Serum metabolic signatures for Alzheimer's Disease reveal alterations in amino acid composition: a validation study.

INTRODUCTION: Alzheimer's Disease (AD) is complex and novel approaches are urgently needed to aid in diagnosis. Blood is frequently used as a source for biomarkers; however, its complexity prevents proper detection. The analytical power of metabolomics, coupled with statistical tools, can assist in reducing this complexity. OBJECTIVES: Thus, we sought to validate a previously proposed panel of metabolic blood-based biomarkers for AD and expand our understanding of the pathological mechanisms involved in AD that are reflected in the blood. METHODS: In the validation cohort serum and plasma were collected from 25 AD patients and 25 healthy controls. Serum was analysed for metabolites using nuclear magnetic resonance (NMR) spectroscopy, while plasma was tested for markers of neuronal damage and AD hallmark proteins using single molecule array (SIMOA). RESULTS: The diagnostic performance of the metabolite biomarker panel was confirmed using sparse-partial least squares discriminant analysis (sPLS-DA) with an area under the curve (AUC) of 0.73 (95% confidence interval: 0.59-0.87). Pyruvic acid and valine were consistently reduced in the discovery and validation cohorts. Pathway analysis of significantly altered metabolites in the validation set revealed that they are involved in branched-chain amino acids (BCAAs) and energy metabolism (glycolysis and gluconeogenesis). Additionally, strong positive correlations were observed for valine and isoleucine between cerebrospinal fluid p-tau and t-tau. CONCLUSIONS: Our proposed panel of metabolites was successfully validated using a combined approach of NMR and sPLS-DA. It was discovered that cognitive-impairment-related metabolites belong to BCAAs and are involved in energy metabolism.

Perfusion system for studying dynamic metabolomics in rat brain slices exposed to oxygen-glucose deprivation using proton and phosphorus nuclear magnetic resonance.

The aim of the current study was to establish a controlled and reproducible model to study metabolic changes during oxygen-glucose deprivation (OGD) in rat brain using a nuclear magnetic resonance (NMR)-compatible perfusion system. Rat brains were cut into 400-µm thick slices and perfused with artificial cerebrospinal fluid (aCSF) in a 10-mm NMR tube inside a 600-MHz NMR spectrometer. Four experimental conditions were tested: (1) continuous perfusion with aCSF with glucose and normoxia, and (2) 30-, (3) 60-, or (4) 120-min periods of OGD followed by reperfusion of aCSF containing glucose and normoxia. The energetic state of perfused brain slices was measured using phosphorus (31 P) NMR and metabolite changes were measured using proton (1 H) NMR. aCSF samples were collected every 30 min and analyzed using 1 H NMR. The sample temperature was maintained at 36.7 ± 0.1°C and was checked periodically throughout the experiments. Brain slice histology was compared before and after OGD in the perfusion system using hematoxylin-eosin-saffron staining. NMR data clearly distinguished three severity groups (mild, moderate, and severe) after 30, 60, and 120 min of OGD, respectively, compared with the control group. 31 P NMR spectra obtained from controls showed that phosphocreatine levels were stable for 5 h inside the perfusion system. Control 1 H NMR spectra showed that lactate, N-acetylaspartic acid, glutamate, γ-aminobutyric acid, and creatine metabolite levels were stable over time, with lactate levels having a tendency to gradually increase due to the recirculation of the aCSF in the perfusion system. A controlled and reproducible perfusion system was established to study the energetic and metabolic changes in rat brain slices during and after OGD of varying severity.

Identifying possible biomarkers of lower urinary tract symptoms using metabolomics and partial least square regression.

