Systems Metabolomics

2022
Suls, J.° ; Salive, M.E. ; Koroukian, S.M. ; Alemi, F. ; Silber, J.H. ; Kastenmüller, G. ; Klabunde, C.N.°
J. Am. Geriatr. Soc., DOI: 10.1111/jgs.17914 (2022)
Older adults experience a higher prevalence of multiple chronic conditions (MCCs). Establishing the presence and pattern of MCCs in individuals or populations is important for healthcare delivery, research, and policy. This report describes four emerging approaches and discusses their potential applications for enhancing assessment, treatment, and policy for the aging population. The National Institutes of Health convened a 2-day panel workshop of experts in 2018. Four emerging models were identified by the panel, including classification and regression tree (CART), qualifying comorbidity sets (QCS), the multimorbidity index (MMI), and the application of omics to network medicine. Future research into models of multiple chronic condition assessment may improve understanding of the epidemiology, diagnosis, and treatment of older persons.
Wissenschaftlicher Artikel
Scientific Article
Milaneschi, Y. ; Arnold, M. ; Kastenmüller, G. ; Dehkordi, S.M. ; Krishnan, R.R. ; Dunlop, B.W. ; Rush, A.J. ; Penninx, B.W.J.H. ; Kaddurah-Daouk, R.
J. Affect. Disord. 307, 254-263 (2022)
Background: Altered metabolism of acylcarnitines – transporting fatty acids to mitochondria – may link cellular energy dysfunction to depression. We examined the potential causal role of acylcarnitine metabolism in depression by leveraging genomics and Mendelian randomization. Methods: Summary statistics were obtained from large GWAS: the Fenland Study (N = 9363), and the Psychiatric Genomics Consortium (246,363 depression cases and 561,190 controls). Two-sample Mendelian randomization analyses tested the potential causal link of 15 endogenous acylcarnitines with depression. Results: In univariable analyses, genetically-predicted lower levels of short-chain acylcarnitines C2 (odds ratio [OR] 0.97, 95% confidence intervals [CIs] 0.95–1.00) and C3 (OR 0.97, 95%CIs 0.96–0.99) and higher levels of medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.01–1.06) and C10 (OR 1.04, 95%CIs 1.02–1.06) were associated with increased depression risk. No reverse potential causal role of depression genetic liability on acylcarnitines levels was found. Multivariable analyses showed that the association with depression was driven by the medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.02–1.06) and C10 (OR 1.04, 95%CIs 1.02–1.06), suggesting a potential causal role in the risk of depression. Causal estimates for C8 (OR = 1.05, 95%CIs = 1.02–1.07) and C10 (OR = 1.05, 95%CIs = 1.02–1.08) were confirmed in follow-up analyses using genetic instruments derived from a GWAS meta-analysis including up to 16,841 samples. Discussion: Accumulation of medium-chain acylcarnitines is a signature of inborn errors of fatty acid metabolism and age-related metabolic conditions. Our findings point to a link between altered mitochondrial energy production and depression pathogenesis. Acylcarnitine metabolism represents a promising access point for the development of novel therapeutic approaches for depression.
Wissenschaftlicher Artikel
Scientific Article
Cadby, G. ; Giles, C. ; Melton, P.E. ; Huynh, K. ; Mellett, N.A. ; Duong, T. ; Nguyen, A. ; Cinel, M. ; Smith, A. ; Olshansky, G. ; Wang, T. ; Brozynska, M. ; Inouye, M. ; McCarthy, N.S. ; Ariff, A. ; Hung, J. ; Hui, J. ; Beilby, J. ; Dubé, M.P. ; Watts, G.F. ; Shah, S. ; Wray, N.R. ; Lim, W.L.F. ; Chatterjee, P. ; Martins, I. ; Laws, S.M. ; Porter, T. ; Vacher, M. ; Bush, A.I. ; Rowe, C.C. ; Villemagne, V.L. ; Ames, D. ; Masters, C.L. ; Taddei, K. ; Arnold, M. ; Kastenmüller, G. ; Nho, K. ; Saykin, A.J. ; Han, X. ; Kaddurah-Daouk, R. ; Martins, R.N. ; Blangero, J. ; Meikle, P.J.° ; Moses, E.K.°
Nat. Commun. 13:3124 (2022)
We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.
Wissenschaftlicher Artikel
Scientific Article
Gehlert, S.° ; Weinisch, P. ; Römisch-Margl, W. ; Jaspers, R.T. ; Artati, A. ; Adamski, J. ; Dyar, K.A. ; Aussieker, T. ; Jacko, D. ; Bloch, W. ; Wackerhage, H. ; Kastenmüller, G.°
Metabolites 12:445 (2022)
Resistance training promotes metabolic health and stimulates muscle hypertrophy, but the precise routes by which resistance exercise (RE) conveys these health benefits are largely unknown. Aim: To investigate how acute RE affects human skeletal muscle metabolism. Methods: We collected vastus lateralis biopsies from six healthy male untrained volunteers at rest, before the first of 13 RE training sessions, and 45 min after the first and last bouts of RE. Biopsies were analysed using untargeted mass spectrometry-based metabolomics. Results: We measured 617 metabolites covering a broad range of metabolic pathways. In the untrained state RE altered 33 metabolites, including increased 3-methylhistidine and N-lactoylvaline, suggesting increased protein breakdown, as well as metabolites linked to ATP (xanthosine) and NAD (N1-methyl-2-pyridone-5-carboxamide) metabolism; the bile acid chenodeoxycholate also increased in response to RE in muscle opposing previous findings in blood. Resistance training led to muscle hypertrophy, with slow type I and fast/intermediate type II muscle fibre diameter increasing by 10.7% and 10.4%, respectively. Comparison of post-exercise metabolite levels between trained and untrained state revealed alterations of 46 metabolites, including decreased N-acetylated ketogenic amino acids and increased beta-citrylglutamate which might support growth. Only five of the metabolites that changed after acute exercise in the untrained state were altered after chronic training, indicating that training induces multiple metabolic changes not directly related to the acute exercise response. Conclusion: The human skeletal muscle metabolome is sensitive towards acute RE in the trained and untrained states and reflects a broad range of adaptive processes in response to repeated stimulation.
Wissenschaftlicher Artikel
Scientific Article
Mutsch, B. ; Heiber, M. ; Grätz, F. ; Hain, R. ; Schönfelder, M. ; Kaps, S. ; Schranner, D. ; Kähler, C.J. ; Wackerhage, H.
Proc. Natl. Acad. Sci. U.S.A. 119:e2202521119 (2022)
SignificanceAirborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or other pathogens is probably increased during indoor exercise, but data on the emission of aerosol particles by an exercising individual are lacking. Here, we report that aerosol particle emission increases on average 132-fold from 580 ± 489 particles/min at rest to 76,200 ± 48,000 particles/min during maximal exercise. Aerosol particle emission increases moderately up to an exercise intensity of ≈2 W/kg and exponentially at higher exercise intensities. These data not only explain SARS-CoV-2 transmissions during indoor group exercise but also can be used to design better targeted mitigation measures for physical activity indoors such as physical education in school, dance events during weddings, or high-intensity gym classes such as spinning.
Wissenschaftlicher Artikel
Scientific Article
Brydges, C.R. ; Bhattacharyya, S. ; Dehkordi, S.M. ; Milaneschi, Y. ; Penninx, B. ; Jansen, R. ; Kristal, B.S. ; Han, X. ; Arnold, M. ; Kastenmüller, G. ; Bekhbat, M. ; Mayberg, H.S. ; Craighead, W.E. ; Rush, A.J. ; Fiehn, O. ; Dunlop, B.W. ; Kaddurah-Daouk, R.
Brain Behav. Immun. 102, 42-52 (2022)
BACKGROUND: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS: The IMD clinical dimension and the inflammatory index were positively correlated (r=0.19, p=.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.
Wissenschaftlicher Artikel
Scientific Article
Wörheide, M. ; Krumsiek, J. ; Kastenmüller, G. ; Kaddurah-Daouk, R.F. ; Arnold, M.
Alzheimers Dement. 17, 3:e056673 (2022)
BACKGROUND: Alzheimer's disease (AD) is a devastating neurodegenerative disorder for which there currently are no disease-modifying treatments available. To accelerate the path to effective intervention strategies, drug repositioning - the application of available compounds in a novel disease context - has gained increasing attention as a promising alternative to de novo drug development. Rich multi-omics data, generated by large international and interdisciplinary AD consortia, is now enabling the implementation of novel methods that have the potential to drive the computational identification and prioritization of promising repositioning candidates. METHOD: We recently developed the AD atlas, a web-based multi-omics resource that integrates multiple layers of heterogenous data from different studies and cohorts, including omics QTLs, transcriptomic, proteomic and metabolomic correlation networks, as well as genetic and multi-omics associations with AD and associated biomarkers/endophenotypes. Using this atlas, we generated and analyzed molecular context networks surrounding AD-associated genes as well as those targeted by drug repositioning candidates proposed in the literature. Subsequent enrichment analysis on AD subnetworks was used to identify drugs with overlapping molecular signatures on the gene expression level, while target networks of repositioning candidates were investigated for their potential involvement in AD pathogenesis. RESULT: We found ample evidence for the potential of integrative multi-omics approaches for drug repositioning in AD. For instance, enrichment analysis of the context network surrounding the AD-associated genes APOE and CLU identified multiple repositioning candidates, where the top hits were drugs that were either previously proposed as promising or already subjected to clinical trials, such as fluoxetine, rosiglitazone, and valproate. Investigating candidate drugs, the exploration of the context network targeted by statins revealed functional links to TYROBP/TREM2 signaling, suggesting a potential protective effect of this drug class through modulation of neuroinflammatory pathways. CONCLUSION: Our results highlight multiple opportunities to advance drug repositioning efforts in AD by integrative analysis of comprehensive multi-omics data. Automation of our analyses using network-based machine learning approaches and extension of the AD atlas with multi-omics data from drug screens to resolve directionalities will allow us to globally identify molecular pathways disturbed in AD that are targetable by drug repositioning candidates.
Meeting abstract
Meeting abstract
Wang, T. ; Huynh, K. ; Giles, C. ; Mellett, N.A. ; Duong, T. ; Nguyen, A. ; Lim, W.L.F. ; Smith, A.A.T. ; Olshansky, G. ; Cadby, G. ; Hung, J. ; Hui, J. ; Beilby, J. ; Watts, G.F. ; Chatterjee, P. ; Martins, I. ; Laws, S.M. ; Bush, A.I. ; Rowe, C.C. ; Villemagne, V.L. ; Ames, D. ; Masters, C.L. ; Taddei, K. ; Doré, V. ; Fripp, J. ; Arnold, M. ; Kastenmüller, G. ; Nho, K. ; Saykin, A.J. ; Baillie, R. ; Han, X. ; Martins, R.N. ; Moses, E.K. ; Kaddurah-Daouk, R. ; Meikle, P.J.
Alzheimers Dement., DOI: 10.1002/alz.12538 (2022)
Introduction: The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. Methods: We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species. Results: A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. Discussion: Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.
Wissenschaftlicher Artikel
Scientific Article
Chetnik, K. ; Benedetti, E. ; Gomari, D.P. ; Schweickart, A. ; Batra, R. ; Buyukozkan, M. ; Wang, Z. ; Arnold, M. ; Zierer, J. ; Suhre, K. ; Krumsiek, J.
Bioinformatics 38, 1168-1170 (2022)
This paper presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results, and visualizations. maplet's key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. AVAILABILITY: The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet.
Wissenschaftlicher Artikel
Scientific Article
2021
Baloni, P. ; Nho, K. ; Arnold, M. ; Louie, G. ; Kueider-Paisley, A. ; Saykin, A.J. ; Ekroos, K. ; Funk, C. ; Hood, L. ; Price, N.D. ; Baillie, R. ; Kastenmüller, G. ; Han, X. ; Kaddurah-Daouk, R.F.
Alzheimers Dement. 17:e056647 (2021)
BACKGROUND: L-carnitine is present in the mammalian cells as free carnitine (FC) and acylcarnitine and the adult human brain contains almost 10% of long chain acylcarnitine. Acylcarnitines are functionally involved in β-oxidation of fatty acids and are also known for their role in neuroprotection. Levels of plasma acylcarnitines are known to decreased on aging. It is important to understand the association of acylcarnitines with cognitive impairment in Alzheimer's disease (AD). METHOD: We integrated the transcriptome data from 1000 post-mortem brain samples from ROS/MAP, Mayo clinic and Mount Sinai Brain bank cohort with the brain region-specific metabolic networks. We calculated the metabolic fluxes for the reactions in the model and identified those that showed differential fluxes in AD samples. We filtered the reactions that are involved in acylcarnitine synthesis and transport namely carnitine transport, fatty acid oxidation, citric acid cycle, and glutathione metabolism. RESULT: We found differences in metabolic fluxes for reactions involved in the acetylcarnitine transport to mitochondria (ACRNtm), carnitine palmitoyl transferase 1 and 2 (CPT1 and CPT2) as well as acyl-CoA dehydrogenase short and medium chain (ACADS, ACADM) located in mitochondria in AD samples. Using gene-based association analysis in participants of the AD Neuroimaging Initiative (ADNI) phases 1, GO and 2, we identified genetic variants linked to CPT1, CPT2, ACADM and ACADS genes suggested from the metabolic flux analysis. CONCLUSION: Our findings suggest that acylcarnitine synthesis and transport is altered in AD. Altered metabolism of short and medium chain acylcarnitines can be used as metabolic features of AD.
Meeting abstract
Meeting abstract
Aboulmaouahib, B. ; Kastenmüller, G. ; Suhre, K. ; Zöllner, S. ; Weissensteiner, H. ; Gieger, C. ; Wang-Sattler, R. ; Lichtner, P. ; Strauch, K. ; Flaquer, A.
Hum. Mol. Genet., DOI: 10.1093/hmg/ddab312 (2021)
INTRODUCTION: In the era of personalized medicine with more and more patient specific targeted therapies being used, we need reliable, dynamic, faster, and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA mtDNA in metabolic regulation, aging, and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles (possibly conveyed by lifestyle and environmental factors) influences the increase of mutation rate in the mtDNA, and thereby contributes to a range of pathophysiological alterations observed in complex diseases. METHODS: We performed an inverted mitochondrial genome wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify genetic variants associated with metabolite profiles. Because of the high coverage, next generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for identification of variants associated with the metabolome. RESULTS: The strongest association was found for mt715G > A located in the MT-12SrRNA with the metabolite ratio C2/C10:1 (p-value = 6.82*10-09, β = 0.909). The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio PC ae C42:5/PC ae C44:5 (p-value = 1.02*10-08, β = 3.631). A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene. CONCLUSION: These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.
Wissenschaftlicher Artikel
Scientific Article
Brydges, C.R. ; Fiehn, O. ; Mayberg, H.S. ; Schreiber, H. ; Dehkordi, S.M. ; Bhattacharyya, S. ; Cha, J. ; Choi, K.S. ; Craighead, W.E. ; Krishnan, R.R. ; Rush, A.J. ; Dunlop, B.W.° ; Kaddurah-Daouk, R.° ; Mood Disorders Precision Medicine Consortium (Kastenmüller, G.) ; Mood Disorders Precision Medicine Consortium (Arnold, M.)
Sci. Rep. 11:21011 (2021)
It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Serum samples (baseline, 12 weeks) were drawn from participants (n = 196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite’s effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, and changes in symptoms were not correlated with changes in indole concentrations. These results suggest that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to modulation of indoles derived from gut microbiota; it remains possible that treatment-related improvement stems from their impact on other aspects of the gut microbiome. A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches. Given the exploratory nature of this study, findings should be replicated in confirmatory studies. Clinical trial NCT00360399 “Predictors of Antidepressant Treatment Response: The Emory CIDAR” https://clinicaltrials.gov/ct2/show/NCT00360399.
Wissenschaftlicher Artikel
Scientific Article
Horgusluoglu, E. ; Neff, R. ; Song, W.M. ; Wang, M. ; Wang, Q. ; Arnold, M. ; Krumsiek, J. ; Galindo-Prieto, B. ; Ming, C. ; Nho, K. ; Kastenmüller, G. ; Han, X. ; Baillie, R. ; Zeng, Q. ; Andrews, S. ; Cheng, H. ; Hao, K. ; Goate, A. ; Bennett, D.A. ; Saykin, A.J. ; Kaddurah-Daouk, R. ; Zhang, B.
Alzheimers Dement., DOI: 10.1002/alz.12468 (2021)
Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.
Wissenschaftlicher Artikel
Scientific Article
Borkowski, K. ; Taha, A.Y. ; Pedersen, T.L. ; de Jager, P.L. ; Bennett, D.A. ; Arnold, M. ; Kaddurah-Daouk, R. ; Newman, J.W.
Sci. Rep. 11:18964 (2021)
Cognitive decline is associated with both normal aging and early pathologies leading to dementia. Here we used quantitative profiling of metabolites involved in the regulation of inflammation, vascular function, neuronal function and energy metabolism, including oxylipins, endocannabinoids, bile acids, and steroid hormones to identify metabolic biomarkers of mild cognitive impairment (MCI). Serum samples (n = 212) were obtained from subjects with or without MCI opportunistically collected with incomplete fasting state information. To maximize power and stratify the analysis of metabolite associations with MCI by the fasting state, we developed an algorithm to predict subject fasting state when unknown (n = 73). In non-fasted subjects, linoleic acid and palmitoleoyl ethanolamide levels were positively associated with perceptual speed. In fasted subjects, soluble epoxide hydrolase activity and tauro-alpha-muricholic acid levels were negatively associated with perceptual speed. Other cognitive domains showed associations with bile acid metabolism, but only in the non-fasted state. Importantly, this study shows unique associations between serum metabolites and cognitive function in the fasted and non-fasted states and provides a fasting state prediction algorithm based on measurable metabolites.
Wissenschaftlicher Artikel
Scientific Article
Pietzner, M.# ; Wheeler, E.# ; Carrasco-Zanini, J. ; Cortes, A. ; Koprulu, M. ; Wörheide, M. ; Oerton, E. ; Cook, J. ; Stewart, I.D. ; Kerrison, N.D. ; Luan, J. ; Raffler, J. ; Arnold, M. ; Arlt, W. ; O'Rahilly, S. ; Kastenmüller, G. ; Gamazon, E.R. ; Hingorani, A.D. ; Scott, R.A. ; Wareham, N.J. ; Langenberg, C.
Science 374:eabj1541 (2021)
Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3,892 plasma proteins to create a cis-anchored gene-protein-disease map of 1,859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to 1) connect etiologically related diseases, 2) provide biological context for new or emerging disorders, and 3) integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at GWAS loci, addressing a major barrier for experimental validation and clinical translation of genetic discoveries.
Wissenschaftlicher Artikel
Scientific Article
Nho, K. ; Kueider-Paisley, A. ; Arnold, M. ; MahmoudianDehkordi, S. ; Risacher, S.L. ; Louie, G. ; Blach, C. ; Baillie, R. ; Han, X. ; Kastenmüller, G. ; Doraiswamy, P.M. ; Kaddurah-Daouk, R.° ; Saykin, A.J.°
Brain Commun. 3:fcab139 (2021)
Metabolomics in the Alzheimer's Disease Neuroimaging Initiative cohort provides a powerful tool for mapping biochemical changes in Alzheimer's disease, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in Alzheimer's disease. We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition and conversion from mild cognitive impairment to Alzheimer's disease in the Alzheimer's Disease Neuroimaging Initiative. Processed [18F] Florbetapir PET images were used to perform a voxel-wise statistical analysis of the effect of metabolite levels on amyloid-β accumulation across the whole brain. We performed a multivariable regression analysis using age, sex, body mass index, apolipoprotein E ε4 status and study phase as covariates. We identified nine metabolites as significantly associated with amyloid-β deposition after multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation and higher memory scores. However, higher levels of seven phosphatidylcholines (lysoPC a C18:2, PC aa C42:0, PC ae C42:3, PC ae C44:3, PC ae C44:4, PC ae C44:5 and PC ae C44:6) were associated with increased brain amyloid-β deposition. In addition, higher levels of PC ae C44:4 were significantly associated with lower memory and executive function scores and conversion from mild cognitive impairment to Alzheimer's disease dementia. Our findings suggest that dysregulation of peripheral phosphatidylcholine metabolism is associated with earlier pathological changes noted in Alzheimer's disease as measured by brain amyloid-β deposition as well as later clinical features including changes in memory and executive functioning. Perturbations in phosphatidylcholine metabolism may point to issues with membrane restructuring leading to the accumulation of amyloid-β in the brain. Additional studies are needed to explore whether these metabolites play a causal role in the pathogenesis of Alzheimer's disease or if they are biomarkers for systemic changes during preclinical phases of the disease.
Wissenschaftlicher Artikel
Scientific Article
Klaus, V.# ; Schriever, S.C.# ; Monroy Kuhn, J.M.# ; Peter, A. ; Irmler, M. ; Tokarz, J. ; Prehn, C. ; Kastenmüller, G. ; Beckers, J. ; Adamski, J. ; Königsrainer, A. ; Müller, T.D. ; Heni, M. ; Tschöp, M.H. ; Pfluger, P.T. ; Lutter, D.
Mol. Metab. 53, 101295 (2021)
Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology, yet tools to fully tap their potential remain scarce. We here present a fully unsupervised and versatile correlation-based method, termed Correlation guided Network Integration (CoNI), to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected metabolic sub-graphs or pathways. By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.
Wissenschaftlicher Artikel
Scientific Article
Portero, V. ; Nicol, T. ; Podliesna, S. ; Marchal, G.A. ; Baartscheer, A. ; Casini, S. ; Tadros, R. ; Treur, J.L. ; Tanck, M.W.T. ; Cox, I.J. ; Probert, F. ; Hough, T.A. ; Falcone, S. ; Beekman, L. ; Müller-Nurasyid, M. ; Kastenmüller, G. ; Gieger, C. ; Peters, A. ; Kääb, S. ; Sinner, M.F. ; Blease, A. ; Verkerk, A.O. ; Bezzina, C.R. ; Potter, P.K. ; Remme, C.A.
Cardiovasc. Res. 118:1742–1757 (2021)
AIM: Cardiac arrhythmias comprise a major health and economic burden and are associated with significant morbidity and mortality, including cardiac failure, stroke and sudden cardiac death (SCD). Development of efficient preventive and therapeutic strategies is hampered by incomplete knowledge of disease mechanisms and pathways. Our aim is to identify novel mechanisms underlying cardiac arrhythmia and SCD using an unbiased approach. METHODS AND RESULTS: We employed a phenotype-driven N-ethyl-N-nitrosourea (ENU) mutagenesis screen and identified a mouse line with a high incidence of sudden death at young age (6-9 weeks) in the absence of prior symptoms. Affected mice were found to be homozygous for the nonsense mutation Bcat2p.Q300*/p.Q300* in the Bcat2 gene encoding branched chain amino acid transaminase 2. At the age of 4-5 weeks, Bcat2p.Q300*/p.Q300* mice displayed drastic increase of plasma levels of branch chain amino acids (BCAAs - leucine, isoleucine, valine) due to the incomplete catabolism of BCAAs, in addition to inducible arrhythmias ex vivo as well as cardiac conduction and repolarization disturbances. In line with these findings, plasma BCAA levels were positively correlated to ECG indices of conduction and repolarization in the German community-based KORA F4 Study. Isolated cardiomyocytes from Bcat2p.Q300*/p.Q300* mice revealed action potential (AP) prolongation, pro-arrhythmic events (early and late afterdepolarizations, triggered APs) and dysregulated calcium homeostasis. Incubation of human pluripotent stem cell-derived cardiomyocytes with elevated concentration of BCAAs induced similar calcium dysregulation and pro-arrhythmic events which were prevented by rapamycin, demonstrating the crucial involvement of mTOR pathway activation. CONCLUSIONS: Our findings identify for the first time a causative link between elevated BCAAs and arrhythmia, which has implications for arrhythmogenesis in conditions associated with BCAA metabolism dysregulation such as diabetes, metabolic syndrome and heart failure. TRANSLATIONAL PERSPECTIVES: Development of efficient anti-arrhythmic strategies is hampered by incomplete knowledge of disease mechanisms. Using an unbiased approach, we here identified for the first time a pro-arrhythmic effect of increased levels of branched chain amino acids (BCAAs). This is of particular relevance for conditions associated with BCAA dysregulation and increased arrhythmia risk, including heart failure, obesity and diabetes, as well as for athletes supplementing their diet with BCAAs. Such metabolic dysregulation is potentially modifiable through dietary interventions, paving the way for novel preventive strategies. Our findings furthermore identify mTOR inhibition as a potential anti-arrhythmic strategy in patients with metabolic syndrome.
Wissenschaftlicher Artikel
Scientific Article
Schranner, D. ; Schönfelder, M. ; Römisch-Margl, W. ; Scherr, J. ; Schlegel, J. ; Zelger, O. ; Riermeier, A. ; Kaps, S. ; Prehn, C. ; Adamski, J. ; Söhnlein, Q. ; Stocker, F. ; Kreuzpointner, F. ; Halle, M. ; Kastenmüller, G. ; Wackerhage, H.
Physiol. Rep. 9:e14885 (2021)
Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.
Wissenschaftlicher Artikel
Scientific Article
Zacharias, H.U. ; Hertel, J. ; Johar, H. ; Pietzner, M. ; Lukaschek, K. ; Atasoy, S. ; Kunze, S. ; Völzke, H. ; Nauck, M. ; Friedrich, N. ; Kastenmüller, G. ; Grabe, H.J. ; Gieger, C. ; Krumsiek, J. ; Ladwig, K.-H.
Mol. Psychiatry 26, 7372–7383 (2021)
Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.
Wissenschaftlicher Artikel
Scientific Article
Pietzner, M. ; Stewart, I.D. ; Raffler, J. ; Khaw, K.T. ; Michelotti, G.A. ; Kastenmüller, G. ; Wareham, N.J. ; Langenberg, C.
Nat. Med. 27, 471-479 (2021)
Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite-disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver ( https://omicscience.org/apps/mwasdisease/ ) to facilitate future research and meta-analyses.
Wissenschaftlicher Artikel
Scientific Article
MahmoudianDehkordi, S. ; Ahmed, A.T. ; Bhattacharyya, S. ; Han, X. ; Baillie, R.A. ; Arnold, M. ; Skime, M.K. ; John-Williams, L.S. ; Moseley, M.A. ; Thompson, J.W. ; Louie, G. ; Riva-Posse, P. ; Craighead, W.E. ; McDonald, W. ; Krishnan, R. ; Rush, A.J. ; Frye, M.A. ; Dunlop, B.W. ; Weinshilboum, R.M. ; Kaddurah-Daouk, R.
Transl. Psychiatry 11:153 (2021)
Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD17). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD17 ≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD17). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.
Wissenschaftlicher Artikel
Scientific Article
Pietzner, M. ; Wheeler, E. ; Carrasco-Zanini, J. ; Raffler, J. ; Kerrison, N.D. ; Oerton, E. ; Auyeung, V.P.W. ; Luan, J. ; Finan, C. ; Casas, J.P. ; Ostroff, R. ; Williams, S.A. ; Kastenmüller, G. ; Ralser, M. ; Gamazon, E.R. ; Wareham, N.J. ; Hingorani, A.D.° ; Langenberg, C.°
Nat. Commun. 12:845 (2021)
The original version of this Article cited “Mehra, M. R., Desai, S. S., Kuy, S., Henry, T. D. & Patel, A. N. Cardiovascular disease, drug therapy, and mortality in Covid-19. N. Engl. J. Med. 382, e102 (2020)” as Ref. 20. The cited paper was retracted; accordingly, Ref. 20 has been replaced with "Grasselli G et al. Risk factors associated with mortality among patients with COVID-19 in intensive care units in Lombardy, Italy. JAMA Intern. Med. 180, 1345–1355 (2020)”. This has been corrected in the PDF and HTML versions of the article.
Crawford, A.A. ; Bankier, S. ; Altmaier, E. ; Barnes, C.L.K. ; Clark, D.W. ; Ermel, R. ; Friedrich, N. ; van der Harst, P. ; Joshi, P.K. ; Karhunen, V. ; Lahti, J. ; Mahajan, A. ; Mangino, M. ; Nethander, M. ; Neumann, A. ; Pietzner, M. ; Sukhavasi, K. ; Wang, C.A. ; Bakker, S.J.L. ; Bjorkegren, J.L.M. ; Campbell, H. ; Eriksson, J. ; Gieger, C. ; Hayward, C. ; Jarvelin, M.R. ; McLachlan, S. ; Morris, A.P. ; Ohlsson, C. ; Pennell, C.E. ; Price, J. ; Rudan, I. ; Ruusalepp, A. ; Spector, T. ; Tiemeier, H. ; Völzke, H. ; Wilson, J.F. ; Michoel, T. ; Timpson, N.J. ; Smith, G.D. ; Walker, B.R.
J. Hum. Genet. 66, 625–636 (2021)
The stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease (0.32, 95% CI 0.06-0.59) and myocardial infarction (0.21, 95% CI 0.00-0.43) in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.
Wissenschaftlicher Artikel
Scientific Article
Huang, J. ; Covic, M. ; Huth, C. ; Rommel, M. ; Adam, J. ; Zukunft, S. ; Prehn, C. ; Wang, L. ; Nano, J. ; Scheerer, M.F. ; Neschen, S. ; Kastenmüller, G. ; Gieger, C. ; Laxy, M. ; Schliess, F. ; Adamski, J. ; Suhre, K. ; Hrabě de Angelis, M. ; Peters, A. ; Wang-Sattler, R.
Metabolites 11:89 (2021)
Biological exploration of early biomarkers for chronic kidney disease (CKD) in (pre)diabetic individuals is crucial for personalized management of diabetes. Here, we evaluated two candidate biomarkers of incident CKD (sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0) concerning kidney function in hyperglycemic participants of the Cooperative Health Research in the Region of Augsburg (KORA) cohort, and in two biofluids and six organs of leptin receptor-deficient (db/db) mice and wild type controls. Higher serum concentrations of SM C18:1 and PC aa C38:0 in hyperglycemic individuals were found to be associated with lower estimated glomerular filtration rate (eGFR) and higher odds of CKD. In db/db mice, both metabolites had a significantly lower concentration in urine and adipose tissue, but higher in the lungs. Additionally, db/db mice had significantly higher SM C18:1 levels in plasma and liver, and PC aa C38:0 in adrenal glands. This cross-sectional human study confirms that SM C18:1 and PC aa C38:0 associate with kidney dysfunction in pre(diabetic) individuals, and the animal study suggests a potential implication of liver, lungs, adrenal glands, and visceral fat in their systemic regulation. Our results support further validation of the two phospholipids as early biomarkers of renal disease in patients with (pre)diabetes.
Wissenschaftlicher Artikel
Scientific Article
Lotta, L.A.# ; Pietzner, M.# ; Stewart, I.D. ; Wittemans, L.B.L. ; Li, C. ; Bonelli, R. ; Raffler, J. ; Biggs, E.K. ; Oliver-Williams, C. ; Auyeung, V.P.W. ; Luan, J. ; Wheeler, E. ; Paige, E. ; Surendran, P. ; Michelotti, G.A. ; Scott, R.A. ; Burgess, S. ; Zuber, V. ; Sanderson, E. ; Koulman, A. ; Imamura, F. ; Forouhi, N.G. ; Khaw, K.T. ; Bahlo, M. ; Griffin, J.L. ; Kastenmüller, G. ; Gribble, F.M. ; Reimann, F. ; Fauman, E. ; Wareham, N.J. ; Langenberg, C.
