The HuMet Repository: Watching human metabolism at work

The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.

Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring

Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson's correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman's correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.

Metabolite Concentration Changes in Humans After a Bout of Exercise: a Systematic Review of Exercise Metabolomics Studies

Background

Exercise changes the concentrations of many metabolites, which are small molecules (< 1.5 kDa) metabolized by the reactions of human metabolism. In recent years, especially mass spectrometry-based metabolomics methods have allowed researchers to measure up to hundreds of metabolites in a single sample in a non-biased fashion. To summarize human exercise metabolomics studies to date, we conducted a systematic review that reports the results of experiments that found metabolite concentrations changes after a bout of human endurance or resistance exercise.

Methods

We carried out a systematic review following PRISMA guidelines and searched for human metabolomics studies that report metabolite concentrations before and within 24 h after endurance or resistance exercise in blood, urine, or sweat. We then displayed metabolites that significantly changed their concentration in at least two experiments.

Results

Twenty-seven studies and 57 experiments matched our search criteria and were analyzed. Within these studies, 196 metabolites changed their concentration significantly within 24 h after exercise in at least two experiments. Human biofluids contain mainly unphosphorylated metabolites as the phosphorylation of metabolites such as ATP, glycolytic intermediates, or nucleotides traps these metabolites within cells. Lactate, pyruvate, TCA cycle intermediates, fatty acids, acylcarnitines, and ketone bodies all typically increase after exercise, whereas bile acids decrease. In contrast, the concentrations of proteinogenic and non-proteinogenic amino acids change in different directions.

Conclusion

Across different exercise modes and in different subjects, exercise often consistently changes the average concentrations of metabolites that belong to energy metabolism and other branches of metabolism. This dataset is a useful resource for those that wish to study human exercise metabolism.

Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C

Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.

Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma

A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.

Metabolic Fingerprints of Circulating IGF-1 and the IGF-1/IGFBP-3 Ratio: A Multifluid Metabolomics Study

OBJECTIVE

IGF-1 is known for its various physiological and severe pathophysiological effects on human metabolism; however, underlying molecular mechanisms still remain unsolved. To reveal possible molecular mechanisms mediating these effects, for the first time, we associated serum IGF-1 levels with multifluid untargeted metabolomics data.

METHODS

Plasma/urine samples of 995 nondiabetic participants of the Study of Health in Pomerania were characterized by mass spectrometry. Sex-specific linear regression analyses were performed to assess the association of IGF-1 and IGF-1/IGF binding protein 3 ratio with metabolites. Additionally, the predictive ability of the plasma and urine metabolome for IGF-1 was assessed by orthogonal partial least squares analyses.

RESULTS AND CONCLUSIONS

We revealed a multifaceted image of associated metabolites with large sex differences. Confirming previous reports, we detected relations between IGF-1 and steroid hormones or related intermediates. Furthermore, various associated metabolites were previously mentioned regarding IGF-1-associated diseases, eg, betaine and cortisol in cardiovascular disease and metabolic syndrome, lipid disorders, and diabetes, or have previously been found to associate with differentiation and proliferation or mitochondrial functionality, eg, phospholipids. bradykinin, fatty acid derivatives, and cortisol, which were inversely associated with IGF-1, might establish a link of IGF-1 with inflammation. For the first time, we showed an association between IGF-1 and pipecolate, a metabolite linked to amino acid metabolism. Our study demonstrates that IGF-1 action on metabolism is tractable, even in healthy subjects, and that the findings provide a solid basis for further experimental/clinical investigation, eg, searching for inflammatory or cardiovascular disease- or metabolic syndrome-associated biomarkers and therapeutic targets.

Lactation is associated with altered metabolomic signatures in women with gestational diabetes

AIMS/HYPOTHESIS

Lactation for >3 months in women with gestational diabetes is associated with a reduced risk of type 2 diabetes that persists for up to 15 years postpartum. However, the underlying mechanisms are unknown. We examined whether in women with gestational diabetes lactation for >3 months is associated with altered metabolomic signatures postpartum.

