Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Multinuclear MRI in Drug Discovery

The continuous development of magnetic resonance imaging broadens the range of applications to newer areas. Using MRI, we can not only visualize, but also track pharmaceutical substances and labeled cells in both in vivo and in vitro tests. ¹H is widely used in the MRI method, which is determined by its high content in the human body. The potential of the MRI method makes it an excellent tool for imaging the morphology of the examined objects, and also enables registration of changes at the level of metabolism. There are several reports in the scientific publications on the use of clinical MRI for in vitro tracking. The use of multinuclear MRI has great potential for scientific research and clinical studies. Tuning MRI scanners to the Larmor frequency of a given nucleus, allows imaging without tissue background. Heavy nuclei are components of both drugs and contrast agents and molecular complexes. The implementation of hyperpolarization techniques allows for better MRI sensitivity. The aim of this review is to present the use of multinuclear MRI for investigations in drug delivery.

A Unique Urinary Metabolic Feature for the Determination of Bladder Cancer, Prostate Cancer, and Renal Cell Carcinoma

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most prevalent cancer among urological cancers. However, there are no cancer-specific symptoms that can differentiate them as well as early clinical signs of urological malignancy. Furthermore, many metabolic studies have been conducted to discover their biomarkers, but the metabolic profiling study to discriminate between these cancers have not yet been described. Therefore, in this study, we aimed to investigate the urinary metabolic differences in male patients with PCa (n = 24), BCa (n = 29), and RCC (n = 12) to find the prominent combination of metabolites between cancers. Based on ¹H NMR analysis, orthogonal partial least-squares discriminant analysis was applied to find distinct metabolites among cancers. Moreover, the ranked analysis of covariance by adjusting a potential confounding as age revealed that 4-hydroxybenzoate, N-methylhydantoin, creatinine, glutamine, and acetate had significantly different metabolite levels among groups. The receiver operating characteristic analysis created by prominent five metabolites showed the great discriminatory accuracy with area under the curve (AUC) > 0.7 for BCa vs. RCC, PCa vs. BCa, and RCC vs. PCa. This preliminary study compares the metabolic profiles of BCa, PCa, and RCC, and reinforces the exploratory role of metabolomics in the investigation of human urine.

Clinical Chemistry for Developing Countries: Mass Spectrometry

Early disease diagnosis is necessary to enable timely interventions. Implementation of this vital task in the developing world is challenging owing to limited resources. Diagnostic approaches developed for resource-limited settings have often involved colorimetric tests (based on immunoassays) due to their low cost. Unfortunately, the performance/sensitivity of such simplistic tests are often limited and significantly hinder opportunities for early disease detection. A new criterion for selecting diagnostic tests in low- and middle-income countries is proposed here that is based on performance-to-cost ratio. For example, modern mass spectrometry (MS) now involves analysis of the native sample in the open laboratory environment, enabling applications in many fields, including clinical research, forensic science, environmental analysis, and agriculture. In this critical review, we summarize recent developments in chemistry that enable MS to be applied effectively in developing countries. In particular, we argue that closed automated analytical systems may not offer the analytical flexibility needed in resource-limited settings. Alternative strategies proposed here have potential to be widely accepted in low- and middle-income countries through the utilization of the open-source ambient MS platform that enables microsampling techniques such as dried blood spot to be coupled with miniature mass spectrometers in a centralized analytical platform. Consequently, costs associated with sample handling and maintenance can be reduced by >50% of the total ownership cost, permitting analytical measurements to be operated at high performance-to-cost ratios in the developing world.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

NMR Spectroscopic Windows on the Systemic Effects of SARS-CoV-2 Infection on Plasma Lipoproteins and Metabolites in Relation to Circulating Cytokines

To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune response to SARS CoV-2 infection interacting with the plasma lipoproteome giving a strong and characteristic immunometabolic phenotype of the disease. We observed that some patients in the respiratory recovery phase and testing virus-free were still metabolically highly abnormal, which indicates a new role for these technologies in assessing full systemic recovery.

