Optimization of Human Plasma 1H NMR Spectroscopic Data Processing for High-Throughput Metabolic Phenotyping Studies and Detection of Insulin Resistance Related to Type 2 Diabetes

Serum Levels of Adiponectin Are Strongly Associated with Lipoprotein Subclasses in Healthy Volunteers but Not in Patients with Metabolic Syndrome

Metabolic syndrome (MS) is a widespread disease in developed countries, accompanied, among others, by decreased adiponectin serum levels and perturbed lipoprotein metabolism. The associations between the serum levels of adiponectin and lipoproteins have been extensively studied in the past under healthy conditions, yet it remains unexplored whether the observed associations also exist in patients with MS. Therefore, in the present study, we analyzed the serum levels of lipoprotein subclasses using nuclear magnetic resonance spectroscopy and examined their associations with the serum levels of adiponectin in patients with MS in comparison with healthy volunteers (HVs). In the HVs, the serum levels of adiponectin were significantly negatively correlated with the serum levels of large buoyant-, very-low-density lipoprotein, and intermediate-density lipoprotein, as well as small dense low-density lipoprotein (LDL) and significantly positively correlated with large buoyant high-density lipoprotein (HDL). In patients with MS, however, adiponectin was only significantly correlated with the serum levels of phospholipids in total HDL and large buoyant LDL. As revealed through logistic regression and orthogonal partial least-squares discriminant analyses, high adiponectin serum levels were associated with low levels of small dense LDL and high levels of large buoyant HDL in the HVs as well as high levels of large buoyant LDL and total HDL in patients with MS. We conclude that the presence of MS weakens or abolishes the strong associations between adiponectin and the lipoprotein parameters observed in HVs and disturbs the complex interplay between adiponectin and lipoprotein metabolism.

Graphical exploration of 600- and 60-MHz proton NMR spectral datasets from ground roast coffee extracts

This article uses a variety of graphical and mathematical approaches to analyse 600- and 60-MHz ('benchtop') proton NMR spectra acquired from lipophilic and hydrophilic extracts of roasted coffee beans. The collection of 40 authenticated samples comprised various coffee species, cultivars and hybrids. The spectral datasets were analysed by a combination of metabolomics approaches, cross-correlation and whole spectrum methods, assisted by visualisation and mathematical techniques not conventionally employed to treat NMR data. A large amount of information content was shared between the 600-MHz and benchtop datasets, including in its magnitude spectral form, suggesting the potential for a lower cost, lower tech route to conducting informative metabolomics studies.

HDL-Related Parameters and COVID-19 Mortality: The Importance of HDL Function

COVID-19, caused by the SARS-CoV-2 coronavirus, emerged as a global pandemic in late 2019, resulting in significant global public health challenges. The emerging evidence suggests that diminished high-density lipoprotein (HDL) cholesterol levels are associated with the severity of COVID-19, beyond inflammation and oxidative stress. Here, we used nuclear magnetic resonance spectroscopy to compare the lipoprotein and metabolic profiles of COVID-19-infected patients with non-COVID-19 pneumonia. We compared the control group and the COVID-19 group using inflammatory markers to ensure that the differences in lipoprotein levels were due to COVID-19 infection. Our analyses revealed supramolecular phospholipid composite (SPC), phenylalanine, and HDL-related parameters as key discriminators between COVID-19-positive and non-COVID-19 pneumonia patients. More specifically, the levels of HDL parameters, including apolipoprotein A-I (ApoA-I), ApoA-II, HDL cholesterol, and HDL phospholipids, were significantly different. These findings underscore the potential impact of HDL-related factors in patients with COVID-19. Significantly, among the HDL-related metrics, the cholesterol efflux capacity (CEC) displayed the strongest negative association with COVID-19 mortality. CEC is a measure of how well HDL removes cholesterol from cells, which may affect the way SARS-CoV-2 enters cells. In summary, this study validates previously established markers of COVID-19 infection and further highlights the potential significance of HDL functionality in the context of COVID-19 mortality.

