Epigenome-wide association study on the plasma metabolome suggests self-regulation of the glycine and serine pathway through DNA methylation.
Cardiometabolic diseases
DNA methylation
Epigenome-wide association study (EWAS)
Gene regulation
Metabolomics
Journal
Clinical epigenetics
ISSN: 1868-7083
Titre abrégé: Clin Epigenetics
Pays: Germany
ID NLM: 101516977
Informations de publication
Date de publication:
13 Aug 2024
13 Aug 2024
Historique:
received:
14
12
2023
accepted:
29
07
2024
medline:
14
8
2024
pubmed:
14
8
2024
entrez:
13
8
2024
Statut:
epublish
Résumé
The plasma metabolome reflects the physiological state of various biological processes and can serve as a proxy for disease risk. Plasma metabolite variation, influenced by genetic and epigenetic mechanisms, can also affect the cellular microenvironment and blood cell epigenetics. The interplay between the plasma metabolome and the blood cell epigenome remains elusive. In this study, we performed an epigenome-wide association study (EWAS) of 1183 plasma metabolites in 693 participants from the LifeLines-DEEP cohort and investigated the causal relationships in DNA methylation-metabolite associations using bidirectional Mendelian randomization and mediation analysis. After rigorously adjusting for potential confounders, including genetics, we identified five robust associations between two plasma metabolites (L-serine and glycine) and three CpG sites located in two independent genomic regions (cg14476101 and cg16246545 in PHGDH and cg02711608 in SLC1A5) at a false discovery rate of less than 0.05. Further analysis revealed a complex bidirectional relationship between plasma glycine/serine levels and DNA methylation. Moreover, we observed a strong mediating role of DNA methylation in the effect of glycine/serine on the expression of their metabolism/transport genes, with the proportion of the mediated effect ranging from 11.8 to 54.3%. This result was also replicated in an independent population-based cohort, the Rotterdam Study. To validate our findings, we conducted in vitro cell studies which confirmed the mediating role of DNA methylation in the regulation of PHGDH gene expression. Our findings reveal a potential feedback mechanism in which glycine and serine regulate gene expression through DNA methylation.
Sections du résumé
BACKGROUND
BACKGROUND
The plasma metabolome reflects the physiological state of various biological processes and can serve as a proxy for disease risk. Plasma metabolite variation, influenced by genetic and epigenetic mechanisms, can also affect the cellular microenvironment and blood cell epigenetics. The interplay between the plasma metabolome and the blood cell epigenome remains elusive. In this study, we performed an epigenome-wide association study (EWAS) of 1183 plasma metabolites in 693 participants from the LifeLines-DEEP cohort and investigated the causal relationships in DNA methylation-metabolite associations using bidirectional Mendelian randomization and mediation analysis.
RESULTS
RESULTS
After rigorously adjusting for potential confounders, including genetics, we identified five robust associations between two plasma metabolites (L-serine and glycine) and three CpG sites located in two independent genomic regions (cg14476101 and cg16246545 in PHGDH and cg02711608 in SLC1A5) at a false discovery rate of less than 0.05. Further analysis revealed a complex bidirectional relationship between plasma glycine/serine levels and DNA methylation. Moreover, we observed a strong mediating role of DNA methylation in the effect of glycine/serine on the expression of their metabolism/transport genes, with the proportion of the mediated effect ranging from 11.8 to 54.3%. This result was also replicated in an independent population-based cohort, the Rotterdam Study. To validate our findings, we conducted in vitro cell studies which confirmed the mediating role of DNA methylation in the regulation of PHGDH gene expression.
CONCLUSIONS
CONCLUSIONS
Our findings reveal a potential feedback mechanism in which glycine and serine regulate gene expression through DNA methylation.
