Thomas Illig

Human metabolic individuality in biomedical and pharmaceutical research

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

SNP-Based Analysis of Genetic Substructure in the German Population

Objective: To evaluate the relevance and necessity to account for the effects of population substructure on association studies under a case-control design in central Europe, we analysed three samples drawn from different geographic areas of Germany. Two of the three samples, POPGEN (n = 720) and SHIP (n = 709), are from north and north-east Germany, respectively, and one sample, KORA (n = 730), is from southern Germany. Methods: Population genetic differentiation was measured by classical F-statistics for different marker sets, either consisting of genome-wide selected coding SNPs located in functional genes, or consisting of selectively neutral SNPs from ‘genomic deserts’. Quantitative estimates of the degree of stratification were performed comparing the genomic control approach [Devlin B, Roeder K: Biometrics 1999;55:997–1004], structured association [Pritchard JK, Stephens M, Donnelly P: Genetics 2000;155:945–959] and sophisticated methods like random forests [Breiman L: Machine Learning 2001;45:5–32]. Results: F-statistics showed that there exists a low genetic differentiation between the samples along a north-south gradient within Germany (FST(KORA/POPGEN): 1.7 · 10–4; FST(KORA/SHIP): 5.4 · 10–4; FST(POPGEN/SHIP): –1.3 · 10–5). Conclusion: Although the FST -values are very small, indicating a minor degree of population structure, and are too low to be detectable from methods without using prior information of subpopulation membership, such as STRUCTURE [Pritchard JK, Stephens M, Donnelly P: Genetics 2000;155:945–959], they may be a possible source for confounding due to population stratification.

DNA methylation of lipid-related genes affects blood lipid levels.

Background—Epigenetic mechanisms might be involved in the regulation of interindividual lipid level variability and thus may contribute to the cardiovascular risk profile. The aim of this study was to investigate the association between genome-wide DNA methylation and blood lipid levels high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol. Observed DNA methylation changes were also further analyzed to examine their relationship with previous hospitalized myocardial infarction. Methods and Results—Genome-wide DNA methylation patterns were determined in whole blood samples of 1776 subjects of the Cooperative Health Research in the Region of Augsburg F4 cohort using the Infinium HumanMethylation450 BeadChip (Illumina). Ten novel lipid-related CpG sites annotated to various genes including ABCG1, MIR33B/SREBF1, and TNIP1 were identified. CpG cg06500161, located in ABCG1, was associated in opposite directions with both high-density lipoprotein cholesterol (&bgr; coefficient=−0.049; P=8.26E-17) and triglyceride levels (&bgr;=0.070; P=1.21E-27). Eight associations were confirmed by replication in the Cooperative Health Research in the Region of Augsburg F3 study (n=499) and in the Invecchiare in Chianti, Aging in the Chianti Area study (n=472). Associations between triglyceride levels and SREBF1 and ABCG1 were also found in adipose tissue of the Multiple Tissue Human Expression Resource cohort (n=634). Expression analysis revealed an association between ABCG1 methylation and lipid levels that might be partly mediated by ABCG1 expression. DNA methylation of ABCG1 might also play a role in previous hospitalized myocardial infarction (odds ratio, 1.15; 95% confidence interval=1.06–1.25). Conclusions—Epigenetic modifications of the newly identified loci might regulate disturbed blood lipid levels and thus contribute to the development of complex lipid-related diseases.

Effects of Metformin on Metabolite Profiles and LDL Cholesterol in Patients With Type 2 Diabetes

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.

Genetic variants in the GATA3 gene are not associated with asthma and atopic diseases in German children

