial, patented software package (Izquierdo-Garcia et al., 2021). ChenoMX contains reference libraries for many types of organic compounds (e.g., amides, amines, and metabolites involved in carbohydrate metabolism), which are available for different pH values (4
9) and NMR field strengths (400
800 MHz). A handy feature of ChenoMX is its ability to automatically adjust the Reference Library to reflect the sample and acquisition conditions (e.g., pH and NMR field strength) by “fitting” the Reference Library to the experimental NMR spectra, which then enables Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Advantages of nuclear magnetic resonance spectroscopy p0530 p0535 p0540 s0115 p0545 identification and quantification of the metabolites. ChenoMX can be used to identify metabolites in biofluids (Rosewell & Vitols, 2006) such as urine (Vitols & Fu, 2006; Weljie et al., 2006), blood serum (Vitols & Weljie, 2006), and plasma (Vitols & Weljie, 2006). ChenoMX has also been used to measure drug metabolism in cell culture, and to discover new biomarkers in the food industry (see http://www.chenomx.com/software/). Clearly, ChenoMX is a tool of interest in metabolite quantification and identification. Perhaps most familiar to NMR spectroscopists are the programs TopSpin (https://www.bruker.com/products/Mr/nmr/software/topspin) and MestreNova (https://mestrelab.com) (Willcott, 2009), both of which are used to analyze and process NMR data, as well to prepare NMR spectra for publication. TopSpin has a user-friendly interface, which is useful for both novice and experienced users. It also provides tools to assist in NMR data acquisition and to make the structural elucidation of small molecules (e.g., metabolites) efficient. MestreNova, on the other hand, can open raw FID files in multiple formats (Varian, Bruker, JEOL, etc.), and show the spectra immediately with no operator intervention (e.g., Fourier transform). It can also open MS files, which can further aid in metabolite identification and quantification, as MS is much more sensitive than NMR. MestreNova has a powerful, user-friendly interface to visualize, process, analyze, and report 1D and 2D NMR spectra. Both TopSpin and MestreNova are commercial software, and licenses are available to businesses and universities alike. The main difficulties in identifying and quantifying peaks in NMR derive from the shifts of the peaks that, especially for urine samples are not fixed. A novel approach to deal with these variations using an automated and accurate prediction of chemical shifts in urine was recently introduced (Takis et al., 2017). This algorithm is driven by the chemical shifts of five “navigator” signals from which, using a regression-based approach, the position of many other signals can be accurately predicted to the 3rd
4th decimal of ppm. As with NMR databases, it would be helpful to have a uniform file format that any program (open source or commercial) can open and process. Efforts in this direction have been performed with international initiatives (Salek et al., 2015). Though not strictly necessary, it would aid in analyzing data that multiple collaborators and researchers are working on, thus facilitating the identification and quantification of metabolites in biofluids. More updates to metabolomics software tools are underway (Misra & Mohapatra, 2019; Misra & van der Hooft, 2016; Spicer et al., 2017) and are likely to continue in the future (Wishart, 2019). Advantages of nuclear magnetic resonance spectroscopy As discussed above, NMR is a versatile analytical instrument with superior advantages including its high reproducibility, nondestructive nature, and nonbiased approach. Furthermore, NMR is an inherently quantitative method for both Troisi-MP-1633011 978-0-323-85062-9 00005 187 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 188 CHAPTER 5 Nuclear magnetic resonance in metabolomics the identification and quantification of different molecules in sample mixtures. NMR is also a rapid and high-throughput technique allowing the acquisition of multiple samples per hour. Standard biological fluid sample preparation is relatively fast, and with minimal sample handling and workup. Finally, despite the high costs of NMR spectrometers and professional staffing, the cost per sample (with economies of scale) can be lower (e.g., 10
20 euros per sample) than other methods. s0120 p0550 p0555 p0560 Reproducibility A fundamental advantage of NMR spectroscopy over MS is high reproducibility. In 1988 a repeatability factor of over 0.8% and a confidence interval of 0.25% were determined during a study across 15 European laboratories, and the reproducibility when using different NMR systems was on the order of 2%
3%. This was improved to 1% with more homogeneous spectrometer systems (Guillou et al., 1988). In contrast, Bauer et al. (1998) observed significant laboratory effects in the integration of complex signal during the course of an interlaboratory study (5 centers), investigating the quantitative use of NMR. In 1999, a German interlaboratory study on quantitative NMR (Malz & Jancke, 2005) found results to differ up to 100% among participating sites; differences were attributed mostly to the individual and independent setup of the measurements, the data processing, and the evaluation procedure of each single laboratory (Gallo et al., 2015; Malz & Jancke, 2005). In a follow up study involving 33 sites (instruments operating from 200 to 600 MHz) (Malz & Jancke, 2005), a common protocol for the experimental setup and data processing was adopted and the determination of mole ratios of different compounds showed a measurement uncertainty of 1.5% for a confidence level of 95%. In another large-scale ring-test (Gallo et al., 2015), the different participants were able to produce NMR spectra of a given mixture that were statistically equivalent in terms of relative intensities of the signals with respect to the internal standard. In a metabolomics context, Dumas et al. (2006) observed a .98% multivariate analytical reproducibility, with most of the inaccuracies originating from sample handling. Keun, Ebbels et al. (2002) assessed the analytical reproducibility of metabolomics protocols at two sites with spectrometers operating at 500 and 600 MHz and found that the relative concentrations of citrate, hippurate, and taurine were in .95% correlation (r2) between the two instruments, with an analytical error comparable to normal physiological variation in concentration (4%
8%). Ward et al. (2010) analyzed plant-derived samples by 1H-NMR spectroscopy across five different sites using instruments with different probes and magnetic field strengths (400, 500 and 600 MHz). They found exceptional comparability of the data sets obtained from different laboratories and reported that field strength differences can be adjusted for in the data preprocessing. They concluded that Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Challenges and limitations 1 p0565 p0570 s0125 p0575 p0580 H-NMR fingerprinting is the ideal technique for large-scale plant metabolomics data collection requiring the participation of multiple laboratories. Further, a large scale assessment of technical reproducibility of NMR metabolomics (Dumas et al., 2006) proved that NMR spectroscopy of biofluids combined with multivariate pattern recognition is a robust and precise approach for metabolomics studies, outperforming other “-omics” technologies in terms of reproducibility. The coefficient of variation for signals in repeated NMR spectra is reported in the range of 0%
10%. However, all studies stressed the necessity of using standardized protocols for sample collection, handling, and measurement for precise quantitative and fingerprinting applications (Emwas, Luchinat et al., 2015), which render NMR spectroscopy highly reproducible. It has been our experience that volatility in physical sample handling (i.e., acquisition protocols, storage, shipping, and preparation) generates a far larger error in results than the NMR instrumentation itself (Sokolenko et al., 2013). Challenges and limitations Although NMR spectroscopy enjoys several advantages as summarized above, a few limitations still represent daunting challenges that need to be appreciated and overcome in order to improve sensitivity, spectral resolution, and both accurate and precise metabolite identification. The main limitation of NMR spectroscopy is intrinsic low sensitivity concerning metabolites below B10 µM or even into submicromolar levels. Without substantially extended experimental times, these metabolites remain undetectable by NMR spectroscopy. For a specific example, many essential metabolites such as hormones are usually below this detection limit. Additionally, secondary metabolites could be the focus of many targeted metabolomics studies, however the detection limit makes these impractical. Despite several developments including ultra-high field NMR magnet, cryoprobes, and using hyperpolarizations methods (Emwas, Roy et al., 2019; Wishart, 2019), many metabolites are still unobtainable. Spectral overlap is another challenge in NMR spectroscopy, mainly in 1D spectra (Emwas, Alghrably et al., 2019; Mohammed et al., 2020; Naser et al., 2019). This presents a major difficulty in metabolic identification and subsequent quantification. For example, the typical spectra width of the entire possible 1D 1H NMR spectra (i.e., in D2O/H2O under typical conditions) is less than 11 ppm. Indeed, the vast majority of 1H NMR signals are mainly obtained between 1 and 4.4 ppm, and the aromatic region (B6.5
8 ppm). As most NMR studies employ 1 H NMR, overlap is still one of the main problems in data analyses. While the spectral redundancy problem can be resolved using higher and higher magnetic fields, there is a limit (B1200 MHz or 28.2 Tesla costing B12.5 M euros) to what researchers can access. Other techniques such as multidimensional (e.g., 2D) Troisi-MP-1633011 978-0-323-85062-9 00005 189 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 190 CHAPTER 5 Nuclear magnetic resonance in metabolomics p0590 and/or multinuclear (e.g., 1H,13C) NMR spectroscopy, pure shift NMR, and projection of JRES spectroscopy (Emwas, Roy et al., 2019) can all assist, at the cost of additional time and experimental complexity. Another issue is the lack of fully developed NMR spectral libraries designed specifically for metabolomics (e.g., calibrated for common field strengths/instruments, gathered in standard format, and freely available). Although projects such as the Human Metabolomics DataBase (e.g., HMDB 4.0) exist and gather extensive information about metabolites (including NMR spectra), the number of experimental data sets provided for NMR (3840) is far behind those for MS/MS (22198) or GC-MS (7418) (Wishart et al., 2018). This aspect was addressed by the NIH with a call to develop a new, sustainable, and open natural products NMR spectral database containing NMR spectra of natural products for NMR metabolomics researchers (Wishart, 2019). Lastly, the practical aspects related to the infrastructure and operational procedure seem to impede the popularization of NMR. NMR requires highly skilled and educated operators to control the instrument and evaluate and interpret the results (the problem increases even more when the lack of an appropriate NMR metabolic database is included). When combined with the fact that NMR magnets have extensive laboratory footprints (especially the new 1.2 GHz models), require special vibration, magnetic field and radio interference free spaces, and a constant supply of liquid helium and nitrogen to maintain the superconducting magnet, the utilization of NMR is therefore obviously hindered by the financial costs and huge equipment footprint (Emwas, Roy et al., 2019; Wishart, 2019). The use of “benchtop,” relatively inexpensive, nonsuperconducting magnets is attractive, however the present limit to 80 MHz and below makes the overlap problems described above even more prominent (Edgar et al., 2021; Percival et al., 2019; Wishart, 2019). s0130 Sample preparation p0585 p0595 The initial stage of any metabolomic investigation (Takis et al., 2019; Tenori et al., 2007; Vignoli et al., 2019) includes the fundamental steps of the collection of the biological samples, their handling, transportation, and storage. All these steps collectively constitute the so-called “preanalytical” phase (see Fig. 5.5). In a complex biological mixture, such as a biofluid, the concentrations of the different analytes could change according to the different conditions or procedures employed for the preanalytical phase. These changes are mostly induced by enzymatic reactions, chemical reactions, and exposure to air and light. Thus it is of utmost importance to establish optimal standard operating procedures (SOPs) for the proper collection and handling of the biological samples. This is important also for biobank activities. In principle, dedicated SOPs need to be developed for each different sample type. This can only be achieved by performing a systematic Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Sample preparation f0030 FIGURE 5.5 NMR samples acquisition overview. Overview of NMR sample acquisition, measurement, and analysis. p0600 evaluation of all the possible effects that potentially could influence the composition of the sample, and analyzing the sample under different preanalytical conditions, in a process that can be dub “evidence-based” SOPs development. Thorough evaluations (Kirwan et al., 2018) exist for some of the most useful biofluids, such as urine, serum, plasma, saliva and CSF. As an example, it was observed that the main source of preanalytical changes in urine samples is due to the presence of human or bacterial cells that may break upon water crystal formation after freezing (Bernini et al., 2011). Indeed, cell breaking releases enzymes that ignite uncontrolled reactions in the sample. For this reason, if cells are eliminated by filtration (and/or mild centrifugation) before NMR sample preparation or long-term storage, these undesirable effects are much reduced. This is preferred to the addition of stabilizers (such as enzyme Troisi-MP-1633011 978-0-323-85062-9 00005 191 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 192 p0605 p0610 o0025 o0030 o0035 o0040 p0635 p0640 p0645 CHAPTER 5 Nuclear magnetic resonance in metabolomics inhibitors) that introduce unwanted signals in the NMR spectra and may also induce changes in the sample. After monitoring the urine metabolic profile over time, it was concluded that urine needed to be processed within 2 hours of collection and maintained at 4 C between collection and processing. For the long-term storage of the samples, liquid nitrogen vapor or 280 C are the recommended conditions. A similar investigation was performed for serum and plasma samples, and the stability of these biofluids was controlled with respect to time and storage temperature before separation from the whole blood (Bernini et al., 2011). They observed time-dependent and temperature-dependent changes both for serum and plasma samples: in both cases storage at 25 C caused deeper changes in the NMR profile, in particular for glucose, lactate, and pyruvate, with a decrement of glucose concentration and a connected increment in lactate. Thus the optimal SOPs developed for serum and plasma collection for NMRbased metabolomic studies prescribe: 1. 