Ex vivo proton spectroscopy (1 H-NMR) analysis of inborn errors of metabolism: Automatic and computer-assisted analyses

There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (¹ H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with ¹ H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same ¹ H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because ¹ H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

The PROVIT Study-Effects of Multispecies Probiotic Add-on Treatment on Metabolomics in Major Depressive Disorder-A Randomized, Placebo-Controlled Trial

The gut–brain axis plays a role in major depressive disorder (MDD). Gut-bacterial metabolites are suspected to reduce low-grade inflammation and influence brain function. Nevertheless, randomized, placebo-controlled probiotic intervention studies investigating metabolomic changes in patients with MDD are scarce. The PROVIT study (registered at clinicaltrials.com NCT03300440) aims to close this scientific gap. PROVIT was conducted as a randomized, single-center, double-blind, placebo-controlled multispecies probiotic intervention study in individuals with MDD (n = 57). In addition to clinical assessments, metabolomics analyses (1H Nuclear Magnetic Resonance Spectroscopy) of stool and serum, and microbiome analyses (16S rRNA sequencing) were performed. After 4 weeks of probiotic add-on therapy, no significant changes in serum samples were observed, whereas the probiotic groups’ (n = 28) stool metabolome shifted towards significantly higher concentrations of butyrate, alanine, valine, isoleucine, sarcosine, methylamine, and lysine. Gallic acid was significantly decreased in the probiotic group. In contrast, and as expected, no significant changes resulted in the stool metabolome of the placebo group. Strong correlations between bacterial species and significantly altered stool metabolites were obtained. In summary, the treatment with multispecies probiotics affects the stool metabolomic profile in patients with MDD, which sets the foundation for further elucidation of the mechanistic impact of probiotics on depression.

Branched-Chain Amino Acids Can Predict Mortality in ICU Sepsis Patients

Sepsis biomarkers and potential therapeutic targets are urgently needed. With proton nuclear magnetic resonance (¹H NMR) spectroscopy, several metabolites can be assessed simultaneously. Fifty-three adult medical ICU sepsis patients and 25 ICU controls without sepsis were prospectively enrolled. ¹H NMR differences between groups and associations with 28-day and ICU mortality were investigated. In multivariate metabolomic analyses, we found separate clustering of ICU controls and sepsis patients, as well as septic shock survivors and non-survivors. Lipoproteins were significantly different between sepsis and control patients. Levels of the branched-chain amino acids (BCAA) valine (median 43.3 [29.0–53.7] vs. 64.3 [47.7–72.3] normalized signal intensity units; p = 0.005), leucine (57.0 [38.4–71.0] vs. 73.0 [54.3–86.3]; p = 0.034) and isoleucine (15.2 [10.9–21.6] vs. 17.9 [16.1–24.4]; p = 0.048) were lower in patients with septic shock compared to those without. Similarly, BCAA were lower in ICU non-survivors compared to survivors, and BCAA were good discriminators for ICU and 28-day mortality. In uni- and multivariable logistic regression analyses, higher BCAA levels were associated with decreased ICU- and 28-day mortality. In conclusion, metabolomics using ¹H NMR spectroscopy showed encouraging potential for personalized medicine in sepsis. BCAA was significantly lower in sepsis non-survivors and may be used as early biomarkers for outcome prediction.

Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection

The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multiplatform metabolic phenotyping with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative measurements of lipoprotein subfractions, α-1-acid glycoprotein, glucose, and biogenic amines were made on samples from symptomatic coronavirus disease 19 (COVID-19) patients who had tested positive for the SARS-CoV-2 virus (n = 17) and from age- and gender-matched controls (n = 25). Data were analyzed using an orthogonal-projections to latent structures (OPLS) method and used to construct an exceptionally strong (AUROC = 1) hybrid NMR-MS model that enabled detailed metabolic discrimination between the groups and their biochemical relationships. Key discriminant metabolites included markers of inflammation including elevated α-1-acid glycoprotein and an increased kynurenine/tryptophan ratio. There was also an abnormal lipoprotein, glucose, and amino acid signature consistent with diabetes and coronary artery disease (low total and HDL Apolipoprotein A1, low HDL triglycerides, high LDL and VLDL triglycerides), plus multiple highly significant amino acid markers of liver dysfunction (including the elevated glutamine/glutamate and Fischer's ratios) that present themselves as part of a distinct SARS-CoV-2 infection pattern. A multivariate training-test set model was validated using independent samples from additional SARS-CoV-2 positive patients and controls. The predictive model showed a sensitivity of 100% for SARS-CoV-2 positivity. The breadth of the disturbed pathways indicates a systemic signature of SARS-CoV-2 positivity that includes elements of liver dysfunction, dyslipidemia, diabetes, and coronary heart disease risk that are consistent with recent reports that COVID-19 is a systemic disease affecting multiple organs and systems. Metabolights study reference: MTBLS2014.

