Nuclear Magnetic Resonance Metabolomics Research Articles

A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls

BackgroundFibromyalgia syndrome (FMS) is a chronic pain syndrome. A plausible pathogenesis of the disease is uncertain and the pursuit of measurable biomarkers for objective identification of affected individuals is a continuing endeavour in FMS research. Our objective was to perform an explorative metabolomics study (1) to elucidate the global urinary metabolite profile of patients suffering from FMS, and (2) to explore the potential of this metabolite information to augment existing medical practice in diagnosing the disease.MethodsWe selected patients with a medical history of persistent FMS (n = 18), who described their recent state of the disease through the Fibromyalgia Impact Questionnaire (FIQR) and an in-house clinical questionnaire (IHCQ). Three control groups were used: first-generation family members of the patients (n = 11), age-related individuals without any indications of FMS or related conditions (n = 10), and healthy young (18–22 years) individuals (n = 20). All subjects were female and the biofluid under investigation was urine. Correlation analysis of the FIQR showed the FMS patients represented a well-defined disease group for this metabolomics study. Spectral analyses of urine were conducted using a 500 MHz 1H nuclear magnetic resonance (NMR) spectrometer; data processing and analyses were performed using Matlab, R, SPSS and SAS software.Results and discussionUnsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, and significant increases in metabolites related to the gut microbiome (hippuric, succinic and lactic acids) were observed. We have developed an algorithm for the diagnosis of FMS consisting of three metabolites — succinic acid, taurine and creatine — that have a good level of diagnostic accuracy (Receiver Operating Characteristic (ROC) analysis — area under the curve 90%) and on the pain and fatigue symptoms for the selected FMS patient group.ConclusionOur data and comparative analyses indicated an altered metabolic profile of patients with FMS, analytically detectable within their urine. Validation studies may substantiate urinary metabolites to supplement information from medical assessment, tender-point measurements and FIQR questionnaires for an improved objective diagnosis of FMS.

Metabolic discrimination of sea buckthorn from different Hippopha\xeb species by 1H NMR based metabolomics

Sea buckthorn (Hippophaë; Elaeagnaceae) berries are widely consumed in traditional folk medicines, nutraceuticals, and as a source of food. The growing demand of sea buckthorn berries and morphological similarity of Hippophaë species leads to confusions, which might cause misidentification of plants used in natural products. Detailed information and comparison of the complete set of metabolites of different Hippophaë species are critical for their objective identification and quality control. Herein, the variation among seven species and seven subspecies of Hippophaë was studied using proton nuclear magnetic resonance (1H NMR) metabolomics combined with multivariate data analysis, and the important metabolites were quantified by quantitative 1H NMR (qNMR) method. The results showed that different Hippophaë species can be clearly discriminated and the important interspecific discriminators, including organic acids, L-quebrachitol, and carbohydrates were identified. Statistical differences were found among most of the Hippophaë species and subspecies at the content levels of the aforementioned interspecific discriminators via qNMR and one-way analysis of variance (ANOVA) test. These findings demonstrated that 1H NMR-based metabolomics is an applicable and effective approach for simultaneous metabolic profiling, species differentiation and quality assessment.

Effect of Yiqi Huoxue Decoction on the metabolomics of acute myocardial infarction rats

ObjectiveTo explore the effect of Yiqi Huoxue Decoction on the metabolomics of acute myocardial infarction rats. MethodLeft anterior descending coronary artery ligation was used to establish an acute myocardial infarction animal model. The rats were randomly divided into model, perindopril, QishenYiqi Dropping Pill and Yiqi Huoxue Decoction groups and compared with a sham-operation group. Blood metabolite changes were detected by nuclear magnetic resonance metabolomics 7 and 28 days after operation. Metabolite differences between the administration and model groups were analyzed. P-values of the differences in metabolite levels between the drug and model groups were statistically analyzed. ResultsCompared with the sham-operation group, there were 22 altered metabolites in the model group identified after 7 days and 15 identified after 28 days. Products of energy, carbohydrate, lipid and amino acid metabolism were the primary metabolites observed in the analyzed samples. ConclusionAfter intervention with traditional Chinese medicine, there were clear changes in energy, carbohydrate, lipid and amino acid metabolism that restored the metabolic phenotype to the normal range, thus repairing the damage caused by myocardial infarction.