Nat. Genet. 53, 54-64 (2021)
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
Wissenschaftlicher Artikel
Scientific Article
Deutelmoser, H.° ; Scherer, D. ; Brenner, H. ; Waldenberger, M. ; Suhre, K. ; Kastenmüller, G. ; Lorenzo Bermejo, J.°
Brief. Bioinform. 22:bbaa230 (2021)
Least absolute shrinkage and selection operator (LASSO) regression is often applied to select the most promising set of single nucleotide polymorphisms (SNPs) associated with a molecular phenotype of interest. While the penalization parameter λ restricts the number of selected SNPs and the potential model overfitting, the least-squares loss function of standard LASSO regression translates into a strong dependence of statistical results on a small number of individuals with phenotypes or genotypes divergent from the majority of the study population-typically comprised of outliers and high-leverage observations. Robust methods have been developed to constrain the influence of divergent observations and generate statistical results that apply to the bulk of study data, but they have rarely been applied to genetic association studies. In this article, we review, for newcomers to the field of robust statistics, a novel version of standard LASSO that utilizes the Huber loss function. We conduct comprehensive simulations and analyze real protein, metabolite, mRNA expression and genotype data to compare the stability of penalization, the cross-iteration concordance of the model, the false-positive and true-positive rates and the prediction accuracy of standard and robust Huber-LASSO. Although the two methods showed controlled false-positive rates ≤2.1% and similar true-positive rates, robust Huber-LASSO outperformed standard LASSO in the accuracy of predicted protein, metabolite and gene expression levels using individual SNP data. The conducted simulations and real-data analyses show that robust Huber-LASSO represents a valuable alternative to standard LASSO in genetic studies of molecular phenotypes.
Wissenschaftlicher Artikel
Scientific Article
Wörheide, M. ; Krumsiek, J. ; Kastenmüller, G. ; Arnold, M.
Anal. Chim. Acta 1141, 144-162 (2021)
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
Review
Review
2020
Pietzner, M. ; Wheeler, E. ; Carrasco-Zanini, J. ; Raffler, J. ; Kerrison, N.D. ; Oerton, E. ; Auyeung, V.P.W. ; Luan, J. ; Finan, C. ; Casas, J.P. ; Ostroff, R. ; Williams, S.A. ; Kastenmüller, G. ; Ralser, M. ; Gamazon, E.R. ; Wareham, N.J. ; Hingorani, A.D. ; Langenberg, C.
Nat. Commun. 11:6397 (2020)
Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).
Wissenschaftlicher Artikel
Scientific Article
Ott, R. ; Pawlow, X. ; Weiss, A. ; Hofelich, A. ; Herbst, M. ; Hummel, N. ; Prehn, C. ; Adamski, J. ; Römisch-Margl, W. ; Kastenmüller, G. ; Ziegler, A.-G. ; Hummel, S.
Int. J. Mol. Sci. 21:9647 (2020)
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.
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Scientific Article
Baloni, P. ; Funk, C.C. ; Yan, J. ; Yurkovich, J.T. ; Kueider-Paisley, A. ; Nho, K. ; Heinken, A. ; Jia, W. ; MahmoudianDehkordi, S. ; Louie, G. ; Saykin, A.J. ; Arnold, M. ; Kastenmüller, G. ; Griffiths, W.J. ; Thiele, I. ; Kaddurah-Daouk, R.° ; Price, N.D.°
Cell Rep. Med. 1:100138 (2020)
Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline. Baloni et al. use a systems biology approach to identify alterations in cholesterol and bile acid metabolism in Alzheimer disease (AD). Expression of alternative bile acid and neural cholesterol clearance pathway along with transporters of taurine and bile acids suggest the role of the gut-brain axis in AD.
Wissenschaftlicher Artikel
Scientific Article
Langenau, J. ; Oluwagbemigun, K. ; Brachem, C. ; Lieb, W. ; di Giuseppe, R. ; Artati, A. ; Kastenmüller, G. ; Weinhold, L. ; Schmid, M. ; Nöthlings, U.
Metabolites 10:468 (2020)
Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.
Wissenschaftlicher Artikel
Scientific Article
Benedetti, E. ; Pučić-Baković, M. ; Keser, T. ; Gerstner, N. ; Büyüközkan, M. ; Štambuk, T. ; Selman, M.H.J. ; Rudan, I. ; Polašek, O. ; Hayward, C. ; Al-Amin, H. ; Suhre, K. ; Kastenmüller, G. ; Lauc, G. ; Krumsiek, J.
Nat. Commun. 11:5153 (2020)
Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.
Wissenschaftlicher Artikel
Scientific Article
Sieberts, S.K.° ; Perumal, T.M. ; Carrasquillo, M.M. ; Allen, M. ; Reddy, J.S. ; Hoffman, G.E. ; Dang, K.K. ; Calley, J. ; Ebert, P.J. ; Eddy, J. ; Wang, X. ; Greenwood, A.K. ; Mostafavi, S. ; Omberg, L. ; Peters, M.A. ; Logsdon, B.A. ; de Jager, P.L. ; Ertekin-Taner, N. ; Mangravite, L.M.° ; The AMP-AD Consortium (Arnold, M.)
Sci. Data 7:340 (2020)
The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).
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Scientific Article
Huynh, K. ; Lim, W.L.F. ; Giles, C. ; Jayawardana, K.S. ; Salim, A. ; Mellett, N.A. ; Smith, A.A.T. ; Olshansky, G. ; Drew, B.G. ; Chatterjee, P. ; Martins, I. ; Laws, S.M. ; Bush, A.I. ; Rowe, C.C. ; Villemagne, V.L. ; Ames, D. ; Masters, C.L. ; Arnold, M. ; Nho, K. ; Saykin, A.J. ; Baillie, R. ; Han, X. ; Kaddurah-Daouk, R.° ; Meikle, P.J.°
Nat. Commun. 11:5698 (2020)
Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 species across 32 classes) allows for detailed lipid separation and characterisation. In this study we examined peripheral samples of two cohorts (AIBL, n = 1112 and ADNI, n = 800). We are able to identify concordant peripheral signatures associated with prevalent AD arising from lipid pathways including; ether lipids, sphingolipids (notably GM3 gangliosides) and lipid classes previously associated with cardiometabolic disease (phosphatidylethanolamine and triglycerides). We subsequently identified similar lipid signatures in both cohorts with future disease. Lastly, we developed multivariate lipid models that improved classification and prediction. Our results provide a holistic view between the lipidome and AD using a comprehensive approach, providing targets for further mechanistic investigation.
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Scientific Article
Wang, J. ; Wei, R. ; Xie, G. ; Arnold, M. ; Kueider-Paisley, A. ; Louie, G. ; Mahmoudian Dehkordi, S. ; Blach, C. ; Baillie, R. ; Han, X. ; de Jager, P.L. ; Bennett, D.A. ; Kaddurah-Daouk, R.° ; Jia, W.°
Sci. Rep. 10:14059 (2020)
The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.
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Scientific Article
Kling, M.A.° ; Goodenowe, D.B. ; Senanayake, V. ; MahmoudianDehkordi, S. ; Arnold, M. ; Massaro, T.J. ; Baillie, R. ; Han, X. ; Leung, Y.Y. ; Saykin, A.J. ; Nho, K. ; Kueider-Paisley, A. ; Tenenbaum, J.D. ; Wang, L.S. ; Shaw, L.M. ; Trojanowski, J.Q. ; Kaddurah-Daouk, R.F.°
Alzheimers Dement. 16, 1234-1247 (2020)
Introduction: Altered lipid metabolism is implicated in Alzheimer's disease (AD), but the mechanisms remain obscure. Aging-related declines in circulating plasmalogens containing omega-3 fatty acids may increase AD risk by reducing plasmalogen availability.Methods: We measured four ethanolamine plasmalogens (PlsEtns) and four closely related phosphatidylethanolamines (PtdEtns) from the Alzheimer's Disease Neu-roimaging Initiative (ADNI; n = 1547 serum) and University of Pennsylvania (U Penn; n = 112 plasma) cohorts, and derived indices reflecting PlsEtn and PtdEtn metabolism: PL-PX (PlsEtns), PL/PE (PlsEtn/PtdEtn ratios), and PBV (plasmalogen biosynthesis value; a composite index). We tested associations with baseline diagnosis, cognition, and cere-brospinal fluid (CSF) AD biomarkers.Results: Results revealed statistically significant negative relationships in ADNI between AD versus CN with PL-PX (P = 0.007) and PBV (P = 0.005), late mild cognitive impairment (LMCI) versus cognitively normal (CN) with PL-PX (P = 2.89 x 10(-5)) and PBV (P = 1.99 x 10(-4), and AD versus LMCI with POPE (P = 1.85 x 10(-4)). In the UPenn cohort, AD versus CN diagnosis associated negatively with PL/PE (P = 0.0191) and PBV (P = 0.0296).In ADNI, cognition was negatively associated with plasmalogen indices, including Alzheimer's Disease Assessment Scale 13-item cognitive subscale (ADAS-Cog13; PL-PX: P = 3.24 x 10(-6); PBV: P = 6.92 x 10(-5)) and Mini-Mental State Examination (MMSE; PL-PX: P = 1.28 x 10(-9); PBV: P = 6.50 x 10(-9)). In the UPenn cohort, there was a trend toward a similar relationship of MMSE with PL/PE (P = 0.0949).In ADN I, CSF total-tau was negatively associated with PL-PX (P = 5.55 x 10(-6)) and PBV (P = 7.77 x 10(-6)). Additionally, CSF t-tau/A beta(1-42) 42 ratio was negatively associated with these same indices (PL-PX, P= 2.73 x 10(-6); PBV, P = 4.39 x 10(-6)). In the UPenn cohort, PL/PE was negatively associated with CSF total-tau (P = 0.031) and t-tau/A beta(1-42) (P = 0.021). CSF A beta(1-42) was not significantly associated with any of these indices in either cohort.Discussion: These data extend previous studies by showing an association of decreased plasmalogen indices with AD, mild cognitive impairment (MCI), cognition, and CSF tau. Future studies are needed to better define mechanistic relationships, and to test the effects of interventions designed to replete serum plasmalogens.
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Scientific Article
Bernath, M.M. ; Bhattacharyya, S. ; Nho, K. ; Barupal, D.K. ; Fiehn, O. ; Baillie, R. ; Risacher, S.L. ; Arnold, M. ; Jacobson, T. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; Kaddurah-Daouk, R. ; Saykin, A.J.
Neurology 94, E2088-E2098 (2020)
ObjectiveTo investigate the association of triglyceride (TG) principal component scores with Alzheimer disease (AD) and the amyloid, tau, neurodegeneration, and cerebrovascular disease (A/T/N/V) biomarkers for AD.MethodsSerum levels of 84 TG species were measured with untargeted lipid profiling of 689 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including 190 cognitively normal older adults (CN), 339 with mild cognitive impairment (MCI), and 160 with AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [F-18]fluorodeoxyglucose-PET) were assessed with a generalized linear model approach. In both cases, the Bonferroni method of adjustment was used to correct for multiple comparisons.ResultsThe 84 TGs yielded 9 principal components, 2 of which, consisting of long-chain, polyunsaturated fatty acid-containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carrying the APOE epsilon 4 allele, these principal components were significantly associated with CSF beta -amyloid(1-42) values and entorhinal cortical thickness.ConclusionThis study shows that PUTG component scores were significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced in APOE epsilon 4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.
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Scientific Article
Zhao, N.° ; Ren, Y. ; Yamazaki, Y. ; Qiao, W. ; Li, F. ; Felton, L.M. ; MahmoudianDehkordi, S. ; Kueider-Paisley, A. ; Sonoustoun, B. ; Arnold, M. ; Shue, F. ; Zheng, J. ; Attrebi, O.N. ; Martens, Y.A. ; Li, Z. ; Bastea, L. ; Meneses, A.D. ; Chen, K. ; Thompson, J.W. ; St John-Williams, L. ; Tachibana, M. ; Aikawa, T. ; Oue, H. ; Job, L. ; Yamazaki, A. ; Liu, C.C. ; Storz, P. ; Asmann, Y.W. ; Ertekin-Taner, N. ; Kanekiyo, T. ; Kaddurah-Daouk, R. ; Bu, G.°
Neuron 106, 727-742 (2020)
Evidence suggests interplay among the three major risk factors for Alzheimer's disease (AD): age, APOE genotype, and sex. Here, we present comprehensive datasets and analyses of brain transcriptomes and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. We found that age had the greatest impact on brain transcriptomes highlighted by an immune module led by Trem2 and Tyrobp, whereas APOE4 was associated with upregulation of multiple Serpina3 genes. Importantly, these networks and gene expression changes were mostly conserved in human brains. Finally, we observed a significant interaction between age, APOE genotype, and sex on unfolded protein response pathway. In the periphery, APOE2 drove distinct blood metabolome profile highlighted by the upregulation of lipid metabolites. Our work identifies unique and interactive molecular pathways underlying AD risk factors providing valuable resources for discovery and validation research in model systems and humans.
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Scientific Article
Arnold, M.# ; Nho, K.# ; Kueider-Paisley, A. ; Massaro, T. ; Huynh, K. ; Brauner, B. ; MahmoudianDehkordi, S. ; Louie, G. ; Moseley, M.A. ; Thompson, J.W. ; St John-Williams, L. ; Tenenbaum, J.D. ; Blach, C. ; Chang, R. ; Brinton, R.D. ; Baillie, R. ; Han, X. ; Trojanowski, J.Q. ; Shaw, L.M. ; Martins, R. ; Weiner, M.W. ; Trushina, E. ; Toledo, J.B. ; Meikle, P.J. ; Bennett, D.A. ; Krumsiek, J. ; Doraiswamy, P.M. ; Saykin, A.J. ; Kaddurah-Daouk, R. ; Kastenmüller, G.
Nat. Commun. 11:1148 (2020)
Sex and the APOE epsilon 4 genotype are important risk factors for late-onset Alzheimer's disease. In the current study, the authors investigate how sex and APOE epsilon 4 genotype modify the association between Alzheimer's disease biomarkers and metabolites in serum.Late-onset Alzheimer's disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE epsilon 4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE epsilon 4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE epsilon 4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE epsilon 4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.
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Scientific Article
Nag, A. ; Kurushima, Y. ; Bowyer, R.C.E. ; Wells, P.M. ; Weiss, S. ; Pietzner, M. ; Kocher, T. ; Raffler, J. ; Völker, U. ; Mangino, M. ; Spector, T.D. ; Milburn, M.V. ; Kastenmüller, G. ; Mohney, R.P. ; Suhre, K. ; Menni, C. ; Steves, C.J.
Hum. Mol. Genet. 29, 864-875 (2020)
Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites, leading to significant insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1419 subjects from the Twins UK cohort (discovery phase), followed by replication in the Study of Health in Pomerania (SHIP-2) cohort. A total of 14 distinct locus-metabolite associations were identified in the discovery phase, most of which were replicated in SHIP-2. While only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there may be regulatory pathways of particular relevance to the salivary metabolome. In addition, some of our findings may have clinical significance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.
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Scientific Article
Schlosser, P.# ; Li, Y.# ; Sekula, P.# ; Raffler, J. ; Grundner-Culemann, F. ; Pietzner, M. ; Cheng, Y. ; Wuttke, M. ; Steinbrenner, I. ; Schultheiss, U.T. ; Kotsis, F. ; Kacprowski, T. ; Forer, L. ; Hausknecht, B. ; Ekici, A.B. ; Nauck, M. ; Völker, U. ; Walz, G. ; Oefner, P.J. ; Kronenberg, F. ; Mohney, R.P. ; Köttgen, M. ; Suhre, K. ; Eckardt, K.U. ; Kastenmüller, G. ; Köttgen, A.
Nat. Genet. 52, 167-176 (2020)
The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.
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Scientific Article
Ahmed, A.T. ; MahmoudianDehkordi, S. ; Bhattacharyya, S. ; Arnold, M. ; Liu, D. ; Neavin, D. ; Moseley, M.A. ; Thompson, J.W. ; Williams, L.S.J. ; Louie, G. ; Skime, M.K. ; Wang, L. ; Riva-Posse, P. ; McDonald, W.M. ; Bobo, W.V. ; Craighead, W.E. ; Krishnan, R. ; Weinshilboum, R.M. ; Dunlop, B.W. ; Millington, D.S. ; Rush, A.J. ; Frye, M.A. ; Kaddurah-Daouk, R.
J. Affect. Disord. 264, 90-97 (2020)
Background: Acylcarnitines have important functions in mitochondrial energetics and beta-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD): core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+).Methods: Depressed outpatients (n = 240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and after eight weeks of treatment with citalopram or escitalopram were profiled for short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ (R) p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminated the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differed between groups.Results: Compared to ANX+, CD+ and NVSM+ had significantly lower concentrations of short- and long-chain acylcarnitines at both baseline and week 8. In NVSM+, the medium- and long-chain acylcarnitines were also significantly lower in NVSM+ compared to ANX+. Short-chain acylcarnitine levels increased significantly from baseline to week 8 in CD+ and ANX+, whereas medium- and long-chain acylcarnitines significantly decreased in CD+ and NVSM+.Conclusions: In depressed patients treated with SSRIs, beta-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.
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Scientific Article
Erzurumluoglu, A.M. ; Liu, M. ; Jackson, V.E. ; Barnes, D.R. ; Datta, G. ; Melbourne, C.A. ; Young, R. ; Batini, C. ; Surendran, P. ; Jiang, T. ; Adnan, S.D. ; Afaq, S. ; Agrawal, A. ; Altmaier, E. ; Antoniou, A.C. ; Asselbergs, F.W. ; Baumbach, C. ; Bierut, L. ; Bertelsen, S. ; Boehnke, M. ; Bots, M.L. ; Brazel, D.M. ; Chambers, J.C. ; Chang-Claude, J. ; Chen, C. ; Corley, J. ; Chou, Y.L. ; David, S.P. ; de Boer, R.A. ; de Leeuw, C.A. ; Dennis, J.G. ; Dominiczak, A.F. ; Dunning, A.M. ; Easton, D.F. ; Eaton, C. ; Elliott, P. ; Evangelou, E. ; Faul, J.D. ; Foroud, T. ; Goate, A. ; Gong, J. ; Grabe, H.J. ; Haessler, J. ; Haiman, C. ; Hallmans, G. ; Hammerschlag, A.R. ; Harris, S.E. ; Hattersley, A. ; Heath, A. ; Hsu, C. ; Iacono, W.G. ; Kanoni, S. ; Kapoor, M. ; Kaprio, J. ; Kardia, S.L. ; Karpe, F. ; Kontto, J. ; Kooner, J.S. ; Kooperberg, C. ; Kuulasmaa, K. ; Laakso, M. ; Lai, D. ; Langenberg, C. ; Le, N. ; Lettre, G. ; Loukola, A. ; Luan, J. ; Madden, P.A.F. ; Mangino, M. ; Marioni, R.E. ; Marouli, E. ; Marten, J. ; Martin, N.G. ; McGue, M. ; Michailidou, K. ; Mihailov, E. ; Moayyeri, A. ; Moitry, M. ; Müller-Nurasyid, M. ; Naheed, A.I. ; Nauck, M. ; Neville, M.J. ; Nielsen, S.F. ; North, K. ; Perola, M. ; Pharoah, P.D.P. ; Pistis, G. ; Polderman, T.J. ; Posthuma, D. ; Poulter, N. ; Qaiser, B. ; Rasheed, A. ; Reiner, A. ; Renström, F. ; Rice, J. ; Rohde, R. ; Rolandsson, O. ; Samani, N.J. ; Samuel, M. ; Schlessinger, D. ; Scholte, S.H. ; Scott, R.A. ; Sever, P. ; Shao, Y. ; Shrine, N. ; Smith, J.A. ; Starr, J.M. ; Stirrups, K. ; Stram, D. ; Stringham, H.M. ; Tachmazidou, I. ; Tardif, J.C. ; Thompson, D.J. ; Tindle, H.A. ; Tragante, V. ; Trompet, S. ; Turcot, V. ; Tyrrell, J. ; Vaartjes, I. ; van der Leij, A.R. ; van der Meer, P. ; Varga, T.V. ; Verweij, N. ; Völzke, H. ; Wareham, N.J. ; Warren, H.R. ; Weir, D.R. ; Weiss, S. ; Wetherill, L. ; Yaghootkar, H. ; Yavas, E. ; Jiang, Y. ; Chen, F. ; Zhan, X. ; Zhang, W. ; Zhao, W. ; Zhou, K. ; Amouyel, P. ; Blankenberg, S. ; Caulfield, M.J. ; Chowdhury, R. ; Cucca, F. ; Deary, I.J. ; Deloukas, P. ; di Angelantonio, E. ; Ferrario, M. ; Ferrières, J. ; Franks, P.W. ; Frayling, T.M. ; Frossard, P. ; Hall, I.P. ; Hayward, C. ; Jansson, J.H. ; Jukema, J.W. ; Kee, F. ; Männistö, S. ; Metspalu, A. ; Munroe, P.B. ; Nørdestgaard, B.G. ; Palmer, C.N.A. ; Salomaa, V. ; Sattar, N. ; Spector, T. ; Strachan, D.P. ; van der Harst, P. ; Zeggini, E. ; Saleheen, D. ; Butterworth, A.S. ; Wain, L.V. ; Abecasis, G.R. ; Danesh, J. ; Tobin, M.D. ; Vrieze, S. ; Liu, D.J. ; Howson, J.M.M.
Mol. Psychiatry 25, 2392-2409 (2020)
Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations withP < 5 x 10(-8)in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 x 10(-8)) in the discovery samples. Ten novel SNVs, including rs12616219 nearTMEM182, were followed-up and five of them (rs462779 inREV3L, rs12780116 inCNNM2, rs1190736 inGPR101, rs11539157 inPJA1, and rs12616219 nearTMEM182) replicated at a Bonferroni significance threshold (P < 4.5 x 10(-3)) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, inCCDC141and two low-frequency SNVs inCEP350andHDGFRP2. Functional follow-up implied that decreased expression ofREV3Lmay lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
Wissenschaftlicher Artikel
Scientific Article
2019
Tsepilov, Y.A. ; Sharapov, S.Z. ; Zaytseva, O.O. ; Krumsiek, J. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Wang-Sattler, R. ; Strauch, K. ; Gieger, C. ; Aulchenko, Y.S.
GigaScience 8:giz162 (2019)
Pietzner, M.° ; Köhrle, J. ; Lehmpuhl, I. ; Budde, K. ; Kastenmüller, G. ; Brabant, G. ; Völzke, H. ; Artati, A. ; Adamski, J. ; Völker, U. ; Nauck, M. ; Friedrich, N. ; Homuth, G.°
Thyroid 29, 1743-1754 (2019)
Background: In numerous studies based predominantly on rodent models, administration of 3,5-diiodo-L-thyronine (3,5-T2), a metabolite of the thyroid hormones (TH) thyroxine (T4) and triiodo-L-thyronine (T3), was reported to cause beneficial health effects, including reversal of steatohepatosis and prevention of insulin resistance, in most instances without adverse thyrotoxic side effects. However, the empirical evidence concerning the physiological relevance of endogenously produced 3,5-T2 in humans is comparatively poor. Therefore, to improve the understanding of 3,5-T2-related metabolic processes, we performed a comprehensive metabolomic study relating serum 3,5-T2 concentrations to plasma and urine metabolite levels within a large general population sample. Methods: Serum 3,5-T2 concentrations were determined for 856 participants of the population-based Study of Health in Pomerania-TREND (SHIP-TREND). Plasma and urine metabolome data were generated using mass spectrometry and nuclear magnetic resonance spectroscopy, allowing quantification of 613 and 578 metabolites in plasma and urine, respectively. To detect thyroid function-independent significant 3,5-T2-metabolite associations, linear regression analyses controlling for major confounders, including thyrotropin and free T4, were performed. The same analyses were carried out using a sample of 16 male healthy volunteers treated for 8 weeks with 250 μg/day levothyroxine to induce thyrotoxicosis. Results: The specific molecular fingerprint of 3,5-T2 comprised 15 and 73 significantly associated metabolites in plasma and urine, respectively. Serum 3,5-T2 concentrations were neither associated with classical thyroid function parameters nor altered during experimental thyrotoxicosis. Strikingly, many metabolites related to coffee metabolism, including caffeine and paraxanthine, formed the clearest positively associated molecular signature. Importantly, these associations were replicated in the experimental human thyrotoxicosis model. Conclusion: The molecular fingerprint of 3,5-T2 demonstrates a clear and strong positive association of the serum levels of this TH metabolite with plasma levels of compounds indicating coffee consumption, therefore pointing to the liver as an organ, the metabolism of which is strongly affected by coffee. Furthermore, 3,5-T2 serum concentrations were found not to be directly TH dependent. Considering the beneficial health effects of 3,5-T2 administration observed in animal models and those of coffee consumption demonstrated in large epidemiological studies, one might speculate that coffee-stimulated hepatic 3,5-T2 production or accumulation represents an important molecular link in this connection.
Wissenschaftlicher Artikel
Scientific Article
St John-Williams, L. ; MahmoudianDehkordi, S. ; Arnold, M. ; Massaro, T. ; Blach, C. ; Kastenmüller, G. ; Louie, G. ; Kueider-Paisley, A. ; Han, X. ; Baillie, R. ; Motsinger-Reif, A.A. ; Rotroff, D. ; Nho, K. ; Saykin, A.J. ; Risacher, S.L. ; Koal, T. ; Moseley, M.A. ; Tenenbaum, J.D. ; Thompson, J.W. ; Kaddurah-Daouk, R. ; Alzheimer Disease Metabolomics Consortium
Sci. Data 6:212 (2019)
Alzheimer's disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI.
Wissenschaftlicher Artikel
Scientific Article
Barupal, D.K. ; Baillie, R. ; Fan, S. ; Saykin, A.J. ; Meikle, P.J. ; Arnold, M. ; Nho, K. ; Fiehn, O. ; Kaddurah-Daouk, R. ; Alzheimer's Disease Neuroimaging Initiative ; Alzheimer Disease Metabolomics Consortium
Alzheimers Dement. 11, 619-627 (2019)
Introduction: Comorbidity with metabolic diseases indicates that lipid metabolism plays a role in the etiology of Alzheimer's disease (AD). Comprehensive lipidomic analysis can provide new insights into the altered lipid metabolism in AD. Method: In this study, a total 349 serum lipids were measured in 806 participants enrolled in the Alzheimer's Disease Neuroimaging Initiative Phase 1 cohort and analyzed using lipid-set enrichment statistics, a data mining method to find coregulated lipid sets. Results: We found that sets of blood lipids were associated with current AD biomarkers and with AD clinical symptoms. AD diagnosis was associated with 7 of 28 lipid sets of which four also correlated with cognitive decline, including polyunsaturated fatty acids. Cerebrospinal fluid amyloid beta (Aβ1-42) correlated with glucosylceramides, lysophosphatidylcholines and unsaturated triacylglycerides; cerebrospinal fluid total tau and brain atrophy correlated with monounsaturated sphingomyelins and ceramides, in addition to EPA-containing lipids. Discussion: AD-associated lipid sets indicated that lipid desaturation, elongation, and acyl chain remodeling processes are disturbed in AD subjects. Monounsaturated lipid metabolism was important in early stages of AD, whereas the polyunsaturated lipid metabolism was associated with later stages of AD. Our study provides several new hypotheses for studying the role of lipid metabolism in AD.
Wissenschaftlicher Artikel
Scientific Article
Deelen, J. ; Kettunen, J. ; Fischer, K. ; van der Spek, A. ; Trompet, S. ; Kastenmüller, G. ; Boyd, A.W. ; Zierer, J. ; van den Akker, E.B. ; Amin, N. ; Demirkan, A. ; Ghanbari, M. ; van Heemst, D. ; Ikram, M.A. ; van Klinken, J.B. ; Mooijaart, S.P. ; Peters, A. ; Salomaa, V. ; Sattar, N. ; Spector, T.D. ; Tiemeier, H. ; Verhoeven, A. ; Waldenberger, M. ; Würtz, P. ; Davey Smith, G. ; Metspalu, A. ; Perola, M. ; Menni, C. ; Geleijnse, J.M. ; Drenos, F. ; Beekman, M. ; Jukema, J.W. ; van Duijn, C.M. ; Slagboom, P.E.
Nat. Commun. 10:3346 (2019)
Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.
Wissenschaftlicher Artikel
Scientific Article
Nho, K. ; Kueider-Paisley, A. ; Ahmad, S. ; MahmoudianDehkordi, S. ; Arnold, M. ; Risacher, S.L. ; Louie, G. ; Blach, C. ; Baillie, R. ; Han, X. ; Kastenmüller, G. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; van Duijn, M. ; Saykin, A.J. ; Kaddurah-Daouk, R.
JAMA net. open 2:e197978 (2019)
IMPORTANCE Increasing evidence suggests an important role of liver function in the pathophysiology of Alzheimer disease (AD). The liver is a major metabolic hub; therefore, investigating the association of liver function with AD, cognition, neuroimaging, and CSF biomarkers would improve the understanding of the role of metabolic dysfunction in AD.OBJECTIVE To examine whether liver function markers are associated with cognitive dysfunction and the "A/T/N" (amyloid, tau, and neurodegeneration) biomarkers for AD.DESIGN, SETTING, AND PARTICIPANTS In this cohort study, serum-based liver function markers were measured from September 1, 2005, to August 31, 2013, in 1581 AD Neuroimaging Initiative participants along with cognitive measures, cerebrospinal fluid (CSF) biomarkers, brain atrophy, brain glucose metabolism, and amyloid-beta accumulation. Associations of liver function markers with AD-associated clinical and A/T/N biomarkers were assessed using generalized linear models adjusted for confounding variables and multiple comparisons. Statistical analysis was performed from November 1, 2017, to February 28, 2019.EXPOSURES Five serum-based liver function markers (total bilirubin, albumin, alkaline phosphatase, alanine aminotransferase, and aspartate aminotransferase) from AD Neuroimaging Initiative participants were used as exposure variables.MAIN OUTCOMES AND MEASURES Primary outcomes included diagnosis of AD, composite scores for executive functioning and memory, CSF biomarkers, atrophy measured by magnetic resonance imaging, brain glucose metabolism measured by fludeoxyglucose F 18 (F-18) positron emission tomography, and amyloid-beta accumulation measured by [F-18]florbetapir positron emission tomography.RESULTS Participants in the AD Neuroimaging Initiative (n = 1581; 697 women and 884 men; mean [SD] age, 73.4 [7.2] years) included 407 cognitively normal older adults, 20 with significant memory concern, 298 with early mild cognitive impairment, 544 with late mild cognitive impairment, and 312 with AD. An elevated aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio and lower levels of ALT were associated with AD diagnosis (AST to ALT ratio: odds ratio, 7.932 [95% CI, 1.673-37.617]; P = .03; ALT: odds ratio, 0.133 [95% CI, 0.042-0.422]; P = .004) and poor cognitive performance (AST to ALT ratio: beta [SE], -0.465 [0.180]; P = .02 for memory composite score; beta [SE], -0.679 [0.215]; P = .006 for executive function composite score; ALT: beta [SE], 0.397 [0.128]; P =.006 for memory composite score; beta [SE], 0.637 [0.152]; P < .001 for executive function composite score). Increased AST to ALT ratio values were associated with lower CSF amyloid-beta 1-42 levels (beta [SE], -0.170 [0.061]; P = .04) and increased amyloid-beta deposition (amyloid biomarkers), higher CSF phosphorylated tau(181) (beta [SE], 0.175 [0.055]; P = .02) (tau biomarkers) and higher CSF total tau levels (beta [SE], 0.160 [0.049]; P = .02) and reduced brain glucose metabolism (beta [SE], -0.123 [0.042]; P = .03) (neurodegeneration biomarkers). Lower levels of ALT were associated with increased amyloid-beta deposition (amyloid biomarkers), and reduced brain glucose metabolism (beta [SE], 0.096 [0.030]; P = .02) and greater atrophy (neurodegeneration biomarkers).CONCLUSIONS AND RELEVANCE Consistent associations of serum-based liver function markers with cognitive performance and A/T/N biomarkers for AD highlight the involvement of metabolic disturbances in the pathophysiology of AD. Further studies are needed to determine if these associations represent a causative or secondary role. Liver enzyme involvement in AD opens avenues for novel diagnostics and therapeutics.
