Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: A case-control study nested in the Northern Sweden Health and Disease Study.

Prostate cancer (PCa) is the most common cancer form in males in many European and American countries, but there are still open questions regarding its etiology. Untargeted metabolomics can produce an unbiased global metabolic profile, with the opportunity for uncovering new plasma metabolites prospectively associated with risk of PCa, providing insights into disease etiology. We conducted a prospective untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis using prediagnostic fasting plasma samples from 752 PCa case-control pairs nested within the Northern Sweden Health and Disease Study (NSHDS). The pairs were matched by age, BMI, and sample storage time. Discriminating features were identified by a combination of orthogonal projection to latent structures-effect projections (OPLS-EP) and Wilcoxon signed-rank tests. Their prospective associations with PCa risk were investigated by conditional logistic regression. Subgroup analyses based on stratification by disease aggressiveness and baseline age were also conducted. Various free fatty acids and phospholipids were positively associated with overall risk of PCa and in various stratification subgroups. Aromatic amino acids were positively associated with overall risk of PCa. Uric acid was positively, and glucose negatively, associated with risk of PCa in the older subgroup. This is the largest untargeted LC-MS based metabolomics study to date on plasma metabolites prospectively associated with risk of developing PCa. Different subgroups of disease aggressiveness and baseline age showed different associations with metabolites. The findings suggest that shifts in plasma concentrations of metabolites in lipid, aromatic amino acid, and glucose metabolism are associated with risk of developing PCa during the following two decades.

Blood pressure interactions with the DASH dietary pattern, sodium, and potassium: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP).

BACKGROUND: Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet enhances potassium intake and reduces sodium intake and blood pressure (BP), but the underlying metabolic pathways are unclear. OBJECTIVES: Among free-living populations, we delineated metabolic signatures associated with the DASH diet adherence, 24-hour urinary sodium and potassium excretions, and the potential metabolic pathways involved. METHODS: We used 24-hour urinary metabolic profiling by proton nuclear magnetic resonance spectroscopy to characterize the metabolic signatures associated with the DASH dietary pattern score (DASH score) and 24-hour excretion of sodium and potassium among participants in the United States (n = 2164) and United Kingdom (n = 496) enrolled in the International Study of Macro- and Micronutrients and Blood Pressure (INTERMAP). Multiple linear regression and cross-tabulation analyses were used to investigate the DASH-BP relation and its modulation by sodium and potassium. Potential pathways associated with DASH adherence, sodium and potassium excretion, and BP were identified using mediation analyses and metabolic reaction networks. RESULTS: Adherence to the DASH diet was associated with urinary potassium excretion (correlation coefficient, r = 0.42; P < 0.0001). In multivariable regression analyses, a 5-point higher DASH score (range, 7 to 35) was associated with a lower systolic BP by 1.35 mmHg (95% CI, -1.95 to -0.80 mmHg; P = 1.2 × 10-5); control of the model for potassium but not sodium attenuated the DASH-BP relation. Two common metabolites (hippurate and citrate) mediated the potassium-BP and DASH-BP relationships, while 5 metabolites (succinate, alanine, S-methyl cysteine sulfoxide, 4-hydroxyhippurate, and phenylacetylglutamine) were found to be specific to the DASH-BP relation. CONCLUSIONS: Greater adherence to the DASH diet is associated with lower BP and higher potassium intake across levels of sodium intake. The DASH diet recommends greater intake of fruits, vegetables, and other potassium-rich foods that may replace sodium-rich processed foods and thereby influence BP through overlapping metabolic pathways. Possible DASH-specific pathways are speculated but confirmation requires further study. INTERMAP is registered as NCT00005271 at www.clinicaltrials.gov.

Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets.

