|Year : 2019 | Volume
| Issue : 4 | Page : 144-150
Metabolic profiling based on nuclear magnetic resonance spectroscopy and mass spectrometry as a tool for clinical application
Herney Andrés Garcia-Perdomo1, Felipe García Vallejo2, Adalberto Sanchez2
1 Associate Professor; UROGIV Research Group, Universidad del Valle, Cali, Colombia
2 Associate Professor; LABIOMOL Research Group, Universidad del Valle, Cali, Colombia
|Date of Submission||14-Jan-2019|
|Date of Decision||20-Feb-2019|
|Date of Acceptance||11-Mar-2019|
|Date of Web Publication||29-Jul-2019|
Herney Andrés Garcia-Perdomo
Cll 4B # 36-00, Universidad del Valle, Cali
Source of Support: None, Conflict of Interest: None
Metabolomics provides an abundance of information with the potential to accurately describe the physiological state of an organism. It aims to identify small molecules under physiological conditions that might serve as biomarkers and aid in the identification and treatment of health problems. Combining nuclear magnetic resonance (NMR) with mass spectrometry (MS) yields better identification and quantification of compounds, especially in mixtures, as well as the ability to cross-analyze data from both techniques and thereby increase the number of compounds identified. Metabolomic profiling using NMR and/or MS provides an important diagnostic tool for identifying metabolites under different conditions. This also requires a valid and reliable way to standardize the way we use it to identify biomarkers. Regarding the clinical application of metabolomics, for bladder cancer, threonine, phenylalanine, valine, isoleucine, lysine, methionine, leucine, glutamate, histidine, arginine, aspartic acid, tyrosine, glutamine, and serine were found discriminative in diagnosing this entity. On the other side, sarcosine, choline, phosphocholines, phosphorylcholines, carnitines, citrate, amino acids (lysine, glutamine, and ornithine), arachidonoyl amine, and lysophospholipids were found discriminative regarding the prostate cancer diagnosis.
Keywords: Cancer, mass spectrometry, metabolic profile, nuclear magnetic resonance spectroscopy, urologic oncology
|How to cite this article:|
Garcia-Perdomo HA, Vallejo FG, Sanchez A. Metabolic profiling based on nuclear magnetic resonance spectroscopy and mass spectrometry as a tool for clinical application. Urol Sci 2019;30:144-50
|How to cite this URL:|
Garcia-Perdomo HA, Vallejo FG, Sanchez A. Metabolic profiling based on nuclear magnetic resonance spectroscopy and mass spectrometry as a tool for clinical application. Urol Sci [serial online] 2019 [cited 2020 Aug 6];30:144-50. Available from: http://www.e-urol-sci.com/text.asp?2019/30/4/144/263649
| Introduction|| |
The metabolic state of an organism depends on its genome, transcriptome, proteome, epigenome, microbiome, and exposome (environment) among other elements, including some that are still undiscovered. Metabolomics as a tool of study provides abundant information with the potential to accurately describe the physiological state of an organism. One of the objectives of this tool is to identify and characterize small molecules under physiological conditions and identify those that may be important biomarkers for the identification and treatment of health problems.
In this review, we will describe a few aspects of metabolomics, the methods used for quantification, and some clinical applications, mainly in cancer.
| General Concepts of Metabolomics|| |
The term metabolomics is a rapidly growing field that was defined as the quantitative study of metabolites (molecules smaller than 1500 kDa) in a biological system and alterations to their concentrations due to environmental or genetic effects. The research has focused on areas such as toxicology, biomedical sciences, nutrition, genetics, innate errors of metabolism, diabetes, cancer, diagnostic tests, and neuronal diseases, among others. These applications are based on the theory that metabolites are the functional outputs of an organism. This might identify an alteration in the system's homeostasis that could occur before the appearance of symptoms of a particular disease, as a single metabolite may be the substrate for a number of different enzymes involved in complex metabolic pathways.,
One of the main advantages of applying metabolomics is the ability to detect hundreds of metabolites in parallel, thereby efficiently monitoring biochemical alterations. In addition, the metabolic profiles of biological specimens can be affected by factors such as diet, age, ethnicity, lifestyle, medications, or microbiota, and these factors should be controlled to obtain disease-specific information.
