Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
23
05
2022
accepted:
26
04
2023
medline:
14
6
2023
pubmed:
6
6
2023
entrez:
5
6
2023
Statut:
ppublish
Résumé
The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.
Identifiants
pubmed: 37277652
doi: 10.1038/s41588-023-01409-8
pii: 10.1038/s41588-023-01409-8
pmc: PMC10260405
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
995-1008Subventions
Organisme : NIDDK NIH HHS
ID : R01 DK124399
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00001
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00002
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00003
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00004
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00005
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL087641
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL086694
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL086694
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR025005
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL141824
Pays : United States
Informations de copyright
© 2023. The Author(s).
Références
Boron, W. F. & Boulpaep, E. L. Medical Physiology (Elsevier, 2017).
Gyimesi, G., Pujol-Gimenez, J., Kanai, Y. & Hediger, M. A. Sodium-coupled glucose transport, the SLC5 family, and therapeutically relevant inhibitors: from molecular discovery to clinical application. Pflugers Arch. 472, 1177–1206 (2020).
pubmed: 32767111
pmcid: 7462921
Anzai, N. & Endou, H. Urate transporters: an evolving field. Semin. Nephrol. 31, 400–409 (2011).
pubmed: 22000646
Evans, A. M. et al. High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Metabolomics 4, 132 (2014).
Schlosser, P. et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat. Genet. 52, 167–176 (2020).
pubmed: 31959995
pmcid: 7484970
Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).
pubmed: 24816252
pmcid: 4064254
Long, T. et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49, 568–578 (2017).
pubmed: 28263315
Lotta, L. A. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat. Genet. 53, 54–64 (2021).
pubmed: 33414548
pmcid: 7612925
Hysi, P. G. et al. Metabolome genome-wide association study identifies 74 novel genomic regions influencing plasma metabolites levels. Metabolites 12, 61 (2022).
pubmed: 35050183
pmcid: 8777659
Yin, X. et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat. Commun. 13, 1644 (2022).
pubmed: 35347128
pmcid: 8960770
Surendran, P. et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat. Med. 28, 2321–2332 (2022).
pubmed: 36357675
pmcid: 9671801
Chen, Y. et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat. Genet. 55, 44–53 (2023).
pubmed: 36635386
Arnold, M., Raffler, J., Pfeufer, A., Suhre, K. & Kastenmuller, G. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics 31, 1334–1336 (2015).
pubmed: 25431330
Schlosser, P. et al. Netboost: boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington’s disease. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 2635–2648 (2021).
Meixner, E. et al. A substrate-based ontology for human solute carriers. Mol. Syst. Biol. 16, e9652 (2020).
pubmed: 32697042
pmcid: 7374931
Gyimesi, G. & Hediger, M. A. Systematic in silico discovery of novel solute carrier-like proteins from proteomes. PLoS ONE 17, e0271062 (2022).
pubmed: 35901096
pmcid: 9333335
Reynolds, L. M. et al. FADS genetic and metabolomic analyses identify the 5 desaturase (FADS1) step as a critical control point in the formation of biologically important lipids. Sci. Rep. 10, 15873 (2020).
pubmed: 32985521
pmcid: 7522985
Veiga-da-Cunha, M. et al. Molecular identification of NAT8 as the enzyme that acetylates cysteine S-conjugates to mercapturic acids. J. Biol. Chem. 285, 18888–18898 (2010).
pubmed: 20392701
pmcid: 2881811
Konig, J., Seithel, A., Gradhand, U. & Fromm, M. F. Pharmacogenomics of human OATP transporters. Naunyn Schmiedebergs Arch. Pharm. 372, 432–443 (2006).
Reimer, R. J. SLC17: a functionally diverse family of organic anion transporters. Mol. Aspects Med. 34, 350–359 (2013).
pubmed: 23506876
pmcid: 3927456
Cheng, H. Y., You, H. Y. & Zhou, T. B. Relationship between GSTM1/GSTT1 null genotypes and renal cell carcinoma risk: a meta-analysis. Ren. Fail. 34, 1052–1057 (2012).
pubmed: 22880812
Wang, Q. et al. Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature 597, 527–532 (2021).
pubmed: 34375979
pmcid: 8458098
Raal, F. J. et al. Evinacumab for homozygous familial hypercholesterolemia. N. Engl. J. Med. 383, 711–720 (2020).
pubmed: 32813947
Woodward, O. M. et al. Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proc. Natl Acad. Sci. USA 106, 10338–10342 (2009).
pubmed: 19506252
pmcid: 2700910
Bustamante, M. et al. A genome-wide association meta-analysis of diarrhoeal disease in young children identifies FUT2 locus and provides plausible biological pathways. Hum. Mol. Genet. 25, 4127–4142 (2016).
pubmed: 27559109
pmcid: 5291237
Barton, S. J. et al. FUT2 genetic variants and reported respiratory and gastrointestinal illnesses during infancy. J. Infect. Dis. 219, 836–843 (2019).
pubmed: 30376117
Nielsen, S. et al. Aquaporins in the kidney: from molecules to medicine. Physiol. Rev. 82, 205–244 (2002).