Metabolic Immunomodulation of Macrophage Polarization by Pseudomonas aeruginosa Biofilms

Despite extensive investment in better healthcare practices, hospital acquired infections (HAIs) remain a major public health concern. In 2011, the CDC's HAI Prevalence Survey identified 722,000 patients with infections contracted during treatment at acute care hospitals, resulting in 75,000 deaths. It has been well demonstrated that the establishment of bacterial biofilm in the wound is a major contributor to the failure of acute wound treatment and development of chronic, non‐healing wounds; however how the metabolic phenotype of a colonizing biofilm influences the wound healing process remains unknown. This gap in knowledge inhibits the development of effective treatment protocols and results in chronic wounds being the leading cause of non‐traumatic limb amputations. Herein, we define how metabolic exchange and metabolite competition between P. aeruginosa biofilms and macrophages promotes a pro‐inflammatory polarization of macrophages and inhibits reprogramming of macrophages into a pro‐resolution phenotype.Using 1D 1H Nuclear Magnetic Resonance (NMR) small molecule metabolomics in conjunction with immunologic profiling, we demonstrate substantial overlap between the metabolic profile of P. aeruginosa biofilm phenotype and the metabolic profile driving macrophage polarization either into the classical inflammatory phenotype (M1) or the non‐classical anti‐inflammatory phenotype (M2). This metabolic overlap included key metabolic pathways that regulate immunomodulation in macrophages such as shifts between the citrate cycle (TCA) and pentose phosphate metabolism (PPP) and selective amino acid metabolism. Furthermore, exposure of non‐polarized macrophages to medium conditioned with soluble, small molecule metabolites (<3 kDa) derived either from P. aeruginosa planktonic cultures (referred to as PCM) or P. aeruginosa biofilm cultures (referred to as BCM) resulted in distinct metabolic profiles directly related to recently characterized metabolic pathways contributing to macrophage immunomodulation such as enhanced glycolysis, uncoupling of the TCA cycle, and selective amino acid uptake and metabolism. Finally, we use novel, naturally‐derived, biofilm‐targeted therapeutics to manipulate the biofilm immunomodulation of macrophage polarization to promote a shift from a pro‐inflammatory phenotype (M1) to a pro‐resolution phenotype (M2) in primary macrophages. Based on these findings, we demonstrate that P. aeruginosa biofilms mediate specific pathological effects against host macrophages through coordinated metabolic interactions, which result in chronic immune polarization and may contribute to deviation from the normal wound healing process and development of a chronic wound. Given the non‐invasive nature of NMR metabolomics profiling, these findings have the potential to lead to novel approaches for prevention and treatment of non‐healing wounds, including development of evidence‐based, personalizable treatment protocols executed at the bedside.Support or Funding InformationNIH NIGMS 1K01GM103821 to M.C.B.A. Kopriva Research Scholarship ‐ Montana State University to A.L.F.

Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study.

Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a 1H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.

Nuclear magnetic resonance metabolomics and human liver diseases: The principles and evidence associated with protein and carbohydrate metabolism.

During the last decade, metabolomics has become widely used in the field of human diseases. Numerous studies have demonstrated that this is a powerful technique for improving the understanding, diagnosis and management of various types of liver disease, such as acute and chronic liver diseases, and liver transplantation. Nuclear magnetic resonance (NMR) spectroscopy is one of the two most commonly applied methods for metabolomics. The aim of the present review was to investigate the results from recent key publications focusing on aspects of protein and carbohydrate metabolism. The review includes existing procedures, which are currently used for NMR data acquisition and statistical analysis. In addition, notable results obtained by these studies on protein and carbohydrate metabolism concerning human liver diseases are presented.

Divergent Relationships between Fecal Microbiota and Metabolome following Distinct Antibiotic-Induced Disruptions.

