Dynamics and determinants of human plasma bile acid profiles during dietary challenges.

In recent years, bile acids (BA) have received great interest due to their pleiotropic biological activity and the presence of plasma membrane-bound and nuclear receptors. Moreover, BA in blood have been identified by metabolite screening approaches as biomarkers that are associated with various diseases and even with a human longevity phenotype. With the growing interest in the microbiota contribution to the health-disease trajectory, BA that undergo deconjugation and other modifications by bacteria in the large intestine have become a prime target as a microbiome diversity modifier. We here profiled BA by a quantitative and a semiquantitative approach in 15 healthy and phenotypically very similar young individuals for over a 36-h fasting period, an oral glucose tolerance test (OGTT), and an oral lipid tolerance test (OLTT). We demonstrate a remarkable heterogeneity of the responses and describe the different dynamics of the plasma changes that likely originate from different routes by which BA enters the peripheral blood, and that may represent a direct secretion from the liver into the blood and a route that reaches the blood as a spill-over after passing from the gallbladder through the intestine and the portal system. We discuss the finding that an individual transport process involved in the passage of BA could be a critical determinant in the kinetics of plasma appearance and the overall phenotypic variability found.

1H NMR spectroscopy quantifies visibility of lipoproteins, subclasses, and lipids at varied temperatures and pressures.

NMR-based quantification of human lipoprotein (sub)classes is a powerful high-throughput method for medical diagnostics. We evaluated select proton NMR signals of serum lipoproteins for elucidating the physicochemical features and the absolute NMR visibility of their lipids. We separated human lipoproteins of different subclasses by ultracentrifugation and analyzed them by 1H NMR spectroscopy at different temperatures (283-323 K) and pressures (0.1-200 MPa). In parallel, we determined the total lipid content by extraction with chloroform/methanol. The visibility of different lipids in the 1H NMR spectra strongly depends on temperature and pressure: it increases with increasing temperatures but decreases with increasing pressures. Even at 313 K, only part of the lipoprotein is detected quantitatively. In LDL and in HDL subclasses HDL2 and HDL3, only 39%, 62%, and 90% of the total cholesterol and only 73%, 70%, and 87% of the FAs are detected, respectively. The choline head groups show visibilities of 43%, 75%, and 87% for LDL, HDL2, and HDL3, respectively. The description of the NMR visibility of lipid signals requires a minimum model of three different compartments, A, B, and C. The thermodynamic analysis of compartment B leads to melting temperatures between 282 K and 308 K and to enthalpy differences that vary for the different lipoproteins as well as for the reporter groups selected. In summary, we describe differences in NMR visibility of lipoproteins and variations in biophysical responses of functional groups that are crucial for the accuracy of absolute NMR quantification.

Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits.

Previously, we reported strong influences of genetic variants on metabolic phenotypes, some of them with clinical relevance. Here, we hypothesize that DNA methylation may have an important and potentially independent effect on human metabolism. To test this hypothesis, we conducted what is to the best of our knowledge the first epigenome-wide association study (EWAS) between DNA methylation and metabolic traits (metabotypes) in human blood. We assess 649 blood metabolic traits from 1814 participants of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) population study for association with methylation of 457 004 CpG sites, determined on the Infinium HumanMethylation450 BeadChip platform. Using the EWAS approach, we identified two types of methylome-metabotype associations. One type is driven by an underlying genetic effect; the other type is independent of genetic variation and potentially driven by common environmental and life-style-dependent factors. We report eight CpG loci at genome-wide significance that have a genetic variant as confounder (P = 3.9 × 10(-20) to 2.0 × 10(-108), r(2) = 0.036 to 0.221). Seven loci display CpG site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (P = 9.2 × 10(-14) to 2.7 × 10(-27), r(2) = 0.008 to 0.107). We further identify several groups of CpG loci that associate with a same metabotype, such as 4-vinylphenol sulfate and 4-androsten-3-beta,17-beta-diol disulfate. In these cases, the association between CpG-methylation and metabotype is likely the result of a common external environmental factor, including smoking. Our study shows that analysis of EWAS with large numbers of metabolic traits in large population cohorts are, in principle, feasible. Taken together, our data suggest that DNA methylation plays an important role in regulating human metabolism.

Reproducibility of NMR analysis of urine samples: impact of sample preparation, storage conditions, and animal health status.

INTRODUCTION: Spectroscopic analysis of urine samples from laboratory animals can be used to predict the efficacy and side effects of drugs. This employs methods combining (1)H NMR spectroscopy with quantification of biomarkers or with multivariate data analysis. The most critical steps in data evaluation are analytical reproducibility of NMR data (collection, storage, and processing) and the health status of the animals, which may influence urine pH and osmolarity. METHODS: We treated rats with a solvent, a diuretic, or a nephrotoxicant and collected urine samples. Samples were titrated to pH 3 to 9, or salt concentrations increased up to 20-fold. The effects of storage conditions and freeze-thaw cycles were monitored. Selected metabolites and multivariate data analysis were evaluated after (1)H NMR spectroscopy. RESULTS: We showed that variation of pH from 3 to 9 and increases in osmolarity up to 6-fold had no effect on the quantification of the metabolites or on multivariate data analysis. Storage led to changes after 14 days at 4°C or after 12 months at -20°C, independent of sample composition. Multiple freeze-thaw cycles did not affect data analysis. CONCLUSION: Reproducibility of NMR measurements is not dependent on sample composition under physiological or pathological conditions.