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples

Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.

Linking 24-h urines to clinical phenotypes: what alternatives does the future bring?

PURPOSE OF REVIEW

The 24-h urine test is recommended as part of the metabolic evaluation for patients with nephrolithiasis to guide preventive interventions. However, this test may be challenging to interpret and has limits in its predictive ability. In this review, we summarize and discuss the most recent research on the opportunities and challenges for utilizing urinary biomarkers for kidney stone prevention.

RECENT FINDINGS

Contemporary studies utilizing the 24-h urine test have improved our understanding of how to better administer testing and interpret test results. Beyond the standard panel of 24-h urine parameters, recent applications of proteomics and metabolomics have identified protein and metabolic profiles of stone formers. These profiles can be assayed in future studies as potential biomarkers for risk stratification and prediction. Broad collaborative efforts to create large datasets and biobanks from kidney stone formers will be invaluable for kidney stone research.

SUMMARY

Recent advances in our understanding of kidney stone risk have opened opportunities to improve metabolic testing for kidney stone formers. These strategies do not appear to be mutually exclusive of 24-h urine testing but instead complementary in their approach. Finally, large clinical datasets hold promise to be leveraged to identify new avenues for stone prevention.

Serum Concentrations of Citrate, Tyrosine, 2- and 3- Hydroxybutyrate are Associated with Increased 3-Month Mortality in Acute Heart Failure Patients

Considering the already established relationship between the extent of the metabolic dysfunction and the severity of heart failure (HF), it is conceivable that the metabolomic profile of the serum may have a prognostic capacity for 3-month mortality in acute heart failure (AHF). Out of 152 recruited patients, 130 serum samples were subjected to the metabolomic analyses. The 3-month mortality rate was 24.6% (32 patients). Metabolomic profiling by nuclear magnetic resonance spectroscopy found that the serum levels of 2-hydroxybutyrate (2-HB), 3-hydoxybutyrate (3-HB), lactate, citrate, and tyrosine, were higher in patients who died within 3 months compared to those who were alive 3 months after onset of AHF, which was confirmed by univariable logistic regression analyses (p = 0.009, p = 0.005, p = 0.008, p<0.001, and p<0.001, respectively). These associations still remained significant for all tested metabolites except for lactate after adjusting for established prognostic parameters in HF. In conclusion, serum levels of 2-HB, 3-HB, tyrosine, and citrate measured at admission are associated with an increased 3-month mortality rate in AHF patients and might thus be of prognostic value in AHF.

N-acetylaspartate pathway is nutrient responsive and coordinates lipid and energy metabolism in brown adipocytes

The discovery of significant amounts of metabolically active brown adipose tissue (BAT) in adult humans renders it a promising target for anti-obesity therapies by inducing weight loss through increased energy expenditure. The components of the N-acetylaspartate (NAA) pathway are highly abundant in BAT. Aspartate N-acetyltransferase (Asp-NAT, encoded by Nat8l) synthesizes NAA from acetyl-CoA and aspartate and increases energy expenditure in brown adipocytes. However, the exact mechanism how the NAA pathway contributes to accelerated mobilization and oxidation of lipids and the physiological regulation of the NAA pathway remained elusive. Here, we demonstrate that the expression of NAA pathway genes corresponds to nutrient availability and specifically responds to changes in exogenous glucose. NAA is preferentially produced from glucose-derived acetyl-CoA and aspartate and its concentration increases during adipogenesis. Overexpression of Nat8l drains glucose-derived acetyl-CoA into the NAA pool at the expense of cellular lipids and certain amino acids. Mechanistically, we elucidated that a combined activation of neutral and lysosomal (acid) lipolysis is responsible for the increased lipid degradation. Specifically, translocation of the transcription factor EB to the nucleus activates the biosynthesis of autophagosomes and lysosomes. Lipid degradation within lysosomes accompanied by adipose triglyceride lipase-mediated lipolysis delivers fatty acids for the support of elevated mitochondrial respiration. Together, our data suggest a crucial role of the NAA pathway in energy metabolism and metabolic adaptation in BAT.

