Biology subjects

Akiyama, J.

Publications and source records attributed to Akiyama, J..

3 recordsLinked to original sources

bioRxiv2023

Combinatorial transcription factor binding encodes cis-regulatory wiring of forebrain GABAergic neurogenesis

Transcription factors (TFs) bind combinatorially to genomic cis-regulatory elements (cREs), orchestrating transcription programs. While studies of chromatin state and chromosomal interactions have revealed dynamic neurodevelopmental cRE landscapes, parallel understanding of the underlying TF binding lags. To elucidate the combinatorial TF-cRE interactions driving mouse basal ganglia development, we integrated ChIP-seq for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and transcriptional state, and transgenic enhancer assays. We identified TF-cREs modules with distinct chromatin features and enhancer activity that have complementary roles driving GABAergic neurogenesis and suppressing other developmental fates. While the majority of distal cREs were bound by one or two TFs, a small proportion were extensively bound, and these enhancers also exhibited exceptional evolutionary conservation, motif density, and complex chromosomal interactions. Our results provide new insights into how modules of combinatorial TF-cRE interactions activate and repress developmental expression programs and demonstrate the value of TF binding data in modeling gene regulatory wiring.

developmental biology↗

bioRxiv2020

De Novo Mutation in an Enhancer of EBF3 in simplex autism

Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of de novo protein-coding variants within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2,671 families with autism, with a specific focus on de novo variation in enhancers with previously characterized in vivo activity. We identified three independent de novo mutations limited to individuals with autism in the enhancer hs737. These mutations result in similar phenotypic characteristics, affect enhancer activity in vitro, and preferentially occur in AAT motifs in the enhancer with predicted disruptions of transcription factor binding. We also find that hs737 is enriched for copy number variation in individuals with NDDs, is dosage sensitive in the human population, is brain-specific, and targets the NDD gene EBF3 that is genome-wide significant for protein coding de novo variants, demonstrating the importance of understanding all forms of variation in the genome. One Sentence SummaryWhole-genome sequencing in thousands of families reveals variants relevant to simplex autism in a brain enhancer of the well-established neurodevelopmental disorder gene EBF3.

genomics↗

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↗