Selected Grantee Publications
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.
Creb5 Establishes the Competence for Prg4 Expression in Articular Cartilage
Zhang et al., Communications Biology. 2021.
https://doi.org/10.1038/s42003-021-01857-0
Cells comprising the superficial zone of articular cartilage express lubricin, encoded by the Prg4 gene, that lubricates joints. Researchers identified Creb5 as a transcription factor that is required for TGF-β and EGFR signaling to induce Prg4 expression. Forced expression of Creb5 in deep-zone chondrocytes of articular cartilage confers competence for TGF-β and EGFR signals to induce Prg4 expression. The researchers showed that Creb5 directly binds to two Prg4 promoter-proximal regulatory elements, which work together with a more distal regulatory element to drive induction of Prg4 by TGF-β. Thus, Creb5 is a critical regulator of Prg4/lubricin expression in the articular cartilage. Supported by ORIP (U42OD11158), NIAMS, and NIDDK.