Identifiants
pubmed: 39138531
doi: 10.1186/s13148-024-01718-7
pii: 10.1186/s13148-024-01718-7
doi:
Substances chimiques
Glycine
TE7660XO1C
Serine
452VLY9402
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
104Subventions
Organisme : Netherlands Organization for Scientific Research (NWO)
ID : ZonMW-VICI 09150182010019
Organisme : Netherlands Organization for Scientific Research (NWO)
ID : VIDI grant 016.178.056
Organisme : Netherlands Organization for Scientific Research (NWO)
ID : VICI grant VI.C.202.022
Organisme : Netherlands Organization for Scientific Research (NWO)
ID : VENI grant 194.006
Organisme : European Research Council
ID : Starting Grant 715772
Pays : International
Organisme : European Research Council
ID : Consolidator grant 101001678
Pays : International
Organisme : Dutch Heart Foundation
ID : IN-CONTROL (CVON2018-27)
Organisme : Netherlands Organ-on-Chip Initiative
ID : NWO Gravitation project (024.003.001)
Informations de copyright
© 2024. The Author(s).
Références
Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50.
pubmed: 24816252
pmcid: 4064254
doi: 10.1038/ng.2982
Long T, Hicks M, Yu H-C, Biggs WH, Kirkness EF, Menni C, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet. 2017;49:568–78.
pubmed: 28263315
doi: 10.1038/ng.3809
Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A. 2009;106:3698–703.
pubmed: 19234110
pmcid: 2656143
doi: 10.1073/pnas.0812874106
Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1098 deeply phenotyped individuals. Nat Med. 2021;27:321–32.
pubmed: 33432175
pmcid: 8353542
doi: 10.1038/s41591-020-01183-8
Playdon MC, Sampson JN, Cross AJ, Sinha R, Guertin KA, Moy KA, et al. Comparing metabolite profiles of habitual diet in serum and urine. Am J Clin Nutr. 2016;104:776–89.
pubmed: 27510537
pmcid: 4997302
doi: 10.3945/ajcn.116.135301
Xu T, Holzapfel C, Dong X, Bader E, Yu Z, Prehn C, et al. Effects of smoking and smoking cessation on human serum metabolite profile: results from the KORA cohort study. BMC Med. 2013;11:60.
pubmed: 23497222
pmcid: 3653729
doi: 10.1186/1741-7015-11-60
Ho JE, Larson MG, Ghorbani A, Cheng S, Chen M-H, Keyes M, et al. Metabolomic profiles of body mass index in the Framingham heart study reveal distinct cardiometabolic phenotypes. PLoS ONE. 2016;11:e0148361.
pubmed: 26863521
pmcid: 4749349
doi: 10.1371/journal.pone.0148361
Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448–53.
pubmed: 21423183
pmcid: 3126616
doi: 10.1038/nm.2307
Villicaña S, Bell JT. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biol. 2021;22:127.
pubmed: 33931130
pmcid: 8086086
doi: 10.1186/s13059-021-02347-6
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.
pubmed: 24138928
pmcid: 4015143
doi: 10.1186/gb-2013-14-10-r115
McCarthy NS, Melton PE, Cadby G, Yazar S, Franchina M, Moses EK, et al. Meta-analysis of human methylation data for evidence of sex-specific autosomal patterns. BMC Genom. 2014;15:981.
doi: 10.1186/1471-2164-15-981
Fraszczyk E, Spijkerman AMW, Zhang Y, Brandmaier S, Day FR, Zhou L, et al. Epigenome-wide association study of incident type 2 diabetes: a meta-analysis of five prospective European cohorts. Diabetologia. 2022;65:763–76.
pubmed: 35169870
pmcid: 8960572
doi: 10.1007/s00125-022-05652-2
Nikpay M, Ravati S, Dent R, McPherson R. Epigenome-wide study identified methylation sites associated with the risk of obesity. Nutrients. 2021;13:1984.
pubmed: 34207686
pmcid: 8229089
doi: 10.3390/nu13061984
Kriebel J, Herder C, Rathmann W, Wahl S, Kunze S, Molnos S, et al. Association between DNA methylation in whole blood and measures of glucose metabolism: KORA F4 study. PLoS ONE. 2016;11:e0152314.