MOTIVATION: Often during the analysis of biological data, it is of importance to interpret the correlation structure that exists between variables. Such correlations may reveal patterns of co-regulation that are indicative of biochemical pathways or common mechanisms of response to a related set of treatments. However, analyses of correlations are usually conducted by either subjective interpretation of the univariate covariance matrix or by applying multivariate modeling techniques, which do not take prior biological knowledge into account. Over-representation analysis (ORA) is a simple method for objectively deciding whether a set of variables of known or suspected biological relevance, such as a gene set or pathway, is more prevalent in a set of variables of interest than we expect by chance. However, ORA is usually applied to a set of variables differentiating a single experimental variable and does not take into account correlations. RESULTS: Over-representation of correlation analysis (ORCA) is a novel combination of ORA and correlation analysis that provides a means to test whether more associations exist between two specific groups of variables than expected by chance. The method is exemplified by application to drug sensitivity and microRNA expression data from a panel of cancer cell lines (NCI60). ORCA highlighted a previously reported correlation between sensitivity to alkylating anticancer agents and topoisomerase inhibitors. We also used this approach to validate microRNA clusters predicted by mRNA correlations. These observations suggest that ORCA has the potential to reveal novel insights from these data, which are not readily apparent using classical ORA. AVAILABILITY AND IMPLEMENTATION: The R code of the method is available at https://github.com/ORCABioinfo/ORCAcode.

Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure.

In this study, we propose a novel statistical framework for detecting progressive changes in molecular traits as response to a pathogenic stimulus. In particular, we propose to employ Bayesian hierarchical models to analyse changes in mean level, variance and correlation of metabolic traits in relation to covariates. To illustrate our approach we investigate changes in urinary metabolic traits in response to cadmium exposure, a toxic environmental pollutant. With the application of the proposed approach, previously unreported variations in the metabolism of urinary metabolites in relation to urinary cadmium were identified. Our analysis highlights the potential effect of urinary cadmium on the variance and correlation of a number of metabolites involved in the metabolism of choline as well as changes in urinary alanine. The results illustrate the potential of the proposed approach to investigate the gradual effect of pathogenic stimulus in molecular traits.

A combination of transcriptomics and metabolomics uncovers enhanced bile acid biosynthesis in HepG2 cells expressing CCAAT/enhancer-binding protein ß (C/EBPß), hepatocyte nuclear factor 4α (HNF4α), and constitutive androstane receptor (CAR).

The development of hepatoma-based in vitro models to study hepatocyte physiology is an invaluable tool for both industry and academia. Here, we develop an in vitro model based on the HepG2 cell line that produces chenodeoxycholic acid, the main bile acid in humans, in amounts comparable to human hepatocytes. A combination of adenoviral transfections for CCAAT/enhancer-binding protein ß (C/EBPß), hepatocyte nuclear factor 4α (HNF4α), and constitutive androstane receptor (CAR) decreased intracellular glutamate, succinate, leucine, and valine levels in HepG2 cells, suggestive of a switch to catabolism to increase lipogenic acetyl CoA and increased anaplerosis to replenish the tricarboxylic acid cycle. Transcripts of key genes involved in bile acid synthesis were significantly induced by approximately 160-fold. Consistently, chenodeoxycholic acid production rate was increased by more than 20-fold. Comparison between mRNA and bile acid levels suggest that 12-alpha hydroxylation of 7-alpha-hydroxy-4-cholesten-3-one is the limiting step in cholic acid synthesis in HepG2 cells. These data reveal that introduction of three hepatocyte-related transcription factors enhance anabolic reactions in HepG2 cells and provide a suitable model to study bile acid biosynthesis under pathophysiological conditions.

Proteomic and metabolomic responses to connexin43 silencing in primary hepatocyte cultures.

Freshly established cultures of primary hepatocytes progressively adopt a foetal-like phenotype and display increased production of connexin43. The latter is a multifaceted cellular entity with variable subcellular locations, including the mitochondrial compartment. Cx43 forms hemichannels and gap junctions that are involved in a plethora of physiological and pathological processes, such as apoptosis. The present study was conducted with the goal of shedding more light onto the role of connexin43 in primary hepatocyte cultures. Connexin43 expression was suppressed by means of RNA interference technology, and the overall outcome of this treatment on the hepatocellular proteome and metabolome was investigated using tandem mass tag-based differential protein profiling and (1)H NMR spectroscopy, respectively. Global protein profiling revealed a number of targets of the connexin43 knock-down procedure, including mitochondrial proteins (heat shock protein 60, glucose-regulated protein 75, thiosulphate sulphurtransferase and adenosine triphosphate synthase) and detoxifying enzymes (glutathione S-transferase µ 2 and cytochrome P450 2C70). At the metabolomic level, connexin43 silencing caused no overt changes, though there was some evidence for a subtle increase in intracellular glycine quantities. Collectively, these data could further substantiate the established existence of a mitochondrial connexin pool and could be reconciled with the previously reported involvement of connexin43 signalling in spontaneously occurring apoptosis in primary hepatocyte cultures.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Meeting-in-the-middle using metabolic profiling - a strategy for the identification of intermediate biomarkers in cohort studies.