The Current Status of Enteric Fever Diagnostics and Implications for Disease Control

Enteric (typhoid) fever remains a problem in low- and middle-income countries that lack the infrastructure to maintain sanitation and where inadequate diagnostic methods have restricted our ability to identify and control the disease more effectively. As we move into a period of potential disease elimination through the introduction of typhoid conjugate vaccine (TCV), we again need to reconsider the role of typhoid diagnostics in how they can aid in facilitating disease control. Recent technological advances, including serology, transcriptomics, and metabolomics, have provided new insights into how we can detect signatures of invasive Salmonella organisms interacting with the host during infection. Many of these new techniques exhibit potential that could be further explored with the aim of creating a new enteric fever diagnostic to work in conjunction with TCV. We need a sustained effort within the enteric fever field to accelerate, validate, and ultimately introduce 1 (or more) of these methods to facilitate the disease control initiative. The window of opportunity is still open, but we need to recognize the need for communication with other research areas and commercial organizations to assist in the progression of these diagnostic approaches. The elimination of enteric fever is now becoming a real possibility, but new diagnostics need to be part of the equation and factored into future calculations for disease control.

NMR-based plasma metabolic profiling in patients with unstable angina

OBJECTIVES

Unstable angina (UA) is a form of the acute coronary syndrome (ACS) that affects more than a third of the population before age 70. Due to the limitations of diagnostic tests, appropriate identification of UA is difficult. In this study, we proceeded to investigate metabolite profiling in UA patients compared with controls to determine potential candidate biomarkers.

MATERIALS AND METHODS

Ninety-four plasma samples from UA and 32 samples from controls were analyzed based on 1H NMR spectroscopy. The raw data were processed, analyzed, and subjected to partial least squares-discrimination analysis (PLS-DA), a supervised classification method with a good separation of control and UA patients was observed. The most important variables (VIP) ≥1 were selected and submitted to MetaboAnalyst pathway enrichment to identify the most important ones.

RESULTS

We identified 17 disturbed metabolites in UA patients in comparison with the controls. These metabolites are involved in various biochemical pathways such as steroid hormone biosynthesis, aminoacyl-tRNA biosynthesis, and lysine degradation. Some of the metabolites were deoxycorticosterone, 17-hydroxyprogesterone, androstenedione, androstanedione, etiocholanolone, estradiol, 2-hydroxyestradiol, 2-hydroxyestrone, 2-methoxyestradiol, and 2-methoxyestrone. In order to determine test applicability in diagnosing UA, a diagnostic model was further created using the receiver operator characteristic (ROC) curve. The areas under the curve (AUC), sensitivity, specificity, and precision were 0.87, 90%, 65%, and 91%, respectively, for diagnosing of UA.

CONCLUSION

These metabolites could not only be useful for the diagnosis of UA patients but also provide more information for further deciphering of the biological processes of UA.

Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

BACKGROUND

Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available.

METHODS

We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548).

RESULTS

Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance.

CONCLUSION

Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables

BACKGROUND

The discovery of new metabolic markers may be helpful for early diagnosis of acute pediatric appendicitis (APA). However, no studies have been reported regarding identification of potential metabolic markers for the APA diagnosis by metabonomics.

METHODS

Serum samples of APA (n=32), non-appendicitis inflammation (NAI, n=32) and healthy children (HS, n=65) were analyzed by the 1H NMR-based metabonomics. A logistic regression model was established to screen the most efficient markers combinations for classification. Forty double-blind samples were further validated the model.

RESULTS

Nine blood metabolites that were different in the APA group from other groups were identified. To differentiate APA from HS, single variable of acetate, formate, white blood cell (WBC) and C-reactive protein (CRP) showed a high diagnostic value (area under the receiver operating characteristic [AUROC]<0.92), while they had a weak diagnostic value (AUROC<0.77) for identifying the APA and NAI. By contrast, the AUROC values of leucine (0.799) were higher than that of WBC and CRP. A combination of five variables, i.e. leucine, lactate, betaine, WBC and CRP, showed a high diagnostic value (AUROC=0.973) for the APA discriminating from the NAI, and the sensitivity and specificity were 93.8% and 93.7%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 85% for 40 unknown samples.

CONCLUSIONS

The current study provides useful information in our understanding of the metabolic alterations associated with APA and indicates that measurement of these metabolites in serum effectively aids in the clinical identification of APA.