pubmed: 27019061
pmcid: 4809492
doi: 10.1371/journal.pone.0152314
Wang Z, Peng H, Gao W, Cao W, Lv J, Yu C, et al. Blood DNA methylation markers associated with type 2 diabetes, fasting glucose, and HbA1c levels: an epigenome-wide association study in 316 adult twin pairs. Genomics. 2021;113:4206–13.
pubmed: 34774679
doi: 10.1016/j.ygeno.2021.11.005
Braun KVE, Dhana K, de Vries PS, Voortman T, van Meurs JBJ, Uitterlinden AG, et al. Epigenome-wide association study (EWAS) on lipids: the Rotterdam Study. Clin Epigenet. 2017;9:15.
doi: 10.1186/s13148-016-0304-4
Gomez-Alonso MDC, Kretschmer A, Wilson R, Pfeiffer L, Karhunen V, Seppälä I, et al. DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures. Clin Epigenet. 2021;13:7.
doi: 10.1186/s13148-020-00957-8
Pfeiffer L, Wahl S, Pilling LC, Reischl E, Sandling JK, Kunze S, et al. DNA methylation of lipid-related genes affects blood lipid levels. Circ Cardiovasc Genet. 2015;8:334–42.
pubmed: 25583993
pmcid: 5012424
doi: 10.1161/CIRCGENETICS.114.000804
Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman ÅK, Aslibekyan S, et al. Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a mendelian randomization approach. PLoS Med. 2017;14:e1002215.
pubmed: 28095459
pmcid: 5240936
doi: 10.1371/journal.pmed.1002215
Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541:81–6.
pubmed: 28002404
doi: 10.1038/nature20784
Reed ZE, Suderman MJ, Relton CL, Davis OSP, Hemani G. The association of DNA methylation with body mass index: distinguishing between predictors and biomarkers. Clin Epigenet. 2020;12:50.
doi: 10.1186/s13148-020-00841-5
Dekkers KF, van Iterson M, Slieker RC, Moed MH, Bonder MJ, van Galen M, et al. Blood lipids influence DNA methylation in circulating cells. Genome Biol. 2016;17:138.
pubmed: 27350042
pmcid: 4922056
doi: 10.1186/s13059-016-1000-6
Sayols-Baixeras S, Tiwari HK, Aslibekyan SW. Disentangling associations between DNA methylation and blood lipids: a Mendelian randomization approach. BMC Proc. 2018;12:23.
pubmed: 30275879
pmcid: 6157243
doi: 10.1186/s12919-018-0119-8
Tigchelaar EF, Zhernakova A, Dekens JAM, Hermes G, Baranska A, Mujagic Z, et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open. 2015;5:e006772.
pubmed: 26319774
pmcid: 4554905
doi: 10.1136/bmjopen-2014-006772
Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.
pubmed: 27571263
pmcid: 5157836
doi: 10.1038/ng.3656
McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.
pubmed: 27548312
pmcid: 5388176
doi: 10.1038/ng.3643
Bonder MJ, Luijk R, Zhernakova DV, Moed M, Deelen P, Vermaat M, et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2016;49:131–8.
pubmed: 27918535
doi: 10.1038/ng.3721
Bonder MJ, Kasela S, Kals M, Tamm R, Lokk K, Barragan I, et al. Genetic and epigenetic regulation of gene expression in fetal and adult human livers. BMC Genom. 2014;15:860.
doi: 10.1186/1471-2164-15-860
Touleimat N, Tost J. Complete pipeline for Infinium® Human Methylation 450 K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics. 2012;4:325–41.
pubmed: 22690668
doi: 10.2217/epi.12.21
Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450 K methylation array data. BMC Genom. 2013;14:293.
doi: 10.1186/1471-2164-14-293
Chen L, Zhernakova DV, Kurilshikov A, Andreu-Sánchez S, Wang D, Augustijn HE, et al. Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome. Nat Med. 2022;28:2333–43.
pubmed: 36216932
doi: 10.1038/s41591-022-02014-8
Fuhrer T, Heer D, Begemann B, Zamboni N. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection–time-of-flight mass spectrometry. Anal Chem. 2011;83:7074–80.