1H-NMR, 1H-NMR T2-edited, and 2D-NMR in bipolar disorder metabolic profiling

Background

The objective of this study was to identify molecular alterations in the human blood serum related to bipolar disorder, using nuclear magnetic resonance (NMR) spectroscopy and chemometrics.

Methods

Metabolomic profiling, employing ¹H-NMR, ¹H-NMR T₂-edited, and 2D-NMR spectroscopy and chemometrics of human blood serum samples from patients with bipolar disorder (n = 26) compared with healthy volunteers (n = 50) was performed.

Results

The investigated groups presented distinct metabolic profiles, in which the main differential metabolites found in the serum sample of bipolar disorder patients compared with those from controls were lipids, lipid metabolism-related molecules (choline, myo-inositol), and some amino acids (N-acetyl-l-phenyl alanine, N-acetyl-l-aspartyl-l-glutamic acid, l-glutamine). In addition, amygdalin, α-ketoglutaric acid, and lipoamide, among other compounds, were also present or were significantly altered in the serum of bipolar disorder patients. The data presented herein suggest that some of these metabolites differentially distributed between the groups studied may be directly related to the bipolar disorder pathophysiology.

Conclusions

The strategy employed here showed significant potential for exploring pathophysiological features and molecular pathways involved in bipolar disorder. Thus, our findings may contribute to pave the way for future studies aiming at identifying important potential biomarkers for bipolar disorder diagnosis or progression follow-up.

Electronic supplementary material

The online version of this article (doi:10.1186/s40345-017-0088-2) contains supplementary material, which is available to authorized users.

Metabolic profiling in kidneys of Atlantic salmon infected with Aeromonas salmonicida based on 1H NMR

Aeromonas salmonicida, an important pathogenic bacterium which induces furunculosis, is globally causing increased risks in Atlantic salmon (Salmo salar) farming. Although the kidney is the main target organ of A. salmonicida, the metabolic profiling of kidney in response to A. salmonicida in vivo remains unknown. Here, we used ¹H nuclear magnetic resonance (NMR) to comprehensively analyze the metabolic changes in the kidney of Atlantic salmon. Through the NOESYPR1D spectrum combined with multi-variate pattern recognition analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models, significant metabolic changes were observed seven and 14 days post-infection and in a control group. Hence, the main objective of this study was to estimate the significant metabolites with resistance to furunculosis and further understand the mechanism of A. salmonicida in Atlantic salmon. Notably, substantial alterations of kidney metabolites were observed, such as with fumarate, alanine, valine, glycine, aspartate, choline, glycerophosphocholine and betaine, and summarized by metabolic pathways including the citrate cycle, glycolysis/gluconeogenesis, tryptophan metabolism, and urea cycle, respectively. Changes were also observed in 3-hydroxybutyrate and phosphocholine which were not involved in these four metabolic pathways. After analyzing the alteration trend of these metabolites, we inferred that A. salmonicida caused absorption inhibition of amino acids and disturbed protein metabolism as well as cell metabolism in favor of its replication. These observations offered novel insights into the mechanisms of infection at a functional level and facilitated further assessment and clarification of fish disease from A. salmonicida exposure.

The promise of metabolic phenotyping in gastroenterology and hepatology

Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.

