Klaus M. Weinberger

Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10−16 to 10−21). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting.

Background Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. Methodology/Principal Findings 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid). Conclusions/Significance Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

Metabolic Profiling Reveals Distinct Variations Linked to Nicotine Consumption in Humans — First Results from the KORA Study

Exposure to nicotine during smoking causes a multitude of metabolic changes that are poorly understood. We quantified and analyzed 198 metabolites in 283 serum samples from the human cohort KORA (Cooperative Health Research in the Region of Augsburg). Multivariate analysis of metabolic profiles revealed that the group of smokers could be clearly differentiated from the groups of former smokers and non-smokers. Moreover, 23 lipid metabolites were identified as nicotine-dependent biomarkers. The levels of these biomarkers are all up-regulated in smokers compared to those in former and non-smokers, except for three acyl-alkyl-phosphatidylcholines (e.g. plasmalogens). Consistently significant results were further found for the ratios of plasmalogens to diacyl-phosphatidylcolines, which are reduced in smokers and regulated by the enzyme alkylglycerone phosphate synthase (alkyl-DHAP) in both ether lipid and glycerophospholipid pathways. Notably, our metabolite profiles are consistent with the strong down-regulation of the gene for alkyl-DHAP (AGPS) in smokers that has been found in a study analyzing gene expression in human lung tissues. Our data suggest that smoking is associated with plasmalogen-deficiency disorders, caused by reduced or lack of activity of the peroxisomal enzyme alkyl-DHAP. Our findings provide new insight into the pathophysiology of smoking addiction. Activation of the enzyme alkyl-DHAP by small molecules may provide novel routes for therapy.

Plasma and Urinary Amino Acid Metabolomic Profiling in Patients with Different Levels of Kidney Function

BACKGROUND AND OBJECTIVES Patients with CKD display altered plasma amino acid profiles. This study estimated the association between the estimated GFR and urinary and plasma amino acid profiles in CKD patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Urine and plasma samples were taken from 52 patients with different stages of CKD, and plasma samples only were taken from 25 patients on maintenance hemodialysis. Metabolic profiling was performed by liquid chromatography coupled with tandem mass spectrometry after phenylisothiocyanate derivatization. RESULTS Most plasma amino acid concentrations were decreased in hemodialysis patients, whereas proline, citrulline, asparagine, asymmetric dimethylarginine, and hydroxykynurenine levels were increased (P<0.05). Both plasma levels and urinary excretion of citrulline were higher in the group of patients with advanced CKD (CKD stages 2 and 3 versus CKD stages 4 and 5; in plasma: 35.9±16.3 versus 61.8±23.6 µmol/L, P<0.01; in urine: 1.0±1.2 versus 7.1±14.3 µmol/mol creatinine, P<0.001). Plasma asymmetric dimethylarginine levels were higher in advanced CKD (CKD stages 2 and 3, 0.57±0.29; CKD stages 4 and 5, 1.02±0.48, P<0.001), whereas urinary excretion was lower (2.37±0.93 versus 1.51±1.43, P<0.001). Multivariate analyses adjusting on estimated GFR, serum albumin, proteinuria, and other covariates revealed associations between diabetes and plasma citrulline (P=0.02) and between serum sodium and plasma asymmetric dimethylarginine (P=0.03). Plasma tyrosine to phenylalanine and valine to glycine ratios were lower in advanced CKD stages (P<0.01). CONCLUSION CKD patients have altered plasma and urinary amino acid profiles that are not corrected by dialysis. Depending on solutes, elevated plasma levels were associated with increased or decreased urinary excretion, depicting situations of uremic retention (asymmetric dimethylarginine) or systemic overproduction (citrulline). These results give some insight in the CKD-associated modifications of amino acid metabolism, which may help improve their handling.

CKD273, a new proteomics classifier assessing CKD and its prognosis.

National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years) as the main outcome measurements. None of the patients with CKD273<0.55 required dialysis or died while all fifteen patients that reached an endpoint had a CKD273 score >0.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman). CKD273 was different in the groups with different renal function (p<0.003). The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry

MOTIVATION Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management. RESULTS We developed a novel feature selection algorithm termed as associative voting (AV) for identifying biomarker candidates in prostate cancer data measured via targeted metabolite profiling MS/MS analysis. We benchmarked our algorithm against two standard entropy-based and correlation-based feature selection methods [Information Gain (IG) and ReliefF (RF)] and observed that, on a variety of classification tasks in prostate cancer diagnosis, our algorithm identified subsets of biomarker candidates that are both smaller and show higher discriminatory power than the subsets identified by IG and RF. A literature study confirms that the highest ranked biomarker candidates identified by AV have independently been identified as important factors in prostate cancer development. AVAILABILITY The algorithm can be downloaded from the following http://biomed.umit.at/page.cfm?pageid=516.