pubmed: 21830798
doi: 10.1021/ac201267k
Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2017;46:D608–17.
doi: 10.1093/nar/gkx1089
Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet. 2016;49:139–45.
pubmed: 27918533
doi: 10.1038/ng.3737
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma Oxf Engl. 2013;29:15–21.
doi: 10.1093/bioinformatics/bts635
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.
pubmed: 20196867
pmcid: 2864565
doi: 10.1186/gb-2010-11-3-r25
Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45:1238–43.
pubmed: 24013639
doi: 10.1038/ng.2756
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.
pubmed: 25826379
doi: 10.1371/journal.pmed.1001779
Huan T, Joehanes R, Song C, Peng F, Guo Y, Mendelson M, et al. Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat Commun. 2019;10:4267.
pubmed: 31537805
doi: 10.1038/s41467-019-12228-z
Li S, Qi C, Deelen P, Boulogne F, de Klein N, Koppelman GH, et al. Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations. bioRxiv. 2021;11:1004958. https://doi.org/10.1101/2021.12.17.473125 .
doi: 10.1101/2021.12.17.473125
Schaid DJ, Dikilitas O, Sinnwell JP, Kullo IJ. Penalized mediation models for multivariate data. Genet Epidemiol. 2022;46:32–50.
pubmed: 34664742
doi: 10.1002/gepi.22433
Ikram MA, Brusselle G, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, et al. Objectives, design and main findings until 2020 from the Rotterdam study. Eur J Epidemiol. 2020;35:483–517.
pubmed: 32367290
pmcid: 7250962
doi: 10.1007/s10654-020-00640-5
Soininen P, Kangas AJ, Würtz P, Tukiainen T, Tynkkynen T, Laatikainen R, et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst. 2009;134:1781.
pubmed: 19684899
doi: 10.1039/b910205a
Soininen P, Kangas AJ, Würtz P, Suna T, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015;8:192–206.
pubmed: 25691689
doi: 10.1161/CIRCGENETICS.114.000216
Petersen A-K, Zeilinger S, Kastenmüller G, Römisch-Margl W, Brugger M, Peters A, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet. 2014;23:534–45.
pubmed: 24014485
doi: 10.1093/hmg/ddt430
Hsu Y-HH, Astley CM, Cole JB, Vedantam S, Mercader JM, Metspalu A, et al. Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome. Int J Obes. 2020;44:1596–606.
doi: 10.1038/s41366-020-0603-x
Holeček M. Serine metabolism in health and disease and as a conditionally essential amino acid. Nutrients. 2022;14:1987.
pubmed: 35565953
pmcid: 9105362
doi: 10.3390/nu14091987
Alves A, Bassot A, Bulteau A-L, Pirola L, Morio B. Glycine metabolism and its alterations in obesity and metabolic diseases. Nutrients. 2019;11:1356.
pubmed: 31208147
pmcid: 6627940
doi: 10.3390/nu11061356
Zhao X, Fu J, Du J, Xu W. The role of D-3-phosphoglycerate dehydrogenase in cancer. Int J Biol Sci. 2020;16:1495–506.
pubmed: 32226297
pmcid: 7097917
doi: 10.7150/ijbs.41051
Klomp LW, de Koning TJ, Malingré HE, van Beurden EA, Brink M, Opdam FL, et al. Molecular characterization of 3-phosphoglycerate dehydrogenase deficiency–a neurometabolic disorder associated with reduced L-serine biosynthesis. Am J Hum Genet. 2000;67:1389–99.
pubmed: 11055895
pmcid: 1287916
doi: 10.1086/316886
Scalise M, Pochini L, Console L, Losso MA, Indiveri C. The human SLC1A5 (ASCT2) amino acid transporter: from function to structure and role in cell biology. Front Cell Dev Biol. 2018;6:96–96.
pubmed: 30234109
pmcid: 6131531
doi: 10.3389/fcell.2018.00096
Scopelliti AJ, Font J, Vandenberg RJ, Boudker O, Ryan RM. Structural characterisation reveals insights into substrate recognition by the glutamine transporter ASCT2/SLC1A5. Nat Commun. 2018;9:38.