Alterations of urinary metabolite profile in model diabetic nephropathy

Countering the diabetes pandemic and consequent complications, such as nephropathy, will require better understanding of disease mechanisms and development of new diagnostic methods. Animal models can be versatile tools in studies of diabetic renal disease when model pathology is relevant to human diabetic nephropathy (DN). Diabetic models using endothelial nitric oxide synthase (eNOS) knock-out mice develop major renal lesions characteristic of human disease. However, it is unknown whether they can also reproduce changes in urinary metabolites found in human DN. We employed Type 1 and Type 2 diabetic mouse models of DN, i.e. STZ-eNOS(-/-) C57BLKS and eNOS(-/-) C57BLKS db/db, with the goal of determining changes in urinary metabolite profile using proton nuclear magnetic resonance (NMR). Six urinary metabolites with significantly lower levels in diabetic compared to control mice have been identified. Specifically, major changes were found in metabolites from tricarboxylic acid (TCA) cycle and aromatic amino acid catabolism including 3-indoxyl sulfate, cis-aconitate, 2-oxoisocaproate, N-phenyl-acetylglycine, 4-hydroxyphenyl acetate, and hippurate. Levels of 4-hydroxyphenyl acetic acid and hippuric acid showed the strongest reverse correlation to albumin-to-creatinine ratio (ACR), which is an indicator of renal damage. Importantly, similar changes in urinary hydroxyphenyl acetate and hippurate were previously reported in human renal disease. We demonstrated that STZ-eNOS(-/-) C57BLKS and eNOS(-/-) C57BLKS db/db mouse models can recapitulate changes in urinary metabolome found in human DN and therefore can be useful new tools in metabolomic studies relevant to human pathology.

Application of (1)h NMR spectroscopy-based metabolomics to sera of tuberculosis patients

Nuclear magnetic resonance (NMR) spectroscopy is an ideal platform for the metabolic analysis of biofluids due to its high reproducibility, nondestructiveness, nonselectivity in metabolite detection, and the ability to simultaneously quantify multiple classes of metabolites. Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. In this study, we combined multivariate pattern recognition (PR) analytical techniques with (1)H NMR spectroscopy to explore the metabolic profile of sera from TB patients. A total of 77 serum samples obtained from patients with TB (n = 38) and healthy controls (n = 39) were investigated. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was capable of distinguishing TB patients from controls and establishing a TB-specific metabolite profile. A total of 17 metabolites differed significantly in concentration between the two groups. Serum samples from TB patients were characterized by increased concentrations of 1-methylhistidine, acetoacetate, acetone, glutamate, glutamine, isoleucine, lactate, lysine, nicotinate, phenylalanine, pyruvate, and tyrosine, accompanied by reduced concentrations of alanine, formate, glycine, glycerolphosphocholine, and low-density lipoproteins relative to control subjects. Our study reveals the metabolic profile of sera from TB patients and indicates that NMR-based methods can distinguish TB patients from healthy controls. NMR-based metabolomics has the potential to be developed into a novel clinical tool for TB diagnosis or therapeutic monitoring and could contribute to an improved understanding of disease mechanisms.

Looking at microbial metabolism by high-resolution (2)H-NMR spectroscopy

We analyzed the applicability of high-resolution ²H-HMR spectroscopy for the analysis of microbe metabolism in samples of mitochondrion isolated from rat liver and from aqueous extracts of homogenates of rat liver and other organs and tissues in the presence of high D₂O contents. Such analysis is possible due to the fast microbe adaptation to life in the heavy water. It is also shown that some enzymatic processes typical for the intact cells are preserved in the homogenized tissue preparations. The microbial and cellular metabolic processes can be differentiated via the strategic use of cell poisons and antibiotics.

GC/TOFMS analysis of metabolites in serum and urine reveals metabolic perturbation of TCA cycle in db/db mice involved in diabetic nephropathy

