Uncovering novel regulatory variants in carbohydrate metabolism: a comprehensive multi-omics study of glycemic traits in the Indian population.
Humans
Quantitative Trait Loci
India
/ epidemiology
Genome-Wide Association Study
Blood Glucose
/ metabolism
Male
Carbohydrate Metabolism
/ genetics
Female
Polymorphism, Single Nucleotide
Diabetes Mellitus, Type 2
/ genetics
Adult
Genetic Predisposition to Disease
Middle Aged
DNA Methylation
/ genetics
Multiomics
Association testing
Carbohydrate metabolism
GWAS
Genetic variants
Glycemic traits
Methylation
Journal
Molecular genetics and genomics : MGG
ISSN: 1617-4623
Titre abrégé: Mol Genet Genomics
Pays: Germany
ID NLM: 101093320
Informations de publication
Date de publication:
04 Sep 2024
04 Sep 2024
Historique:
received:
29
12
2023
accepted:
02
08
2024
medline:
4
9
2024
pubmed:
4
9
2024
entrez:
4
9
2024
Statut:
epublish
Résumé
Clinical biomarkers such as fasting glucose, HbA1c, and fasting insulin, which gauge glycemic status in the body, are highly influenced by diet. Indians are genetically predisposed to type 2 diabetes and their carbohydrate-centric diet further elevates the disease risk. Despite the combined influence of genetic and environmental risk factors, Indians have been inadequately explored in the studies of glycemic traits. Addressing this gap, we investigate the genetic architecture of glycemic traits at genome-wide level in 4927 Indians (without diabetes). Our analysis revealed numerous variants of sub-genome-wide significance, and their credibility was thoroughly assessed by integrating data from various levels. This identified key effector genes, ZNF470, DPP6, GXYLT2, PITPNM3, BEND7, and LORICRIN-PGLYRP3. While these genes were weakly linked with carbohydrate intake or glycemia earlier in other populations, our findings demonstrated a much stronger association in the Indian population. Associated genetic variants within these genes served as expression quantitative trait loci (eQTLs) in various gut tissues essential for digestion. Additionally, majority of these gut eQTLs functioned as methylation quantitative trait loci (meth-QTLs) observed in peripheral blood samples from 223 Indians, elucidating the underlying mechanism of their regulation of target gene expression. Specific co-localized eQTLs-meth-QTLs altered the binding affinity of transcription factors targeting crucial genes involved in glucose metabolism. Our study identifies previously unreported genetic variants that strongly influence the diet-glycemia relationship. These findings set the stage for future research into personalized lifestyle interventions integrating genetic insights with tailored dietary strategies to mitigate disease risk based on individual genetic profiles.
Identifiants
pubmed: 39230791
doi: 10.1007/s00438-024-02176-9
pii: 10.1007/s00438-024-02176-9
doi:
Substances chimiques
Blood Glucose
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
85Subventions
Organisme : CSIR
ID : BSC0122
Organisme : DST
ID : DST/SR/PURSE Phase II/11
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Abdellaoui A, Dolan CV, Verweij KJH, Nivard MG (2022) Gene–environment correlations across geographic regions affect genome-wide association studies. Nat Genet 54:1345–1354. https://doi.org/10.1038/s41588-022-01158-0
doi: 10.1038/s41588-022-01158-0
pubmed: 35995948
pmcid: 9470533
Abhiman S, Iyer LM, Aravind L (2008) BEN: a novel domain in chromatin factors and DNA viral proteins. Bioinformatics 24:458–461. https://doi.org/10.1093/bioinformatics/btn007
doi: 10.1093/bioinformatics/btn007
pubmed: 18203771
Agius L, Chachra SS, Ford BE (2020) The protective role of the carbohydrate response element binding protein in the liver: the metabolite perspective. Front Endocrinol (lausanne) 11:594041. https://doi.org/10.3389/fendo.2020.594041
doi: 10.3389/fendo.2020.594041
pubmed: 33281747
Aryee MJ, Jaffe AE, Corrada-Bravo H et al (2014) Minfi: A flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics 30:1363–1369. https://doi.org/10.1093/bioinformatics/btu049
doi: 10.1093/bioinformatics/btu049
pubmed: 24478339
pmcid: 4016708