METHODS

We enrolled 197 women with gestational diabetes at a median of 3.6 years (interquartile range 0.7-6.5 years) after delivery. Targeted metabolomics profiles (including 156 metabolites) were obtained during a glucose challenge test. Comparisons of metabolite concentrations and ratios between women who lactated for >3 months and women who lactated for ≤3 months or not at all were performed using linear regression with adjustment for age and BMI at the postpartum visit, time since delivery, and maternal education level, and correction for multiple testing. Gaussian graphical modelling was used to generate metabolite networks.

RESULTS

Lactation for >3 months was associated with a higher total lysophosphatidylcholine/total phosphatidylcholine ratio; in women with short-term follow-up, it was also associated with lower leucine concentrations and a lower total branched-chain amino acid concentration. Gaussian graphical modelling identified subgroups of closely linked metabolites within phosphatidylcholines and branched-chain amino acids that were affected by lactation for >3 months and have been linked to the pathophysiology of type 2 diabetes in previous studies.

CONCLUSIONS/INTERPRETATION

Lactation for >3 months in women with gestational diabetes is associated with changes in the metabolomics profile that have been linked to the early pathogenesis of type 2 diabetes.

The Pharmacogenetic Footprint of ACE Inhibition: A Population-Based Metabolomics Study

Angiotensin-I-converting enzyme (ACE) inhibitors are an important class of antihypertensives whose action on the human organism is still not fully understood. Although it is known that ACE especially cleaves COOH-terminal dipeptides from active polypeptides, the whole range of substrates and products is still unknown. When analyzing the action of ACE inhibitors, effects of genetic variation on metabolism need to be considered since genetic variance in the ACE gene locus was found to be associated with ACE-concentration in blood as well as with changes in the metabolic profiles of a general population. To investigate the interactions between genetic variance at the ACE-locus and the influence of ACE-therapy on the metabolic status we analyzed 517 metabolites in 1,361 participants from the KORA F4 study. We replicated our results in 1,964 individuals from TwinsUK. We observed differences in the concentration of five dipeptides and three ratios of di- and oligopeptides between ACE inhibitor users and non-users that were genotype dependent. Such changes in the concentration affected major homozygotes, and to a lesser extent heterozygotes, while minor homozygotes showed no or only small changes in the metabolite status. Two of these resulting dipeptides, namely aspartylphenylalanine and phenylalanylserine, showed significant associations with blood pressure which qualifies them—and perhaps also the other dipeptides—as readouts of ACE-activity. Since so far ACE activity measurement is substrate specific due to the usage of only one oligopeptide, taking several dipeptides as potential products of ACE into account may provide a broader picture of the ACE activity.

A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function and can be used to estimate filtration (e.g., the established marker creatinine) or may precede and potentially contribute to CKD development. Here, we applied a nontargeted metabolomics approach using gas and liquid chromatography coupled to mass spectrometry to quantify 493 small molecules in human serum. The associations of these molecules with GFR estimated on the basis of creatinine (eGFRcr) and cystatin C levels were assessed in ≤1735 participants in the KORA F4 study, followed by replication in 1164 individuals in the TwinsUK registry. After correction for multiple testing, 54 replicated metabolites significantly associated with eGFRcr, and six of these showed pairwise correlation (r≥0.50) with established kidney function measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, and N-acetylcarnosine. Higher C-mannosyltryptophan, pseudouridine, and O-sulfo-L-tyrosine concentrations associated with incident CKD (eGFRcr <60 ml/min per 1.73 m(2)) in the KORA F4 study. In contrast with serum creatinine, C-mannosyltryptophan and pseudouridine concentrations showed little dependence on sex. Furthermore, correlation with measured GFR in 200 participants in the AASK study was 0.78 for both C-mannosyltryptophan and pseudouridine concentration, and highly significant associations of both metabolites with incident ESRD disappeared upon adjustment for measured GFR. Thus, these molecules may be alternative or complementary markers of kidney function. In conclusion, our study provides a comprehensive list of kidney function-associated metabolites and highlights potential novel filtration markers that may help to improve the estimation of GFR.

Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples

Advances in the "omics" field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between 'good' and 'bad' quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between 'good' and 'bad' quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation.

