Identification of paired-related Homeobox Protein 1 as a key mesenchymal transcription factor in pulmonary fibrosis

circGRHPR inhibits aberrant epithelial-mesenchymal transformation progression of lung epithelial cells associated with idiopathic pulmonary fibrosis

Air pollution has greatly increased the risk of idiopathic pulmonary fibrosis (IPF). Circular RNAs (circRNAs) have been found to play a significant role in the advancement of IPF, but there is limited evidence of correlation between circRNAs and lung epithelial cells (LECs) in IPF. This research aimed to explore the influence of circRNAs on the regulation of EMT progression in LECs, with the objective of elucidating its mechanism and establishing its association with IPF. Our results suggested that the downregulation of circGRHPR in peripheral blood of clinical cases was associated with the diagnosis of IPF. Meanwhile, we found that circGRHPR was downregulated in transforming growth factor-beta1 (TGF-β1)-induced A549 and Beas-2b cells. It is a valid model to study the abnormal EMT progression of IPF-associated LECs in vitro. The overexpression of circGRHPR inhibited the abnormal EMT progression of TGF-β1-induced LECs. Furthermore, as the sponge of miR-665, circGRHPR released the expression of E3 ubiquitin-protein ligase NEDD4-like (NEDD4L), thus promoting its downstream transforming growth factor beta receptor 2 (TGFBR2) ubiquitination. It is helpful to reduce the response of LECs to TGF-β1 signaling. In summary, circGRHPR/miR-665/NEDD4L axis inhibited the abnormal EMT progression of TGF-β1-induced LECs by promoting TGFBR2 ubiquitination, which provides new ideas and potential targets for the treatment of IPF.

Transcription factors in fibroblast plasticity and CAF heterogeneity

In recent years, research focused on the multifaceted landscape and functions of cancer-associated fibroblasts (CAFs) aimed to reveal their heterogeneity and identify commonalities across diverse tumors for more effective therapeutic targeting of pro-tumoral stromal microenvironment. However, a unified functional categorization of CAF subsets remains elusive, posing challenges for the development of targeted CAF therapies in clinical settings.The CAF phenotype arises from a complex interplay of signals within the tumor microenvironment, where transcription factors serve as central mediators of various cellular pathways. Recent advances in single-cell RNA sequencing technology have emphasized the role of transcription factors in the conversion of normal fibroblasts to distinct CAF subtypes across various cancer types.This review provides a comprehensive overview of the specific roles of transcription factor networks in shaping CAF heterogeneity, plasticity, and functionality. Beginning with their influence on fibroblast homeostasis and reprogramming during wound healing and fibrosis, it delves into the emerging insights into transcription factor regulatory networks. Understanding these mechanisms not only enables a more precise characterization of CAF subsets but also sheds light on the early regulatory processes governing CAF heterogeneity and functionality. Ultimately, this knowledge may unveil novel therapeutic targets for cancer treatment, addressing the existing challenges of stromal-targeted therapies.

A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.