Wissenschaftlicher Artikel
Scientific Article
Bhattacharyya, S. ; Ahmed, A.T. ; Arnold, M. ; Liu, D. ; Luo, C. ; Zhu, H. ; MahmoudianDehkordi, S. ; Neavin, D. ; Louie, G. ; Dunlop, B.W. ; Frye, M.A. ; Wang, L. ; Weinshilboum, R.M. ; Krishnan, R.R. ; Rush, A.J. ; Kaddurah-Daouk, R.
Transl. Psychiatry 9:173 (2019)
Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD17) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD17 scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine-homogentisic acid and methionine-tyrosine interactions associated with HRSD17. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.
Wissenschaftlicher Artikel
Scientific Article
Ahmed, A.T. ; Dehkordi, S.M. ; Bhattacharyya, S. ; Arnold, M. ; Louie, G. ; Dunlop, B. ; Wang, L. ; Weinshilboum, R. ; Krishnan, R. ; Bobo, W.V. ; Posse, P.R. ; Craighead, W.E. ; McDonald, W. ; Skime, M.K. ; Rush, A.J. ; Frye, M.A. ; Kaddurah-Daouk, R.
Biol. Psychiatry 85, S177-S178 (2019)
Meeting abstract
Meeting abstract
Quell, J. ; Römisch-Margl, W. ; Haid, M. ; Krumsiek, J. ; Skurk, T. ; Halama, A. ; Stephan, N. ; Adamski, J. ; Hauner, H. ; Mook-Kanamori, D.O. ; Mohney, R.P. ; Daniel, H. ; Suhre, K. ; Kastenmüller, G.
Metabolites 9:109 (2019)
Kit-based assays, such as AbsoluteIDQ(TM) p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving Lipidyzer(TM) platform, analyzing 223 samples in parallel to the AbsoluteIDQ(TM). Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.
Wissenschaftlicher Artikel
Scientific Article
Yu, B.# ; Flexeder, C.# ; McGarrah, R.W. ; Wyss, A. ; Morrison, A.C. ; North, K.E. ; Boerwinkle, E. ; Kastenmüller, G. ; Gieger, C. ; Suhre, K. ; Karrasch, S. ; Peters, A. ; Wagner, G.R. ; Michelotti, G.A. ; Mohney, R.P. ; Schulz, H.° ; London, S.J.°
Metabolites 9:61 (2019)
Determination of metabolomic signatures of pulmonary function and chronic obstructive pulmonary disease (COPD) in the general population could aid in identification and understanding of early disease processes. Metabolome measurements were performed on serum from 4742 individuals (2354 African-Americans and 1529 European-Americans from the Atherosclerosis Risk in Communities study and 859 Europeans from the Cooperative Health Research in the Region of Augsburg study). We examined 368 metabolites in relation to cross-sectional measures of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), their ratio (FEV1/FVC) and COPD using multivariable regression followed by meta-analysis. At a false discovery rate of 0.05, 95 metabolites were associated with FEV1 and 100 with FVC (73 overlapping), including inverse associations with branched-chain amino acids and positive associations with glutamine. Ten metabolites were associated with FEV1/FVC and seventeen with COPD (393 cases). Enriched pathways of amino acid metabolism were identified. Associations with FEV1 and FVC were not driven by individuals with COPD. We identified novel metabolic signatures of pulmonary function and COPD in African and European ancestry populations. These may allow development of biomarkers in the general population of early disease pathogenesis, before pulmonary function has decreased to levels diagnostic for COPD.
Wissenschaftlicher Artikel
Scientific Article
Goudey, B. ; Fung, B.J. ; Schieber, C. ; Faux, N.G. ; Alzheimer's Disease Neuroimaging Initiative (Kastenmüller, G. ; Arnold, M.)
Sci. Rep. 9:4163 (2019)
It is increasingly recognized that Alzheimer's disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid beta(1-42) (A beta(1-42)) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual's CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A beta(1-42) levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOE epsilon 4 carrier status and four plasma analytes (CGA, A beta(1-42), Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A beta(1-42) levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF A beta(1-42) levels and that the resulting model also validates reasonably across PET A beta(1-42) status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOE epsilon 4 carrier status, is able to predict CSF A beta(1-42) status, the earliest risk indicator for AD, with high accuracy.
Wissenschaftlicher Artikel
Scientific Article
Nho, K. ; Kueider-Paisley, A. ; MahmoudianDehkordi, S. ; Arnold, M. ; Risacher, S.L. ; Louie, G. ; Blach, C. ; Baillie, R.A. ; Han, X. ; Kastenmüller, G. ; Jia, W. ; Xie, G. ; Ahmad, S. ; Hankemeier, T. ; van Duijn, C.M. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; Saykin, A.J. ; Kaddurah-Daouk, R.
Alzheimers Dement. 15, 232-244 (2019)
Introduction: Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-beta deposition.Method: Serum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n = 1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the "A/T/N" (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF) biomarkers, atrophy (magnetic resonance imaging), and brain glucose metabolism ([F-18]FDG PET).Results: Of 23 BAs and relevant calculated ratios after quality control procedures, three BA signatures were associated with CSFA beta(1-42) ("A") and three with CSF p-tau181 ("T") (corrected P < .05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy ("N"), respectively (corrected P < .05).Discussion: This is the first study to show serum-based BA metabolites are associated with "A/T/N" AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association. (C) 2018 Published by Elsevier Inc. on behalf of the Alzheimer's Association.
Wissenschaftlicher Artikel
Scientific Article
Hamad, S.#° ; Adornetto, G.# ; Naveja, J.J. ; Ravindranath, A.C. ; Raffler, J. ; Campillos, M.°
Bioinformatics 35, 1239-1240 (2019)
Motivation The identification of protein targets of novel compounds is essential to understand compounds' mechanisms of action leading to biological effects. Experimental methods to determine these protein targets are usually slow, costly and time consuming. Computational tools have recently emerged as cheaper and faster alternatives that allow the prediction of targets for a large number of compounds.Results Here, we present HitPickV2, a novel ligand-based approach for the prediction of human druggable protein targets of multiple compounds. For each query compound, HitPickV2 predicts up to 10 targets out of 2739 human druggable proteins. To that aim, HitPickV2 identifies the closest, structurally similar compounds in a restricted space within a vast chemical-protein interaction area, until 10 distinct protein targets are found. Then, HitPickV2 scores these 10 targets based on three parameters of the targets in such space: the Tanimoto coefficient (Tc) between the query and the most similar compound interacting with the target, a target rank that considers Tc and Laplacian-modified naive Bayesian target models scores and a novel parameter introduced in HitPickV2, the number of compounds interacting with each target (occur). We present the performance results of HitPickV2 in cross-validation as well as in an external dataset.Availability and implementation HitPickV2 is available in www.hitpickv2.com.Supplementary informationSupplementary data are available at Bioinformatics online.
Wissenschaftlicher Artikel
Scientific Article
Do, K.T. ; Rasp, D.J.N.P. ; Kastenmüller, G. ; Suhre, K. ; Krumsiek, J.
Bioinformatics 35, 532-534 (2019)
Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data.
Wissenschaftlicher Artikel
Scientific Article
2018
Ward-Caviness, C.K. ; Agha, G. ; Chen, B.H. ; Pfeiffer, L. ; Wilson, R. ; Wolf, P. ; Gieger, C. ; Schwartz, J. ; Vokonas, P.S. ; Hou, L. ; Just, A.C. ; Bandinelli, S. ; Hernandez, D.G. ; Singleton, A.B. ; Prokisch, H. ; Meitinger, T. ; Kastenmüller, G. ; Ferrucci, L. ; Baccarelli, A.A. ; Waldenberger, M. ; Peters, A.
Clin. Epigenet. 10:161 (2018)
BackgroundMost research into myocardial infarctions (MIs) have focused on preventative efforts. For survivors, the occurrence of an MI represents a major clinical event that can have long-lasting consequences. There has been little to no research into the molecular changes that can occur as a result of an incident MI. Here, we use three cohorts to identify epigenetic changes that are indicative of an incident MI and their association with gene expression and metabolomics.ResultsUsing paired samples from the KORA cohort, we screened for DNA methylation loci (CpGs) whose change in methylation is potentially indicative of the occurrence of an incident MI between the baseline and follow-up exams. We used paired samples from the NAS cohort to identify 11 CpGs which were predictive in an independent cohort. After removing two CpGs associated with medication usage, we were left with an epigenetic fingerprint of MI composed of nine CpGs. We tested this fingerprint in the InCHIANTI cohort where it moderately discriminated incident MI occurrence (AUC=0.61, P=6.5x10(-3)). Returning to KORA, we associated the epigenetic fingerprint loci with cis-gene expression and integrated it into a gene expression-metabolomic network, which revealed links between the epigenetic fingerprint CpGs and branched chain amino acid (BCAA) metabolism.ConclusionsThere are significant changes in DNA methylation after an incident MI. Nine of these CpGs show consistent changes in multiple cohorts, significantly discriminate MI in independent cohorts, and were independent of medication usage. Integration with gene expression and metabolomics data indicates a link between MI-associated epigenetic changes and BCAA metabolism.
Wissenschaftlicher Artikel
Scientific Article
Müller-Nurasyid, M. ; Schramm, K. ; Heier, M. ; Pietzner, M. ; Budde, K. ; Adamski, J. ; Gieger, C. ; Suhre, K. ; Kastenmüller, G. ; Strauch, K.
Genet. Epidemiol. 42, 719-720 (2018)
Meeting abstract
Meeting abstract
MahmoudianDehkordi, S.# ; Arnold, M.# ; Nho, K. ; Ahmad, S. ; Jia, W. ; Xie, G. ; Louie, G. ; Kueider-Paisley, A. ; Moseley, M.A. ; Thompson, J.W. ; St John Williams, L. ; Tenenbaum, J.D. ; Blach, C. ; Baillie, R.A. ; Han, X. ; Bhattacharyya, S. ; Toledo, J.B. ; Schafferer, S. ; Klein, S. ; Koal, T. ; Risacher, S.L. ; Kling, M.A. ; Motsinger-Reif, A. ; Rotroff, D.M. ; Jack, J.R. ; Hankemeier, T. ; Bennett, D.A. ; de Jager, P.L. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; van Duijn, C.M. ; Saykin, A.J. ; Kastenmüller, G. ; Kaddurah-Daouk, R.
Alzheimers Dement. 15, 76-92 (2018)
Introduction: Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD).Methods: Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for confounders and multiple testing.Results: In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid: CA, which reflects 7 alpha-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response-related genes implicated in AD showed associations with BA profiles.Discussion: We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut-liver-brain axis in the pathogenesis of AD. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.
Wissenschaftlicher Artikel
Scientific Article
Arnold, M. ; Raffler, J. ; Suhre, K. ; Kastenmüller, G.
BioSpektrum 24, 662-663 (2018)
Wissenschaftlicher Artikel
Scientific Article
Barrios, C. ; Zierer, J.° ; Würtz, P. ; Haller, T. ; Metspalu, A. ; Gieger, C. ; Thorand, B. ; Meisinger, C. ; Waldenberger, M. ; Raitakari, O. ; Lehtimäki, T. ; Otero, S. ; Rodríguez, E. ; Pedro-Botet, J. ; Kähönen, M. ; Ala-Korpela, M. ; Kastenmüller, G. ; Spector, T.D. ; Pascual, J. ; Menni, C.°
Sci. Rep. 8:15249 (2018)
Using targeted NMR spectroscopy of 227 fasting serum metabolic traits, we searched for novel metabolic signatures of renal function in 926 type 2 diabetics (T2D) and 4838 non-diabetic individuals from four independent cohorts. We furthermore investigated longitudinal changes of metabolic measures and renal function and associations with other T2D microvascular complications. 142 traits correlated with glomerular filtration rate (eGFR) after adjusting for confounders and multiple testing: 59 in diabetics, 109 in non-diabetics with 26 overlapping. The amino acids glycine and phenylalanine and the energy metabolites citrate and glycerol were negatively associated with eGFR in all the cohorts, while alanine, valine and pyruvate depicted opposite association in diabetics (positive) and non-diabetics (negative). Moreover, in all cohorts, the triglyceride content of different lipoprotein subclasses showed a negative association with eGFR, while cholesterol, cholesterol esters (CE), and phospholipids in HDL were associated with better renal function. In contrast, phospholipids and CEs in LDL showed positive associations with eGFR only in T2D, while phospholipid content in HDL was positively associated with eGFR both cross-sectionally and longitudinally only in non-diabetics. In conclusion, we provide a wide list of kidney function-associated metabolic traits and identified novel metabolic differences between diabetic and non-diabetic kidney disease.
Wissenschaftlicher Artikel
Scientific Article
Do, K.T.# ; Wahl, S.# ; Raffler, J. ; Molnos, S. ; Laimighofer, M. ; Adamski, J. ; Suhre, K. ; Strauch, K. ; Peters, A. ; Gieger, C. ; Langenberg, C. ; Stewart, I.D. ; Theis, F.J. ; Grallert, H. ; Kastenmüller, G.° ; Krumsiek, J.°
Metabolomics 14:128 (2018)
BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.
Wissenschaftlicher Artikel
Scientific Article
Saulnier-Blache, J.S.# ; Wilson, R.# ; Klavins, K. ; Graham, D. ; Alesutan, I. ; Kastenmüller, G. ; Wang-Sattler, R. ; Adamski, J. ; Roden, M. ; Rathmann, W. ; Seissler, J. ; Meisinger, C. ; Koenig, W. ; Thiery, J. ; Suhre, K. ; Peters, A. ; Kuro-O, M. ; Lang, F. ; Dallmann, G. ; Delles, C. ; Voelkl, J. ; Waldenberger, M. ; Bascands, J.-L. ; Klein, J. ; Schanstra, J.P.
Atherosclerosis 276, 140-147 (2018)
Background and aims: Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD.Methods: A targeted mass spectrometry assay was used to quantify and compare a series of blood metabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE(-/-), Ldlr(-/-), and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates.Results: In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcarnitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr(-/-) and ApoE(-/-) mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr(-/-) mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE(-/-) mice shared 10.Conclusions: The human cIMT signature was partially mimicked by Ldlr(-/-) and ApoE(-/-) mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD. (c) 2018 Elsevier B.V. All rights reserved.
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Scientific Article
Jackson, M.A. ; Verdi, S. ; Maxan, M.E. ; Shin, C.M. ; Zierer, J. ; Bowyer, R.C.E. ; Martin, T. ; Williams, F.M.K. ; Menni, C. ; Bell, J.T. ; Spector, T.D. ; Steves, C.J.
Nat. Commun. 9:2655 (2018)
The human gut microbiome has been associated with many health factors but variability between studies limits exploration of effects between them. Gut microbiota profiles are available for >2700 members of the deeply phenotyped TwinsUK cohort, providing a uniform platform for such comparisons. Here, we present gut microbiota association analyses for 38 common diseases and 51 medications within the cohort. We describe several novel associations, highlight associations common across multiple diseases, and determine which diseases and medications have the greatest association with the gut microbiota. These results provide a reference for future studies of the gut microbiome and its role in human health.
Wissenschaftlicher Artikel
Scientific Article
Pitchika, A. ; Jolink, M. ; Winkler, C. ; Hummel, S. ; Hummel, N. ; Krumsiek, J. ; Kastenmüller, G. ; Raab, J. ; Kordonouri, O. ; Ziegler, A.-G. ; Beyerlein, A.
Diabetologia 61, 2319–2332 (2018)
Exposure to an intrauterine hyperglycaemic environment has been suggested to increase the offspring's later risk for being overweight or having metabolic abnormalities, but conclusive evidence for pregnancies affected by maternal type 1 diabetes is still lacking. This study aims to analyse the relationship between maternal type 1 diabetes and the offspring's metabolic health and investigate whether birthweight and/or changes in the offspring's metabolome are in the potential pathway.We analysed data from 610 and 2169 offspring having a first-degree relative with type 1 diabetes from the TEENDIAB and BABYDIAB/BABYDIET cohorts, respectively. Anthropometric and metabolic outcomes, assessed longitudinally at 0.3-18 years of age, were compared between offspring of mothers with type 1 diabetes and offspring of non-diabetic mothers but with fathers or siblings with type 1 diabetes using mixed regression models. Non-targeted metabolomic measurements were carried out in 500 individuals from TEENDIAB and analysed with maternal type 1 diabetes and offspring overweight status.The offspring of mothers with type 1 diabetes had a higher BMI SD score (SDS) and an increased risk for being overweight than the offspring of non-diabetic mothers (e.g. OR for overweight status in TEENDIAB 2.40 [95% CI 1.41, 4.06]). Further, waist circumference SDS, fasting levels of glucose, insulin and C-peptide, and insulin resistance and abdominal obesity were significantly increased in the offspring of mothers with type 1 diabetes, even when adjusted for potential confounders and birthweight. Metabolite patterns related to androgenic steroids and branched-chain amino acids were found to be associated with offspring's overweight status, but no significant associations were observed between maternal type 1 diabetes and metabolite concentrations in the offspring.Maternal type 1 diabetes is associated with offspring's overweight status and metabolic health in later life, but this is unlikely to be caused by alterations in the offspring's metabolome.
Wissenschaftlicher Artikel
Scientific Article
Jackson, V.E.° ; Latourelle, J.C. ; Wain, L.V. ; Smith, A.V. ; Grove, M.L. ; Bartz, T.M. ; Obeidat, M. ; Altmaier, E. ; Marten, J. ; Harris, S.E. ; Rawal, R. ; Karrasch, S. ; Huffmann, J.E. ; Smith, B.H. ; Schulz, H. ; Polasek, O. ; Campbell, A. ; Strauch, K. ; Morrison, A.C. ; Hall, I.P. ; Tobin, M.D ; London, S.J.°
Wellcome Open Res. 3:4 (2018)
Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P<2·8x10-7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs (SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.
Wissenschaftlicher Artikel
Scientific Article
Menni, C. ; Gudelj, I. ; Macdonald-Dunlop, E. ; Mangino, M. ; Zierer, J. ; Besic, E. ; Joshi, P.K. ; Trbojević-Akmačić, I. ; Chowienczyk, P.J. ; Spector, T.D. ; Wilson, J.F. ; Lauc, G. ; Valdes, A.M.
J. Virol. 122, 1555-1564 (2018)
Human adenovirus (HAdV) E1B-55K is a multifunctional regulator of productive viral replication and oncogenic transformation in nonpermissive mammalian cells. These functions depend on E1B-55K's posttranslational modification with the SUMO protein and its binding to HAdV E4orf6. Both early viral proteins recruit specific host factors to form an E3 ubiquitin ligase complex that targets antiviral host substrates for proteasomal degradation. Recently, we reported that the PML-NB associated factor Daxx represses efficient HAdV productive infection and is proteasomally degraded via a SUMO-E1B-55K-dependent, E4orf6-independent pathway, the details of which remained to be established. RNF4, a cellular SUMO-targeted ubiquitin ligase (STUbL), induces ubiquitinylation of specific SUMOy lated proteins and plays an essential role during DNA repair. Here, we show that E1B-55K recruits RNF4 to the insoluble nuclear matrix fraction of the infected cell to support RNF4/Daxx association, promoting Daxx PTM and thus inhibiting this antiviral factor. Removing RNF4 from infected cells using RNA interference resulted in blocking the proper establishment of viral replication centers and significantly diminished viral gene expression. These results provide a model for how HAdV antagonize the antiviral host responses by exploiting the functional capacity of cellular STUbLs. Thus, RNF4 and its STUbL function represent a positive factor during lytic infection and a novel candidate for future therapeutic antiviral intervention strategies.IMPORTANCE Daxx is a PML-NB-associated transcription factor that was recently shown to repress efficient HAdV productive infection. To counteract this antiviral measurement during infection, Daxx is degraded via a novel pathway including viral E1B-55K and host proteasomes. This virus-mediated degradation is independent of the classical HAdV E3 ubiquitin ligase complex, which is essential during viral infection to target other host antiviral substrates. To maintain a productive viral life cycle, HAdV E1B-55K early viral protein inhibits the chromatin-remodeling factor Daxx in a SUMO-dependent manner. In addition, viral E1B-55K protein recruits the STUbL RNF4 and sequesters it into the insoluble fraction of the infected cell. E1B-55K promotes complex formation between RNF4-and E1B-55K-targeted Daxx protein, supporting Daxx posttranslational modification prior to functional inhibition. Hence, RNF4 represents a novel host factor that is beneficial for HAdV gene expression by supporting Daxx counteraction. In this regard, RNF4 and other STUbL proteins might represent novel targets for therapeutic intervention.
Wissenschaftlicher Artikel
Scientific Article
Zierer, J. ; Jackson, M.A. ; Kastenmüller, G. ; Mangino, M. ; Long, T.C. ; Telenti, A. ; Mohney, R.P. ; Small, K.S. ; Bell, J.T. ; Steves, C.J. ; Valdes, A.M. ; Spector, T.D.° ; Menni, C.°
Nat. Genet. 50, 790-795 (2018)
Skin affections after sulfur mustard (SM) exposure include erythema, blister formation and severe inflammation. An antidote or specific therapy does not exist. Anti-inflammatory compounds as well as substances counteracting SM-induced cell death are under investigation. In this study, we investigated the benzylisoquinoline alkaloide berberine (BER), a metabolite in plants like berberis vulgaris, which is used as herbal pharmaceutical in Asian countries, against SM toxicity using a well-established in vitro approach. Keratinocyte (HaCaT) mono-cultures (MoC) or HaCaT/THP-1 co-cultures (CoC) were challenged with 100, 200 or 300 mM SM for 1 h. Post-exposure, both MoC and CoC were treated with 10, 30 or 50 mu M BER for 24 h. At that time, supernatants were collected and analyzed both for interleukine (IL) 6 and 8 levels and for content of adenylate-kinase (AK) as surrogate marker for cell necrosis. Cells were lysed and nucleosome formation as marker for late apoptosis was assessed. In parallel, AK in cells was determined for normalization purposes. BER treatment did not influence necrosis, but significantly decreased apoptosis. Anti-inflammatory effects were moderate, but also significant, primarily in CoC. Overall, BER has protective effects against SM toxicity in vitro. Whether this holds true should be evaluated in future in vivo studies.
Wissenschaftlicher Artikel
Scientific Article
Halama, A. ; Kulinski, M. ; Dib, S.S. ; Zaghlool, S.B. ; Siveen, K.S. ; Iskandarani, A. ; Zierer, J. ; Prabhu, K.S. ; Satheesh, N.J. ; Bhagwat, A.M. ; Uddin, S. ; Kastenmüller, G. ; Elemento, O. ; Gross, S.S. ; Suhre, K.
Cancer Lett. 430, 133-147 (2018)
Suppressing glutaminolysis does not always induce cancer cell death in glutamine dependent tumors because cells may switch to alternative energy sources. To reveal compensatory metabolic pathways, we investigated the metabolome-wide cellular response to inhibited glutaminolysis in cancer cells. Glutaminolysis inhibition with C.968 suppressed cell proliferation but was insufficient to induce cancer cell death. We found that lipid catabolism was activated as a compensation for glutaminolysis inhibition. Accelerated lipid catabolism, together with oxidative stress induced by glutaminolysis inhibition, triggered autophagy. Simultaneously inhibiting glutaminolysis and either beta oxidation with trimetazidine or autophagy with chloroquine both induced cancer cell death. Here we identified metabolic escape mechanisms contributing to cancer cell survival under treatment and we suggest potentially translational strategy for combined cancer therapy, given that chloroquine is an FDA approved drug. Our findings are first to show efficiency of combined inhibition of glutaminolysis and beta oxidation as potential anti-cancer strategy as well as add to the evidence that combined inhibition of glutaminolysis and autophagy may be effective in glutamine-addicted cancers.
Wissenschaftlicher Artikel
Scientific Article
Varma, V.R. ; Oommen, A.M. ; Varma, S. ; Casanova, R. ; An, Y. ; Andrews, R.M. ; O'Brien, R.M. ; Pletnikova, O. ; Troncoso, J.C. ; Toledo, J.B. ; Baillie, R.A. ; Arnold, M. ; Kastenmüller, G. ; Nho, K. ; Doraiswamy, P.M. ; Saykin, A.J. ; Kaddurah-Daouk, R. ; Legido-Quigley, C. ; Thambisetty, M.
PLoS Med. 15:e1002482 (2018)
Background: The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. Methods and findings: Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and “asymptomatic Alzheimer’s disease” (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes—sphingolipids and glycerophospholipids—that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer’s Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer’s Disease [EASE-AD] ). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703–11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516–7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373–9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047–4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples. Conclusions: We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.
Wissenschaftlicher Artikel
Scientific Article
Köttgen, A. ; Raffler, J. ; Sekula, P. ; Kastenmüller, G.
Semin. Nephrol. 38, 151-174 (2018)
Metabolites are small molecules that are intermediates or products of metabolism, many of which are freely filtered by the kidneys. In addition, the kidneys have a central role in metabolite anabolism and catabolism, as well as in active metabolite reabsorption and/or secretion during tubular passage. This review article illustrates how the coupling of genomics and metabolomics in genome-wide association analyses of metabolites can be used to illuminate mechanisms underlying human metabolism, with a special focus on insights relevant to nephrology. First, genetic susceptibility loci for reduced kidney function and chronic kidney disease (CKD) were reviewed systematically for their associations with metabolite concentrations in metabolomics studies of blood and urine. Second, kidney function and CKD-associated metabolites reported from observational studies were interrogated for metabolite-associated genetic variants to generate and discuss complementary insights. Finally, insights originating from the simultaneous study of both blood and urine or by modeling intermetabolite relationships are summarized. We also discuss methodologic questions related to the study of metabolite concentrations in urine as well as among CKD patients. In summary, genome-wide association analyses of metabolites using metabolite concentrations quantified from blood and/or urine are a promising avenue of research to illuminate physiological and pathophysiological functions of the kidney.
Review
Review
Adler, A. ; Kirchmeier, P. ; Reinhard, J. ; Brauner, B. ; Dunger, I. ; Fobo, G. ; Frishman, G. ; Montrone, C. ; Mewes, H.-W. ; Arnold, M. ; Ruepp, A.
Orphanet J. Rare Dis. 13:22 (2018)
Background: Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity for the interpretation of inherited diseases. We have therefore developed PhenoDis, a comprehensive, manually annotated database providing symptomatic, genetic and imprinting information about rare cardiac diseases. Results: PhenoDis includes 214 rare cardiac diseases from Orphanet and 94 more from OMIM. For phenotypic characterization of the diseases, we performed manual annotation of diseases with articles from the biomedical literature. Detailed description of disease symptoms required the use of 2247 different terms from the Human Phenotype Ontology (HPO). Diseases listed in PhenoDis frequently cover a broad spectrum of symptoms with 28% from the branch of 'cardiovascular abnormality' and others from areas such as neurological (11.5%) and metabolism (6%). We collected extensive information on the frequency of symptoms in respective diseases as well as on disease-associated genes and imprinting data. The analysis of the abundance of symptoms in patient studies revealed that most of the annotated symptoms (71%) are found in less than half of the patients of a particular disease. Comprehensive and systematic characterization of symptoms including their frequency is a pivotal prerequisite for computer based prediction of diseases and disease causing genetic variants. To this end, PhenoDis provides in-depth annotation for a complete group of rare diseases, including information on pathogenic and likely pathogenic genetic variants for 206 diseases as listed in ClinVar. We integrated all results in an online database (http://mips.helmholtz-muenchen.de/phenodis/) with multiple search options and provide the complete dataset for download. Conclusion: PhenoDis provides a comprehensive set of manually annotated rare cardiac diseases that enables computational approaches for disease prediction via decision support systems and phenotype-driven strategies for the identification of disease causing genes.
Wissenschaftlicher Artikel
Scientific Article
Jackson, M.A. ; Bonder, M.J. ; Kuncheva, Z. ; Zierer, J. ; Fu, J. ; Kurilshikov, A. ; Wijmenga, C. ; Zhernakova, A. ; Bell, J.T. ; Spector, T.D. ; Steves, C.J.
PeerJ 6:e4303 (2018)
Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.
Wissenschaftlicher Artikel
Scientific Article
Haid, M. ; Muschet, C. ; Wahl, S. ; Römisch-Margl, W. ; Prehn, C. ; Möller, G. ; Adamski, J.
J. Proteome Res. 17, 203-211 (2018)
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 degrees 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%; sphingomye-lins, -14.8%. We conclude that storage of plasma samples at -80 degrees 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.
Wissenschaftlicher Artikel
Scientific Article
2017
Sekula, P. ; Dettmer, K. ; Vogl, F.C. ; Gronwald, W. ; Ellmann, L. ; Mohney, R.P. ; Eckardt, K.U. ; Suhre, K. ; Kastenmüller, G. ; Oefner, P.J. ; Köttgen, A.
Sci. Rep. 7:17400 (2017)
Using a non-targeted metabolomics platform, we recently identified C-mannosyltryptophan and pseudouridine as non-traditional kidney function markers. The aims of this study were to obtain absolute concentrations of both metabolites in blood and urine from individuals with and without CKD to provide reference ranges and to assess their fractional excretions (FE), and to assess the agreement with their non-targeted counterparts. In individuals without/with CKD, mean plasma and urine concentrations for C-mannosyltryptophan were 0.26/0.72 μmol/L and 3.39/4.30 μmol/mmol creatinine, respectively. The respective concentrations for pseudouridine were 2.89/5.67 μmol/L and 39.7/33.9 μmol/mmol creatinine. Median (25 th , 75 th percentiles) FEs were 70.8% (65.6%, 77.8%) for C-mannosyltryptophan and 76.0% (68.6%, 82.4%) for pseudouridine, indicating partial net reabsorption. Association analyses validated reported associations between single metabolites and eGFR. Targeted measurements of both metabolites agreed well with the non-targeted measurements, especially in urine. Agreement for composite nephrological measures FE and urinary metabolite-to-creatinine ratio was lower, but could be improved by replacing non-targeted creatinine measurements with a standard clinical creatinine test. In summary, targeted quantification and additional characterization in relevant populations are necessary steps in the translation of non-traditional biomarkers in nephrology from non-targeted discovery to clinical application.
Wissenschaftlicher Artikel
Scientific Article
Rueedi, R. ; Mallol, R. ; Raffler, J. ; Lamparter, D. ; Friedrich, N. ; Vollenweider, P. ; Waeber, G. ; Kastenmüller, G. ; Kutalik, Z. ; Bergmann, S.
PLoS Comput. Biol. 13, e1005839:e1005839 (2017)
A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
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Scientific Article
Pietzner, M.# ; Kaul, A.K.# ; Henning, A.-K. ; Kastenmüller, G. ; Artati, A. ; Lerch, M.M. ; Adamski, J. ; Nauck, M. ; Friedrich, N.
BMC Med. 15:210 (2017)
BACKGROUND: Inflammation occurs as an immediate protective response of the immune system to a harmful stimulus, whether locally confined or systemic. In contrast, a persisting, i.e., chronic, inflammatory state, even at a low-grade, is a well-known risk factor in the development of common diseases like diabetes or atherosclerosis. In clinical practice, laboratory markers like high-sensitivity C-reactive protein (hsCRP), white blood cell count (WBC), and fibrinogen, are used to reveal inflammatory processes. In order to gain a deeper insight regarding inflammation-related changes in metabolism, the present study assessed the metabolic patterns associated with alterations in inflammatory markers. METHODS: Based on mass spectrometry and nuclear magnetic resonance spectroscopy we determined a comprehensive panel of 613 plasma and 587 urine metabolites among 925 apparently healthy individuals. Associations between inflammatory markers, namely hsCRP, WBC, and fibrinogen, and metabolite levels were tested by linear regression analyses controlling for common confounders. Additionally, we tested for a discriminative signature of an advanced inflammatory state using random forest analysis. RESULTS: HsCRP, WBC, and fibrinogen were significantly associated with 71, 20, and 19 plasma and 22, 3, and 16 urine metabolites, respectively. Identified metabolites were related to the bradykinin system, involved in oxidative stress (e.g., glutamine or pipecolate) or linked to the urea cycle (e.g., ornithine or citrulline). In particular, urine 3'-sialyllactose was found as a novel metabolite related to inflammation. Prediction of an advanced inflammatory state based solely on 10 metabolites was well feasible (median AUC: 0.83). CONCLUSIONS: Comprehensive metabolic profiling confirmed the far-reaching impact of inflammatory processes on human metabolism. The identified metabolites included not only those already described as immune-modulatory but also completely novel patterns. Moreover, the observed alterations provide molecular links to inflammation-associated diseases like diabetes or cardiovascular disorders.  
