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1.  An atlas of genetic influences on human blood metabolites 
Nature genetics  2014;46(6):543-550.
Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism to date, including 7,824 adult individuals from two European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity regarding more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information regarding gene expression, heritability, overlap with known drug targets, previous association with complex disorders and inborn errors of metabolism. We further developed a database and web-based resources for data mining and results visualization. Our findings contribute to a greater understanding of the role of inherited variation in blood metabolic diversity, and identify potential new opportunities for pharmacologic development and disease understanding.
PMCID: PMC4064254  PMID: 24816252
2.  Uremic solutes and risk of end stage renal disease in type 2 diabetes 
Kidney international  2014;85(5):1214-1224.
Here we studied plasma metabolomic profiles as determinants of progression to ESRD in patients with Type 2 diabetes (T2D). This nested case-control study evaluated 40 cases who progressed to ESRD during 8-12 years of follow-up and 40 controls who remained alive without ESRD from the Joslin Kidney Study cohort. Controls were matched with cases for baseline clinical characteristics; although controls had slightly higher eGFR and lower levels of urinary albumin excretion than T2D cases. Plasma metabolites at baseline were measured by mass spectrometry-based global metabolomic profiling. Of the named metabolites in the library, 262 were detected in at least 80% of the study patients. The metabolomic platform recognized 78 metabolites previously reported to be elevated in ESRD (uremic solutes). Sixteen were already elevated in the baseline plasma of our cases years before ESRD developed. Other uremic solutes were either not different or not commonly detectable. Essential amino acids and their derivatives were significantly depleted in the cases, whereas certain amino acid-derived acylcarnitines were increased. All findings remained statistically significant after adjustment for differences between study groups in albumin excretion rate, eGFR or HbA1c. Uremic solute differences were confirmed by quantitative measurements. Thus, abnormal plasma concentrations of putative uremic solutes and essential amino acids either contribute to progression to ESRD or are a manifestation of an early stage(s) of the disease process that leads to ESRD in T2D.
PMCID: PMC4072128  PMID: 24429397
3.  Metabolomics in pneumonia and sepsis: an analysis of the GenIMS cohort study 
Intensive care medicine  2013;39(8):1423-1434.
To determine the global metabolomic profile as measured in circulating plasma from surviving and non-surviving patients with community-acquired pneumonia (CAP) and sepsis.
Random, outcome-stratified case–control sample from a prospective study of 1,895 patients hospitalized with CAP and sepsis. Cases (n = 15) were adults who died before 90 days, and controls (n = 15) were adults who survived, matched on demographics, infection type, and procalcitonin. We determined the global metabolomic profile in the first emergency department blood sample using non-targeted mass-spectrometry. We derived metabolite-based prognostic models for 90-day mortality. We determined if metabolites stimulated cytokine production by differentiated Thp1 monocytes in vitro, and validated metabolite profiles in mouse liver and kidney homogenates at 8 h in cecal ligation and puncture (CLP) sepsis.
We identified 423 small molecules, of which the relative levels of 70 (17 %) were different between survivors and non-survivors (p ≤ 0.05). Broad differences were present in pathways of oxidative stress, bile acid metabolism, and stress response. Metabolite-based prognostic models for 90-day survival performed modestly (AUC = 0.67, 95 % CI 0.48, 0.81). Five nucleic acid metabolites were greater in non-survivors (p ≤ 0.05). Of these, pseudouridine increased monocyte expression of TNFα and IL1β versus control (p < 0.05). Pseudouridine was also increased in liver and kidney homogenates from CLP mice versus sham (p < 0.05 for both).
Although replication is required, we show the global metabolomic profile in plasma broadly differs between survivors and non-survivors of CAP and sepsis. Metabolite-based prognostic models had modest performance, though metabolites of oxidative stress may act as putative damage-associated molecular patterns.
PMCID: PMC3932707  PMID: 23673400
Sepsis; Pneumonia; Metabolomics; Biomarker
4.  The Mycobacterium tuberculosis regulatory network and hypoxia 
Nature  2013;499(7457):178-183.
We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.
PMCID: PMC4087036  PMID: 23823726
5.  2,3,7,8-Tetrachlorodibenzo-p-dioxin-Mediated Production of Reactive Oxygen Species Is An Essential Step in the Mechanism of Action to Accelerate Human Keratinocyte Differentiation 
Toxicological Sciences  2012;132(1):235-249.