The intestinal microbiome plays an essential role in regulating many aspects of host physiology, and its disruption through antibiotic exposure has been implicated in the development of a range of serious pathologies. The complex metabolic relationships that exist between members of the intestinal microbiota and the potential redundancy in functional pathways mean that an integrative analysis of changes in both structure and function are needed to understand the impact of antibiotic exposure. We used a combination of next-generation sequencing and nuclear magnetic resonance (NMR) metabolomics to characterize the effects of two clinically important antibiotic treatments, ciprofloxacin and vancomycin-imipenem, on the intestinal microbiomes of female C57BL/6 mice. This assessment was performed longitudinally and encompassed both antibiotic challenge and subsequent microbiome reestablishment. Both antibiotic treatments significantly altered the microbiota and metabolite compositions of fecal pellets during challenge and recovery. Spearman's correlation analysis of microbiota and NMR data revealed that, while some metabolites could be correlated with individual operational taxonomic units (OTUs), frequently multiple OTUs were associated with a significant change in a given metabolite. Furthermore, one metabolite, arginine, can be associated with increases/decreases in different sets of OTUs under differing conditions. Taken together, these findings indicate that reliance on shifts in one data set alone will generate an incomplete picture of the functional effect of antibiotic intervention. A full mechanistic understanding will require knowledge of the baseline microbiota composition, combined with both a comparison and an integration of microbiota, metabolomics, and phenotypic data. IMPORTANCE Despite the fundamental importance of antibiotic therapies to human health, their functional impact on the intestinal microbiome and its subsequent ability to recover are poorly understood. Much research in this area has focused on changes in microbiota composition, despite the interdependency and overlapping functions of many members of the microbial community. These relationships make prediction of the functional impact of microbiota-level changes difficult, while analyses based on the metabolome alone provide relatively little insight into the taxon-level changes that underpin changes in metabolite levels. Here, we used combined microbiota and metabolome profiling to characterize changes associated with clinically important antibiotic combinations with distinct effects on the gut. Correlation analysis of changes in the metabolome and microbiota indicate that a combined approach will be essential for a mechanistic understanding of the functional impact of distinct antibiotic classes.

Metabolites as biomarkers of adverse reactions following vaccination: A pilot study using nuclear magnetic resonance metabolomics

An Adverse Event Following Immunization (AEFI) is an adverse reaction to a vaccination that goes above and beyond the usual side effects associated with vaccinations. One serious AEFI related to the smallpox vaccine is myopericarditis. Metabolomics involves the study of the low molecular weight metabolite profile of cells, tissues, and biological fluids, and provides a functional readout of the phenotype. Metabolomics may help identify a particular metabolic signature in serum of subjects who are predisposed to developing AEFIs. The goal of this study was to identify metabolic markers that may predict the development of adverse events following smallpox vaccination. Serum samples were collected from military personnel prior to and following receipt of smallpox vaccine. The study population included five subjects who were clinically diagnosed with myopericarditis, 30 subjects with asymptomatic elevation of troponins, and 31 subjects with systemic symptoms following immunization, and 34 subjects with no AEFI, serving as controls. Two-hundred pre- and post-smallpox vaccination sera were analyzed by untargeted metabolomics using 1H nuclear magnetic resonance (NMR) spectroscopy. Baseline (pre-) and post-vaccination samples from individuals who experienced clinically verified myocarditis or asymptomatic elevation of troponins were more metabolically distinguishable pre- and post-vaccination compared to individuals who only experienced systemic symptoms, or controls. Metabolomics profiles pre- and post-receipt of vaccine differed substantially when an AEFI resulted. This study is the first to describe pre- and post-vaccination metabolic profiles of subjects who developed an adverse event following immunization. The study demonstrates the promise of metabolites for determining mechanisms associated with subjects who develop AEFI and the potential to develop predictive biomarkers.

Recent breakthroughs in metabolomics promise to reveal the cryptic chemical traits that mediate plant community composition, character evolution and lineage diversification.

Contents 952 I. 952 II. 953 III. 955 IV. 956 V. 957 957 References 957 SUMMARY: Much of our understanding of the mechanisms by which biotic interactions shape plant communities has been constrained by the methods available to study the diverse secondary chemistry that defines plant relationships with other organisms. Recent innovations in analytical chemistry and bioinformatics promise to reveal the cryptic chemical traits that mediate plant ecology and evolution by facilitating simultaneous structural comparisons of hundreds of unknown molecules to each other and to libraries of known compounds. Here, I explore the potential for mass spectrometry and nuclear magnetic resonance metabolomics to enable unprecedented tests of seminal, but largely untested hypotheses that propose a fundamental role for plant chemical defenses against herbivores and pathogens in the evolutionary origins and ecological coexistence of plant species diversity.

