Research Interests

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. In several ground-breaking studies since 2008, we reported comprehensive analyses of genotype-dependent metabolic phenotypes using a GWAS with targeted and non-targeted metabolomics in blood and urine. We identified until now over 150 genetic loci associated with blood metabolite concentrations, of which many show effect sizes that are unusually high for GWAS and account for up to 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. These studies advance our knowledge of the genetic basis of metabolic individuality in humans and generate many new hypotheses for biomedical and pharmaceutical research.

Here is a list of all studies that we published so far on the discovery of new genetically influenced metabotypes (GIMs):

1. Gieger, C, Geistlinger, L, Altmaier, E, Hrabé de Angelis, M, Kronenberg, F, Meitinger, T, Mewes, HW, Wichmann, HE, Weinberger, KM, Adamski, J, Illig, T, Suhre K, Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum , PLoS Genetics, 4(11):e1000282, 2008. [This paper reports the first GWAS with metabolomics and establishes the concept of GIMs] 
2. Illig, T, Gieger, C, Zhai, G, Römisch-Margl, W, Wang-Sattler, R, Prehn, C, Altmaier, E, Kastenmüller, G, Kato, BS , Mewes, HW, Meitinger, T, Hrabé de Angelis, M, Kronenberg, F, Soranzo, N , Wichmann, HE, Spector, TD, Adamski, J, Suhre, K, A genome-wide perspective of genetic variation in human metabolism, Nature Genetics, 42:137-141, 2010, doi:10.1038/ng.507. [This paper describes the first fully powered GWAS with targeted metabolomics/lipidomics]
3. Suhre K., Wallaschofski H., RafflerJ., 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., GiegerC., Römisch-Margl W., Nauck M., A genome-wide association study of metabolic traits in human urine Nature Genetics, 43:565-569, 2011. [This paper describes the first GWAS with NMR-derived metabolites in urine]
4. Suhre K, Shin S-Y, Petersen A-K, Mohney RP, Meredith D, Wagele B, Altmaier E, CARDIoGRAM, Deloukas P, Erdmann J, Grundberg E, Hammond CJ, de Angelis MH, Kastenmüller G, Kottgen A, Kronenberg F, Mangino M, Meisinger C, Meitinger T, Mewes H-W, Milburn MV, Prehn C, Raffler J, Ried JS, Romisch-Margl W, Samani NJ, Small KS, Erich Wichmann H, Zhai G, Illig T, Spector TD, Adamski J, Soranzo N, Gieger C. Human metabolic individuality in biomedical and pharmaceutical research. Nature, 477:54-60, 2011. [This paper describes a GWAS with the most comprehensive set of non-targeted metabolomics at the time and reports 37 GIMs]
5. Krumsiek J, Suhre K, Evans AM, Mitchell MW, Mohney RP, Milburn MV, Wägele B, Römisch-Margl W, Illig T, Adamski J, Gieger C, Theis FJ, Kastenmüller G., Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genetics, 8:e1003005, 2012. [This paper uses a GWAS with metabolites of unknown biochemical identity to reveal their biochemical properties]
6. Raffler J, Römisch-Margl W, Petersen AK, Pagel P, Blöchl F, Hengstenberg C, Illig T, Meisinger C, Stark K, Wichmann HE, Adamski J, Gieger C, Kastenmüller G, Suhre K, Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med., 5:13, 2013. [This paper reports a GWAS with NMR peaks and relates them to MS-derived metabotypes]
7. Petersen AK, Zeilinger S, Kastenmüller G, Römisch-Margl W, Brugger M, Peters A, Meisinger C, Strauch K, Hengstenberg C, Pagel P, Huber F, Mohney RP, Grallert H, Illig T, Adamski J, Waldenberger M, Gieger C, Suhre K, Epigenetics meets metabolomics: An epigenome-wide association study with blood serum metabolic traits, Hum Mol Genet., 23:534-545, 2014. [This paper describes the first EWAS with metabolomics]
8. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, Walter K, Menni C, Chen L, Vasquez L, Valdes AM, Hyde CL, Wang V, Ziemek D, Roberts P, Xi L, Grundberg E, The MuTHER Consortium, Waldenberger M, Richards JB, Mohney RP, Milburn MV, John SL, Trimmer J, Theis FJ, Overington JP, Suhre K*, Brosnan MJ*, Gieger C*, Kastenmüller G*, Spector TD*, Soranzo N*, An atlas of genetic influences on human blood metabolites, Nature Genetics, 46:543-550, 2014. [This paper reports the largest GWAS with non-targeted metabolomics to date and describes 145 GIMs, each of them telling its own biological story]