2. 3. 4. the the the the serum/plasma separation within 2 hours after blood collection maintenance of the blood samples at 4 C during this delay immediate freezing of the samples after separation long-term storage at 280 C The suitability of these recommendations was also monitored for long-term storage (Ghini et al., 2019). These results obtained for urine, serum, and plasma derive from the activities performed under the EC founded project SPIDIA (FP7, #222916), and have been the basis for the production of technical specifications for the preanalytical processes for NMR metabolomics in urine, venous blood serum, and plasma published by CEN (CEN/TS 16945:2016) (Bernini et al., 2011; Molecular in Vitro Diagnostic Examinations—Specifications for Pre-Examination Processes for Metabolomics in Urine, Venous Blood Serum, & Plasma., 2016). This document is now in the process of being translated into ISO/IS specifications (ISO/DIS 23118) (Molecular in Vitro Diagnostic Examinations—Specifications for PreExamination Processes in Metabolomics in Urine, Venous Blood Serum, & Plasma, 2020) as part of the activities of the EC founded project SPIDIA4P (H2020, #733112), and these recommendations have been adopted by some biobanks (Carotenuto et al., 2015; Marcon & Nincheri, 2014). Saliva is an interesting biofluid that, although less exploited than blood and urine, has demonstrated potentially useful applications in recent years (Aimetti et al., 2012; Romano et al., 2018, 2019). For this reason, a recent paper (Duarte, Castro et al., 2020) reported an untargeted NMR metabolomics study to assess the effects of different storage temperatures and times on saliva composition. The authors showed that, after collection, saliva may be kept at 22 C or 4 C for up to 6 hours, after which some metabolite levels start to change, especially at 22 C. For longer periods, saliva is stable at 220 C for at least 4 weeks (Duarte, Castro et al., 2020). Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Summary and future perspectives p0650 p0655 s0135 p0660 p0665 p0670 The composition of CSF, a secretion product of the central nervous system, indirectly reflects the biochemical processes occurring in the brain (Albrecht et al., 2020). Thus CSF metabolomics has received much attention in the research of neurological disorders including Alzheimer’s disease (Vignoli et al., 2020), Parkinson’s disease, multiple sclerosis and brain injury, amongst others. Optimal procedures for the collection and biobanking of CSF for clinical purposes have been proposed by Teunissen et al. (2009). These procedures are not specifically developed or validated for NMR metabolomics. However, according to a paper of the Metabolomic Society Initiative (Kirwan et al., 2018), these recommendations could be used for metabolomics with minor changes, including a centrifugation step (2000 g for 10 minutes at 4 C) before storage to remove and prevent cell lysis on thawing. In any case, further systematic validations are still needed. Unfortunately, for many other potentially interesting biofluids (e.g., sweat, tears, sperm, vaginal fluid, synovial fluid, breast milk) specific SOPs for metabolomics investigations are not yet established. In these cases, the recommendation (Kirwan et al., 2018) is that, in the absence of specific studies and until proper validations, the procedures should be based on existing clinical protocols, reasonably adapted following similar metabolomics research. Summary and future perspectives The Human Genome Project (1993
2003) was termed as one of “the great feats of exploration in history.” Even if officially “complete,” it is still active in thousands of laboratories around the world delivering new data with an estimated 60 million human genomes available in the coming years (Birney et al., 2017; Langmead & Nellore, 2018). Genomics, which depicts in detail genotypes, gave birth to transcriptomics, proteomics, metabolomics, and single-cell-omics techniques that define phenotypes. Presently high-resolution imaging, electronic health and medical records, “big-data” analytics, and numerous internet-connected health devices have the potential to combine all into a far clearer picture of a “human being.” Innovative cloud systems maintain and allow access to “-omics,” collaborating with other relevant clinical data sources in a secure manner. A standardized meta/ data format could vastly simplify data sharing and increase the findability (Aarestrup et al., 2020) of key results. NMR, with its high reproducibility and standard data output, fits perfectly with the cloud data-storage concept. Matching genotypes with phenotypes and environmental factors is a complicated process, in which metabolomics has a fundamental role. In the future, the analysis of metabolites related to the aging process, virus and bacteria pathologies, ingested drugs and food, and climate changing factors (e.g., pCO2, temperature) will define better pathologies and therapies. Applying these ideas with information and communication technology-based medicine will permit the monitoring of the changes in the metabolome and the deviations from an individual baseline. Medical doctors and supporting staff can Troisi-MP-1633011 978-0-323-85062-9 00005 193 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 194 p0675 CHAPTER 5 Nuclear magnetic resonance in metabolomics follow the development of diseases and subsequent response(s) to developed therapies. Of course, genomic and other
omic information will need to be recorded and integrated in the “virtual patient” description (Bertini et al., 2012). Because of its simplicity and minimal sample processing, NMR is ideally suited for systems medicine applications (Dos Santos et al., 2020), profiling blood and tissue samples collected in the operating theater can provide almost real time information to the surgeons and to clinicians (Nicholson et al., 2012). Although NMR spectroscopy has the limitation of intrinsically low sensitivity, the continuous development of NMR methods, probes, and ultra-high magnetic fields along with new hyperpolarization methods may alleviate many of NMR’s limitations. Moreover, recent developments of computer power, related molecular identification software, and metabolomics databases will facilitate more applications of NMR-based metabolomics in a wider range of research areas. References Aarestrup, F. M., Albeyatti, A., Armitage, W., Auffray, C., Augello, L., Balling, R., Benhabiles, N., Bertolini, G., Bjaalie, J., & Black, M. (2020). Towards a European health research and innovation cloud (HRIC). Genome Medicine, 12(1), 1
14. Abd Ghafar, S. Z., Mediani, A., Maulidiani, M., Rudiyanto, R., Ghazali, H. M., Ramli, N. S., & Abas, F. (2020). Complementary NMR-and MS-based metabolomics approaches reveal the correlations of phytochemicals and biological activities in Phyllanthus acidus leaf extracts. Food Research International, 136, 109312. Abdul-Hamid, N. A., et al. (2019). 1H-NMR-based metabolomics to investigate the effects of Phoenix dactylifera seed extracts in LPS-IFN-γ-induced RAW 264.7 cells. Food Research International (Ottawa, Ont.), 125, 108565. Available from https://doi.org/ 10.1016/j.foodres.2019.108565. Abdul Jameel, A. G., Alquaity, A. B. S., Campuzano, F., Emwas, A.-H., Saxena, S., Sarathy, S. M., & Roberts, W. L. (2021). Surrogate formulation and molecular characterization of sulfur species in vacuum residues using APPI and ESI FT-ICR mass spectrometry. Fuel, 293, 120471. Available from https://doi.org/10.1016/j.fuel.2021.120471. Acciardo, S., et al. (2020). Metabolic imaging using hyperpolarized (13) C-pyruvate to assess sensitivity to the B-Raf inhibitor vemurafenib in melanoma cells and xenografts. Journal of cellular and molecular medicine, 24(2), 1934
1944. Available from https:// doi.org/10.1111/jcmm.14890. Adams, R. W. (2007). Pure shift NMR spectroscopy. Emagres, 295
310. Agrawal, P. (2020). NMR spectroscopy in drug discovery and development. Materials and Methods. Available from https://doi.org/10.13070/mm.en.4.599. Aimetti, M., Cacciatore, S., Graziano, A., & Tenori, L. (2012). Metabonomic analysis of saliva reveals generalized chronic periodontitis signature. Metabolomics: Official Journal of the Metabolomic Society, 8(3), 465
474. Alahmari, F., Davaasuren, B., Emwas, A.-H., & Rothenberger, A. (2018). Thioaluminogermanate M (AlS2)(GeS2) 4 (M 5 Na, Ag, Cu): Synthesis, crystal structures, characterization, ion-exchange and solid-state 27Al and 23Na NMR spectroscopy. Inorganic Chemistry, 57(7), 3713
3719. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Albrecht, B., Voronina, E., Schipke, C., Peters, O., Parr, M. K., Dı́az-Hernández, M. D., & Schlörer, N. E. (2020). Pursuing experimental reproducibility: An efficient protocol for the preparation of cerebrospinal fluid samples for NMR-based metabolomics and analysis of sample degradation. Metabolites, 10(6), 251. Aljuhani, M. A., Zhang, Z., Barman, S., El Eter, M., Failvene, L., Ould-Chikh, S., Guan, E., Abou-Hamad, E., Emwas, A.-H., & Pelletier, J. D. (2019). Mechanistic study of hydroamination of alkyne through tantalum-based silica-supported surface species. ACS Catalysis, 9(9), 8719
8725. Alonso, J., Arús, C., Westler, W. M., & Markley, J. L. (1989). Two-dimensional correlated spectroscopy (COSY) of intact frog muscle: Spectral pattern characterization and lactate quantitation. Magnetic Resonance in Medicine, 11(3), 316
330. Alsiary, R. A., Alghrably, M., Saoudi, A., Al-Ghamdi, S., Jaremko, L., Jaremko, M., & Emwas, A.-H. (2020). Using NMR spectroscopy to investigate the role played by copper in prion diseases. Neurological Sciences, 1
18. Altes, T. A., & Salerno, M. (2004). Hyperpolarized gas MR imaging of the lung. Journal of Thoracic Imaging, 19(4), 250
258. Altieri, A., Miller, K., & Byrd, R. (1996). A comparison of water suppression techniques using pulsed field gradients for high-resolution NMR of biomolecules. Magn Res Rev, 17, 27
82. Apperley, D. C., Harris, R. K., & Hodgkinson, P. (2012). Solid-state NMR: Basic principles and practice. Momentum Press. Ashbrook, S. E., Griffin, J. M., & Johnston, K. E. (2018). Recent advances in solid-state nuclear magnetic resonance spectroscopy. Annual Review of Analytical Chemistry, 11, 485
508. Aue, W., Karhan, J., & Ernst, R. (1976). Homonuclear broad band decoupling and twodimensional J-resolved NMR spectroscopy. The Journal of Chemical Physics, 64(10), 4226
4227. Babgi, B. A., Alsayari, J., Alenezi, H. M., Abdellatif, M. H., Eltayeb, N. E., Emwas, A.H. M., Jaremko, M., & Hussien, M. A. (2021). Alteration of anticancer and proteinbinding properties of gold(I) alkynyl by phenolic Schiff bases moieties. Pharmaceutics, 13(4), 461. Available from https://doi.org/10.3390/pharmaceutics13040461. Banci, L., Barbieri, L., Calderone, V., Cantini, F., Cerofolini, L., Ciofi-Baffoni, S., Felli, I. C., Fragai, M., Lelli, M., & Luchinat, C. (2019). Biomolecular NMR at 1.2 GHz. ArXiv Preprint ArXiv:1910.07462. Barskiy, D. A., Coffey, A. M., Nikolaou, P., Mikhaylov, D. M., Goodson, B. M., Branca, R. T., Lu, G. J., Shapiro, M. G., Telkki, V.-V., & Zhivonitko, V. V. (2017). NMR hyperpolarization techniques of gases. Chemistry (Weinheim an Der Bergstrasse, Germany), 23(4), 725. Barskiy, D. A., Shchepin, R. V., Coffey, A. M., Theis, T., Warren, W. S., Goodson, B. M., & Chekmenev, E. Y. (2016). Over 20% 15N hyperpolarization in under one minute for metronidazole, an antibiotic and hypoxia probe. Journal of the American Chemical Society, 138(26), 8080
8083. Bartel, J., Krumsiek, J., & Theis, F. J. (2013). Statistical methods for the analysis of highthroughput metabolomics data. Computational and Structural Biotechnology Journal, 4 (5), e201301009. Bauer, M., Bertario, A., Boccardi, G., Fontaine, X., Rao, R., & Verrier, D. (1998). Reproducibility of 1H-NMR integrals: A collaborative study. Journal of Pharmaceutical and Biomedical Analysis, 17(3), 419
425. Troisi-MP-1633011 978-0-323-85062-9 00005 195 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 196 CHAPTER 5 Nuclear magnetic resonance in metabolomics Bax, A., & Summers, M. F. (1986). Proton and carbon-13 assignments from sensitivityenhanced detection of heteronuclear multiple-bond connectivity by 2D multiple quantum NMR. Journal of the American Chemical Society, 108(8), 2093
2094. Beckonert, O., Coen, M., Keun, H. C., Wang, Y., Ebbels, T. M., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2010). High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nature Protocols, 5(6), 1019
1032. Beckonert, O., Keun, H. C., Ebbels, T. M. D., Bundy, J., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2007). Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols, 2(11), 2692
