Mary E. Donohoe , Robert Morey , Yingchun Li , Donald Pizzo , Sampada Kallol , Hee-Young Cho , Francesca Soncin , Mana M. Parast
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引用次数: 0
Abstract
The placenta plays a crucial role in pregnancy success. ΔNp63α (p63), a transcription factor from the TP53 family, is highly expressed in villous cytotrophoblasts (CTBs), the epithelial stem cells of the human placenta, and is involved in CTB maintenance and differentiation. We examined the mechanisms of action of p63 by identifying its downstream targets. Gene expression changes were evaluated following overexpression and knockdown of p63 in the JEG3 choriocarcinoma cell line, using microarray-based RNA profiling. High-temperature requirement A4 (HTRA4), a placenta-specific serine protease involved in trophoblast differentiation and altered in preeclampsia, was identified as a gene reciprocally regulated by p63, and its expression was characterized in primary human placental tissues by RNA-sequencing and in situ hybridization. Potential p63 DNA-binding motifs were identified in the HTRA4 promoter, and p63 occupancy at some of these sites was confirmed using chromatin immunoprecipitation, followed by quantitative PCR in both JEG3 and trophoblast stem cells. These data begin to identify members of the transcriptional network downstream of p63, thus laying the groundwork for probing mechanisms by which this important transcription factor regulates trophoblast stemness and differentiation.
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.