Webservers for Genomics, Metabolomics, and Protein Structure Analyses

1. 
   SNIPA: Single Nucleotide Polymorphism Annotator.
   This server was inspired by the Broad Institute's SNAP server; SNIPA offers up-to-date functional annotations and linkage disequilibrium information for bi-allelic genomic variants (SNPs and SNVs). 
2. 
   Metabolomics GWAS Server: An atlas of genetic influences on human blood metabolites.
   This server provides interactive access to association results of our two largest genome-wide association studies on the human metabolome (Suhre et al., Nature, 2011; Shin et al., Nature Genetics, 2014).
3. 
   MetaP Server: Automated analysis of metabolomics data
   This server provides automated data analysis for the processing of metabolomics experiments, including data QC, PCA, and hypothesis tests.
4. 
   MassTRIX: Mass TRanslator into Pathways
   This server annotates metabolites in high precision mass spectrometry data.
5. 
   ElNemo: The Elastic Network Model
   This server is the Web-interface to the Elastic Network Model (ENM), a fast and simple way for computing the low frequency normal modes of a macromolecule.

BOOK:
Genetics Meets Metabolomics
from Experiment to Systems Biology

Summary: Unlike previous books published on metabolomics, this book switches the focus from experimental questions and technical challenges, to the application of metabolomics with an emphasis on the underlying genetics. The chapters provide a thorough basis for the understanding of the underlying experimental techniques, concepts and potential biomedical applications of this exciting field. The interdisciplinary approach of this book addresses a wide readership, and contains educational aids for anyone not familiar with a particular area of metabolomics. The area of research where genetics and metabolomics meet is likely to represent a field where systems biology shall prosper highly in the years to come.

Content: Pre-conditions for high quality biobanking in large human epidemiological cohorts for metabolomics and other -omics studies.- Assay Tools for Metabolomics.- Statistical methods in genetic and molecular epidemiology and their application in studies with metabolic phenotypes.- Ultrahigh resolution mass spectrometry based non-targeted microbial metabolomics.- etabolomic systems biology of protozoan parasites.- Mouse genetics and metabolic mouse phenotyping.- Metabolomics in animal breeding.- Metabolomics applications in human nutrition.- Metabolomics for the individualized therapy of androgen deficiency syndrome in male adults.- Systems biology resources arising from the human metabolome project.- Understanding cancer metabolism through global metabolomics.- Genetic and metabolic determinants of fatty acid chain length and desaturation, their incorporation into lipid classes and their effects on risk of vascular and metabolic disease.- Mapping metabolomic quantitative trait loci (mQTL) – A link between metabolome-wide association studies and systems biology.- Metabolic traits as intermediate phenotype.- Genome-wide association studies with metabolomics.- Systems biology meets metabolism.

Establishing world leadership in date palm research in Qatar

Weill Cornell Medicine - Qatar won the first award in the Qatar National Research Fund (QNRF) Exceptional Proposal program with Dr. Karsten Suhre, Professor of Physiology and Director of the Bioinformatics Core, and Dr. Joel Malek, Director of the Genomics Core, awarded a five-year grant of $US4.5 million for their project Establishing World Leadership in Date Palm Research in Qatar.

The proposal combines two innovative technologies that are well established at WCM-Q, genomics and metabolomics, in an interdisciplinary approach to date palm research, addressing major challenges of the field. WCM-Q is collaborating with the Ministry of Environment’s Biotechnology Center in Qatar, the Helmholtz Centre in Munich, the French National Institute for Agricultural Research, and the European Institute for Research and Development.

The goal of the project is to better understand date palm biology and link the genetics of the date palm to date palm characteristics such as fruit color, flavor and ability to resist disease or environmental stress. Go to the dactylifera.org web-site

The following studies report results related to this project:

1. Mathew LS, Spannagl M, Al-Malki A, George B, Torres MF, Al-Dous EK, Al-Azwani EK, Hussein E, Mathew S, Mayer KF, Mohamoud YA, Suhre K, Malek JA, A first genetic map of date palm (Phoenix dactylifera) reveals long-range genome structure conservation in the palms, BMC Genomics, 15:285, 2014.
2. Mathew S, Krug S, Skurk T, Halama A, Stank A, Artati A, Prehn C, Malek JA, Kastenmüller G, Römisch-Margl W, Adamski J, Hauner H, Suhre K., Metabolomics of Ramadan fasting: an opportunity for the controlled study of physiological responses to food intake, J Transl Med, 12:161, 2014.