To the Editor: In asthma and atopy a disturbed regulation of T cells can lead to an imbalance between TH1 and TH2 cells and toward an increased TH2 response. The transcription factor GATA3 is necessary and sufficient for the differentiation of naive T cells into TH2 cells and also the maintenance of TH2 cytokine expression (IL-4, IL-5, and IL-13) in already differentiated TH2 cells. 1 GATA3 is not only involved in T-cell regulation but directly influences asthma symptoms and development. Selectively silencing GATA3, Sel et al recently demonstrated the essential involvement of GATA3 in the development of asthma in a murine model of acute experimental allergic asthma. Thus the aim of the present study was to investigate whether polymorphisms in the GATA3 gene exist that might affect TH cell differentiation and influence the pathogenesis of asthma and allergy. By using the HapMap database, GATA3 polymorphisms (minor allele frequency 3%) were identified and genotyped by means of matrix-assisted laser desorption/ionization time of flight mass spectrometry in German children from Munich (n 5 1159) and Dresden (n 5 1940) recruited in the cross-sectional International Study of Asthma and Allergy in Childhood phase II and children from Leipzig (n 5 1165) phenotyped with a similar protocol. The prevalence of asthma and allergies was assessed by means of self-administered questionnaires and objective measurements, such as lung function tests, skin prick tests, and serum IgE measurements. Further information on primer sequences, technical details, or population characteristics are available from the corresponding author on request.

Zentralisierte Biobanken als Grundlage für die medizinische Forschung

ZusammenfassungBiobanken bilden die Grundlage für einen Großteil der biomedizinischen Forschung. Mit der Entwicklung, dem Aufbau und dem Betrieb von Biobanken ist eine Vielzahl von Fragen verbunden, die vor allem die Erhebung, Speicherung, Nutzung und Weitergabe von Proben und Daten sowie die gesellschaftliche Einbindung dieser Prozesse betreffen. Diese komplexen Anforderungen können in der Regel nur von großen zentralisierten Biobanken erfolgreich bewältigt werden. Aus diesem Grund wurden in den letzten Jahren im klinischen Umfeld zentralisierte Biobanken in zahlreichen deutschen Universitäten gegründet und ausgebaut. Ähnliche Aktivitäten finden auch im europäischen Ausland sowie weltweit statt. Der vorliegende Beitrag beleuchtet die Anforderungen an zentrale Biobanken und deren Hauptaufgabengebiete wie z.xa0B. die hoch qualitative Probenpräanalytik und Probenlagerung, die Schaffung professioneller IT-Strukturen, den Datenschutz, ethische Aspekte sowie das Qualitäts- sowie Projektmanagement.AbstractBiobanks are the basis for a substantial part of biomedical research. The development, establishment and operation of biobanks are connected to a broad range of aspects, mainly concerning the preparation, storage, usage and dissemination of samples and associated data, in addition to the social and public involvement of these processes. These complex requirements can often only be managed in large centralized biobanks. In recent years, centralized clinical biobanks have been established in several university clinics in Germany. Similar activities take place in other European countries and worldwide. This article highlights the requirements and main tasks of centralized clinical biobanks: high-quality pre-analytics and sample storage, the creation of professional IT structures, data protection, ethical issues, in addition to quality and project management.Biobanks are the basis for a substantial part of biomedical research. The development, establishment and operation of biobanks are connected to a broad range of aspects, mainly concerning the preparation, storage, usage and dissemination of samples and associated data, in addition to the social and public involvement of these processes. These complex requirements can often only be managed in large centralized biobanks. In recent years, centralized clinical biobanks have been established in several university clinics in Germany. Similar activities take place in other European countries and worldwide. This article highlights the requirements and main tasks of centralized clinical biobanks: high-quality pre-analytics and sample storage, the creation of professional IT structures, data protection, ethical issues, in addition to quality and project management.