| Types of Fluids Used in Metabolite Analysis|| |
Analyses of metabolites can use various fluids including urine, blood plasma, serum, cerebrospinal fluid, saliva, and tissue. The former three are the most commonly used biofluids in metabolomic studies because they contain thousands of detectable metabolites and are obtained noninvasively or minimally invasively. The use of each fluid type presents processing and analysis challenges and different possible associations with diseases and drug effects.
The analysis of urine samples offers a number of advantages over the use of other fluids such as blood (serum), plasma, saliva, cerebrospinal fluid, and homogenized tissues. Among these, advantages are the following: (1) urine can be collected in large quantities, (2) the sample collection process is not invasive, and samples can be taken repeatedly, and (3) urine requires less complex pretreatment steps for analysis because it has low protein levels and high concentrations of low-molecular-weight compounds. Thus, urine is a sample with low complexity of analysis and few molecular interactions. For these reasons, urine is the most commonly used fluid in the analysis of metabolites for the early detection of diseases., However, given its high-salt content, mass spectrometry (MS) measurements of urine are slightly more challenging than measurements of other fluids and still require pretreatment of the sample.
Blood plasma and serum
The use of these fluids for metabolite analysis allows the determination of the presence and stage of several diseases, but these fluids contain a wide range of macromolecules that, when analyzed, can overlap with the results from small molecule metabolites.
Relationships can be found between serum and plasma metabolic profiles that can provide an overview of metabolic status at a point in time. In nuclear magnetic resonance (NMR) data, this relationship includes narrow signals from small molecule metabolites and broad signals from proteins and lipids.
The metabolic profiling of intact tissue has gained interest in recent years; its purpose is to obtain an approximate understanding of the molecular basis of disease and pathway analysis. The global determination of metabolite concentrations in anatomical tissues might provide information on unique aspects of the tissue during pathological development, which cannot be derived from measurements taken in other fluids, such as the relationship between metabolism and changes in tissues. Nonetheless, the information derived from the analyzing tissue samples is difficult to obtain, requiring invasive methods and sample preparation for storage and analysis.
| Metabolite Analysis Techniques|| |
Two of the most important techniques currently used in metabolomics are NMR spectroscopy and MS. NMR requires little to no preparation, is rapid and noninvasive, does not destroy tissue, and has highly reproducible results (coefficient of variation: 1%–2%). Combining NMR with MS might increase the diagnostic yield, but the data obtained from NMR/MS experiments are quite complex, as they provide qualitative and quantitative information on several metabolites, and distinguishing statistically between disease and control markers can be difficult [Figure 1].
We would like to go deeper describing the techniques used for metabolite analysis in the following paragraphs:
Nuclear magnetic resonance spectroscopy
At present, single-pulse (one-dimensional [1D]) and 1D nuclear Overhauser enhancement spectroscopy with water suppression is one of the most widely used methods in metabolomics. This technique is robust, provides a flat baseline under similar conditions, and allows pulses to suppress elevated signals from water, leaving metabolite signals intact., Two additional important methods used to eliminate large signals from large molecules (e.g., tissue and serum) are the Carr-Purcell-Meiboom-Gill sequence and the “edited diffusion” method that allows the observation of large molecules such as lipids.
Other methods, such as 2D NMR, have been used in the detection of metabolites to reduce spectrum complexity, although these methods are expensive, time-consuming, and complex to analyze. These methods include 2D-J spectroscopy, correlation spectroscopy, total correlation spectroscopy, heteronuclear single-quantum coherence spectroscopy, and heteronuclear multiple bond correlation.,
High-resolution magic-angle spinning (HR-MAS) NMR is another technique used with tissues. This technique obtains high-resolution spectra from heterogeneous samples that are neither solids nor pure liquids, such as solvated resins, allowing the in situ characterization of organic molecules and the quantification of compounds in the solid support phase [Table 1].