Impaired autophagy induces chronic atrophic pancreatitis in mice via sex- and nutrition-dependent processes

BACKGROUND & AIMS

Little is known about the mechanisms of the progressive tissue destruction, inflammation, and fibrosis that occur during development of chronic pancreatitis. Autophagy is involved in multiple degenerative and inflammatory diseases, including pancreatitis, and requires the protein autophagy related 5 (ATG5). We created mice with defects in autophagy to determine its role in pancreatitis.

METHODS

We created mice with pancreas-specific disruption of Atg5 (Ptf1aCreex1;Atg5F/F mice) and compared them to control mice. Pancreata were collected and histology, immunohistochemistry, transcriptome, and metabolome analyses were performed. ATG5-deficient mice were placed on diets containing 25% palm oil and compared with those on a standard diet. Another set of mice received the antioxidant N-acetylcysteine. Pancreatic tissues were collected from 8 patients with chronic pancreatitis (CP) and compared with pancreata from ATG5-deficient mice.

RESULTS

Mice with pancreas-specific disruption of Atg5 developed atrophic CP, independent of β-cell function; a greater proportion of male mice developed CP than female mice. Pancreata from ATG5-deficient mice had signs of inflammation, necrosis, acinar-to-ductal metaplasia, and acinar-cell hypertrophy; this led to tissue atrophy and degeneration. Based on transcriptome and metabolome analyses, ATG5-deficient mice produced higher levels of reactive oxygen species than control mice, and had insufficient activation of glutamate-dependent metabolism. Pancreata from these mice had reduced autophagy, increased levels of p62, and increases in endoplasmic reticulum stress and mitochondrial damage, compared with tissues from control mice; p62 signaling to Nqo1 and p53 was also activated. Dietary antioxidants, especially in combination with palm oil-derived fatty acids, blocked progression to CP and pancreatic acinar atrophy. Tissues from patients with CP had many histologic similarities to those from ATG5-deficient mice.

CONCLUSIONS

Mice with pancreas-specific disruption of Atg5 develop a form of CP similar to that of humans. CP development appears to involve defects in autophagy, glutamate-dependent metabolism, and increased production of reactive oxygen species. These mice might be used to identify therapeutic targets for CP.

Metabolomics of Ramadan fasting: an opportunity for the controlled study of physiological responses to food intake

High-throughput screening techniques that analyze the metabolic endpoints of biological processes can identify the contributions of genetic predisposition and environmental factors to the development of common diseases. Studies applying controlled physiological challenges can reveal dysregulation in metabolic responses that may be predictive for or associated with these diseases. However, large-scale epidemiological studies with well controlled physiological challenge conditions, such as extended fasting periods and defined food intake, pose logistic challenges. Culturally and religiously motivated behavioral patterns of life style changes provide a natural setting that can be used to enroll a large number of study volunteers. Here we report a proof of principle study conducted within a Muslim community, showing that a metabolomics study during the Holy Month of Ramadan can provide a unique opportunity to explore the pre-prandial and postprandial response of human metabolism to nutritional challenges. Up to five blood samples were obtained from eleven healthy male volunteers, taken directly before and two hours after consumption of a controlled meal in the evening on days 7 and 26 of Ramadan, and after an over-night fast several weeks after Ramadan. The observed increases in glucose, insulin and lactate levels at the postprandial time point confirm the expected physiological response to food intake. Targeted metabolomics further revealed significant and physiologically plausible responses to food intake by an increase in bile acid and amino acid levels and a decrease in long-chain acyl-carnitine and polyamine levels. A decrease in the concentrations of a number of phospholipids between samples taken on days 7 and 26 of Ramadan shows that the long-term response to extended fasting may differ from the response to short-term fasting. The present study design is scalable to larger populations and may be extended to the study of the metabolic response in defined patient groups such as individuals with type 2 diabetes.

Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up

BACKGROUND

Recently, five branched-chain and aromatic amino acids were shown to be associated with the risk of developing type 2 diabetes (T2D).

AIM

We set out to examine whether amino acids are also associated with the development of hypertriglyceridemia.