INTRODUCTION: The objective of this study was to explore potential novel biomarkers for moderate to severe lower urinary tract symptoms (LUTS) using a metabolomics-based approach, and statistical methods with significant different features than previous reported. MATERIALS AND METHODS: The patients and the controls were selected to participate in the study according to inclusion/exclusion criteria (n = 82). We recorded the following variables: International prostatic symptom score (IPSS), prostate volume, comorbidities, PSA, height, weight, triglycerides, glycemia, HDL cholesterol, and blood pressure. The study of 41 plasma metabolites was done using the nuclear magnetic resonance spectroscopy technique. First, the correlations between the metabolites and the IPSS were done using Pearson. Second, significant biomarkers of LUTS from metabolites were further analysed using a multiple linear regression model. Finally, we validated the findings using partial least square regression (PLS). RESULTS: Small to moderate correlations were found between IPSS and methionine (-0.301), threonine (-0.320), lactic acid (0.294), pyruvic acid (0.207) and 2-aminobutyric-acid (0.229). The multiple linear regression model revealed that only threonine (p = 0.022) was significantly associated with IPSS, whereas methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847) and 2-aminobutyric-acid (p = 0.244) lost their significance. However, all metabolites lost their significance in the PLS model. CONCLUSION: When using the robust PLS-regression method, none of the metabolites in our analysis had a significant association with lower urinary tract symptoms. This highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology.

Serum NMR-Based Metabolomics Profiling Identifies Lipoprotein Subfraction Variables and Amino Acid Reshuffling in Myeloma Development and Progression.

Multiple myeloma (MM) is an incurable hematological cancer. It is preceded by monoclonal gammopathy of uncertain significance (MGUS)-an asymptomatic phase. It has been demonstrated that early detection increases the 5-year survival rate. However, blood-based biomarkers that enable early disease detection are lacking. Metabolomic and lipoprotein subfraction variable profiling is gaining traction to expand our understanding of disease states and, more specifically, for identifying diagnostic markers in patients with hematological cancers. This study aims to enhance our understanding of multiple myeloma (MM) and identify candidate metabolites, allowing for a more effective preventative treatment. Serum was collected from 25 healthy controls, 20 patients with MGUS, and 30 patients with MM. 1H-NMR (Nuclear Magnetic Resonance) spectroscopy was utilized to evaluate serum samples. The metabolite concentrations were examined using multivariate, univariate, and pathway analysis. Metabolic profiles of the MGUS patients revealed lower levels of alanine, lysine, leucine but higher levels of formic acid when compared to controls. However, metabolic profiling of MM patients, compared to controls, exhibited decreased levels of total Apolipoprotein-A1, HDL-4 Apolipoprotein-A1, HDL-4 Apolipoprotein-A2, HDL Free Cholesterol, HDL-3 Cholesterol and HDL-4 Cholesterol. Lastly, metabolic comparison between MGUS to MM patients primarily indicated alterations in lipoproteins levels: Total Cholesterol, HDL Cholesterol, HDL Free Cholesterol, Total Apolipoprotein-A1, HDL Apolipoprotein-A1, HDL-4 Apolipoprotein-A1 and HDL-4 Phospholipids. This study provides novel insights into the serum metabolic and lipoprotein subfraction changes in patients as they progress from a healthy state to MGUS to MM, which may allow for earlier clinical detection and treatment.

Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study.

BACKGROUND: The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease. METHODS: From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs). RESULTS: Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women. CONCLUSIONS: We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.

Phosphatase of regenerating liver-3 regulates cancer cell metabolism in multiple myeloma.

Cancer cells often depend on microenvironment signals from molecules such as cytokines for proliferation and metabolic adaptations. PRL-3, a cytokine-induced oncogenic phosphatase, is highly expressed in multiple myeloma cells and associated with poor outcome in this cancer. We studied whether PRL-3 influences metabolism. Cells transduced to express PRL-3 had higher aerobic glycolytic rate, oxidative phosphorylation, and ATP production than the control cells. PRL-3 promoted glucose uptake and lactate excretion, enhanced the levels of proteins regulating glycolysis and enzymes in the serine/glycine synthesis pathway, a side branch of glycolysis. Moreover, mRNAs for these proteins correlated with PRL-3 expression in primary patient myeloma cells. Glycine decarboxylase (GLDC) was the most significantly induced metabolism gene. Forced GLDC downregulation partly counteracted PRL-3-induced aerobic glycolysis, indicating GLDC involvement in a PRL-3-driven Warburg effect. AMPK, HIF-1α, and c-Myc, important metabolic regulators in cancer cells, were not mediators of PRL-3's metabolic effects. A phosphatase-dead PRL-3 mutant, C104S, promoted many of the metabolic changes induced by wild-type PRL-3, arguing that important metabolic effects of PRL-3 are independent of its phosphatase activity. Through this study, PRL-3 emerges as one of the key mediators of metabolic adaptations in multiple myeloma.