2703. Available from https://doi.org/10.1038/nprot.2007.376. Beirnaert, C., Meysman, P., Vu, T. N., Hermans, N., Apers, S., Pieters, L., Covaci, A., & Laukens, K. (2018). speaq 2.0: A complete workflow for high-throughput 1D NMR spectra processing and quantification. PLoS Computational Biology, 14(3), e1006018. Berg, J. M., Tymoczko, J. L., & Stryer, L. Biochemistry. 5th edition. New York: W. H. Freeman; 2002. Section 16.1, Glycolysis Is an Energy-Conversion Pathway in Many Organisms. Available from https://www.ncbi.nlm.nih.gov/books/NBK22593/. Bernini, P., Bertini, I., Luchinat, C., Nepi, S., Saccenti, E., Schażfer, H., Schużtz, B., Spraul, M., & Tenori, L. (2009). Individual human phenotypes in metabolic space and time. Journal of Proteome Research, 8(9), 4264
4271. Bernini, P., Bertini, I., Luchinat, C., Nincheri, P., Staderini, S., & Turano, P. (2011). Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. Journal of Biomolecular NMR, 49(3), 231
243. Bertelli, D., Lolli, M., Papotti, G., Bortolotti, L., Serra, G., & Plessi, M. (2010). Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance. Journal of Agricultural and Food Chemistry, 58(15), 8495
8501. Bertini, I., Luchinat, C., & Tenori, L. (2012). Metabolomics for the future of personalized medicine through information and communication technologies. Personalized Medicine, 9(2), 133
136. Bhinderwala, F., Evans, P., Jones, K., Laws, B. R., Smith, T. G., Morton, M., & Powers, R. (2020). Phosphorus NMR and its application to metabolomics. Analytical Chemistry, 92(14), 9536
9545. Bhinderwala, F., Lonergan, S., Woods, J., Zhou, C., Fey, P. D., & Powers, R. (2018). Expanding the coverage of the Metabolome with nitrogen-based NMR. Analytical Chemistry, 90(7), 4521
4528. Bhinderwala, F., Wase, N., DiRusso, C., & Powers, R. (2018). Combining mass spectrometry and NMR improves metabolite detection and annotation. Journal of Proteome Research, 17(11), 4017
4022. Bingol, K. (2018). Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High-Throughput, 7(2), 9. Birney, E., Vamathevan, J., & Goodhand, P. (2017). Genomics in healthcare: GA4GH looks to 2022. BioRxiv, 203554. Blaive, B., Pietri, S., Miollan, M., Martel, S., Le Moigne, F., & Culcasi, M. (2000). Alphaand beta-phosphorylated amines and pyrrolidines, a new class of low toxic highly sensitive 31P NMR pH Indicators. Modeling of pKa and chemical shift values as a Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References function of substituents. Journal of Biological Chemistry, 275(26), 19505
19512. Available from https://doi.org/10.1074/jbc.M001784200. Blasco, H., Corcia, P., Moreau, C., Veau, S., Fournier, C., Vourc’h, P., Emond, P., Gordon, P., Pradat, P.-F., & Praline, J. (2010). 1 H-NMR-based metabolomic profiling of CSF in early amyotrophic lateral sclerosis. PLoS One, 5(10), e13223. Blasco, H., Nadal-Desbarats, L., Pradat, P.-F., Gordon, P. H., Antar, C., Veyrat-Durebex, C., Moreau, C., Devos, D., Mavel, S., & Emond, P. (2014). Untargeted 1H-NMR metabolomics in CSF: Toward a diagnostic biomarker for motor neuron disease. Neurology, 82(13), 1167
1174. Blümich, B. (2019). Low-field and benchtop NMR. Journal of Magnetic Resonance, 306, 27
35. Bodenhausen, G., & Ruben, D. J. (1980). Natural abundance nitrogen-15 NMR by enhanced heteronuclear spectroscopy. Chemical Physics Letters, 69(1), 185
189. Boersma, M., Solyanikova, I., Van Berkel, W., Vervoort, J., Golovleva, L., & Rietjens, I. (2001). 19F NMR metabolomics for the elucidation of microbial degradation pathways of fluorophenols. Journal of Industrial Microbiology and Biotechnology, 26(1
2), 22
34. Bosc, C., Broin, N., Fanjul, M., Saland, E., Farge, T., Courdy, C., Batut, A., Masoud, R., Larrue, C., & Skuli, S. (2020). Autophagy regulates fatty acid availability for oxidative phosphorylation through mitochondria-endoplasmic reticulum contact sites. Nature Communications, 11(1), 1
14. Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., & Liu, P. (2013). The human urine metabolome. PLoS One, 8(9), e73076. Braunschweiler, L., & Ernst, R. (1983). Coherence transfer by isotropic mixing: Application to proton correlation spectroscopy. Journal of Magnetic Resonance (1969), 53(3), 521
528. Brinson, R. G., Arbogast, L. W., Marino, J. P., & Delaglio, F. (2020). Best practices in utilization of 2D-NMR spectral data as the input for chemometric analysis in biopharmaceutical applications. Journal of Chemical Information and Modeling, 60(4), 2339
2355. Buchli, R., Meier, D., Martin, E., & Boesiger, P. (1994). Assessment of absolute metabolite concentrations in human tissue by 31P MRS in vivo. Part II: Muscle, liver, kidney. Magnetic Resonance in Medicine, 32(4), 453
458. Bunescu, A., Garric, J., Vollat, B., Canet-Soulas, E., Graveron-Demilly, D., & Fauvelle, F. (2010). In vivo proton HR-MAS NMR metabolic profile of the freshwater cladoceran Daphnia magna. Molecular Biosystems, 6(1), 121
125. Cady, E., Dawson, M. J., Hope, P., Tofts, P., Costello, A., de, L., Delpy, D., Reynolds, E., & Wilkie, D. (1983). Non-invasive investigation of cerebral metabolism in newborn infants by phosphorus nuclear magnetic resonance spectroscopy. The Lancet, 321 (8333), 1059
1062. Cao, Q., Liu, H., Zhang, G., Wang, X., Manyande, A., & Du, H. (2020). 1H-NMR based metabolomics reveals the nutrient differences of two kinds of freshwater fish soups before and after simulated gastrointestinal digestion. Food & Function, 11(4), 3095
3104. Carlbom, L., Weis, J., Johansson, L., Korsgren, O., & Ahlström, H. (2017). Pretransplantation 31P-magnetic resonance spectroscopy for quality assessment of human pancreatic grafts
A feasibility study. Magnetic Resonance Imaging, 39, 98
102. Troisi-MP-1633011 978-0-323-85062-9 00005 197 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 198 CHAPTER 5 Nuclear magnetic resonance in metabolomics Carotenuto, D., Luchinat, C., Marcon, G., Rosato, A., & Turano, P. (2015). The Da Vinci European BioBank: A metabolomics-driven infrastructure. Journal of Personalized Medicine, 5(2), 107
119. Castañar, L. (2017). Pure shift 1H NMR: What is next? Magnetic Resonance in Chemistry, 55(1), 47
53. Cavallari, E., et al. (2020). In-vitro NMR Studies of Prostate Tumor Cell Metabolism by Means of Hyperpolarized [1-13C]Pyruvate Obtained Using the PHIP-SAH Method. Frontiers in Oncology, 10(497). Available from https://doi.org/10.3389/fonc.2020. 00497. Cavanagh, J., Fairbrother, W. J., Palmer, A. G., III, & Skelton, N. J. (1995). Protein NMR spectroscopy: Principles and practice. Elsevier. Chan, E. C. Y., Koh, P. K., Mal, M., Cheah, P. Y., Eu, K. W., Backshall, A., Cavill, R., Nicholson, J. K., & Keun, H. C. (2009). Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). Journal of Proteome Research, 8(1), 352
361. Chandra, K., Al-Harthi, S., Sukumaran, S., Almulhim, F., Emwas, A.-H., Atreya, H. S., Jaremko, Ł., & Jaremko, M. (2021). NMR-based metabolomics with enhanced sensitivity. RSC Advances, 11(15), 8694
8700. Chiechio, S., Canonico, P. L., & Grilli, M. (2018). L-Acetylcarnitine: A mechanistically distinctive and potentially rapid-acting antidepressant drug. International Journal of Molecular Sciences, 19(1), 11. Chisca, S., Duong, P., Emwas, A.-H., Sougrat, R., & Nunes, S. P. (2015). Crosslinked copolyazoles with a zwitterionic structure for organic solvent resistant membranes. Polymer Chemistry, 6(4), 543
554. Chorao, C., Traı̈kia, M., Besse-Hoggan, P., Sancelme, M., Bligny, R., Gout, E., Mailhot, G., & Delort, A. (2010). In vivo31P and 13C NMR investigations of Rhodococcus rhodochrous metabolism and behaviour during biotransformation processes. Journal of Applied Microbiology, 108(5), 1733
1743. Chu, S., Maltsev, S., Emwas, A.-H., & Lorigan, G. A. (2010). Solid-state NMR paramagnetic relaxation enhancement immersion depth studies in phospholipid bilayers. Journal of Magnetic Resonance, 207(1), 89
94. Claridge, T. D. (2016). High-resolution NMR techniques in organic chemistry (Vol. 27). Elsevier. Clendinen, C. S., Lee-McMullen, B., Williams, C. M., Stupp, G. S., Vandenborne, K., Hahn, D. A., Walter, G. A., & Edison, A. S. (2014). 13C NMR metabolomics: Applications at natural abundance. Analytical Chemistry, 86(18), 9242
9250. Clendinen, C. S., Stupp, G. S., Ajredini, R., Lee-McMullen, B., Beecher, C., & Edison, A. S. (2015). An overview of methods using 13C for improved compound identification in metabolomics and natural products. Frontiers in Plant Science, 6, 611. Cloarec, O., Dumas, M.-E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., & Holmes, E. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77(5), 1282
1289. Cui, G., Liew, Y. J., Li, Y., Kharbatia, N., Zahran, N. I., Emwas, A.-H., Eguiluz, V. M., & Aranda, M. (2019). Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLoS Genetics, 15(6), e1008189. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Cui, J., Zhu, D., Su, M., Tan, D., Zhang, X., Jia, M., & Chen, G. (2019). The combined use of 1H and 2D NMR-based metabolomics and chemometrics for non-targeted screening of biomarkers and identification of reconstituted milk. Journal of the Science of Food and Agriculture, 99(14), 6455
6461. Cui, Q., Lewis, I. A., Hegeman, A. D., Anderson, M. E., Li, J., Schulte, C. F., Westler, W. M., Eghbalnia, H. R., Sussman, M. R., & Markley, J. L. (2008). Metabolite identification via the Madison metabolomics consortium database. Nature Biotechnology, 26 (2), 162
164. Čuperlović-Culf, M. (2012). NMR metabolomics in cancer research. Elsevier. Darpolor, M. M., et al. (2014). The aspartate metabolism pathway is differentiable in human hepatocellular carcinoma: transcriptomics and 13C-isotope based metabolomics. NMR in Biomedicine, 27(4), 381
389. Available from https://doi.org/10.1002/nbm.3072. da Silva, R. R., Dorrestein, P. C., & Quinn, R. A. (2015). Illuminating the dark matter in metabolomics. Proceedings of the National Academy of Sciences, 112(41), 12549
12550. de Brouwer, H., & Stegeman, G. (2011). A LEAN approach Toward automated analysis and data processing of polymers using proton NMR spectroscopy. JALA: Journal of the Association for Laboratory Automation, 16(1), 1
16. De Livera, A. M., Olshansky, M., & Speed, T. P. (2013). Statistical analysis of metabolomics data. Metabolomics tools for natural product discovery (pp. 291
307). Springer. Dey, A., Charrier, B., Martineau, E., Deborde, C., Gandriau, E., Moing, A., Jacob, D., Eshchenko, D., Schnell, M., Melzi, R., Kurzbach, D., Ceillier, M., Chappuis, Q., Cousin, S. F., Kempf, J. G., Jannin, S., Dumez, J.-N., & Giraudeau, P. (2020). Hyperpolarized NMR metabolomics at natural 13C abundance. Analytical Chemistry, 92(22), 14867
14871. Available from https://doi.org/10.1021/acs.analchem.0c03510. Dhahri, M., Sioud, S., Dridi, R., Hassine, M., Boughattas, N. A., Almulhim, F., Al Talla, Z., Jaremko, M., & Emwas, A.-H. M. (2020). Extraction, characterization, and anticoagulant activity of a sulfated polysaccharide from Bursatella leachii viscera. ACS Omega, 5(24), 14786
14795. Diaz, G., Miranda, I. L., & Diaz, M. A. N. (2015). Quinolines, isoquinolines, angustureine, and congeneric alkaloids—occurrence, chemistry, and biological activity. Phytochemicals-Isolation, Characterisation and Role in Human Health. Diaz, O., Fernandez, M., De Fernando, G. D. G., de la Hoz, L., & Ordoñez, J. A. (1997). Proteolysis in dry fermented sausages: The effect of selected exogenous proteases. Meat Science, 46(1), 115
128. Dona, A. C., Kyriakides, M., Scott, F., Shephard, E. A., Varshavi, D., Veselkov, K., & Everett, J. R. (2016). A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Computational and Structural Biotechnology Journal, 14, 135
153. Dos Santos, V. A. M., Hardt, C., Skrede, S., & Saccenti, E. (2020). Systems and precision medicine in necrotizing soft tissue infections. Necrotizing Soft Tissue Infections (pp. 187
207). Springer. Dossey, A. T., Walse, S. S., Conle, O. V., & Edison, A. S. (2007). Parectadial, a monoterpenoid from the defensive spray of Parectatosoma mocquerysi. Journal of Natural Products, 70(8), 1335
1338. Duarte, C. M., Jaremko, Ł., & Jaremko, M. (2020). Hypothesis: Potentially systemic impacts of elevated CO2 on the human proteome and health. Frontiers in Public Health, 8. Troisi-MP-1633011 978-0-323-85062-9 00005 199 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 200 CHAPTER 5 Nuclear magnetic resonance in metabolomics Duarte, D., Castro, B., Pereira, J. L., Marques, J. F., Costa, A. L., & Gil, A. M. (2020). Evaluation of saliva stability for NMR metabolomics: Collection and handling protocols. Metabolites, 10(12), 515. Duarte, I. F., Diaz, S. O., & Gil, A. M. (2014). NMR metabolomics of human blood and urine in disease research. Journal of Pharmaceutical and Biomedical Analysis, 93, 17
26. Dudka, I., et al. (2020). Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status. BMC cancer, 20 (1), 437. Available from https://doi.org/10.1186/s12885-020-06908-z. Dumas, M.-E., Maibaum, E. C., Teague, C., Ueshima, H., Zhou, B., Lindon, J. C., Nicholson, J. K., Stamler, J., Elliott, P., & Chan, Q. (2006). Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: The INTERMAP Study. Analytical Chemistry, 78(7), 2199
2208. Dumez, J.-N., et al. (2015). Hyperpolarized NMR of plant and cancer cell extracts at natural abundance. Analyst, 140(17), 5860
5863. Available from https://doi.org/10.1039/ C5AN01203A. Eckhardt, B. J., Gulick, R. M., Cohen, J., Powderly, W. G., & Opal, S. M. (2017). 152— Drugs for HIV Infection (pp. 1293
1308). Elsevier e2. Available from https://doi.org/ 10.1016/B978-0-7020-6285-8.00152-0. Edgar, M., Percival, B. C., Gibson, M., Jafari, F., & Grootveld, M. (2021). Low-field benchtop NMR spectroscopy as a potential non-stationary tool for point-of-care urinary metabolite tracking in diabetic conditions. Diabetes Research and Clinical Practice, 171, 108554. Edison, A. S., Le Guennec, A., Delaglio, F., & Kupče, Ē. (2019). Practical guidelines for 13 C-based NMR metabolomics. NMR-Based Metabolomics (pp. 69
95). Springer. Ellinger, J. J., Chylla, R. A., Ulrich, E. L., & Markley, J. L. (2013). Databases and software for NMR-based metabolomics. Current Metabolomics, 1(1), 28
40. Emwas, A.-H., Alghrably, M., Al-Harthi, S., Gabriel Poulson, B., Szczepski, K., Chandra, K., & Jaremko, M. (2019). New advances in fast methods of 2D NMR experiments. Nuclear magnetic resonance. IntechOpen. Emwas, A.-H., Luchinat, C., Turano, P., Tenori, L., Roy, R., Salek, R. M., Ryan, D., Merzaban, J. S., Kaddurah-Daouk, R., & Zeri, A. C. (2015). Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: A review. Metabolomics: Official Journal of the Metabolomic Society, 11(4), 872