The dynamic range of the human metabolome revealed by challenges.

Metabolic challenge protocols, such as the oral glucose tolerance test, can uncover early alterations in metabolism preceding chronic diseases. Nevertheless, most metabolomics data accessible today reflect the fasting state. To analyze the dynamics of the human metabolome in response to environmental stimuli, we submitted 15 young healthy male volunteers to a highly controlled 4 d challenge protocol, including 36 h fasting, oral glucose and lipid tests, liquid test meals, physical exercise, and cold stress. Blood, urine, exhaled air, and breath condensate samples were analyzed on up to 56 time points by MS- and NMR-based methods, yielding 275 metabolic traits with a focus on lipids and amino acids. Here, we show that physiological challenges increased interindividual variation even in phenotypically similar volunteers, revealing metabotypes not observable in baseline metabolite profiles; volunteer-specific metabolite concentrations were consistently reflected in various biofluids; and readouts from a systematic model of ß-oxidation (e.g., acetylcarnitine/palmitylcarnitine ratio) showed significant and stronger associations with physiological parameters (e.g., fat mass) than absolute metabolite concentrations, indicating that systematic models may aid in understanding individual challenge responses. Due to the multitude of analytical methods, challenges and sample types, our freely available metabolomics data set provides a unique reference for future metabolomics studies and for verification of systems biology models.

Genetic associations with lipoprotein subfractions provide information on their biological nature.

Adverse levels of lipoproteins are highly heritable and constitute risk factors for cardiovascular outcomes. Hitherto, genome-wide association studies revealed 95 lipid-associated loci. However, due to the small effect sizes of these associations large sample numbers (>100 000 samples) were needed. Here we show that analyzing more refined lipid phenotypes, namely lipoprotein subfractions, can increase the number of significantly associated loci compared with bulk high-density lipoprotein and low-density lipoprotein analysis in a study with identical sample numbers. Moreover, lipoprotein subfractions provide novel insight into the human lipid metabolism. We measured 15 lipoprotein subfractions (L1-L15) in 1791 samples using (1)H-NMR (nuclear magnetic resonance) spectroscopy. Using cluster analyses, we quantified inter-relationships among lipoprotein subfractions. Additionally, we analyzed associations with subfractions at known lipid loci. We identified five distinct groups of subfractions: one (L1) was only marginally captured by serum lipids and therefore extends our knowledge of lipoprotein biochemistry. During a lipid-tolerance test, L1 lost its special position. In the association analysis, we found that eight loci (LIPC, CETP, PLTP, FADS1-2-3, SORT1, GCKR, APOB, APOA1) were associated with the subfractions, whereas only four loci (CETP, SORT1, GCKR, APOA1) were associated with serum lipids. For LIPC, we observed a 10-fold increase in the variance explained by our regression models. In conclusion, NMR-based fine mapping of lipoprotein subfractions provides novel information on their biological nature and strengthens the associations with genetic loci. Future clinical studies are now needed to investigate their biomedical relevance.

Large-scale candidate gene analysis of HDL particle features.

BACKGROUND: HDL cholesterol (HDL-C) is an established marker of cardiovascular risk with significant genetic determination. However, HDL particles are not homogenous, and refined HDL phenotyping may improve insight into regulation of HDL metabolism. We therefore assessed HDL particles by NMR spectroscopy and conducted a large-scale candidate gene association analysis. METHODOLOGY/PRINCIPAL FINDINGS: We measured plasma HDL-C and determined mean HDL particle size and particle number by NMR spectroscopy in 2024 individuals from 512 British Caucasian families. Genotypes were 49,094 SNPs in >2,100 cardiometabolic candidate genes/loci as represented on the HumanCVD BeadChip version 2. False discovery rates (FDR) were calculated to account for multiple testing. Analyses on classical HDL-C revealed significant associations (FDR<0.05) only for CETP (cholesteryl ester transfer protein; lead SNP rs3764261: p = 5.6*10(-15)) and SGCD (sarcoglycan delta; rs6877118: p = 8.6*10(-6)). In contrast, analysis with HDL mean particle size yielded additional associations in LIPC (hepatic lipase; rs261332: p = 6.1*10(-9)), PLTP (phospholipid transfer protein, rs4810479: p = 1.7*10(-8)) and FBLN5 (fibulin-5; rs2246416: p = 6.2*10(-6)). The associations of SGCD and Fibulin-5 with HDL particle size could not be replicated in PROCARDIS (n = 3,078) and/or the Women's Genome Health Study (n = 23,170). CONCLUSIONS: We show that refined HDL phenotyping by NMR spectroscopy can detect known genes of HDL metabolism better than analyses on HDL-C.

