Yanqiang Wang1, Huiling He1, Sandya Liyanarachchi1, Luke K Genutis1, Wei Li1, Lianbo Yu2,3, John E Phay4, Rulong Shen5, Pamela Brock6, Albert de la Chapelle7. 1. Human Cancer Genetics Program and Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA. 2. Center for Biostatistics, The Ohio State University, Columbus, Ohio, USA. 3. Department of Biomedical Informatics, The Ohio State University, Ohio, Columbus, USA. 4. Department of Surgery, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA. 5. Department of Pathology, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA. 6. Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA. 7. Human Cancer Genetics Program and Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA. albert.delachapelle@osumc.edu.
Abstract
PURPOSE: To identify and characterize the functional variants, regulatory gene networks, and potential binding targets of SMAD3 in the 15q22 thyroid cancer risk locus. METHODS: We performed linkage disequilibrium (LD) and haplotype analyses to fine map the 15q22 locus. Luciferase reporter assays were applied to evaluate the regulatory effects of the candidate variants. Knockdown by small interfering RNA, microarray analysis, chromatin immunoprecipitation (ChIP) and quantitative real-time polymerase chain reaction assays were performed to reveal the regulatory gene network and identify its binding targets. RESULTS: We report a 25.6-kb haplotype within SMAD3 containing numerous single-nucleotide polymorphisms (SNPs) in high LD. SNPs rs17293632 and rs4562997 were identified as functional variants of SMAD3 by luciferase assays within the LD region. These variants regulate SMAD3 transcription in an allele-specific manner through enhancer elements in introns of SMAD3. Knockdown of SMAD3 in thyroid cancer cell lines revealed its regulatory gene network including two upregulated genes, SPRY4 and SPRY4-IT1. Sequence analysis and ChIP assays validated the actual binding of SMAD3 protein to multiple SMAD binding element sites in the region upstream of SPRY4. CONCLUSION: Our data provide a functional annotation of the 15q22 thyroid cancer risk locus.
PURPOSE: To identify and characterize the functional variants, regulatory gene networks, and potential binding targets of SMAD3 in the 15q22 thyroid cancer risk locus. METHODS: We performed linkage disequilibrium (LD) and haplotype analyses to fine map the 15q22 locus. Luciferase reporter assays were applied to evaluate the regulatory effects of the candidate variants. Knockdown by small interfering RNA, microarray analysis, chromatin immunoprecipitation (ChIP) and quantitative real-time polymerase chain reaction assays were performed to reveal the regulatory gene network and identify its binding targets. RESULTS: We report a 25.6-kb haplotype within SMAD3 containing numerous single-nucleotide polymorphisms (SNPs) in high LD. SNPs rs17293632 and rs4562997 were identified as functional variants of SMAD3 by luciferase assays within the LD region. These variants regulate SMAD3 transcription in an allele-specific manner through enhancer elements in introns of SMAD3. Knockdown of SMAD3 in thyroid cancer cell lines revealed its regulatory gene network including two upregulated genes, SPRY4 and SPRY4-IT1. Sequence analysis and ChIP assays validated the actual binding of SMAD3 protein to multiple SMAD binding element sites in the region upstream of SPRY4. CONCLUSION: Our data provide a functional annotation of the 15q22 thyroid cancer risk locus.
Authors: Julius Gudmundsson; Gudmar Thorleifsson; Jon K Sigurdsson; Lilja Stefansdottir; Jon G Jonasson; Sigurjon A Gudjonsson; Daniel F Gudbjartsson; Gisli Masson; Hrefna Johannsdottir; Gisli H Halldorsson; Simon N Stacey; Hannes Helgason; Patrick Sulem; Leigha Senter; Huiling He; Sandya Liyanarachchi; Matthew D Ringel; Esperanza Aguillo; Angeles Panadero; Enrique Prats; Almudena Garcia-Castaño; Ana De Juan; Fernando Rivera; Li Xu; Lambertus A Kiemeney; Gudmundur I Eyjolfsson; Olof Sigurdardottir; Isleifur Olafsson; Hoskuldur Kristvinsson; Romana T Netea-Maier; Thorvaldur Jonsson; Jose I Mayordomo; Theo S Plantinga; Hannes Hjartarson; Jon Hrafnkelsson; Erich M Sturgis; Unnur Thorsteinsdottir; Thorunn Rafnar; Albert de la Chapelle; Kari Stefansson Journal: Nat Commun Date: 2017-02-14 Impact factor: 14.919
Authors: Alan C Mullen; David A Orlando; Jamie J Newman; Jakob Lovén; Roshan M Kumar; Steve Bilodeau; Jessica Reddy; Matthew G Guenther; Rodney P DeKoter; Richard A Young Journal: Cell Date: 2011-10-28 Impact factor: 41.582
Authors: Christopher J Ott; Neil P Blackledge; Jenny L Kerschner; Shih-Hsing Leir; Gregory E Crawford; Calvin U Cotton; Ann Harris Journal: Proc Natl Acad Sci U S A Date: 2009-11-06 Impact factor: 11.205
Authors: Yanqiang Wang; Sandya Liyanarachchi; Katherine E Miller; Taina T Nieminen; Daniel F Comiskey; Wei Li; Pamela Brock; David E Symer; Keiko Akagi; Katherine E DeLap; Huiling He; Daniel C Koboldt; Albert de la Chapelle Journal: Thyroid Date: 2019-05-13 Impact factor: 6.568
Authors: Ammar J Alsheikh; Sabrina Wollenhaupt; Emily A King; Jonas Reeb; Sujana Ghosh; Lindsay R Stolzenburg; Saleh Tamim; Jozef Lazar; J Wade Davis; Howard J Jacob Journal: BMC Med Genomics Date: 2022-04-01 Impact factor: 3.063
Authors: Daniel F Comiskey; Huiling He; Sandya Liyanarachchi; Mehek S Sheikh; Isabella V Hendrickson; Lianbo Yu; Pamela L Brock; Albert de la Chapelle Journal: Sci Rep Date: 2020-11-17 Impact factor: 4.379