894. Emwas, A.-H., Roy, R., McKay, R. T., Ryan, D., Brennan, L., Tenori, L., Luchinat, C., Gao, X., Zeri, A. C., & Gowda, G. N. (2016). Recommendations and standardization of biomarker quantification using NMR-based metabolomics with particular focus on urinary analysis. Journal of Proteome Research, 15(2), 360
373. Emwas, A.-H., Roy, R., McKay, R. T., Tenori, L., Saccenti, E., Gowda, G., Raftery, D., Alahmari, F., Jaremko, L., & Jaremko, M. (2019). NMR spectroscopy for metabolomics research. Metabolites, 9(7), 123. Emwas, A.-H., Saccenti, E., Gao, X., McKay, R. T., Dos Santos, V. A. M., Roy, R., & Wishart, D. S. (2018). Recommended strategies for spectral processing and postprocessing of 1D 1 H-NMR data of biofluids with a particular focus on urine. Metabolomics: Official Journal of the Metabolomic Society, 14(3), 1
23. Emwas, A.-H., Saunders, M., Ludwig, C., & Günther, U. (2008). Determinants for optimal enhancement in ex situ DNP experiments. Applied Magnetic Resonance, 34(3
4), 483
494. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Emwas, A.-H. M. (2015). The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Metabonomics (pp. 161
193). Springer. Emwas, A.-H. M., Al-Talla, Z. A., & Kharbatia, N. M. (2015). Sample collection and preparation of biofluids and extracts for gas chromatography
mass spectrometry. Metabonomics (pp. 75
90). Springer. Emwas, A.-H. M., Salek, R. M., Griffin, J. L., & Merzaban, J. (2013). NMR-based metabolomics in human disease diagnosis: Applications, limitations, and recommendations. Metabolomics: Official Journal of the Metabolomic Society, 9(5), 1048
1072. Emwas, A. M., Al-Talla, Z. A., Guo, X., Al-Ghamdi, S., & Al-Masri, H. T. (2013). Utilizing NMR and EPR spectroscopy to probe the role of copper in prion diseases. Magnetic Resonance in Chemistry, 51(5), 255
268. Enderle, J. D. (2012). Biochemical reactions and enzyme kinetics. Introduction to biomedical engineering (pp. 447
508). Elsevier. Esmaeili, M., Bathen, T. F., Engebråten, O., Mælandsmo, G. M., Gribbestad, I. S., & Moestue, S. A. (2014). Quantitative 31P HR-MAS MR spectroscopy for detection of response to PI3K/mTOR inhibition in breast cancer xenografts. Magnetic Resonance in Medicine, 71(6), 1973
1981. Euceda, L. R., Giskeødegård, G. F., & Bathen, T. F. (2015). Preprocessing of NMR metabolomics data. Scandinavian Journal of Clinical and Laboratory Investigation, 75(3), 193
203. Fan, T. W. M., et al. (2012). Stable isotope resolved metabolomics analysis of ribonucleotide and RNA metabolism in human lung cancer cells. Metabolomics, 8(3), 517
527. Available from https://doi.org/10.1007/s11306-011-0337-9. Fan, T. W.-M., & Lane, A. N. (2016). Applications of NMR spectroscopy to systems biochemistry. Progress in Nuclear Magnetic Resonance Spectroscopy, 92, 18
53. Farag, M. A., Mahrous, E. A., Lübken, T., Porzel, A., & Wessjohann, L. (2014). Classification of commercial cultivars of Humulus lupulus L. (hop) by chemometric pixel analysis of two dimensional nuclear magnetic resonance spectra. Metabolomics: Official Journal of the Metabolomic Society, 10(1), 21
32. Farjon, J. (2017). How to face the low intrinsic sensitivity of 2D heteronuclear NMR with fast repetition techniques: Go faster to go higher!. Magnetic Resonance in Chemistry, 55(10), 883
892. Felsenfeld, A. J., & Levine, B. S. (2015). Pathophysiology of calcium, phosphorus, and magnesium in chronic kidney disease. Chronic renal disease (pp. 391
405). Elsevier. Feraud, B., Leenders, J., Martineau, E., Giraudeau, P., Govaerts, B., & De Tullio, P. (2019). Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics. Metabolomics: Official Journal of the Metabolomic Society, 15(4), 1
14. Féraud, B., Martineau, E., Leenders, J., Govaerts, B., de Tullio, P., & Giraudeau, P. (2020). Combining rapid 2D NMR experiments with novel pre-processing workflows and MIC quality measures for metabolomics. Metabolomics: Official Journal of the Metabolomic Society, 16(4), 42. Available from https://doi.org/10.1007/s11306-020-01662-6. Flores, A., Manfron Schiefer, E., Sassaki, G., Menezes, L., Fonseca, R., Cunha, R., Canziani, M. E., Guedes, M., Moreno-Amaral, A. N., & Souza, W. (2020). P1057 untargeted 1h NMR-based serum metabolic profile analysis of patients treated with high volume hemodiafiltration (HDF). Nephrology Dialysis Transplantation, 35(Suppl. 3), gfaa142
P1057. Troisi-MP-1633011 978-0-323-85062-9 00005 201 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 202 CHAPTER 5 Nuclear magnetic resonance in metabolomics Flores-Sanchez, I. J., Choi, Y. H., & Verpoorte, R. (2012). Metabolite analysis of Cannabis sativa L. by NMR spectroscopy. Functional Genomics (pp. 363
375). Springer. Foxall, P., Parkinson, J., Sadler, I., Lindon, J., & Nicholson, J. (1993). Analysis of biological fluids using 600 MHz proton NMR spectroscopy: Application of homonuclear twodimensional J-resolved spectroscopy to urine and blood plasma for spectral simplification and assignment. Journal of Pharmaceutical and Biomedical Analysis, 11(1), 21
31. Freeman, C. D., Klutman, N. E., & Lamp, K. C. (1997). Metronidazole. Drugs, 54(5), 679
708. Frydman, L., Scherf, T., & Lupulescu, A. (2002). The acquisition of multidimensional NMR spectra within a single scan. Proceedings of the National Academy of Sciences, 99(25), 15858
15862. Gallo, A., Farinha, A. S., Dinis, M., Emwas, A.-H., Santana, A., Nielsen, R. J., Goddard, W. A., & Mishra, H. (2019). The chemical reactions in electrosprays of water do not always correspond to those at the pristine air
water interface. Chemical Science, 10 (9), 2566
2577. Gallo, V., Intini, N., Mastrorilli, P., Latronico, M., Scapicchio, P., Triggiani, M., Bevilacqua, V., Fanizzi, P., Acquotti, D., & Airoldi, C. (2015). Performance assessment in fingerprinting and multi component quantitative NMR analyses. Analytical Chemistry, 87(13), 6709
6717. Garcı́a-Garcı́a, A. B., Lamichhane, S., Castejón, D., Cambero, M. I., & Bertram, H. C. (2018). 1H HR-MAS NMR-based metabolomics analysis for dry-fermented sausage characterization. Food Chemistry, 240, 514
523. Gargallo-Garriga, A., et al. (2020). (31)P-NMR Metabolomics Revealed Species-Specific Use of Phosphorous in Trees of a French Guiana Rainforest. Molecules, 25(17). Available from https://doi.org/10.3390/molecules25173960. Gattineni, J., & Friedman, P. A. (2015). Regulation of hormone-sensitive renal phosphate transport. Vitamins & Hormones, 98, 249
306. Gebregiworgis, T., & Powers, R. (2012). Application of NMR metabolomics to search for human disease biomarkers. Combinatorial Chemistry & High Throughput Screening, 15(8), 595
610. Geier, F. M., Leroi, A. M., & Bundy, J. G. (2019). 13C labeling of nematode worms to improve metabolome coverage by heteronuclear nuclear magnetic resonance experiments. Frontiers in Molecular Biosciences, 6, 27. Ghini, V., Quaglio, D., Luchinat, C., & Turano, P. (2019). NMR for sample quality assessment in metabolomics. New Biotechnology, 52, 25
34. Gil, A., Duarte, I., Cabrita, E., Goodfellow, B., Spraul, M., & Kerssebaum, R. (2004). Exploratory applications of diffusion ordered spectroscopy to liquid foods: An aid towards spectral assignment. Analytica Chimica Acta, 506(2), 215
223. Giraudeau, P. (2020). NMR-based metabolomics and fluxomics: Developments and future prospects. Analyst, 145(7), 2457
2472. Giraudeau, P., Guignard, N., Hillion, E., Baguet, E., & Akoka, S. (2007). Optimization of homonuclear 2D NMR for fast quantitative analysis: Application to tropine
nortropine mixtures. Journal of Pharmaceutical and Biomedical Analysis, 43(4), 1243
1248. Giraudeau, P., Silvestre, V., & Akoka, S. (2015). Optimizing water suppression for quantitative NMR-based metabolomics: A tutorial review. Metabolomics: Official Journal of the Metabolomic Society, 11(5), 1041
1055. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Gogiashvili, M., Nowacki, J., Hergenröder, R., Hengstler, J. G., Lambert, J., & Edlund, K. (2019). HR-MAS NMR based quantitative metabolomics in breast cancer. Metabolites, 9(2), 19. Gout, E., Bligny, R., Douce, R., Boisson, A., & Rivasseau, C. (2011). Early response of plant cell to carbon deprivation: In vivo 31P-NMR spectroscopy shows a quasi-instantaneous disruption on cytosolic sugars, phosphorylated intermediates of energy metabolism, phosphate partitioning, and intracellular pHs. New Phytologist, 189(1), 135
147. Griffiths, L., & Horton, R. (1998). Optimization of LC
NMR. III—Increased signal-to-noise ratio through column trapping. Magnetic Resonance in Chemistry, 36(2), 104
109. Guennec, A. L., Giraudeau, P., & Caldarelli, S. (2014). Evaluation of fast 2D NMR for metabolomics. Analytical Chemistry, 86(12), 5946
5954. Guillou, C., Trierweiler, M., & Martin, G. (1988). Repeatability and reproducibility of sitespecific isotope ratios in quantitative 2H NMR. Magnetic Resonance in Chemistry, 26 (6), 491
496. Halabalaki, M., Vougogiannopoulou, K., Mikros, E., & Skaltsounis, A. L. (2014). Recent advances and new strategies in the NMR-based identification of natural products. Current Opinion in Biotechnology, 25, 1
7. Halse, M. E. (2016). Perspectives for hyperpolarisation in compact NMR. TrAC Trends in Analytical Chemistry, 83, 76
83. Händel, H., Gesele, E., Gottschall, K., & Albert, K. (2003). Application of HRMAS 1H NMR spectroscopy to investigate interactions between ligands and synthetic receptors. Angewandte Chemie International Edition, 42(4), 438
442. Haner, R. L., & Keifer, P. A. (2007). Flow Probes for NMR spectroscopy. EMagRes. Hao, J., Astle, W., De Iorio, M., & Ebbels, T. M. (2012). BATMAN—An R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics (Oxford, England), 28(15), 2088
2090. Harris, R. K., Becker, E. D., Cabral De Menezes, S. M., Granger, P., Hoffman, R. E., & Zilm, K. W. (2007). Further conventions for NMR shielding and chemical shifts (IUPAC Recommendations 2008). EMagRes. Hasanpour, M., Saberi, S., & Iranshahi, M. (2020). Metabolic profiling and untargeted 1HNMR-based metabolomics study of different Iranian pomegranate (Punica granatum) ecotypes. Planta Medica, 86(03), 212
219. Haviland, J. A., et al. (2013). NMR-based metabolomics and breath studies show lipid and protein catabolism during low dose chronic T1AM treatment. Obesity, 21(12), 2538
2544. Available from https://doi.org/10.1002/oby.20391. Heude, C., Lemasson, E., Elbayed, K., & Piotto, M. (2015). Rapid assessment of fish freshness and quality by 1 H HR-MAS NMR spectroscopy. Food Analytical Methods, 8(4), 907
915. Hill, D. K., Mariotti, E., & Eykyn, T. R. (2018). Imaging metabolic processes in living systems with hyperpolarised 13C magnetic resonance (pp. 280
309). The Royal Society of Chemistry Chapter 11. Available from https://doi.org/10.1039/978178262793700280. Holmes, E., Wilson, I. D., & Nicholson, J. K. (2008). Metabolic phenotyping in health and disease. Cell, 134(5), 714
717. Hong, S.-M., Choi, C.-H., Magill, A. W., Shah, N. J., & Felder, J. (2018). Design of a quadrature 1H/31P coil using bent dipole antenna and four-channel loop at 3T MRI. IEEE Transactions on Medical Imaging, 37(12), 2613
2618. Troisi-MP-1633011 978-0-323-85062-9 00005 203 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 204 CHAPTER 5 Nuclear magnetic resonance in metabolomics Hoult, D. (1976). Solvent peak saturation with single phase and quadrature Fourier transformation. Journal of Magnetic Resonance (1969), 21(2), 337
347. Howarth, A., Ermanis, K., & Goodman, J. M. (2020). DP4-AI automated NMR data analysis: Straight from spectrometer to structure. Chemical Science, 11(17), 4351
4359. Hunt, C. T., Boulanger, Y., Fesik, S. W., & Armitage, I. M. (1984). NMR analysis of the structure and metal sequestering properties of metallothioneins. Environmental Health Perspectives, 54, 135
145. Izquierdo-Garcia, J. L., Comella-Del-Barrio, P., Campos-Olivas, R., Casanova, F., Dominguez, J., & Ruiz-Cabello, J. (2019). Benchtop NMR-based metabolomic analysis as a diagnostic tool for tuberculosis in clinical urine samples. Eur Respiratory Soc. Izquierdo-Garcia, J. L., Padro, D., Villa, P., Fadon, L., & Cifuentes, A. (2021). 2.25— NMR-based metabolomics (pp. 353
369). Elsevier. Available from https://doi.org/ 10.1016/B978-0-08-100596-5.22909-0. Jiang, C., Yang, K., Yang, L., Miao, Z., Wang, Y., & Zhu, H. (2013). A 1 H NMR-based metabonomic investigation of time-related metabolic trajectories of the plasma, urine and liver extracts of hyperlipidemic hamsters. PLoS One, 8(6), e66786. Johnson, K., Barrientos, L. G., Le, L., & Murthy, P. P. (1995). Application of twodimensional total correlation spectroscopy for structure determination of individual inositol phosphates in a mixture. Analytical Biochemistry, 231(2), 421
431. Johnson, S. R., & Lange, B. M. (2015). Open-access metabolomics databases for natural product research: Present capabilities and future potential. Frontiers in Bioengineering and Biotechnology, 3, 22. Jordan, K., Adkins, C., Su, L., Halpern, E., Mark, E., Christiani, D., & Cheng, L. (2010). Comparison of squamous cell carcinoma and adenocarcinoma of the lung by metabolomic analysis of tissue
serum pairs. Lung Cancer (Amsterdam, Netherlands), 68(1), 44
50. Jung, Y., Lee, J., Kwon, J., Lee, K.-S., Ryu, D. H., & Hwang, G.-S. (2010). Discrimination of the geographical origin of beef by 1H NMR-based metabolomics. Journal of Agricultural and Food Chemistry, 58(19), 10458
10466. Kaebisch, E., Fuss, T. L., Vandergrift, L. A., Toews, K., Habbel, P., & Cheng, L. L. (2017). Applications of high-resolution magic angle spinning MRS in biomedical studies I—cell line and animal models. NMR in Biomedicine, 30(6), e3700. Kaluarachchi, M., et al. (2018). A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS. Metabolomics, 14(3), 32. Available from https://doi.org/10.1007/s11306-018-1332-1. Kamal, A., Shaik, A. B., Kumar, C. G., Mongolla, P., Rani, P. U., Krishna, K. V. S. R., Mamidyala, S. K., & Joseph, J. (2012). Metabolic profiling and biological activities of bioactive compounds produced by Pseudomonas sp. strain ICTB-745 isolated from Ladakh, India. Journal of Microbiology and Biotechnology, 22(1), 69