Bandesh K, Prasad G, Giri AK et al (2019a) Genome-wide association study of blood lipids in Indians confirms universality of established variants. J Hum Genet 64:573–587. https://doi.org/10.1038/s10038-019-0591-7
doi: 10.1038/s10038-019-0591-7
pubmed: 30911093
Bandesh K, Prasad G, Giri AK et al (2019b) Genomewide association study of C-peptide surfaces key regulatory genes in Indians. J Genet 98:8. https://doi.org/10.1007/s12041-018-1046-1
doi: 10.1007/s12041-018-1046-1
pubmed: 30945665
Bird A (1992) The essentials of DNA methylation. Cell 70:5–8. https://doi.org/10.1016/0092-8674(92)90526-I
doi: 10.1016/0092-8674(92)90526-I
pubmed: 1377983
Costanzo MC, von Grotthuss M, Massung J et al (2023) The Type 2 diabetes knowledge portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab 35:695-710.e6. https://doi.org/10.1016/j.cmet.2023.03.001
doi: 10.1016/j.cmet.2023.03.001
pubmed: 36963395
pmcid: 10231654
Das S, Forer L, Schönherr S et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287. https://doi.org/10.1038/ng.3656
doi: 10.1038/ng.3656
pubmed: 27571263
pmcid: 5157836
Davegårdh C, García-Calzón S, Bacos K, Ling C (2018) DNA methylation in the pathogenesis of type 2 diabetes in humans. Mol Metab 14:12–25. https://doi.org/10.1016/j.molmet.2018.01.022
doi: 10.1016/j.molmet.2018.01.022
pubmed: 29496428
pmcid: 6034041
Davis CA, Hitz BC, Sloan CA et al (2018) The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res 46:D794–D801. https://doi.org/10.1093/nar/gkx1081
doi: 10.1093/nar/gkx1081
pubmed: 29126249
de Leeuw CA, Mooij JM, Heskes T, Posthuma D (2015) MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 11:e1004219. https://doi.org/10.1371/journal.pcbi.1004219
doi: 10.1371/journal.pcbi.1004219
pubmed: 25885710
pmcid: 4401657
Dukes ID, Philipson LH (1996) K+ Channels: generating excitement in pancreatic β-cells. Diabetes 45:845–853. https://doi.org/10.2337/diab.45.7.845
doi: 10.2337/diab.45.7.845
pubmed: 8666132
Fuchsberger C, Abecasis GR, Hinds DA (2015) minimac2: faster genotype imputation. Bioinformatics 31:782–784. https://doi.org/10.1093/bioinformatics/btu704
doi: 10.1093/bioinformatics/btu704
pubmed: 25338720
Giri AK, Prasad G, Bandesh K et al (2020) Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians. Mol Genet Genom 295:1013–1026. https://doi.org/10.1007/s00438-020-01678-6
doi: 10.1007/s00438-020-01678-6
Giri AK, Prasad G, Parekatt V et al (2023) Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient’s classifications. Hum Mol Genet 32:2576–2586. https://doi.org/10.1093/hmg/ddad084
doi: 10.1093/hmg/ddad084
pubmed: 37184252
INdian DIabetes COnsortium (2011) INDICO: The development of a resource for epigenomic study of Indians undergoing socioeconomic transition. HUGO J 5:65–69. https://doi.org/10.1007/s11568-011-9157-2
doi: 10.1007/s11568-011-9157-2
pmcid: 3238020
Kahn SE, Cooper ME, Del Prato S (2014) Pathophysiology and treatment of type 2 diabetes: Perspectives on the past, present, and future. The Lancet 383:1068–1083
doi: 10.1016/S0140-6736(13)62154-6
Kheradpour P, Kellis M (2014) Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res 42:2976–2987. https://doi.org/10.1093/nar/gkt1249
doi: 10.1093/nar/gkt1249
pubmed: 24335146
Lee YB, Hwang HJ, Kim E, Lim SH, Chung CH, Choi EH. Hyperglycemia-activated 11β-hydroxysteroid dehydrogenase type 1 increases endoplasmic reticulum stress and skin barrier dysfunction. Scientific reports. 2023 Jun 6;13(1):9206.. https://doi.org/10.1038/s41598-023-36294-y
doi: 10.1038/s41598-023-36294-y
pubmed: 37280272
pmcid: 10244460
Lin H-M, Lee J-H, Yadav H et al (2009) Transforming growth factor-β/Smad3 signaling regulates insulin gene transcription and pancreatic islet β-cell function. J Biol Chem 284:12246–12257. https://doi.org/10.1074/jbc.M805379200
doi: 10.1074/jbc.M805379200
pubmed: 19265200
pmcid: 2673293
Ling C, Rönn T (2019) Epigenetics in human obesity and Type 2 diabetes. Cell Metab 29:1028–1044. https://doi.org/10.1016/j.cmet.2019.03.009
doi: 10.1016/j.cmet.2019.03.009
pubmed: 30982733
pmcid: 6509280
Lonsdale J, Thomas J, Salvatore M et al (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45:580–585. https://doi.org/10.1038/ng.2653