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Hertel, J. ; König, J. ; Homuth, G. ; Van der Auwera, S. ; Wittfeld, K. ; Pietzner, M. ; Kacprowski, T. ; Pfeiffer, L. ; Kretschmer, A. ; Waldenberger, M. ; Kastenmüller, G. ; Artati, A. ; Suhre, K. ; Adamski, J. ; Langner, S. ; Völker, U. ; Völzke, H. ; Nauck, M. ; Friedrich, N. ; Grabe, H.J.
Sci. Rep. 7:14111 (2017)
Using oral contraceptives has been implicated in the aetiology of stress-related disorders like depression. Here, we followed the hypothesis that oral contraceptives deregulate the HPA-axis by elevating circulating cortisol levels. We report for a sample of 233 pre-menopausal women increased circulating cortisol levels in those using oral contraceptives. For women taking oral contraceptives, we observed alterations in circulating phospholipid levels and elevated triglycerides and found evidence for increased glucocorticoid signalling as the transcript levels of the glucocorticoid-regulated genes DDIT4 and FKBP5 were increased in whole blood. The effects were statistically mediated by cortisol. The associations of oral contraceptives with higher FKBP5 mRNA and altered phospholipid levels were modified by rs1360780, a genetic variance implicated in psychiatric diseases. Accordingly, the methylation pattern of FKBP5 intron 7 was altered in women taking oral contraceptives depending on the rs1360780 genotype. Moreover, oral contraceptives modified the association of circulating cortisol with depressive symptoms, potentially explaining conflicting results in the literature. Finally, women taking oral contraceptives displayed smaller hippocampal volumes than non-using women. In conclusion, the integrative analyses of different types of physiological data provided converging evidence indicating that oral contraceptives may cause effects analogous to chronic psychological stressors regarding the regulation of the HPA axis.
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St John-Williams, L. ; Blach, C. ; Toledo, J.B. ; Rotroff, D.M. ; Kim, S. ; Klavins, K. ; Baillie, R.A. ; Han, X. ; MahmoudianDehkordi, S. ; Jack, J.R. ; Massaro, T.J. ; Lucas, J.E. ; Louie, G. ; Motsinger-Reif, A.A. ; Risacher, S.L. ; Saykin, A.J. ; Kastenmüller, G. ; Arnold, M. ; Koal, T. ; Moseley, M.A. ; Mangravite, L.M. ; Peters, M.A. ; Tenenbaum, J.D. ; Thompson, J.W. ; Kaddurah-Daouk, R.
Sci. Data 4:170140 (2017)
Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.
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Pallister, T. ; Jackson, M.A. ; Martin, T.C. ; Zierer, J. ; Jennings, A. ; Mohney, R.P. ; MacGregor, A. ; Steves, C.J. ; Cassidy, A. ; Spector, T.D. ; Menni, C.
Sci. Rep. 7:13670 (2017)
Reduced gut microbiome diversity is associated with multiple disorders including metabolic syndrome (MetS) features, though metabolomic markers have not been investigated. Our objective was to identify blood metabolite markers of gut microbiome diversity, and explore their relationship with dietary intake and MetS. We examined associations between Shannon diversity and 292 metabolites profiled by the untargeted metabolomics provider Metabolon Inc. in 1529 females from TwinsUK using linear regressions adjusting for confounders and multiple testing (Bonferroni: P < 1.71 × 10 -4 ). We replicated the top results in an independent sample of 420 individuals as well as discordant identical twin pairs and explored associations with self-reported intakes of 20 food groups. Longitudinal changes in circulating levels of the top metabolite, were examined for their association with food intake at baseline and with MetS at endpoint. Five metabolites were associated with microbiome diversity and replicated in the independent sample. Higher intakes of fruit and whole grains were associated with higher levels of hippurate cross-sectionally and longitudinally. An increasing hippurate trend was associated with reduced odds of having MetS (OR: 0.795[0.082]; P = 0.026). These data add further weight to the key role of the microbiome as a potential mediator of the impact of dietary intake on metabolic status and health.
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Molnos, S. ; Baumbach, C. ; Wahl, S. ; Müller-Nurasyid, M. ; Strauch, K. ; Wang-Sattler, R. ; Waldenberger, M. ; Meitinger, T. ; Adamski, J. ; Kastenmüller, G. ; Suhre, K. ; Peters, A. ; Grallert, H. ; Theis, F.J. ; Gieger, C.
BMC Bioinformatics 18:429 (2017)
Background Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different “omics” layers. Existing tools only consider single-nucleotide polymorphism (SNP)–SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. Results We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different “omics” layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. Conclusions The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/.  
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Do, K.T. ; Pietzner, M. ; Rasp, D.J.N.P. ; Friedrich, N. ; Nauck, M. ; Kocher, T. ; Suhre, K. ; Mook-Kanamori, D.O. ; Kastenmüller, G. ; Krumsiek, J.
NPJ Syst. Biol. Appl. 3:28 (2017)
The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.
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Menni, C. ; Zierer, J. ; Pallister, T. ; Jackson, M.A. ; Long, T.C. ; Mohney, R.P. ; Steves, C.J. ; Spector, T.D. ; Valdes, A.M.
Sci. Rep. 7:11079 (2017)
Omega-3 fatty acids may influence human physiological parameters in part by affecting the gut microbiome. The aim of this study was to investigate the links between omega-3 fatty acids, gut microbiome diversity and composition and faecal metabolomic profiles in middle aged and elderly women. We analysed data from 876 twins with 16S microbiome data and DHA, total omega-3, and other circulating fatty acids. Estimated food intake of omega-3 fatty acids were obtained from food frequency questionnaires. Both total omega-3and DHA serum levels were significantly correlated with microbiome alpha diversity (Shannon index) after adjusting for confounders (DHA Beta(SE) = 0.13(0.04), P = 0.0006 total omega-3: 0.13(0.04), P = 0.001). These associations remained significant after adjusting for dietary fibre intake. We found even stronger associations between DHA and 38 operational taxonomic units (OTUs), the strongest ones being with OTUs from the Lachnospiraceae family (Beta(SE) = 0.13(0.03), P = 8 × 10 -7 ). Some of the associations with gut bacterial OTUs appear to be mediated by the abundance of the faecal metabolite N-carbamylglutamate. Our data indicate a link between omega-3 circulating levels/intake and microbiome composition independent of dietary fibre intake, particularly with bacteria of the Lachnospiraceae family. These data suggest the potential use of omega-3 supplementation to improve the microbiome composition.
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Kim, H.I. ; Raffler, J. ; Lu, W. ; Lee, J.J. ; Abbey, D. ; Saleheen, D. ; Rabinowitz, J.D. ; Bennett, M.J. ; Hand, N.J. ; Brown, C.T. ; Rader, D.J.
Am. J. Hum. Genet. 101, 489-502 (2017)
Genome-wide association studies have identified a signal at the SLC22A1 locus for serum acylcarnitines, intermediate metabolites of mitochondrial oxidation whose plasma levels associate with metabolic diseases. Here, we refined the association signal, performed conditional analyses, and examined the linkage structure to find coding variants of SLC22A1 that mediate independent association signals at the locus. We also employed allele-specific expression analysis to find potential regulatory variants of SLC22A1 and demonstrated the effect of one variant on the splicing of SLC22A1. SLC22A1 encodes a hepatic plasma membrane transporter whose role in acylcarnitine physiology has not been described. By targeted metabolomics and isotope tracing experiments in loss- and gain-of-function cell and mouse models of Slc22a1, we uncovered a role of SLC22A1 in the efflux of acylcarnitines from the liver to the circulation. We further validated the impacts of human variants on SLC22A1-mediated acylcarnitine efflux in vitro, explaining their association with serum acylcarnitine levels. Our findings provide the detailed molecular mechanisms of the GWAS association for serum acylcarnitines at the SLC22A1 locus by functionally validating the impact of SLC22A1 and its variants on acylcarnitine transport.
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Entenmann, L. ; Pietzner, M. ; Artati, A. ; Hannemann, A. ; Henning, A.K. ; Kastenmüller, G. ; Völzke, H. ; Nauck, M. ; Adamski, J. ; Wallaschofski, H. ; Friedrich, N.
PLoS ONE 12:e0184721 (2017)
Recent research suggested a metabolic implication of osteocalcin (OCN) in e.g. insulin sensitivity or steroid production. We used an untargeted metabolomics approach by analyzing plasma and urine samples of 931 participants using mass spectrometry to reveal further metabolic actions of OCN. Several detected relations between OCN and metabolites were strongly linked to renal function, however, a number of associations remained significant after adjustment for renal function. Intermediates of proline catabolism were associated with OCN reflecting the implication in bone metabolism. The association to kynurenine points towards a pro-inflammatory state with increasing OCN. Inverse relations with intermediates of branch-chained amino acid metabolism suggest a link to energy metabolism. Finally, urinary surrogate markers of smoking highlight its adverse effect on OCN metabolism. In conclusion, the present study provides a read-out of metabolic actions of OCN. However, most of the associations were weak arguing for a limited role of OCN in whole-body metabolism.
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Menni, C. ; Migaud, M. ; Kastenmüller, G. ; Pallister, T. ; Zierer, J. ; Peters, A. ; Mohney, R.P. ; Spector, T.D. ; Bagnardi, V. ; Gieger, C. ; Moore, S.C. ; Valdes, A.M.
Obesity 25, 1618-1624 (2017)
OBJECTIVE: To investigate the association between long-term weight change and blood metabolites. METHODS: Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3-79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow-up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study (n = 1,760; age range: 25-70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study (n = 471; age range: 55-74, baseline BMI: 27.24 ± 5.37). RESULTS: Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta-analysis β [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10(-19) ), gamma-glutamyl valine (β [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10(-20) ), butyrylcarnitine (β [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10(-12) ), and 3-phenylpropionate (β [95% CI] = -0.03 [-0.041 to -0.019]; P = 9.8 × 10(-8) ), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO. CONCLUSIONS: Metabolites linked to higher oxidative stress are associated with increased long-term weight gain.
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Keuper, M. ; Sachs, S. ; Walheim, E. ; Berti, L. ; Raedle, B. ; Tews, D. ; Fischer-Posovszky, P. ; Wabitsch, M. ; Hrabě de Angelis, M. ; Kastenmüller, G. ; Tschöp, M.H. ; Jastroch, M. ; Staiger, H. ; Hofmann, S.M.
Mol. Metab. 6, 1226-1239 (2017)
Objective Obesity-associated WAT inflammation is characterized by the accumulation and local activation of macrophages (MΦs), and recent data from mouse studies suggest that macrophages are modifiers of adipocyte energy metabolism and mitochondrial function. As mitochondrial dysfunction has been associated with obesity and the metabolic syndrome in humans, herein we aimed to delineate how human macrophages may affect energy metabolism of white adipocytes. Methods Human adipose tissue gene expression analysis for markers of macrophage activation and tissue inflammation (CD11c, CD40, CD163, CD206, CD80, MCP1, TNFα) in relationship to mitochondrial complex I (NDUFB8) and complex III (UQCRC2) was performed on subcutaneous WAT of 24 women (BMI 20–61 kg/m2). Guided by these results, the impact of secreted factors of LPS/IFNγ- and IL10/TGFβ-activated human macrophages (THP1, primary blood-derived) on mitochondrial function in human subcutaneous white adipocytes (SGBS, primary) was determined by extracellular flux analysis (Seahorse technology) and gene/protein expression. Results Stepwise regression analysis of human WAT gene expression data revealed that a linear combination of CD40 and CD163 was the strongest predictor for mitochondrial complex I (NDUFB8) and complex III (UQCRC2) levels, independent of BMI. IL10/TGFβ-activated MΦs displayed high CD163 and low CD40 expression and secreted factors that decreased UQCRC2 gene/protein expression and ATP-linked respiration in human white adipocytes. In contrast, LPS/IFNγ-activated MΦs showed high CD40 and low CD163 expression and secreted factors that enhanced adipocyte mitochondrial activity resulting in a total difference of 37% in ATP-linked respiration of white adipocytes (p = 0.0024) when comparing the effect of LPS/IFNγ- vs IL10/TGFβ-activated MΦs. Conclusion Our data demonstrate that macrophages modulate human adipocyte energy metabolism via an activation-dependent paracrine mechanism.  
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Kremer, L.S. ; Bader, D.M. ; Mertes, C. ; Kopajtich, R. ; Pichler, G. ; Iuso, A. ; Haack, T.B. ; Graf, E. ; Schwarzmayr, T. ; Terrile, C. ; Konarikova, E. ; Repp, B. ; Kastenmüller, G. ; Adamski, J. ; Lichtner, P. ; Leonhardt, C. ; Funalot, B. ; Donati, A. ; Tiranti, V. ; Lombes, A. ; Jardel, C. ; Gläser, D. ; Taylor, R.W. ; Ghezzi, D. ; Mayr, J.A. ; Rötig, A. ; Freisinger, P. ; Distelmaier, F. ; Strom, T.M. ; Meitinger, T. ; Gagneur, J. ; Prokisch, H.
Nat. Commun. 8:15824 (2017)
Across a variety of Mendelian disorders, ∼50-75% of patients do not receive a genetic diagnosis by exome sequencing indicating disease-causing variants in non-coding regions. Although genome sequencing in principle reveals all genetic variants, their sizeable number and poorer annotation make prioritization challenging. Here, we demonstrate the power of transcriptome sequencing to molecularly diagnose 10% (5 of 48) of mitochondriopathy patients and identify candidate genes for the remainder. We find a median of one aberrantly expressed gene, five aberrant splicing events and six mono-allelically expressed rare variants in patient-derived fibroblasts and establish disease-causing roles for each kind. Private exons often arise from cryptic splice sites providing an important clue for variant prioritization. One such event is found in the complex I assembly factor TIMMDC1 establishing a novel disease-associated gene. In conclusion, our study expands the diagnostic tools for detecting non-exonic variants and provides examples of intronic loss-of-function variants with pathological relevance.
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Quell, J. ; Römisch-Margl, W. ; Colombo, M. ; Krumsiek, J. ; Evans, A.M. ; Mohney, R.P. ; Salomaa, V. ; de Faire, U. ; Groop, L.C. ; Agakov, F.V. ; Looker, H.C. ; McKeigue, P.M. ; Colhoun, H.M. ; Kastenmüller, G.
J. Chromatogr. B 1071, 58-67 (2017)
Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.
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Toledo, J.B. ; Arnold, M. ; Kastenmüller, G. ; Chang, R. ; Baillie, R.A. ; Han, X. ; Thambisetty, M. ; Tenenbaum, J.D. ; Suhre, K. ; Thompson, J.W. ; John-Williams, L.S. ; MahmoudianDehkordi, S. ; Rotroff, D.M. ; Jack, J.R. ; Motsinger-Reif, A. ; Risacher, S.L. ; Blach, C. ; Lucas, J.E. ; Massaro, T. ; Louie, G. ; Zhu, H. ; Dallmann, G. ; Klavins, K. ; Koal, T. ; Kim, S. ; Nho, K. ; Shen, L. ; Casanova, R. ; Varma, S. ; Legido-Quigley, C. ; Moseley, M.A. ; Zhu, K. ; Henrion, M.Y. ; van der Lee, S.J. ; Harms, A.C. ; Demirkan, A. ; Hankemeier, T. ; van Duijn, C.M. ; Trojanowski, J.Q. ; Shaw, L.M. ; Saykin, A.J. ; Weiner, M.W. ; Doraiswamy, P.M. ; Kaddurah-Daouk, R.
Alzheimers Dement. 13, 965-984 (2017)
INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.
Wissenschaftlicher Artikel
Scientific Article
Day, F.R. ; Thompson, D.J. ; Helgason, H. ; Chasman, D.I. ; Finucane, H. ; Sulem, P. ; Ruth, K.S. ; Whalen, S. ; Sarkar, A.K. ; Albrecht, E. ; Altmaier, E. ; Amini, M. ; Barbieri, C.M. ; Boutin, T. ; Campbell, A. ; Demerath, E. ; Giri, A. ; He, C. ; Hottenga, J.J. ; Karlsson, R. ; Kolcic, I. ; Loh, P.R. ; Lunetta, K.L. ; Mangino, M. ; Marco, B. ; McMahon, G. ; Medland, S.E. ; Nolte, I.M. ; Noordam, R. ; Nutile, T. ; Paternoster, L. ; Perjakova, N. ; Porcu, E. ; Rose, L.M. ; Schraut, K.E. ; Segrè, A.V. ; Smith, A.V. ; Stolk, L. ; Andrulis, I.L. ; Bandinelli, S. ; Beckmann, M.W. ; Benítez, J. ; Bergmann, S. ; Bochud, M. ; Boerwinkle, E. ; Bojesen, S.E. ; Bolla, M.K. ; Brand, J.S. ; Brauch, H. ; Brenner, H. ; Broer, L. ; Brüning, T. ; Buring, J.E. ; Campbell, H. ; Catamo, E. ; Chanock, S. ; Chenevix-Trench, G. ; Corre, T. ; Couch, F.J. ; Cousminer, D.L. ; Cox, A. ; Crisponi, L. ; Czene, K. ; Davey Smith, G. ; de Geus, E.J.C.N. ; de Mutsert, R. ; de Vivo, I. ; Dennis, J. ; Devilee, P. ; Dos-Santos-Silva, I. ; Dunning, A.M. ; Eriksson, J.G. ; Fasching, P.A. ; Fernández-Rhodes, L. ; Ferrucci, L. ; Flesch-Janys, D. ; Franke, L. ; Gabrielson, M. ; Gandin, I. ; Giles, G.G. ; Grallert, H. ; Gudbjartsson, D.F. ; Guénel, P. ; Hall, P. ; Hallberg, E. ; Hamann, U. ; Harris, T.B. ; Hartman, C.A. ; Heiss, G. ; Hooning, M.J. ; Hopper, J.L. ; Hu, F. ; Hunter, D.J. ; Ikram, M.A. ; Im, H.K. ; Jarvelin, M.R. ; Joshi, P.K. ; Karasik, D. ; Kellis, M. ; Kutalik, Z. ; Lachance, G. ; Lambrechts, D. ; Langenberg, C. ; Launer, L.J. ; Laven, J.S.E. ; Lenarduzzi, S. ; Li, J. ; Lind, P.A. ; Lindström, S. ; Liu, Y. ; Luan, J. ; Mägi, R. ; Mannermaa, A. ; Mbarek, H. ; McCarthy, M.I. ; Meisinger, C. ; Meitinger, T. ; Menni, C. ; Metspalu, A. ; Michailidou, K. ; Milani, L. ; Milne, R.L. ; Montgomery, G.W. ; Mulligan, A.M. ; Nalls, M.A. ; Navarro, P. ; Nevanlinna, H. ; Nyholt, D.R. ; Oldehinkel, A.J. ; O'Mara, T.A. ; Padmanabhan, S. ; Palotie, A. ; Pedersen, N. ; Peters, A. ; Peto, J. ; Pharoah, P.D.P. ; Pouta, A. ; Radice, P. ; Rahman, I. ; Ring, S.M. ; Robino, A. ; Rosendaal, F.R. ; Rudan, I. ; Rueedi, R. ; Ruggiero, D. ; Sala, C.F. ; Schmidt, M.K. ; Scott, R.A. ; Shah, M. ; Sorice, R. ; Southey, M.C. ; Sovio, U. ; Stampfer, M. ; Steri, M. ; Strauch, K. ; Tanaka, T. ; Tikkanen, E. ; Timpson, N.J. ; Traglia, M. ; Truong, T. ; Tyrer, J.P. ; Uitterlinden, A.G. ; Edwards, D.R.V. ; Vitart, V. ; Völker, U. ; Vollenweider, P. ; Wang, Q. ; Widen, E. ; van Dijk, K.W. ; Willemsen, G. ; Winqvist, R. ; Wolffenbuttel, B.H.R. ; Zhao, J.H. ; Zoledziewska, M. ; Zygmunt, M. ; Alizadeh, B.Z. ; Boomsma, D.I. ; Ciullo, M. ; Cucca, F. ; Esko, T. ; Franceschini, N. ; Gieger, C. ; Gudnason, V. ; Hayward, C. ; Kraft, P. ; Lawlor, D.A. ; Magnusson, P.K.E. ; Martin, N.G. ; Mook-Kanamori, D.O. ; Nohr, E.A. ; Polasek, O. ; Porteous, D.J. ; Price, A.L. ; Ridker, P.M. ; Snieder, H. ; Spector, T.D. ; Stöckl, D. ; Toniolo, D. ; Ulivi, S. ; Visser, J.A. ; Völzke, H. ; Wareham, N.J. ; Wilson, J.F. ; Spurdle, A.B. ; Thorsteindottir, U. ; Pollard, K.S. ; Easton, D.F. ; Tung, J.Y. ; Chang-Claude, J. ; Hinds, D.A. ; Murray, A. ; Murabito, J.M. ; Stefansson, K. ; Ong, K.K. ; Perry, J.R.B.
Nat. Genet. 49, 834-841 (2017)
The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to ∼370,000 women, we identify 389 independent signals (P < 5 × 10(-8)) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain ∼7.4% of the population variance in age at menarche, corresponding to ∼25% of the estimated heritability. We implicate ∼250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility.
Wissenschaftlicher Artikel
Scientific Article
Koch, M. ; Freitag-Wolf, S. ; Schlesinger, S. ; Borggrefe, J. ; Hov, J.R. ; Jensen, M.K. ; Pick, J. ; Markus, M.R. ; Höpfner, T. ; Jacobs, G. ; Siegert, S. ; Artati, A. ; Kastenmüller, G. ; Römisch-Margl, W. ; Adamski, J. ; Illig, T. ; Nothnagel, M. ; Karlsen, T.H. ; Schreiber, S. ; Franke, A. ; Krawczak, M. ; Nöthlings, U. ; Lieb, W.
Eur. J. Clin. Nutr. 71, 995–1001 (2017)
BACKGROUND/OBJECTIVES: Fatty liver disease (FLD) is an important intermediate trait along the cardiometabolic disease spectrum and strongly associates with type 2 diabetes. Knowledge of biological pathways implicated in FLD is limited. An untargeted metabolomic approach might unravel novel pathways related to FLD. SUBJECTS/METHODS: In a population-based sample (n=555) from Northern Germany, liver fat content was quantified as liver signal intensity using magnetic resonance imaging. Serum metabolites were determined using a non-targeted approach. Partial least squares regression was applied to derive a metabolomic score, explaining variation in serum metabolites and liver signal intensity. Associations of the metabolomic score with liver signal intensity and FLD were investigated in multivariable-adjusted robust linear and logistic regression models, respectively. Metabolites with a variable importance in the projection >1 were entered in in silico overrepresentation and pathway analyses. RESULTS: In univariate analysis, the metabolomics score explained 23.9% variation in liver signal intensity. A 1-unit increment in the metabolomic score was positively associated with FLD (n=219; odds ratio: 1.36; 95% confidence interval: 1.27-1.45) adjusting for age, sex, education, smoking and physical activity. A simplified score based on the 15 metabolites with highest variable importance in the projection statistic showed similar associations. Overrepresentation and pathway analyses highlighted branched-chain amino acids and derived gamma-glutamyl dipeptides as significant correlates of FLD. CONCLUSIONS: A serum metabolomic profile was associated with FLD and liver fat content. We identified a simplified metabolomics score, which should be evaluated in prospective studies.European Journal of Clinical Nutrition advance online publication, 5 April 2017; doi:10.1038/ejcn.2017.43.
Wissenschaftlicher Artikel
Scientific Article
Hastreiter, M. ; Jeske, T. ; Hoser, J.D.S. ; Kluge, M. ; Ahomaa, K. ; Friedl, M.-S. ; Kopetzky, S.J. ; Quell, J. ; Mewes, H.-W. ; Küffner, R.
Bioinformatics 33, 1565-1567 (2017)
Analysis of Next Generation Sequencing (NGS) data requires the processing of large datasets by chaining various tools with complex input and output formats. In order to automate data analysis, we propose to standardize NGS tasks into modular workflows. This simplifies reliable handling and processing of NGS data, and corresponding solutions become substantially more reproducible and easier to maintain. Here, we present a documented, linux-based, toolbox of 42 processing modules that are combined to construct workflows facilitating a variety of tasks such as DNAseq and RNAseq analysis. We also describe important technical extensions. The high throughput executor (HTE) helps to increase the reliability and to reduce manual interventions when processing complex datasets. We also provide a dedicated binary manager that assists users in obtaining the modules' executables and keeping them up to date. As basis for this actively developed toolbox we use the workflow management software KNIME.
Wissenschaftlicher Artikel
Scientific Article
Piontek, U. ; Wallaschofski, H. ; Kastenmüller, G. ; Suhre, K. ; Völzke, H. ; Do, K.T. ; Artati, A. ; Nauck, M. ; Adamski, J. ; Friedrich, N. ; Pietzner, M.
Sci. Rep. 7:2235 (2017)
The role of androgens in metabolism with respect to sex-specific disease associations is poorly understood. Therefore, we aimed to provide molecular signatures in plasma and urine of androgen action in a sex-specific manner using state-of-the-art metabolomics techniques. Our study population consisted of 430 men and 343 women, aged 20-80 years, who were recruited for the cross-sectional population-based Study of Health in Pomerania (SHIP-TREND), Germany. We used linear regression models to identify associations between testosterone, androstenedione and dehydroepiandrosterone-sulfate (DHEAS) as well as sex hormone-binding globulin and plasma or urine metabolites measured by mass spectrometry. The analyses revealed major sex-specific differences in androgen-associated metabolites, particularly for levels of urate, lipids and metabolic surrogates of lifestyle factors, like cotinine or piperine. In women, in particular in the postmenopausal state, androgens showed a greater impact on the metabolome than in men (especially DHEAS and lipids were highly related in women). We observed a novel association of androstenedione on the metabolism of biogenic amines and only a small sex-overlap of associations within steroid metabolism. The present study yields new insights in the interaction between androgens and metabolism, especially about their implication in female metabolism.
Wissenschaftlicher Artikel
Scientific Article
Menni, C. ; Zierer, J. ; Valdes, A.M. ; Spector, T.D.
Nat. Rev. Rheumatol. 13, 174-181 (2017)
Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.
Review
Review
Adam, J. ; Brandmaier, S. ; Troll, M. ; Rotter, M. ; Mohney, R.P. ; Heier, M. ; Adamski, J. ; Li, Y. ; Neschen, S. ; Kastenmüller, G. ; Suhre, K. ; Ankerst, D.P. ; Meitinger, T. ; Wang-Sattler, R.
Diabetes 66, e3-e4 (2017)
Letter to the Editor
Letter to the Editor
Keser, T. ; Vučković, F. ; Barrios, C. ; Zierer, J. ; Wahl, A. ; Akinkuolie, A.O. ; Stambuk, J. ; Nakić, N. ; Pavić, T. ; Periša, J. ; Mora, S. ; Gieger, C. ; Menni, C. ; Spector, T.D. ; Gornik, O. ; Lauc, G.
Biochim. Biophys. Acta-Gen. Subj. 1861, 1152-1158 (2017)
BACKGROUND: Statins are among the most widely prescribed medications worldwide and usually many individuals involved in clinical and population studies are on statin therapy. Immunoglobulin G (IgG) glycosylation has been associated with numerous cardiometabolic risk factors. METHODS: The aim of this study was to investigate the possible association of statin use with N-glycosylation of IgG. The association was analyzed in two large population cohorts (TwinsUK and KORA) using hydrophilic interaction liquid chromatography (HILIC-UPLC) in the TwinsUK cohort and reverse phase liquid chromatography coupled with electrospray mass spectrometry (LC-ESI-MS) in the KORA cohort. Afterwards we investigated the same association for only one statin (rosuvastatin) in a subset of individuals from the randomized double-blind placebo-controlled JUPITER study using LC-ESI-MS for IgG glycome and HILIC-UPLC for total plasma N-glycome. RESULTS: In the TwinsUK population, the use of statins was associated with higher levels of core-fucosylated biantennary glycan structure with bisecting N-acetylglucosamine (FA2B) and lower levels of core-fucosylated biantennary digalactosylated monosialylated glycan structure (FA2G2S1). The association between statin use and FA2B was replicated in the KORA cohort. In the JUPITER trial we found no statistically significant differences between the randomly allocated placebo and rosuvastatin groups. CONCLUSIONS: In the TwinsUK and KORA cohorts, statin use was associated with a small increase of pro-inflammatory IgG glycan, although this finding was not confirmed in a subset of participants from the JUPITER trial. GENERAL SIGNIFICANCE: Even if the association between IgG N-glycome and statins exists, it is not large enough to pose a problem for glycomic studies.
Wissenschaftlicher Artikel
Scientific Article
Suhre, K.° ; Arnold, M. ; Bhagwat, A.M. ; Cotton, R.J. ; Engelke, R. ; Raffler, J. ; Sarwath, H. ; Thareja, G. ; Wahl, A. ; DeLisle, R.K. ; Gold, L. ; Pezer, M. ; Lauc, G. ; El-Din Selim, M.A. ; Mook-Kanamori, D.O. ; Al-Dous, E.K. ; Mohamoud, Y.A. ; Malek, J.A. ; Strauch, K. ; Grallert, H. ; Peters, A. ; Kastenmüller, G. ; Gieger, C.° ; Graumann, J.
Nat. Commun. 8:14357 (2017)
Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.
Wissenschaftlicher Artikel
Scientific Article
Siskos, A.P.# ; Jain, P.# ; Römisch-Margl, W. ; Bennett, M.J. ; Achaintre, D. ; Asad, Y. ; Marney, L. ; Richardson, L. ; Koulman, A. ; Griffin, J.L. ; Raynaud, F. ; Scalbert, A. ; Adamski, J. ; Prehn, C. ; Keun, H.C.
Anal. Chem. 89, 656-665 (2017)
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.
Wissenschaftlicher Artikel
Scientific Article
2016
Much, D. ; Beyerlein, A. ; Kindt, A. ; Krumsiek, J. ; Rossbauerl, M. ; Hofelich, A. ; Hivner, S. ; Herbst, M. ; Römisch-Margl, W. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Theis, F.J. ; Ziegler, A.-G. ; Hummel, S.
Diabetologia 59, S187-S187 (2016)
Meeting abstract
Meeting abstract
Zierer, J. ; Pallister, T. ; Tsai, P.C. ; Krumsiek, J. ; Bell, J.T. ; Lauc, G. ; Spector, T.D. ; Menni, C. ; Kastenmüller, G.
Sci. Rep. 6:37646 (2016)
Although association studies have unveiled numerous correlations of biochemical markers with age and age-related diseases, we still lack an understanding of their mutual dependencies. To find molecular pathways that underlie age-related diseases as well as their comorbidities, we integrated aging markers from four different high-throughput omics datasets, namely epigenomics, transcriptomics, glycomics and metabolomics, with a comprehensive set of disease phenotypes from 510 participants of the TwinsUK cohort. We used graphical random forests to assess conditional dependencies between omics markers and phenotypes while eliminating mediated associations. Applying this novel approach for multi-omics data integration yields a model consisting of seven modules that represent distinct aspects of aging. These modules are connected by hubs that potentially trigger comorbidities of age-related diseases. As an example, we identified urate as one of these key players mediating the comorbidity of renal disease with body composition and obesity. Body composition variables are in turn associated with inflammatory IgG markers, mediated by the expression of the hormone oxytocin. Thus, oxytocin potentially contributes to the development of chronic low-grade inflammation, which often accompanies obesity. Our multi-omics graphical model demonstrates the interconnectivity of age-related diseases and highlights molecular markers of the aging process that might drive disease comorbidities.
