Endocytic trafficking is an under-appreciated pathway in cardiac development. Several genes related to endocytic trafficking have been uncovered in a mutagenic ENU screen, in which mutations led to congenital heart defects (CHDs). In this article, we review the relationship between these genes (including and ) and cardiac neural crest cells (CNCCs) during cardiac development. Mice with an ENU-induced mutation exhibit a spectrum of CHDs. Conditional deletion using a floxed allele with different Cre drivers showed that targeting neural crest cells with Wnt1-Cre expression replicated the full cardiac phenotypes of the ENU-induced mutation. In addition, LRP1 function in CNCCs is required for normal OFT lengthening and survival/expansion of the cushion mesenchyme, with other cell lineages along the NCC migratory path playing an additional role. Mice with an ENU-induced and targeted mutation demonstrated the cardiac phenotype of common arterial trunk (CAT). Although there is no impact on CNCCs in mutants, the loss of LRP2 results in the depletion of sonic hedgehog (SHH)-dependent cells in the second heart field. SHH is known to be crucial for CNCC survival and proliferation, which suggests LRP2 has a non-autonomous role in CNCCs. In this article, other endocytic trafficking proteins that are associated with CHDs that may play roles in the NCC pathway during development, such as AP1B1, AP2B1, FUZ, MYH10, and HECTD1, are reviewed.
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http://dx.doi.org/10.3390/ijms22168816 | DOI Listing |
Cell Tissue Res
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Laboratory of Anatomy and Cell Biology, Department of Health Sciences, Kyorin University, 5-4-1 Shimorenjaku, Mitaka, Tokyo, 181-8612, Japan.
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Department of Biomedical Data Science, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan.
Large-scale reconstitution of neuronal circuits from volumetric electron microscopy images is a remarkable research goal in neuroanatomy. However, the large-scale reconstruction is a result of automatic segmentation using convolutional neural networks (CNNs), which is still challenging for general researchers to perform. This review focuses on two representative CNNs for dense neuronal segmentation: flood-filling networks (FFN) and local shape descriptors (LSD)-predicting U-Net (LSD network).
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