doi: 10.1038/ng.2653
Mesuraca M, Galasso O, Guido L et al (2014) Expression profiling and functional implications of a set of zinc finger proteins, ZNF423, ZNF470, ZNF521, and ZNF780B, in primary osteoarthritic articular chondrocytes. Mediators Inflamm 2014:1–11. https://doi.org/10.1155/2014/318793
doi: 10.1155/2014/318793
Mobasheri A, Vannucci SJ, Bondy CA, et al (2002) Glucose transport and metabolism in chondrocytes: a key to understanding chondrogenesis, skeletal development and cartilage degradation in osteoarthritis. Histol Histopathol 17:1239–1267. https://doi.org/10.14670/HH-17.1239
Moore F, Naamane N, Colli ML et al (2011) STAT1 Is a master regulator of pancreatic β-cell apoptosis and islet inflammation. J Biol Chem 286:929–941. https://doi.org/10.1074/jbc.M110.162131
doi: 10.1074/jbc.M110.162131
pubmed: 20980260
Murphy N, Carreras-Torres R, Song M et al (2020) Circulating levels of insulin-like growth factor 1 and insulin-like growth factor binding protein 3 associate with risk of colorectal cancer based on serologic and mendelian randomization analyses. Gastroenterology 158:1300-1312.e20. https://doi.org/10.1053/j.gastro.2019.12.020
doi: 10.1053/j.gastro.2019.12.020
pubmed: 31884074
Ni YG, Wang N, Cao DJ et al (2007) FoxO transcription factors activate Akt and attenuate insulin signaling in heart by inhibiting protein phosphatases. Proc Natl Acad Sci 104:20517–20522. https://doi.org/10.1073/pnas.0610290104
doi: 10.1073/pnas.0610290104
pubmed: 18077353
pmcid: 2154463
Pessah M, Prunier C, Marais J et al (2001) c-Jun interacts with the corepressor TG-interacting factor (TGIF) to suppress Smad2 transcriptional activity. Proc Natl Acad Sci 98:6198–6203. https://doi.org/10.1073/pnas.101579798
doi: 10.1073/pnas.101579798
pubmed: 11371641
pmcid: 33445
Prasad G, Bandesh K, Giri A et al (2019) Genome-wide association study of metabolic syndrome reveals primary genetic variants at CETP locus in Indians. Biomolecules 9:321. https://doi.org/10.3390/biom9080321
doi: 10.3390/biom9080321
pubmed: 31366177
pmcid: 6723498
Scott RA, Fall T, Pasko D et al (2014) Common genetic variants highlight the role of insulin resistance and body fat distribution in Type 2 diabetes, independent of obesity. Diabetes 63:4378–4387. https://doi.org/10.2337/db14-0319
doi: 10.2337/db14-0319
pubmed: 24947364
Semenza GL (1999) Regulation of mammalian O
doi: 10.1146/annurev.cellbio.15.1.551
pubmed: 10611972
Soh H, Goldstein SAN (2008) ISA channel complexes include four subunits each of DPP6 and Kv4.2. J Biol Chem 283:15072–15077. https://doi.org/10.1074/jbc.M706964200
doi: 10.1074/jbc.M706964200
pubmed: 18364354
pmcid: 2397469
Tabassum R, Chauhan G, Dwivedi OP et al (2013) Genome-wide association study for type 2 diabetes in indians identifies a new susceptibility locus at 2q21. Diabetes 62:977–986. https://doi.org/10.2337/db12-0406
doi: 10.2337/db12-0406
pubmed: 23209189
pmcid: 3581193
Taliun D, Harris DN, Kessler MD et al (2021) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590:290–299. https://doi.org/10.1038/s41586-021-03205-y
doi: 10.1038/s41586-021-03205-y
pubmed: 33568819
pmcid: 7875770
Teschendorff AE, Marabita F, Lechner M et al (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29:189–196. https://doi.org/10.1093/bioinformatics/bts680
doi: 10.1093/bioinformatics/bts680
pubmed: 23175756
Urata Y, Takeuchi H (2020) Effects of Notch glycosylation on health and diseases. Dev Growth Differ 62:35–48. https://doi.org/10.1111/dgd.12643
doi: 10.1111/dgd.12643
pubmed: 31886522
Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190–2191. https://doi.org/10.1093/bioinformatics/btq340
doi: 10.1093/bioinformatics/btq340
pubmed: 20616382
pmcid: 2922887
Xu Z, Niu L, Li L, Taylor JA (2016) ENmix: A novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res 44:1–6. https://doi.org/10.1093/nar/gkv907
doi: 10.1093/nar/gkv907
Yu Y, Ross SA, Halseth AE et al (2005) Role of PYK2 in the development of obesity and insulin resistance. Biochem Biophys Res Commun 334:1085–1091. https://doi.org/10.1016/j.bbrc.2005.06.198
doi: 10.1016/j.bbrc.2005.06.198
pubmed: 16039993