79. Available from https://doi.org/10.4014/jmb.1105.05008. Kanamori, K. (2017). In vivo N-15 MRS study of glutamate metabolism in the rat brain. Analytical Biochemistry, 529, 179
192. Kanwal, S., Ann, N.-u., Fatima, S., Emwas, A.-H., Alazmi, M., Gao, X., Ibrar, M., Zaib Saleem, R. S., & Chotana, G. A. (2020). Facile synthesis of NH-free 5-(hetero)aryl-pyrrole-2-carboxylates by catalytic C
H borylation and Suzuki coupling. Molecules (Basel, Switzerland), 25(9), 210. Available from https://doi.org/10.3390/molecules25092106. Karaman, I. (2017). Preprocessing and pretreatment of metabolomics data for statistical analysis. Metabolomics: From Fundamentals to Clinical Applications, 145
161. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Karaman, I., Ferreira, D. L., Boulangé, C. L., Kaluarachchi, M. R., Herrington, D., Dona, A. C., Castagné, R., Moayyeri, A., Lehne, B., & Loh, M. (2016). Workflow for integrated processing of multicohort untargeted 1H NMR metabolomics data in large-scale metabolic epidemiology. Journal of Proteome Research, 15(12), 4188
4194. Keeler, J. (2011). Understanding NMR spectroscopy. John Wiley & Sons. Keifer, P. (2007). Flow techniques in NMR spectroscopy. Annual Reports on NMR Spectroscopy, 62, 1
47. Keifer, P. A. (2003). Flow injection analysis NMR (FIA
NMR): A novel flow NMR technique that complements LC
NMR and direct injection NMR (DI
NMR). Magnetic Resonance in Chemistry, 41(7), 509
516. Keifer, P. A., Smallcombe, S. H., Williams, E. H., Salomon, K. E., Mendez, G., Belletire, J. L., & Moore, C. D. (2000). Direct-injection NMR (DI-NMR): A flow NMR technique for the analysis of combinatorial chemistry libraries. Journal of Combinatorial Chemistry, 2(2), 151
171. Kern, S., Wander, L., Meyer, K., Guhl, S., Mukkula, A. R. G., Holtkamp, M., Salge, M., Fleischer, C., Weber, N., & King, R. (2019). Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals. Analytical and Bioanalytical Chemistry, 411(14), 3037
3046. Keun, H. C., Beckonert, O., Griffin, J. L., Richter, C., Moskau, D., Lindon, J. C., & Nicholson, J. K. (2002). Cryogenic probe 13C NMR spectroscopy of urine for metabonomic studies. Analytical Chemistry, 74(17), 4588
4593. Keun, H. C., Ebbels, T. M., Antti, H., Bollard, M. E., Beckonert, O., Schlotterbeck, G., Senn, H., Niederhauser, U., Holmes, E., & Lindon, J. C. (2002). Analytical reproducibility in 1H NMR-based metabonomic urinalysis. Chemical Research in Toxicology, 15(11), 1380
1386. Khasaeva, F., Parshikov, I., & Zaraisky, E. (2016). Biodegradation of 4-methylpyridine by Arthrobacter sp. Asian Journal of Microbiology, Biotechnology and Environmental Sciences, 18(1), 75
77. Kijewska, M., Sharfalddin, A. A., Jaremko, Ł., Cal, M., Setner, B., Siczek, M., Stefanowicz, P., Hussien, M. A., Emwas, A.-H., & Jaremko, M. (2021). Lossen rearrangement of p-toluenesulfonates of N-oxyimides in basic condition, theoretical study, and molecular docking. Frontiers in Chemistry, 9, 189. Kim, H. K., Choi, Y. H., & Verpoorte, R. (2010). NMR-based metabolomic analysis of plants. Nature Protocols, 5(3), 536
549. Kirwan, J. A., Brennan, L., Broadhurst, D., Fiehn, O., Cascante, M., Dunn, W. B., Schmidt, M. A., & Velagapudi, V. (2018). Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for “Precision Medicine and Pharmacometabolomics Task Group”— The Metabolomics Society Initiative). Clinical Chemistry, 64(8), 1158
1182. Kogler, H., Sørensen, O., Bodenhausen, G., & Ernst, R. (1983). Low-pass J filters. Suppression of neighbor peaks in heteronuclear relayed correlation spectra. Journal of Magnetic Resonance, 55, 157
163. Kohl, S. M., Klein, M. S., Hochrein, J., Oefner, P. J., Spang, R., & Gronwald, W. (2012). State-of-the art data normalization methods improve NMR-based metabolomic analysis. Metabolomics: Official Journal of the Metabolomic Society, 8(1), 146
160. Komoroski, R. A., Pearce, J. M., & Mrak, R. E. (2008). 31P NMR spectroscopy of phospholipid metabolites in postmortem schizophrenic brain. Magnetic Resonance in Troisi-MP-1633011 978-0-323-85062-9 00005 205 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 206 CHAPTER 5 Nuclear magnetic resonance in metabolomics Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 59(3), 469
474. Kono, H. (2013). 1H and 13C chemical shift assignment of the monomers that comprise carboxymethyl cellulose. Carbohydrate Polymers, 97(2), 384
390. Koskela, H., et al. (2018). pH-Dependent Piecewise Linear Correlation of 1H,31P Chemical Shifts: Application in NMR Identification of Nerve Agent Metabolites in Urine Samples. Analytical Chemistry, 90(14), 8495
8500. Available from https://doi. org/10.1021/acs.analchem.8b01308. Kovacs, H., Moskau, D., & Spraul, M. (2005). Cryogenically cooled probes—a leap in NMR technology. Progress in Nuclear Magnetic Resonance Spectroscopy, 2(46), 131
155. Kövér, K. E., & Batta, G. (1987). Strong coupling effects and their suppression in twodimensional heteronuclear NOE experiments. Journal of Magnetic Resonance (1969), 74(3), 397
405. Kovtunov, K. V., Pokochueva, E. V., Salnikov, O. G., Cousin, S. F., Kurzbach, D., Vuichoud, B., Jannin, S., Chekmenev, E. Y., Goodson, B. M., & Barskiy, D. A. (2018). Hyperpolarized NMR spectroscopy: d-DNP, PHIP, and SABRE Techniques. Chemistry—An Asian Journal, 13(15), 1857
1871. Krikken, E., et al. (2019). Early detection of changes in phospholipid metabolism during neoadjuvant chemotherapy in breast cancer patients using phosphorus magnetic resonance spectroscopy at 7T. NMR in Biomedicine, 32(6), e4086. Available from https:// doi.org/10.1002/nbm.4086. Krishnamurthy, K. (2013). CRAFT (complete reduction to amplitude frequency table)
Robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR. Magnetic Resonance in Chemistry, 51(12), 821
829. Kupče, Ē., & Freeman, R. (2003). Frequency-domain Hadamard spectroscopy. Journal of Magnetic Resonance, 162(1), 158
165. Lai, Z., Tsugawa, H., Wohlgemuth, G., Mehta, S., Mueller, M., Zheng, Y., Ogiwara, A., Meissen, J., Showalter, M., & Takeuchi, K. (2018). Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nature Methods, 15(1), 53
56. Lalaleo, L., et al. (2020). Differentiating, evaluating, and classifying three quinoa ecotypes by washing, cooking and germination treatments, using 1H NMR-based metabolomic approach. Food Chemistry, 331, 127351. Available from https://doi.org/10.1016/j. foodchem.2020.127351. Langmead, B., & Nellore, A. (2018). Cloud computing for genomic data analysis and collaboration. Nature Reviews. Genetics, 19(4), 208. Larson, P. E. Z., et al. (2018). Investigation of analysis methods for hyperpolarized 13Cpyruvate metabolic MRI in prostate cancer patients. NMR in Biomedicine, 31(11), e3997. Available from https://doi.org/10.1002/nbm.3997. Laserna, A. K. C., Lai, Y., Fang, G., Ganapathy, R., Atan, M. S. B. M., Lu, J., Wu, J., Uttamchandani, M., Moochhala, S. M., & Li, S. F. Y. (2020). Metabolic profiling of a porcine combat trauma-injury model using NMR and multi-mode LC-MS metabolomics—A preliminary study. Metabolites, 10(9), 373. Lawson, I. J., Ewart, C., Kraft, A., & Ellis, D. (2020). Demystifying NMR spectroscopy: Applications of benchtop spectrometers in the undergraduate teaching laboratory. Magnetic Resonance in Chemistry, 58(12), 1256
1260. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Le Guennec, A., Dumez, J., Giraudeau, P., & Caldarelli, S. (2015). Resolution-enhanced 2D NMR of complex mixtures by non-uniform sampling. Magnetic Resonance in Chemistry, 53(11), 913
920. Le Guennec, A., Tea, I., Antheaume, I., Martineau, E., Charrier, B., Pathan, M., Akoka, S., & Giraudeau, P. (2012). Fast determination of absolute metabolite concentrations by spatially encoded 2D NMR: Application to breast cancer cell extracts. Analytical Chemistry, 84(24), 10831
10837. Lee, W., et al. (2019). Discrimination of Human Urine from Animal Urine Using 1HNMR. Journal of Analytical Toxicology, 43(1), 51
60. Available from https://doi.org/ 10.1093/jat/bky061. Lee, W., Ko, B. J., Sim, Y. E., Suh, S., Yoon, D., & Kim, S. (2019). Discrimination of human urine from animal urine using 1H-NMR. Journal of Analytical Toxicology, 43 (1), 51
60. Levin, Y. S., Albers, M. J., Butler, T. N., Spielman, D., Peehl, D. M., & Kurhanewicz, J. (2009). Methods for metabolic evaluation of prostate cancer cells using proton and 13C HR-MAS spectroscopy and [3-13C] pyruvate as a metabolic substrate. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 62(5), 1091
1098. Levine, J., Panchalingam, K., McClure, R., Gershon, S., & Pettegrew, J. (2003). Effects of acetyl-L-carnitine and myo-inositol on high-energy phosphate and membrane phospholipid metabolism in Zebra Fish: A 31 P-NMR-Spectroscopy Study. Neurochemical Research, 28(5), 687
690. Lewis, I. A., Karsten, R. H., Norton, M. E., Tonelli, M., Westler, W. M., & Markley, J. L. (2010). NMR method for measuring carbon-13 isotopic enrichment of metabolites in complex solutions. Analytical Chemistry, 82(11), 4558
4563. Lewis, I. A., Schommer, S. C., Hodis, B., Robb, K. A., Tonelli, M., Westler, W. M., Sussman, M. R., & Markley, J. L. (2007). Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H
13C NMR spectra. Analytical Chemistry, 79(24), 9385
9390. Lewis, I. A., Schommer, S. C., & Markley, J. L. (2009). rNMR: Open source software for identifying and quantifying metabolites in NMR spectra. Magnetic Resonance in Chemistry, 47(S1), S123
S126. Li, X., Luo, H., Huang, T., Xu, L., Shi, X., & Hu, K. (2019). Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures. Analytical and Bioanalytical Chemistry, 411(7), 1301
1309. Li, Y., Wang, C., Li, D., Deng, P., Shao, X., Hu, J., Liu, C., Jie, H., Lin, Y., & Li, Z. (2017). 1H-NMR-based metabolic profiling of a colorectal cancer CT-26 lung metastasis model in mice. Oncology Reports, 38(5), 3044
3054. Liang, Y.-S., Kim, H., Lefeber, A., Erkelens, C., Choi, Y., & Verpoorte, R. (2006). Identification of phenylpropanoids in methyl jasmonate treated Brassica rapa leaves using two-dimensional nuclear magnetic resonance spectroscopy. Journal of Chromatography A, 1112(1
2), 148
155. Lipfert, M., Rout, M. K., Berjanskii, M., & Wishart, D. S. (2019). Automated tools for the analysis of 1D-NMR and 2D-NMR spectra. NMR-based metabolomics (pp. 429
449). Springer. Lloyd, L. S., Adams, R. W., Bernstein, M., Coombes, S., Duckett, S. B., Green, G. G., Lewis, R. J., Mewis, R. E., & Sleigh, C. J. (2012). Utilization of SABRE-derived Troisi-MP-1633011 978-0-323-85062-9 00005 207 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 208 CHAPTER 5 Nuclear magnetic resonance in metabolomics hyperpolarization to detect low-concentration analytes via 1D and 2D NMR methods. Journal of the American Chemical Society, 134(31), 12904
12907. Lopez, J. M., Cabrera, R., & Maruenda, H. (2019). Ultra-clean pure shift 1 H-NMR applied to metabolomics profiling. Scientific Reports, 9(1), 1
8. Lown, J. W., & Hanstock, C. C. (1985). High field 1H-NMR analysis of the 1:1 intercalation complex of the antitumor agent mitoxantrone and the DNA duplex [d (CpGpCpG)] 2. Journal of Biomolecular Structure and Dynamics, 2(6), 1097
1106. Ludwig, C., Easton, J. M., Lodi, A., Tiziani, S., Manzoor, S. E., Southam, A. D., Byrne, J. J., Bishop, L. M., He, S., & Arvanitis, T. N. (2012). Birmingham Metabolite Library: A publicly accessible database of 1-D 1 H and 2-D 1 HJ-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics: Official Journal of the Metabolomic Society, 8(1), 8
18. Ludwig, C., Marin-Montesinos, I., Saunders, M. G., Emwas, A.-H., Pikramenou, Z., Hammond, S. P., & Günther, U. L. (2010). Application of ex situ dynamic nuclear polarization in studying small molecules. Physical Chemistry Chemical Physics, 12 (22), 5868
5871. Ludwig, C., & Viant, M. R. (2010). Two-dimensional J-resolved NMR spectroscopy: Review of a key methodology in the metabolomics toolbox. Phytochemical Analysis: An International Journal of Plant Chemical and Biochemical Techniques, 21(1), 22
32. Luke, T. D., Pryce, J. E., Wales, W. J., & Rochfort, S. J. (2020). A tale of two biomarkers: Untargeted 1H NMR metabolomic fingerprinting of BHBA and NEFA in early lactation dairy cows. Metabolites, 10(6), 247. Lundberg, P., & Lundquist, P.-O. (2004). Primary metabolism in N2-fixing Alnus incana
Frankia symbiotic root nodules studied with 15N and 31P nuclear magnetic resonance spectroscopy. Planta, 219(4), 661
672. Available from https://doi.org/ 10.1007/s00425-004-1271-0. Lutz, N. W., & Hull, W. E. (1999). Assignment and pH dependence of the 19F-NMR resonances from the fluorouracil anabolites involved in fluoropyrimidine chemotherapy. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo, 12(4), 237
248. Lutz, N. W., Maillet, S., Nicoli, F., Viout, P., & Cozzone, P. J. (1998). Further assignment of resonances in 1H NMR spectra of cerebrospinal fluid (CSF). FEBS Letters, 425(2), 345
351. Macura, S., Kumar, N. G., & Brown, L. R. (1983). Combined use of COSY and double quantum two-dimensional NMR spectroscopy for elucidation of spin systems in polymyxin B. Biochemical and Biophysical Research Communications, 117(2), 486
492. Madrid-Gambin, F., Brunius, C., Garcia-Aloy, M., Estruel-Amades, S., Landberg, R., & Andres-Lacueva, C. (2018). Untargeted 1H NMR-based metabolomics analysis of urine and serum profiles after consumption of lentils, chickpeas, and beans: An extended meal study to discover dietary biomarkers of pulses. Journal of Agricultural and Food Chemistry, 66(27), 6997
7005. Mahrous, E. A., & Farag, M. A. (2015). Two dimensional NMR spectroscopic approaches for exploring plant metabolome: A review. Journal of Advanced Research, 6(1), 3
15. Malloy, C. R., Maher, E., Marin-Valencia, I., Mickey, B., Deberardinis, R. J., & Sherry, A. D. (2010). Carbon-13 nuclear magnetic resonance for analysis of metabolic pathways. Methodologies for Metabolomics: Experimental Strategies and Techniques (pp. 415
445). Cambridge university Press. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Malz, F., & Jancke, H. (2005). Validation of quantitative NMR. Journal of Pharmaceutical and Biomedical Analysis, 38(5), 813
823. Marcon, G., & Nincheri, P. (2014). The multispecialistic da Vinci European BioBank. Open Journal of Bioresources, 1. Marion, D. (2013). An introduction to biological NMR spectroscopy. Molecular & Cellular Proteomics, 12(11), 3006
