Biology subjects

Zhu, J.

Publications and source records attributed to Zhu, J..

At least 613 records · Page 34Linked to original sources

bioRxiv2018

Intercellular signaling network underlies biological time across multiple temporal scales

MotivationCellular, physiological and molecular processes must be organized and regulated across multiple time domains throughout the lifespan of an organism. The technological revolution in molecular biology has led to the identification of numerous genes implicated in the regulation of diverse temporal biological processes. However, it is natural to question whether there is an underlying regulatory network governing multiple timescales simultaneously.\n\nResultsUsing queries of relevant databases and literature searches, a single dense multiscale temporal regulatory network was identified involving core sets of genes that regulate circadian, cell cycle, and aging processes. The network was highly enriched for genes involved in signal transduction (P = 1.82e-82), with p53 and its regulators such as p300 and CREB binding protein forming key hubs, but also for genes involved in metabolism (P = 6.07e-127) and cellular response to stress (P = 1.56e-93). These results suggest an intertwined molecular signaling network that affects biological time across multiple temporal scales in response to environmental stimuli and available resources.\n\nContactjoshua.millstein@usc.edu\n\nSupplementary informationSupplementary data are available online.

systems biology↗

bioRxiv2018

Normal observers show no evidence for blindsight in facial emotion perception

It is commonly assumed that normal human observers can exhibit blindsight-like behavior: the ability to discriminate or identify a stimulus without being aware of it. However, we recently used a bias-free task to show that what looks like blindsight may in fact be an artifact of typical experimental paradigms susceptibility to response bias. While those findings challenge many previous reports of blindsight in normal observers, they do not rule out the possibility that different stimuli or techniques could reveal such perception without awareness. One particularly intriguing candidate for this unconscious perception is emotion processing, as processing of emotional stimuli (e.g. fearful/happy faces) has been reported to potentially bypass conscious visual circuits. Here we used the bias-free blindsight paradigm to investigate whether emotion processing might reveal featural blindsight, i.e. the ability to identify a faces emotion without having introspective access to the task-relevant features of the face that led to the discrimination decision. However, despite the purported ability of emotional stimuli to bypass conscious visual processing, we saw no evidence for such emotion processing featural blindsight: like in our previous study, as soon as participants could identify a faces emotion they reported introspective access to the task-relevant features, matching predictions of a Bayesian ideal observer. The present results challenge dominant theory, adding to the growing body of evidence that perceptual discrimination ability in the complete absence of introspective access may not be possible for neurologically intact observers.

neuroscience↗

bioRxiv2018

S-Nitrosylation of STIM1 by neuronal nitric oxide synthase inhibits store-operated Ca2+ entry

Store-operated Ca2+ entry (SOCE) mediated by stromal interacting molecule-1 (STIM1) and Orai1 represents a major route of Ca2+ entry in mammalian cells and is initiated by STIM1 oligomerization in the endoplasmic or sarcoplasmic reticulum (ER/SR). However, the effects of nitric oxide (NO) on STIM1 function are unknown. Neuronal NO synthase (nNOS) is located in the SR of cardiomyocytes. Here, we show that STIM1 is susceptible to S-nitrosylation. nNOS deficiency or inhibition enhanced Ca2+ release-activated Ca2+ channel current (ICRAC) and SOCE in cardiomyocytes. Consistently, NO donor S-nitrosoglutathione (GSNO) inhibited STIM1 puncta formation and ICRAC in HEK293 cells, but this effect was absent in cells expressing the Cys49Ser/Cys56Ser STIM1 double mutant. Furthermore, NO donors caused Cys49 and Cys56-specific structural changes associated with reduced protein backbone mobility, increased thermal stability and suppressed Ca2+-depletion-dependent oligomerization of the luminal Ca2+-sensing region of STIM1. Collectively, our data show that S-nitrosylation of STIM1 suppresses oligomerization via enhanced luminal domain stability and rigidity, and inhibits SOCE in cardiomyocytes.

physiology↗

bioRxiv2018

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high -fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe.

systems biology↗

bioRxiv2018

Inference of Species Phylogenies from Bi-allelic Markers Using Pseudo-likelihood

MotivationPhylogenetic networks represent reticulate evolutionary histories. Statistical methods for their inference under the multispecies coalescent have recently been developed. A particularly powerful approach uses data that consist of bi-allelic markers (e.g., single nucleotide polymorphism data) and allows for exact likelihood computations of phylogenetic networks while numerically integrating over all possible gene trees per marker. While the approach has good accuracy in terms of estimating the network and its parameters, likelihood computations remain a major computational bottleneck and limit the methods applicability.\n\nResultsIn this paper, we first demonstrate why likelihood computations of networks take orders of magnitude more time when compared to trees. We then propose an approach for inference of phylo-genetic networks based on pseudo-likelihood using bi-allelic markers. We demonstrate the scalability and accuracy of phylogenetic network inference via pseudo-likelihood computations on simulated data. Furthermore, we demonstrate aspects of robustness of the method to violations in the underlying assumptions of the employed statistical model. Finally, we demonstrate the application of the method to biological data. The proposed method allows for analyzing larger data sets in terms of the numbers of taxa and reticulation events. While pseudo-likelihood had been proposed before for data consisting of gene trees, the work here uses sequence data directly, offering several advantages as we discuss.\n\nAvailabilityThe methods have been implemented in PhyloNet (http://bioinfocs.rice.edu/phylonet).\n\nContactjiafan.zhu@rice.edu, nakhleh@rice.edu

