Ying Xu

Sichuan University, Hua-yang, Sichuan, China

Are you Ying Xu?

Claim your profile

Publications (320)1471.29 Total impact

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Bacterial operons are considerably more complex than what were thought. At least their components are dynamically rather than statically defined as previously assumed. Here we present a computational study of the landscape of the transcriptional units (TUs) of E. coli K12, revealed by the available genomic and transcriptomic data, providing new understanding about the complexity of TUs as a whole encoded in the genome of E. coli K12. Results and conclusion Our main findings include that (i) different TUs may overlap with each other by sharing common genes, giving rise to clusters of overlapped TUs (TUCs) along the genomic sequence; (ii) the intergenic regions in front of the first gene of each TU tend to have more conserved sequence motifs than those of the other genes inside the TU, suggesting that TUs each have their own promoters; (iii) the terminators associated with the 3’ ends of TUCs tend to be Rho-independent terminators, substantially more often than terminators of TUs that end inside a TUC; and (iv) the functional relatedness of adjacent gene pairs in individual TUs is higher than those in TUCs, suggesting that individual TUs are more basic functional units than TUCs. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0805-8) contains supplementary material, which is available to authorized users.
    Full-text · Article · Nov 2015 · BMC Bioinformatics
  • [Show abstract] [Hide abstract]
    ABSTRACT: Jatropha curcas L. (further referred to as Jatropha), as a rapidly emerging biofuel crop, has attracted worldwide interest. However, Jatropha is still an undomesticated plant, the true potential of this shrub has not yet been fully realized. To explore the potential of Jatropha, breeding and domestication are needed. Seed size is one of the most important traits of seed yield and has been selected since the beginning of agriculture. Increasing the seed size is a main goal of Jatropha domestication for increasing the seed yield, but the genetic regulation of seed size in Jatropha has not been fully understood.
    No preview · Article · Nov 2015 · Electronic Journal of Biotechnology
  • [Show abstract] [Hide abstract]
    ABSTRACT: Sheep red blood cells (SRBCs) have long been used as a model antigen for eliciting systemic immune responses, yet the basis for their adjuvant activity has been unknown. Here, we show that SRBCs failed to engage the inhibitory mouse SIRPα receptor on splenic CD4(+) dendritic cells (DCs), and this failure led to DC activation. Removal of the SIRPα ligand, CD47, from self-RBCs was sufficient to convert them into an adjuvant for adaptive immune responses. DC capture of Cd47(-/-) RBCs and DC activation occurred within minutes in a Src-family-kinase- and CD18-integrin-dependent manner. These findings provide an explanation for the adjuvant mechanism of SRBCs and reveal that splenic DCs survey blood cells for missing self-CD47, a process that might contribute to detecting and mounting immune responses against pathogen-infected RBCs.
    No preview · Article · Oct 2015 · Immunity
  • Source
    Fang Yao · Chi Zhang · Wei Du · Chao Liu · Ying Xu
    [Show abstract] [Hide abstract]
    ABSTRACT: The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.
    Preview · Article · Sep 2015 · PLoS ONE
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Large numbers of plant cell-wall (CW)-related genes have been identified or predicted in several plant genomes such as Arabidopsis thaliana, Oryza sativa (rice), and Zea mays (maize), as results of intensive studies of these organisms in the past 2 decades. However, no such gene list has been identified in switchgrass (Panicum virgatum), a key bioenergy crop. Here, we present a computational study for prediction of CW genes in switchgrass using a two-step procedure: (i) homology mapping of all annotated CW genes in the fore-mentioned species to switchgrass, giving rise to a total of 991 genes, and (ii) candidate prediction of CW genes based on switchgrass genes co-expressed with the 991 genes under a large number of experimental conditions. Specifically, our co-expression analyses using the 991 genes as seeds led to the identification of 104 large clusters of co-expressed genes, each referred to as a co-expression module (CEM), covering 830 of the 991 genes plus 823 additional genes that are strongly co-expressed with some of the 104 CEMs. These 1653 genes represent our prediction of CW genes in switchgrass, 112 of which are homologous to predicted CW genes in Arabidopsis. Functional inference of these genes is conducted to derive the possible functional relations among these predicted CW genes. Overall, these data may offer a highly useful information source for cell-wall biologists of switchgrass as well as plants in general.
    Full-text · Article · Sep 2015 · BioEnergy Research
  • [Show abstract] [Hide abstract]
    ABSTRACT: Cysteine proteinase inhibitor (cystatin, CPI) is one of the most important molecules involved in plant development and defense, especially in the regulation of stress responses. However, it is still unclear whether the Jatropha curcas CPI (JcCPI) gene functions in salinity response and tolerance. In this study, the sequence of the JcCPI gene, its expression pattern, and the effects of overexpression in Escherichia coli and Nicotiana benthamiana were examined. The purpose of this study was to evaluate the regulatory role of JcCPI in salinity stress tolerance.
    No preview · Article · Sep 2015 · Electronic Journal of Biotechnology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Regulatory T cell (T reg cell) numbers and activities are tightly calibrated to maintain immune homeostasis, but the mechanisms involved are incompletely defined. Here, we report that the lysophosphatidylserine (LysoPS) receptor GPR174 is abundantly expressed in developing and mature T reg cells. In mice that lacked this X-linked gene, T reg cell generation in the thymus was intrinsically favored, and a higher fraction of peripheral T reg cells expressed CD103. LysoPS could act in vitro via GPR174 to suppress T cell proliferation and T reg cell generation. In vivo, LysoPS was detected in lymphoid organ and spinal cord tissues and was abundant in the colon. Gpr174(-/Y) mice were less susceptible to experimental autoimmune encephalomyelitis than wild-type mice, and GPR174 deficiency in T reg cells contributed to this phenotype. This study provides evidence that a bioactive lipid, LysoPS, negatively influences T reg cell accumulation and activity through GPR174. As such, GPR174 antagonists might have therapeutic potential for promoting immune regulation in the context of autoimmune disease. © 2015 Barnes et al.
