Biology subjects

Wen, D.

Publications and source records attributed to Wen, D..

23 records · Page 2Linked to original sources

bioRxiv2019

The glycine locating at random coil of picornaviruses VP3 enhances viral pathogenicity by targeting p53 to promote apoptosis and autophagy

Picornaviruses, comprising important and widespread pathogens of humans and animals, have evolved to control apoptosis and autophagy for their replication and spread. However, the underlying mechanism of the association between apoptosis/autophage and viral pathogenicity remains unclear. In the present study, VP3 of picornaviruses was demonstrated to induce apoptosis and autophagy. Foot-and-mouth disease virus (FMDV), which served as a research model here, can strongly induce both apoptosis and autophagy in the skin lesions. By directly interacting with p53, FMDV-VP3 facilitates its phosphorylation and translocation, resulting in Bcl-2 family-mediated apoptosis and LC3-dependent autophagy. The single residue Gly129 of FMDV-VP3 plays a crucial role in apoptosis and autophagy induction and the interaction with p53. Consistently, the comparison of rescued FMDV with mutated Gly129 and parental virus showed that the Gly129 is indispensable for viral replication and pathogenicity. More importantly, the Gly129 locates at a bend region of random coil structure, the mutation of Gly to Ala remarkably shrunk the volume of viral cavity. Coincidentally, the Gly is conserved in the similarly location of other picornaviruses, including poliovirus (PV), enterovirus 71 (EV71), coxsackievirus (CV) and seneca valley virus (SVA). This study demonstrates that picornaviruses induce apoptosis and autophagy to facilitate its pathogenicity and the Gly is functional site, providing novel insights into picornavirus biology.

microbiology↗

bioRxiv2019

Long noncoding RNAs sustain high expression of exogenous Oct4 by sponging miRNA during reprogramming

Long noncoding RNAs (lncRNAs) modulate gene expression as competing endogenous RNAs (ceRNAs) via sponging microRNAs (miRNAs). However, the extent and functional consequences of ceRNAs in diverse cellular context still need to be proven. Using a doxycycline inducible expression of Yamanaka four factors to generate induced pluripotent stem cells (iPSCs) from mouse embryonic fibroblasts (MEFs), we found the miRNAs from MEFs remained highly expressed from day 0 to day 6 after doxycycline induction; unexpectedly, many genes targeted by these miRNAs were actually up-regulated; meanwhile, long intergenic noncoding RNAs (lincRNAs) and circular RNAs (circRNAs) which have complementary binding sites with the miRNAs were highly expressed, indicating lincRNAs and circRNAs (linc/circRNAs) may serve as sponges for miRNAs to block their activities during reprogramming. Intriguingly, the knockdown of the linc/circRNAs sponging the miRNAs targeting Oct4 mRNA resulted in down-regulation of exogenous Oct4 expression, decrease of reprogramming efficiency, and low-grade chimera forming iPSCs. Our results suggest that the ceRNA network plays an important role in reprogramming somatic cells to pluripotent stem cells.

cell biology↗

bioRxiv2017

Inferring Phylogenetic Networks Using PhyloNet

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or bi-allelic markers) is implemented. Maximum parsimony is based on an extension of the \"minimizing deep coalescences\" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudo-likelihood measure. PhyloNet summarizes the results of the various analyses, and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software, [phylogenetic networks; reticulation; incomplete lineage sorting; multispecies network coalescent; Bayesian inference; maximum likelihood; maximum parsimony.]

evolutionary biology↗

bioRxiv2017

Bayesian Inference Of Phylogenetic Networks From Bi-allelic Genetic Markers

Phylogenetic networks are rooted, directed, acyclic graphs that model reticulate evolutionary histories. Recently, statistical methods were devised for inferring such networks from either gene tree estimates or the sequence alignments of multiple unlinked loci. Bi-allelic markers, most notably single nucleotide polymorphisms (SNPs) and amplified fragment length polymorphisms (AFLPs), provide a powerful source of genome-wide data. In a recent paper, a method called SNAPP was introduced for statistical inference of species trees from unlinked bi-allelic markers. The generative process assumed by the method combined both a model of evolution for the bi-allelic markers, as well as the multispecies coalescent. A novel component of the method was a polynomial-time algorithm for exact computation of the likelihood of a fixed species tree via integration over all possible gene trees for a given marker. Here we report on a method for Bayesian inference of phylogenetic networks from bi-allelic markers. Our method significantly extends the algorithm for exact computation of phylogenetic network likelihood via integration over all possible gene trees. Unlike the case of species trees, the algorithm is no longer polynomial-time on all instances of phylogenetic networks. Furthermore, the method utilizes a reversible-jump MCMC technique to sample the posterior of phylogenetic networks given bi-allelic marker data. Our method has a very good performance in terms of accuracy and robustness as we demonstrate on simulated data, as well as a data set of multiple New Zealand species of the plant genus Ourisia(Plantaginaceae). We implemented the method in the publicly available, open-source PhyloNet software package.\n\nAuthor summaryThe availability of genomic data has revolutionized the study of evolutionary histories and phylogeny inference. Inferring evolutionary histories from genomic data requires, in most cases, accounting for the fact that different genomic regions could have evolutionary histories that differ from each other as well as from that of the species from which the genomes were sampled. In this paper, we introduce a method for inferring evolutionary histories while accounting for two processes that could give rise to such differences across the genomes, namely incomplete lineage sorting and hybridization. We introduce a novel algorithm for computing the likelihood of phylogenetic networks from bi-allelic genetic markers and use it in a Bayesian inference method. Analyses of synthetic and empirical data sets show a very good performance of the method in terms of the estimates it obtains.

evolutionary biology↗

bioRxiv2016

Co-estimating Reticulate Phylogenies and Gene Trees from Multi-locus Sequence Data

The multispecies network coalescent (MSNC) is a stochastic process that captures how gene trees grow within the branches of a phylogenetic network. Coupling the MSNC with a stochastic mutational process that operates along the branches of the gene trees gives rise to a generative model of how multiple loci from within and across species evolve in the presence of both incomplete lineage sorting (ILS) and reticulation (e.g., hybridization). We report on a Bayesian method for sampling the parameters of this generative model, including the species phylogeny, gene trees, divergence times, and population sizes, from DNA sequences of multiple independent loci. We demonstrate the utility of our method by analyzing simulated data and reanalyzing three biological data sets. Our results demonstrate the significance of not only co-estimating species phylogenies and gene trees, but also accounting for reticulation and ILS simultaneously. In particular, we show that when gene flow occurs, our method accurately estimates the evolutionary histories, coalescence times, and divergence times. Tree inference methods, on the other hand, underestimate divergence times and overestimate coalescence times when the evolutionary history is reticulate. While the MSNC corresponds to an abstract model of \"intermixture,\" we study the performance of the model and method on simulated data generated under a gene flow model. We show that the method accurately infers the most recent time at which gene flow occurs. Finally, we demonstrate the application of the new method to a 106-locus yeast data set. [Multispecies network coalescent; reticulation; incomplete lineage sorting; phylogenetic network; Bayesian inference; RJMCMC.]

evolutionary biology↗