Early diagnosis of diabetic nephropathy (DN) is difficult although it is of crucial importance to prevent its development. To probe potential markers and the underlying mechanism of DN, an animal model of DN, the db/db mice, was used and serum and urine metabolites were profiled using gas chromatography/time-of-flight mass spectrometry. Metabolic patterns were evaluated based on serum and urine data. Principal component analysis of the data revealed an obvious metabonomic difference between db/db mice and controls, and db/db mice showed distinctly different metabolic patterns during the progression from diabetes to early, medium, and later DN. The identified metabolites discriminating between db/db mice and controls suggested that db/db mice have perturbations in the tricarboxylic acid cycle (TCA, citrate, malate, succinate, and aconitate), lipid metabolism, glycolysis, and amino acid turnover. The db/db mice were characterized by acidic urine, high TCA intermediates in serum at week 6 and a sharp decline thereafter, and gradual elevation of free fatty acids in the serum. The sharp drop of serum TCA intermediates from week 6 to 8 indicated the downregulated glycolysis and insulin resistance. However, urinary TCA intermediates did not decrease in parallel with those in the serum from week 6 to 10, and an increased portion of TCA intermediates in the serum was excreted into the urine at 8, 10, and 12 wk than at 6 wk, indicating kidney dysfunction occurred. The relative abundances of TCA intermediates in urine relative to those in serum were suggested as an index of renal damage.

NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers

To facilitate the high-throughput acquisition of nuclear magnetic resonance (NMR) experimental data on large sets of samples, we have developed a simple and straightforward automated methodology that capitalizes on recent advances in Bruker BioSpin NMR spectrometer hardware and software. Given the daunting challenge for non-NMR experts to collect quality spectra, our goal was to increase user accessibility, provide customized functionality, and improve the consistency and reliability of resultant data. This methodology, NMRbot, is encoded in a set of scripts written in the Python programming language accessible within the Bruker BioSpin TopSpin™ software. NMRbot improves automated data acquisition and offers novel tools for use in optimizing experimental parameters on the fly. This automated procedure has been successfully implemented for investigations in metabolomics, small-molecule library profiling, and protein-ligand titrations on four Bruker BioSpin NMR spectrometers at the National Magnetic Resonance Facility at Madison. The investigators reported benefits from ease of setup, improved spectral quality, convenient customizations, and overall time savings.

A pilot metabolic profiling study in serum of patients with chronic kidney disease based on (1) H-NMR-spectroscopy

BACKGROUND

Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology.

METHODS

A (1) H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages (N= 80) and healthy controls (N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile.

RESULTS

The classification models could grade CKD patients with considerably high value of Q(2) and R(2) . The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%.

CONCLUSION

The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.

Chemometrics in Medicine and Pharmacy

This minireview summarizes the basic ways of application of chemometrics in medicine and pharmacy. It brings a collection of applications of chemometric used for the solution of diverse practical problems, e.g. exploitation of biologically active species, effective use of biomarkers, advancement of clinical diagnosis, monitoring of the patient's state and prediction of its perspectives, drug design or classification of toxic chemical substances. The aim of this contribution is a brief presentation of versatile potentialities of contemporary chemometrical techniques and relevant software. They are exemplified by typical cases from literature as well as by own research results of the Chemometrics group at Department of Chemistry, the University of Ss. Cyril & Methodius in Trnava.

A proton nuclear magnetic resonance-based metabonomics study of metabolic profiling in immunoglobulin a nephropathy

OBJECTIVES

Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis patients. The ability to diagnose immunoglobulin A nephropathy remains poor. However, renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. The aims of the present study were to identify immunoglobulin A nephropathy patients, to identify useful biomarkers of immunoglobulin A nephropathy and to establish a human immunoglobulin A nephropathy metabolic profile.

METHODS

Serum samples were collected from immunoglobulin A nephropathy patients who were not using immunosuppressants. A pilot study was undertaken to determine disease-specific metabolite biomarker profiles in three groups: healthy controls (N = 23), low-risk patients in whom immunoglobulin A nephropathy was confirmed as grades I-II by renal biopsy (N = 23), and high-risk patients with nephropathies of grades IV-V (N = 12). Serum samples were analyzed using proton nuclear magnetic resonance spectroscopy and by applying multivariate pattern recognition analysis for disease classification.

RESULTS

Compared with the healthy controls, both the low-risk and high-risk patients had higher levels of phenylalanine, myo-Inositol, lactate, L6 lipids ( = CH-CH2-CH = O), L5 lipids (-CH2-C = O), and L3 lipids (-CH2-CH2-C = O) as well as lower levels of β -glucose, α-glucose, valine, tyrosine, phosphocholine, lysine, isoleucine, glycerolphosphocholine, glycine, glutamine, glutamate, alanine, acetate, 3-hydroxybutyrate, and 1-methylhistidine.