Associations of lipoprotein particle profile and objectively measured physical activity and sedentary time in schoolchildren: a prospective cohort study.

BACKGROUND: Our understanding of the mechanisms through which physical activity might benefit lipoprotein metabolism is inadequate. Here we characterise the continuous associations between physical activity of different intensities, sedentary time, and a comprehensive lipoprotein particle profile. METHODS: Our cohort included 762 fifth grade (mean [SD] age = 10.0 [0.3] y) Norwegian schoolchildren (49.6% girls) measured on two separate occasions across one school year. We used targeted proton nuclear magnetic resonance (1H NMR) spectroscopy to produce 57 lipoprotein measures from fasted blood serum samples. The children wore accelerometers for seven consecutive days to record time spent in light-, moderate-, and vigorous-intensity physical activity, and sedentary time. We used separate multivariable linear regression models to analyse associations between the device-measured activity variables-modelled both prospectively (baseline value) and as change scores (follow-up minus baseline value)-and each lipoprotein measure at follow-up. RESULTS: Higher baseline levels of moderate-intensity and vigorous-intensity physical activity were associated with a favourable lipoprotein particle profile at follow-up. The strongest associations were with the larger subclasses of triglyceride-rich lipoproteins. Sedentary time was associated with an unfavourable lipoprotein particle profile, the pattern of associations being the inverse of those in the moderate-intensity and vigorous-intensity physical activity analyses. The associations with light-intensity physical activity were more modest; those of the change models were weak. CONCLUSION: We provide evidence of a prospective association between time spent active or sedentary and lipoprotein metabolism in schoolchildren. Change in activity levels across the school year is of limited influence in our young, healthy cohort. TRIAL REGISTRATION: ClinicalTrials.gov , # NCT02132494 . Registered 7th April 2014.

Effect of Repeated Freeze-Thaw Cycles on NMR-Measured Lipoproteins and Metabolites in Biofluids.

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze-thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9%  for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze-thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform.

Nanoparticle Ligand-Decoration Procedures Affect in Vivo Interactions with Immune Cells.

Ligand-decorated nanoparticles are extensively studied and applied for in vivo drug delivery and molecular imaging. Generally, two different ligand-decoration procedures are utilized; ligands are either conjugated with nanoparticle ingredients and incorporated during nanoparticle preparation, or they are attached to preformed nanoparticles by utilizing functionalized reactive surface groups (e.g., maleimide). Although the two procedures result in nanoparticles with very similar physicochemical properties, formulations obtained through the latter manufacturing process typically contain nonconjugated reactive surface groups. In the current study, we hypothesized that the different ligand-decoration procedures might affect the extent of interaction between nanoparticles and immune cells (especially phagocytes). In order to investigate our hypothesis, we decorated lipidic nanoparticles with a widely used cyclic Arg-Gly-Asp (cRGD) peptide using the two different procedures. As proven from in vivo experiments in mice, the presence of nonconjugated surface moieties results in increased recognition by the immune system. This is important knowledge considering the emerging focus on understanding and optimizing ways to target and track immune cells and the development of nanomedicine-based strategies in the field of immunotherapy.

Identification of metabolites from 2D (1)H-(13)C HSQC NMR using peak correlation plots.

BACKGROUND: Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in (1)H NMR spectra has previously been successfully employed. Similar correlation of 2D (1)H-(13)C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine). RESULTS: From 50 (1)H-(13)C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database. CONCLUSIONS: Correlation plots prepared by statistically correlating (1)H-(13)C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Benzoylated uronic acid building blocks and synthesis of N-uronate conjugates of lamotrigine.