3025. Markley, J. L., Anderson, M. E., Cui, Q., Eghbalnia, H. R., Lewis, I. A., Hegeman, A. D., Li, J., Schulte, C. F., Sussman, M. R., & Westler, W. M. (2007). New bioinformatics resources for metabolomics. Biocomputing 2007 (pp. 157
168). World Scientific. Markley, J. L., Bax, A., Arata, Y., Hilbers, C., Kaptein, R., Sykes, B. D., Wright, P. E., & Wüthrich, K. (1998). Recommendations for the presentation of NMR structures of proteins and nucleic acids (IUPAC Recommendations 1998). Pure and Applied Chemistry. Chimie Pure et Appliquee, 70(1), 117
142. Markley, J. L., Brüschweiler, R., Edison, A. S., Eghbalnia, H. R., Powers, R., Raftery, D., & Wishart, D. S. (2017). The future of NMR-based metabolomics. Current Opinion in Biotechnology, 43, 34
40. Martineau, E., & Giraudeau, P. (2019). Fast quantitative 2D NMR for untargeted and targeted metabolomics. NMR-based metabolomics (pp. 365
383). Springer. Mattar, S. M., Emwas, A. H., & Calhoun, L. A. (2004). Spectroscopic studies of the intermediates in the conversion of 1, 4, 11, 12-tetrahydro-9, 10-anthraquinone to 9, 10anthraquinone by reaction with oxygen under basic conditions. The Journal of Physical Chemistry A, 108(52), 11545
11553. McKay, R. T. (2009). Recent advances in solvent suppression for solution NMR: A practical reference. Annual Reports on NMR Spectroscopy, 66, 33
76. Mckay, R. T. (2011). How the 1D-NOESY suppresses solvent signal in metabonomics NMR spectroscopy: An examination of the pulse sequence components and evolution. Concepts in Magnetic Resonance Part A, 38(5), 197
220. McNally, D. J., Lamoureux, M., Li, J., Kelly, J., Brisson, J.-R., Szymanski, C. M., & Jarrell, H. C. (2006). HR-MAS NMR studies of 15N-labeled cells confirm the structure of the O-methyl phosphoramidate CPS modification in Campylobacter jejuni and provide insight into its biosynthesis. Canadian Journal of Chemistry, 84(4), 676
684. Mediani, A., Khatib, A., Ismail, A., Hamid, M., Lajis, N. H., Shaari, K., & Abas, F. (2017). Application of BATMAN and BAYESIL for quantitative 1 H-NMR based metabolomics of urine: Discriminant analysis of lean, obese, and obese-diabetic rats. Metabolomics: Official Journal of the Metabolomic Society, 13(11), 1
14. Meier, S., Jensen, P. R., Karlsson, M., & Lerche, M. H. (2014). Hyperpolarized NMR probes for biological assays. Sensors, 14(1), 1576
1597. Mercier, P., Lewis, M. J., Chang, D., Baker, D., & Wishart, D. S. (2011). Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra. Journal of Biomolecular NMR, 49(3), 307
323. Mestrelab Research S.L.—Analytical Chemistry Software. (2021). https://mestrelab.com. Michel, N., & Akoka, S. (2004). The application of the ERETIC method to 2D-NMR. Journal of Magnetic Resonance, 168(1), 118
123. Miccheli, A., et al. (2015). Urinary 1H-NMR-based metabolic profiling of children with NAFLD undergoing VSL#3 treatment. International Journal of Obesity, 39(7), 1118
1125. Available from https://doi.org/10.1038/ijo.2015.40. Troisi-MP-1633011 978-0-323-85062-9 00005 209 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 210 CHAPTER 5 Nuclear magnetic resonance in metabolomics Misra, B. B., & Mohapatra, S. (2019). Tools and resources for metabolomics research community: A 2017
2018 update. Electrophoresis, 40(2), 227
246. Misra, B. B., & van der Hooft, J. J. (2016). Updates in metabolomics tools and resources: 2014
2015. Electrophoresis, 37(1), 86
110. Mobli, M., & Hoch, J. C. (2014). Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR. Progress in Nuclear Magnetic Resonance Spectroscopy, 83, 21
41. Moe, S. M., & Daoud, J. R. (2014). Disorders of mineral metabolism: Calcium, phosphorus, and magnesium. In National Kidney Foundation Primer on Kidney Diseases (pp. 100
112). Elsevier. Mohammed, S. A. A., Khan, R. A., El-Readi, M. Z., Emwas, A.-H., Sioud, S., Poulson, B. G., Jaremko, M., Eldeeb, H. M., Al-Omar, M. S., & Mohammed, H. A. (2020). Suaeda vermiculata aqueous-ethanolic extract-based mitigation of CCl4-induced hepatotoxicity in rats, and HepG-2 and HepG-2/ADR cell-lines-based cytotoxicity evaluations. Plants, 9(10). Available from https://doi.org/10.3390/plants9101291. European Committee for Standardization. Molecular in vitro diagnostic examinations— Specifications for pre-examination processes for metabolomics in urine, venous blood serum and plasma. (2016). CEN Standard CEN/TS 16945 2016. https://shop.bsigroup. com/ProductDetail?pid 5 000000000030339067. International Organization for Standardization. Molecular in vitro diagnostic examinations—Specifications for pre-examination processes in metabolomics in urine, venous blood serum and plasma. (2020). ISO/DIS 23118. https://www.iso.org/obp/ui#iso:std: iso:23118:dis:ed-1:v1:en. Mroue, K. H., Emwas, A.-H. M., & Power, W. P. (2010). Solid-state 27Al nuclear magnetic resonance investigation of three aluminum-centered dyes. Canadian Journal of Chemistry, 88(2), 111
123. Mulder, F. A., Tenori, L., & Luchinat, C. (2019). Fast and quantitative NMR metabolite analysis afforded by a paramagnetic co-solute. Angewandte Chemie International Edition, 58(43), 15283
15286. Nadal-Desbarats, L., et al. (2014). Combined 1H-NMR and 1H
13C HSQC-NMR to improve urinary screening in autism spectrum disorders. Analyst, 139(13), 3460
3468. Available from https://doi.org/10.1039/C4AN00552J. Nagana Gowda, G., & Raftery, D. (2017). Recent advances in NMR-based metabolomics. Analytical Chemistry, 89(1), 490
510. Nasca, C., Xenos, D., Barone, Y., Caruso, A., Scaccianoce, S., Matrisciano, F., Battaglia, G., Mathé, A. A., Pittaluga, A., & Lionetto, L. (2013). L-Acetylcarnitine causes rapid antidepressant effects through the epigenetic induction of mGlu2 receptors. Proceedings of the National Academy of Sciences, 110(12), 4804
4809. Naser, N., Abdul Jameel, A. G., Emwas, A.-H., Singh, E., Chung, S. H., & Sarathy, S. M. (2019). The influence of chemical composition on ignition delay times of gasoline fractions. Combustion and Flame, 209, 418
429. Available from https://doi.org/10.1016/j. combustflame.2019.07.030. Nemets, B., Fux, M., Levine, J., & Belmaker, R. (2001). Combination of antidepressant drugs: The case of inositol. Human Psychopharmacology: Clinical and Experimental, 16(1), 37
43. Nicholson, J. K., Holmes, E., Kinross, J. M., Darzi, A. W., Takats, Z., & Lindon, J. C. (2012). Metabolic phenotyping in clinical and surgical environments. Nature, 491(7424), 384
392. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Nikolaou, P., Goodson, B. M., & Chekmenev, E. Y. (2015). NMR hyperpolarization techniques for biomedicine. Chemistry
A European Journal, 21(8), 3156
3166. Nishikawa, Y., et al. (2003). Vitamin C metabolomic mapping in experimental diabetes with 6-deoxy-6-fluoro-ascorbic acid and high resolution 19F-nuclear magnetic resonance spectroscopy. Metabolism, 52(6), 760
770. Available from https://doi.org/ 10.1016/S0026-0495(03)00069-6. Nizioł, J., Ossoliński, K., Tripet, B. P., Copié, V., Arendowski, A., & Ruman, T. (2020). Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer. Analytical and Bioanalytical Chemistry, 412(23), 5827
5841. Ogunade, I. M., & Jiang, Y. (2019). PSIX-7 1H NMR-based plasma metabolomics reveals a potential biomarker of aflatoxin ingestion in dairy cows. Journal of Animal Science, 97(Suppl 3), 395
396. Available from https://doi.org/10.1093/jas/skz258.789. O’Sullivan, A., Avizonis, D., German, J. B., & Slupsky, C. M. (2007). Software tools for NMR metabolomics. EMagRes. Okazaki, Y., & Saito, K. (2012). Recent advances of metabolomics in plant biotechnology. Plant Biotechnology Reports, 6(1), 1
15. Orfali, R., & Perveen, S. (2019). Secondary metabolites from the Aspergillus sp. in the rhizosphere soil of Phoenix dactylifera (Palm tree). BMC Chemistry, 13(1), 103. Available from https://doi.org/10.1186/s13065-019-0624-5. Otto, A., Porzel, A., Schmidt, J., Wessjohann, L., & Arnold, N. (2015). A study on the biosynthesis of hygrophorone B12 in the mushroom Hygrophorus abieticola reveals an unexpected labelling pattern in the cyclopentenone moiety. Phytochemistry, 118, 174
180. Padayachee, T., Khamiakova, T., Louis, E., Adriaensens, P., & Burzykowski, T. (2019). The impact of the method of extracting metabolic signal from 1H-NMR data on the classification of samples: A case study of binning and BATMAN in lung cancer. PLoS One, 14(2), e0211854. Palmnas, M. S., & Vogel, H. J. (2013). The future of NMR metabolomics in cancer therapy: Towards personalizing treatment and developing targeted drugs? Metabolites, 3 (2), 373
396. Pan, Q., Mustafa, N. R., Verpoorte, R., & Tang, K. (2016). 13C-isotope-labeling experiments to study metabolism in Catharanthus roseus. Metabolomics—Fundamentals and applications. InTechOpen. Park, B. K., Kitteringham, N. R., & O’Neill, P. M. (2001). Metabolism of fluorinecontaining drugs. Annual Review of Pharmacology and Toxicology, 41(1), 443
470. Park, J. M., & Park, J. H. (2001). Human in-vivo 31P MR spectroscopy of benign and malignant breast tumors. Korean Journal of Radiology, 2(2), 80. Parsons, H. M., Ludwig, C., Günther, U. L., & Viant, M. R. (2007). Improved classification accuracy in 1-and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation. BMC Bioinformatics, 8(1), 1
16. Pathan, M., Akoka, S., Tea, I., Charrier, B., & Giraudeau, P. (2011). “Multi-scan single shot” quantitative 2D NMR: A valuable alternative to fast conventional quantitative 2D NMR. Analyst, 136(15), 3157
3163. Pawłowski, P. H., Szcze˛sny, P., Rempoła, B., Poznańska, A., & Poznański, J. (2019). Combined in silico and 19F NMR analysis of 5-fluorouracil metabolism in yeast at low ATP conditions. Bioscience Reports, 39(12). Troisi-MP-1633011 978-0-323-85062-9 00005 211 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 212 CHAPTER 5 Nuclear magnetic resonance in metabolomics Pearson, D., et al. (2019). 19F-NMR-based determination of the absorption, metabolism and excretion of the oral phosphatidylinositol-3-kinase (PI3K) delta inhibitor leniolisib (CDZ173) in healthy volunteers. Xenobiotica, 49(8), 953
960. Available from https:// doi.org/10.1080/00498254.2018.1523488. Pendland, S. L., Piscitelli, S. C., Schreckenberger, P. C., & Danziger, L. H. (1994). In vitro activities of metronidazole and its hydroxy metabolite against Bacteroides spp. Antimicrobial Agents and Chemotherapy, 38(9), 2106
2110. Percival, B. C., Grootveld, M., Gibson, M., Osman, Y., Molinari, M., Jafari, F., Sahota, T., Martin, M., Casanova, F., Mather, M. L., Edgar, M., Masania, J., & Wilson, P. B. (2019). Low-field, benchtop NMR spectroscopy as a potential tool for point-of-care diagnostics of metabolic conditions: validation, protocols and computational models. High Throughput, 8(1), 2. Price, W. S. (1999). Water signal suppression in NMR spectroscopy. Annual Reports on NMR Spectroscopy, 38, 289
354. Psychogios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., Sinelnikov, I., Krishnamurthy, R., Eisner, R., & Gautam, B. (2011). The human serum metabolome. PLoS One, 6(2), e16957. Puchades-Carrasco, L., Palomino-Schätzlein, M., Pérez-Rambla, C., & Pineda-Lucena, A. (2016). Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers. Briefings in Bioinformatics, 17(3), 541
552. Qiao, H., Zhang, X., Zhu, X.-H., Du, F., & Chen, W. (2006). In vivo 31P MRS of human brain at high/ultrahigh fields: A quantitative comparison of NMR detection sensitivity and spectral resolution between 4 T and 7 T. Magnetic Resonance Imaging, 24(10), 1281
1286. Qiu, X., Redwine, D., Beshah, K., Livazovic, S., Canlas, C. G., Guinov, A., & Emwas, A.H. M. (2019). Amide vs amine ratio in the discrimination layer of reverse osmosis membrane by solid state 15N NMR and DNP NMR. Journal of Membrane Science, 581, 243
251. Quinn, R. H. (2012). Rabbit colony management and related health concerns. The laboratory rabbit, guinea pig, hamster, and other rodents (pp. 217
241). Elsevier. Radjursoga, M., et al. (2018). Nutritional Metabolomics: Postprandial Response of Meals Relating to Vegan, Lacto-Ovo Vegetarian, and Omnivore Diets. Nutrients, 10(8). Available from https://doi.org/10.3390/nu10081063. Raji, M., Amad, M., & Emwas, A. (2013). Dehydrodimerization of pterostilbene during electrospray ionization mass spectrometry. Rapid Communications in Mass Spectrometry, 27(11), 1260
1266. Ramirez, B., Durst, M. A., Lavie, A., & Caffrey, M. (2019). NMR-based metabolite studies with 15 N amino acids. Scientific Reports, 9(1), 1
5. Ravanbakhsh, S., Liu, P., Bjordahl, T. C., Mandal, R., Grant, J. R., Wilson, M., Eisner, R., Sinelnikov, I., Hu, X., & Luchinat, C. (2015). Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS One, 10(5), e0124219. Reineri, F., et al. (2010). Para-hydrogenated Glucose Derivatives as Potential 13CHyperpolarized Probes for Magnetic Resonance Imaging. Journal of the American Chemical Society, 132(20), 7186
7193. Available from https://doi.org/10.1021/ja101399q. Reineri, F., Boi, T., & Aime, S. (2015). ParaHydrogen Induced Polarization of 13C carboxylate resonance in acetate and pyruvate. Nature Communications, 6(1), 5858. Available from https://doi.org/10.1038/ncomms6858. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Ren, S., Hinzman, A. A., Kang, E. L., Szczesniak, R. D., & Lu, L. J. (2015). Computational and statistical analysis of metabolomics data. Metabolomics: Official Journal of the Metabolomic Society, 11(6), 1492
1513. Renault, S., et al. (2010). Uranyl nitrate inhibits lactate gluconeogenesis in isolated human and mouse renal proximal tubules: A 13C-NMR study. Toxicology and Applied Pharmacology, 242(1), 9
17. Available from https://doi.org/10.1016/j.taap.2009.09. 002. Renault, M., Cukkemane, A., & Baldus, M. (2010). Solid-state NMR spectroscopy on complex biomolecules. Angewandte Chemie International Edition, 49(45), 8346
8357. Richardson, P. M., Parrott, A. J., Semenova, O., Nordon, A., Duckett, S. B., & Halse, M. E. (2018). SABRE hyperpolarization enables high-sensitivity 1 H and 13 C benchtop NMR spectroscopy. Analyst, 143(14), 3442
3450. Riegel, S. D., & Leskowitz, G. M. (2016). Benchtop NMR spectrometers in academic teaching. TrAC Trends in Analytical Chemistry, 83, 27
38. Roberts, L. D., Souza, A. L., Gerszten, R. E., & Clish, C. B. (2012). Targeted metabolomics. Current Protocols in Molecular Biology, 98(1), 30