evolutionary biology↗

bioRxiv2018

Gut microbiota density influences host physiology and is shaped by host and microbial factors

To identify factors that regulate gut microbiota density and the impact of varied microbiota density on health, we assayed this fundamental ecosystem property in fecal samples across mammals, human disease, and therapeutic interventions. Physiologic features of the host (carrying capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohns disease, ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between microbiota and disease in terms of microbiota density, host carrying capacity, and microbiota fitness provide new insights into microbiome structure and microbiome targeted therapeutics.

microbiology↗

bioRxiv2018

Verified the effectiveness of AsCpf1 system in a variety of vertebrate species

CRISPR/Cpf1 system is a novel genomic editing tool. Because of its more sophisticated components, and lower off-target rate, it has the potential to become a better gene-editing tool. Previous reports showed that CRISPR/Cpf1 could work effectively in multiple species. But our data show that AsCpf1 activity has a big difference in different vertebrates. Using in vitro experiments, we finally learned that the difference between species is due to temperature.

developmental biology↗

bioRxiv2018

The plasma miR-122 basal levels respond to circulating catecholamine in rats

miR-122 in circulation is a promising non-invasive biomarker as a replacement or supplement of current serum biomarkers for liver injuries. But the concept was questioned by recent studies, mainly due to its release from hepatocytes in absence of overt cellular injuries. In this study, we reported that the hepatic metabolism of circulating catecholamines resulted in the release of hepatocyte-specific miR-122. Acute stress-induced hepatocellular deformation was histopathologically different from drug-induced liver injury with significant increases of plasma miR-122 levels. The basal levels of human plasma miR-122 could be significantly altered by emotional responses. Interday variances of plasma miR-122 measurements were reduced effectively by stress-relief measures. The metabolism of basal circulating norepinephrine and epinephrine in liver might contribute to the basal levels of plasma miRNAs expressed in hepatocytes.

molecular biology↗

bioRxiv2018

High-throughput automatic training system for odor-based learned behaviors in head-fixed mice

Understanding neuronal mechanisms of cognitive behaviors requires efficient behavioral assays. We designed a high-throughput automatic training system (HATS) for olfactory behaviors in head-fixed mice. The hardware and software were constructed to enable automatic training with minimal human intervention. The integrated system was composed of customized 3D-printing supporting components, an odor-delivery unit with fast response, Arduino based hardware-controlling and data-acquisition unit. Furthermore, the customized software was designed to enable automatic training in all training phases, including lick-teaching, shaping, and learning. Using HATS, we trained mice to perform delayed non-match to sample (DNMS), delayed paired association (DPA), Go/No-go (GNG), and GNG reversal tasks. These tasks probed cognitive functions including sensory discrimination, working memory, decision making, and cognitive flexibility. Mice reached stable levels of performance within several days in the tasks. HATS enabled an experimenter to train eight mice simultaneously, therefore greatly enhanced the experimental efficiency. Combined with causal perturbation and activity recording techniques, HATS can greatly facilitate our understanding of the neural-circuitry mechanisms underlying cognitive behaviors.

neuroscience↗

bioRxiv2018

Oncogenic role of sFRP2 in P53-mutant osteosarcoma development via autocrine and paracrine mechanism

Osteosarcoma (OS), the most common primary bone tumor, is highly metastatic with high chemotherapeutic resistance and poor survival rates. Using induced pluripotent stem cells (iPSCs) generated from Li-Fraumeni syndrome (LFS) patients, we investigated an oncogenic role of secreted frizzled-related protein 2 (sFRP2) in P53 mutation-associated OS development. Interestingly, we found that high sFRP2 expression in OS patient samples correlates with poor survival. Systems-level analyses identified that expression of sFRP2 increases during LFS OS development and can induce angiogenesis. Ectopic sFRP2 overexpression in normal osteoblast precursors is sufficient to suppress normal osteoblast differentiation and to promote OS phenotypes through induction of oncogenic molecules such as FOXM1 and CYR61 in a {beta}-catenin independent manner. Conversely, inhibition of sFRP2, FOXM1 or CYR61 represses the tumorigenic potential. In summary, these findings demonstrate the oncogenic role of sFRP2 in P53 mutation-associated OS development and that inhibition of sFRP2 is a potential therapeutic strategy.