pubmed: 11773613
Sohara, E. et al. Defective water and glycerol transport in the proximal tubules of Aqp7 knockout mice. Am. J. Physiol. Renal Physiol. 289, F1195–F1200 (2005).
pubmed: 15998844
Goubau, C. et al. Homozygosity for aquaporin 7 G264V in three unrelated children with hyperglyceroluria and a mild platelet secretion defect. Genet. Med. 15, 55–63 (2013).
pubmed: 22899094
Dawson, P. A., Lan, T. & Rao, A. Bile acid transporters. J. Lipid Res. 50, 2340–2357 (2009).
pubmed: 19498215
pmcid: 2781307
Wilson, F. A., Burckhardt, G., Murer, H., Rumrich, G. & Ullrich, K. J. Sodium-coupled taurocholate transport in the proximal convolution of the rat kidney in vivo and in vitro. J. Clin. Invest. 67, 1141–1150 (1981).
pubmed: 7204571
pmcid: 370675
Craddock, A. L. et al. Expression and transport properties of the human ileal and renal sodium-dependent bile acid transporter. Am. J. Physiol. 274, G157–G169 (1998).
pubmed: 9458785
Ho, R. H. et al. Functional characterization of genetic variants in the apical sodium-dependent bile acid transporter (ASBT; SLC10A2). J. Gastroenterol. Hepatol. 26, 1740–1748 (2011).
pubmed: 21649730
pmcid: 3170668
Love, M. W. et al. Analysis of the ileal bile acid transporter gene, SLC10A2, in subjects with familial hypertriglyceridemia. Arterioscler. Thromb. Vasc. Biol. 21, 2039–2045 (2001).
pubmed: 11742882
Ferkingstad, E. et al. Genome-wide association meta-analysis yields 20 loci associated with gallstone disease. Nat. Commun. 9, 5101 (2018).
pubmed: 30504769
pmcid: 6269469
Grosser, G., Muller, S. F., Kirstgen, M., Doring, B. & Geyer, J. Substrate specificities and inhibition pattern of the solute carrier family 10 members NTCP, ASBT and SOAT. Front. Mol. Biosci. 8, 689757 (2021).
pubmed: 34079822
pmcid: 8165160
St-Pierre, M. V., Kullak-Ublick, G. A., Hagenbuch, B. & Meier, P. J. Transport of bile acids in hepatic and non-hepatic tissues. J. Exp. Biol. 204, 1673–1686 (2001).
pubmed: 11316487
Liu, H. et al. Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease. Nat. Genet. 54, 950–962 (2022).
pubmed: 35710981
Sheng, X. et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat. Genet. 53, 1322–1333 (2021).
pubmed: 34385711
pmcid: 9338440
Breljak, D. et al. Distribution of organic anion transporters NaDC3 and OAT1–3 along the human nephron. Am. J. Physiol. Renal Physiol. 311, F227–F238 (2016).
pubmed: 27053689
Chen, X., Tsukaguchi, H., Chen, X. Z., Berger, U. V. & Hediger, M. A. Molecular and functional analysis of SDCT2, a novel rat sodium-dependent dicarboxylate transporter. J. Clin. Invest. 103, 1159–1168 (1999).
pubmed: 10207168
pmcid: 408276
Wang, H. et al. Structure, function, and genomic organization of human Na
pubmed: 10794676
Pajor, A. M., Gangula, R. & Yao, X. Cloning and functional characterization of a high-affinity Na
pubmed: 11287335
McIntyre, T. & Curthoys, N. P. Renal catabolism of glutathione. Characterization of a particulate rat renal dipeptidase that catalyzes the hydrolysis of cysteinylglycine. J. Biol. Chem. 257, 11915–11921 (1982).
pubmed: 6126478
Nitanai, Y., Satow, Y., Adachi, H. & Tsujimoto, M. Crystal structure of human renal dipeptidase involved in β-lactam hydrolysis. J. Mol. Biol. 321, 177–184 (2002).
pubmed: 12144777
Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).
pubmed: 34857953
Setti, T. et al. The protective role of glutathione in osteoarthritis. J. Clin. Orthop. Trauma 15, 145–151 (2021).
pubmed: 33717929
Xu, X. et al. Molecular insights into genome-wide association studies of chronic kidney disease-defining traits. Nat. Commun. 9, 4800 (2018).
pubmed: 30467309
pmcid: 6250666
Kumar, V. et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet. 9, e1003201 (2013).
pubmed: 23341781
pmcid: 3547830
Giral, H., Landmesser, U. & Kratzer, A. Into the wild: GWAS exploration of non-coding RNAs. Front. Cardiovasc. Med. 5, 181 (2018).
pubmed: 30619888
pmcid: 6304420
Gagliano Taliun, S. A. et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat. Genet. 52, 550–552 (2020).
pubmed: 32504056
pmcid: 7754083
Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
pubmed: 20634204
pmcid: 2935401
Eckardt, K. U. et al. The German Chronic Kidney Disease (GCKD) study: design and methods. Nephrol. Dial. Transplant. 27, 1454–1460 (2012).