MS is used with various separation methods, such as gas chromatography (GC), liquid chromatography (LC), and capillary electrophoresis (CE), to provide chemical information for metabolomic studies [Table 1].,
Liquid chromatography coupled to mass spectrometry (high-performance liquid chromatography-mass spectrometry)
LC-MS is one of the most important techniques used for metabolic analysis, with high sensitivity and the ability to provide a wide range of information on metabolite content. This technique separates a sample by LC for subsequent analysis by MS. LC-MS has been widely used to analyze complexed substances (including nonvolatile) and can be used to separate macromolecules such as proteins. LC-MS is considered a moderate to high-performance method, and ultra performance LC increases the chromatographic resolution of this technique 3–5 fold. Urine can be input directly into an LC system, but serum and other liquids require preparation for protein precipitation.
Gas chromatography coupled to mass spectrometry
GC-MS is usually used to analyze samples of fluids, such as urine, and volatile metabolites including fatty acids, steroids, and flavonoids. GC-MS has lower costs of analysis than high-performance LC (HPLC)-MS, but its use is limited for nonpolar thermally stable metabolites. Thus, it is commonly used to derivatize compounds that produce other volatile compounds. The advantages of GC-MS include its rapid ability to identify metabolites, supported by the commercial availability of extensive libraries.,
The use of different agents in the derivatization of compounds has been studied in human urine samples treated with N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA), N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), and methyl-bis-trifluoroacetamide (MBTFA) and analyzed in GC-quadruple MS. BSTFA and MSTFA exhibit similar efficiency with respect to the number of peaks detected, peak intensity, and reproducibility. MBTFA combined with BSTFA exhibits better secondary and tertiary amine derivation. Some amino acids were detected when derivation with BSTFA was followed by the use of MBTFA; however, these results were not reproducible, and BSTFA is recommended as an optimal derivation agent for GC-MS.
Combining nuclear magnetic resonance and mass spectrometry
NMR has low sensitivity for the identification of metabolites; however, it offers the possibility for broad observation of the most abundant compounds in biological fluids and tissues without the need to prepare or fractionate samples. In addition, NMR allows the identification of compounds with identical masses and the identification of dynamics that can reveal metabolic pathways and their compartmentalization. Combining NMR and MS yields increased identification and quantification of compounds, especially in complexed ones. In addition, data from both techniques can be cross-analyzed to increase the number of compounds identified using the principle of a relatively constant abundance/intensity ratio for the same metabolites across different samples., The combination of NMR and MS also allows the performance of isotope screening experiments and metabolic flow analysis (NMR provides position, and MS provides quantification).
| Platforms to Establish Standards in Nuclear Magnetic Resonance-Based Metabolomics|| |
Information on biological molecules associated with NMR spectra is available in databases including Human Metabolome Database, Biological Magnetic Resonance Data Bank, TOCSY Customized Carbon Trace Archive, and Complex Mixture Analysis by NMR, but information on many metabolites is still needed.,,, Repositories of results from metabolomic studies have been generated by the National Institutes of Health's Common Fund Centers  and by initiatives to coordinate metabolomic standards, for example, Complex Systems Modelling and Simulation, which currently develops a robust infrastructure that allows the exchange of data or metadata between researchers and the development of applications (http://nmrml.org and http://metabolomexchange.org). Many other important databases (pathway analysis and viewers) are now available for metabolomic analysis, and these include KEGG (http://www.genome.ad.jp/kegg/), MetaCyc (http://metacyc.org/), AraCyc (http://www. Arabidopsis.org/tools/aracyc/), MapMan (http://gabi.rzpd.de/projects/MapMan/), KaPPA-View (http://kpv.kazusa.or.jp/kappa-view/), the data model for plant metabolomic experiment ArMet (http://www.armet.org/), and functional genomic databases MetNet (http://metnet.vrac.iastate.edu/) and DOME (http://medicago.vbi.vt.edu). In addition, standards and best practices have been published for the metabolic phenotyping of biological fluids.,