cancer 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

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC

The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. We have developed a user-friendly R/Bioconductor package, named GDCRNATools, to facilitate downloading, organizing, and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs (ceRNAs) regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis.\n\nAvailabilityGDCRNATools is an R/Bioconductor package that is freely available at https://github.com/Jialab-UCR/GDCRNATools

bioinformatics↗

bioRxiv2017

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features

The CCCTC-binding zinc finger protein (CTCF)-mediated network of long-range chromatin interactions is important for genome organization and function. Although this network has been considered largely invariant, we found that it exhibits extensive cell-type-specific interactions that contribute to cell identity. Here we present Lollipop--a machine-learning framework--which predicts CTCF-mediated long-range interactions using genomic and epigenomic features. Using ChIA-PET data as benchmark, we demonstrated that Lollipop accurately predicts CTCF-mediated chromatin interactions both within and across cell-types, and outperforms other methods based only on CTCF motif orientation. Predictions were confirmed computationally and experimentally by Chromatin Conformation Capture (3C). Moreover, our approach reveals novel determinants of CTCF-mediated chromatin wiring, such as gene expression within the loops. Our study contributes to a better understanding about the underlying principles of CTCF-mediated chromatin interactions and their impact on gene expression.

systems biology↗

bioRxiv2017

Disruption of the interfacial membrane leads to Magnaporthe oryzae effector re-location and lifestyle switch during rice blast disease

The hemibiotrophic fungus Magnaporthe oryzae produces invasive hyphae enclosed in a plant-derived interfacial membrane, known as the extra-invasive hyphal membrane (EIHM), in living rice cells. Little is known about when the EIHM is disrupted and how the disruption contributes to blast disease. Here we show that EIHM disruption correlates with the hyphal growth stage in first-invaded susceptible rice cells. Our approach utilized GFP secreted from invasive hyphae as an EIHM integrity reporter. Secreted-GFP accumulated in the EIHM compartment but appeared in the rice cytoplasm when the EIHM integrity was compromised. Live-cell imaging of secreted-GFP and various fluorescent reporters revealed that EIHM disruption led to rice vacuole rupture and cell death limited to the invaded cell with closed plasmodesmata. We report that EIHM disruption and host cell death are landmarks delineating three distinct infection phases (early biotrophic, late biotrophic, and transient necrotrophic phases) within the first-invaded cell before reestablishment of biotrophy in second-invaded cells. M. oryzae effectors exhibited phase-specific localizations, including entry of the apoplastic effector Bas4 into the rice cytoplasm during the late biotrophic phase. Understanding how the phase-specific dynamics are regulated and linked to host susceptibility will offer potential targets that can be exploited to control blast disease.

cell 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↗

bioRxiv2017

SIMLR: A Tool For Large-Scale Single-Cell Analysis By Multi-Kernel Learning

MotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.\n\nAvailability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on bioconductor.org.\n\nContactbowang87@stanford.edu or daniele.ramazzotti@stanford.edu\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

scRNASeqDB: a database for gene expression profiling in human single cell by RNA-seq

Summary: Single-cell RNA sequencing (scRNA-Seq) is quickly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. So far, there is no database that comprehensively collects and curates scRNA-Seq data in humans. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n= 36) covering 71 human cell lines or types and 8910 samples. Our online web interface allows user to query and visualize expression profiles of the gene(s) of interest, search for genes that are expressed in different cell types or groups, or retrieve differentially expressed genes between cell types or groups. The scRNASeqDB is a valuable resource for single cell transcriptional studies.\n\nAvailability: The database is available at https://bioinfo.uth.edu/scrnaseqdb/.\n\nContact: zhongming.zhao@uth.tmc.edu

bioinformatics↗

bioRxiv2017

An overexpressed Q allele leads to increased spike density and improved processing quality

Spike density and processing quality are important traits during the evolution of wheat, which is controlled by multiple gene loci. The associated gene loci have been heavily studied with slow progress. A common wheat mutant with extremely compact spikes and good processing quality was isolated. The gene (Qc1) responsible for the mutant phenotype was mapped and cloned, and the cellular mechanism for the mutant phenotype was investigated. Qc1 originated from a point mutation that interferes with the miR172-directed cleavage of the Q gene, leading to its overexpression. Qc1 reduces the longitudinal cell size of rachises, resulting in an increased spike density. Qc1 increases the number of vascular bundles, which suggests a higher efficiency in the transportation of assimilates in the spikes of the mutant than in the WT. This could account for the improved processing quality. The effects of Qc1 on spike density and wheat processing quality were confirmed by the identification of nine common wheat mutants having four different Qc alleles. These results deepen our understanding of the key role of Q gene, one of the most important domestication gene for wheat, and provide new insights for the potential application of Qc allele in wheat breeding.

genetics↗