NMR-Based Urinary Metabolomics Applications

The field of metabolomics has been growing tremendously over the recent years and, consistent with that growth, a number of investigators have been looking at the potential of NMR-based urinary metabolomics for several applications. While such applications have shown promising results, there still remains an enormous amount of work to be done before this approach becomes accepted and widely used in clinical diagnostics and other biomedical applications. To achieve such goals, optimization of parameters and standardization of protocols are of paramount importance. In view of this, in this chapter, we present some recommended methods and procedures that can help researchers in the field. Furthermore, we have highlighted some of the challenges encountered in such applications and suggested some possible ways to overcome those challenges.

The metabolomic quest for a biomarker in chronic kidney disease

Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient-centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made.

Metabolic profiling of metformin treatment for low-level Pb-induced nephrotoxicity in rat urine

Chronic kidney disease is a worldwide problem, and Pb contamination is a potential risk factor. Since current biomarkers are not sensitive for the diagnosis of Pb-induced nephrotoxicity, novel biomarkers are needed. Metformin has both hypoglycaemic effects and reno-protection ability. However, its mechanism of action is unknown. We aimed to discover the early biomarkers for the diagnosis of low-level Pb-induced nephrotoxicity and understand the mechanism of reno-protection of metformin. Male Wistar rats were randomly divided into control, Pb, Pb + ML, Pb + MH and MH groups. Pb (250 ppm) was given daily via drinking water. Metformin (50 or 100 mg/kg/d) was orally administered. Urine was analysed by nuclear magnetic resonance (NMR)-based metabolomics coupled with multivariate statistical analysis, and potential biomarkers were subsequently quantified. The results showed that Pb-induced nephrotoxicity was closely correlated with the elevation of 5-aminolevulinic acid, D-lactate and guanidinoacetic acid in urine. After co-treatment with metformin, 5-aminolevulinic acid and D-lactate were decreased. This is the first demonstration that urinary 5-aminolevulinic acid, D-lactate and guanidinoacetic acid could be early biomarkers of low-level Pb-induced nephrotoxicity in rats. The reno-protection of metformin might be attributable to the reduction of D-lactate excretion.

Urine metabolomics insight into acute kidney injury point to oxidative stress disruptions in energy generation and H2S availability

Acute kidney injury (AKI) is one of the main complications in acute care medicine and a risk factor for chronic kidney disease (CKD). AKI incidence has increased; however, its diagnosis has limitations and physiopathological mechanisms are underexplored. We investigated urine samples, aiming to identify major metabolite changes during human AKI evolution. Metabolic signatures found were further explored for a potential link to severity of injury. Twenty-four control subjects and 38 hospitalized patients with AKI were recruited and urine samples were collected at the time of diagnosis, during follow-up and at discharge. Nuclear magnetic resonance (NMR) was used in a first discovery phase for identifying potential metabolic differences. Target metabolites of interest were confirmed by liquid chromatography-mass spectrometry (LC-MS/MS) in an independent group. Underlying metabolic defects were further explored by kidney transcriptomics of murine toxic AKI. Urinary 2-hydroxybutyric acid, pantothenic acid, and hippuric acid were significantly downregulated and urinary N-acetylneuraminic acid, phosphoethanolamine, and serine were upregulated during AKI. Hippuric acid, phosphoethanolamine, and serine showed further downregulation/upregulation depending on the metabolite in acute tubular necrosis (ATN) AKI compared to prerenal AKI. Kidney transcriptomics disclosed decreased expression of cystathionase, cystathionine-β-synthase, and ethanolamine-phosphate cytidylyltransferase, and increased N-acetylneuraminate synthase as the potentially underlying cause of changes in urinary metabolites. A urinary metabolite panel identified AKI patients and provided insight into intrarenal events. A urine fingerprint made up of six metabolites may be related to pathophysiological changes in oxidative stress, energy generation, and H₂S availability associated with AKI.

KEY MESSAGES

The urinary metabolome reflects AKI evolution and severity of injury. Kidney transcriptomics revealed enzymatic expression changes. Enzymatic expression changes may be the potentially underlying cause of changes in urine metabolites. Identified metabolite changes link oxidative stress, energy generation, and H₂S availability to AKI.