Crystallographic characterization of the C-terminal coiled-coil region of mouse Bicaudal-D1 (BICD1)

Role of Microtubule-Associated Factors in HIF1α Nuclear Translocation

Adaptation to hypoxia is essential for regulating the survival and functions of hypoxic cells; it is mainly mediated by the hypoxia-inducible factor 1 (HIF1). The alpha subunit of HIF1 (HIF1α) is a well-known regulatory component of HIF1, which is tightly controlled by various types of HIF1α-regulating processes. Previous research has shown that microtubule-regulated HIF1α nuclear translocation is a key factor for HIF1 activation under hypoxia. In this review, we summarize experimental reports on the role of microtubule-associated factors, such as microtubule, dynein, and dynein adaptor protein, in nuclear translocation of HIF1α. Based upon scientific evidence, we propose a bicaudal D homolog (BICD) as a novel HIF1α translocation regulating factor. A deeper understanding of the mechanism of the action of regulatory factors in controlling HIF1α nuclear translocation will provide novel insights into cell biology under hypoxia.

BICD1 mediates HIF1α nuclear translocation in mesenchymal stem cells during hypoxia adaptation

Hypoxia inducible factor 1α (HIF1α) is a master regulator leading to metabolic adaptation, an essential physiological process to maintain the survival of stem cells under hypoxia. However, it is poorly understood how HIF1α translocates into the nucleus in stem cells under hypoxia. Here, we investigated the role of a motor adaptor protein Bicaudal D homolog 1 (BICD1) in dynein-mediated HIF1α nuclear translocation and the effect of BICD1 regulation on hypoxia adaptation and its therapeutic potential on human umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs). In our results, silencing of BICD1 but not BICD2 abolished HIF1α nuclear translocation and its activity. BICD1 overexpression further enhanced hypoxia-induced HIF1α nuclear translocation. Hypoxia stimulated direct bindings of HIF1α to BICD1 and the intermediate chain of dynein (Dynein IC), which was abolished by BICD1 silencing. Akt inhibition reduced the binding of BICD1 to HIF1α and nuclear translocation of HIF1α. Conversely, Akt activation or GSK3β silencing further enhanced the hypoxia-induced HIF1α nuclear translocation. Furthermore, BICD1 silencing abolished hypoxia-induced glycolytic reprogramming and increased mitochondrial ROS accumulation and apoptosis in UCB-MSCs under hypoxia. In the mouse skin wound healing model, the transplanted cell survival and skin wound healing capacities of hypoxia-pretreated UCB-MSCs were reduced by BICD1 silencing and further increased by GSK3β silencing. In conclusion, we demonstrated that BICD1-induced HIF1α nuclear translocation is critical for hypoxia adaptation, which determines the regenerative potential of UCB-MSCs.

Exploring the Molecular Mechanism of Thoracic Aortic Aneurysm via Bioinformatics Analysis

BACKGROUND The aim of this study was to identify some key genes related to the pathogenesis of thoracic aortic aneurysm (TAA) and gain more insights to the molecular mechanism of TAA. MATERIAL AND METHODS The expression profile of GSE9106 was downloaded from the Gene Expression Omnibus (GEO) database. The data contained 58 TAA peripheral blood samples and 36 normal peripheral blood samples. The differently expressed genes (DEGs) between the TAA samples and the normal samples were identified via limma package of R. Functional enrichment analysis of the DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differentially co-expressed genes in TAA samples compared to normal samples were identified via the DCGL package in R. The protein-protein interaction (PPI) network of the DEGs was constructed through the Search Tool for the Retrieval of Interacting Proteins (STARING) database and visualized by Cytoscape software. RESULTS A total of 407 DEGs were obtained in TAA samples compared with normal samples. The DEGs were enriched in 29 Gene Ontology (GO) terms. There were 1,441 co-expression gene pairs that had significant changes in the co-expression status in TAA samples compared with normal samples and a differential co-expression network was constructed based on them. Moreover, a PPI network of the DEGs was constructed, containing 101 nodes. CONCLUSIONS Bioinformatics methods could identify significant biological processes and genes related to TAA. KRTDAP, BICD1, and genes in the OR family might play an important role in TAA.