Diabetes Research - a non-invasive marker for diabetes screening

In most ethnicities at least a quarter of all cases with diabetes is assumed to be undiagnosed. Screening for diabetes using saliva has been suggested as an effective approach to identify affected individuals. The objective of this study is to identify a non-invasive metabolic marker of type 2 diabetes in saliva. In a case-control study of type 2 diabetes, we used a clinical metabolomics discovery study to screen for diabetes-relevant metabolic readouts in saliva, using blood and urine as a reference. With a combination of three metabolomics platforms based on non-targeted mass spectrometry we examined 2,178 metabolites in saliva, blood plasma, and urine samples from 188 subjects with type 2 diabetes and 181 controls of Arab and Asian ethnicities. We found a strong association of type 2 diabetes with 1,5-anhydroglucitol (1,5-AG) in saliva (p=3.6×10−13). Levels of 1,5-AG in saliva highly correlated with 1,5-AG levels in blood and inversely correlated with blood glucose and HbA1c levels. These findings were robust across three different non-Caucasian ethnicities (Arabs, South Asians, and Philippines) irrespective of body mass index, age and gender. Clinical studies have already established 1,5-AG in blood as a reliable marker of short-term glycemic control. Our study suggests that 1,5-AG in saliva can be used in national screening programs for undiagnosed diabetes, which are of particular interest for Middle Eastern countries with young populations and exceptionally high diabetes rates.

Mook-Kanamori DO, El-Din Selim MM, Takiddin AH, Al-Homsi H, Al-Mahmoud KA, Al-Obaidli A, Zirie MA, Rowe J, Yousri NA, Karoly ED, Kocher T, Sekkal Gherbi W, Chidiac OM, Mook-Kanamori MJ, Abdul Kader S, Al Muftah WA, McKeon C, Suhre K, 1,5-anhydroglucitol in saliva is a non-invasive marker of short-term glycemic control, J Clin Endocrinol Metab, [Epub ahead of print], 2014. 

Ethnic differences in skin autofluorescence - a predictor of cardiovascular risk

Advanced glycation end products (AGEs) have been shown to be a predictor of cardiovascular risk in Caucasian subjects. In this study we examine whether the existing reference values are useable for non-Caucasian ethnicities. Furthermore, we assessed whether gender and smoking affect AGEs. AGEs were determined by a non-invasive method of skin auto-fluorescence (AF). AF was measured in 200 Arabs, 99 South Asians, 35 Filipinos and 14 subjects of other/mixed ethnicity in the Qatar Metabolomics Study on Diabetes (QMDiab). Using multivariate linear regression analysis and adjusting for age and type 2 diabetes, we assessed whether ethnicity, gender and smoking were associated with AF. The mean AF was 2.27 arbitrary units (AU) (SD: 0.63). Arabs and Filipinos had a significant higher AF than the South Asian population (0.25 arbitrary units (AU) (95% CI: 0.11–0.39), p = 0.001 and 0.34 (95% CI: 0.13–0.55), p = 0.001 respectively). Also, AF was significantly higher in females (0.41 AU (95% CI: 0.29–0.53), p < 0.001). AF associated with smoking (0.21 AU (95% CI: 0.01–0.41), p = 0.04) and increased with the number of pack-years smoked (p = 0.02). This study suggests that the existing reference values should take ethnicity, gender and smoking into account. Larger studies in specific ethnicities are necessary to create ethnic- and gender-specific reference values.

Mook-Kanamori MJ, Selim MM, Takiddin AH, Al-Homsi H, Al-Mahmoud KA, Al-Obaidli A, Zirie MA, Rowe J, Gherbi WS, Chidiac OM, Kader SA, Al Muftah WA, McKeon C, Suhre K, Mook-Kanamori DO, Ethnic and gender differences in advanced glycation end products measured by skin auto-fluorescence. Dermato-Endocrinology, 5:325-330, 2013.