Wissenschaftlicher Artikel
Scientific Article
Muschaweckh, A. ; Buchholz, V.R. ; Fellenzer, A. ; Hessel, C. ; König, P.A ; Tao, S. ; Tao, R. ; Heikenwälder, M. ; Busch, D.H. ; Korn, T. ; Kastenmüller, G. ; Drexler, I. ; Gasteiger, G.
J. Exp. Med. 213, 3075-3086 (2016)
Tissue-resident memory CD8+ T cells (TRM) constitute a major component of the immune-surveillance system in nonlymphoid organs. Local, noncognate factors are both necessary and sufficient to support the programming of TRM cell fate in tissue-infiltrating T cells. Recent evidence suggests that TCR signals received in infected nonlymphoid tissues additionally contribute to TRM cell formation. Here, we asked how antigen-dependent pathways influence the generation of skin-resident memory T cells that arise from a polyclonal repertoire of cells induced by infection with an antigenically complex virus and recombinant vaccine vector. We found that CD8+ T cells of different specificities underwent antigen-dependent competition in the infected tissue, which shaped the composition of the local pool of TRM cells. This local cross-competition was active for T cells recognizing antigens that are coexpressed by infected cells. In contrast, TRM cell development remained largely undisturbed by the presence of potential competitors when antigens expressed in the same tissue were segregated through infection with antigenically distinct viral quasispecies. Functionally, local cross-competition might serve as a gatekeeping mechanism to regulate access to the resident memory niche and to fine-tune the local repertoire of antiviral TRM cells.
Wissenschaftlicher Artikel
Scientific Article
Ward-Caviness, C.K. ; Breitner, S. ; Wolf, K. ; Cyrys, J. ; Kastenmüller, G. ; Wang-Sattler, R. ; Schneider, A.E. ; Peters, A.
Int. J. Epidemiol. 45, 1528-1538 (2016)
Background: Short-term exposure to air pollution is associated with morbidity and mortality. Metabolites are intermediaries in biochemical processes, and associations between air pollution and metabolites can yield unique mechanistic insights. Methods: We used independent cross-sectional samples with targeted metabolomics (138 metabolites across five metabolite classes) from three cohort studies, each a part of the Cooperative Health Research in the Region of Augsburg (KORA). The KORA cohorts are numbered (1 to 4) according to which survey they belong to, and lettered S or F according to whether the survey was a baseline or follow-up survey. KORA F4 (N = 3044) served as our discovery cohort, with KORA S4 (N = 485) serving as the primary replication cohort. KORA F4 and KORA S4 were primarily fasting cohorts. We used the non-fasting KORA F3 (N = 377) cohort to evaluate replicated associations in non-fasting individuals, and we performed a random effects meta-analysis of all three cohorts. Associations between the 0–4-day lags and the 5-day average of particulate matter (PM)2.5, NO2 and ozone were modelled via generalized additive models. All air pollution exposures were scaled to the interquartile range, and effect estimates presented as percent changes relative to the geometric mean of the metabolite concentration (ΔGM). Results: There were 10 discovery cohort associations, of which seven were lysophosphatidylcholines (LPCs); NO2 was the most ubiquitous exposure (5/10). The 5-day average NO2-LPC(28:0) association was associated at a Bonferroni corrected P-value threshold (P < 1.2x10−4) in KORA F4 [ΔGM = 11.5%; 95% confidence interval (CI) = 6.60, 16.3], and replicated (P < 0.05) in KORA S4 (ΔGM = 21.0%; CI = 4.56, 37.5). This association was not observed in the non-fasting KORA F3 cohort (ΔGM = −5.96%; CI = −26.3, 14.3), but remained in the random effects meta-analysis (ΔGM = 10.6%; CI = 0.16, 21). Conclusions: LPCs are associated with short-term exposure to air pollutants, in particular NO2. Further research is needed to understand the effect of nutritional/fasting status on these associations and the causal mechanisms linking air pollution exposure and metabolite profiles.
Wissenschaftlicher Artikel
Scientific Article
Lacruz, M.E. ; Kluttig, A. ; Tiller, D. ; Medenwald, D. ; Giegling, I. ; Rujescu, D. ; Prehn, C. ; Adamski, J. ; Frantz, S. ; Greiser, K.H. ; Emeny, R.T. ; Kastenmüller, G. ; Haerting, J.
Circ. Cardiovasc. Genet. 9, 487-494 (2016)
BACKGROUND: -The effects of lifestyle risk-factors considered collectively on the human metabolism are so far unknown. We aim to investigate the association of these risk-factors with metabolites and their changes over 4 years. METHODS AND RESULTS: -163 metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45-83 at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lyso-phosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (BMI, alcohol consumption, smoking, diet and exercise). Multilevel or simple linear regression modelling adjusted for relevant covariates was used for the evaluation of cross-sectional and longitudinal associations respectively, multiple testing correction was based on false discovery rate. BMI, alcohol and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk-factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI, and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines. CONCLUSIONS: -This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.
Wissenschaftlicher Artikel
Scientific Article
Knacke, H. ; Pietzner, M. ; Do, K.T. ; Römisch-Margl, W. ; Kastenmüller, G. ; Völker, U. ; Völzke, H. ; Krumsiek, J. ; Artati, A. ; Wallaschofski, H. ; Nauck, M. ; Suhre, K. ; Adamski, J. ; Friedrich, N.
J. Clin. Endocrinol. Metab. 101, 4730-4742:jc20162588 (2016)
OBJECTIVE: Insulin-like Growth Factor (IGF-I) 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-I levels with multi-fluid untargeted metabolomics data. METHODS: Plasma/urine samples of 995 non-diabetic participants of the Study of Health in Pomerania (SHIP-TREND) were characterized by mass spectrometry. Sex-specific linear regression analyses were performed to assess the association of IGF-I and IGF-I/IGFBP3 ratio with metabolites. Additionally, the predictive ability of the plasma and urine metabolome for IGF-I was assessed by OPLS analyses. RESULTS: and discussion: We revealed a multi-faceted image of associated metabolites with large sex differences. Confirming previous reports, we detected relations between IGF-I and steroid hormones or related intermediates. Furthermore, various associated metabolites were previously mentioned regarding IGF-I associated diseases, e.g. 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, e.g. phospholipids. Bradykinin, fatty acid derivatives and cortisol, which were inversely associated with IGF-I, might establish a link of IGF-I with inflammation. For the first time we showed an association between IGF-I and pipecolate, a metabolite linked to amino acid metabolism. Our study demonstrates that IGF-I action on metabolism is tractable even in healthy subjects and that the findings provide a solid basis for further experimental/clinical investigation, e.g. searching for inflammatory, cardiovascular disease or metabolic syndrome associated biomarkers and therapeutic targets.
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Adam, J. ; Brandmaier, S. ; Leonhardt, J. ; Scheerer, M.F. ; Mohney, R.P. ; Xu, T. ; Bi, J. ; Rotter, M. ; Troll, M. ; Chi, S. ; Heier, M. ; Herder, C. ; Rathmann, W.G. ; Giani, G. ; Adamski, J. ; Illig, T. ; Strauch, K. ; Li, Y. ; Gieger, C. ; Peters, A. ; Suhre, K. ; Ankerst, D.P. ; Meitinger, T. ; Hrabě de Angelis, M. ; Roden, M. ; Neschen, S. ; Kastenmüller, G. ; Wang-Sattler, R.
Diabetes 65, 3776-3785 (2016)
Metformin is the first-line oral medication to increase insulin sensitivity in patients with type 2 diabetes (T2D). Our aim is to investigate metformin's pleiotropic effect using a non-targeted metabolomics approach. We analyzed 353 metabolites in fasting serum samples of the population-based human KORA F4 cohort. To compare T2D patients treated with metformin (mt-T2D, n=74) and those without antidiabetic medication (ndt-T2D, n=115), we used multivariable linear regression models in a cross-sectional study. We applied generalized estimating equation to confirm the initial findings in longitudinal samples of 683 KORA participants. In a translational approach, we used murine plasma, liver, skeletal muscle, and epididymal adipose tissue samples from metformin treated-db/db mice to further corroborate our findings from the human study. We identified two metabolites significantly (P<1.42E-04) associated with metformin treatment. Citrulline showed lower values and an unknown metabolite X-21365 showed higher relative concentrations in human serum when comparing mt-T2D with ndt-T2D. Citrulline was confirmed to be significantly (P<2.96E-04) decreased at seven years' follow up in patients who started metformin treatment. In mice, we validated significantly (P<4.52E-07) lower citrulline values in plasma, skeletal muscle, and adipose tissue of metformin treated animals, but not in their liver. The lowered values of citrulline we observed by using a non-targeted approach, most likely result from metformin's pleiotropic effect on the interlocked urea and nitric oxide cycle. The translational data derived from of multiple murine tissues corroborated and complemented the findings from the human cohort.
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Beger, R.D. ; Dunn, W.A, Jr. ; Schmidt, M.A. ; Gross, S.S. ; Kirwan, J.A. ; Cascante, M. ; Brennan, L. ; Wishart, D.S. ; Oresič, M. ; Hankemeier, T. ; Broadhurst, D.I. ; Lane, A.N. ; Suhre, K. ; Kastenmüller, G. ; Sumner, S.J. ; Thiele, I. ; Fiehn, O. ; Kaddurah-Daouk, R.
Metabolomics 12, 149 (2016)
Introduction: Background to metabolomics: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Conclusions: Key scientific concepts and recommendations for precision medicine: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
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Vogt, S. ; Wahl, S. ; Kettunen, J. ; Breitner, S. ; Kastenmüller, G. ; Gieger, C. ; Suhre, K. ; Waldenberger, M. ; Kratzsch, J. ; Perola, M. ; Salomaa, V. ; Blankenberg, S. ; Zeller, T. ; Soininen, P. ; Kangas, A.J. ; Peters, A. ; Grallert, H. ; Ala-Korpela, M. ; Thorand, B.
Int. J. Epidemiol. 45, 1469-1481 (2016)
BACKGROUND: Numerous observational studies have observed associations between vitamin D deficiency and cardiometabolic diseases, but these findings might be confounded by obesity. A characterization of the metabolic profile associated with serum 25-hydroxyvitamin D [25(OH)D] levels, in general and stratified by abdominal obesity, may help to untangle the relationship between vitamin D, obesity and cardiometabolic health. METHODS: Serum metabolomics measurements were obtained from a nuclear magnetic resonance spectroscopy (NMR)- and a mass spectrometry (MS)-based platform. The discovery was conducted in 1726 participants of the population-based KORA-F4 study, in which the associations of the concentrations of 415 metabolites with 25(OH)D levels were assessed in linear models. The results were replicated in 6759 participants (NMR) and 609 (MS) participants, respectively, of the population-based FINRISK 1997 study. RESULTS: Mean [standard deviation (SD)] 25(OH)D levels were 15.2 (7.5) ng/ml in KORA F4 and 13.8 (5.9) ng/ml in FINRISK 1997; 37 metabolites were associated with 25(OH)D in KORA F4 at P < 0.05/415. Of these, 30 associations were replicated in FINRISK 1997 at P < 0.05/37. Among these were constituents of (very) large very-low-density lipoprotein and small low-density lipoprotein subclasses and related measures like serum triglycerides as well as fatty acids and measures reflecting the degree of fatty acid saturation. The observed associations were independent of waist circumference and generally similar in abdominally obese and non-obese participants. CONCLUSIONS: Independently of abdominal obesity, higher 25(OH)D levels were associated with a metabolite profile characterized by lower concentrations of atherogenic lipids and a higher degree of fatty acid polyunsaturation. These results indicate that the relationship between vitamin D deficiency and cardiometabolic diseases is unlikely to merely reflect obesity-related pathomechanisms.
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Pallister, T.° ; Haller, T. ; Thorand, B. ; Altmaier, E. ; Cassidy, A. ; Martin, T. ; Jennings, A. ; Mohney, R.P. ; Gieger, C. ; MacGregor, A. ; Kastenmüller, G. ; Metspalu, A. ; Spector, T.D. ; Menni, C.°
Eur. J. Nutr. 56, 2379-2391 (2016)
PURPOSE: Milk provides a significant source of calcium, protein, vitamins and other minerals to Western populations throughout life. Due to its widespread use, the metabolic and health impact of milk consumption warrants further investigation and biomarkers would aid epidemiological studies. METHODS: Milk intake assessed by a validated food frequency questionnaire was analyzed against fasting blood metabolomic profiles from two metabolomic platforms in females from the TwinsUK cohort (n = 3559). The top metabolites were then replicated in two independent populations (EGCUT, n = 1109 and KORA, n = 1593), and the results from all cohorts were meta-analyzed. RESULTS: Four metabolites were significantly associated with milk intake in the TwinsUK cohort after adjustment for multiple testing (P < 8.08 × 10(-5)) and covariates (BMI, age, batch effects, family relatedness and dietary covariates) and replicated in the independent cohorts. Among the metabolites identified, the carnitine metabolite trimethyl-N-aminovalerate (β = 0.012, SE = 0.002, P = 2.98 × 10(-12)) and the nucleotide uridine (β = 0.004, SE = 0.001, P = 9.86 × 10(-6)) were the strongest novel predictive biomarkers from the non-targeted platform. Notably, the association between trimethyl-N-aminovalerate and milk intake was significant in a group of MZ twins discordant for milk intake (β = 0.050, SE = 0.015, P = 7.53 × 10(-4)) and validated in the urine of 236 UK twins (β = 0.091, SE = 0.032, P = 0.004). Two metabolites from the targeted platform, hydroxysphingomyelin C14:1 (β = 0.034, SE = 0.005, P = 9.75 × 10(-14)) and diacylphosphatidylcholine C28:1 (β = 0.034, SE = 0.004, P = 4.53 × 10(-16)), were also replicated. CONCLUSIONS: We identified and replicated in independent populations four novel biomarkers of milk intake: trimethyl-N-aminovalerate, uridine, hydroxysphingomyelin C14:1 and diacylphosphatidylcholine C28:1. Together, these metabolites have potential to objectively examine and refine milk-disease associations.
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Fall, T. ; Salihovic, S. ; Brandmaier, S. ; Nowak, C. ; Ganna, A. ; Gustafsson, S. ; Broeckling, C.D. ; Prenni, J.E. ; Kastenmüller, G. ; Peters, A. ; Magnusson, P.K. ; Wang-Sattler, R. ; Giedraitis, V. ; Berne, C. ; Gieger, C. ; Pedersen, N.L. ; Ingelsson, E. ; Lind, L.
Diabetologia 59, 2114-2124 (2016)
AIMS/HYPOTHESIS: Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. METHODS: In this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies. RESULTS: Out of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes. CONCLUSIONS/INTERPRETATION: We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. ACCESS TO RESEARCH MATERIALS: Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).
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Much, D. ; Beyerlein, A. ; Kindt, A. ; Krumsiek, J. ; Stückler, F. ; Rossbauer, M. ; Hofelich, A. ; Wiesenäcker, D. ; Hivner, S. ; Herbst, M. ; Römisch-Margl, W. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Theis, F.J. ; Ziegler, A.-G. ; Hummel, S.
Diabetologia 59, 2193-2202 (2016)
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.
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Altmaier, E. ; Menni, C. ; Heier, M. ; Meisinger, C. ; Thorand, B. ; Quell, J. ; Kobl, M. ; Römisch-Margl, W. ; Valdes, A.M. ; Mangino, M. ; Waldenberger, M. ; Strauch, K. ; Illig, T. ; Adamski, J. ; Spector, T. ; Gieger, C. ; Suhre, K. ; Kastenmüller, G.
PLoS ONE 11:e0153163 (2016)
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.
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Scientific Article
Yet, I. ; Menni, C. ; Shin, S.Y. ; Mangino, M. ; Soranzo, N. ; Adamski, J. ; Suhre, K. ; Spector, T.D. ; Kastenmüller, G.° ; Bell, J.T.°
PLoS ONE 11:e0153672 (2016)
Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.
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Fard, D. ; Läer, K. ; Rothämel, T. ; Schürmann, P. ; Arnold, M. ; Cohen, M. ; Vennemann, M. ; Pfeiffer, H. ; Bajanowski, T. ; Pfeufer, A. ; Dörk, T. ; Klintschar, M.
Int. J. Legal Med. 130, 1025-1033 (2016)
BACKGROUND: Sudden infant death syndrome (SIDS) causes early infant death with an incidence between 0.5 and 2.5 cases among 1000 live births. Besides central sleep apnea and thermal dysregulation, infections have been repeatedly suggested to be implicated in SIDS etiology. METHODS: To test the risk contribution of common genetic variants related to infection, we genotyped 40 single-nucleotide polymorphisms (SNPs) from 15 candidate genes for association with SIDS in a total of 579 cases and 1124 controls from Germany and the UK in a two-stage case control design. RESULTS: The discovery-stage series (267 SIDS cases and 303 controls) revealed nominally significant associations for variants in interleukin 6 (IL6) (rs1880243), interleukin 10 (IL10) (rs1800871, rs1800872), and mannose-binding lectin 2 (MBL2) (rs930506), and for several other variants in subgroups. Meta-analyses were then performed in adding genotype information from a genome-wide association study of another 312 European SIDS cases and 821 controls. Overall associations were observed for two independent variants in MBL2: rs930506 in a co-dominant model (odds ratio (OR) = 0.82, p = 0.04) and rs1838065 in a dominant model (OR = 1.27, p = 0.03). CONCLUSION: Our study did not replicate published associations of IL10 variants with SIDS. However, the evidence for two independent MBL2 variants in the combined analysis of two large series seems consistent with the hypothesis that infection may play a role in SIDS pathogenesis.
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Zierer, J. ; Kastenmüller, G. ; Suhre, K. ; Gieger, C. ; Codd, V. ; Tsai, P.C. ; Bell, J. ; Peters, A. ; Strauch, K. ; Schulz, H. ; Weidinger, S. ; Mohney, R.P. ; Samani, N.J. ; Spector, T. ; Mangino, M. ; Menni, C.
Aging 8, 77-94 (2016)
Leukocyte telomere length (LTL) is considered one of the most predictive markers of biological aging. The aim of this study was to identify novel pathways regulating LTL using a metabolomics approach. To this end, we tested associations between 280 blood metabolites and LTL in 3511 females from TwinsUK and replicated our results in the KORA cohort. We furthermore tested significant metabolites for associations with several aging-related phenotypes, gene expression markers and epigenetic markers to investigate potential underlying pathways. Five metabolites were associated with LTL: Two lysolipids, 1-stearoylglycerophosphoinositol (P=1.6×10-5) and 1-palmitoylglycerophosphoinositol (P=1.6×10-5), were found to be negatively associated with LTL and positively associated with phospholipase A2 expression levels suggesting an involvement of fatty acid metabolism and particularly membrane composition in biological aging. Moreover, two gamma-glutamyl amino acids, gamma-glutamyltyrosine (P=2.5×10-6) and gamma-glutamylphenylalanine (P=1.7×10-5), were negatively correlated with LTL. Both are products of the glutathione cycle and markers for increased oxidative stress. Metabolites were also correlated with functional measures of aging, i.e. higher blood pressure and HDL cholesterol levels and poorer lung, liver and kidney function. Our results suggest an involvement of altered fatty acid metabolism and increased oxidative stress in human biological aging, reflected by LTL and age-related phenotypes of vital organ systems.
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Schulte, E.C.# ; Altmaier, E.# ; Berger, H.S. ; Do, K.T. ; Kastenmüller, G. ; Wahl, S. ; Adamski, J. ; Peters, A. ; Krumsiek, J. ; Suhre, K. ; Haslinger, B. ; Ceballos-Baumann, A. ; Gieger, C. ; Winkelmann, J.
PLoS ONE 11:e0147129 (2016)
BACKGROUND: Serum metabolite profiling can be used to identify pathways involved in the pathogenesis of and potential biomarkers for a given disease. Both restless legs syndrome (RLS) and Parkinson`s disease (PD) represent movement disorders for which currently no blood-based biomarkers are available and whose pathogenesis has not been uncovered conclusively. We performed unbiased serum metabolite profiling in search of signature metabolic changes for both diseases. METHODS: 456 metabolites were quantified in serum samples of 1272 general population controls belonging to the KORA cohort, 82 PD cases and 95 RLS cases by liquid-phase chromatography and gas chromatography separation coupled with tandem mass spectrometry. Genetically determined metabotypes were calculated using genome-wide genotyping data for the 1272 general population controls. RESULTS: After stringent quality control, we identified decreased levels of long-chain (polyunsaturated) fatty acids of individuals with PD compared to both RLS (PD vs. RLS: p = 0.0001 to 5.80x10-9) and general population controls (PD vs. KORA: p = 6.09x10-5 to 3.45x10-32). In RLS, inositol metabolites were increased specifically (RLS vs. KORA: p = 1.35x10-6 to 3.96x10-7). The impact of dopaminergic drugs was reflected in changes in the phenylalanine/tyrosine/dopamine metabolism observed in both individuals with RLS and PD. CONCLUSIONS: A first discovery approach using serum metabolite profiling in two dopamine-related movement disorders compared to a large general population sample identified significant alterations in the polyunsaturated fatty acid metabolism in PD and implicated the inositol metabolism in RLS. These results provide a starting point for further studies investigating new perspectives on factors involved in the pathogenesis of the two diseases as well as possible points of therapeutic intervention.
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Vehmas, A.P. ; Adam, M. ; Laajala, T.D. ; Kastenmüller, G. ; Prehn, C. ; Rozman, J. ; Ohlsson, C. ; Fuchs, H. ; Hrabě de Angelis, M. ; Gailus-Durner, V. ; Elo, L.L. ; Aittokallio, T. ; Adamski, J. ; Corthals, G.L. ; Poutanen, M. ; Strauss, L.
J. Proteomics 133, 66-75 (2016)
Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. The cytochrome P450 aromatase (P450arom) is an enzyme converting androgens to estrogens. Male mice overexpressing human aromatase enzyme (AROM+ mice), and thus have high circulating E/A, were used as a model in this study. Proteomics and gene expression analyses indicated an increase in the peroxisomal β-oxidation in the liver of AROM+ mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we noted that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM+ liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM+ mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal β-oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism. BIOLOGICAL SIGNIFICANCE: The role of sex steroid hormones in the development of metabolic diseases is a topical issue. Lipid metabolism in both sexes supposedly benefits from estrogens, and low circulating estrogen to androgen ratio has been shown to lead to liver steatosis in males. However, there are no comprehensive studies showing the effects of sex steroid hormones on the expression of genes regulating liver lipid metabolism on both mRNA and protein levels. In this study a combination of quantitative MS-based proteome measurements and mRNA microarray both consistently indicated a set of genes that are deregulated in the liver of male mice having high circulating E/A. Interestingly, the results of targeted profiling of phospholipids in the plasma by LC-MS/MS were in line with the mRNA and protein measurements carried out in the liver, suggesting that plasma phospholipid profile could be used as an indicator of altered liver lipid metabolism.
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Suhre, K. ; Raffler, J. ; Kastenmüller, G.
Arch. Biochem. Biophys. 589, 168-176 (2016)
Genome-wide association studies with concentrations of hundreds of small molecules in samples collected from thousands of individuals (mGWAS) access otherwise inaccessible natural genetic experiments and their influence on the metabolic capacities of the human body. By sampling the natural metabolic and genetic variability that is present in the general population, mGWAS identified over 150 associations between genetic variants and variation in the metabolic composition of human body fluids. Many of these genetic variants were found to be located in enzyme or transporter coding genes, whose functions match the biochemical nature of the associated metabolites. Associations identified by mGWAS can reveal novel biochemical knowledge, such as the function of uncharacterized genes, the biochemical identity of small molecules, and the structure of entire biochemical pathways. Here we review findings of recent mGWAS and discuss concrete examples of how their results can be interpreted in a biochemical context. We describe online resources that are available for mining mGWAS results. In this context, we present two concepts that also find more general applications in the field of metabolomics: strengthening of associations by looking at ratios between metabolite pairs and reconstruction of metabolic pathways by Gaussian graphical modeling.
Wissenschaftlicher Artikel
Scientific Article
Yousri, N.A. ; Kastenmüller, G. ; AlHaq, W.G. ; Holle, R. ; Kääb, S. ; Mohney, R.P. ; Gieger, C. ; Peters, A. ; Adamski, J. ; Suhre, K. ; Arayssi, T.
J. Proteome Res. 15, 554-562 (2016)
This study aims at identifying metabolites that significantly associate with self-reported joint symptoms (diagnostic) and metabolites that can predict the change from a symptom-free status to the development of self-reported joint symptoms after a 7 years period (prognostic). More than 300 metabolites were analyzed for 2246 subjects from the longitudinal study of the KORA (Cooperative Health Research in the Region of Augsburg, Germany), specifically the fourth survey S4 and its 7-year follow-up study F4. Two types of self-reported symptoms, chronic joint inflammation and worn out joints, were used for the analyses. Diagnostic analysis identified dysregulated metabolites in cases with symptoms compared with controls. Prognostic analysis identified metabolites that differentiate subjects in S4 who remained symptom-free after 7 years (F4) from those who developed any combination of symptoms. 48 metabolites were identified as nominally significantly (p < 0.05) associated with the self-reported symptoms in the diagnostic analysis, among which steroids show Bonferroni significance. 45 metabolites were identified as nominally significantly associated with developing symptoms after 7 years, among which hippurate showed Bonferroni significance. We show that metabolic profiles of self-reported joint symptoms are in line with metabolites known to associate with various forms of arthritis and suggest that future studies may benefit from that by investigating the possible use of self-reporting/questionnaire along with metabolic markers for the early referral of patients for further diagnostic workup and treatment of arthritis.
Wissenschaftlicher Artikel
Scientific Article
Sekula, P. ; Goek, O.N. ; Quaye, L. ; Barrios, C. ; Levey, A.S. ; Römisch-Margl, W. ; Menni, C. ; Yet, I. ; Gieger, C. ; Inker, L.A. ; Adamski, J. ; Gronwald, W. ; Illig, T. ; Dettmer, K. ; Krumsiek, J. ; Oefner, P.J. ; Valdes, A.M. ; Meisinger, C. ; Coresh, J. ; Spector, T.D. ; Mohney, R.P. ; Suhre, K. ; Kastenmüller, G.° ; Köttgen, A.°
J. Am. Soc. Nephrol. 27, 1175-1188 (2016)
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
Wissenschaftlicher Artikel
Scientific Article
Barrios, C. ; Zierer, J. ; Gudelj, I. ; Stambuk, J. ; Ugrina, I. ; Rodriguez, E. ; Soler, M.J. ; Pavić, T. ; Simurina, M. ; Keser, T. ; Pučić-Baković, M. ; Mangino, M. ; Pascual, J. ; Spector, T.D. ; Lauc, G. ; Menni, C.
J. Am. Soc. Nephrol. 27, 933-941 (2016)
Glycans constitute the most abundant and diverse form of the post-translational modifications, and animal studies have suggested the involvement of IgG glycosylation in mechanisms of renal damage. Here, we explored the associations between IgG glycans and renal function in 3274 individuals from the TwinsUK registry. We analyzed the correlation between renal function measured as eGFR and 76 N-glycan traits using linear regressions adjusted for covariates and multiple testing in the larger population. We replicated our results in 31 monozygotic twin pairs discordant for renal function. Results from both analyses were then meta-analyzed. Fourteen glycan traits were associated with renal function in the discovery sample (P<6.5×10(-4)) and remained significant after validation. Those glycan traits belong to three main glycosylation features: galactosylation, sialylation, and level of bisecting N-acetylglucosamine of the IgG glycans. These results show the role of IgG glycosylation in kidney function and provide novel insight into the pathophysiology of CKD and potential diagnostic and therapeutic targets.
Wissenschaftlicher Artikel
Scientific Article
2015
Raffler, J.
München, Ludwig-Maximilians-Universität, Fakultät für Biologie, Diss., 2015, 187 S.
Kastenmüller, G.
Ann. Nutr. Metab. 67, 39-40 (2015)
Meeting abstract
Meeting abstract
Pallister, T. ; Haller, T. ; Thorand, B. ; Altmaier, E. ; Cassidy, A. ; MacGregor, A. ; Kastenmüller, G. ; Metspalu, A. ; Spector, T.D. ; Menni, C.
