Biology subjects

Sutton, R.

Publications and source records attributed to Sutton, R..

2 recordsLinked to original sources

bioRxiv2018

Dental and facial characteristics of osteogenesis imperfecta type V

Osteogenesis imperfecta (OI) type V is an ultrarare heritable bone disorder caused by the heterozygous c.-14C>T mutation in IFITM5. The dental and craniofacial phenotype has not been described in detail. In the present multicenter study (Brittle Bone Disease Consortium) 14 individuals with OI type V (age 3 to 50 years; 10 females, 4 males) underwent dental and craniofacial assessment. None of the individuals had dentinogenesis imperfecta. Six of the 9 study participants (66%) for whom panoramic radiographs were obtained had at least one missing tooth (range 1 to 9). Class II molar occlusion was present in 8 (57%) of the 14 study participants. The facial profile was retrusive and lower face height was decreased in 8 (57%) individuals. Cephalometry, performed in three study participants, revealed a severely retrusive maxilla and mandible, and poorly angulated incisors in a 14-year old girl, a protrusive maxilla and a retrusive mandible in a 14-year old boy. Cone beam computed tomograpy scans were obtained from two study participants and demonstrated intervertebral disc calcification at the C2-C3 level in one individual. Our study observed that OI type V is associated with missing permanent teeth, especially permanent premolar, but not with dentinogenesis imperfecta. The pattern of craniofacial abnormalities in OI type V thus differs from that in other severe OI types, such as OI type III and IV, and could be described as a bimaxillary retrusive malocclusion with reduced lower face height and multiple missing teeth.

genetics↗

bioRxiv2018

A cross-organism framework for supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancer-associated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

bioinformatics↗