Biology subjects

Wang, X.-W.

Publications and source records attributed to Wang, X.-W..

3 recordsLinked to original sources

bioRxiv2018

Acidic microenvironment shaped by lactate accumulation promotes pluripotency through multiple mechanisms

Enhanced glycolysis is a distinct feature associated with numerous stem cells and cancer cells. However, little is known about its regulatory roles in gene expression and cell fate determination. Here we show that acidic environment shaped by lactate accumulation promotes the self-renewal and pluripotency of both mouse and human embryonic stem cells (ESCs). Mechanistically, acidic pH reduces the tri-methylation of H3K27 globally at transcriptional start sites to partially prevent ESC differentiation. In addition, acidic pH stabilizes a large number of mRNAs including pluripotency genes. Furthermore, we found that AGO1 protein is downregulated at acidic conditions, leading to the de-repression of a subset of microRNA targets in low-pH treated ESCs. Altogether, our study provides insights into mechanisms whereby acidic microenvironment produced by enhanced glycolysis regulates gene expression to determine cell fate and has broad implications in the fields of regenerative medicine and cancer biology.

developmental biology↗

bioRxiv2018

Novel role of Lin28 signaling in regulation of mammalian PNS and CNS axon regeneration

Several signaling molecules involved in cellular reprogramming have been shown to regulate mammalian axon regeneration. We hypothesized that reprogramming factors are key regulators of axon regeneration. Here we investigated the role of Lin28, an important reprogramming factor, in the regulation of axon regeneration. We found that Lin28a and Lin28b and their regulatory partners, let-7 microRNAs (miRNAs), were both necessary and sufficient in regulating mature sensory axon regeneration in vivo. More importantly, overexpression of either Lin28a or Lin28b in mature retinal ganglion cells (RGCs) promoted robust and sustained optic nerve regeneration. Additionally, combined overexpression of Lin28a and downregulation of PTEN in RGCs acted additively to promote optic nerve regeneration by reducing the backward turning of regenerating RGC axons. Our findings not only identified a novel molecule promoting optic nerve regeneration but also suggested that reprogramming factors may play vital roles in regulating axon regeneration in mammals.

neuroscience↗

bioRxiv2018

Link Prediction through Deep Learning

Inferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1-3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7-9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13-17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance. SummaryWe propose a new link prediction method based on deep generative models.

bioinformatics↗