Neural Differentiation Is Inhibited through HIF1/-Catenin Signaling in Embryoid Bodies.

Stem Cells Int

Department of Experimental Biology, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic.

Published: December 2017

Extensive research in the field of stem cells and developmental biology has revealed evidence of the role of hypoxia as an important factor regulating self-renewal and differentiation. However, comprehensive information about the exact hypoxia-mediated regulatory mechanism of stem cell fate during early embryonic development is still missing. Using a model of embryoid bodies (EBs) derived from murine embryonic stem cells (ESC), we here tried to encrypt the role of hypoxia-inducible factor 1 (HIF1) in neural fate during spontaneous differentiation. EBs derived from ESC with the ablated gene for HIF1 had abnormally increased neuronal characteristics during differentiation. An increased neural phenotype in EBs was accompanied by the disruption of -catenin signaling together with the increased cytoplasmic degradation of -catenin. The knock-in of , as well as -catenin ectopic overexpression in EBs, induced a reduction in neural markers to the levels observed in wild-type EBs. Interestingly, direct interaction between HIF1 and -catenin was demonstrated by immunoprecipitation analysis of the nuclear fraction of wild-type EBs. Together, these results emphasize the regulatory role of HIF1 in -catenin stabilization during spontaneous differentiation, which seems to be a crucial mechanism for the natural inhibition of premature neural differentiation.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750467PMC
http://dx.doi.org/10.1155/2017/8715798DOI Listing

Publication Analysis

Top Keywords

neural differentiation
8
embryoid bodies
8
stem cells
8
ebs derived
8
spontaneous differentiation
8
wild-type ebs
8
hif1 -catenin
8
ebs
6
neural
5
differentiation
5

Similar Publications

A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.

NPJ Digit Med

January 2025

Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.

View Article and Find Full Text PDF

Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, Stacking, and Categorical Boosting were applied as artificial intelligence methods to predict chemical oxygen demand (COD) removal efficiency, biomass productivity, biomass yield, and energy yield. Among these, the Stacking model demonstrated superior predictive performance across all targets.

View Article and Find Full Text PDF

Physics-informed Neural Implicit Flow neural network for parametric PDEs.

Neural Netw

January 2025

Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.

The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.

View Article and Find Full Text PDF

Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research department is developing the CAM, based on the Current-Assisted Photonic Sampler, to achieve real-time fluorescence lifetime imaging in the NIR (700-900 nm) region.

View Article and Find Full Text PDF

Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology.

J Clin Med

January 2025

Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal.

Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4's performance in interpreting gastroenterology images.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!