Changes in human hepatic metabolism in steatosis and cirrhosis.

AIMTo understand the underlying metabolic changes in human liver disease we have applied nuclear magnetic resonance (NMR) metabolomics analysis to human liver tissue.METHODSWe have carried out pilot study using 1H-NMR to derive metabolomic signatures from human liver from patients with steatosis, nonalcoholic steatohepatitis (NASH) or alcohol-related liver damage (ARLD) to identify species that can predict outcome and discriminate between alcohol and metabolic-induced liver injuries.RESULTSChanges in branched chain amino acid homeostasis, tricarboxylic acid cycle and purine biosynthesis intermediates along with betaine were associated with the development of cirrhosis in both ARLD and nonalcoholic fatty liver disease. Species such as propylene glycol and as yet unidentified moieties that allowed discrimination between NASH and ARLD samples were also detected using our approach.CONCLUSIONOur high throughput, non-destructive technique for multiple analyte quantification in human liver specimens has potential for identification of biomarkers with prognostic and diagnostic significance.

Metabolic profiling of fatty liver in young and middle-aged adults: Cross-sectional and prospective analyses of the Young Finns Study.

Nonalcoholic fatty liver is associated with obesity‐related metabolic disturbances, but little is known about the metabolic perturbations preceding fatty liver disease. We performed comprehensive metabolic profiling to assess how circulating metabolites, such as lipoprotein lipids, fatty acids, amino acids, and glycolysis‐related metabolites, reflect the presence of and future risk for fatty liver in young adults. Sixty‐eight lipids and metabolites were quantified by nuclear magnetic resonance metabolomics in the population‐based Young Finns Study from serum collected in 2001 (n = 1,575), 2007 (n = 1,509), and 2011 (n = 2,002). Fatty liver was diagnosed by ultrasound in 2011 when participants were aged 34‐49 years (19% prevalence). Cross‐sectional associations as well as 4‐year and 10‐year risks for fatty liver were assessed by logistic regression. Metabolites across multiple pathways were strongly associated with the presence of fatty liver (P < 0.0007 for 60 measures in age‐adjusted and sex‐adjusted cross‐sectional analyses). The strongest direct associations were observed for extremely large very‐low‐density lipoprotein triglycerides (odds ratio [OR] = 4.86 per 1 standard deviation, 95% confidence interval 3.48‐6.78), other very‐low‐density lipoprotein measures, and branched‐chain amino acids (e.g., leucine OR = 2.94, 2.51‐3.44). Strong inverse associations were observed for high‐density lipoprotein measures, e.g., high‐density lipoprotein size (OR = 0.36, 0.30‐0.42) and several fatty acids including omega‐6 (OR = 0.37, 0.32‐0.42). The metabolic associations were attenuated but remained significant after adjusting for waist, physical activity, alcohol consumption, and smoking (P < 0.0007). Similar aberrations in the metabolic profile were observed already 10 years before fatty liver diagnosis. Conclusion: Circulating lipids, fatty acids, and amino acids reflect fatty liver independently of routine metabolic risk factors; these metabolic aberrations appear to precede the development of fatty liver in young adults. (Hepatology 2017;65:491‐500).

NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients.

The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using 1H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data.

1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.

Metabolic signatures of birthweight in 18288 adolescents and adults.