Chloracne is commonly observed in humans exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD); yet, the mechanism of toxicity is not well understood. Using normal human epidermal keratinocytes, we investigated the mechanism of TCDD-mediated enhancement of epidermal differentiation by integrating functional genomic, metabolomic, and biochemical analyses. TCDD increased the expression of 40% of the genes of the epidermal differentiation complex found on chromosome 1q21 and 75% of the genes required for de novo ceramide biosynthesis. Lipid analysis demonstrated that eight of the nine classes of ceramides were increased by TCDD, altering the ratio of ceramides to free fatty acids. TCDD decreased the expression of the glucose transporter, SLC2A1, and most of the glycolytic transcripts, followed by decreases in glycolytic intermediates, including pyruvate. NADH and Krebs cycle intermediates were decreased, whereas NAD+ was increased. Mitochondrial glutathione (GSH) reductase activity and the GSH/glutathione disulfide ratio were decreased by TCDD, ultimately leading to mitochondrial dysfunction, characterized by decreased inner mitochondrial membrane potential and ATP production, and increased production of the reactive oxygen species (ROS), hydrogen peroxide. Aryl hydrocarbon receptor (AHR) antagonists blocked the response of many transcripts to TCDD, and the endpoints of decreased ATP production and differentiation, suggesting regulation by the AHR. Cotreatment of cells with chemical antioxidants or the enzyme catalase blocked the TCDD-mediated acceleration of keratinocyte cornified envelope formation, an endpoint of terminal differentiation. Thus, TCDD-mediated ROS production is a critical step in the mechanism of this chemical to accelerate keratinocyte differentiation.
PMCID: PMC3576006  PMID: 23152189
2; 3; 7; 8-tetrachlorodibenzo-p-dioxin; epidermal barrier; differentiation; reactive oxygen species; mitochondrial dysfunction; glycolysis; ceramide biosynthesis.
6.  Long term conservation of human metabolic phenotypes and link to heritability 
Metabolomics  2014;10(5):1005-1017.
Changes in an individual’s human metabolic phenotype (metabotype) over time can be indicative of disorder-related modifications. Studies covering several months to a few years have shown that metabolic profiles are often specific for an individual. This “metabolic individuality” and detected changes may contribute to personalized approaches in human health care. However, it is not clear whether such individual metabotypes persist over longer time periods. Here we investigate the conservation of metabotypes characterized by 212 different metabolites of 818 participants from the Cooperative Health Research in the Region of Augsburg; Germany population, taken within a 7-year time interval. For replication, we used paired samples from 83 non-related individuals from the TwinsUK study. Results indicated that over 40 % of all study participants could be uniquely identified after 7 years based on their metabolic profiles alone. Moreover, 95 % of the study participants showed a high degree of metabotype conservation (>70 %) whereas the remaining 5 % displayed major changes in their metabolic profiles over time. These latter individuals were likely to have undergone important biochemical changes between the two time points. We further show that metabolite conservation was positively associated with heritability (rank correlation 0.74), although there were some notable exceptions. Our results suggest that monitoring changes in metabotypes over several years can trace changes in health status and may provide indications for disease onset. Moreover, our study findings provide a general reference for metabotype conservation over longer time periods that can be used in biomarker discovery studies.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-014-0629-y) contains supplementary material, which is available to authorized users.
PMCID: PMC4145193  PMID: 25177233
Metabolomics; Longitudinal study; Heritability; Population study
7.  An integrated clinico-metabolomic model improves prediction of death in sepsis 
Science translational medicine  2013;5(195):195ra95.
Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features, and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would die differed markedly from those who would survive. The different profiles of proteins and metabolites clustered into fatty acid transport and β-oxidation, gluconeogenesis and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of seven metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.
PMCID: PMC3924586  PMID: 23884467
8.  Human metabolic individuality in biomedical and pharmaceutical research 
Nature  2011;477(7362):10.1038/nature10354.
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 exhibit effect sizes that are unusually high for GWAS and account for 10-60% of metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism, and Crohn’s disease. Taken together our study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
PMCID: PMC3832838  PMID: 21886157
9.  Phospho-ΔNp63α/SREBF1 protein interactions 
Cell Cycle  2012;11(20):3810-3827.
Tumor protein (TP)-p53 family members (TP63, TP63 and TP73) are guardians of the genome and key players in orchestrating the cellular response to cisplatin treatment. Cisplatin-induced phosphorylation of ΔNp63α was shown to have a role in regulating intracellular ΔNp63α protein levels. We previously found that squamous cell carcinoma (SCC) cells exposed to cisplatin displayed the ATM-dependent phosphorylation of ΔNp63α (p-ΔNp63α), which is critical for the transcriptional regulation of specific downstream mRNAs and microRNAs and is likely to underlie the chemoresistance of SCC cells. However, SCC cells expressing non-p-ΔNp63α became more cisplatin-resistant. We also found that p-ΔNp63α forms complexes with a number of proteins involved in cell death response through regulation of cell cycle arrest, apoptosis, autophagy, RNA splicing and chromatin modifications. Here, we showed that p-ΔNp63α induced ARG1, GAPDH, and CPT2 gene transcription in cisplatin-sensitive SCC cells, while non-p-ΔNp63α increased a transcription of CAD, G6PD and FASN genes in cisplatin-resistant SCC cells. We report that the p-ΔNp63α-dependent regulatory mechanisms implicated in the modulation of plethora of pathways, including amino acid, carbohydrate, lipid and nucleotide metabolisms, thereby affect tumor cell response to cisplatin-induced cell death, suggesting that the ATM-dependent ΔNp63α pathway plays a role in the resistance of tumor cells to platinum therapy.
PMCID: PMC3495824  PMID: 22951905
P53; p63; cisplatin; squamous cell carcinomas; protein interactions; metabolomics
10.  Autophagy 
Autophagy  2012;8(10):1477-1493.