32. Roberts, M. J., Schirra, H., Lavin, M. F. Martin, F., & Gardiner Robert, A. (2014) NMR-based metabolomics: global analysis of metabolites to address problems in prostate cancer. Cervical, Breast and Prostate Cancer. Tokwawan, Kowloon, Hong Kong. iConcept Press. 1
43. Available from https://espace.library.uq.edu.au/view/ UQ:319160. Rocha, C. M., Barros, A. S., Gil, A. M., Goodfellow, B. J., Humpfer, E., Spraul, M., Carreira, I. M., Melo, J. B., Bernardo, J., & Gomes, A. (2010). Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy. Journal of Proteome Research, 9(1), 319
332. Rocha, C. M., Carrola, J., Barros, A. S., Gil, A. M., Goodfellow, B. J., Carreira, I. M., Bernardo, J., Gomes, A., Sousa, V., & Carvalho, L. (2011). Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma. Journal of Proteome Research, 10(9), 4314
4324. Romano, F., Meoni, G., Manavella, V., Baima, G., Mariani, G. M., Cacciatore, S., Tenori, L., & Aimetti, M. (2019). Effect of non-surgical periodontal therapy on salivary metabolic fingerprint of generalized chronic periodontitis using nuclear magnetic resonance spectroscopy. Archives of Oral Biology, 97, 208
214. Romano, F., Meoni, G., Manavella, V., Baima, G., Tenori, L., Cacciatore, S., & Aimetti, M. (2018). Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics. Journal of Periodontology, 89(12), 1452
1460. Romero, J. A., Kazimierczuk, K., & Gołowicz, D. (2020). Enhancing benchtop NMR spectroscopy by means of sample shifting. Analyst, 145(22), 7406
7411. Rosewell, R., & Vitols, C. (2006). Identifying metabolites in biofluids. Edmonton, AB: Chenomx Inc. Rouger, L., Gouilleux, B., & Nantes, F. P. G. (2017). Fast n-dimensional data acquisition methods. Laetitia ROUGER, 23. Rubtsov, D. V., Jenkins, H., Ludwig, C., Easton, J., Viant, M. R., Günther, U., Griffin, J. L., & Hardy, N. (2007). Proposed reporting requirements for the description of NMR-based metabolomics experiments. Metabolomics: Official Journal of the Metabolomic Society, 3(3), 223
229. Troisi-MP-1633011 978-0-323-85062-9 00005 213 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 214 CHAPTER 5 Nuclear magnetic resonance in metabolomics Rutar, V. (1984). Suppression of long-range couplings in heteronuclear two-dimensional J spectroscopy. Effects of nonuniform one-bond couplings. Journal of Magnetic Resonance (1969), 58(1), 132
142. Saccenti, E., Hoefsloot, H. C., Smilde, A. K., Westerhuis, J. A., & Hendriks, M. M. (2014). Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics: Official Journal of the Metabolomic Society, 10(3), 361
374. Sahoo, N. K., Tejaswini, G., Sahu, M., & Muralikrishna, K. (2020). An overview on NMR spectroscopy based metabolomics. International Journal of Pharmaceutical Sciences and Developmental Research, 6(1), 016
020. Salek, R. M., Neumann, S., Schober, D., Hummel, J., Billiau, K., Kopka, J., Correa, E., Reijmers, T., Rosato, A., & Tenori, L. (2015). COordination of Standards in MetabOlomicS (COSMOS): Facilitating integrated metabolomics data access. Metabolomics: Official Journal of the Metabolomic Society, 11(6), 1587
1597. Sanders, J. K. M., & Hunter, B. K. (1993). Modern NMR spectroscopy - a guide for chemists; Oxford University Press: New York. Sands, C. J., Coen, M., Maher, A. D., Ebbels, T. M., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2009). Statistical total correlation spectroscopy editing of 1H NMR spectra of biofluids: Application to drug metabolite profile identification and enhanced information recovery. Analytical Chemistry, 81(15), 6458
6466. Sarfaraz, M. O., Myers, R. P., Coffin, C. S., Gao, Z.-H., Shaheen, A. A. M., Crotty, P. M., Zhang, P., Vogel, H. J., & Weljie, A. M. (2016). A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C. Clinical and Translational Medicine, 5(1), 1
13. Satake, M., et al. (2003). Vitamin C Metabolomic Mapping in the Lens with 6-Deoxy-6fluoro-ascorbic Acid and High-Resolution 19F-NMR Spectroscopy. Investigative Ophthalmology & Visual Science, 44(5), 2047
2058. Available from https://doi.org/ 10.1167/iovs.02-057. Schilling, F., Warner, L. R., Gershenzon, N. I., Skinner, T. E., Sattler, M., & Glaser, S. J. (2014). Next-generation heteronuclear decoupling for high-field biomolecular NMR spectroscopy. Angewandte Chemie International Edition, 53(17), 4475
4479. Seitz, J. D., et al. (2015). Design, synthesis and application of fluorine-labeled taxoids as 19F NMR probes for the metabolic stability assessment of tumor-targeted drug delivery systems. Journal of Fluorine Chemistry, 171, 148
161. Available from https://doi.org/ 10.1016/j.jfluchem.2014.08.006. Sekiyama, Y., Chikayama, E., & Kikuchi, J. (2011). Evaluation of a semipolar solvent system as a step toward heteronuclear multidimensional NMR-based metabolomics for 13C-labeled bacteria, plants, and animals. Analytical Chemistry, 83(3), 719
726. Separovic, F., & Sani, M.-A. (2020). Solid-state NMR. Applications in biomembrane structure. IOP Publishing. Available from https://doi.org/10.1088/978-0-7503-2532-5. Serkova, N. J., & Niemann, C. U. (2006). Pattern recognition and biomarker validation using quantitative 1H-NMR-based metabolomics. Expert Review of Molecular Diagnostics, 6(5), 717
731. Sethi, S., Pedrini, M., Rizzo, L. B., Zeni-Graiff, M., Dal Mas, C., Cassinelli, A. C., Noto, M. N., Asevedo, E., Cordeiro, Q., & Pontes, J. G. (2017). 1 H-NMR, 1 H-NMR T 2edited, and 2D-NMR in bipolar disorder metabolic profiling. International Journal of Bipolar Disorders, 5(1), 1
9. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Shah, P. K., Ye, F., Liu, M., Jayaraman, A., Baligand, C., Walter, G., & Vandenborne, K. (2014). In vivo 31 P NMR spectroscopy assessment of skeletal muscle bioenergetics after spinal cord contusion in rats. European Journal of Applied Physiology, 114(4), 847
858. Shanaiah, N., et al. (2007). Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced ,sup . 13 , /sup . C NMR. Proceedings of the National Academy of Sciences, 104(28), 11540
11544. Available from https:// doi.org/10.1073/pnas.0704449104. Sharma, R., Gogna, N., Singh, H., & Dorai, K. (2017). Fast profiling of metabolite mixtures using chemometric analysis of a speeded-up 2D heteronuclear correlation NMR experiment. RSC Advances, 7(47), 29860
29870. Shchepin, R. V., et al. (2014). Parahydrogen Induced Polarization of 1-13CPhospholactate-d2 for Biomedical Imaging with .30,000,000-fold NMR Signal Enhancement in Water. Analytical Chemistry, 86(12), 5601
5605. Available from https://doi.org/10.1021/ac500952z. Shchepin, R. V., et al. (2016). 15N Hyperpolarization of Imidazole-15N2 for Magnetic Resonance pH Sensing via SABRE-SHEATH. ACS Sensors, 1(6), 640
644. Available from https://doi.org/10.1021/acssensors.6b00231. Shchepin, R. V., Birchall, J. R., Chukanov, N. V., Kovtunov, K. V., Koptyug, I. V., Theis, T., Warren, W. S., Gelovani, J. G., Goodson, B. M., & Shokouhi, S. (2019). Hyperpolarizing concentrated metronidazole 15NO2 group over six chemical bonds with more than 15% polarization and 20 minute lifetime. Chemistry (Weinheim an Der Bergstrasse, Germany), 25(37), 8829. Shchepin, R. V., & Chekmenev, E. Y. (2014). Toward hyperpolarized molecular imaging of HIV: Synthesis and longitudinal relaxation properties of 15N-Azidothymidine. Journal of Labelled Compounds and Radiopharmaceuticals, 57(10), 621
624. Sheedy, J. R., Ebeling, P. R., Gooley, P. R., & McConville, M. J. (2010). A sample preparation protocol for 1H nuclear magnetic resonance studies of water-soluble metabolites in blood and urine. Analytical Biochemistry, 398(2), 263
265. Silva, C. L., et al. (2019). Untargeted Urinary 1H NMR-Based Metabolomic Pattern as a Potential Platform in Breast Cancer Detection. Metabolites, 9(11), 269. Available from https://doi.org/10.3390/metabo9110269. Silva, C. L., Olival, A., Perestrelo, R., Silva, P., Tomás, H., & Câmara, J. S. (2019). Untargeted urinary 1H NMR-based metabolomic pattern as a potential platform in breast cancer detection. Metabolites, 9(11), 269. Silver, J., Naveh-Many, T., & Kronenberg, H. M. (2002). Parathyroid hormone: Molecular biology. In Principles of bone biology (pp. 407
422). Elsevier. Singh, A., et al. (2017). 1H NMR Metabolomics Reveals Association of High Expression of Inositol 1, 4, 5 Trisphosphate Receptor and Metabolites in Breast Cancer Patients. PLOS ONE, 12(1), e0169330. Available from https://doi.org/10.1371/journal.pone. 0169330. Smolinska, A., Blanchet, L., Buydens, L. M., & Wijmenga, S. S. (2012). NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review. Analytica Chimica Acta, 750, 82
97. Sokolenko, S., McKay, R., Blondeel, E. J., Lewis, M. J., Chang, D., George, B., & Aucoin, M. G. (2013). Understanding the variability of compound quantification from targeted profiling metabolomics of 1D-1 H-NMR spectra in synthetic mixtures and urine with Troisi-MP-1633011 978-0-323-85062-9 00005 215 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 216 CHAPTER 5 Nuclear magnetic resonance in metabolomics additional insights on choice of pulse sequences and robotic sampling. Metabolomics: Official Journal of the Metabolomic Society, 9(4), 887
903. Song, C., Jiao, C., Jin, Q., Chen, C., Cai, Y., & Lin, Y. (2020). Metabolomics analysis of nitrogen-containing metabolites between two Dendrobium plants. Physiology and Molecular Biology of Plants, 26(7), 1425
1435. Spicer, R., Salek, R. M., Moreno, P., Cañueto, D., & Steinbeck, C. (2017). Navigating freely-available software tools for metabolomics analysis. Metabolomics: Official Journal of the Metabolomic Society, 13(9), 1
16. Straadt, I. K., et al. (2010). Oxidative Stress-Induced Metabolic Changes in Mouse C2C12 Myotubes Studied with High-Resolution 13C, 1H, and 31P NMR Spectroscopy. Journal of Agricultural and Food Chemistry, 58(3), 1918
1926. Available from https://doi.org/10.1021/jf903505a. Sterin, M., Cohen, J. S., Mardor, Y., Berman, E., & Ringel, I. (2001). Levels of phospholipid metabolites in breast cancer cells treated with antimitotic drugs: A 31P-magnetic resonance spectroscopy study. Cancer Research, 61(20), 7536
7543. Stilbs, P. (1987). Fourier transform pulsed-gradient spin-echo studies of molecular diffusion. Progress in Nuclear Magnetic Resonance Spectroscopy, 19(1), 1
45. Stringer, K. A., Puskarich, M. P., Finkel, M. A., Karnovsky, A., & Jones, A. E. (2014). LCarnitine treatment impacts amino acid and energy metabolism in sepsis as detected by untargeted 1H-nuclear magnetic resonance (NMR) pharmacometabolomics. C15. Central nervous system and motor impairment in critical illness (p. A3932) American Thoracic Society. Sykes, B. D. (2007). Urine stability for metabolomic studies: Effects of preparation and storage. Metabolomics: Official Journal of the Metabolomic Society, 3(1), 19
27. Taggi, A. E., Meinwald, J., & Schroeder, F. C. (2004). A new approach to natural products discovery exemplified by the identification of sulfated nucleosides in spider venom. Journal of the American Chemical Society, 126(33), 10364
10369. Taglienti, A., Tiberini, A., Ciampa, A., Piscopo, A., Zappia, A., Tomassoli, L., Poiana, M., & Dell’Abate, M. T. (2020). Metabolites response to onion yellow dwarf virus (OYDV) infection in ‘Rossa di Tropea’ onion during storage: A 1H HR-MAS NMR study. Journal of the Science of Food and Agriculture, 100(8), 3418
3427. Takis, P. G., Ghini, V., Tenori, L., Turano, P., & Luchinat, C. (2019). Uniqueness of the NMR approach to metabolomics. TrAC Trends in Analytical Chemistry, 120, 115300. Takis, P. G., Schäfer, H., Spraul, M., & Luchinat, C. (2017). Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool. Nature Communications, 8(1), 1
12. Tarachiwin, L., Ute, K., Kobayashi, A., & Fukusaki, E. (2007). 1H NMR based metabolic profiling in the evaluation of Japanese green tea quality. Journal of Agricultural and Food Chemistry, 55(23), 9330
9336. Tasic, L., et al. (2019). Peripheral biomarkers allow differential diagnosis between schizophrenia and bipolar disorder. Journal of Psychiatric Research, 119, 67
75. Available from https://doi.org/10.1016/j.jpsychires.2019.09.009. Tasic, L., Larcerda, A. L. T., Pontes, J. G. M., da Costa, T. B. B. C., Nani, J. V., Martins, L. G., Santos, L. A., Nunes, M. F. Q., Adelino, M. P. M., Pedrini, M., Cordeiro, Q., Bachion de Santana, F., Poppi, R. J., Brietzke, E., & Hayashi, M. A. F. (2019). Peripheral biomarkers allow differential diagnosis between schizophrenia and bipolar Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References disorder. Journal of Psychiatric Research, 119, 67
75. Available from https://doi.org/ 10.1016/j.jpsychires.2019.09.009. Tasic, L., Pontes, J. G. M., Carvalho, M. S., Cruz, G., Dal Mas, C., Sethi, S., Pedrini, M., Rizzo, L. B., Zeni-Graiff, M., Asevedo, E., Lacerda, A. L. T., Bressan, R. A., Poppi, R. J., Brietzke, E., & Hayashi, M. A. F. (2017). Metabolomics and lipidomics analyses by 1H nuclear magnetic resonance of schizophrenia patient serum reveal potential peripheral biomarkers for diagnosis. Schizophrenia Research, 185, 182
189. Available from https://doi.org/10.1016/j.schres.2016.12.024. Tavel, L., Fontana, F., Garcia Manteiga, J. M., Mari, S., Mariani, E., Caneva, E., Sitia, R., Camnasio, F., Marcatti, M., & Cenci, S. (2016). Assessing heterogeneity of osteolytic lesions in multiple myeloma by 1H HR-MAS NMR Metabolomics. International Journal of Molecular Sciences, 17(11), 1814. Tayyari, F., et al. (2013). 15N-Cholamine
A Smart Isotope Tag for Combining NMRand MS-Based Metabolite Profiling. Analytical chemistry, 85(18), 8715
8721. Available from https://doi.org/10.1021/ac401712a. Tayyari, F., Gowda, G. N., Gu, H., & Raftery, D. (2013). 15N-cholamine: A smart isotope tag for combining NMR-and MS-based metabolite profiling. Analytical Chemistry, 85 (18), 8715
8721. Tebben, P. J., Berndt, T. J., & Kumar, R. (2013). Phosphatonins. Osteoporosis (pp. 373
390). Elsevier. Tenori, L., Turano, P., & Luchinat, C. (2007). Metabolic profiling by NMR. EMagRes, 199
204. Teunissen, C., Petzold, A., Bennett, J., Berven, F., Brundin, L., Comabella, M., Franciotta, D., Frederiksen, J., Fleming, J., & Furlan, R. (2009). A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology, 73(22), 1914