MATERIALS AND METHODS

We determined the serum amino acids concentrations of 1,125 individuals of the KORA S4 baseline study, for which follow-up data were available also at the KORA F4 7 years later. After exclusion for hypertriglyceridemia (defined as having a fasting triglyceride level above 1.70 mmol/L) and diabetes at baseline, 755 subjects remained for analyses.

RESULTS

Increased levels of leucine, arginine, valine, proline, phenylalanine, isoleucine and lysine were significantly associated with an increased risk of hypertriglyceridemia. These associations remained significant when restricting to those individuals who did not develop T2D in the 7-year follow-up. The increase per standard deviation of amino acid level was between 26 and 40 %.

CONCLUSIONS

Seven amino acids were associated with an increased risk of developing hypertriglyceridemia after 7 years. Further studies are necessary to elucidate the complex role of these amino acids in the pathogenesis of metabolic disorders.

Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids

BACKGROUND

Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits.

METHODS

We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another.

RESULTS

A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci.

CONCLUSIONS

These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.

Plasma metabolomics reveal alterations of sphingo- and glycerophospholipid levels in non-diabetic carriers of the transcription factor 7-like 2 polymorphism rs7903146

AIMS/HYPOTHESIS

Polymorphisms in the transcription factor 7-like 2 (TCF7L2) gene have been shown to display a powerful association with type 2 diabetes. The aim of the present study was to evaluate metabolic alterations in carriers of a common TCF7L2 risk variant.

METHODS

Seventeen non-diabetic subjects carrying the T risk allele at the rs7903146 TCF7L2 locus and 24 subjects carrying no risk allele were submitted to intravenous glucose tolerance test and euglycemic-hyperinsulinemic clamp. Plasma samples were analysed for concentrations of 163 metabolites through targeted mass spectrometry.

RESULTS

TCF7L2 risk allele carriers had a reduced first-phase insulin response and normal insulin sensitivity. Under fasting conditions, carriers of TCF7L2 rs7903146 exhibited a non-significant increase of plasma sphingomyelins (SMs), phosphatidylcholines (PCs) and lysophosphatidylcholines (lysoPCs) species. A significant genotype effect was detected in response to challenge tests in 6 SMs (C16:0, C16:1, C18:0, C18:1, C24:0, C24:1), 5 hydroxy-SMs (C14:1, C16:1, C22:1, C22:2, C24:1), 4 lysoPCs (C14:0, C16:0, C16:1, C17:0), 3 diacyl-PCs (C28:1, C36:6, C40:4) and 4 long-chain acyl-alkyl-PCs (C40:2, C40:5, C44:5, C44:6).

DISCUSSION

Plasma metabolomic profiling identified alterations of phospholipid metabolism in response to challenge tests in subjects with TCF7L2 rs7903146 genotype. This may reflect a genotype-mediated link to early metabolic abnormalities prior to the development of disturbed glucose tolerance.

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, r² = 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, r² = 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.

Metabolomic profiles in individuals with negative affectivity and social inhibition: a population-based study of Type D personality

BACKGROUND

Individuals with negative affectivity who are inhibited in social situations are characterized as distressed, or Type D, and have an increased risk of cardiovascular disease (CVD). The underlying biomechanisms that link this psychological affect to a pathological state are not well understood. This study applied a metabolomic approach to explore biochemical pathways that may contribute to the Type D personality.

METHODS

Type D personality was determined by the Type D Scale-14. Small molecule biochemicals were measured using two complementary mass-spectrometry based metabolomics platforms. Metabolic profiles of Type D and non-Type D participants within a population-based study in Southern Germany were compared in cross-sectional regression analyses. The PHQ-9 and GAD-7 instruments were also used to assess symptoms of depression and anxiety, respectively, within this metabolomic study.

RESULTS

668 metabolites were identified in the serum of 1502 participants (age 32-77); 386 of these individuals were classified as Type D. While demographic and biomedical characteristics were equally distributed between the groups, a higher level of depression and anxiety was observed in Type D individuals. Significantly lower levels of the tryptophan metabolite kynurenine were associated with Type D (p-value corrected for multiple testing=0.042), while no significant associations could be found for depression and anxiety. A Gaussian graphical model analysis enabled the identification of four potentially interesting metabolite networks that are enriched in metabolites (androsterone sulfate, tyrosine, indoxyl sulfate or caffeine) that associate nominally with Type D personality.