Autophagy is a catabolic process that functions in recycling and degrading cellular proteins, and is also induced as an adaptive response to the increased metabolic demand upon nutrient starvation. However, the prosurvival role of autophagy in response to metabolic stress due to deprivation of glutamine, the most abundant nutrient for mammalian cells, is not well understood. Here, we demonstrated that when extracellular glutamine was withdrawn, autophagy provided cells with sub-mM concentrations of glutamine, which played a critical role in fostering cell metabolism. Moreover, we uncovered a previously unknown connection between metabolic responses to ATG5 deficiency and glutamine deprivation, and revealed that WT and atg5−/− MEFs utilized both common and distinct metabolic pathways over time during glutamine deprivation. Although the early response of WT MEFs to glutamine deficiency was similar in many respects to the baseline metabolism of atg5−/− MEFs, there was a concomitant decrease in the levels of essential amino acids and branched chain amino acid catabolites in WT MEFs after 6 h of glutamine withdrawal that distinguished them from the atg5−/− MEFs. Metabolomic profiling, oxygen consumption and pathway focused quantitative RT-PCR analyses revealed that autophagy and glutamine utilization were reciprocally regulated to couple metabolic and transcriptional reprogramming. These findings provide key insights into the critical prosurvival role of autophagy in maintaining mitochondrial oxidative phosphorylation and cell growth during metabolic stress caused by glutamine deprivation.
PMCID: PMC3679231  PMID: 22906967
ATG5; autophagy; glutamine; ATP; transcriptional reprogramming; altered metabolism
11.  Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function  
The Journal of Clinical Investigation  2013;123(10):4479-4488.
Naive CD8+ T cells rely upon oxidation of fatty acids as a primary source of energy. After antigen encounter, T cells shift to a glycolytic metabolism to sustain effector function. It is unclear, however, whether changes in glucose metabolism ultimately influence the ability of activated T cells to become long-lived memory cells. We used a fluorescent glucose analog, 2-NBDG, to quantify glucose uptake in activated CD8+ T cells. We found that cells exhibiting limited glucose incorporation had a molecular profile characteristic of memory precursor cells and an increased capacity to enter the memory pool compared with cells taking up high amounts of glucose. Accordingly, enforcing glycolytic metabolism by overexpressing the glycolytic enzyme phosphoglycerate mutase-1 severely impaired the ability of CD8+ T cells to form long-term memory. Conversely, activation of CD8+ T cells in the presence of an inhibitor of glycolysis, 2-deoxyglucose, enhanced the generation of memory cells and antitumor functionality. Our data indicate that augmenting glycolytic flux drives CD8+ T cells toward a terminally differentiated state, while its inhibition preserves the formation of long-lived memory CD8+ T cells. These results have important implications for improving the efficacy of T cell–based therapies against chronic infectious diseases and cancer.
PMCID: PMC3784544  PMID: 24091329
12.  Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits 
Human Molecular Genetics  2013;23(2):534-545.
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.
PMCID: PMC3869358  PMID: 24014485
13.  Oncogene-induced senescence results in marked metabolic and bioenergetic alterations 
Cell Cycle  2012;11(7):1383-1392.
Oncogene-induced senescence (OIS) is characterized by permanent growth arrest and the acquisition of a secretory, pro-inflammatory state. Increasingly, OIS is viewed as an important barrier to tumorgenesis. Surprisingly, relatively little is known about the metabolic changes that accompany and therefore may contribute to OIS. Here, we have performed a metabolomic and bioenergetic analysis of Ras-induced senescence. Profiling approximately 300 different intracellular metabolites reveals that cells that have undergone OIS develop a unique metabolic signature that differs markedly from cells undergoing replicative senescence. A number of lipid metabolites appear uniquely increased in OIS cells, including a marked increase in the level of certain intracellular long chain fatty acids. Functional studies reveal that this alteration in the metabolome reflects substantial changes in overall lipid metabolism. In particular, Ras-induced senescent cells manifest a decline in lipid synthesis and a significant increase in fatty acid oxidation. Increased fatty acid oxidation results in an unexpectedly high rate of basal oxygen consumption in cells that have undergone OIS. Pharmacological or genetic inhibition of carnitine palmitoyltransferase 1, the rate-limiting step in mitochondrial fatty acid oxidation, restores a presenescent metabolic rate and, surprisingly, selectively inhibits the secretory, pro-inflammatory state that accompanies OIS. Thus, Ras-induced senescent cells demonstrate profound alterations in their metabolic and bioenergetic profiles, particularly with regards to the levels, synthesis and oxidation of free fatty acids. Furthermore, the inflammatory phenotype that accompanies OIS appears to be related to these underlying changes in cellular metabolism.
PMCID: PMC3350879  PMID: 22421146
oncogene-induced senescence; metabolomics; Ras; fatty acid oxidation
15.  Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information 
PLoS Genetics  2012;8(10):e1003005.
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.
Author Summary
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.
PMCID: PMC3475673  PMID: 23093944

Results 1-15 (15)