Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10 -10 ) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10 -30 )]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.M.P. was supported by a fellowship from the German Research Foundation (DFG PI 1446/2-1). C.O. was founded by an early career fellowship at Homerton College, University of Cambridge. L. B. L. W. acknowledges funding by the Wellcome Trust (WT083442AIA). J.G. was supported by grants from the Medical Research Council (MC_UP_A090_1006, MC_PC_13030, MR/P011705/1 and MR/P01836X/1). Work in the Reimann/Gribble laboratories was supported by the Wellcome Trust (106262/Z/14/Z and 106263/Z/14/Z), UK Medical Research Council (MRC_MC_UU_12012/3) and PhD funding for EKB from MedImmune/AstraZeneca. Praveen Surendran is supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1. A. W. is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). J.D. is funded by the National Institute for Health Research [Senior Investigator Award] [*]. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372. We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. The Fenland Study is supported by the UK Medical Research Council (MC_UU_12015/1 and MC_PC_13046). Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. DNA extraction and genotyping was co-funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). Metabolon Metabolomics assays were funded by the NIHR 26 BioResource and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. The academic coordinating centre for INTERVAL was supported by core funding from: NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024), UK Medical Research Council (MR/L003120/1), British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946) and the NIHR [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*].The academic coordinating centre would like to thank blood donor centre staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. *The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. UK Biobank: This research has been conducted using the UK Biobank resource under Application Number 44448

Late Pliocene marine pCO2 reconstructions from the Subarctic Pacific Ocean

The development of large ice-sheets across the Northern Hemisphere during the late Pliocene and the emergence of the glacial-interglacial cycles that punctuate the Quaternary mark a significant threshold in Earth's climate history. Although a number of different mechanisms have been proposed to initiate this cooling and the onset of major Northern Hemisphere glaciation, reductions in atmospheric concentrations of CO2 likely played a key role. The emergence of a stratified (halocline) water column in the subarctic north-west Pacific Ocean at 2.73 Ma has often been interpreted as an event which would have limited oceanic ventilation of CO2 to the atmosphere, thereby helping to cool the global climate system. Here, diatom carbon isotopes (δ13Cdiatom) are used to reconstruct changes in regional carbon dynamics through this interval. Results show that the development of a salinity stratification did not fundamental alter the net oceanic/atmospheric flux of CO2 in the subarctic north-west Pacific Ocean through the late Pliocene/early Quaternary. These results provide further insights into the long-term controls on global carbon cycling and the role of the subarctic Pacific Ocean in instigating global climatic changes

Factors affecting the reaction of lecithin cholesterol acyltransferase with water-soluble substrates

To separate interfacial binding from the catalytic reactions of LCAT, the first investigation of its reaction with water soluble substrates was performed to characterize the molecular specificity of the active site as well as the effects of external modulators on the enzyme. Two substrates were chosen: fatty acid esters of p-nitrophenol and 1,2-Bis (4-(1-pyreno)butanoyl) -sn-glycero-3-phosphocholine (DPydPC).The fatty acid esters of p-nitrophenol were used to determine the chain length specificity of LCAT. The kinetics of the LCAT reaction were measured for the PNP esters with fatty acids having up to six (C-6) carbons in length. With increasing acyl chain lengths, the initial velocity of PNP ester hydrolysis went through a maximum at the C-5 esters. The Km and Vmax values decreased progressively, while the kcat/km values increased with increasing acyl chain length.Both water soluble substrates were used to study the effects of modulators of the LCAT reaction (e.g., lysolecithin, salt concentration, and apolipoprotein A-I) on the enzyme itself. Inhibition of the LCAT reaction by lysolecithin, fatty acids, and detergents of the hydrolysis of the water soluble substrates by LCAT indicated that the effects of these compounds are due to a direct interaction with the enzyme.The effects of anions, cations, and increasing salt concentration had no effect on the hydrolysis of the water soluble substrates, in contrast to their effects on the LCAT reaction in the presence of an interface. Thus, these effects are mediated primarily on the interaction of the enzyme with the interface. The involvement of cationic residues on LCAT in interfacial binding is also postulated.Hydrolysis of the water soluble substrates occurred at high reaction rates relative to rHDL containing apolipoprotein A-I. This observation as well as the fact that solubilized apolipoproteins do not affect hydrolysis of the water soluble substrates suggests that activation of the LCAT reaction by apolipoprotein A-I also is exclusively an interfacial effect.U of I OnlyETDs are only available to UIUC Users without author permissio

A cross-platform approach identifies genetic regulators of human metabolism and health.

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

Rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, inflammatory, autoimmune disease that primarily affects the joints and is associated with autoantibodies that target various molecules including modified self-epitopes. The identification of novel autoantibodies has improved diagnostic accuracy, and newly developed classification criteria facilitate the recognition and study of the disease early in its course. New clinical assessment tools are able to better characterize disease activity states, which are correlated with progression of damage and disability, and permit improved follow-up. In addition, better understanding of the pathogenesis of RA through recognition of key cells and cytokines has led to the development of targeted disease-modifying antirheumatic drugs. Altogether, the improved understanding of the pathogenetic processes involved, rational use of established drugs and development of new drugs and reliable assessment tools have drastically altered the lives of individuals with RA over the past 2 decades. Current strategies strive for early referral, early diagnosis and early start of effective therapy aimed at remission or, at the least, low disease activity, with rapid adaptation of treatment if this target is not reached. This treat-to-target approach prevents progression of joint damage and optimizes physical functioning, work and social participation. In this Primer, we discuss the epidemiology, pathophysiology, diagnosis and management of RA