A chemoenzymatic approach towards benzoylated uronic acid building blocks has been investigated starting with benzoylated hexapyranosides using regioselective C-6 enzymatic hydrolysis as the key step. Two of the building blocks were reacted with the antiepileptic drug lamotrigine. Glucuronidation of lamotrigine using methyl (2,3,4-tri-O-benzoyl-α-D-glycopyranosyl bromide)uronate proceeded to give the N2-conjugate. However, lamotrigine-N2-glucuronide was most efficiently synthesised from methyl (2,3,4-tri-O-acetyl-α-D-glucopyranosyl bromide)uronate. Employing nitromethane as solvent with CdCO(3) as a base lamotrigine-N2 glucuronide was prepared in a high yield (41%). Also methyl (2,3-di-O-benzoyl-4-deoxy-4-fluoro-α-D-glucosyl bromide)uronate underwent N-glucuronidation, but the product was unstable, eliminating hydrogen fluoride to give the corresponding enoate conjugate.

Longitudinal Changes in Circulating Metabolites and Lipoproteins After Breast Cancer Treatment.

The multimodal treatment of breast cancer may induce long term effects on the metabolic profile and increase the risk of future cardiovascular disease. In this study, we characterized longitudinal changes in serum lipoprotein subfractions and metabolites after breast cancer treatment, aiming to determine the long-term effect of different treatment modalities. Further, we investigated the prognostic value of treatment-induced changes in breast cancer-specific and overall 10-year survival. In this study, serum samples from breast cancer patients (n = 250) were collected repeatedly before and after radiotherapy, and serum metabolites and lipoprotein subfractions were quantified by NMR spectroscopy. Longitudinal changes were assessed by univariate and multivariate data analysis methods applicable for repeated measures. Distinct changes were detectable in levels of lipoprotein subfractions and circulating metabolites during the first year, with similar changes despite large differences in treatment regimens. We detect increased free cholesterol and decreased esterified cholesterol levels of HDL subfractions, a switch towards larger LDL particles and higher total LDL-cholesterol, in addition to a switch in the glutamine-glutamate ratio. Non-survivors had different lipid profiles from survivors already at baseline. To conclude, our results show development towards an atherogenic lipid profile in breast cancer patients with different treatment regimens.

Highly sensitive GC/MS/MS method for quantitation of amino and nonamino organic acids.

Metabolite profiling methods are important tools for measurement of metabolite pools in biological systems. While most metabolite profiling methods report relative intensities or depend on a few internal standards representing all metabolites, the ultimate requirement for a quantitative description of the metabolite pool in biological cells and fluids is absolute concentration determination. We report here a high-throughput and sensitive gas chromatography/tandem mass spectrometry (GC/MS/MS) targeted metabolite profiling method enabling absolute quantification of all detected metabolites. The method is based on methyl chloroformate derivatization and quantification by spiking samples with metabolite standards separately derivatized with deuterated derivatization reagents. The traditional electron impact ionization is replaced with positive chemical ionization since the latter to a much larger extent preserve the molecular ion and other high molecular weight fragments. This made it easier to select unique MS/MS transitions among the many coeluting metabolites. Currently, the novel GC/MS/MS method comprises 67 common primary metabolites of which most belong to the groups of amino and nonamino organic acids. We show the applicability of the method on urine and serum samples. The method is a significant improvement of present methodology for quantitative GC/MS metabolite profiling of amino acids and nonamino organic acids.

New nystatin-related antifungal polyene macrolides with altered polyol region generated via biosynthetic engineering of Streptomyces noursei.

Polyene macrolide antibiotics, including nystatin and amphotericin B, possess fungicidal activity and are being used as antifungal agents to treat both superficial and invasive fungal infections. Due to their toxicity, however, their clinical applications are relatively limited, and new-generation polyene macrolides with an improved therapeutic index are highly desirable. We subjected the polyol region of the heptaene nystatin analogue S44HP to biosynthetic engineering designed to remove and introduce hydroxyl groups in the C-9-C-10 region. This modification strategy involved inactivation of the P450 monooxygenase NysL and the dehydratase domain in module 15 (DH15) of the nystatin polyketide synthase. Subsequently, these modifications were combined with replacement of the exocyclic C-16 carboxyl with the methyl group through inactivation of the P450 monooxygenase NysN. Four new polyene macrolides with up to three chemical modifications were generated, produced at relatively high yields (up to 0.51 g/liter), purified, structurally characterized, and subjected to in vitro assays for antifungal and hemolytic activities. Introduction of a C-9 hydroxyl by DH15 inactivation also blocked NysL-catalyzed C-10 hydroxylation, and these modifications caused a drastic decrease in both antifungal and hemolytic activities of the resulting analogues. In contrast, single removal of the C-10 hydroxyl group by NysL inactivation had only a marginal effect on these activities. Results from the extended antifungal assays strongly suggested that the 9-hydroxy-10-deoxy S44HP analogues became fungistatic rather than fungicidal antibiotics.