| Analysis of Metabolites in Clinical Conditions|| |
The development of a pathology can be identified through different metabolic pathways, but we are only describing three of them: glycolisis, glutamine and fatty acids, in which adenosine triphosphate (ATP) production is increased, mediated by the increased activity of enzymes such as hexokinase and lactate dehydrogenase and the increased production of lactate induced by the overexpression of hypoxia-inducible factor-1. Under these conditions, the action of pyruvate dehydrogenase kinase 1 is induced, which in turn inhibits pyruvate dehydrogenase, thus reducing pyruvate input to the tricarboxylic acid (TCA) cycle and reducing levels of associated metabolites. The consumption of glutamine also increases through the action of the glutaminase enzyme. This effect presents a possible pathway for supplementing the deficit induced in TCA under anaerobic conditions. Some studies suggest that this pathway provides the main energy source used under aerobic conditions by proliferating cells such as lymphocytes, fibroblasts, and some cancer cells. This pathway is also a precursor of oxaloacetate, which is involved in the synthesis of fatty acids and cholesterol. The alteration of these pathways, in turn, alters the metabolism of fatty acids, which are produced in greater quantities in cancer cells due to the hyperactivity of enzymes such as ATP citrate lyase, acetyl-CoA carboxylase, and fatty acid synthase. The latter is expressed at low levels in normal cells and tissues but is highly expressed in cancerous tissues. Cancerous tissues also exhibit an increase in messenger lipids such as phosphatidylinositol-3, 4, 5-triphosphate which is formed by the action of phosphatidylinositol 3-kinase and active protein kinase B/Akt. Alterations in these pathways can be used in the detection of metabolites and the preventive diagnosis, treatment, and identification of biomarkers for diseases. Therefore, the study of alterations to the levels of these compounds in tissue, plasma, or urine has been used to study various pathologies.
Urological cancer is a condition in which early detection based on biochemical methods is required to offer rapid treatment based on personalized medicine. Metabolomic tools based on NMR and MS have the potential to assist the provision of early diagnosis and targeted therapy. Various biomarkers for urological cancer based on different pathways of disease in human and animal models are currently being studied.
Metabolomics has been extensively used in the study of cancer, with studies on bladder cancer using LC/MS to compare the urine metabolites of healthy controls and those of patients with bladder cancer and identify differences.
Recently, Cheng et al. pooled 11 studies which described metabolites to detect bladder cancer in a systematic review, with different techniques and high heterogeneity: GC–time-of-flight MS (two studies), LC-MS/CE-MS (three studies), HR-MAS NMR spectroscopy (one study), proton NMR (three studies), reversed-phase LC-MS/hydrophilic interaction chromatography-MS (one study), and HPLC–quadrupole time-of-flight MS (one study). Regarding the altered glucose metabolites expression, they found no conclusion about glucose, fructose, and lactic acid since they were found up in some studies and down in others; nevertheless, pyruvic acid was found down in only one study (anaerobic oxidation). Similarly, citric acid and fumaric acid were also found down in different studies (aerobic oxidation).
Regarding the altered amino acid metabolites expression, they found that threonine, phenylalanine, valine, isoleucine, lysine, methionine, leucine (essential amino acids), glutamate, histidine, arginine, aspartic acid, tyrosine, glutamine, and serine (nonessential amino acids) were found up. They found some studies regarding the lipid and nucleotide metabolites expression; however, it was not enough to pool information and recommend for the diagnosis of bladder cancer.
Another study found that metabolite profiles in urine can discriminate between bladder cancers with and without muscular invasion and between healthy patients and those with bladder cancer and can trace relationships between different metabolic pathways and identify where pathology-associated alterations appear.
There are few studies using different samples and analytical platforms with similar results for some metabolites; nonetheless, it is important to standardize these two fundamental variables to establish a way to diagnose bladder cancer nowadays.