Background: Lower birthweight is associated with increased susceptibility to cardiometabolic diseases in adulthood, but the underlying molecular pathways are incompletely understood. We examined associations of birthweight with a comprehensive metabolic profile measured in adolescents and adults. Methods: High-throughput nuclear magnetic resonance metabolomics and biochemical assays were used to quantify 87 circulating metabolic measures in seven cohorts from Finland and the UK, comprising altogether 18 288 individuals (mean age 26 years, range 15–75). Metabolic associations with birthweight were assessed by linear regression models adjusted for sex, gestational age and age at blood sampling. The metabolic associations with birthweight were compared with the corresponding associations with adult body mass index (BMI). Results: Lower birthweight adjusted for gestational age was adversely associated with cardiometabolic biomarkers, including lipoprotein subclasses, fatty acids, amino acids and markers of inflammation and impaired liver function (P < 0.0015 for 46 measures). Associations were consistent across cohorts with different ages at metabolic profiling, but the magnitudes were weak. The pattern of metabolic deviations associated with lower birthweight resembled the metabolic signature of higher adult BMI (R2 = 0.77) assessed at the same time as the metabolic profiling. The resemblance indicated that 1 kg lower birthweight is associated with similar metabolic aberrations as caused by 0.92 units higher BMI in adulthood. Conclusions: Lower birthweight adjusted for gestational age is associated with adverse biomarker aberrations across multiple metabolic pathways. Coherent metabolic signatures between lower birthweight and higher adult adiposity suggest that shared molecular pathways may potentially underpin the metabolic deviations. However, the magnitudes of metabolic associations with birthweight are modest in comparison to the effects of adiposity, implying that birthweight is only a weak indicator of the metabolic risk profile in adulthood.

Metabolic profiling of alcohol consumption in 9778 young adults.

Background: High alcohol consumption is a major cause of morbidity, yet alcohol is associated with both favourable and adverse effects on cardiometabolic risk markers. We aimed to characterize the associations of usual alcohol consumption with a comprehensive systemic metabolite profile in young adults. Methods: Cross-sectional associations of alcohol intake with 86 metabolic measures were assessed for 9778 individuals from three population-based cohorts from Finland (age 24–45 years, 52% women). Metabolic changes associated with change in alcohol intake during 6-year follow-up were further examined for 1466 individuals. Alcohol intake was assessed by questionnaires. Circulating lipids, fatty acids and metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. Results: Increased alcohol intake was associated with cardiometabolic risk markers across multiple metabolic pathways, including higher lipid concentrations in HDL subclasses and smaller LDL particle size, increased proportions of monounsaturated fatty acids and decreased proportion of omega-6 fatty acids, lower concentrations of glutamine and citrate (P < 0.001 for 56 metabolic measures). Many metabolic biomarkers displayed U-shaped associations with alcohol consumption. Results were coherent for men and women, consistent across the three cohorts and similar if adjusting for body mass index, smoking and physical activity. The metabolic changes accompanying change in alcohol intake during follow-up resembled the cross-sectional association pattern (R2 = 0.83, slope = 0.72 ± 0.04). Conclusions: Alcohol consumption is associated with a complex metabolic signature, including aberrations in multiple biomarkers for elevated cardiometabolic risk. The metabolic signature tracks with long-term changes in alcohol consumption. These results elucidate the double-edged effects of alcohol on cardiovascular risk.

Preterm neonatal urinary renal developmental and acute kidney injury metabolomic profiling: an exploratory study.

Acute kidney injury (AKI) staging has been developed in the adult and pediatric populations, but these do not yet exist for the neonatal population. Metabolomics was utilized to uncover biomarkers of normal and AKI-associated renal function in preterm infants. The study comprised 20 preterm infants with an AKI diagnosis who were matched by gestational age and gender to 20 infants without an AKI diagnosis. Urine samples from pre-term newborn infants collected on day 2 of life were analyzed using broad-spectrum nuclear magnetic resonance (NMR) metabolomics. Multivariate analysis methods were used to identify metabolite profiles that differentiated AKI and no AKI, and to identify a metabolomics profile correlating with gestational age in infants with and without AKI. There was a clear distinction between the AKI and no-AKI profiles. Two previously identified biomarkers of AKI, hippurate and homovanillate, differentiated AKI from no-AKI profiles. Pathway analysis revealed similarities to cholinergic neurons, prenatal nicotine exposure on pancreatic β cells, and amitraz-induced inhibition of insulin secretion. Additionally, a pH difference was noted. Both pH and the metabolites were found to be associated with AKI; however, only the metabotype was a significant predictor of AKI. Pathways for the no-AKI group that correlated uniquely with gestational age included aminoacyl-t-RNA biosynthesis, whereas pathways in the AKI group yielded potential metabolite changes in pyruvate metabolism. Metabolomics was able to differentiate the urinary profiles of neonates with and without an AKI diagnosis and metabolic developmental profiles correlated with gestational age. Further studies in larger cohorts are needed to validate these results.