pubmed: 29295993
pmcid: 5750217
doi: 10.1038/s41467-017-02444-w
Palmer ND, Stevens RD, Antinozzi PA, Anderson A, Bergman RN, Wagenknecht LE, et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J Clin Endocrinol Metab. 2015;100:E463–8.
pubmed: 25423564
doi: 10.1210/jc.2014-2357
Guasch-Ferré M, Hruby A, Toledo E, Clish CB, Martínez-González MA, Salas-Salvadó J, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016;39:833–46.
pubmed: 27208380
pmcid: 4839172
doi: 10.2337/dc15-2251
Merino J, Leong A, Liu C-T, Porneala B, Walford GA, von Grotthuss M, et al. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose. Diabetologia. 2018;61:1315–24.
pubmed: 29626220
pmcid: 5940516
doi: 10.1007/s00125-018-4599-x
Ding Y, Svingen GFT, Pedersen ER, Gregory JF, Ueland PM, Tell GS, et al. Plasma glycine and risk of acute myocardial infarction in patients with suspected stable angina pectoris. J Am Heart Assoc. 2015;5:e002621.
pubmed: 26722126
pmcid: 4859380
doi: 10.1161/JAHA.115.002621
Ottosson F, Smith E, Melander O, Fernandez C. Altered asparagine and glutamate homeostasis precede coronary artery disease and type 2 diabetes. J Clin Endocrinol Metab. 2018;103:3060–9.
pubmed: 29788285
doi: 10.1210/jc.2018-00546
Huang Y, Ollikainen M, Muniandy M, Zhang T, van Dongen J, Hao G, et al. Identification, heritability, and relation with gene expression of novel DNA methylation loci for blood pressure. Hypertens Dallas Tex. 1979;2020(76):195–205.
Richard MA, Huan T, Ligthart S, Gondalia R, Jhun MA, Brody JA, et al. DNA methylation analysis identifies loci for blood pressure regulation. Am J Hum Genet. 2017;101:888–902.
pubmed: 29198723
pmcid: 5812919
doi: 10.1016/j.ajhg.2017.09.028
Ma J, Nano J, Ding J, Zheng Y, Hennein R, Liu C, et al. A peripheral blood DNA methylation signature of hepatic fat reveals a potential causal pathway for nonalcoholic fatty liver disease. Diabetes. 2019;68:1073–83.
pubmed: 30936141
pmcid: 6477898
doi: 10.2337/db18-1193
Hedman ÅK, Mendelson MM, Marioni RE, Gustafsson S, Joehanes R, Irvin MR, et al. Epigenetic patterns in blood associated with lipid traits predict incident coronary heart disease events and are enriched for results from genome-wide association studies. Circ Cardiovasc Genet. 2017;10:e001487.
pubmed: 28213390
pmcid: 5331877
doi: 10.1161/CIRCGENETICS.116.001487
Nano J, Ghanbari M, Wang W, de Vries PS, Dhana K, Muka T, et al. Epigenome-wide association study identifies methylation sites associated with liver enzymes and hepatic steatosis. Gastroenterology. 2017;153:1096–106.
pubmed: 28624579
doi: 10.1053/j.gastro.2017.06.003
Vazquez A, Markert EK, Oltvai ZN. Serine biosynthesis with one carbon catabolism and the glycine cleavage system represents a novel pathway for ATP generation. PLoS ONE. 2011;6:e25881.
pubmed: 22073143
pmcid: 3206798
doi: 10.1371/journal.pone.0025881
Ducker GS, Rabinowitz JD. One-carbon metabolism in health and disease. Cell Metab. 2017;25:27–42.
pubmed: 27641100
doi: 10.1016/j.cmet.2016.08.009
Kleiveland CR, et al. Peripheral blood mononuclear cells. In: Verhoeckx K, Cotter P, López-Expósito I, Kleiveland C, Lea T, Mackie A, et al., editors. Impact food bioact health vitro ex vivo models. Cham: Springer; 2015.