Plasma and tissue samples from prostate cancer (PCa) patients have been evaluated using different methods such as HR-MAS and LC/MS; therefore, alterations in amino acid levels have been found, such as increased levels of lactate, phospholipids, and choline; low levels of citrate and polyamines; and regulation of spermine.,,,, One of the advantages of this marker is that noninvasive methods can be used for the detection of PCa, as it can be measured in urine.
Regarding the complete spectrum of metabolites, so far, the most promising biomarkers for PCa diagnosis are sarcosine (area under the curve [AUC]: 0.67), choline, phosphocholines (AUC: 0.982), phosphorylcholines, carnitines (AUC: 0.97), citrate (AUC: 0.89), amino acids (lysine, glutamine, and ornithine),,,, arachidonoyl amine (AUC: 0.86), and lysophospholipids (steroid hormone biosynthesis pathway and bile acids – sensitivity and specificity: 92%–94%).
The following five constituents are also important when discriminating between PCa and hyperplasia: dihydroxybutanoic acid, xylonic acid, pyrimidine, xylopyranose, and ribofuranoside, with an AUC: 0.825. In addition, citrate, glutamate, and taurine have important discriminatory roles, with a sensitivity of 100% and specificity of 96%.
Regarding the metabolic pathways, many of them have been associated with PCa. The following are the most described in the literature:
- Energy metabolism, including TCA cycle intermediates,,, lactate,, citrate, phosphoenolpyruvate, and adenosine diphosphate 
- Chronic stress is another important pathway involved in developing cancer, and cortisol  is thought to be related by this way 
- Cell proliferation pathway through de novo lipid biosynthesis has participated by different metabolites such as citrate, inositol, lactate, and cortisol,, along with phosphoethanolamine, glycerophosphoethanolamine, and acetate.
As readers can see, there are many metabolites that might be associated or altered in PCa; therefore, there is still place to study through this molecular method how to diagnose and establish prognosis in patients with this condition.
Kidney cancer is one of the most important urological cancers since it has high mortality. The research has been focused on the clear cell type since it is the most frequent, and different studies have shown results from human and animal samples. Here, we present some of the most promising metabolite biomarkers for this kind of tumor according to critical revisions from Rodrigues et al. and Hakimi et al.:, Regarding the amino acid metabolism, they found that creatine, glutamate, glutamine, and quinolinate were found upregulated mostly in renal tissue; however, 4-hydroxybenzoate, gentisate, and hippuric acid were found downregulated mainly in urine. Regarding the fatty acid metabolism, carnitine and acetylcarnitine were found upregulated in renal tissue, but choline/choline-containing compounds were found downregulated in the same kind of sample. The glutathione (reduced form) was found upregulated in renal tissue, and regarding the glycolysis, glucose was found to be inconclusive in different kinds of samples; on the other side, lactate and pyruvate were found upregulated in different samples. Citrate, fumarate, malate, and succinate had inconclusive findings, regarding the TCA metabolism. In addition, alpha-tocopherol (Vitamin E metabolism) was found upregulated in renal tissue. Most of the inconsistencies are because of differences in type, collection, handling, and manipulation of the samples; experimental designs; and the kind of analytical platforms.
The identification of biomarkers has been used in renal cancer patients, allowing differentiation between healthy controls and cancer patients and suggesting the involvement of alterations in the quinolinate pathways (pathways of nicotinate and nicotinamide metabolism), gentisate (benzoate degradation pathway), and alpha-ketoglutarate (alanine, aspartate, and glutamine metabolism pathway) in the disease. However, these results have not been validated for use in clinical practice.,
| Conclusions|| |
Metabolomic profiling using NMR and/or MS provides an important diagnostic tool for identifying metabolites under different conditions. These tools have been tried for conditions that greatly affect the quality of life, such as cancer; in such cases, the fast, valid, and reliable identification of biomarkers is required. Different methods involving NMR and/or MS can result in variations in their results; however, the appropriate control of confounding variables and the use of statistical and bioinformatic analyses can offer a wide range of information, useful for clinical applications.
We wish to thank Fabián Hernández BSc MSc PhD (c), who supplied some of the information for this review.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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