Identifying metabolic alterations in newly diagnosed small cell lung cancer patients.

BACKGROUND: Small cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer. METHODS: A total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed. RESULTS: Several metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC. CONCLUSIONS: This study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.

Classification and biomarker identification of prostate tissue from TRAMP mice with hyperpolarized 13C-SIRA.

Hyperpolarized 13C isotope resolved spectroscopy boosts NMR signal intensity, which improves signal detection and allows metabolic fluxes to be analyzed. Such hyperpolarized flux data may offer new approaches to tissue classification and biomarker identification that could be translated in vivo. Here we used hyperpolarized stable isotope resolved analysis (SIRA) to measure metabolite specific 13C isotopic enrichments in the central carbon metabolism of mouse prostate. Prostate and tumor tissue samples were acquired from transgenic adenocarcinomas of the mouse prostate (TRAMP) mice. Before euthanasia, mice were injected with [U-13C]glucose intraperitoneally (i.p.). Polar metabolite extracts were prepared, and hyperpolarized 1D-13C NMR spectra were obtained from normal prostate (n = 19) and cancer tissue (n = 19) samples. Binary classification and feature analysis was performed to make a separation model and to investigate differences between samples originating from normal and cancerous prostate tissue, respectively. Hyperpolarized experiments were carried out according to a standardized protocol, which showed a high repeatability (CV = 15%) and an average linewidth in the 1D-13C NMR spectra of 2 ± 0.5 Hz. The resolution of the hyperpolarized 1D-13C spectra was high with little signal overlap in the carbonyl region and metabolite identification was easily accomplished. A discrimination with 95% success rate could be made between samples originating from TRAMP mice prostate and tumor tissue based on isotopomers from uniquely identified metabolites. Hyperpolarized 13C-SIRA allowed detailed metabolic information to be obtained from tissue specimens. The positional information of 13C isotopic enrichments lead to easily interpreted features responsible for high predictive classification of tissue types. This analytical approach has matured, and the robust experimental protocols currently available allow systematic tracking of metabolite flux ex vivo.

Cross-sectional and prospective associations between aerobic fitness and lipoprotein particle profile in a cohort of Norwegian schoolchildren.

BACKGROUND AND AIMS: The associations between aerobic fitness and traditional measures of lipid metabolism in children are uncertain. We investigated whether higher levels of aerobic fitness benefit lipoprotein metabolism by exploring associations with a comprehensive lipoprotein particle profile. METHODS: In our prospective cohort study, we used targeted proton nuclear magnetic resonance (1H NMR) spectroscopy to profile 57 measures of lipoprotein metabolism from fasting serum samples of 858 fifth-grade Norwegian schoolchildren (49.0% girls; mean age 10.0 years). Aerobic fitness was measured using an intermittent shuttle run aerobic fitness test. We used multiple linear regression adjusted for potential confounders to examine cross-sectional and prospective associations between aerobic fitness and lipoprotein particle profile. RESULTS: Higher levels of aerobic fitness were associated with a favourable lipoprotein particle profile in the cross-sectional analysis, which included inverse associations with all measures of very low-density lipoprotein (VLDL) particles (e.g., -0.06 mmol·L-1 or -0.23 SD units; 95% CI = -0.31, -0.16 for VLDL cholesterol concentration). In the prospective analysis, the favourable pattern of associations persisted, though the individual associations tended to be more consistent with those of the cross-sectional analysis for the VLDL subclass measures compared to the low-density lipoproteins and high-density lipoproteins. Adjustment for adiposity attenuated the associations in both cross-sectional and prospective models. Nevertheless, an independent effect of aerobic fitness remained for some measures. CONCLUSIONS: Improving children's aerobic fitness levels should benefit lipoprotein metabolism, though a concomitant reduction in adiposity would likely potentiate this effect.