Abstract 4215: Hyperpolarized magnetic resonance metabolic imaging and NMR metabolomics to assess the progression and aggressiveness of patient-derived pancreatic cancer xenografts

Abstract Pancreatic cancer, the most lethal of solid tumors is an aggressive disease that develops relatively symptom-free. The absence of early symptoms has created a critical need for identifying and developing new noninvasive biomarkers and therapeutic intervention to improve the survival rate of pancreatic cancer patients. Our goal is to identify metabolic biomarkers for early detection with an understanding of pancreatic cancer progression from benign lesions to a malignant state. Hyperpolarized magnetic resonance imaging (HP-MRI) provides a &amp;gt;10,000-fold signal enhancement for detecting of endogenous metabolic substrates to monitor metabolic fluxes through the multiple key biochemical pathways including glycolysis and citric acid cycle. In particular, conversion between hyperpolarized 13C-labeled pyruvate and lactate, catalyzed by lactate dehydrogenase (LDH), has been shown to have a number of potential applications such as diagnosis, staging tumor grade and monitoring therapy response. Genetic mutation is evident in causing tumorigenesis and often time these mutations trigger the signaling pathways that are associated with “metabolic transformations. There is currently tremendous research interest in dissecting the mechanisms of metabolic transformation as the cancers progress. In this effort, we are applying high-resolution NMR (nuclear magnetic resonance) metabolomics and the real-time hyperpolarized metabolic imaging of patient-derived xenograft tumors for probing the underlying mechanism of altered-metabolism and correlate with pancreatic cancer progression and its aggressiveness. The protocol detailing heterotopic engraftment of pancreatic cancer patient tumors into immunodeficient mice and expansion of direct xenograft tumors was recently reported elsewhere. These patient-derived xenografts (PDX) were well annotated with original patient tumors. For the NMR metabolomics study, the fresh-frozen tumor samples were homogenized, lyophilized, and resuspended in D2O and 1-D proton NMR spectroscopy was employed to characterize the water-soluble portion of the metabolome. These data suggest that elevated glycolysis (production of high lactate) and alterations in choline metabolism may arise in pancreatic cancer development. Also, the metabolite concentrations were significantly higher in aggressive tumors compare to non-aggressive one. The real-time metabolic imaging shows the immediate conversion of lactate after injection (i.v.) of hyperpolarized 13C pyruvate and probe the upregulated LDH activity as the cancer progress. The basic genomic profiling of these tumors is in progress. This model may provide an excellent platform where mutational status of these patient tumors can be correlated with both high resolution and hyperpolarized dynamical metabolomics data for characterizing individual tumor phenotypes. Citation Format: Prasanta Dutta, Mayrim Rios Perez, Travis Cole Salzillo, Michael Pratt, Yaan Kang, Niki Zacharias, Anirban Maitra, Jason B. Fleming, Pratip Bhattacharya. Hyperpolarized magnetic resonance metabolic imaging and NMR metabolomics to assess the progression and aggressiveness of patient-derived pancreatic cancer xenografts. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4215.

Pro-Oxidant Role of Silibinin in DMBA/TPA Induced Skin Cancer: 1H NMR Metabolomic and Biochemical Study.

