Biology subjects

Mannion, B.

Publications and source records attributed to Mannion, B..

3 recordsLinked to original sources

bioRxiv2020

A gene desert required for regulatory control of pleiotropic Shox2 expression and embryonic survival

Gene deserts are defined as genomic regions devoid of protein coding genes and spanning more than 500 kilobases, collectively encompassing about 25% of the human genome. Approximately 30% of all gene deserts are enriched for conserved elements with cis-regulatory signatures. These are located predominantly near developmental transcription factors (TFs) but despite predicted critical functions, the transcriptional contributions and biological necessity of most gene deserts remain elusive. Here, we explore the cis-regulatory impact of a gene desert flanking the Shox2 gene, a TF indispensable for proximal limb, craniofacial and cardiac pacemaker development. Using a functional genomics approach in mouse embryos we identify the gene desert as a hub for numerous Shox2-overlapping enhancers arranged in a globular chromatin domain with tissue-specific features. In accordance, using endogenous CRISPR deletion, we demonstrate that the gene desert interval is essential for Shox2 transcriptional control in developing limbs, craniofacial compartments, and the heart. Phenotypically, gene desert ablation leads to pacemaker-related embryonic lethality due to Shox2 depletion in the cardiac sinus venosus. We show that this role is partially mediated through a distal gene desert enhancer, providing evidence for intra-gene desert regulatory robustness. Finally, we uncover a multi-layered functional role of the gene desert by revealing an additional requirement for stylopod morphogenesis, mediated through an array of proximal limb enhancers (PLEs). In summary, our study establishes the Shox2 gene desert as a fundamental genomic unit that controls pleiotropic gene expression through modular arrangement and coordinated dynamics of tissue-specific enhancers.

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↗