Biosynthetic pathway for γ-cyclic sarcinaxanthin in Micrococcus luteus: heterologous expression and evidence for diverse and multiple catalytic functions of C(50) carotenoid cyclases.

We report the cloning and characterization of the biosynthetic gene cluster (crtE, crtB, crtI, crtE2, crtYg, crtYh, and crtX) of the γ-cyclic C(50) carotenoid sarcinaxanthin in Micrococcus luteus NCTC2665. Expression of the complete and partial gene cluster in Escherichia coli hosts revealed that sarcinaxanthin biosynthesis from the precursor molecule farnesyl pyrophosphate (FPP) proceeds via C(40) lycopene, C(45) nonaflavuxanthin, C(50) flavuxanthin, and C(50) sarcinaxanthin. Glucosylation of sarcinaxanthin was accomplished by the crtX gene product. This is the first report describing the biosynthetic pathway of a γ-cyclic C(50) carotenoid. Expression of the corresponding genes from the marine M. luteus isolate Otnes7 in a lycopene-producing E. coli host resulted in the production of up to 2.5 mg/g cell dry weight sarcinaxanthin in shake flasks. In an attempt to experimentally understand the specific difference between the biosynthetic pathways of sarcinaxanthin and the structurally related ε-cyclic decaprenoxanthin, we constructed a hybrid gene cluster with the γ-cyclic C(50) carotenoid cyclase genes crtYg and crtYh from M. luteus replaced with the analogous ε-cyclic C(50) carotenoid cyclase genes crtYe and crtYf from the natural decaprenoxanthin producer Corynebacterium glutamicum. Surprisingly, expression of this hybrid gene cluster in an E. coli host resulted in accumulation of not only decaprenoxanthin, but also sarcinaxanthin and the asymmetric ε- and γ-cyclic C(50) carotenoid sarprenoxanthin, described for the first time in this work. Together, these data contributed to new insight into the diverse and multiple functions of bacterial C(50) carotenoid cyclases as key catalysts for the synthesis of structurally different carotenoids.

The effect of sampling procedures and day-to-day variations in metabolomics studies of biofluids.

Metabolomics analysis of biofluids is a feasible tool for disease characterization and monitoring due to its minimally invasive nature. To reduce unwanted variation in biobanks and clinical studies, it is important to determine the effect of external factors on metabolic profiles of biofluids. In this study we examined the effect of sample collection and sample processing procedures on NMR measured serum lipoproteins and small-molecule metabolites in serum and urine, using a cohort of men diagnosed with either prostate cancer or benign prostatic hyperplasia. We determined day-to-day reliability of metabolites by systematic sample collection at two different days, in both fasting and non-fasting conditions. Study participants received prostate massage the first day to assess the differences between urine with and without prostate secretions. Further, metabolic differences between first-void and mid-stream urine samples, and the effect of centrifugation of urine samples before storage were assessed. Our results show that day-to-day reliability is highly variable between metabolites in both serum and urine, while lipoprotein subfractions possess high reliability. Further, fasting status clearly influenced the metabolite concentrations, demonstrating the importance of keeping this condition constant within a study cohort. Day-to-day reliabilities were however comparable in fasting and non-fasting samples. Urine sampling procedures such as sampling of first-void or mid-stream urine, and centrifugation or not before sample storage, were shown to only have minimal effect on the overall metabolic profile, and is thus unlikely to constitute a confounder in clinical studies utilizing NMR derived metabolomics.

Biomarker Discovery Using NMR-Based Metabolomics of Tissue.

NMR-based metabolomics has shown promise in the diagnosis of diseases as it enables identification and quantification of metabolic biomarkers. Using high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy, metabolic profiles from intact tissue specimens can be obtained with high spectral resolution. In addition, HR-MAS NMR requires minimal sample preparation and the sample is kept intact for subsequent analyses. In this chapter, we describe a typical protocol for NMR-based metabolomics of tissue samples. We cover all major steps ranging from tissue sample collection to determination of biomarkers, including experimental precautions taken to ensure reproducible and reliable reporting of data in the area of clinical application.