BACKGROUND: Predictive disease risk biomarkers that can be linked to exposure have proved difficult to identify in case-control studies. METHODS: Parallel statistical analysis of the correlation between (1)H NMR profiles from plasma samples collected before disease onset (EPIC cohort), versus exposure to dietary compounds, and follow-up disease endpoints (colon and breast cancer) was performed. RESULTS: Metabonomic signatures associated with colon cancer and dietary fiber intake (a protective factor according to epidemiological studies) were identified. CONCLUSION: This implementation of the novel "meet-in-the-middle" analytical strategy indicates how case-control studies nested in prospectively collected cohorts may reveal intermediate biomarkers linking exposure and disease.

High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues.

Metabolic profiling, metabolomic and metabonomic studies require robust study protocols for any large-scale comparisons and evaluations. Detailed methods for solution-state NMR spectroscopy have been summarized in an earlier protocol. This protocol details the analysis of intact tissue samples by means of high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy and we provide a detailed description of sample collection, preparation and analysis. Described here are (1)H NMR spectroscopic techniques such as the standard one-dimensional, relaxation-edited, diffusion-edited and two-dimensional J-resolved pulse experiments, as well as one-dimensional (31)P NMR spectroscopy. These are used to monitor different groups of metabolites, e.g., sugars, amino acids and osmolytes as well as larger molecules such as lipids, non-invasively. Through the use of NMR-based diffusion coefficient and relaxation times measurements, information on molecular compartmentation and mobility can be gleaned. The NMR methods are often combined with statistical analysis for further metabonomics analysis and biomarker identification. The standard acquisition time per sample is 8-10 min for a simple one-dimensional (1)H NMR spectrum, giving access to metabolite information while retaining tissue integrity and hence allowing direct comparison with histopathology and MRI/MRS findings or the evaluation together with biofluid metabolic-profiling data.

NMR-based metabolic profiling identifies biomarkers of liver regeneration following partial hepatectomy in the rat.

Tissue injury and repair are often overlapping consequences of disease or toxic exposure, but are not often considered as distinct processes in molecular studies. To establish the systemic metabolic response to liver regeneration, the partial hepatectomy (PH) model has been studied in the rat by an integrated metabonomics strategy, utilizing (1)H NMR spectroscopy of urine, liver and serum. Male Sprague-Dawley rats were subjected to either surgical removal of approximately two-thirds of the liver, sham operated (SO) surgery, or no treatment (n = 10/group) and samples collected over a 7 day period. A number of urinary metabolic perturbations were observed in PH rats compared with SO and control animals, including elevated levels of taurine, hypotaurine, creatine, guanidinoacetic acid, betaine, dimethylglycine and bile acids. Serum betaine and creatine were also elevated after PH, while levels of triglyceride were reduced. In the liver, triglycerides, cholesterol, alanine and betaine were elevated after PH, while choline and its derivatives were reduced. Upon examining the dynamic pattern of urinary response (the 'metabolic trajectory'), several metabolites could be categorized into groups likely to reflect perturbations to different processes such as dietary intake or hepatic 1-carbon metabolism. Several of the urinary perturbations observed during the regenerative phase of the PH model have also been observed after exposure to liver toxins, indicating that hepatic regeneration may make a contribution to the systemic alterations in metabolism associated with hepatotoxicity. The observed changes in 1-carbon and lipid metabolism are consistent with the proposed role of these pathways in the activation of a regenerative response and provide further evidence regarding the utility of urinary NMR profiles in the detection of liver-specific pathology. Biofluid (1)H NMR-based metabolic profiling provides new insight into the role of metabolism of liver regeneration, and suggests putative biomarkers for the noninvasive monitoring of the regeneration process.