CONCLUSIONS

These metabolites investigated in this study may serve as potential biomarkers of immunoglobulin A nephropathy. Point scoring of pattern recognition analysis was able to distinguish immunoglobulin A nephropathy patients from healthy controls. However, there were no obvious differences between the low-risk and high-risk groups in our research. These results offer new, sensitive and specific, noninvasive approaches that may be of great benefit to immunoglobulin A nephropathy patients by enabling earlier diagnosis.

1H NMR-based metabolomic study of metabolic profiling for systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic inflammatory disease characterized by multi-system involvement, diverse clinical presentation, and alterations in circulating metabolites. In this study, a 1H NMR spectroscopy-based metabolomics approach was applied to establish a human SLE serum metabolic profile. Serum samples were obtained from patients with SLE ( n = 64), patients with rheumatoid arthritis (RA) ( n = 30) and healthy controls ( n = 35). The NOESYPR1D spectrum combined with multi-variate pattern recognition analysis was used to cluster the groups and establish a disease-specific metabolites phenotype. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models were capable of distinguishing SLE or RA patients from healthy subjects. The OPLS-DA model was able to predict diagnosis of SLE with a sensitivity rate of 60.9% and a specificity rate of 97.1%. For diagnosing RA, the model has much higher sensitivity (96.7%) and specificity (91.4%). The SLE serum samples were characterized by reduced concentrations of valine, tyrosine, phenylalanine, lysine, isoleucine, histidine, glutamine, alanine, citrate, creatinine, creatine, pyruvate, high-density lipoprotein, cholesterol, glycerol, formate and increased concentrations of N-acetyl glycoprotein, very low-density lipoprotein and low-density lipoprotein in comparison with the control population. Theresults not only indicated that serum NMR-based metabolomic methods had sufficient sensitivity and specificity to distinguish SLE and RA from healthy controls, but also have the potential to be developed into a clinically useful diagnostic tool, and could also contribute to a further understanding of disease mechanisms.

NMR-spectroscopic analysis of mixtures: from structure to function

NMR spectroscopy as a particularly information-rich method offers unique opportunities for improving the structural and functional characterization of metabolomes, which will be essential for advancing the understanding of many biological processes. Whereas traditionally NMR spectroscopy was mostly relegated to the characterization of pure compounds, the past few years have seen a surge of interest in using NMR-spectroscopic techniques for characterizing complex metabolite mixtures. Development of new methods was motivated partly by the realization that using NMR for the analysis of metabolite mixtures can help identify otherwise inaccessible small molecules, for example compounds that are prone to chemical decomposition and thus cannot be isolated. Furthermore, comparative metabolomics and statistical analyses of NMR spectra have proven highly effective at identifying novel and known metabolites that correlate with changes in genotype or phenotype. In this review, we provide an overview of the range of NMR-spectroscopic techniques recently developed for characterizing metabolite mixtures, including methods used in discovery-oriented natural product chemistry, in the study of metabolite biosynthesis and function, or for comparative analyses of entire metabolomes.

NMR methods for unravelling the spectra of complex mixtures

The main methods for the simplification of the NMR of complex mixtures by selective attenuation/suppression of the signals of certain components are presented. The application of relaxation, diffusion and PSR filters and other techniques to biological samples, pharmaceuticals, foods, living organisms and natural products are illustrated with examples.

Advances in NMR-based biofluid analysis and metabolite profiling

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

Standard operating procedure for metabonomics studies of blood serum and plasma samples using a 1H-NMR micro-flow probe

A standard operating procedure (SOP) is presented for high-throughput metabonomics studies using a 600 MHz NMR spectrometer equipped with a micro-flow probe that is connected to an auto-sampler. The procedure is designed to minimize random and systematic variation of NMR data collection. In addition, a protocol is described to assess the quality of the data acquired by a given NMR spectroscopist to ensure that (i) all researchers involved in the NMR data acquisition of a metabonomics research program perform equally and (ii) operator-associated variation of NMR data collection is statistically not relevant for the interpretation of results obtained from multivariate data analyses.

