Biology subjects

Wang, X.-W.

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

31 records · Page 2Linked to original sources

bioRxiv2022

Ecological Dynamics Imposes Fundamental Challenges in Microbial Source Tracking

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. Numerous computational methods, referred to as MST solvers hereafter, have been developed in the past and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for any MST solver. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in solving the MST problem using computational approaches.

microbiology↗

bioRxiv2022

Histone methyltransferase Ezh2 coordinates mammalian axon regeneration via epigenetic regulation of key regenerative pathways

Current clinical treatment for neurodegenerative diseases and neural injuries falls short of success, and one primary reason is that neurons in the mammalian central nervous system (CNS) lose their regeneration ability as they mature. Previous studies indicated that the regeneration ability of neurons is governed by complex signaling networks involving many genes. Therefore, here we investigated the roles of Ezh2, a histone methyltransferase, in regulation of mammalian axon regeneration at the epigenetic level. We found that Ezh2 level was gradually downregulated in the mouse nervous system during maturation but significantly upregulated in mature sensory neurons during spontaneous axon regeneration in the peripheral nerve system (PNS), suggesting its role in supporting axon regeneration. Indeed, Ezh2 loss-of-function in sensory neurons impaired PNS axon regeneration in vitro and in vivo. In contrast, overexpression of Ezh2 in retinal ganglion cells in the CNS induced optic nerve regeneration after optic nerve injury in both methyltransferase-dependent and -independent manners. Mechanistic exploration with multiomics sequencing, together with functional analyses, revealed that Ezh2 supported axon regeneration by systematically silencing the transcription of genes regulating synaptic function and axon regeneration inhibitory signaling, while broadly activating factors promoting axon regeneration. Our study not only reveals that Ezh2 coordinates axon regeneration via epigenetically regulating multiple key regenerative pathways, but also suggests that modulating chromatin accessibility is a promising strategy to promote CNS axon regeneration.

neuroscience↗

bioRxiv2022

A Statistical Physics Approach for Disease Module Detection

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the human protein-protein interaction (PPI) network (a.k.a. the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach with genome-wide association studies (GWAS) of six complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

systems biology↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗

bioRxiv2021

Predicting microbiome compositions through deep learning.

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, here we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowledge of any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply it to experimental data of both in vitro and in vivo communities, including ocean and soil microbial communities, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. In particular, we show how our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

microbiology↗

bioRxiv2021

Origins of Scaling Laws in Microbial Dynamics

Analysis of high-resolution time series data from the human and mouse gut microbiomes revealed that the gut microbial dynamics can be characterized by several robust and simple scaling laws. It is still unknown if those scaling laws are universal across different body sites, host species, or even free-living microbial communities. Moreover, the underlying mechanisms responsible for those scaling laws remain poorly understood. Here, we demonstrate that those scaling laws are not unique to gut microbiome, but universal across different habitats, from human skin and oral microbiome to marine plankton bacteria and eukarya communities. Since completely shuffled time series yield very similar scaling laws, we conjecture that the universal scaling laws in various microbiomes are largely driven by temporal stochasticity of the host or environmental factors. We leverage a simple population dynamics model with both deterministic inter-species interactions and stochastic noise to confirm our conjecture. In particular, we find that those scaling laws are jointly determined by inter-species interactions and linear multiplicative noises. The presented results deepen our understanding of the nature of scaling laws in microbial dynamics.

microbiology↗

bioRxiv2021

Nutritional Redundancy in Human Diet

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as nutritional redundancy and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

systems biology↗

bioRxiv2021

Characterizing scaling laws in gut microbial dynamics from time series data: caution is warranted

Many studies have revealed that both host and environmental factors can impact the gut microbial compositions, implying that the gut microbiota is considerably dynamic1-5. In their Article, Ji et al.6 performed comprehensive analysis of multiple high-resolution time series data of human and mouse gut microbiota. They found that both human and mouse gut microbiota dynamics can be characterized by several robust scaling laws describing short- and long-term changes in gut microbiota abundances, distributions of species residence and return times, and the correlation between the mean and the temporal variance of species abundances. They claimed that those scaling laws characterize both short- and long-term dynamics of gut microbiota. However, we are concerned that their interpretation is quite misleading, because all the scaling laws can be reproduced by the shuffled time series with completely randomized time stamps of the microbiome samples.

microbiology↗

bioRxiv2020

Gut expressed vitellogenin is hijacked by a virus to facilitate its spread

Insect vitellogenin (Vg) has been considered to be synthesized in the female fat body, secreted into hemolymph, and taken up by developing oocytes to serve as nutrition for embryo development. However, the expression and function of Vg in other tissues are largely unknown. Here, we show that Vg is expressed in the whitefly midgut epithelial cells and secreted into gut lumen where it binds to the microvillar membrane. Furthermore, we find that the midgut expressed Vg is hijacked by Tomato yellow leaf curl virus to facilitate the movement of virus crossing the midgut wall. Silencing of Vg or immune-blocking the interaction between viral coat protein and midgut Vg inhibited virus movement across the midgut wall and decreased virus transmission. Our findings show possession of functional Vg in the midgut of an insect, and reveal a novel mechanism involved in the movement of virus crossing the midgut barrier of vector insect.

microbiology↗

bioRxiv2019

Knocking out non-muscle myosin II in retinal ganglion cells promotes long-distance optic nerve regeneration

In addition to altered gene expression, pathological cytoskeletal dynamics in the axon are another key intrinsic barrier for axon regeneration in the central nervous system (CNS). Here we showed that knocking out myosin IIA/B in retinal ganglion cells alone either before or after optic nerve crush induced marked and sustained optic nerve regeneration. Combined Lin28 overexpression and myosin IIA/B knockout led to synergistic promoting effect and long-distance axon regeneration. Immunostaining, RNA-seq and western blot analyses revealed that myosin II deletion did not affect known axon regeneration signaling pathways or the expression of regeneration associated genes. Instead, it abolished the retraction bulb formation and significantly enhanced the axon extension efficiency. The study provided clear evidence that directly targeting neuronal cytoskeleton was sufficient to induce strong CNS axon regeneration, and combining gene expression in the soma and modified cytoskeletal dynamics in the axon was a promising approach for long-distance CNS axon regeneration.

neuroscience↗

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↗