Neue Biomarker und Gene in der Prädiktion des Typ-2-Diabetes

ZusammenfassungHintergrundTyp-2-Diabetes stellt eine multifaktorielle Erkrankung dar, die auf nichtgenetischen und genetischen Risikofaktoren beruht. Für Prädiktionsmodelle werden derzeit im Wesentlichen nichtgenetische Faktoren wie Patientenalter, Übergewicht/Adipositas oder Lebensstilfaktoren verwendet, was zu einer moderaten bis guten Vorhersage des persönlichen Diabetesrisikos führt.Ziel der ArbeitEs soll ein Update zu der Frage geliefert werden, inwiefern Genvarianten und Metaboliten als bislang am besten messbare Biomarker im Rahmen der neuen „omiks“-Technologien zur Verbesserung der Risikoprädiktion dienen können.ErgebnisseSeit 2008 hat sich aufgrund „Microarray“-basierter genomweiter Assoziationsstudien ein enormer Zuwachs an Informationen zur genetischen Architektur des Typ-2-Diabetes ergeben. Bislang erlauben diese neuen Erkenntnisse jedoch eher ein besseres Verständnis der Pathophysiologie, die zum Typ-2-Diabetes führt, während der prädiktive Wert der neuen genetischen Biomarker noch gering ist. In Metabolomikstudien werden hauptsächlich im Blut zirkulierende Metaboliten wie Aminosäuren und Lipide untersucht. Ihr prädiktiver Wert scheint höher zu sein als der von Genvarianten.SchlussfolgerungWeitere Studien, die die komplette Sequenzierung des menschlichen Genoms umfassen, werden in Zukunft helfen, die genetische Prädisposition für Typ-2-Diabetes besser zu erklären als bisher. Der nächste Schritt muss dann in der Integration der Daten aus omik-Studien (Genomik, Epigenomik, Transkriptomik, Proteomik, Metabolomik) bestehen, um neue pathogenetische Mechanismen zu charakterisieren und um Biomarkermuster zu identifizieren, die einen höheren prädiktiven Wert besitzen als die derzeit verfügbaren Genvarianten und Metaboliten.AbstractBackgroundType 2 diabetes is a multifactorial disease caused by non-genetic and genetic risk factors. Current prediction models use mostly non-genetic factors, such as age, overweight, obesity and lifestyle factors, which result in a moderate or good prediction of the individual diabetes risk.AimThis review provides an update on the question to what extent gene variants and metabolites, which are currently the best measurable biomarkers under the new “omics” technologies, can be used to improve risk prediction.ResultsSince 2008 microarray-based genome-wide association studies have led to substantially deeper insights into the genetic architecture of type 2 diabetes. This knowledge has improved the understanding of the pathophysiology leading to type 2 diabetes, whereas the predictive value of the novel genetic biomarkers remains fairly low. Metabolomic studies analyze circulating metabolites, such as amino acids and lipids in blood. The predictive value of these metabolites seems to be higher than that of genetic variants.ConclusionFurther large scale studies including whole-genome sequencing will help to obtain a better understanding of the genetic susceptibility for type 2 diabetes. Subsequently, data from different “omics” studies (i.e. genomics, epigenomics, transcriptomics, proteomics and metabolomics) need to be integrated to characterize novel pathogenetic mechanisms and to identify patterns of biomarkers which predict type 2 diabetes better than currently available gene variants and metabolites.

Typ-2-Diabetes-assoziierte Gene

ZusammenfassungTyp-2-Diabetes gewinnt zunehmend an Bedeutung, da die durch verringerte Insulinsensitivität und -sekretion gekennzeichnete Krankheit in der Bevölkerung stark zunimmt. Durch diese Störung kommt es zu einer reduzierten Glukoseaufnahme in den Zielgeweben, wodurch schließlich der Blutzuckerspiegel ansteigt. Dabei spielen sowohl genetische Faktoren als auch Umwelteinflüsse eine Rolle. Die grundlegende Pathogenese ist allerdings bis heute nicht vollständig geklärt. Es wurden bereits zahlreiche Gene hauptsächlich mithilfe von genomweiten Assoziationsstudien (GWAS) entdeckt, die zur Entstehung beitragen. Ein Großteil der Heritabilität von Typ-2-Diabetes sowie die funktionellen Mechanismen bleiben unklar. Diese aufzuklären ist ein erster Schritt, um verbesserte sowie personalisierte Therapieansätze zu entwickeln.AbstractType 2 diabetes is becoming more and more important because the prevalence of the disease, which is characterized by reduced insulin sensitivity and secretion, keeps rising in the population. This dysfunction leads to a decreased glucose uptake in target tissues, whereby the blood glucose level eventually increases. In this process genetic as well as environmental factors play a role but the fundamental pathogenesis is still not fully understood. Numerous genes contributing to the manifestation have already been identified mainly via genome-wide association studies (GWAS). The main part of the heritability of type 2 diabetes as well as functional mechanisms remain unclear. The elucidation is the first step towards the development of improved as well as personalized therapeutic approaches.

