Multi-platform NMR Study of Pluripotent Stem Cells Unveils Complementary Metabolic Signatures towards Differentiation.

Stem cells, poised to revolutionize current medicine, stand as major workhorses for monitoring changes in cell fate. Characterizing metabolic phenotypes is key to monitor in differentiating cells transcriptional and epigenetic shifts at a functional level and provides a non-genetic means to control cell specification. Expanding the arsenal of analytical tools for metabolic profiling of cell differentiation is therefore of importance. Here, we describe the metabolome of whole pluripotent stem cells (PSCs) using high-resolution magic angle spinning (HR-MAS), a non-destructive approach for Nuclear Magnetic Resonance (NMR) analysis. The integrated 1H NMR analysis results in detection of metabolites of various groups, including energy metabolites, amino acids, choline derivatives and short chain fatty acids. It unveils new metabolites that discriminate PSCs from differentiated counterparts and directly measures substrates and co-factors of histone modifying enzymes, suggesting that NMR stands as a strategic technique for deciphering metabolic regulations of histone post-translational modifications. HR-MAS NMR analysis of whole PSCs complements the much used solution NMR of cell extracts. Altogether, our multi-platform NMR investigation provides a consolidated picture of PSC metabolic signatures and of metabolic pathways involved in differentiation.

Placental physiology monitored by hyperpolarized dynamic 13C magnetic resonance.

Placental functions, including transport and metabolism, play essential roles in pregnancy. This study assesses such processes in vivo, from a hyperpolarized MRI perspective. Hyperpolarized urea, bicarbonate, and pyruvate were administered to near-term pregnant rats, and all metabolites displayed distinctive behaviors. Little evidence of placental barrier crossing was observed for bicarbonate, at least within the timescales allowed by 13C relaxation. By contrast, urea was observed to cross the placental barrier, with signatures visible from certain fetal organs including the liver. This was further evidenced by the slower decay times observed for urea in placentas vis-à-vis other maternal compartments and validated by mass spectrometric analyses. A clear placental localization, as well as concurrent generation of hyperpolarized lactate, could also be detected for [1-13C]pyruvate. These metabolites also exhibited longer lifetimes in the placentas than in maternal arteries, consistent with a metabolic activity occurring past the trophoblastic interface. When extended to a model involving the administration of a preeclampsia-causing chemical, hyperpolarized MR revealed changes in urea's transport, as well as decreases in placental glycolysis vs. the naïve animals. These distinct behaviors highlight the potential of hyperpolarized MR for the early, minimally invasive detection of aberrant placental metabolism.

Following Metabolism in Living Microorganisms by Hyperpolarized (1)H NMR.

Dissolution dynamic nuclear polarization (dDNP) is used to enhance the sensitivity of nuclear magnetic resonance (NMR), enabling monitoring of metabolism and specific enzymatic reactions in vivo. dDNP involves rapid sample dissolution and transfer to a spectrometer/scanner for subsequent signal detection. So far, most biologically oriented dDNP studies have relied on hyperpolarizing long-lived nuclear spin species such as (13)C in small molecules. While advantages could also arise from observing hyperpolarized (1)H, short relaxation times limit the utility of prepolarizing this sensitive but fast relaxing nucleus. Recently, it has been reported that (1)H NMR peaks in solution-phase experiments could be hyperpolarized by spontaneous magnetization transfers from bound (13)C nuclei following dDNP. This work demonstrates the potential of this sensitivity-enhancing approach to probe the enzymatic process that could not be suitably resolved by (13)C dDNP MR. Here we measured, in microorganisms, the action of pyruvate decarboxylase (PDC) and pyruvate formate lyase (PFL)-enzymes that catalyze the decarboxylation of pyruvate to form acetaldehyde and formate, respectively. While (13)C NMR did not possess the resolution to distinguish the starting pyruvate precursor from the carbonyl resonances in the resulting products, these processes could be monitored by (1)H NMR at 500 MHz. These observations were possible in both yeast and bacteria in minute-long kinetic measurements where the hyperpolarized (13)C enhanced, via (13)C → (1)H cross-relaxation, the signals of protons binding to the (13)C over the course of enzymatic reactions. In addition to these spontaneous heteronuclear enhancement experiments, single-shot acquisitions based on J-driven (13)C → (1)H polarization transfers were also carried out. These resulted in higher signal enhancements of the (1)H resonances but were not suitable for multishot kinetic studies. The potential of these (1)H-based approaches for measurements in vivo is briefly discussed.

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort.

BACKGROUND: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers. METHODS: To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort. RESULTS: A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis. CONCLUSION: Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study.

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.

Batch profiling calibration for robust NMR metabonomic data analysis.

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.