CONCLUSIONS

This study identified novel biochemical pathways associated with Type D personality and demonstrates that the application of metabolomic approaches in population studies can reveal mechanisms that may contribute to psychological health and disease.

Metabolites associate with kidney function decline and incident chronic kidney disease in the general population

BACKGROUND

Serum metabolites are associated cross-sectionally with kidney function in population-based studies.

METHODS

Using flow injection and liquid chromatography tandem mass spectrometry methods, we examined longitudinal associations of baseline concentrations of 140 metabolites and their 19 460 ratios with kidney function decline and chronic kidney disease (CKD) incidence over 7 years in 1104 participants of the Cooperative Health Research in the Region of Augsburg S4/F4 study.

RESULTS

Corrected for multiple testing, a significant association with annual change in the estimated glomerular filtration rate was observed for spermidine (P = 5.8 × 10(-7)) and two metabolite ratios, the phosphatidylcholine diacyl C42:5-to-phosphatidylcholine acyl-alkyl C36:0 ratio (P = 1.5 × 10(-6)) and the kynurenine-to-tryptophan ratio (P = 1.9 × 10(-6)). The kynurenine-to-tryptophan ratio was also associated with significantly higher incidence of CKD at the follow-up visit with an odds ratio of 1.36 per standard deviation increase; 95% confidence interval 1.11-1.66, P = 2.7 × 10(-3)). In separate analyses, the predictive ability of the metabolites was assessed: both the three significantly associated metabolite (ratios) as well as a panel of 35 metabolites selected from all metabolites in an unbiased fashion provided as much but not significantly more prognostic information than selected clinical predictors as judged by the area under the curve.

CONCLUSIONS

Baseline serum concentrations of spermidine and two metabolite ratios were associated with kidney function change over subsequent years in the general population. In separate analyses, baseline serum metabolites were able to predict incident CKD to a similar but not better extent than selected clinical parameters. Our longitudinal findings provide a basis for targeted studies of certain metabolic pathways, e.g. tryptophan metabolism, and their relation to kidney function decline.

Targeted metabolomics profiles are strongly correlated with nutritional patterns in women

Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ™ Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a "traditional English" diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 × 10^-5) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 × 10^-9) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 × 10^-13). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research.

Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in (1)H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.

Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels. The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system, providing valuable insights for both basic research as well as clinical applications. In this study, we borrow the metabolomics GWAS concept for a novel, entirely different purpose. Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample, but it has not yet been elucidated which specific metabolite this signal actually represents. The concept is comparable to a fingerprint: each one is uniquely identifiable, but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs. Obviously, this issue tremendously reduces the usability of a metabolomics analyses. The genetic associations of such an “unknown,” however, give us concrete evidence of the metabolic pathway this substance is most probably involved in. Moreover, we complement the approach with a specific measure of correlation between metabolites, providing further evidence of the metabolic processes of the unknown. For a number of cases, this even allows for a concrete identity prediction, which we then experimentally validate in the lab.

Changing metabolic signatures of amino acids and lipids during the prediabetic period in a pig model with impaired incretin function and reduced β-cell mass

Diabetes is generally diagnosed too late. Therefore, biomarkers indicating early stages of β-cell dysfunction and mass reduction would facilitate timely counteraction. Transgenic pigs expressing a dominant-negative glucose-dependent insulinotropic polypeptide receptor (GIPR(dn)) reveal progressive deterioration of glucose control and reduction of β-cell mass, providing a unique opportunity to study metabolic changes during the prediabetic period. Plasma samples from intravenous glucose tolerance tests of 2.5- and 5-month-old GIPR(dn) transgenic and control animals were analyzed for 163 metabolites by targeted mass spectrometry. Analysis of variance revealed that 26 of 163 parameters were influenced by the interaction Genotype × Age (P ≤ 0.0001) and thus are potential markers for progression within the prediabetic state. Among them, the concentrations of seven amino acids (Phe, Orn, Val, xLeu, His, Arg, and Tyr) were increased in 2.5-month-old but decreased in 5-month-old GIPR(dn) transgenic pigs versus controls. Furthermore, specific sphingomyelins, diacylglycerols, and ether phospholipids were decreased in plasma of 5-month-old GIPR(dn) transgenic pigs. Alterations in plasma metabolite concentrations were associated with liver transcriptome changes in relevant pathways. The concentrations of a number of plasma amino acids and lipids correlated significantly with β-cell mass of 5-month-old pigs. These metabolites represent candidate biomarkers of early phases of β-cell dysfunction and mass reduction.

