Implications of genetic variations, differential gene expression, and allele-specific expression on metformin response in drug-naive type 2 diabetes

被引:8
作者
Vohra, M. [1 ]
Sharma, A. R. [1 ]
Mallya, S. [2 ]
Prabhu, N. B. [1 ]
Jayaram, P. [3 ]
Nagri, S. K. [4 ]
Umakanth, S. [5 ]
Rai, P. S. [1 ]
机构
[1] Manipal Acad Higher Educ, Manipal Sch Life Sci, Dept Biotechnol, Manipal, India
[2] Manipal Acad Higher Educ, Manipal Sch Life Sci, Dept Bioinformat, Manipal, India
[3] Manipal Acad Higher Educ, Manipal Sch Life Sci, Dept Cell & Mol Biol, Manipal, India
[4] Manipal Acad Higher Educ, Kasturba Med Coll, Dept Med, Manipal, India
[5] Manipal Acad Higher Educ, Dr TMA Pai Hosp, Dept Med, Manipal, India
关键词
Allele-specific gene expression; Metformin; RNA-Seq; Targeted exome sequencing; Type; 2; diabetes; LIFE-STYLE INTERVENTION; REPURPOSING METFORMIN; EUROPEAN ASSOCIATION; INSULIN-RESISTANCE; MELLITUS; STATEMENT; VARIANTS; THERAPY; PHARMACOGENOMICS; POLYMORPHISMS;
D O I
10.1007/s40618-022-01989-y
中图分类号
R5 [内科学];
学科分类号
100201 [内科学];
摘要
Purpose Metformin is widely used to treat type 2 diabetes mellitus (T2DM) individuals. Clinically, inter-individual variability of metformin response is of significant concern and is under interrogation. In this study, a targeted exome and whole transcriptome analysis were performed to identify predictive biomarkers of metformin response in drug-naive T2DM individuals. Methods The study followed a prospective study design. Drug-naive T2DM individuals (n = 192) and controls (n = 223) were enrolled. T2DM individuals were administered with metformin monotherapy and defined as responders and non-responders based on their glycated haemoglobin change over three months. 146 T2DM individuals were used for the final analysis and remaining samples were lost during the follow-up. Target exome sequencing and RNA-seq was performed to analyze genetic and transcriptome profile. The selected SNPs were validated by genotyping and allele specific gene expression using the TaqMan assay. The gene prioritization, enrichment analysis, drug-gene interactions, disease-gene association, and correlation analysis were performed using various tools and databases. Results rs1050152 and rs272893 in SLC22A4 were associated with improved response to metformin. The copy number loss was observed in PPARGC1A in the non-responders. The expression analysis highlighted potential differentially expressed targets for predicting metformin response (n = 35) and T2DM (n = 14). The expression of GDF15, TWISTNB, and RPL36A genes showed a maximum correlation with the change in HbA1c levels. The disease-gene association analysis highlighted MAGI2 rs113805659 to be linked with T2DM. Conclusion The results provide evidence for the genetic variations, perturbed transcriptome, allele-specific gene expression, and pathways associated with metformin drug response in T2DM.
引用
收藏
页码:1205 / 1218
页数:14
相关论文
共 57 条
[41]
Genetic Variants in CPA6 and PRPF31 Are Associated With Variation in Response to Metformin in Individuals With Type 2 Diabetes [J].
Rotroff, Daniel M. ;
Yee, Sook Wah ;
Zhou, Kaixin ;
Marvel, Skylar W. ;
Shah, Hetal S. ;
Jack, John R. ;
Havener, Tammy M. ;
Hedderson, Monique M. ;
Kubo, Michiaki ;
Herman, Mark A. ;
Gao, He ;
Mychaleckyi, Josyf C. ;
McLeod, Howard L. ;
Doria, Alessandro ;
Giacomini, Kathleen M. ;
Pearson, Ewan R. ;
Wagner, Michael J. ;
Buse, John B. ;
Motsinger-Reif, Alison A. .
DIABETES, 2018, 67 (07) :1428-1440
[42]
Metformin therapy for the reproductive and metabolic consequences of polycystic ovary syndrome [J].
Sam, Susan ;
Ehrmann, David A. .
DIABETOLOGIA, 2017, 60 (09) :1656-1661
[43]
The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics [J].
Schwarz, Ute, I ;
Gulilat, Markus ;
Kim, Richard B. .
COLD SPRING HARBOR PERSPECTIVES IN MEDICINE, 2019, 9 (02)
[44]
Human organic cation transporter (OCT1 and OCT2) gene polymorphisms and therapeutic effects of metformin [J].
Shikata, Eriko ;
Yamamoto, Rei ;
Takane, Hiroshi ;
Shigemasa, Chiaki ;
Ikeda, Tadasu ;
Otsubo, Kenji ;
Ieiri, Ichiro .
JOURNAL OF HUMAN GENETICS, 2007, 52 (02) :117-122
[45]
Effect of genetic variation in the organic cation transporter 1, OCT1, on metformin pharmacokinetics [J].
Shu, Y. ;
Brown, C. ;
Castro, R. A. ;
Shi, R. J. ;
Lin, E. T. ;
Owen, R. P. ;
Sheardown, S. A. ;
Yue, L. ;
Burchard, E. G. ;
Brett, C. M. ;
Giacomini, K. M. .
CLINICAL PHARMACOLOGY & THERAPEUTICS, 2008, 83 (02) :273-280
[46]
Quantitative In Vivo Proteomics of Metformin Response in Liver Reveals AMPK-Dependent and -Independent Signaling Networks [J].
Stein, Benjamin D. ;
Calzolari, Diego ;
Hellberg, Kristina ;
Hu, Ying S. ;
He, Lin ;
Hung, Chien-Min ;
Toyama, Erin Q. ;
Ross, Debbie S. ;
Lillemeier, Bjorn F. ;
Cantley, Lewis C. ;
Yates, John R., III ;
Shaw, Reuben J. .
CELL REPORTS, 2019, 29 (10) :3331-+
[47]
CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing [J].
Talevich, Eric ;
Shain, A. Hunter ;
Botton, Thomas ;
Bastian, Boris C. .
PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (04)
[48]
Pharmacogenomic association between a variant in SLC47A1 gene and therapeutic response to metformin in type 2 diabetes [J].
Tkac, I. ;
Klimcakova, L. ;
Javorsky, M. ;
Fabianova, M. ;
Schroner, Z. ;
Hermanova, H. ;
Babjakova, E. ;
Tkacova, R. .
DIABETES OBESITY & METABOLISM, 2013, 15 (02) :189-191
[49]
Combined transcriptome and metabolome analyses of metformin effects reveal novel links between metabolic networks in steroidogenic systems [J].
Udhane, Sameer S. ;
Legeza, Balazs ;
Marti, Nesa ;
Hertig, Damian ;
Diserens, Gaelle ;
Nuoffer, Jean-Marc ;
Vermathen, Peter ;
Fluck, Christa E. .
SCIENTIFIC REPORTS, 2017, 7
[50]
Unluturk U, 2015, MOLEC INTEGR TOXICOL, P147, DOI 10.1007/978-3-319-15630-9_7