Rapid sample preparation and simultaneous quantitation of prostaglandins and lipoxygenase derived fatty acid metabolites by liquid chromatography-mass spectrometry from small sample volumes

Abstract Background: Fatty acid metabolites play a key role in numerous physiological and pathological processes. A rapid liquid chromatography-mass spectrometry assay for the simultaneous determination of prostanoids, isoprostane and lipoxygenase (LOX) derived fatty acid metabolites in a small biological sample of only 20 μL was developed. Methods: Human plasma samples were applied to a filter spot, extracted without prior derivatization and analyzed within 13 min. Detection of metabolites was performed on a triple quadrupole mass spectrometer in negative multiple-reaction monitoring detection mode. Application of this assay to various biological matrices was performed. Results: The validated assay was linear over the concentration range of 5–500 nmol/L for prostanoids and isoprostane, 50–5000 nmol/L for LOX-derived metabolites and 400–40,000 nmol/L for fatty acids. Limits of quantitation were 0.4–233 nmol/L, depending on the metabolite. Plasma samples from diabetic patients and controls showed significant increases in (±)9-HODE and 15(S)-HETE with p-values of 0.019 and 0.024, respectively. Conclusions: The small amount of 20 μL sample volume used in this assay and the demonstrated application to various sample types makes it an ideal routine analysis method for fatty acid metabolites. The resulting values for LOX-derived metabolites in diabetes mellitus type 2 samples support earlier findings about the role of lipid oxidation products in diabetes. Clin Chem Lab Med 2008;46:1589–97.

Metabolomic profiles indicate distinct physiological pathways affected by two loci with major divergent effect on Bos taurus growth and lipid deposition

Identifying trait-associated genetic variation offers new prospects to reveal novel physiological pathways modulating complex traits. Taking advantage of a unique animal model, we identified the I442M mutation in the non-SMC condensin I complex, subunit G (NCAPG) gene and the Q204X mutation in the growth differentiation factor 8 (GDF8) gene as substantial modulators of pre- and/or postnatal growth in cattle. In a combined metabolomic and genotype association approach, which is the first respective study in livestock, we surveyed the specific physiological background of the effects of both loci on body-mass gain and lipid deposition. Our data provided confirming evidence from two historically and geographically distant cattle populations that the onset of puberty is the key interval of divergent growth. The locus-specific metabolic patterns obtained from monitoring 201 plasma metabolites at puberty mirror the particular NCAPG I442M and GDF8 Q204X effects and represent biosignatures of divergent physiological pathways potentially modulating effects on proportional and disproportional growth, respectively. While the NCAPG I442M mutation affected the arginine metabolism, the 204X allele in the GDF8 gene predominantly raised the carnitine level and had concordant effects on glycerophosphatidylcholines and sphingomyelins. Our study provides a conclusive link between the well-described growth-regulating functions of arginine metabolism and the previously unknown specific physiological role of the NCAPG protein in mammalian metabolism. Owing to the confirmed effect of the NCAPG/LCORL locus on human height in genome-wide association studies, the results obtained for bovine NCAPG might add valuable, comparative information on the physiological background of genetically determined divergent mammalian growth.

Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics

The effect of coffee consumption on human health is still discussed controversially. Here, we report results from a metabolomics study of coffee consumption, where we measured 363 metabolites in blood serum of 284 male participants of the Cooperative Health Research in the Region of Augsburg study population, aged between 55 and 79 years. A statistical analysis of the association of metabolite concentrations and the number of cups of coffee consumed per day showed that coffee intake is positively associated with two classes of sphingomyelins, one containing a hydroxy-group (SM(OH)) and the other having an additional carboxy-group (SM(OH,COOH)). In contrast, long- and medium-chain acylcarnitines were found to decrease with increasing coffee consumption. It is noteworthy that the concentration of total cholesterol also rises with an increased coffee intake in this study group. The association observed here between these hydroxylated and carboxylated sphingolipid species and coffee intake may be induced by changes in the cholesterol levels. Alternatively, these molecules may act as scavengers of oxidative species, which decrease with higher coffee intake. In summary, we demonstrate strong positive associations between coffee consumption and two classes of sphingomyelins and a negative association between coffee consumption and long- and medium-chain acylcarnitines.