pubmed: 21862458
Prokosch, H. U. et al. Designing and implementing a biobanking IT framework for multiple research scenarios. Stud. Health Technol. Inform. 180, 559–563 (2012).
pubmed: 22874253
Titze, S. et al. Disease burden and risk profile in referred patients with moderate chronic kidney disease: composition of the German Chronic Kidney Disease (GCKD) cohort. Nephrol. Dial. Transplant. 30, 441–451 (2015).
pubmed: 25271006
Li, Y. et al. Genome-wide association studies of metabolites in patients with CKD identify multiple loci and illuminate tubular transport mechanisms. J. Am. Soc. Nephrol. 29, 1513–1524 (2018).
pubmed: 29545352
pmcid: 5967769
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
pubmed: 27571263
pmcid: 5157836
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).
pubmed: 24039616
pmcid: 3772505
Schrimpe-Rutledge, A. C., Codreanu, S. G., Sherrod, S. D. & McLean, J. A. Untargeted metabolomics strategies—challenges and emerging directions. J. Am. Soc. Mass. Spectrom. 27, 1897–1905 (2016).
pubmed: 27624161
pmcid: 5110944
Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in
pubmed: 16808434
Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009).
pubmed: 19414839
pmcid: 2763564
Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).
pubmed: 34554658
pmcid: 8822996
Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).
pubmed: 21886157
Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
pubmed: 20517342
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
pubmed: 19541911
pmcid: 2752132
Konig, E. et al. Whole exome sequencing enhanced imputation identifies 85 metabolite associations in the Alpine CHRIS Cohort. Metabolites 12, 604 (2022).
pubmed: 35888728
pmcid: 9320943
Bomba, L. et al. Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites. Am. J. Hum. Genet. 109, 1038–1054 (2022).
pubmed: 35568032
pmcid: 9247822
Feofanova, E. V. et al. A genome-wide association study discovers 46 loci of the human metabolome in the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 107, 849–863 (2020).
pubmed: 33031748
pmcid: 7675000
Yousri, N. A. et al. Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population. Nat. Commun. 9, 333 (2018).
pubmed: 29362361
pmcid: 5780481
Li-Gao, R. et al. Genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Diabetes 70, 2932–2946 (2021).
pubmed: 34610981
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
pubmed: 21167468
pmcid: 3014363
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).
pubmed: 20562875
pmcid: 3232052
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, S1–S3 (2012).
Wakefield, J. Bayes factors for genome-wide association studies: comparison with P-values. Genet. Epidemiol. 33, 79–86 (2009).
pubmed: 18642345
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
pubmed: 24830394
pmcid: 4022491
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
pubmed: 24930139
Gillies, C. E. et al. An eQTL landscape of kidney tissue in human nephrotic syndrome. Am. J. Hum. Genet. 103, 232–244 (2018).
pubmed: 30057032
pmcid: 6081280
The GTEx Consortium. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
pubmed: 29875488
pmcid: 6697541
Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).
pubmed: 29892013
pmcid: 6309610
Sinnott-Armstrong, N. et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat. Genet. 53, 185–194 (2021).
pubmed: 33462484
Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).
pubmed: 34737426
Stanzick, K. J. et al. Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals. Nat. Commun. 12, 4350 (2021).
pubmed: 34272381
pmcid: 8285412
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).
pubmed: 36653562
pmcid: 9849126
Aizarani, N. et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572, 199–204 (2019).
pubmed: 31292543
pmcid: 6687507
Wu, H. et al. Comparative analysis and refinement of human PSC-derived kidney organoid differentiation with single-cell transcriptomics. Cell Stem Cell 23, 869–881 (2018).
Stewart, B. J. et al. Spatiotemporal immune zonation of the human kidney. Science 365, 1461–1466 (2019).
pubmed: 31604275
pmcid: 7343525
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).
pubmed: 29622724
pmcid: 6188645
Wang, Y. et al. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J. Exp. Med. 217, e20191130 (2020).
pubmed: 31753849
Hansen, J. et al. A reference tissue atlas for the human kidney. Sci. Adv. 8, eabn4965 (2022).
pubmed: 35675394
pmcid: 9176741
Cheng, Y. et al. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat. Commun. 12, 964 (2021).
pubmed: 33574263
pmcid: 7878905
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
pubmed: 31178118
pmcid: 6687398
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).
pubmed: 25722852
pmcid: 4342193
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173
pmcid: 102409
Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
pubmed: 25613900
Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci. Adv. 7, eabh2169 (2021).
Whirl-Carrillo, M. et al. An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 110, 563–572 (2021).
pubmed: 34216021
pmcid: 8457105
Relling, M. V. et al. The Clinical Pharmacogenetics Implementation Consortium: 10 years later. Clin. Pharmacol. Ther. 107, 171–175 (2020).
pubmed: 31562822
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).
Motenko, H., Neuhauser, S. B., O’Keefe, M. & Richardson, J. E. MouseMine: a new data warehouse for MGI. Mamm. Genome 26, 325–330 (2015).
pubmed: 26092688
pmcid: 4534495
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
pubmed: 19617889
pmcid: 3159387