Serum metabolite concentrations and decreased GFR in the general population

BACKGROUND

Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning normal kidney function and chronic kidney disease (CKD).

STUDY DESIGN

Cross-sectional observational studies of the general population.

SETTING AND PARTICIPANTS

2 independent samples: KORA F4 (discovery sample, n = 3,011) and Twins UK (validation sample, n = 984). EXPOSURE FACTORS: 151 serum metabolites, quantified by targeted mass spectrometry.

OUTCOMES AND MEASUREMENTS

Metabolites and their 22,650 ratios were analyzed by multivariable-adjusted linear regression for their association with glomerular filtration rate (eGFR), estimated separately from creatinine and cystatin C levels by CKD-EPI (CKD Epidemiology Collaboration) equations. After correction for multiple testing, significant metabolites (P < 3.3 × 10(-4) for single metabolites; P < 2.2 × 10(-6) for ratios) were meta-analyzed with independent data from the TwinsUK Study.

RESULTS

Replicated associations with eGFR were observed for 22 metabolites and 516 metabolite ratios. Pooled P values ranged from 7.1 × 10(-7) to 1.8 × 10(-69) for the replicated single metabolites. Acylcarnitines such as glutarylcarnitine were associated inversely with eGFR (-3.73 mL/min/1.73 m(2) per standard deviation [SD] increase, pooled P = 1.8 × 10(-69)). The replicated ratio with the strongest association was the ratio of serine to glutarylcarnitine (P = 3.6 × 10(-81)). Almost all replicated phenotypes associated with decreased eGFR (<60 mL/min/1.73 m(2); n = 172 cases) in KORA F4: per 1-SD increment, ORs ranged from 0.29-2.06. Across categories of a metabolic score consisting of 3 uncorrelated metabolites, the prevalence of decreased eGFR increased from 3% to 53%.

LIMITATIONS

Cross-sectional study design, GFR was estimated, limited number of metabolites.

CONCLUSIONS

Distinct metabolic phenotypes were reproducibly associated with eGFR in 2 separate population studies. They may provide novel insights into renal metabolite handling, improve understanding of pathophysiology, or aid in the diagnosis of kidney disease. Longitudinal studies are needed to clarify whether changes in metabolic phenotypes precede or result from kidney function impairment.

Childhood obesity is associated with changes in the serum metabolite profile

OBJECTIVE

The human serum metabolite profile is reflective of metabolic processes, including pathophysiological changes characteristic of diseases. Therefore, investigation of serum metabolite concentrations in obese children might give new insights into biological mechanisms associated with childhood obesity.

METHODS

Serum samples of 80 obese and 40 normal-weight children between 6 and 15 years of age were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Metabolite concentrations and metabolite ratios were compared between obese and normal-weight children as well as between children of different pubertal stages.

RESULTS

Metabolite concentration profiles of obese children could be distinguished from those of normal-weight children. After correction for multiple testing, we observed 14 metabolites (glutamine, methionine, proline, nine phospholipids, and two acylcarnitines, p < 3.8 × 10⁻⁴) and 69 metabolite ratios (p < 6.0 × 10⁻⁶) to be significantly altered in obese children. The identified metabolite markers are indicative of oxidative stress and of changes in sphingomyelin metabolism, in β-oxidation, and in pathways associated with energy expenditure. In contrast, pubertal stage was not associated with metabolite concentration differences.

CONCLUSION

Our study shows that childhood obesity influences the composition of the serum metabolome. If replicated in larger studies, the altered metabolites might be considered as potential biomarkers in the generation of new hypotheses on the biological mechanisms behind obesity.

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.