Ann. Nutr. Metab. 67, 256 (2015)
Meeting abstract
Meeting abstract
Lunetta, K.L.# ; Day, F.R.# ; Sulem, P. ; Ruth, K.S. ; Tung, J.Y. ; Hinds, D.A. ; Esko, T. ; Elks, C.E. ; Altmaier, E. ; He, C. ; Huffman, J.E. ; Mihailov, E. ; Porcu, E. ; Robino, A. ; Rose, L.M. ; Schick, U.M. ; Stolk, L. ; Teumer, A. ; Thompson, D.J. ; Traglia, M. ; Wang, C.A. ; Yerges-Armstrong, L.M. ; Antoniou, A.C. ; Barbieri, C. ; Coviello, A.D. ; Cucca, F. ; Demerath, E.W. ; Dunning, A.M. ; Gandin, I. ; Grove, M.L. ; Gudbjartsson, D.F. ; Hocking, L.J. ; Hofman, A. ; Huang, J. ; Jackson, R.D. ; Karasik, D. ; Kriebel, J. ; Lange, E.M. ; Lange, L.A. ; Langenberg, C. ; Li, X. ; Luan, J. ; Mägi, R. ; Morrison, A.C. ; Padmanabhan, S. ; Pirie, A. ; Polasek, O. ; Porteous, D.J. ; Reiner, A.P. ; Rivadeneira, F. ; Rudan, I. ; Sala, C.F. ; Schlessinger, D. ; Scott, R.A. ; Stöckl, D. ; Visser, J.A. ; Völker, U. ; Vozzi, D. ; Wilson, J.G. ; Zygmunt, M. ; EPIC-Interact Consortium () ; Generation Scotland Consortium () ; Boerwinkle, E. ; Buring, J.E. ; Crisponi, L. ; Easton, D.F. ; Hayward, C. ; Hu, F.B. ; Liu, S. ; Metspalu, A. ; Pennell, C.E. ; Ridker, P.M. ; Strauch, K. ; Streeten, E.A. ; Toniolo, D. ; Uitterlinden, A.G. ; Ulivi, S. ; Völzke, H. ; Wareham, N.J. ; Wellons, M. ; Franceschini, N. ; Chasman, D.I. ; Thorsteinsdottir, U. ; Murray, A. ; Stefansson, K. ; Murabito, J.M. ; Ong, K.K.° ; Perry, J.R.°
Nat. Commun. 6:10257 (2015)
Day, F.R. ; Ruth, K.S. ; Thompson, D.J. ; Lunetta, K.L. ; Pervjakova, N. ; Chasman, D.I. ; Stolk, L. ; Finucane, H.K. ; Sulem, P. ; Bulik-Sullivan, B. ; Esko, T. ; Johnson, A.D. ; Elks, C.E. ; Franceschini, N. ; He, C. ; Altmaier, E. ; Brody, J.A. ; Franke, L.L. ; Huffman, J.E. ; Keller, M.F. ; McArdle, P.F. ; Nutile, T. ; Porcu, E. ; Robino, A. ; Rose, L.M. ; Schick, U.M. ; Smith, J.A. ; Teumer, A. ; Traglia, M. ; Vuckovic, D. ; Yao, J. ; Zhao, W. ; Albrecht, E. ; Amin, N. ; Corre, T. ; Hottenga, J.J. ; Mangino, M. ; Smith, A.V. ; Tanaka, T. ; Abecasis, G.R. ; Andrulis, I.L. ; Anton-Culver, H. ; Antoniou, A.C. ; Arndt, V. ; Arnold, A.M. ; Barbieri, C. ; Beckmann, M.W. ; Beeghly-Fadiel, A. ; Benítez, J. ; Bernstein, L. ; Bielinski, S.J. ; Blomqvist, C. ; Boerwinkle, E. ; Bogdanova, N.V. ; Bojesen, S.E. ; Bolla, M.K. ; Borresen-Dale, A.L. ; Boutin, T.S. ; Brauch, H. ; Brenner, H. ; Brüning, T ; Burwinkel, B. ; Campbell, A. ; Campbell, H. ; Chanock, S.J. ; Chapman, J.R. ; Chen, Y.D. ; Chenevix-Trench, G. ; Couch, F.J. ; Coviello, A.D. ; Cox, A. ; Czene, K. ; Darabi, H. ; de Vivo, I. ; Demerath, E.W. ; Dennis, J. ; Devilee, P. ; Dörk, T. ; Dos-Santos-Silva, I. ; Dunning, A.M. ; Eicher, J.D. ; Fasching, P.A. ; Faul, J.D. ; Figueroa, J. ; Flesch-Janys, D. ; Gandin, I. ; Garcia, M.E. ; Garcia-Closas, M. ; Giles, G.G. ; Girotto, G.G. ; Goldberg, M.S. ; González-Neira, A. ; Goodarzi, M.O. ; Grove, M.L. ; Gudbjartsson, D.F. ; Guénel, P. ; Guo, X. ; Haiman, C.A. ; Hall, P. ; Hamann, U. ; Henderson, B.E. ; Hocking, L.J. ; Hofman, A. ; Homuth, G. ; Hooning, M.J. ; Hopper, J.L. ; Hu, F.B. ; Huang, J. ; Humphreys, K. ; Hunter, D.J. ; Jakubowska, A. ; Jones, S.E. ; Kabisch, M. ; Karasik, D. ; Knight, J.A. ; Kolcic, I. ; Kooperberg, C. ; Kosma, V.M. ; Kriebel, J. ; Kristensen, V. ; Lambrechts, D. ; Langenberg, C. ; Li, J. ; Li, X. ; Lindström, S. ; Liu, Y. ; Luan, J. ; Lubinski, J. ; Mägi, R. ; Mannermaa, A. ; Manz, J. ; Margolin, S. ; Marten, J. ; Martin, N.G. ; Masciullo, C. ; Meindl, A. ; Michailidou, K. ; Mihailov, E. ; Milani, L. ; Milne, R.L. ; Müller-Nurasyid, M. ; Nalls, M. ; Neale, B.M. ; Nevanlinna, H. ; Neven, P. ; Newman, A.B. ; Nørdestgaard, B.G. ; Olson, J.E. ; Padmanabhan, S. ; Peterlongo, P. ; Peters, U. ; Petersmann, A. ; Peto, J. ; Pharoah, P.D. ; Pirastu, N.N. ; Pirie, A. ; Pistis, G. ; Polasek, O. ; Porteous, D.J. ; Psaty, B.M. ; Pylkäs, K. ; Radice, P. ; Raffel, L.J. ; Rivadeneira, F. ; Rudan, I. ; Rudolph, A. ; Ruggiero, D. ; Sala, C.F. ; Sanna, S. ; Sawyer, E.J. ; Schlessinger, D. ; Schmidt, M.K. ; Schmidt, F. ; Schmutzler, R.K. ; Schoemaker, M.J. ; Scott, R.A. ; Seynaeve, C.M. ; Simard, J. ; Sorice, R. ; Southey, M.C. ; Stöckl, D. ; Strauch, K. ; Swerdlow, A. ; Taylor, K.D. ; Thorsteinsdottir, U. ; Toland, A.E. ; Tomlinson, I. ; Truong, T. ; Tryggvadottir, L. ; Turner, S.T. ; Vozzi, D. ; Wang, Q. ; Wellons, M. ; Willemsen, G. ; Wilson, J.F. ; Winqvist, R. ; Wolffenbuttel, B.B. ; Wright, A.F. ; Yannoukakos, D. ; Zemunik, T. ; Zheng, W. ; Zygmunt, M. ; Bergmann, S. ; Boomsma, D.I. ; Buring, J.E. ; Ferrucci, L. ; Montgomery, G.W. ; Gudnason, V. ; Spector, T.D. ; van Duijn, C.M. ; Alizadeh, B.Z. ; Ciullo, M. ; Crisponi, L. ; Easton, D.F. ; Gasparini, P.P. ; Gieger, C. ; Harris, T.B. ; Hayward, C. ; Kardia, S.L. ; Kraft, P. ; McKnight, B. ; Metspalu, A. ; Morrison, A.C. ; Reiner, A.P. ; Ridker, P.M. ; Rotter, J.I. ; Toniolo, D. ; Uitterlinden, A.G. ; Ulivi, S. ; Völzke, H. ; Wareham, N.J. ; Weir, D.R. ; Yerges-Armstrong, L.M. ; Price, A.L. ; Stefansson, K. ; Visser, J.A. ; Ong, K.K. ; Chang-Claude, J. ; Murabito, J.M. ; Perry, J.R. ; Murray, A.
Obstet. Gynecol. 70, 758-762 (2015)
ABSTRACT: Menopause timing has a major impact on infertility and risk of disease. Younger age at natural (nonsurgical) menopause (ANM) is associated with a higher risk of osteoporosis, cardiovascular disease, and type 2 diabetes and a lower risk of breast cancer. Late menopause is associated with a higher risk of breast cancer. It is well known that the age at which women go through menopause is partly determined by genes, but the underlying mechanisms are poorly understood. Genome-wide association studies have identified 18 common genetic variants associated with ANM. These variants explain less than 5% of the variation in ANM compared with the 21% explained by all common variants on genome-wide association study arrays. This genome-wide association study was the collaborative effort of researchers from 177 institutions worldwide. The study was designed to investigate genetic variants associated with timing of menopause among a population of approximately 70,000 women of European ancestry. A dual strategy was used to identify both common and, for the first time, low-frequency coding variants associated with ANM. The causal relationship between ANM and breast cancer was investigated using a Mendelian randomization approach. Combined analysis identified 1208 single-nucleotide polymorphisms (SNPs) of a total of approximately 2.6 million that reached the genome-wide significance threshold for association with ANM. Forty-four regions with common variants were identified; among these 44 loci were 2 rare low-frequency missense alleles of large effect. A majority of ANM SNPs were enriched in DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal relationship between delayed ANM and breast cancer risk; there was approximately 6% increase in risk per year; P = 3 × 10−14); increased risk with delayed menopause appeared to be mediated primarily by prolonged sex hormone exposure in a woman’s lifetime, not DDR mechanisms. This is the first study to confirm the link between early and late menopause and breast cancer risk using genetic information. Age at natural menopause genetic variants influence breast cancer risk primarily through variation in menopause timing. Although carrying higher numbers of ANM-increasing variants and enrichment in DDR genes are associated with a modest increase in breast cancer risk, the major mechanism for increased risk appears to be prolonged estrogen and/or progesterone exposure due to delayed menopause.  
Sonstiges: Meinungsartikel
Other: Opinion
Paternoster, L. ; Standl, M. ; Waage, J. ; Baurecht, H. ; Hotze, M. ; Strachan, D.P. ; Curtin, J.A. ; Bonnelykke, K. ; Tian, C. ; Takahashi, A. ; Esparza-Gordillo, J. ; Alves, A.C. ; Thyssen, J.P. ; den Dekker, H.T. ; Ferreira, M.A. ; Altmaier, E. ; Sleiman, P.M.A. ; Xiao, F.L. ; Gonzalez, J.R. ; Marenholz, I. ; Kalb, B. ; Pino-Yanes, M. ; Xu, C. ; Carstensen, L. ; Groen-Blokhuis, M.M. ; Venturini, C. ; Pennell, C.E. ; Barton, S.J. ; Levin, A.M. ; Curjuric, I. ; Bustamante, M. ; Kreiner-Moller, E. ; Lockett, G.A. ; Bacelis, J. ; Bunyavanich, S. ; Myers, R.A. ; Matanovic, A. ; Kumar, A. ; Tung, J.Y. ; Hirota, T. ; Kubo, M. ; McArdle, W.L. ; Henderson, A.J. ; Kemp, J.P. ; Zheng, J.H. ; Smith, G.D. ; Rueschendorf, F. ; Bauerfeind, A. ; Lee-Kirsch, M.A. ; Arnold, A. ; Homuth, G. ; Schmidt, C.O. ; Mangold, E. ; Cichon, S. ; Keil, T. ; Rodriguez, E. ; Peters, A. ; Franke, A. ; Lieb, W. ; Novak, N. ; Foelster-Holst, R. ; Horikoshi, M. ; Pekkanen, J. ; Sebert, S. ; Husemoen, L.L. ; Grarup, N. ; de Jongste, J.C. ; Rivadeneira, F. ; Hofman, A. ; Jaddoe, V.W.V. ; Pasmans, S.G.M.A. ; Elbert, N.J. ; Uitterlinden, A.G. ; Marks, G.B. ; Thompson, P.J. ; Matheson, M.C. ; Robertson, C.F. ; Ried, J.S. ; Li, J. ; Zuo, X.B. ; Zheng, X.D. ; Yin, X.Y. ; Sun, L.D. ; McAleer, M.A. ; O'Regan, G.M. ; Fahy, C.M.R. ; Campbell, L.E. ; Macek, M. ; Kurek, M. ; Hu, D. ; Eng, C. ; Postma, D.S. ; Feenstra, B. ; Geller, F. ; Hottenga, J.J. ; Middeldorp, C.M. ; Hysi, P. ; Bataille, V. ; Spector, T. ; Tiesler, C.M. ; Thiering, E. ; Pahukasahasram, B. ; Yang, J.J. ; Imboden, M. ; Huntsman, S. ; Vilor-Tejedor, N. ; Relton, C.L. ; Myhre, R. ; Nystad, W. ; Custovic, A. ; Weiss, S.T. ; Meyers, D.A. ; Soederhaell, C. ; Melén, E. ; Ober, C. ; Raby, B.A. ; Simpson, A. ; Jacobsson, B. ; Holloway, J.W. ; Bisgaard, H. ; Sunyer, J. ; Probst-Hensch, N.M. ; Williams, L.K. ; Godfrey, K.M. ; Wang, C.A. ; Boomsma, D.I. ; Melbye, M. ; Koppelman, G.H. ; Jarvis, D. ; McLean, W.H.I. ; Irvine, A.D. ; Zhang, X.J. ; Hakonarson, H. ; Gieger, C. ; Burchard, E.G. ; Martin, N.G. ; Duijts, L. ; Linneberg, A. ; Jarvelin, M.R. ; Nöthen, M.M. ; Lau, S. ; Huebner, N. ; Lee, Y. ; Tamari, M. ; Hinds, D.A. ; Glass, D. ; Brown, S.J. ; Heinrich, J. ; Evans, D.M. ; Weidinger, S. ; Australian Asthma Genetics Consortium (AAGC) () ; EArly Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium ()
Nat. Genet. 47, 1449-1456 (2015)
Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common, complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified ten new risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with new secondary signals at four of these loci). Notably, the new loci include candidate genes with roles in the regulation of innate host defenses and T cell function, underscoring the important contribution of (auto)immune mechanisms to atopic dermatitis pathogenesis.
Wissenschaftlicher Artikel
Scientific Article
Day, F.R. ; Ruth, K.S. ; Thompson, D.J. ; Lunetta, K.L. ; Pervjakova, N. ; Chasman, D.I. ; Stolk, L. ; Finucane, H.K. ; Sulem, P. ; Bulik-Sullivan, B. ; Esko, T. ; Johnson, A.D. ; Elks, C.E. ; Franceschini, N. ; He, C. ; Altmaier, E. ; Brody, J.A. ; Franke, L.L. ; Huffman, J.E. ; Keller, M.F. ; McArdle, P.F. ; Nutile, T. ; Porcu, E. ; Robino, A. ; Rose, L.M. ; Schick, U.M. ; Smith, J.A. ; Teumer, A. ; Traglia, M. ; Vuckovic, D. ; Yao, J. ; Zhao, W. ; Albrecht, E. ; Amin, N. ; Corre, T. ; Hottenga, J.J. ; Mangino, M. ; Smith, A.V. ; Tanaka, T. ; Abecasis, G.R. ; Andrulis, I.L. ; Anton-Culver, H. ; Antoniou, A.C. ; Arndt, V. ; Arnold, A.M. ; Barbieri, C. ; Beckmann, M.W. ; Beeghly-Fadiel, A. ; Benítez, J. ; Bernstein, L. ; Bielinski, S.J. ; Blomqvist, C. ; Boerwinkle, E. ; Bogdanova, N.V. ; Bojesen, S.E. ; Bolla, M.K. ; Borresen-Dale, A.L. ; Boutin, T.S. ; Brauch, H. ; Brenner, H. ; Brüning, T ; Burwinkel, B. ; Campbell, A. ; Campbell, H. ; Chanock, S.J. ; Chapman, J.R. ; Chen, Y.D. ; Chenevix-Trench, G. ; Couch, F.J. ; Coviello, A.D. ; Cox, A. ; Czene, K. ; Darabi, H. ; de Vivo, I. ; Demerath, E.W. ; Dennis, J. ; Devilee, P. ; Dörk, T. ; Dos-Santos-Silva, I. ; Dunning, A.M. ; Eicher, J.D. ; Fasching, P.A. ; Faul, J.D. ; Figueroa, J. ; Flesch-Janys, D. ; Gandin, I. ; Garcia, M.E. ; Garcia-Closas, M. ; Giles, G.G. ; Girotto, G.G. ; Goldberg, M.S. ; González-Neira, A. ; Goodarzi, M.O. ; Grove, M.L. ; Gudbjartsson, D.F. ; Guénel, P. ; Guo, X. ; Haiman, C.A. ; Hall, P. ; Hamann, U. ; Henderson, B.E. ; Hocking, L.J. ; Hofman, A. ; Homuth, G. ; Hooning, M.J. ; Hopper, J.L. ; Hu, F.B. ; Huang, J. ; Humphreys, K. ; Hunter, D.J. ; Jakubowska, A. ; Jones, S.E. ; Kabisch, M. ; Karasik, D. ; Knight, J.A. ; Kolcic, I. ; Kooperberg, C. ; Kosma, V.M. ; Kriebel, J. ; Kristensen, V. ; Lambrechts, D. ; Langenberg, C. ; Li, J. ; Li, X. ; Lindström, S. ; Liu, Y. ; Luan, J. ; Lubinski, J. ; Mägi, R. ; Mannermaa, A. ; Manz, J. ; Margolin, S. ; Marten, J. ; Martin, N.G. ; Masciullo, C. ; Meindl, A. ; Michailidou, K. ; Mihailov, E. ; Milani, L. ; Milne, R.L. ; Müller-Nurasyid, M. ; Nalls, M. ; Neale, B.M. ; Nevanlinna, H. ; Neven, P. ; Newman, A.B. ; Nørdestgaard, B.G. ; Olson, J.E. ; Padmanabhan, S. ; Peterlongo, P. ; Peters, U. ; Petersmann, A. ; Peto, J. ; Pharoah, P.D. ; Pirastu, N.N. ; Pirie, A. ; Pistis, G. ; Polasek, O. ; Porteous, D.J. ; Psaty, B.M. ; Pylkäs, K. ; Radice, P. ; Raffel, L.J. ; Rivadeneira, F. ; Rudan, I. ; Rudolph, A. ; Ruggiero, D. ; Sala, C.F. ; Sanna, S. ; Sawyer, E.J. ; Schlessinger, D. ; Schmidt, M.K. ; Schmidt, F. ; Schmutzler, R.K. ; Schoemaker, M.J. ; Scott, R.A. ; Seynaeve, C.M. ; Simard, J. ; Sorice, R. ; Southey, M.C. ; Stöckl, D. ; Strauch, K. ; Swerdlow, A. ; Taylor, K.D. ; Thorsteinsdottir, U. ; Toland, A.E. ; Tomlinson, I. ; Truong, T. ; Tryggvadottir, L. ; Turner, S.T. ; Vozzi, D. ; Wang, Q. ; Wellons, M. ; Willemsen, G. ; Wilson, J.F. ; Winqvist, R. ; Wolffenbuttel, B.B. ; Wright, A.F. ; Yannoukakos, D. ; Zemunik, T. ; Zheng, W. ; Zygmunt, M. ; Bergmann, S. ; Boomsma, D.I. ; Buring, J.E. ; Ferrucci, L. ; Montgomery, G.W. ; Gudnason, V. ; Spector, T.D. ; van Duijn, C.M. ; Alizadeh, B.Z. ; Ciullo, M. ; Crisponi, L. ; Easton, D.F. ; Gasparini, P.P. ; Gieger, C. ; Harris, T.B. ; Hayward, C. ; Kardia, S.L. ; Kraft, P. ; McKnight, B. ; Metspalu, A. ; Morrison, A.C. ; Reiner, A.P. ; Ridker, P.M. ; Rotter, J.I. ; Toniolo, D. ; Uitterlinden, A.G. ; Ulivi, S. ; Völzke, H. ; Wareham, N.J. ; Weir, D.R. ; Yerges-Armstrong, L.M. ; Price, A.L. ; Stefansson, K. ; Visser, J.A. ; Ong, K.K. ; Chang-Claude, J. ; Murabito, J.M. ; Perry, J.R. ; Murray, A.
Nat. Genet. 47, 1294-1303 (2015)
Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms.
Wissenschaftlicher Artikel
Scientific Article
Halama, A. ; Horsch, M. ; Kastenmüller, G. ; Möller, G. ; Kumar, P. ; Prehn, C. ; Laumen, H. ; Hauner, H. ; Hrabě de Angelis, M. ; Beckers, J. ; Suhre, K. ; Adamski, J.
Arch. Biochem. Biophys. 589, 93-107 (2015)
Fat cell metabolism has an impact on body homeostasis and its proper function. Nevertheless, the knowledge about simultaneous metabolic processes, which occur during adipogenesis and in mature adipocytes, is limited. Identification of key metabolic events associated with fat cell metabolism could be beneficial in the field of novel drug development, drug repurposing, as well as for the discovery of patterns predicting obesity risk. The main objective of our work was to provide comprehensive characterization of metabolic processes occurring during adipogenesis and in mature adipocytes. In order to globally determine crucial metabolic pathways involved in fat cell metabolism, metabolomics and transcriptomics approaches were applied. We observed significantly regulated metabolites correlating with significantly regulated genes at different stages of adipogenesis. We identified the synthesis of phosphatidylcholines, the metabolism of even and odd chain fatty acids, as well as the catabolism of branched chain amino acids (BCAA; leucine, isoleucine and valine) as key regulated pathways. Our further analysis led to identification of an enzymatic switch comprising the enzymes Hmgcs2 (3-hydroxy-3-methylglutaryl-CoA synthase) and Auh (AU RNA binding protein/enoyl-CoA hydratase) which connects leucine degradation with cholesterol synthesis and which is strongly regulated during adipogenesis. In addition, propionyl-CoA, a product of isoleucine degradation, was identified as a putative substrate for odd chain fatty acid synthesis. The uncovered crosstalk's between BCAA and lipid metabolism during adipogenesis might contribute to the understanding of molecular mechanisms of obesity and have potential implications in obesity prediction.
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Scientific Article
Raffler, J. ; Friedrich, N. ; Arnold, M. ; Kacprowski, T. ; Rueedi, R. ; Altmaier, E. ; Bergmann, S. ; Budde, K. ; Gieger, C. ; Homuth, G. ; Pietzner, M. ; Römisch-Margl, W. ; Strauch, K. ; Völzke, H. ; Waldenberger, M. ; Wallaschofski, H. ; Nauck, M. ; Völker, U. ; Kastenmüller, G. ; Suhre, K.
PLoS Genet. 11:e1005487 (2015)
Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases.
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Scientific Article
Zierer, J. ; Menni, C. ; Kastenmüller, G. ; Spector, T.D.
Aging Cell 14, 933–944 (2015)
Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.
Review
Review
Krumsiek, J.# ; Mittelstraß, K.# ; Do, K.T. ; Stückler, F. ; Ried, J.S. ; Adamski, J. ; Peters, A. ; Illig, T. ; Kronenberg, F. ; Friedrich, N. ; Nauck, M. ; Pietzner, M. ; Mook-Kanamori, D.O. ; Suhre, K. ; Gieger, C. ; Grallert, H. ; Theis, F.J.° ; Kastenmüller, G.°
Metabolomics 11, 1815-1833 (2015)
The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.
Wissenschaftlicher Artikel
Scientific Article
Lunetta, K.L.# ; Day, F.R.# ; Sulem, P. ; Ruth, K.S. ; Tung, J.Y. ; Hinds, D.A. ; Esko, T. ; Elks, C.E. ; Altmaier, E. ; He, C. ; Huffman, J.E. ; Mihailov, E. ; Porcu, E. ; Robino, A. ; Rose, L.M. ; Schick, U.M. ; Stolk, L. ; Teumer, A. ; Thompson, D.J. ; Traglia, M. ; Wang, C.A. ; Yerges-Armstrong, L.M. ; Antoniou, A.C. ; Barbieri, C. ; Coviello, A.D. ; Cucca, F. ; Demerath, E.W. ; Dunning, A.M. ; Gandin, I. ; Grove, M.L. ; Gudbjartsson, D.F. ; Hocking, L.J. ; Hofman, A. ; Huang, J. ; Jackson, R.D. ; Karasik, D. ; Kriebel, J. ; Lange, E.M. ; Lange, L.A. ; Langenberg, C. ; Li, X. ; Luan, J. ; Mägi, R. ; Morrison, A.C. ; Padmanabhan, S. ; Pirie, A. ; Polasek, O. ; Porteous, D.J. ; Reiner, A.P. ; Rivadeneira, F. ; Rudan, I. ; Sala, C.F. ; Schlessinger, D. ; Scott, R.A. ; Stöckl, D. ; Visser, J.A. ; Völker, U. ; Vozzi, D. ; Wilson, J.G. ; Zygmunt, M. ; EPIC-Interact Consortium () ; Generation Scotland Consortium () ; Boerwinkle, E. ; Buring, J.E. ; Crisponi, L. ; Easton, D.F. ; Hayward, C. ; Hu, F.B. ; Liu, S. ; Metspalu, A. ; Pennell, C.E. ; Ridker, P.M. ; Strauch, K. ; Streeten, E.A. ; Toniolo, D. ; Uitterlinden, A.G. ; Ulivi, S. ; Völzke, H. ; Wareham, N.J. ; Wellons, M. ; Franceschini, N. ; Chasman, D.I. ; Thorsteinsdottir, U. ; Murray, A. ; Stefansson, K. ; Murabito, J.M. ; Ong, K.K.° ; Perry, J.R.°
Nat. Commun. 6:7756 (2015)
More than 100 loci have been identified for age at menarche by genome-wide association studies; however, collectively these explain only ∼3% of the trait variance. Here we test two overlooked sources of variation in 192,974 European ancestry women: low-frequency protein-coding variants and X-chromosome variants. Five missense/nonsense variants (in ALMS1/LAMB2/TNRC6A/TACR3/PRKAG1) are associated with age at menarche (minor allele frequencies 0.08-4.6%; effect sizes 0.08-1.25 years per allele; P<5 × 10(-8)). In addition, we identify common X-chromosome loci at IGSF1 (rs762080, P=9.4 × 10(-13)) and FAAH2 (rs5914101, P=4.9 × 10(-10)). Highlighted genes implicate cellular energy homeostasis, post-transcriptional gene silencing and fatty-acid amide signalling. A frequently reported mutation in TACR3 for idiopathic hypogonatrophic hypogonadism (p.W275X) is associated with 1.25-year-later menarche (P=2.8 × 10(-11)), illustrating the utility of population studies to estimate the penetrance of reportedly pathogenic mutations. Collectively, these novel variants explain ∼0.5% variance, indicating that these overlooked sources of variation do not substantially explain the 'missing heritability' of this complex trait.
Wissenschaftlicher Artikel
Scientific Article
Xu, T. ; Brandmaier, S. ; Messias, A.C. ; Herder, C. ; Draisma, H.H. ; Demirkan, A. ; Yu, Z. ; Ried, J.S. ; Haller, T. ; Heier, M. ; Campillos, M. ; Fobo, G. ; Stark, R.G. ; Holzapfel, C. ; Adam, J. ; Chi, S. ; Rotter, M. ; Panni, T. ; Quante, A.S. ; He, Y. ; Prehn, C. ; Römisch-Margl, W. ; Kastenmüller, G. ; Willemsen, G. ; Pool, R. ; Kasa, K. ; van Dijk, K.W. ; Hankemeier, T. ; Meisinger, C. ; Thorand, B. ; Ruepp, A. ; Hrabě de Angelis, M. ; Li, Y. ; Wichmann, H.-E. ; Stratmann, B. ; Strauch, K. ; Metspalu, A. ; Gieger, C. ; Suhre, K. ; Adamski, J. ; Illig, T. ; Rathmann, W. ; Roden, M. ; Peters, A. ; van Duijn, C.M. ; Boomsma, D.I. ; Meitinger, T. ; Wang-Sattler, R.
Diabetes Care 38, 1858-1867 (2015)
OBJECTIVE Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. RESEARCH DESIGN AND METHODS We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. RESULTS We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years’ follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. CONCLUSIONS Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.  
Wissenschaftlicher Artikel
Scientific Article
Kastenmüller, G. ; Raffler, J. ; Gieger, C. ; Suhre, K.
Hum. Mol. Genet. 24, R93-R101 (2015)
Genome-wide association studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interest, including the functional understanding of genetic associations with clinical endpoints, design of strategies to correct dysregulations in metabolic disorders, and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems based downstream analyses. The generated large data sets of genetic associations can now be mined by the biomedical research community and provide valuable resources for hypothesis driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.
Wissenschaftlicher Artikel
Scientific Article
Anton, G. ; Wilson, R. ; Yu, Z. ; Prehn, C. ; Zukunft, S. ; Adamski, J. ; Heier, M. ; Meisinger, C. ; Römisch-Margl, W. ; Wang-Sattler, R. ; Hveem, K. ; Wolfenbuttel, B.H.R. ; Peters, A. ; Kastenmüller, G. ; Waldenberger, M.
PLoS ONE 10:e0121495 (2015)
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.
Wissenschaftlicher Artikel
Scientific Article
Yousri, N.A. ; Mook-Kanamori, D.O. ; El-Din Selim, M.M. ; Takiddin, A.H. ; Al-Homsi, H. ; Al-Mahmoud, K.A.S. ; Karoly, E.D. ; Krumsiek, J. ; Do, K.T. ; Neumaier, U. ; Mook-Kanamori, M.J. ; Rowe, J. ; Chidiac, O.M. ; McKeon, C. ; Al Muftah, W.A. ; Kader, S.A. ; Kastenmüller, G. ; Suhre, K.
Diabetologia 58, 2199 (2015)
Fischer, L. ; Arnold, M. ; Kirsch, F. ; Leidl, R.
Gesundheitswesen, DOI: 10.1055/s-0035-1549989 (2015)
Ziel der Studie: Brustkrebs ist die häufigste Krebserkrankung von Frauen. Die meisten Leitlinien empfehlen Patientinnen mit Lymphknoten-positivem (LN +) Brustkrebs im Frühstadium eine adjuvante Chemotherapie, um das Risiko eines Rezidivs zu vermeiden. Dadurch kann es zu einem häufigen, undifferenzierten Einsatz von Chemotherapie kommen, der mit hohen Kosten und beträchtlichen Nebenwirkungen verbunden ist. Der Oncotype DX, auch genannt 21 Gene Test, von Genomic Health ist ein diagnostischer Multigentest, der das Rezidivrisiko von Brustkrebs und damit eine zentrale Nutzendeterminante der Chemotherapie bestimmt. Für Patientinnen mit LN− Brustkrebs haben eine Reihe von Studien Hinweise auf die Kosten-Effektivität des 21 Gene Tests erbracht. Dieser Beitrag gibt eine Übersicht zum Einsatz dieses Tests bei Patientinnen mit LN+ Brustkrebs. Methodik: Auf Basis der Datenbanken Pubmed, Embase, Business Source Complete und EconLit wurde eine systematische Literaturrecherche durchgeführt und die gefundenen Studien nach Ansatz, Parametern und Unsicherheitsanalysen ausgewertet. Die Generalisierbarkeit und Übertragbarkeit der Studienergebnisse auf Deutschland wurden anhand einer Kriterienliste überprüft. Ergebnisse: Es wurden 7 relevante ökonomische Studien identifiziert. Die Kosten-Nutzwert-Relation bewegte sich zwischen Kosteneinsparungen in Höhe von € 3 548 pro Patientin bis zu zusätzlichen Kosten von € 9 113 pro gewonnenen QALY. Eine Übertragbarkeit der Ergebnisse auf Deutschland wird vor allem durch Studienunterschiede im Ansatz medizinischer Kosten, in absoluten und relativen Preisen im Gesundheitswesen und im untersuchten Behandlungsablauf eingeschränkt. Schlussfolgerung: Es gibt Hinweise, dass ein Einsatz des Tests bei Patientinnen mit LN+ Brustkrebs eine ähnliche Größenordnung der Wirtschaftlichkeit erreicht wie bei der LN– Form. Präzisere Ergebnisse für Deutschland würden valide Daten bezüglich der Rückfallrisiken und der Beschreibung und Bewertung der gesundheitsbezogenen Lebensqualität der Patientinnen erfordern.
Review
Review
Bartel, J.# ; Krumsiek, J.# ; Schramm, K. ; Adamski, J. ; Gieger, C. ; Herder, C. ; Carstensen, M. ; Peters, A. ; Rathmann, W. ; Roden, M. ; Strauch, K. ; Suhre, K. ; Kastenmüller, G. ; Prokisch, H. ; Theis, F.J.
PLoS Genet. 11:e1005274 (2015)
Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the 'human blood metabolome-transcriptome interface' (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.
Wissenschaftlicher Artikel
Scientific Article
Yousri, N.A. ; Mook-Kanamori, D.O. ; Selim, M.M.E.D. ; Takiddin, A.H. ; Al-Homsi, H. ; Al-Mahmoud, K.A.S. ; Karoly, E.D. ; Krumsiek, J. ; Do, K.T. ; Neumaier, U. ; Mook-Kanamori, M.J. ; Rowe, J. ; Chidiac, O.M. ; McKeon, C. ; Al Muftah, W.A. ; Kader, S.A. ; Kastenmüller, G. ; Suhre, K.
Diabetologia 58, 1855-1867 (2015)
Aims/hypothesis: Metabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking. Methods: Here we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control. Results: Ninety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p < 2.3 × 10−5), 16 of which have never been reported. Sixty-five of these diabetes-associated metabolites were associated with at least one marker of glycaemic control in the diabetes group. Using Gaussian graphical modelling, we constructed a metabolic network that links diabetes-associated metabolites from three biofluids across three different timescales of glycaemic control. Conclusions/interpretation: Our study reveals a complex network of biochemical dysregulation involving metabolites from different pathways of diabetes pathology, and provides a reference framework for future diabetes studies with metabolic endpoints.
Wissenschaftlicher Artikel
Scientific Article
Draisma, H.H.# ; Pool, R.# ; Kobl, M. ; Jansen, R.C. ; Petersen, A.-K. ; Vaarhorst, A.A. ; Yet, I. ; Haller, T. ; Demirkan, A. ; Esko, T. ; Zhu, G. ; Böhringer, S. ; Beekman, M. ; van Klinken, J.B. ; Römisch-Margl, W. ; Prehn, C. ; Adamski, J. ; de Craen, A.J. ; van Leeuwen, E.M. ; Amin, N. ; Dharuri, H. ; Westra, H.J. ; Franke, L. ; de Geus, E.J. ; Hottenga, J.J. ; Willemsen, G. ; Henders, A.K. ; Montgomery, G.W. ; Nyholt, D.R. ; Whitfield, J.B. ; Penninx, B.W. ; Spector, T.D. ; Metspalu, A. ; Slagboom, P.E. ; van Dijk, K.W. ; 't Hoen, P.A. ; Strauch, K. ; Martin, N.G. ; van Ommen, G.J. ; Illig, T. ; Bell, J.T. ; Mangino, M. ; Suhre, K. ; McCarthy, M.I. ; Gieger, C. ; Isaacs, A. ; van Duijn, C.M. ; Boomsma, D.I.
