Biology subjects

Hong, L.

Publications and source records attributed to Hong, L..

3 recordsLinked to original sources

bioRxiv2018

TCF7L2 Regulation of GATA6-dependent and -Independent Vascular Smooth Muscle Cell Plasticity and Intimal Hyperplasia

Genetic variations in Wnt-coreceptor LRP6 and Wnt-regulated transcription factor TCF7L2 have been among the strongest genetic signals for type2 diabetes (T2DM) and coronary artery disease (CAD). Mice with a CAD-linked LRP6 mutation exhibit obstructive coronary artery disease characterized by reduced TCF7L2 expression and dedifferentiation of vascular smooth muscle cell (VSMC). While TCF7L2 maintains stemness and promotes proliferation in embryonic tissues and adult stem cells, its role and mechanisms of action in VSMC differentiation is not understood. Using multiple mouse models, we demonstrate here that TCF7L2 promotes differentiation and inhibits proliferation of VSMCs. TCF7L2 accomplishes these effects by stabilization of GATA6 and upregulation of SM-MHC and cell cycle inhibitors. Accordingly, TCF7L2 haploinsufficient mice exhibited increased susceptibility to, while mice overexpressing TCF7L2 were protected against injury-induced intimal hyperplasia compared to wildtype littermates. Consequently, the overexpression of TCF7L2 in LRP6 mutant mice rescued the injury induced intimal hyperplasia. These novel findings imply cell type-specific functional role of TCF7L2 and provide critical insight into poorly understood mechanisms underlying pathogenesis of intimal hyperplasia.

molecular biology↗

bioRxiv2018

Virtual navigation tested on a mobile app (Sea Hero Quest) is predictive of real-world navigation performance: preliminary data

Virtual reality environments presented on smart-phone and tablet devices have potential to aid the early diagnosis of conditions such as Alzheimers dementia by quantifying impairments in navigation performance. However, it is unclear whether performance on mobile devices can predict navigation errors in the real-world. In a preliminary study we tested 30 participants (15 female, 18-30 years old) on their wayfinding ability in our mobile app Sea Hero Quest and on novel real-world wayfinding task in London (UK). We find a significant correlation between virtual and real-world navigation performance and a male advantage on both tasks, although smaller in the real-world environment. These results are consistent with prior studies which have reported navigation in the virtual environments are predictive of real-world navigation performance and a consistent male advantage. Future research will need to test a larger sample size and older participants.

neuroscience↗

bioRxiv2018

NeoDTI: Neural integration of neighborinformation from a heterogeneous network fordiscovering new drug-target interactions

MotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.\n\nResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.\n\nAvailability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI.\n\nContactzengjy321@tsinghua.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