1922. Thebault, M., Pichavant, K., & Kervarec, N. (2009). 31 P nuclear magnetic resonance measurements of phosphate metabolites and intracellular pH in turbot Psetta maxima red blood cells using a novel flow method. Journal of Fish Biology, 75(3), 747
754. Theis, T., et al. (2015). Microtesla SABRE enables 10% nitrogen-15 nuclear spin polarization. Journal of the American Chemical Society, 137(4), 1404
1407. Available from https://doi.org/10.1021/ja512242d. Timári, I., Wang, C., Hansen, A. L., Costa dos Santos, G., Yoon, S. O., Bruschweiler-Li, L., & Brüschweiler, R. (2019). Real-time pure shift HSQC NMR for untargeted metabolomics. Analytical Chemistry, 91(3), 2304
2311. Tiret, B., Brouillet, E., & Valette, J. (2016). Evidence for a “metabolically inactive” inorganic phosphate pool in adenosine triphosphate synthase reaction using localized 31P saturation transfer magnetic resonance spectroscopy in the rat brain at 11.7 T. Journal of Cerebral Blood Flow & Metabolism, 36(9), 1513
1518. Tokumaru, O., Kuroki, C., Yoshimura, N., Sakamoto, T., Takei, H., Ogata, K., Kitano, T., Nisimaru, N., & Yokoi, I. (2009). Neuroprotective effects of ethyl pyruvate on brain energy metabolism after ischemia-reperfusion injury: A 31 P-nuclear magnetic resonance study. Neurochemical Research, 34(4), 775
785. Tomah Al-Masri, H., Emwas, A.-H. M., Al-Talla, Z. A., & Alkordi, M. H. (2012). Synthesis and characterization of new N-(diphenylphosphino)-naphthylamine chalcogenides: X-ray structures of (1-NHC10H7)P(Se)Ph2 and Ph2P(S)OP(S)Ph2. Null, 187 (9), 1082
1090. Available from https://doi.org/10.1080/10426507.2012.668985. Troisi-MP-1633011 978-0-323-85062-9 00005 217 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 218 CHAPTER 5 Nuclear magnetic resonance in metabolomics Tredwell, G. D., Bundy, J. G., De Iorio, M., & Ebbels, T. M. (2016). Modelling the acid/ base 1 H NMR chemical shift limits of metabolites in human urine. Metabolomics: Official Journal of the Metabolomic Society, 12(10), 1
10. Tsujimoto, T., et al. (2018). 13C-NMR-based metabolic fingerprinting of Citrus-type crude drugs. Journal of Pharmaceutical and Biomedical Analysis, 161, 305
312. Available from https://doi.org/10.1016/j.jpba.2018.08.044. Tynkkynen, T., Tiainen, M., Soininen, P., & Laatikainen, R. (2009). From proton nuclear magnetic resonance spectra to pH. Assessment of 1H NMR pH indicator compound set for deuterium oxide solutions. Analytica Chimica Acta, 648(1), 105
112. Uday, S., Högler, W., Huhtaniemi, I., & Martini, L. (2019). Rickets and osteomalacia (pp. 339
354). Academic Press. Available from https://doi.org/10.1016/B978-0-12801238-3.65426-0. Ulrich, E. L., Akutsu, H., Doreleijers, J. F., Harano, Y., Ioannidis, Y. E., Lin, J., Livny, M., Mading, S., Maziuk, D., & Miller, Z. (2007). BioMagResBank. Nucleic Acids Research, 36(Suppl. 1), D402
D408. van Beek, T. A. (2021). Low-field benchtop NMR spectroscopy: Status and prospects in natural product analysis. Phytochemical Analysis, 32(1), 24
37. Van, Q. N., Issaq, H. J., Jiang, Q., Li, Q., Muschik, G. M., Waybright, T. J., Lou, H., Dean, M., Uitto, J., & Veenstra, T. D. (2008). Comparison of 1D and 2D NMR spectroscopy for metabolic profiling. Journal of Proteome Research, 7(2), 630
639. Vassilev, N. G., Simova, S. D., Dangalov, M., Velkova, L., Atanasov, V., Dolashki, A., & Dolashka, P. (2020). An 1H NMR-and MS-based study of metabolites profiling of garden snail helix aspersa mucus. Metabolites, 10(9), 360. Vauclare, P., Bligny, R., Gout, E., & Widmer, F. (2013). An overview of the metabolic differences between Bradyrhizobium japonicum 110 bacteria and differentiated bacteroids from soybean (Glycine max) root nodules: An in vitro 13C-and 31P-nuclear magnetic resonance spectroscopy study. FEMS Microbiology Letters, 343(1), 49
56. Vignoli, A., Ghini, V., Meoni, G., Licari, C., Takis, P. G., Tenori, L., Turano, P., & Luchinat, C. (2019). High-throughput metabolomics by 1D NMR. Angewandte Chemie International Edition, 58(4), 968
994. Vignoli, A., Paciotti, S., Tenori, L., Eusebi, P., Biscetti, L., Chiasserini, D., Scheltens, P., Turano, P., Teunissen, C., & Luchinat, C. (2020). Fingerprinting Alzheimer’s disease by 1H nuclear magnetic resonance spectroscopy of cerebrospinal fluid. Journal of Proteome Research, 19(4), 1696
1705. Vinci, G., et al. (2018). An alternative to mineral phosphorus fertilizers: The combined effects of Trichoderma harzianum and compost on Zea mays, as revealed by 1H NMR and GC-MS metabolomics. PLOS ONE, 13(12), e0209664. Available from https://doi. org/10.1371/journal.pone.0209664. Viola, R., Tucci, A., Timellini, G., & Fantazzini, P. (2006). NMR techniques: A nondestructive analysis to follow microstructural changes induced in ceramics. Journal of the European Ceramic Society, 26(15), 3343
3349. Vitols, C., & Fu, H. (2006). Targeted profiling of common metabolites in urine. Edmonton, AB: Chenomx Inc. Vitols, C., & Weljie, A. (2006). Identifying and quantifying metabolites in blood serum and plasma. Chenomx Inc. Vitorge, B., Bieri, S., Humam, M., Christen, P., Hostettmann, K., Muñoz, O., Loss, S., & Jeannerat, D. (2009). High-precision heteronuclear 2D NMR experiments using Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References 10-ppm spectral window to resolve carbon overlap. Chemical Communications, 8, 950
952. Walker, T. G., & Happer, W. (1997). Spin-exchange optical pumping of noble-gas nuclei. Reviews of Modern Physics, 69(2), 629. Wang, J., Pu, S., Sun, Y., Li, Z., Niu, M., Yan, X., Zhao, Y., Wang, L., Qin, X., & Ma, Z. (2014). Metabolomic profiling of autoimmune hepatitis: The diagnostic utility of nuclear magnetic resonance spectroscopy. Journal of Proteome Research, 13(8), 3792
3801. Wang, Z. J., Ohliger, M. A., Larson, P. E., Gordon, J. W., Bok, R. A., Slater, J., Villanueva-Meyer, J. E., Hess, C. P., Kurhanewicz, J., & Vigneron, D. B. (2019). Hyperpolarized 13C MRI: State of the art and future directions. Radiology, 291(2), 273
284. Ward, J. L., Baker, J. M., Miller, S. J., Deborde, C., Maucourt, M., Biais, B., Rolin, D., Moing, A., Moco, S., Vervoort, J., Lommen, A., Schäfer, H., Humpfer, E., & Beale, M. H. (2010). An inter-laboratory comparison demonstrates that [1H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection. Metabolomics: Official Journal of the Metabolomic Society, 6(2), 263
273. Available from https://doi.org/10.1007/s11306-010-0200-4. Watanabe, N., & Niki, E. (1978). Direct-coupling of FT-NMR to high performance liquid chromatography. Proceedings of the Japan Academy, Series B, 54(4), 194
199. Watts, A. (2005). Solid-state NMR in drug design and discovery for membrane-embedded targets. Nature Reviews. Drug Discovery, 4(7), 555
568. Webb, A. (2006). Advances in probe design for protein NMR. Annual Reports on NMR Spectroscopy, 58, 1
50. Wei, L., Liao, P., Wu, H., Li, X., Pei, F., Li, W., & Wu, Y. (2009). Metabolic profiling studies on the toxicological effects of realgar in rats by 1H NMR spectroscopy. Toxicology and Applied Pharmacology, 234(3), 314
325. Weljie, A. M., Newton, J., Mercier, P., Carlson, E., & Slupsky, C. M. (2006). Targeted profiling: Quantitative analysis of 1H NMR metabolomics data. Analytical Chemistry, 78(13), 4430
4442. Wijnen, J. P., van der Kemp, W. J., Luttje, M. P., Korteweg, M. A., Luijten, P. R., & Klomp, D. W. (2012). Quantitative 31P magnetic resonance spectroscopy of the human breast at 7 T. Magnetic Resonance in Medicine, 68(2), 339
348. Willcott, M. R. (2009). MestRe Nova. ACS Publications. Winter, G., & Krömer, J. O. (2013). Fluxomics
connecting ‘omics analysis and phenotypes. Environmental Microbiology, 15(7), 1901
1916. Wishart, D. S. (2019). NMR metabolomics: A look ahead. Journal of Magnetic Resonance, 306, 155
161. Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., & Karu, N. (2018). HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research, 46(D1), D608
D617. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., Djoumbou, Y., Mandal, R., Aziat, F., & Dong, E. (2012). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41(D1), D801
D807. Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., Hau, D. D., Psychogios, N., Dong, E., & Bouatra, S. (2009). HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research, 37(suppl_1), D603
D610. Troisi-MP-1633011 978-0-323-85062-9 00005 219 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. 220 CHAPTER 5 Nuclear magnetic resonance in metabolomics Wishart, D. S., Mandal, R., Stanislaus, A., & Ramirez-Gaona, M. (2016). Cancer metabolomics and the human metabolome database. Metabolites, 6(1), 10. Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., & Sawhney, S. (2007). HMDB: The human metabolome database. Nucleic Acids Research, 35(Suppl 1), D521
D526. Wu, R., Chen, J., Zhang, L., Wang, X., Yang, Y., & Ren, X. (2021). LC/MS-based metabolomics to evaluate the milk composition of human, horse, goat and cow from China. European Food Research and Technology, 1
13. Wüthrich, K. (1986). NMR with proteins and nucleic acids. Europhysics News, 17(1), 11
13. Yang, W., Wang, Y., Zhou, Q., & Tang, H. (2008). Analysis of human urine metabolites using SPE and NMR spectroscopy. Science in China Series B: Chemistry, 51(3), 218
225. Ye, T., et al. (2009). Chemoselective 15N Tag for Sensitive and High-Resolution Nuclear Magnetic Resonance Profiling of the Carboxyl-Containing Metabolome. Analytical Chemistry, 81(12), 4882
4888. Available from https://doi.org/10.1021/ ac900539y. Ye, T., Mo, H., Shanaiah, N., Gowda, G. N., Zhang, S., & Raftery, D. (2009). Chemoselective 15N tag for sensitive and high-resolution nuclear magnetic resonance profiling of the carboxyl-containing metabolome. Analytical Chemistry, 81(12), 4882
4888. Yilmaz, A., et al. (2017). Diagnostic Biomarkers of Alzheimer’s Disease as Identified in Saliva using 1H NMR-Based Metabolomics. Journal of Alzheimer’s Disease, 58(2), 355
359. Available from https://doi.org/10.3233/JAD-161226. Yilmaz, A., Geddes, T., Han, B., Bahado-Singh, R. O., Wilson, G. D., Imam, K., Maddens, M., & Graham, S. F. (2017). Diagnostic biomarkers of Alzheimer’s disease as identified in saliva using 1H NMR-based metabolomics. Journal of Alzheimer’s Disease, 58 (2), 355
359. Zacharias, H. U., Altenbuchinger, M., & Gronwald, W. (2018). Statistical analysis of NMR metabolic fingerprints: Established methods and recent advances. Metabolites, 8(3), 47. Zacharias, N. M., Chan, H. R., Sailasuta, N., Ross, B. D., & Bhattacharya, P. (2012). Realtime molecular imaging of tricarboxylic acid cycle metabolism in vivo by hyperpolarized 1
13C diethyl succinate. Journal of the American Chemical Society, 134(2), 934
943. Zacharias, N. M., McCullough, C. R., Wagner, S., Sailasuta, N., Chan, H. R., Lee, Y., Hu, J., Perman, W. H., Henneberg, C., & Ross, B. D. (2016). Towards real-time metabolic profiling of cancer with hyperpolarized succinate. Journal of Molecular Imaging & Dynamics, 6(1). Zambon, A., et al. (2019). Nucleoside 20 ,30 -Cyclic Monophosphates in Aphanizomenon flos-aquae Detected through Nuclear Magnetic Resonance and Mass Spectrometry. Journal of Agricultural and Food Chemistry, 67(46), 12780
12785. Available from https://doi.org/10.1021/acs.jafc.9b05991. Zangger, K. (2015). Pure shift NMR. Progress in Nuclear Magnetic Resonance Spectroscopy, 86, 1
20. Zangger, K., & Sterk, H. (1997). Homonuclear broadband-decoupled NMR spectra. Journal of Magnetic Resonance, 124(2), 486
489. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. References Zhang, G., Emwas, A.-H., Hameed, U. F. S., Arold, S. T., Yang, P., Chen, A., Xiang, J.-F., & Khashab, N. M. (2020). Shape-induced selective separation of ortho-substituted benzene isomers enabled by cucurbit [7] uril host macrocycles. Chem, 6(5), 1082
1096. Zhang, M.-H., Jia-Qing, C., Hui-Min, G., Rui-Ting, L., Yi-Qiao, G., Yuan, T., Zhang, Z.J., & Huang, Y. (2017). Combination of LC/MS and GC/MS based metabolomics to study the hepatotoxic effect of realgar nanoparticles in rats. Chinese Journal of Natural Medicines, 15(9), 684
694. Zheng, A., Liu, S.-B., & Deng, F. (2017). 31P NMR chemical shifts of phosphorus probes as reliable and practical acidity scales for solid and liquid catalysts. Chemical Reviews, 117(19), 12475
12531. Zheng, H., Chen, M., Lu, S., Zhao, L., Ji, J., & Gao, H. (2017). Metabolic characterization of hepatitis B virus-related liver cirrhosis using NMR-based serum metabolomics. Metabolomics: Official Journal of the Metabolomic Society, 13(10), 1
9. Troisi-MP-1633011 978-0-323-85062-9 00005 221 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. Troisi-MP-1633011 978-0-323-85062-9 00005 To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for internal business use only by the author(s), editor(s), reviewer(s), Elsevier and typesetter MPS. It is not allowed to publish this proof online or in print. This proof copy is the copyright property of the publisher and is confidential until formal publication. NON-PRINT ITEM Abstract Nuclear magnetic resonance (NMR) is one of the most common and powerful techniques used in metabolomics. The inherent quantitative, nondestructive, and nonbiased properties, together with minimal sample preparation/manipulation make NMR a potent approach to any investigative metabolic study involving biological systems. NMR spectroscopy offers several unique monitoring opportunities such as extremely high reproducibility, relatively short experiment times, a wide range of available experiments (e.g., multidimensional and multinuclear based), and advanced highly automated robotic sample handling/exchange technologies enabling potentially hundreds of samples per instrument in a single day. In this chapter, we highlight the primary advantages and limitations of NMR spectroscopy, introduce the most commonly applied NMR experiments in metabolomics, and review some of the recent advances with selected examples of novel applications, such as high-resolution magic-angle spinning for tissue samples, and pure shift NMR method as an example of a promising new approach that can be used to overcome the overlapping of 1D NMR spectra. The main advantages of NMR spectroscopy with a particular focus on reproducibility are also presented. Keywords: Nuclear magnetic resonance; NMR; nuclear magnetic resonance spectroscopy; 1D NMR; 2D NMR; HRMAS; metabolomics; metabolomics databases Troisi-MP-1633011 978-0-323-85062-9 00005