Nat. Commun. 6:7208 (2015)
Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P<1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.
Wissenschaftlicher Artikel
Scientific Article
Suhre, K. ; Schwartz, J.E. ; Sharma, V.K. ; Chen, Q. ; Lee, J.R. ; Muthukumar, T. ; Dadhania, D.M. ; Ding, R. ; Ikle, D.N. ; Bridges, N.D. ; Williams, N.M. ; Kastenmüller, G. ; Karoly, E.D. ; Mohney, R.P. ; Abecassis, M. ; Friedewald, J. ; Knechtle, S.J. ; Becker, Y.T. ; Samstein, B. ; Shaked, A. ; Gross, S.S. ; Suthanthiran, M.
J. Am. Soc. Nephrol. 27, 626-636 (2015)
Noninvasive diagnosis and prognostication of acute cellular rejection in the kidney allograft may help realize the full benefits of kidney transplantation. To investigate whether urine metabolites predict kidney allograft status, we determined levels of 749 metabolites in 1516 urine samples from 241 kidney graft recipients enrolled in the prospective multicenter Clinical Trials in Organ Transplantation-04 study. A metabolite signature of the ratio of 3-sialyllactose to xanthosine in biopsy specimen-matched urine supernatants best discriminated acute cellular rejection biopsy specimens from specimens without rejection. For clinical application, we developed a high-throughput mass spectrometry-based assay that enabled absolute and rapid quantification of the 3-sialyllactose-to-xanthosine ratio in urine samples. A composite signature of ratios of 3-sialyllactose to xanthosine and quinolinate to X-16397 and our previously reported urinary cell mRNA signature of 18S ribosomal RNA, CD3ε mRNA, and interferon-inducible protein-10 mRNA outperformed the metabolite signatures and the mRNA signature. The area under the receiver operating characteristics curve for the composite metabolite-mRNA signature was 0.93, and the signature was diagnostic of acute cellular rejection with a specificity of 84% and a sensitivity of 90%. The composite signature, developed using solely biopsy specimen-matched urine samples, predicted future acute cellular rejection when applied to pristine samples taken days to weeks before biopsy. We conclude that metabolite profiling of urine offers a noninvasive means of diagnosing and prognosticating acute cellular rejection in the human kidney allograft, and that the combined metabolite and mRNA signature is diagnostic and prognostic of acute cellular rejection with very high accuracy.
Wissenschaftlicher Artikel
Scientific Article
Menni, C.# ; Graham, D.# ; Kastenmüller, G. ; Alharbi, N.H. ; Alsanos, S.M. ; McBride, M. ; Mangino, M. ; Titcombe, P. ; Shin, S.Y. ; Psatha, M. ; Geisendorfer, T. ; Huber, A. ; Peters, A. ; Wang-Sattler, R. ; Xu, T. ; Brosnan, M.J. ; Trimmer, J. ; Reichel, C. ; Mohney, R.P. ; Soranzo, N. ; Edwards, M.H. ; Cooper, C. ; Church, A.C. ; Suhre, K. ; Gieger, C. ; Dominiczak, A.F. ; Spector, T.D. ; Padmanabhan, S. ; Valdes, A.M.
Hypertension 66, 422-429 (2015)
High blood pressure is a major contributor to the global burden of disease and discovering novel causal pathways of blood pressure regulation has been challenging. We tested blood pressure associations with 280 fasting blood metabolites in 3980 TwinsUK females. Survival analysis for all-cause mortality was performed on significant independent metabolites (P<8.9 10(-5)). Replication was conducted in 2 independent cohorts KORA (n=1494) and Hertfordshire (n=1515). Three independent animal experiments were performed to establish causality: (1) blood pressure change after increasing circulating metabolite levels in Wistar-Kyoto rats; (2) circulating metabolite change after salt-induced blood pressure elevation in spontaneously hypertensive stroke-prone rats; and (3) mesenteric artery response to noradrenaline and carbachol in metabolite treated and control rats. Of the15 metabolites that showed an independent significant association with blood pressure, only hexadecanedioate, a dicarboxylic acid, showed concordant association with blood pressure (systolic BP [SBP]: β [95% confidence interval], 1.31 [0.83-1.78], P=6.81×10(-8); diastolic BP [DBP]: 0.81 [0.5-1.11], P=2.96×10(-7)) and mortality (hazard ratio [95% confidence interval], 1.49 [1.08-2.05]; P=0.02) in TwinsUK. The blood pressure association was replicated in KORA and Hertfordshire. In the animal experiments, we showed that oral hexadecanedioate increased both circulating hexadecanedioate and blood pressure in Wistar-Kyoto rats, whereas blood pressure elevation with oral sodium chloride in hypertensive rats did not affect hexadecanedioate levels. Vascular reactivity to noradrenaline was significantly increased in mesenteric resistance arteries from hexadecanedioate-treated rats compared with controls, indicated by the shift to the left of the concentration-response curve (P=0.013). Relaxation to carbachol did not show any difference. Our findings indicate that hexadecanedioate is causally associated with blood pressure regulation through a novel pathway that merits further investigation.
Wissenschaftlicher Artikel
Scientific Article
Tsepilov, Y.A. ; Shin, S.Y. ; Soranzo, N. ; Spector, T.D. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Wang-Sattler, R. ; Strauch, K. ; Gieger, C. ; Aulchenko, Y.S. ; Ried, J.S.
Genetics 200, 707-718 (2015)
Genome-wide association studies (GWAS) are widely applied to analyze the genetic effects on phenotypes. With the availability of high-throughput technologies for metabolite measurements, GWAS successfully identified loci that affect metabolite concentrations and underlying pathways. In most GWAS the effect of each SNP on the phenotype is assumed to be additive. Other genetic models such as recessive, dominant or over-dominant were considered only by very few studies. In contrast to that, there are theories that emphasize the relevance of non-additive effects as a consequence of physiological mechanisms. This might be especially important for metabolites as these intermediate phenotypes are closer to the underlying pathways than other traits or diseases. In this study we analyzed systematically non-additive effects on a large panel of serum metabolites and all possible ratios (22,801 in total) in a population based study (KORA F4, N=1,785). We applied four different 1 df tests corresponding to an additive, dominant, recessive and over-dominant trait model and additionally a genotypic model with 2 df that allows a more general consideration of genetic effects. Twenty three loci were found to be genome-wide significantly associated (Bonferroni corrected p-value ≤2.19x10(-12)) with at least one metabolite or ratio. For five of them we show the evidence of non-additive effects. We replicated seventeen loci including three loci with non-additive effects in an independent study (TwinsUK, N=846). In conclusion, we found that most genetic effects on metabolite concentrations and ratios were indeed additive, which verifies the practice of using the additive model for analyzing SNP effects on metabolites.
Wissenschaftlicher Artikel
Scientific Article
Livshits, G. ; Macgregor, A.J. ; Gieger, C. ; Malkin, I. ; Moayyeri, A. ; Grallert, H. ; Emeny, R.T. ; Spector, T. ; Kastenmüller, G. ; Williams, F.M.
Pain 156, 1845-1851 (2015)
Chronic widespread musculoskeletal pain (CWP) is common, having a population prevalence of 10%. This study aimed to define the biological basis of the CWP/body mass association by using a systems biology approach. Adult female twins (n=2,444) from the TwinsUK registry who had extensive clinical, anthropometric, and "omic" data were included. Non-targeted metabolomics screening including 324 metabolites was carried out for CWP and body composition, assessed by DXA. The biological basis of these associations were explored through GWAS and replicated in an independent population sample (KORA study, n=2,483). A causal role for the genetic variants identified was sought in CWP using a Mendelian randomisation study design. Fat mass/height was the body composition variable most strongly associated with CWP (TwinsUK p=2.4x10 and KORA p=1.59x10). Of 324 metabolites examined, epiandrosterone sulphate (EAS) was highly associated with both CWP (p=1.05 x 10 in TwinsUK and p=3.70x10 in KORA) and fat mass/height. GWAS of EAS identified imputed SNP rs1581492 at 7q22.1 to be strikingly associated with EAS levels (p ≤2.49 x10) and this result was replicated in KORA (p=2.12x10). Mendelian randomization by rs1581492 genotype showed that EAS is unlikely to be causally related to CWP. Using an agnostic omics approach to focus on the association of CWP with BMI, we have confirmed a steroid hormone association and identified a genetic variant upstream of the CYP genes which likely controls this response. This study suggests that steroid hormone abnormalities result from pain rather than causing it, and EAS may provide a biomarker which identifies subgroups at risk of CWP.
Wissenschaftlicher Artikel
Scientific Article
Schwab, S. ; Zierer, A. ; Schneider, A.E. ; Heier, M. ; Koenig, W. ; Kastenmüller, G. ; Waldenberger, M. ; Peters, A. ; Thorand, B.
Br. J. Nutr. 113, 1782-1791 (2015)
The aim of the present study was to examine the association between intake of five common antioxidative nutrients from supplements and medications (vitamin E, vitamin C, carotenoids, Se, and Zn) and levels of high-sensitivity C-reactive protein (hs-CRP) in the general population. For this purpose, a total of 2924 participants of the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4 study (2006-8) were investigated cross-sectionally. Intake of dietary supplements and medication during the last 7 d was recorded in a personal interview, when participants were asked to show product packages of ingested preparations. Linear regression models were calculated; first, the exposure to regular nutrient intake was treated with a binary response (yes/no); then regularly ingested amounts were divided into quartiles to examine dose-response relationships. Effect of single v. combined supplementation of antioxidants was assessed through the inclusion of interaction terms into the models. Regular intake of any of the five investigated antioxidants per se was not associated with hs-CRP levels. However, dose-response analyses revealed that participants who regularly ingested more than 78 mg vitamin E/d, which corresponds to the upper quartile, had 22 % lower hs-CRP levels (95 % CI 0·63, 0·97) compared to those of persons who were not exposed to any vitamin E supplementation. Stratified analyses showed that this association was found only in persons who took vitamin E in combination with other antioxidants. The combined supplementation of vitamin E with other antioxidants could thus be a promising strategy for the prevention of inflammation-related diseases in the general population, if further studies could confirm that the proposed association is causal.
Wissenschaftlicher Artikel
Scientific Article
Wahl, S.#° ; Vogt, S.# ; Stückler, F. ; Krumsiek, J. ; Bartel, J. ; Kacprowski, T. ; Schramm, K. ; Carstensen, M. ; Rathmann, W. ; Roden, M. ; Jourdan, C. ; Kangas, A.J. ; Soininen, P. ; Ala-Korpela, M. ; Nöthlings, U. ; Boeing, H. ; Theis, F.J. ; Meisinger, C. ; Waldenberger, M. ; Suhre, K. ; Homuth, G. ; Gieger, C. ; Kastenmüller, G. ; Illig, T. ; Linseisen, J. ; Peters, A. ; Prokisch, H. ; Herder, C. ; Thorand, B.° ; Grallert, H.°
BMC Med. 13:48 (2015)
Background Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study. Methods We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits. Results Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10−4 to 1.2 × 10−24). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules. Conclusions Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.
Wissenschaftlicher Artikel
Scientific Article
Diakopoulos, K.N. ; Lesina, M. ; Wörmann, S. ; Song, L. ; Aichler, M. ; Schild, L. ; Artati, A. ; Römisch-Margl, W. ; Wartmann, T. ; Fischer, R. ; Kabiri, Y. ; Zischka, H. ; Halangk, W. ; Demir, I.E. ; Pilsak, C. ; Walch, A.K. ; Mantzoros, C.S. ; Steiner, J.M. ; Erkan, M. ; Schmid, R.M. ; Witt, H. ; Adamski, J. ; Algül, H.
Gastroenterology 148, 626-638 (2015)
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 (Ptf1aCre(ex1);Atg5(F/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 to 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 to 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 females. 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.
Wissenschaftlicher Artikel
Scientific Article
Do, K.T. ; Kastenmüller, G. ; Mook-Kanamori, D.O. ; Yousri, N.A. ; Theis, F.J. ; Suhre, K. ; Krumsiek, J.
J. Proteome Res. 14, 1183-1194 (2015)
Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.
Wissenschaftlicher Artikel
Scientific Article
Arnold, M.# ; Raffler, J.# ; Pfeufer, A. ; Suhre, K. ; Kastenmüller, G.
Bioinformatics 31, 1334-1336 (2015)
MOTIVATION: Linking genes and functional information to genetic variants identified by association studies remains difficult. Resources containing extensive genomic annotations are available but often not fully utilized due to heterogeneous data formats. To enhance their accessibility, we integrated many annotation datasets into a user-friendly webserver. Availability and implementation: http://www.snipa.org/ CONTACT: g.kastenmueller@helmholtz-muenchen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Wissenschaftlicher Artikel
Scientific Article
Kahle-Stephan, M. ; Schäfer, A. ; Seelig, A. ; Schultheiß, J. ; Wu, M. ; Aichler, M. ; Leonhardt, J. ; Rathkolb, B. ; Rozman, J. ; Sarioglu, H. ; Hauck, S.M. ; Ueffing, M. ; Wolf, E. ; Kastenmüller, G. ; Adamski, J. ; Walch, A.K. ; Hrabě de Angelis, M. ; Neschen, S.
Mol. Metab. 4, 39-50 (2015)
Objective Excess lipid intake has been implicated in the pathophysiology of hepatosteatosis and hepatic insulin resistance. Lipids constitute approximately 50% of the cell membrane mass, define membrane properties, and create microenvironments for membrane-proteins. In this study we aimed to resolve temporal alterations in membrane metabolite and protein signatures during high-fat diet (HF)-mediated development of hepatic insulin resistance. Methods We induced hepatosteatosis by feeding C3HeB/FeJ male mice a HF enriched with long-chain polyunsaturated C18:2n6 fatty acids for 7, 14, or 21 days. Longitudinal changes in hepatic insulin sensitivity were assessed via the euglycemic-hyperinsulinemic clamp, in membrane lipids via t-metabolomics- and membrane proteins via quantitative proteomics-analyses, and in hepatocyte morphology via electron microscopy. Data were compared to those of age- and litter-matched controls maintained on a low-fat diet. Results Excess long-chain polyunsaturated C18:2n6 intake for 7 days did not compromise hepatic insulin sensitivity, however induced hepatosteatosis and modified major membrane lipid constituent signatures in liver, e.g. increased total unsaturated, long-chain fatty acid-containing acyl-carnitine or membrane-associated diacylglycerol moieties and decreased total short-chain acyl-carnitines, glycerophosphocholines, lysophosphatidylcholines, or sphingolipids. Hepatic insulin sensitivity tended to decrease within 14 days HF-exposure. Overt hepatic insulin resistance developed until day 21 of HF-intervention and was accompanied by morphological mitochondrial abnormalities and indications for oxidative stress in liver. HF-feeding progressively decreased the abundance of protein-components of all mitochondrial respiratory chain complexes, inner and outer mitochondrial membrane substrate transporters independent from the hepatocellular mitochondrial volume in liver. Conclusions We assume HF-induced modifications in membrane lipid- and protein-signatures prior to and during changes in hepatic insulin action in liver alter membrane properties – in particular those of mitochondria which are highly abundant in hepatocytes. In turn, a progressive decrease in the abundance of mitochondrial membrane proteins throughout HF-exposure likely impacts on mitochondrial energy metabolism, substrate exchange across mitochondrial membranes, contributes to oxidative stress, mitochondrial damage, and the development of insulin resistance in liver.
Wissenschaftlicher Artikel
Scientific Article
2014
Windhager, L. ; Zierer, J. ; Küffner, R.
PLoS ONE 9:e84596 (2014)
Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate predictions for a gene regulatory network, e.g. due to probabilistic optimization or a cross-validation procedure.
Wissenschaftlicher Artikel
Scientific Article
Padmanabhan, S. ; Menni, C. ; Alsanosi, S. ; Kastenmüller, G. ; McBride, M. ; Mangino, M. ; Brosnan, J. ; Trimmer, J. ; Mohney, R.P. ; Suhre, K. ; Gieger, C. ; Melander, O. ; Dominiczak, A. ; Spector, T. ; Valdes, A.
J. Hum. Hypertens. 28, 638 (2014)
Meeting abstract
Meeting abstract
Altmaier, E. ; Emeny, R.T.
Adv. Biol. Psychiatry 29, 128-138 (2014)
With a frequent occurrence of approximately 10.6% in adult populations, anxiety disorders are among the most common mental health problems worldwide. Although anxiety disorders are rather prevalent, their underlying biochemical mechanisms remain unclear. As a functional endpoint of all biological events, the metabolome represents the most precise and direct molecular expression of a phenotype. Combining metabolic information with proteome data, systems biology can draw an even more comprehensive picture of the biological processes. Here, we provide a review summarizing the results from human as well as animal studies analyzing metabolic and proteomic traits in different tissues for associations with anxiety. In addition, we give an overview of animal studies that applied a systems biology approach using metabolic as well as proteomic data to identify anxiety-related pathways.
Wissenschaftlicher Artikel
Scientific Article
Mathew, S. ; Krug, S. ; Skurk, T. ; Halama, A. ; Stank, A. ; Artati, A. ; Prehn, C. ; Malek, J.A. ; Kastenmüller, G. ; Römisch-Margl, W. ; Adamski, J. ; Hauner, H. ; Suhre, K.
J. Transl. Med. 12:161 (2014)
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.
Wissenschaftlicher Artikel
Scientific Article
Shin, S.Y.# ; Fauman, E.B.# ; Petersen, A.-K.# ; Krumsiek, J.# ; Santos, R. ; Huang, J. ; Arnold, M. ; Erte, I. ; Forgetta, V. ; Yang, T.P. ; Walter, K. ; Menni, C. ; Chen, L. ; Vasquez, L. ; Valdes, A.M. ; Hyde, C.L. ; Wang, V. ; Ziemek, D. ; Roberts, P. ; Xi, L. ; Grundberg, E. ; Waldenberger, M. ; Richards, J.B. ; Mohney, R.P. ; Milburn, M.V. ; John, S.L. ; Trimmer, J. ; Theis, F.J. ; Overington, J.P. ; Suhre, K. ; Brosnan, M.J. ; Gieger, C. ; Kastenmüller, G.° ; Spector, T.D.° ; Soranzo, N.°
Nat. Genet. 46, 543-550 (2014)
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 thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with 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 on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
Wissenschaftlicher Artikel
Scientific Article
Altmaier, E. ; Fobo, G. ; Heier, M. ; Thorand, B. ; Meisinger, C. ; Römisch-Margl, W. ; Waldenberger, M. ; Gieger, C. ; Illig, T. ; Adamski, J. ; Suhre, K. ; Kastenmüller, G.
Eur. J. Epidemiol. 29, 325-336 (2014)
The mechanism of antihypertensive and lipid-lowering drugs on the human organism is still not fully understood. New insights on the drugs' action can be provided by a metabolomics-driven approach, which offers a detailed view of the physiological state of an organism. Here, we report a metabolome-wide association study with 295 metabolites in human serum from 1,762 participants of the KORA F4 (Cooperative Health Research in the Region of Augsburg) study population. Our intent was to find variations of metabolite concentrations related to the intake of various drug classes and-based on the associations found-to generate new hypotheses about on-target as well as off-target effects of these drugs. In total, we found 41 significant associations for the drug classes investigated: For beta-blockers (11 associations), angiotensin-converting enzyme (ACE) inhibitors (four assoc.), diuretics (seven assoc.), statins (ten assoc.), and fibrates (nine assoc.) the top hits were pyroglutamine, phenylalanylphenylalanine, pseudouridine, 1-arachidonoylglycerophosphocholine, and 2-hydroxyisobutyrate, respectively. For beta-blockers we observed significant associations with metabolite concentrations that are indicative of drug side-effects, such as increased serotonin and decreased free fatty acid levels. Intake of ACE inhibitors and statins associated with metabolites that provide insight into the action of the drug itself on its target, such as an association of ACE inhibitors with des-Arg(9)-bradykinin and aspartylphenylalanine, a substrate and a product of the drug-inhibited ACE. The intake of statins which reduce blood cholesterol levels, resulted in changes in the concentration of metabolites of the biosynthesis as well as of the degradation of cholesterol. Fibrates showed the strongest association with 2-hydroxyisobutyrate which might be a breakdown product of fenofibrate and, thus, a possible marker for the degradation of this drug in the human organism. The analysis of diuretics showed a heterogeneous picture that is difficult to interpret. Taken together, our results provide a basis for a deeper functional understanding of the action and side-effects of antihypertensive and lipid-lowering drugs in the general population.
Wissenschaftlicher Artikel
Scientific Article
Schramm, K.# ; Marzi, C.# ; Schurmann, C.# ; Carstensen, M.# ; Reinmaa, E. ; Biffar, R. ; Eckstein, G.N. ; Gieger, C. ; Grabe, H.J. ; Homuth, G. ; Kastenmüller, G. ; Mägi, R. ; Metspalu, A. ; Mihailov, E. ; Peters, A. ; Petersmann, A. ; Roden, M. ; Strauch, K. ; Suhre, K. ; Teumer, A. ; Völker, U. ; Völzke, H. ; Wang-Sattler, R. ; Waldenberger, M. ; Meitinger, T. ; Illig, T. ; Herder, C. ; Grallert, H. ; Prokisch, H.
PLoS ONE 9:e93844 (2014)
BACKGROUND: We aimed to assess whether whole blood expression quantitative trait loci (eQTLs) with effects in cis and trans are robust and can be used to identify regulatory pathways affecting disease susceptibility. MATERIALS AND METHODS: We performed whole-genome eQTL analyses in 890 participants of the KORA F4 study and in two independent replication samples (SHIP-TREND, N = 976 and EGCUT, N = 842) using linear regression models and Bonferroni correction. RESULTS: In the KORA F4 study, 4,116 cis-eQTLs (defined as SNP-probe pairs where the SNP is located within a 500 kb window around the transcription unit) and 94 trans-eQTLs reached genome-wide significance and overall 91% (92% of cis-, 84% of trans-eQTLs) were confirmed in at least one of the two replication studies. Different study designs including distinct laboratory reagents (PAXgene™ vs. Tempus™ tubes) did not affect reproducibility (separate overall replication overlap: 78% and 82%). Immune response pathways were enriched in cis- and trans-eQTLs and significant cis-eQTLs were partly coexistent in other tissues (cross-tissue similarity 40-70%). Furthermore, four chromosomal regions displayed simultaneous impact on multiple gene expression levels in trans, and 746 eQTL-SNPs have been previously reported to have clinical relevance. We demonstrated cross-associations between eQTL-SNPs, gene expression levels in trans, and clinical phenotypes as well as a link between eQTLs and human metabolic traits via modification of gene regulation in cis. CONCLUSIONS: Our data suggest that whole blood is a robust tissue for eQTL analysis and may be used both for biomarker studies and to enhance our understanding of molecular mechanisms underlying gene-disease associations.
Wissenschaftlicher Artikel
Scientific Article
Mook-Kanamori, D.O. ; Römisch-Margl, W. ; Kastenmüller, G. ; Prehn, C. ; Petersen, A.K. ; Illig, T. ; Gieger, C. ; Wang-Sattler, R. ; Meisinger, C. ; Peters, A. ; Adamski, J. ; Suhre, K.
J. Endocrinol. Invest. 37, 369-374 (2014)
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.
Wissenschaftlicher Artikel
Scientific Article
Shin, S.-Y.# ; Petersen, A.-K.# ; Wahl, S. ; Zhai, G. ; Römisch-Margl, W. ; Small, K.S. ; Döring, A. ; Kato, B.S. ; Peters, A. ; Grundberg, E. ; Prehn, C. ; Wang-Sattler, R. ; Wichmann, H.-E. ; Hrabě de Angelis, M. ; Illig, T. ; Adamski, J. ; Deloukas, P. ; Spector, T.D. ; Suhre, K. ; Gieger, C. ; Soranzo, N.
Genome Med. 6:25 (2014)
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 Modelling, 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 Modelling 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.
Wissenschaftlicher Artikel
Scientific Article
Yousri, N.A. ; Kastenmüller, G. ; Gieger, C. ; Shin, S.-Y. ; Erte, I. ; Menni, C. ; Peters, A. ; Meisinger, C. ; Mohney, R.P. ; Illig, T. ; Adamski, J. ; Soranzo, N. ; Spector, T.D. ; Suhre, K.
Catal. Lett. 10, 1005-1017 (2014)
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.
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Scientific Article
Bleves, S. ; Dunger, I. ; Walter, M.C. ; Frangoulidis, D. ; Kastenmüller, G. ; Voulhoux, R.° ; Ruepp, A.°
Nucleic Acids Res. 42, D671-D676 (2014)
Bacterial infectious diseases are the result of multifactorial processes affected by the interplay between virulence factors and host targets. The host-Pseudomonas and Coxiella interaction database (HoPaCI-DB) is a publicly available manually curated integrative database (http://mips.helmholtz-muenchen.de/HoPaCI/) of host-pathogen interaction data from Pseudomonas aeruginosa and Coxiella burnetii. The resource provides structured information on 3585 experimentally validated interactions between molecules, bioprocesses and cellular structures extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make HoPaCI-DB a versatile knowledge base for biologists and network biology approaches.
Wissenschaftlicher Artikel
Scientific Article
Wahl, S. ; Krug, S. ; Then, C. ; Kirchhofer, A. ; Kastenmüller, G. ; Brand, T. ; Skurk, T. ; Claussnitzer, M. ; Huth, C. ; Heier, M. ; Meisinger, C. ; Peters, A. ; Thorand, B. ; Gieger, C. ; Prehn, C. ; Römisch-Margl, W. ; Adamski, J. ; Suhre, K. ; Illig, T. ; Grallert, H. ; Laumen, H. ; Seissler, J. ; Hauner, H.
Catal. Lett. 10, 386-401 (2014)
The measurement of metabolites during intravenous or nutritional challenges may improve the identification of novel metabolic signatures which are not detectable in the fasting state. Here, we comprehensively characterized the plasma metabolomics response to five defined challenge tests and explored their use to identify interactions with the FTO rs9939609 obesity risk genotype. Fifty-six non-diabetic male participants of the KORA S4/F4 cohort, including 25 homozygous carriers of the FTO risk allele (AA genotype) and 31 carriers of the TT genotype were recruited. Challenges comprised an oral glucose tolerance test, a standardized high-fat high-carbohydrate meal and a lipid tolerance test, as well as an intravenous glucose tolerance test and a euglycemic hyperinsulinemic clamp. Blood was sampled for biochemical and metabolomics measurement before and during the challenges. Plasma samples were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Linear mixed-effects models and cluster analysis were performed. In both genotype groups, we observed significant challenge-induced changes for all major metabolite classes (amino acids, hexose, acylcarnitines, phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, with corrected p-values ranging from 0.05 to 6.7E-37), which clustered in five distinct metabolic response profiles. Our data contribute to the understanding of plasma metabolomics response to diverse metabolic challenges, including previously unreported metabolite changes in response to intravenous challenges. The FTO genotype had only minor effects on the metabolite fluxes after standardized metabolic challenges.
Wissenschaftlicher Artikel
Scientific Article
Petersen, A.-K.# ; Zeilinger, S.# ; Kastenmüller, G. ; Römisch-Margl, W. ; Brugger, M. ; Peters, A. ; Meisinger, C. ; Strauch, K. ; Hengstenberg, C. ; Pagel, P. ; Huber, F. ; Mohney, R.P. ; Grallert, H. ; Illig, T. ; Adamski, J. ; Waldenberger, M. ; Gieger, C. ; Suhre, K.
Hum. Mol. Genet. 23, 534-545 (2014)
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 1,814 participants of the 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=3.9x10-20 to 2.0x10-108, r2=0.036 to 0.221). Seven loci display CpG-site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (p=9.2x10-14 to 2.7x10-27, 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-3beta,17beta-diol disulfate. In these cases the association between CpG-methylation and metabotype are 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 suggests that DNA methylation plays an important role in regulating human metabolism.
Wissenschaftlicher Artikel
Scientific Article
Albrecht, E.# ; Waldenberger, M.# ; Krumsiek, J. ; Evans, A.M. ; Jeratsch, U. ; Breier, M. ; Adamski, J. ; Koenig, W. ; Zeilinger, S. ; Fuchs, C. ; Klopp, N. ; Theis, F.J. ; Wichmann, H.-E. ; Suhre, K. ; Illig, T. ; Strauch, K. ; Peters, A. ; Gieger, C. ; Kastenmüller, G. ; Döring, A. ; Meisinger, C.
Metabolomics 10, 141-151 (2014)
Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum urate levels highly differ between men and women; however the underlying biological processes in its regulation are still not completely understood and are assumed to result from a complex interplay between genetic, environmental and lifestyle factors. In order to describe the metabolic vicinity of serum urate, we analyzed 355 metabolites in 1,764 individuals of the population-based KORA F4 study and constructed a metabolite network around serum urate using Gaussian Graphical Modeling in a hypothesis-free approach. We subsequently investigated the effect of sex and urate lowering medication on all 38 metabolites assigned to the network. Within the resulting network three main clusters could be detected around urate, including the well-known pathway of purine metabolism, as well as several dipeptides, a group of essential amino acids, and a group of steroids. Of the 38 assigned metabolites, 25 showed strong differences between sexes. Association with uricostatic medication intake was not only confined to purine metabolism but seen for seven metabolites within the network. Our findings highlight pathways that are important in the regulation of serum urate and suggest that dipeptides, amino acids, and steroid hormones are playing a role in its regulation. The findings might have an impact on the development of specific targets in the treatment and prevention of hyperuricemia.
Wissenschaftlicher Artikel
Scientific Article
Jourdan, C. ; Linseisen, J. ; Meisinger, C. ; Petersen, A.-K. ; Gieger, C. ; Rawal, R. ; Illig, T. ; Heier, M. ; Peters, A. ; Wallaschofski, H. ; Nauck, M. ; Kastenmüller, G. ; Suhre, K. ; Prehn, C. ; Adamski, J. ; Koenig, W. ; Roden, M. ; Wichmann, H.-E. ; Völzke, H.
Catal. Lett. 10, 152-164 (2014)
The aim was to characterise associations between circulating thyroid hormones-free thyroxine (FT4) and thyrotropin (TSH)-and the metabolite profiles in serum samples from participants of the German population-based KORA F4 study. Analyses were based on the metabolite profile of 1463 euthyroid subjects. In serum samples, obtained after overnight fasting (≥8), 151 different metabolites were quantified in a targeted approach including amino acids, acylcarnitines (ACs), and phosphatidylcholines (PCs). Associations between metabolites and thyroid hormone concentrations were analysed using adjusted linear regression models. To draw conclusions on thyroid hormone related pathways, intra-class metabolite ratios were additionally explored. We discovered 154 significant associations (Bonferroni p < 1.75 × 10-04) between FT4 and various metabolites and metabolite ratios belonging to AC and PC groups. Significant associations with TSH were lacking. High FT4 levels were associated with increased concentrations of many ACs and various sums of ACs of different chain length, and the ratio of C2 by C0. The inverse associations observed between FT4 and many serum PCs reflected the general decrease in PC concentrations. Similar results were found in subgroup analyses, e.g., in weight-stable subjects or in obese subjects. Further, results were independent of different parameters for liver or kidney function, or inflammation, which supports the notion of an independent FT4 effect. In fasting euthyroid adults, higher serum FT4 levels are associated with increased serum AC concentrations and an increased ratio of C2 by C0 which is indicative of an overall enhanced fatty acyl mitochondrial transport and β-oxidation of fatty acids.
Wissenschaftlicher Artikel
Scientific Article
2013
Zierer, J.
München, Ludwig-Maximilians-Universität & Technische Universität, Bioinformatik, Master-Thesis, 2013, 147 S.
