ORCID Profile
0000-0001-9638-3912
Current Organisations
Weill Cornell Medicine
,
Weill Cornell Medicine - Qatar
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Publisher: Cold Spring Harbor Laboratory
Date: 05-05-2017
DOI: 10.1101/134551
Abstract: Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-in idual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 in iduals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.
Publisher: American Physiological Society
Date: 03-2016
DOI: 10.1152/PHYSIOLGENOMICS.00105.2015
Abstract: Despite numerous attempts to discover genetic variants associated with elite athletic performance, injury predisposition, and elite/world-class athletic status, there has been limited progress to date. Past reliance on candidate gene studies predominantly focusing on genotyping a limited number of single nucleotide polymorphisms or the insertion/deletion variants in small, often heterogeneous cohorts (i.e., made up of athletes of quite different sport specialties) have not generated the kind of results that could offer solid opportunities to bridge the gap between basic research in exercise sciences and deliverables in biomedicine. A retrospective view of genetic association studies with complex disease traits indicates that transition to hypothesis-free genome-wide approaches will be more fruitful. In studies of complex disease, it is well recognized that the magnitude of genetic association is often smaller than initially anticipated, and, as such, large s le sizes are required to identify the gene effects robustly. A symposium was held in Athens and on the Greek island of Santorini from 14–17 May 2015 to review the main findings in exercise genetics and genomics and to explore promising trends and possibilities. The symposium also offered a forum for the development of a position stand (the Santorini Declaration). Among the participants, many were involved in ongoing collaborative studies (e.g., ELITE, GAMES, Gene SMART, GENESIS, and POWERGENE). A consensus emerged among participants that it would be advantageous to bring together all current studies and those recently launched into one new large collaborative initiative, which was subsequently named the Athlome Project Consortium.
Publisher: Cold Spring Harbor Laboratory
Date: 20-07-2021
DOI: 10.1101/2021.07.16.21260601
Abstract: Dysregulation of sphingomyelin (SM) and ceramide metabolism have been implicated in Alzheimer’s Disease (AD). Genome-wide and transcriptome wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with AD. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. Evaluation of peripheral lipidomic profiles is useful in providing perspective on metabolic dysregulation in preclinical and clinical AD states. In this study, we focused on the sphingolipid pathway and carried out multi-omic analyses to identify central and peripheral metabolic changes in AD patients and correlate them to imaging features and cognitive performance in amyloidogenic mouse models. Our multi-omic approach was based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes (b) in silico metabolic flux analysis on 1708 context-specific metabolic networks to identify differential reaction fluxes (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in SM pathway with AD pathogenesis (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in AD (e) metabolite genome-wide association studies (mGWAS) to define receptors within pathway as potential drug target. Our findings from complementary approaches suggested that depletion of S1P compensated for AD cellular pathology, likely by upregulating the SM pathway, suggesting that modulation of S1P signaling may have protective effects in AD. We tested this hypothesis in APP/PS1 mice and showed that prolonged exposure to fingolimod, an S1P signaling modulator approved for treatment of multiple sclerosis, alleviated the cognitive impairment in mice. Our multi-omic approach identified potential targets in the SM pathway and suggested modulators of S1P metabolism as possible candidates for AD treatment.
Publisher: Springer Science and Business Media LLC
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 08-10-2022
DOI: 10.1038/S42003-022-04011-6
Abstract: Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer’s disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer’s disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer’s patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer’s disease pathogenesis (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer’s and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer’s disease treatment.
Publisher: Elsevier BV
Date: 10-2019
Publisher: MDPI AG
Date: 10-12-2014
DOI: 10.3390/MD12125960
Publisher: eLife Sciences Publications, Ltd
Date: 25-11-2021
Publisher: eLife Sciences Publications, Ltd
Date: 13-01-2022
DOI: 10.7554/ELIFE.71802
Abstract: Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent s le (Generation Scotland n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
Publisher: Oxford University Press (OUP)
Date: 20-09-2018
DOI: 10.1093/NAR/GKY837
Publisher: Springer Science and Business Media LLC
Date: 12-06-2015
DOI: 10.1038/NCOMMS8208
Publisher: Springer Science and Business Media LLC
Date: 06-2018
Publisher: Springer Science and Business Media LLC
Date: 20-01-2020
Publisher: Wiley
Date: 03-2013
DOI: 10.1111/ALL.12110
Abstract: Genome-wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites. We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic in iduals from the population-based KORA F4 study (N = 2925). In addition, we analysed effects of single-nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites. Increased levels of various phosphatidylcholines and decreased levels of various lyso-phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions. Our study demonstrated the potential of metabolomics to infer asthma-related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.
Publisher: MDPI AG
Date: 16-03-2022
Abstract: Modern metabolomics platforms are able to identify many drug-related metabolites in blood s les. Applied to population-based biobank studies, the detection of drug metabolites can then be used as a proxy for medication use or serve as a validation tool for questionnaire-based health assessments. However, it is not clear how well detection of drug metabolites in blood s les matches information on self-reported medication provided by study participants. Here, we curate free-text responses to a drug-usage questionnaire from 6000 participants of the Qatar Biobank (QBB) using standardized WHO Anatomical Therapeutic Chemical (ATC) Classification System codes and compare the occurrence of these ATC terms to the detection of drug-related metabolites in matching blood plasma s les from 2807 QBB participants for which we collected non-targeted metabolomics data. We found that the detection of 22 drug-related metabolites significantly associated with the self-reported use of the corresponding medication. Good agreement of self-reported medication with non-targeted metabolomics was observed, with self-reported drugs and their metabolites being detected in a same blood s le in 79.4% of the cases. On the other hand, only 29.5% of detected drug metabolites matched to self-reported medication. Possible explanations for differences include under-reporting of over-the-counter medications from the study participants, such as paracetamol, misannotation of low abundance metabolites, such as metformin, and inability of the current methods to detect them. Taken together, our study provides a broad real-world view of what to expect from large non-targeted metabolomics measurements in population-based biobank studies and indicates areas where further improvements can be made.
Publisher: Oxford University Press (OUP)
Date: 29-06-2013
DOI: 10.1093/IJE/DYT094
Publisher: Springer Science and Business Media LLC
Date: 11-05-2014
DOI: 10.1038/NG.2982
Publisher: Wiley
Date: 27-08-2012
Publisher: Cold Spring Harbor Laboratory
Date: 02-12-2020
DOI: 10.1101/2020.12.01.404681
Abstract: Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent s le, (Generation Scotland n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore – disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
Publisher: American Diabetes Association
Date: 08-05-2020
DOI: 10.2337/DB19-1070
Abstract: The increasing prevalence of type 2 diabetes poses a major challenge to societies worldwide. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach, measuring serum levels of 4,137 proteins in 5,438 elderly Icelanders, and identified 536 proteins associated with prevalent and/or incident type 2 diabetes. We validated a subset of the observed associations in an independent case-control study of type 2 diabetes. These protein associations provide novel biological insights into the molecular mechanisms that are dysregulated prior to and following the onset of type 2 diabetes and can be detected in serum. A bidirectional two-s le Mendelian randomization analysis indicated that serum changes of at least 23 proteins are downstream of the disease or its genetic liability, while 15 proteins were supported as having a causal role in type 2 diabetes.
Publisher: Springer Science and Business Media LLC
Date: 15-05-2011
DOI: 10.1038/NG.837
Abstract: 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 s les of the same study, including a 5-year follow-up, and 992 s les 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.
Publisher: Public Library of Science (PLoS)
Date: 08-09-2011
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Germany
No related grants have been discovered for Karsten Suhre.