Macrophage cholesterol efflux correlates with lipoprotein subclass distribution and risk of obstructive coronary artery disease in patients undergoing coronary angiography.

BACKGROUND: Studies in patients with low HDL have suggested that impaired cellular cholesterol efflux is a heritable phenotype increasing atherosclerosis risk. Less is known about the association of macrophage cholesterol efflux with lipid profiles and CAD risk in normolipidemic subjects. We have therefore measured macrophage cholesterol efflux in 142 normolipidemic subjects undergoing coronary angiography. METHODS: Monocytes isolated from blood samples of patients scheduled for cardiac catheterization were differentiated into macrophages over seven days. Isotopic cholesterol efflux to exogenously added apolipoprotein A-I and HDL2 was measured. Quantitative cholesterol efflux from macrophages was correlated with lipoprotein subclass distribution in plasma from the same individuals measured by NMR-spectroscopy of lipids and with the extent of coronary artery disease seen on coronary angiography. RESULTS: Macrophage cholesterol efflux was positively correlated with particle concentration of smaller HDL and LDL particles but not with total plasma concentrations of HDL or LDL-cholesterol. We observed an inverse relationship between macrophage cholesterol efflux and the concentration of larger and triglyceride rich particles (VLDL, chylomicrons). Subjects with significant stenosis on coronary angiography had lower cholesterol efflux from macrophages compared to individuals without significant stenosis (adjusted p = 0.02). CONCLUSION: Macrophage cholesterol efflux is inversely correlated with lipoprotein particle size and risk of CAD.

Chemical shift optimization in multidimensional NMR spectra by AUREMOL-SHIFTOPT.

A problem often encountered in multidimensional NMR-spectroscopy is that an existing chemical shift list of a protein has to be used to assign an experimental spectrum but does not fit sufficiently well for a safe assignment. A similar problem occurs when temperature or pressure series of n-dimensional spectra are to be evaluated automatically. We have developed two different algorithms, AUREMOL-SHIFTOPT1 and AUREMOL-SHIFTOPT2 that fulfill this task. In the present contribution their performance is analyzed employing a set of simulated and experimental two-dimensional and three-dimensional spectra obtained from three different proteins. A new z-score based on atom and amino acid specific chemical shift distributions is introduced to weight the chemical shift contributions in different dimensions properly.

Structure of the leech protein saratin and characterization of its binding to collagen.

The leech protein Saratin from Hirudo medicinalis prevents thrombocyte aggregation by interfering with the first binding step of the thrombocytes to collagen by binding to collagen. We solved the three-dimensional structure of the leech protein Saratin in solution and identified its collagen binding site by NMR titration experiments. The NMR structure of Saratin consists of one alpha-helix and a five-stranded beta-sheet arranged in the topology betabetaalphabetabetabeta. The C-terminal region, of about 20 amino acids in length, adopts no regular structure. NMR titration experiments with collagen peptides show that the collagen interaction of Saratin takes place in a kind of notch that is formed by the end of the alpha-helix and the beta-sheet. NMR data-driven docking experiments to collagen model peptides were used to elucidate the putative binding mode of Saratin and collagen. Mainly, parts of the first and the end of the fifth beta-strand, the loop connecting the alpha-helix and the third beta-strand, and a short part of the loop connecting the fourth and fifth beta-strand participate in binding.

The lipoprotein subfraction profile: heritability and identification of quantitative trait loci.

The HDL and LDL subclass profile is an emerging cardiovascular risk factor. Yet, the biological and genetic mechanisms controlling the lipoprotein subclass distribution are unclear. Therefore, we aimed 1) to determine the heritability of the entire spectrum of LDL and HDL subclass features and 2) to identify gene loci influencing the lipoprotein subfraction pattern. Using NMR spectroscopy, we analyzed the lipoprotein subclass distribution in 1,275 coronary artery disease patients derived from the Regensburg Myocardial Infarction Family Study. We calculated heritabilities, performed a microsatellite genome scan, and calculated linkage. HDL and LDL subclass profiles showed heritabilities ranging from 23% to 67% (all P < 10(-3)) of traits using univariate calculation. After multivariate adjustment, we found heritabilities of 27-48% (all P < 0.05) for HDL and 21-44% for LDL traits. The linkage analysis revealed a significant logarithm of the odds (LOD) score (3.3) for HDL particle concentration on chromosome 18 and a highly suggestive signal for HDL particle size on chromosome 12 (2.9). After multivariate adjustment, we found a significant maximum LOD score of 3.7 for HDL size. Our study is the first to analyze heritability and linkage for the entire spectrum of LDL and HDL subclass features. Our findings may lead to the identification of genes controlling the lipoprotein subclass distribution.