Human metabolic individuality in biomedical and pharmaceutical research

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Identification of a potential biomarker for FABP4 inhibition: the power of lipidomics in preclinical drug testing

The fatty acid binding protein 4 (FABP4) belongs to the family of lipid chaperones that control intracellular fluxes and compartmentalization of their respective ligands (e.g., fatty acids). FABP4, which is almost exclusively expressed in adipocytes and macrophages, contributes to the development of insulin resistance and atherosclerosis in mice. Lack of FABP4 protects against the development of insulin resistance associated with genetic or diet-induced obesity in mice. Furthermore, total or macrophage-specific FABP4 deficiency is protective against atherosclerosis in apolipoprotein E-deficient mice. The FABP4 small-molecule inhibitor BMS309403 has demonstrated efficacy in mouse models for type 2 diabetes mellitus and atherosclerosis, resembling phenotypes of mice with FABP4 deficiency. However, despite the therapeutically attractive long-term effects of FABP4 inhibition, an acute biomarker for drug action is lacking. The authors applied mass spectrometry lipidomics analysis to in vitro and in vivo (plasma and adipose tissue) samples upon inhibitor treatment. They report the identification of a potential biomarker for acute in vivo FABP4 inhibition that is applicable for further investigations and can be implemented in simple and fast-flow injection mass spectrometry assays. In addition, this approach can be considered a proof-of-principle study that can be applied to other lipid-pathway targeting mechanisms.

Ultrafast infrared spectroscopy of an isotope-labeled photoactivatable flavoprotein

The blue light using flavin (BLUF) domain photosensors, such as the transcriptional antirepressor AppA, utilize a noncovalently bound flavin as the chromophore for photoreception. Since the isoalloxazine ring of the chromophore is unable to undergo large-scale structural change upon light absorption, there is intense interest in understanding how the BLUF protein matrix senses and responds to flavin photoexcitation. Light absorption is proposed to result in alterations in the hydrogen-bonding network that surrounds the flavin chromophore on an ultrafast time scale, and the structural changes caused by photoexcitation are being probed by vibrational spectroscopy. Here we report ultrafast time-resolved infrared spectra of the AppA BLUF domain (AppA(BLUF)) reconstituted with isotopically labeled riboflavin (Rf) and flavin adenine dinucleotide (FAD), which permit the first unambiguous assignment of ground and excited state modes arising directly from the flavin carbonyl groups. Studies of model compounds and DFT calculations of the ground state vibrational spectra reveal the sensitivity of these modes to their environment, indicating that they can be used as probes of structural dynamics.

Mouse phenotyping

Model organisms like the mouse are important tools to learn more about gene function in man. Within the last 20 years many mutant mouse lines have been generated by different methods such as ENU mutagenesis, constitutive and conditional knock-out approaches, knock-down, introduction of human genes, and knock-in techniques, thus creating models which mimic human conditions. Due to pleiotropic effects, one gene may have different functions in different organ systems or time points during development. Therefore mutant mouse lines have to be phenotyped comprehensively in a highly standardized manner to enable the detection of phenotypes which might otherwise remain hidden. The German Mouse Clinic (GMC) has been established at the Helmholtz Zentrum München as a phenotyping platform with open access to the scientific community (www.mousclinic.de; [1]). The GMC is a member of the EUMODIC consortium which created the European standard workflow EMPReSSslim for the systemic phenotyping of mouse models (http://www.eumodic.org/[2]).

Artemisinin biosynthesis in growing plants of Artemisia annua. A 13CO2 study

Artemisinin from Artemisia annua has become one of the most important drugs for malaria therapy. Its biosynthesis proceeds via amorpha-4,11-diene, but it is still unknown whether the isoprenoid precursors units are obtained by the mevalonate pathway or the more recently discovered non-mevalonate pathway. In order to address that question, a plant of A. annua was grown in an atmosphere containing 700 ppm of 13CO2 for 100 min. Following a chase period of 10 days, artemisinin was isolated and analyzed by 13C NMR spectroscopy. The isotopologue pattern shows that artemisinin was predominantly biosynthesized from (E,E)-farnesyl diphosphate (FPP) whose central isoprenoid unit had been obtained via the non-mevalonate pathway. The isotopologue data confirm the previously proposed mechanisms for the cyclization of (E,E)-FPP to amorphadiene and its oxidative conversion to artemisinin. They also support deprotonation of a terminal allyl cation intermediate as the final step in the enzymatic conversion of FPP to amorphadiene and show that either of the two methyl groups can undergo deprotonation.

