Selected Grantee Publications
Plural Molecular and Cellular Mechanisms of Pore Domain KCNQ2 Encephalopathy
Abreo et al., eLife. 2025.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11703504
This study investigates the cellular and molecular mechanisms underlying KCNQ2 encephalopathy, a severe type of early-onset epilepsy caused by mutations in the KCNQ2 gene. Researchers describe a case study of a child with a specific KCNQ2 gene mutation, G256W, and found that it disrupts normal brain activity, leading to seizures and developmental impairments. Male and female Kcnq2G256W/+ mice have reduced KCNQ2 protein levels, epilepsy, brain hyperactivity, and premature deaths. As seen in the patient study, ezogabine treatment rescued seizures in mice, suggesting a potential treatment avenue. These findings provide important insights into KCNQ2-related epilepsy and highlight possible therapeutic strategies. Supported by ORIP (U54OD020351, S10OD026804, U54OD030187), NCI, NHLBI, NICHD, NIGMS, NIMH, and NINDS.
Identification of a Heterogeneous and Dynamic Ciliome during Embryonic Development and Cell Differentiation
Elliott et al., Development. 2023.
Ciliopathies are a class of diseases that arise when the structure or function of the cilium is compromised. To definitively determine the extent of heterogeneity within the ciliome, investigators compared the ciliomes of six distinct embryonic domains. The data comprehensively revealed that about 30% of the ciliome is differentially expressed across analyzed tissues in the developing embryo. Furthermore, upregulation of numerous ciliary genes correlated with osteogenic cell-fate decisions, suggesting that changes in the ciliome contribute to distinct functions of cell types in vertebrate species. Supported by ORIP (UM1OD023222), NIDCR, and NIGMS.
Deep Learning Is Widely Applicable to Phenotyping Embryonic Development and Disease
Naert et al., Development. 2021.
https://pubmed.ncbi.nlm.nih.gov/34739029/
Genome editing simplifies the generation of new animal models for congenital disorders. The authors illustrate how deep learning (U-Net) automates segmentation tasks in various imaging modalities. They demonstrate this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). They provide a library of pre-trained networks and detailed instructions for applying deep learning to datasets and demonstrate the versatility, precision, and scalability of deep neural network phenotyping on embryonic disease models. Supported by ORIP (P40OD010997, R24OD030008), NICHD, NIDDK, and NIMH.