Seong Kyu Han1, Jungho Kong1, Sanguk Kim1,2, Jae-Hoon Lee2, Dong-Hoo Han2. 1. Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea. 2. Department of Prosthodontics, College of Dentistry, Yonsei University, Seoul, Korea.
Abstract
OBJECTIVE: Hereditary gingival fibromatosis (HGF) is a rare oral disease characterized by either localized or generalized gradual, benign, non-hemorrhagic enlargement of gingivae. Although several genetic causes of HGF are known, the genetic etiology of HGF as a non-syndromic and idiopathic entity remains uncertain. SUBJECTS AND METHODS: We performed exome and RNA-seq of idiopathic HGF patients and controls, and then devised a computational framework that specifies exomic/transcriptomic alterations interconnected by a regulatory network to unravel genetic etiology of HGF. Moreover, given the lack of animal model or large-scale cohort data of HGF, we developed a strategy to cross-check their clinical relevance through in silico gene-phenotype mapping with biomedical literature mining and semantic analysis of disease phenotype similarities. RESULTS: Exomic variants and differentially expressed genes of HGF were connected by members of TGF-β/SMAD signaling pathway and craniofacial development processes, accounting for the molecular mechanism of fibroblast overgrowth mimicking HGF. Our cross-check supports that genes derived from the regulatory network analysis have pathogenic roles in fibromatosis-related diseases. CONCLUSIONS: The computational approach of connecting exomic and transcriptomic alterations through regulatory networks is applicable in the clinical interpretation of genetic variants in HGF patients.
OBJECTIVE:Hereditary gingival fibromatosis (HGF) is a rare oral disease characterized by either localized or generalized gradual, benign, non-hemorrhagic enlargement of gingivae. Although several genetic causes of HGF are known, the genetic etiology of HGF as a non-syndromic and idiopathic entity remains uncertain. SUBJECTS AND METHODS: We performed exome and RNA-seq of idiopathic HGF patients and controls, and then devised a computational framework that specifies exomic/transcriptomic alterations interconnected by a regulatory network to unravel genetic etiology of HGF. Moreover, given the lack of animal model or large-scale cohort data of HGF, we developed a strategy to cross-check their clinical relevance through in silico gene-phenotype mapping with biomedical literature mining and semantic analysis of disease phenotype similarities. RESULTS: Exomic variants and differentially expressed genes of HGF were connected by members of TGF-β/SMAD signaling pathway and craniofacial development processes, accounting for the molecular mechanism of fibroblast overgrowth mimicking HGF. Our cross-check supports that genes derived from the regulatory network analysis have pathogenic roles in fibromatosis-related diseases. CONCLUSIONS: The computational approach of connecting exomic and transcriptomic alterations through regulatory networks is applicable in the clinical interpretation of genetic variants in HGF patients.