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.

2,5-diamino-6-ribitylamino-4(3H)-pyrimidinone 5'-phosphate synthases of fungi and archaea

The pathway of riboflavin (vitamin B2) biosynthesis is significantly different in archaea, eubacteria, fungi and plants. Specifically, the first committed intermediate, 2,5-diamino-6-ribosylamino-4(3H)-pyrimidinone 5'-phosphate, can either undergo hydrolytic cleavage of the position 2 amino group by a deaminase (in plants and most eubacteria) or reduction of the ribose side chain by a reductase (in fungi and archaea). We compare 2,5-diamino-6-ribitylamino-4(3H)-pyrimidinone 5'-phosphate synthases from the yeast Candida glabrata, the archaeaon Methanocaldococcus jannaschii and the eubacterium Aquifex aeolicus. All three enzymes convert 2,5-diamino-6-ribosylamino-4(3H)-pyrimidinone 5'-phosphate into 2,5-diamino-6-ribitylamino-4(3H)-pyrimidinone 5'-phosphate, as shown by 13C-NMR spectroscopy using [2,1',2',3',4',5'-13C6]2,5-diamino-6-ribosylamino-4(3H)-pyrimidinone 5'-phosphate as substrate. The beta anomer was found to be the authentic substrate, and the alpha anomer could serve as substrate subsequent to spontaneous anomerisation. The M. jannaschii and C. glabrata enzymes were shown to be A-type reductases catalysing the transfer of deuterium from the 4(R) position of NADPH to the 1' (S) position of the substrate. These results are in agreement with the known three-dimensional structure of the M. jannaschii enzyme.

13C Isotopologue editing of FMN bound to phototropin domains

The plant blue light receptor phototropin comprises a protein kinase domain and two FMN-binding LOV domains (LOV1 and LOV2). Blue light irradiation of recombinant LOV domains is conducive to the addition of a cysteinyl thiolate group to carbon 4a of the FMN chromophore, and spontaneous cleavage of that photoadduct completes the photocycle of the receptor. The present study is based on (13)C NMR signal modulation observed after reconstitution of LOV domains of different origins with random libraries of (13)C-labeled FMN isotopologues. Using this approach, all (13)C signals of FMN bound to LOV1 and LOV2 domains of Avena sativa and to the LOV2 domain of the fern, Adiantum capillus-veneris, could be unequivocally assigned under dark and under blue light irradiation conditions. (13)C Chemical shifts of FMN are shown to be differently modulated by complexation with the LOV domains under study, indicating slight differences in the binding interactions of FMN and the apoproteins.

13CO2 as a universal metabolic tracer in isotopologue perturbation experiments

A tobacco plant was illuminated for 5h in an atmosphere containing (13)CO(2) and then maintained for 10 days under standard greenhouse conditions. Nicotine, glucose, and amino acids from proteins were isolated chromatographically. Isotopologue abundances of isolated metabolites were determined quantitatively by NMR spectroscopy and mass spectrometry. The observed non-stochastic isotopologue patterns indicate (i) formation of multiply labeled photosynthetic carbohydrates during the (13)CO(2) pulse phase followed by (ii) partial catabolism of the primary photosynthetic products, and (iii) recombination of the (13)C-labeled fragments with unlabeled intermediary metabolites during the chase period. The detected and simulated isotopologue profiles of glucose and amino acids reflect carbon partitioning that is dominated by the Calvin cycle and glycolysis/glucogenesis. Retrobiosynthetic analysis of the nicotine pattern is in line with its known formation from nicotinic acid and putrescine via aspartate, glyceraldehyde phosphate and alpha-ketoglutarate as basic building blocks. The study demonstrates that pulse/chase labeling with (13)CO(2) as precursor is a powerful tool for the analysis of quantitative aspects of plant metabolism in completely unperturbed whole plants.