Silibinin, a major bioactive flavonolignan in Silybum marianum, has received considerable attention in view of its anticarcinogenic activity. The present study examines its anticancer potential against 7, 12-dimethylbenz(a)anthracene (DMBA) and 12-O-tetradecanoylphorbol-13-acetate (TPA) induced skin cancer. Male LACA mice were randomly segregated into 4 groups: Control, DMBA/TPA, Silibinin and Silibinin+DMBA/TPA. Tumors in DMBA/TPA and Silibinin+DMBA/TPA groups were histologically graded as squamous cell carcinoma. In the Silibinin+DMBA/TPA group, significant reduction in tumor incidence (23%), tumor volume (64.4%), and tumor burden (84.8%) was observed when compared to the DMBA/TPA group. The underlying protective mechanism of Silibinin action was studied at pre-initiation (2 weeks), post-initiation (10 weeks) and promotion (22 weeks) stages of the skin carcinogenesis. The antioxidant nature of Silibinin was evident at the end of 2 weeks of its treatment. However, towards the end of 10 and 22 weeks, elevated lipid peroxidation (LPO) levels indicate the pro-oxidative nature of Silibinin in the cancerous tissue. TUNEL assay revealed enhanced apoptosis in the Silibinin+DMBA/TPA group with respect to the DMBA/TPA group. Therefore, it may be suggested that raised LPO could be responsible for triggering apoptosis in the Silibinin+DMBA/TPA group. 1H Nuclear Magnetic Resonance (NMR) spectroscopy was used to determine the metabolic profile of the skin /skin tumors. Dimethylamine (DMA), glycerophosphocholine (GPC), glucose, lactic acid, taurine and guanine were identified as the major contributors for separation between the groups from the Principal Component Analysis (PCA) of the metabolite data. Enhanced DMA levels with no alteration in GPC, glucose and lactate levels reflect altered choline metabolism with no marked Warburg effect in skin tumors. However, elevated guanine levels with potent suppression of taurine and glucose levels in the Silibinin+DMBA/TPA group are suggestive of the pro-oxidative nature of Silibinin in regressing tumors. Thus, supporting the theory of augmented LPO levels resulting in increased apoptosis in the skin tumors treated with Silibinin.

Cerebrospinal fluid in tuberculous meningitis exhibits only the L-enantiomer of lactic acid.

BackgroundThe defining feature of the cerebrospinal fluid (CSF) collected from infants and children with tuberculous meningitis (TBM), derived from an earlier untargeted nuclear magnetic resonance (NMR) metabolomics study, was highly elevated lactic acid. Undetermined was the contribution from host response (L-lactic acid) or of microbial origin (D-lactic acid), which was set out to be determined in this study.MethodsIn this follow-up study, we used targeted ultra-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC–ESI–MS/MS) to determine the ratio of the L and D enantiomers of lactic acid in these CSF samples.ResultsHere we report for the first time that the lactic acid observed in the CSF of confirmed TBM cases was in the L-form and solely a response from the host to the infection, with no contribution from any bacteria. The significance of elevated lactic acid in TBM appears to be that it is a crucial energy substrate, used preferentially over glucose by microglia, and exhibits neuroprotective capabilities.ConclusionThese results provide experimental evidence to support our conceptual astrocyte–microglia lactate shuttle model formulated from our previous NMR-based metabolomics study — highlighting the fact that lactic acid plays an important role in neuroinflammatory diseases such as TBM. Furthermore, this study reinforces our belief that the determination of enantiomers of metabolites corresponding to infectious diseases is of critical importance in substantiating the clinical significance of disease markers.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1597-9) contains supplementary material, which is available to authorized users.

Metabolic Characterization of a Rare Genetic Variation Within APOC3 and Its Lipoprotein Lipase-Independent Effects.

Plasma triglyceride levels have been implicated in atherosclerosis and coronary heart disease. Apolipoprotein C-III (APOC3) plays a key role in the hydrolysis of triglyceride-rich lipoproteins to remnant particles by lipoprotein lipase (LPL) and their uptake by the liver. A rare variant in APOC3(rs138326449) has been associated with triglyceride, very low-density lipoprotein, and high-density lipoprotein levels, as well as risk of coronary heart disease. We aimed to characterize the impact of this locus across a broad set of mainly lipids-focused metabolic measures. A high-throughput serum nuclear magnetic resonance metabolomics platform was used to quantify 225 metabolic measures in 13 285 participants from 2 European population cohorts. We analyzed the effect of the APOC3 variant on the metabolic measures and used the common LPL(rs12678919) polymorphism to test for LPL-independent effects. Eighty-one metabolic measures showed evidence of association with APOC3(rs138326449). In addition to previously reported triglyceride and high-density lipoprotein associations, the variant was also associated with very low-density lipoprotein and high-density lipoprotein composition measures, other cholesterol measures, and fatty acids. Comparison of the APOC3 and LPL associations revealed that APOC3 association results for medium and very large very low-density lipoprotein composition are unlikely to be solely predictable by the action of APOC3 through LPL. We characterized the effects of the rare APOC3(rs138326449) loss of function mutation in lipoprotein metabolism, as well as the effects of LPL(rs12678919). Our results improve our understanding of the role of APOC3 in triglyceride metabolism, its LPL independent action, and the complex and correlated nature of human metabolites.