Transcriptomics und Typ-2-Diabetes

ZusammenfassungModerne Methoden der Genexpressionsanalyse erlauben es, alle Transkripte, also das gesamte Transkriptom einer Zelle oder eines Gewebes, simultan zu untersuchen. Derzeit können mit DNA-Microarray-Techniken Transkriptspiegel zu fast allen proteinkodierenden Genen im Genom gemessen werden. Sequenzierbasierte Ansätzen ermöglichen eine noch vollständigere Erfassung, da mit ihnen alle Sequenzen der exprimierten Transkripte bestimmt und somit auch unbekannte und nichtproteinkodierende RNAs identifiziert werden können. Erste Querschnittsstudien zur Genexpression in Skelettmuskel, Leber, Fett und Blut haben gezeigt, dass Genexpressionsprofile in diesen Geweben mit Insulinresistenz und Diabetes korrelieren. Untersuchungen im bevölkerungsbasierten KORA-Surveyxa0F4 belegten insbesondere Assoziationen zwischen mRNA-Transkripten im Vollblut und 2-h-Glukosespiegeln aus dem oGTT. Ob RNA-Transkripte helfen, Hochrisikopersonen frühzeitig zu erkennen und klinisch relevante Unterformen des Typ-2-Diabetes aufzudecken, muss in prospektiven Studien untersucht werden. Ebenso ist noch unbekannt, ob die Kombinationen von Biomarkern aus verschiedenen „Omics“-Technologien unser Verständnis der Entwicklung des Typ-2-Diabetes verbessern kann.AbstractModern methods of gene expression analysis allow the simultaneous characterization of all transcripts, i.e. the entire transcriptome of a cell or tissue. Current DNA microarray-based technologies are able to quantify transcript levels of almost all protein coding genes in the genome. Sequencing-based approaches enable an even more complete investigation, because they provide all sequences of expressed transcripts including previously unknown and non-protein coding RNAs. Cross-sectional studies on gene expression in skeletal muscle, liver, adipose tissue and blood have demonstrated that gene expression profiles in these tissues correlate with insulin resistance and diabetes. Analyses in the population-based KORA survey F4 revealed that mRNA transcripts in whole blood were especially associated with 2xa0h glucose levels in the oral glucose tolerance test (OGTT). Prospective studies will have to show whether RNA transcripts can help in the early identification of high-risk individuals and in the definition of novel clinically relevant subtypes of type 2 diabetes. Also it remains to be seen whether the combination of biomarkers from different “omics” technologies can give further insight into the development of type 2 diabetes.

Epigenetische Prozesse beim Typ-2-Diabetes

ZusammenfassungTyp-2-Diabetes (T2D) entwickelt sich durch die weltweit dramatische Zunahme erkrankter Personen immer stärker zur Belastung der Gesundheitssysteme. Voraussichtlich wird die Zahl der T2D-Patienten von den in 2011 366xa0Mio. Betroffenen bis 2030 auf 522xa0Mio. ansteigen. Der Anteil genetischer Prädisposition, der für T2D auf etwa 25u2009% geschätzt wird, kann durch die bisher detektierten Risikovarianten mit 5–10u2009% nur teilweise erklärt werden. Die hierbei verwendeten Methoden stoßen mittlerweile an ihre Grenzen, um die fehlende Vererbung aufzuklären, und brachten nicht den postulierten Erfolg bei der Prädiktionsverbesserung. Neben neuen Methoden zur Analyse genetischer Daten bietet die Erweiterung der Ansätze auf die Epigenetik, die regulatorische Elemente untersucht, Möglichkeiten, um das Bild der komplexen genetischen Zusammenhänge in der Krankheitsentwicklung zu vervollständigen.AbstractDue to the worldwide dramatically increasing numbers of type 2 diabetes (T2D) patients, the disease becomes a heavy burden for the health systems. It is estimated that the number of individuals with T2D will rise from 366 million in 2011 to 522 million by 2030. The genetic part of the predisposition which is estimated to be around 25u2009% for T2D can be only explained in 5–10u2009% by the risk variants detected so far. The methods used so far have reached their limitations for discovering the missing heritability or improving disease prediction. Besides new methods for the analysis of genetic data the extension to epigenetic approaches, which investigate regulatory elements offers a possibility to complete the picture of complex genetic associations in the development of the disease.