Menni, C. ; Fauman, E. ; Erte, I. ; Perry, J.R. ; Kastenmüller, G. ; Shin, S.Y. ; Petersen, A.-K. ; Hyde, C. ; Psatha, M. ; Ward, K.J. ; Yuan, W. ; Milburn, M.V. ; Palmer, C.N. ; Frayling, T.M. ; Trimmer, J. ; Bell, J.T. ; Gieger, C. ; Mohney, R.P. ; Brosnan, M.J. ; Suhre, K. ; Soranzo, N. ; Spector, T.D.
Diabetes 62, 4270-4276 (2013)
Using a non-targeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycaemia in a large population-based cohort of 2,204 females (115 Type 2 Diabetes-T2D cases, 192 individuals with impaired fasting glucose- IFG and 1,897 controls) from TwinsUK.Forty-two metabolites from three major fuel sources, carbohydrates, lipids and proteins, were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain-keto-acid metabolite 3-methyl-2-oxovalerate, was the strongest predictive biomarker for IFG after glucose (OR=1.65, 95%CI=1.39,1.95, P=8.46x10(-9)) and was moderately heritable (h(2)=0.20). The association was replicated in an independent population (n=720, OR=1.68, 95%CI=1.34, 2.11, P=6.52x10(-6)) and validated in 189 Twins with urine metabolomics taken at the same time as plasma (OR=1.87, 95%CI=1.27,2.75, P=1x10(-3)). Results confirm an important role for catabolism of branched-chain-amino-acids in T2D and IFG.In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of non-targeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.
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Scientific Article
Menni, C. ; Kastenmüller, G. ; Petersen, A.-K. ; Bell, J.T. ; Psatha, M. ; Tsai, P.C. ; Gieger, C. ; Schulz, H. ; Erte, I. ; John, S. ; Brosnan, M.J. ; Wilson, S.G. ; Tsaprouni, L. ; Lim, E.M. ; Stuckey, B. ; Deloukas, P. ; Mohney, R.P. ; Suhre, K. ; Spector, T.D. ; Valdes, A.M.
Int. J. Epidemiol. 42, 1111-1119 (2013)
BACKGROUND: Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age. METHODS: Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels. RESULTS: We identified a panel of 22 metabolites which combined are strongly correlated with age (R(2) = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10(-157)) and lung function (FEV1 beta = -0.04, SE = 0.008, P = 1.8 × 10(-8) adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = -0.01, SE = 0.002, P = 1.9 × 10(-6)) and birthweight (beta = -0.06, SE = 0.01, P = 2.5 × 10(-9)). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10(-6)). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = -0.20, SE = 0.04, P = 2.9 × 10(-8)). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes. CONCLUSIONS: Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.
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Scientific Article
Then, C. ; Wahl, S. ; Kirchhofer, A. ; Grallert, H. ; Krug, S. ; Kastenmüller, G. ; Römisch-Margl, W. ; Claussnitzer, M. ; Illig, T. ; Heier, M. ; Meisinger, C. ; Adamski, J. ; Thorand, B. ; Huth, C. ; Peters, A. ; Prehn, C. ; Heukamp, I. ; Laumen, H. ; Lechner, A. ; Hauner, H. ; Seissler, J.
PLoS ONE 8:e78430 (2013)
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).
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Scientific Article
Montrone, C. ; Kokkaliaris, K.D. ; Loeffler, D. ; Lechner, M. ; Kastenmüller, G. ; Schroeder, T. ; Ruepp, A.
PLoS ONE 8:e70348 (2013)
HSC-Explorer (http://mips.helmholtz-muenchen.de/HSC/) is a publicly available, integrative database containing detailed information about the early steps of hematopoiesis. The resource aims at providing fast and easy access to relevant information, in particular to the complex network of interacting cell types and molecules, from the wealth of publications in the field through visualization interfaces. It provides structured information on more than 7000 experimentally validated interactions between molecules, bioprocesses and environmental factors. Information is manually derived by critical reading of the scientific literature from expert annotators. Hematopoiesis-relevant interactions are accompanied with context information such as model organisms and experimental methods for enabling assessment of reliability and relevance of experimental results. Usage of established vocabularies facilitates downstream bioinformatics applications and to convert the results into complex networks. Several predefined datasets (Selected topics) offer insights into stem cell behavior, the stem cell niche and signaling processes supporting hematopoietic stem cell maintenance. HSC-Explorer provides a versatile web-based resource for scientists entering the field of hematopoiesis enabling users to inspect the associated biological processes through interactive graphical presentation.
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Scientific Article
Goek, O.-N. ; Prehn, C. ; Sekula, P. ; Römisch-Margl, W. ; Döring, A. ; Gieger, C. ; Heier, M. ; Koenig, W. ; Wang-Sattler, R. ; Illig, T. ; Suhre, K. ; Adamski, J. ; Köttgen, A.° ; Meisinger, C.°
Nephrol. Dial. Transplant. 28, 2131-2138 (2013)
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. KEYWORDS: GFR, incident chronic kidney disease, kidney function loss, metabolites, prediction  
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Scientific Article
Xu, T. ; Holzapfel, C. ; Dong, X. ; Bader, E. ; Yu, Z. ; Prehn, C. ; Perstorfer, K. ; Jaremek, M. ; Römisch-Margl, W. ; Rathmann, W. ; Li, Y. ; Wichmann, H.-E. ; Wallaschofski, H. ; Ladwig, K.-H. ; Theis, F.J. ; Suhre, K. ; Adamski, J. ; Illig, T. ; Peters, A. ; Wang-Sattler, R.
BMC Med. 11:60 (2013)
BACKGROUND: Metabolomics helps to identify links between environmental exposures and intermediate biomarkers of disturbed pathways. We previously reported variations in phosphatidylcholines in male smokers compared with non-smokers in a cross-sectional pilot study with a small sample size, but knowledge of the reversibility of smoking effects on metabolite profiles is limited. Here, we extend our metabolomics study with a large prospective study including female smokers and quitters. METHODS: Using targeted metabolomics approach, we quantified 140 metabolite concentrations for 1,241 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) human cohort at two time points: baseline survey conducted between 1999 and 2001 and follow-up after seven years. Metabolite profiles were compared among groups of current smokers, former smokers and never smokers, and were further assessed for their reversibility after smoking cessation. Changes in metabolite concentrations from baseline to the follow-up were investigated in a longitudinal analysis comparing current smokers, never smokers and smoking quitters, who were current smokers at baseline but former smokers by the time of follow-up. In addition, we constructed protein-metabolite networks with smoking-related genes and metabolites. RESULTS: We identified 21 smoking-related metabolites in the baseline investigation (18 in men and six in women, with three overlaps) enriched in amino acid and lipid pathways, which were significantly different between current smokers and never smokers. Moreover, 19 out of the 21 metabolites were found to be reversible in former smokers. In the follow-up study, 13 reversible metabolites in men were measured, of which 10 were confirmed to be reversible in male quitters. Protein-metabolite networks are proposed to explain the consistent reversibility of smoking effects on metabolites. CONCLUSIONS: We showed that smoking-related changes in human serum metabolites are reversible after smoking cessation, consistent with the known cardiovascular risk reduction. The metabolites identified may serve as potential biomarkers to evaluate the status of smoking cessation and characterize smoking-related diseases.  
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Scientific Article
Raffler, J. ; Römisch-Margl, W. ; Petersen, A.-K. ; Pagel, P. ; Blöchl, F. ; Hengstenberg, C. ; Illig, T. ; Meisinger, C. ; Stark, K. ; Wichmann, H.-E. ; Adamski, J. ; Gieger, C. ; Kastenmüller, G. ; Suhre, K.
Genome Med. 5:13 (2013)
Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H 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.
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Scientific Article
Wjst, M. ; Sargurupremraj, M. ; Arnold, M.
Curr. Opin. Allergy Clin. Immunol. 13, 112-118 (2013)
PURPOSE OF REVIEW: Over the past years, several consortia have provided a data deluge from large-scale, genome-wide association studies (GWASs) for numerous asthma and allergy related traits. Dozens of reviews have already summarized the main results, although a coherent picture is still missing, referred to as 'missing' or 'unexplained' heritability. RECENT FINDINGS: We identify the factors responsible for the unexplained heritability including imprecise phenotyping, biased single-nucleotide polymorphism selection (preferentially gene-based and high allele frequency with poor linkage disequilibrium tagging capacity), heterogeneity and insufficient significance ranking test statistics. In spite of these problems, three major outcomes can already be identified. First, rare variants give the highest risk estimates but are limited to small subgroups indicating a complex origin of asthma that may involve hundreds of variants that are either population, family or individual specific. Second, only a few common variants are shared amongst all asthmatics where the IL33/ST2 pathway turns out to be the most relevant factor. Third, transcription factor binding sites are enriched amongst the top association results pointing towards disturbed regulatory network function in asthma. SUMMARY: The next wave of asthma genetic studies will use full-genome sequencing and overcome most GWAS-associated problems. It will be the last step of a century-long search for asthma genes, satisfying scientific curiosity and, hopefully, also providing data applicable in translational medicine.
Review
Review
Altmaier, E. ; Emeny, R.T. ; Krumsiek, J. ; Lacruz, M.E. ; Lukaschek, K. ; Häfner, S. ; Kastenmüller, G. ; Römisch-Margl, W. ; Prehn, C. ; Mohney, R.P. ; Evans, A.M. ; Milburn, M.V. ; Illig, T. ; Adamski, J. ; Theis, F.J. ; Suhre, K. ; Ladwig, K.-H.
Psychoneuroendocrinology 38, 1299-1309 (2013)
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.
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Scientific Article
Menni, C. ; Zhai, G. ; MacGregor, A. ; Prehn, C. ; Römisch-Margl, W. ; Suhre, K. ; Adamski, J. ; Cassidy, A. ; Illig, T. ; Spector, T.D. ; Valdes, A.M.
Catal. Lett. 9, 506-514 (2013)
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 (TM) 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 x 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 x 10(-9)) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 x 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.
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Scientific Article
Ferrannini, E. ; Natalai, A. ; Camastra, S. ; Nannipieri, M. ; Mari, A. ; Adam, K.-P. ; Milburn, M.V. ; Kastenmüller, G. ; Adamski, J. ; Tuomi, T. ; Lyssenko, V. ; Groop, L. ; Gall, W.E.
Diabetes 62, 1730-1737 (2013)
Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of β-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00–1.60] and 1.26 [1.07–1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48–0.85] and 0.67 [0.54–0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding area under the receiver operating characteristic curves were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and β-cell dysfunction.
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Scientific Article
2012
Wahl, S. ; Yu, Z. ; Kleber, M. ; Singmann, P. ; Holzapfel, C. ; He, Y. ; Mittelstraß, K. ; Polonikov, A. ; Prehn, C. ; Römisch-Margl, W. ; Adamski, J. ; Suhre, K. ; Grallert, H. ; Illig, T. ; Wang-Sattler, R. ; Reinehr, T.
Obes. Facts 5, 660-670 (2012)
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.
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Scientific Article
Krumsiek, J. ; Stückler, F. ; Kastenmüller, G. ; Theis, F.J.
In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: from Experiment to Systems Biology. New York: Springer, 2012. 281-313
In the preceding chapters many aspects of metabolite quantification and relation to trait and disease phenotypes have been described, in particular the linkage of intermediate metabolic traits to genetic heterogeneities. Although many analyses start on the genome-wide level, they end up picking out single polymorphisms or other variations and study these in detail. This reductionist approach is very common in molecular biology and has proven hugely successful over the past decades. In recent years however, a second paradigm has become increasingly popular, namely that of integrating multiple such analyses into larger ones commonly called models. This paradigm, nowadays, is known as systems biology and is expected to penetrate many classical molecular analyses.
Arnold, M. ; Hartsperger, M.L. ; Baurecht, H. ; Rodriguez, E. ; Wachinger, B. ; Franke, A. ; Kabesch, M. ; Winkelmann, J. ; Pfeufer, A. ; Romanos, M. ; Illig, T. ; Mewes, H.-W. ; Stuempflen, V. ; Weidinger, S.
BMC Genomics 13:490 (2012)
ABSTRACT: BACKGROUND: Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases. RESULTS: Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. We demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. We further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches. CONCLUSIONS: The observation that a surprisingly high fraction (>15%) of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further.
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Scientific Article
Krumsiek, J. ; Suhre, K. ; Evans, A.M. ; Mitchell, M.W. ; Mohney, R.P. ; Milburn, M.V. ; Wägele, B. ; Römisch-Margl, W. ; Illig, T. ; Adamski, J. ; Gieger, C. ; Theis, F.J. ; Kastenmüller, G.
PLoS Genet. 8:e1003005 (2012)
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.
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Scientific Article
Lechner, M. ; Höhn, V. ; Brauner, B. ; Dunger, I. ; Fobo, G. ; Frishman, G. ; Montrone, C. ; Kastenmüller, G. ; Wägele, B. ; Ruepp, A.
Genome Biol. 13:R62 (2012)
ABSTRACT: The pathobiology of common diseases is influenced by heterogeneous factors interacting in complex networks. CIDeR http://mips.helmholtz-muenchen.de/cider/ is a publicly available, manually curated, integrative database of metabolic and neurological disorders. The resource provides structured information on 18,813 experimentally validated interactions between molecules, bioprocesses and environmental factors extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make CIDeR a versatile knowledge base for biologists, analysis of large-scale data and systems biology approaches.
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Scientific Article
Wang-Sattler, R. ; Yu, Z. ; Herder, C. ; Messias, A.C. ; Floegel, A. ; He, Y. ; Heim, K. ; Campillos, M.J. ; Holzapfel, C. ; Thorand, B. ; Grallert, H. ; Xu, T. ; Bader, E. ; Huth, C. ; Mittelstraß, K. ; Döring, A. ; Meisinger, C. ; Gieger, C. ; Prehn, C. ; Römisch-Margl, W. ; Carstensen, M. ; Xie, L. ; Yamanaka-Okumura, H. ; Xing, G. ; Ceglarek, U. ; Thiery, J. ; Giani, G. ; Lickert, H. ; Lin, X. ; Li, Y. ; Boeing, H. ; Joost, H.-G. ; Hrabě de Angelis, M. ; Rathmann, W. ; Suhre, K. ; Prokisch, H. ; Peters, A. ; Meitinger, T. ; Roden, M. ; Wichmann, H.-E. ; Pischon, T. ; Adamski, J. ; Illig, T.
Mol. Syst. Biol. 8:615 (2012)
Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10(-4) to 2.1 × 10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.
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Scientific Article
Yu, Z.° ; Zhai, G. ; Singmann, P. ; He, Y. ; Xu, T. ; Prehn, C. ; Römisch-Margl, W. ; Lattka, E. ; Gieger, C. ; Soranzo, N. ; Heinrich, J. ; Standl, M. ; Thiering, E. ; Mittelstraß, K. ; Wichmann, H.-E. ; Peters, A. ; Suhre, K. ; Li, Y. ; Adamski, J. ; Spector, T.D. ; Illig, T. ; Wang-Sattler, R.°
Aging Cell 11, 960-967 (2012)
Understanding the complexity of aging is of utmost importance. This can now be addressed by the novel and powerful approach of metabolomics. However, to date, only a few metabolic studies based on large samples are available. Here, we provide novel and specific information on age-related metabolite concentration changes in human homeostasis. We report results from two population-based studies: the KORA F4 study from Germany as a discovery cohort, with 1038 female and 1124 male participants (32-81 years), and the TwinsUK study as replication, with 724 female participants. Targeted metabolomics of fasting serum samples quantified 131 metabolites by FIA-MS/MS. Among these, 71/34 metabolites were significantly associated with age in women/men (BMI adjusted). We further identified a set of 13 independent metabolites in women (with P values ranging from 4.6 × 10(-04) to 7.8 × 10(-42) , α(corr)  = 0.004). Eleven of these 13 metabolites were replicated in the TwinsUK study, including seven metabolite concentrations that increased with age (C0, C10:1, C12:1, C18:1, SM C16:1, SM C18:1, and PC aa C28:1), while histidine decreased. These results indicate that metabolic profiles are age dependent and might reflect different aging processes, such as incomplete mitochondrial fatty acid oxidation. The use of metabolomics will increase our understanding of aging networks and may lead to discoveries that help enhance healthy aging.
Wissenschaftlicher Artikel
Scientific Article
Goek, O.N. ; Döring, A. ; Gieger, C. ; Heier, M. ; Koenig, W. ; Prehn, C. ; Römisch-Margl, W. ; Wang-Sattler, R. ; Illig, T. ; Suhre, K. ; Sekula, P. ; Zhai, G.J. ; Adamski, J. ; Köttgen, A. ; Meisinger, C.
Am. J. Kidney Dis. 60, 197-206 (2012)
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 & Participants: 2 independent samples: KORA F4 (discovery sample, n = 3,011) and TwinsUK (validation sample, n = 984). Exposure Factors: 151 serum metabolites, quantified by targeted mass spectrometry. Outcomes & 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 x 10(-4) for single metabolites; P < 2.2 x 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 x 10(-7) to 1.8 x 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 x 10(-69)). The replicated ratio with the strongest association was the ratio of serine to glutarylcarnitine (P = 3.6 x 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.
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Scientific Article
Renner, S. ; Römisch-Margl, W. ; Prehn, C. ; Krebs, S. ; Adamski, J. ; Göke, B. ; Blum, H. ; Suhre, K. ; Roscher, A.A. ; Wolf, E.
Diabetes 61, 2166-2175 (2012)
Diabetes is generally diagnosed too late. Therefore, biomarkers indicating early stages of beta-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 beta-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 x 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 beta-cell mass of 5-monthold pigs. These metabolites represent candidate biomarkers of early phases of beta-cell dysfunction and mass reduction. Diabetes 61:2166-2175, 2012
Wissenschaftlicher Artikel
Scientific Article
Jourdan, C. ; Petersen, A.-K. ; Gieger, C. ; Döring, A. ; Illig, T. ; Wang-Sattler, R. ; Meisinger, C. ; Peters, A. ; Adamski, J. ; Prehn, C. ; Suhre, K. ; Altmaier, E. ; Kastenmüller, G. ; Römisch-Margl, W. ; Theis, F.J. ; Krumsiek, J. ; Wichmann, H.-E. ; Linseisen, J.
PLoS ONE 7:e40009 (2012)
Objective: To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study. Subjects and Methods: Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs). Results: We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 x 10(-16) -8.95 x 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism. Conclusion: A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.
Wissenschaftlicher Artikel
Scientific Article
Arnold, B. ; Hauser, W. ; Arnold, M. ; Bernateck, M. ; Bernardy, K. ; Bruckle, W. ; Friedel, E. ; Hesselschwerdt, H.J. ; Jackel, W. ; Köllner, V. ; Kühn, E. ; Petzke, F. ; Settan, M. ; Weigl, M. ; Winter, E. ; Offenbacher, M.
Schmerz 26, 287-290 (2012)
The scheduled update to the German S3 guidelines on fibromyalgia syndrome (FMS) by the Association of the Scientific Medical Societies ("Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften", AWMF; registration number 041/004) was planned starting in March 2011. The development of the guidelines was coordinated by the German Interdisciplinary Association for Pain Therapy ("Deutsche Interdisziplinaren Vereinigung fur Schmerztherapie", DIVS), 9 scientific medical societies and 2 patient self-help organizations. Eight working groups with a total of 50 members were evenly balanced in terms of gender, medical field, potential conflicts of interest and hierarchical position in the medical and scientific fields. Literature searches were performed using the Medline, PsycInfo, Scopus and Cochrane Library databases (until December 2010). The grading of the strength of the evidence followed the scheme of the Oxford Centre for Evidence-Based Medicine. The formulation and grading of recommendations was accomplished using a multi-step, formal consensus process. The guidelines were reviewed by the boards of the participating scientific medical societies. The use of a multicomponent therapy (the combination of aerobic exercise with at least one psychological therapy) for a minimum of 24 h is strongly recommended for patients with severe FMS. The English full-text version of this article is available at SpringerLink (under "Supplemental").
Review
Review
Arnold, M. ; Ellwanger, D.C. ; Hartsperger, M.L. ; Pfeufer, A. ; Stuempflen, V.
PLoS ONE 7:e36694 (2012)
Genome-wide association studies (GWAS) have become an effective tool to map genes and regions contributing to multifactorial human diseases and traits. A comparably small number of variants identified by GWAS are known to have a direct effect on protein structure whereas the majority of variants is thought to exert their moderate influences on the phenotype through regulatory changes in mRNA expression. MicroRNAs (miRNAs) have been identified as powerful posttranscriptional regulators of mRNAs. Binding to their target sites, which are mostly located within the 3'-untranslated region (3'-UTR) of mRNA transcripts, they modulate mRNA expression and stability. Until today almost all human mRNA transcripts are known to harbor at least one miRNA target site with an average of over 20 miRNA target sites per transcript. Among 5,101 GWAS-identified sentinel single nucleotide polymorphisms (SNPs) that correspond to 18,884 SNPs in linkage disequilibrium (LD) with the sentinels (r(2) >= 0.8) we identified a significant overrepresentation of SNPs that affect the 3'-UTR of genes (OR = 2.33, 95% CI = 2.12-2.57, P
Wissenschaftlicher Artikel
Scientific Article
Petersen, A.-K. ; Stark, K. ; Musameh, M.D. ; Nelson, C.P. ; Römisch-Margl, W. ; Kremer, W. ; Raffler, J. ; Krug, S. ; Skurk, T. ; Rist, M.J. ; Daniel, H. ; Hauner, H. ; Adamski, J. ; Tomaszewski, M. ; Döring, A. ; Peters, A. ; Wichmann, H.-E. ; Kaess, B.M. ; Kalbitzer, H.R. ; Huber, F. ; Pfahlert, V. ; Samani, N.J. ; Kronenberg, F. ; Dieplinger, H. ; Illig, T. ; Hengstenberg, C. ; Suhre, K. ; Gieger, C. ; Kastenmüller, G.
Hum. Mol. Genet. 21, 1433-1443 (2012)
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.
Wissenschaftlicher Artikel
Scientific Article
Krug, S.# ; Kastenmüller, G.# ; Stückler, F.# ; Rist, M.J.# ; Skurk, T.# ; Sailer, M. ; Raffler, J. ; Römisch-Margl, W. ; Adamski, J. ; Prehn, C. ; Frank, T. ; Engel, K.-H. ; Hofmann, T. ; Luy, B. ; Zimmermann, R. ; Moritz, F. ; Schmitt-Kopplin, P. ; Krumsiek, J. ; Kremer, W. ; Huber, F. ; Oeh, U. ; Theis, F.J. ; Szymczak, W. ; Hauner, H. ; Suhre, K. ; Daniel, H.
FASEB J. 26, 2607-2619 (2012)
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 beta-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.-Krug, S., Kastenmuller, G., Stuckler, F., Rist, M. J., Skurk, T., Sailer, M., Raffler, J., Romisch-Margl, W., Adamski, J., Prehn, C., Frank, T., Engel, K.-H., Hofmann, T., Luy, B., Zimmermann, R., Moritz, F., Schmitt-Kopplin, P., Krumsiek, J., Kremer, W., Huber, F., Oeh, U., Theis, F. J., Szymczak, W., Hauner, H., Suhre, K., Daniel, H. The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607-2619 (2012). www.fasebj.org
Wissenschaftlicher Artikel
Scientific Article
Römisch-Margl, W. ; Prehn, C. ; Bogumil, R. ; Röhring, C. ; Suhre, K. ; Adamski, J.
Metabolomics 8, 133-142 (2012)
Reproducible quantification of metabolites in tissue samples is of high importance for characterization of animal models and identification of metabolic changes that occur in different tissue types in specific diseases. However, the extraction of metabolites from tissue is often the most labor-intensive and error-prone step in metabolomics studies. Here, we report the development of a standardized high-throughput method for rapid and reproducible extraction of metabolites from multiple tissue samples from different organs of several species. The method involves a bead-based homogenizer in combination with a simple extraction protocol and is compatible with state-ofthe-art metabolomics kit technology for quantitative and targeted flow injection tandem mass spectrometry. We analyzed different extraction solvents for both reproducibility as well as suppression effects for a range of different animal tissue types including liver, kidney, muscle, brain, and fat tissue from mouse and bovine. In this study, we show that for most metabolites a simple methanolic extraction is best suited for reliable results. An additional extraction step with phosphate buffer can be used to improve the extraction yields for a few more polar metabolites. We provide a verified tissue extraction setup to be used with different indications. Our results demonstrate that this high-throughput procedure provides a basis for metabolomic assays with a wide spectrum of metabolites. The developed method can be used for tissue extraction setup for different indications like studies of metabolic syndrome, obesity, diabetes or cardiovascular disorders and nutrient transformation in livestock.
Wissenschaftlicher Artikel
Scientific Article
2011
Suhre, K. ; Shin, S.Y. ; Petersen, A.-K. ; Mohney, R.P. ; Meredith, D. ; Wägele, B. ; Altmaier, E. ; CARDIoGRAM Consortium (Lichtner, P. ; Eckstein, G.N. ; Fischer, G. ; Strom, T.M. ; Peters, A. ; Holle, R. ; John, J.) ; Deloukas, P. ; Erdmann, J. ; Grundberg, E. ; Hammond, C.J. ; Hrabě de Angelis, M. ; Kastenmüller, G. ; Köttgen, A. ; Kronenberg, F. ; Mangino, M. ; Meisinger, C. ; Meitinger, T. ; Mewes, H.-W. ; Milburn, M.V. ; Prehn, C. ; Raffler, J. ; Ried, J.S. ; Römisch-Margl, W. ; Samani, N.J. ; Small, K.S. ; Wichmann, H.-E. ; Zhai, G. ; Illig, T. ; Spector, T.D. ; Adamski, J. ; Soranzo, N. ; Gieger, C.
Nature 477, 54-60 (2011)
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.
Wissenschaftlicher Artikel
Scientific Article
Mittelstraß, K.# ; Ried, J.S.# ; Yu, Z.# ; Krumsiek, J. ; Gieger, C. ; Prehn, C. ; Römisch-Margl, W. ; Polonikov, A. ; Peters, A. ; Theis, F.J. ; Meitinger, T. ; Kronenberg, F. ; Weidinger, S. ; Wichmann, H.-E. ; Suhre, K. ; Wang-Sattler, R. ; Adamski, J.° ; Illig, T.°
PLoS Genet. 7:e1002215 (2011)
Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.
Wissenschaftlicher Artikel
Scientific Article
Yu, Z. ; Kastenmüller, G. ; He, Y. ; Belcredi, P. ; Möller, G. ; Prehn, C. ; Mendes, J. ; Wahl, S. ; Römisch-Margl, W. ; Ceglarek, U. ; Polonikov, A. ; Dahmen, N. ; Prokisch, H. ; Xie, L. ; Li, Y. ; Wichmann, H.-E. ; Peters, A. ; Kronenberg, F. ; Suhre, K. ; Adamski, J. ; Illig, T. ; Wang-Sattler, R.
PLoS ONE 6:e21230 (2011)
BACKGROUND: Human plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p = 0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r = 0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices. CONCLUSIONS/SIGNIFICANCE: Our study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.
Wissenschaftlicher Artikel
Scientific Article
Suhre, K. ; Wallaschofski, H. ; Raffler, J. ; Friedrich, N. ; Haring, R. ; Michael, K. ; Wasner, C. ; Krebs, A. ; Kronenberg, F. ; Chang, D. ; Meisinger, C. ; Wichmann, H.-E. ; Hoffmann, W. ; Völzke, H. ; Völker, U. ; Teumer, A. ; Biffar, R. ; Kocher, T. ; Felix, S.B. ; Illig, T. ; Kroemer, H.K. ; Gieger, C. ; Römisch-Margl, W. ; Nauck, M.
Nat. Genet. 43, 565-571 (2011)
We present a genome-wide association study of metabolic traits in human urine, designed to investigate the detoxification capacity of the human body. Using NMR spectroscopy, we tested for associations between 59 metabolites in urine from 862 male participants in the population-based SHIP study. We replicated the results using 1,039 additional samples of the same study, including a 5-year follow-up, and 992 samples from the independent KORA study. We report five loci with joint P values of association from 3.2 × 10(-19) to 2.1 × 10(-182). Variants at three of these loci have previously been linked with important clinical outcomes: SLC7A9 is a risk locus for chronic kidney disease, NAT2 for coronary artery disease and genotype-dependent response to drug toxicity, and SLC6A20 for iminoglycinuria. Moreover, we identify rs37369 in AGXT2 as the genetic basis of hyper-β-aminoisobutyric aciduria.
Wissenschaftlicher Artikel
Scientific Article
Suhre, K. ; Römisch-Margl, W. ; Hrabě de Angelis, M. ; Adamski, J. ; Luippold, G. ; Augustin, R.
J. Biomol. Screen. 16, 467-475 (2011)
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.
Wissenschaftlicher Artikel
Scientific Article
Fuchs, H. ; Gailus-Durner, V. ; Adler, T. ; Aguilar-Pimentel, J.A. ; Becker, L. ; Calzada-Wack, J. ; Da Silva-Buttkus, P. ; Neff, F. ; Götz, A.A. ; Hans, W. ; Hölter, S.M. ; Horsch, M. ; Kastenmüller, G. ; Kemter, E. ; Lengger, C. ; Maier, H. ; Matloka, M. ; Möller, G. ; Naton, B. ; Prehn, C. ; Puk, O. ; Rácz, I. ; Rathkolb, B. ; Römisch-Margl, W. ; Rozman, J. ; Wang-Sattler, R. ; Schrewe, A. ; Stöger, C. ; Tost, M. ; Adamski, J. ; Aigner, B. ; Beckers, J. ; Behrendt, H. ; Busch, D.H. ; Esposito, I. ; Graw, J. ; Illig, T. ; Ivandic, B. ; Klingenspor, M. ; Klopstock, T. ; Kremmer, E. ; Mempel, M. ; Neschen, S. ; Ollert, M. ; Schulz, S. ; Suhre, K. ; Wolf, E. ; Wurst, W. ; Zimmer, A. ; Hrabě de Angelis, M.
Methods 53, 120-135 (2011)
Model organisms like the mouse are important tools to learn more about gene function in man. Within the last 20years 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]).
Review
Review
Altmaier, E. ; Kastenmüller, G. ; Römisch-Margl, W. ; Thorand, B. ; Weinberger, K.M. ; Illig, T. ; Adamski, J. ; Döring, A. ; Suhre, K.
Eur. J. Epidemiol. 26, 145-156 (2011)
Nutrition plays an important role in human metabolism and health. However, it is unclear in how far self-reported nutrition intake reflects de facto differences in body metabolite composition. To investigate this question on an epidemiological scale we conducted a metabolomics study analyzing the association of self-reported nutrition habits with 363 metabolites quantified in blood serum of 284 male participants of the KORA population study, aged between 55 and 79 years. Using data from an 18-item food frequency questionnaire, the consumption of 18 different food groups as well as four derived nutrition indices summarizing these food groups by their nutrient content were analyzed for association with the measured metabolites. The self-reported nutrition intake index "polyunsaturated fatty acids" associates with a decrease in saturation of the fatty acid chains of glycero-phosphatidylcholines analyzed in serum samples. Using a principal component analysis dietary patterns highly associating with serum metabolite concentrations could be identified. The first principal component, which was interpreted as a healthy nutrition lifestyle, associates with a decrease in the degree of saturation of the fatty acid moieties of different glycero-phosphatidylcholines. In summary, this analysis shows that on a population level metabolomics provides the possibility to link self-reported nutrition habits to changes in human metabolic profiles and that the associating metabolites reflect the self-reported nutritional intake. Moreover, we could show that the strength of association increases when composed nutrition indices are used. Metabolomics may, thus, facilitate evaluating questionnaires and improving future questionnaire-based epidemiological studies on human health.
Wissenschaftlicher Artikel
Scientific Article
Kastenmüller, G. ; Römisch-Margl, W. ; Wägele, B. ; Altmaier, E. ; Suhre, K.
J. Biomed. Biotechnol. 2011:839862 (2011)
Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developed metaP-server to facilitate data interpretation. metaP-server provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided. metaP-server is freely accessible at http://metabolomics.helmholtz-muenchen.de/metap2/.
Wissenschaftlicher Artikel
Scientific Article