Metabolomics in evaluation of glucose disorders

PURPOSE OF REVIEW

Recent advances in metabolomic tools now permit to characterize dysregulated metabolic pathways in various diseases associated with the identification of sensitive and specific early responding biomarkers that are critical both for the diagnosis of the type of insult as well as for the selection and evaluation of therapy.

RECENT FINDINGS

This short review describes progresses made in analytical science and their applications in the field of glucose disorders. Recent studies focused mainly on type 2 diabetes both in human and animal models in order to validate early biomarkers and effects of drugs on disease progression. The potential of using the metabolomic approach was also demonstrated for diagnosing diabetic complications such as diabetic nephropathy.

SUMMARY

In addition to its application in the discovery of disease biomarkers, metabolomics can contribute to the elucidation of pathophysiological mechanisms.

Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies

The application of a (1)H nuclear magnetic resonance (NMR) spectroscopy-based screening method for determining the use of two widely available analgesics (acetaminophen and ibuprofen) in epidemiologic studies has been investigated. We used samples and data from the cross-sectional INTERMAP Study involving participants from Japan (n = 1145), China (n = 839), U.K. (n = 501), and the U.S. (n = 2195). An orthogonal projection to latent structures discriminant analysis (OPLS-DA) algorithm with an incorporated Monte Carlo resampling function was applied to the NMR data set to determine which spectra contained analgesic metabolites. OPLS-DA preprocessing parameters (normalization, bin width, scaling, and input parameters) were assessed systematically to identify an optimal acetaminophen prediction model. Subsets of INTERMAP spectra were examined to verify and validate the presence/absence of acetaminophen/ibuprofen based on known chemical shift and coupling patterns. The optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens containing these drug metabolites. The acetaminophen and ibuprofen models were subsequently used to predict the presence/absence of these drug metabolites for the remaining INTERMAP specimens. The acetaminophen model identified 415 out of 8436 spectra as containing acetaminophen metabolite signals while the ibuprofen model identified 245 out of 8604 spectra as containing ibuprofen metabolite signals from the global data set after excluding samples used to construct the prediction models. The NMR-based metabolic screening strategy provides a new objective approach for evaluation of self-reported medication data and is extendable to other aspects of population xenometabolome profiling.

1H NMR-based metabonomics for investigating diabetes

Diabetes is characterized by hyperglycemia due to dysfunction of insulin secretion or action. The two most common forms are Type 1 diabetes, in which pancreatic β-cells are destroyed, and Type 2 diabetes, in which a combination of disordered insulin action and secretion results in abnormal carbohydrate, lipid and protein metabolism. Metabonomics employs analytical technologies to measure ‘global’ metabolic responses to a disease state. With the aid of statistical pattern recognition, this can reveal novel insights into the biochemical consequences of diabetes. The metabonomic method can be divided into four stages: sample collection; preparation; data acquisition and processing; and statistical analyses. In this review, we describe the most recent developments at each experimental stage in detail, and comment on specific precautions or improvements that should be taken into account when studying diabetes. Finally, we end with speculations as to where and how the field will develop in the future. Metabonomics provides a logical framework for understanding the global metabolic effects of diabetes. Continuing technological improvements will expand our knowledge of the causes and progression of this disease, and enhance treatment options for individuals.

Two-dimensional DOSY experiment with Excitation Sculpting water suppression for the analysis of natural and biological media

The Bipolar Pulse Pair Stimulated Echo NMR pulse sequence was modified to blend the original Excitation Sculpting water signal suppression. The sequence is a powerful tool to generate rapidly, with a good spectrum quality, bidimensional DOSY experiments without solvent signal, thus allowing the analysis of complex mixtures such as plant extracts or biofluids. The sequence has also been successfully implemented for a protein at very-low concentration in interaction with a small ligand, namely the salivary IB5 protein binding the polyphenol epigallocatechine gallate. The artifacts created by this sequence can be observed, checked and removed thanks to NPK and NMRnotebook softwares to give a perfect bidimensional DOSY spectrum.