    Full-text · Article · Jun 2015 · Journal of Experimental Medicine
  • Sha Cao · Chi Zhang · Ying Xu
    [Show abstract] [Hide abstract]
    ABSTRACT: Tomasetti and Vogelstein recently published two articles in Science proposing that random mutations arising during DNA replication in normal, noncancerous stem cells are key contributors to cancer, based on their observation that there is a strong and positive correlation between the total number of stem cell divisions and the lifetime cancer risk in a tissue. Our recent analyses of their and additional data revealed that there is a fundamental disconnection between their observation and their conclusion. In addition, our data suggest that (1) a combination of basal metabolic rate and oxidative stress level in a tissue offers a more plausible explanation of the lifetime risk of cancers than their model; and (2) somatic mutations may be predominantly selected to serve as facilitators rather than primary drivers of cancer formation. This article is protected by copyright. All rights reserved. © 2015 UICC.
    No preview · Article · Jun 2015 · International Journal of Cancer
  • Source
    Chi Zhang · Chao Liu · Sha Cao · Ying Xu
    [Show abstract] [Hide abstract]
    ABSTRACT: Lactates play key roles in facilitating or protecting the development of a cancer in most cancer types. While its beneficial effects to cancer development have been extensively studied, very little is known about what derives the high-level production of lactates in a cancer throughout its entire development. Here we present a novel computational analysis of transcriptomic data of nine primary cancer types, plus a few precancerous and metastatic cancer, to address this issue. Our approach is to identify stress types, which are known to play key roles in cancer development and show strong co-expressions with lactate dehydrogenase-A (LDHA), at different stages of cancer development. A number of interesting observations are made through our analyses, including (i) all nine primary cancer types show similar association patterns between stresses and LDHA, namely the strengths of the associations increase from early- to intermediate-stage cancer tissues but then make a substantial down turn at the most advanced stage; (ii) while the detailed stress types associated with LDHA may vary across different cancer types, stresses induced by apoptosis and adaptive immune responses are present universally, suggesting that these two stresses are possibly two key drivers to keep the high-level production of lactates; and (iii) there is a clear distinction between stress types associated with LDHA in precancerous tissues vs. cancer and metastasis tissues. We anticipate that the analyses can provide highly useful information for designing personalized treatments for different cancers at different stages, as stopping lactate production could have devastating effects on a cancer development. © The Author (2015). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. All rights reserved.
    Full-text · Article · May 2015 · Journal of Molecular Cell Biology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/. We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
    Full-text · Article · Feb 2015 · Nucleic Acids Research
  • Source
    Lei Wei · Wei Zhang · Li Yin · Fang Yan · Ying Xu · Fang Chen
    [Show abstract] [Hide abstract]
    ABSTRACT: Triterpenoids are multifunctional secondary metabolites in plants. But little information is available concerning the actual yield, optimal extraction method and pharmacologic activity with regard to triterpenoids from Jatropha curcas leaves (TJL). Hence, response surface methodology (RSM) was used to optimize the extraction parameters. The effects of three independent variables, namely liquid-to-solid ratio, ethanol concentration and extraction time on TJL yield were investigated. TJL obtained by silica column chromatography was tested against bacterial and fungal species relevant to oral disease and wounds through broth microdilution. Antioxidant activity was assessed using the 2,2-diphenyl-2-picrylhydrazyl and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) assays.
    Preview · Article · Jan 2015 · Electronic Journal of Biotechnology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Gastric cancer is one of the most prevalent and aggressive cancers worldwide, and its molecular mechanism remains largely elusive. Here we report the genomic landscape in primary gastric adenocarcinoma of human, based on the complete genome sequences of five pairs of cancer and matching normal samples. In total, 103,464 somatic point mutations, including 407 non-synonymous ones, were identified and the most recurrent mutations were harbored by Mucins (MUC3A and MUC12) and transcription factors (ZNF717, ZNF595 and TP53). 679 genomic rearrangements were detected, which affect 355 protein-coding genes; and 76 genes show copy number changes. Through mapping the boundaries of the rearranged regions to the folded three-dimensional structure of human chromosomes, we determined that 79.6% of the chromosomal rearrangements happen among DNA fragments in close spatial proximity, especially when two endpoints stay in a similar replication phase. We demonstrated evidences that microhomology-mediated break induced replication was utilized as a mechanism in inducing ~40.9% of the identified genomic changes in gastric tumor. Our data analyses revealed potential integrations of Helicobacter pylori DNA into the gastric cancer genomes. Overall a large set of novel genomic variations were detected in these gastric cancer genomes, which may be essential to the study of the genetic basis and molecular mechanism of the gastric tumorigenesis. This article is protected by copyright. All rights reserved.
    Full-text · Article · Dec 2014 · International Journal of Cancer
  • Source
    Lei Wei · Li Yin · Xiaole Hu · Ying Xu · Fang Chen
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Jatropha curcas is a rich reservoir of pharmaceutically active terpenoids. More than 25 terpenoids have been isolated from this plant, and their activities are anti-bacterial, anti-fungal, anti-cancer, insecticidal, rodenticidal, cytotoxic and molluscicidal. But not much is known about the pathway involved in the biosynthesis of terpenoids. The present investigation describes the cloning, characterization and subcellular localization of isopentenyl diphosphate isomerase (IPI) gene from J. curcas. IPI is one of the rate limiting enzymes in the biosynthesis of terpenoids, catalyzing the crucial interconversion of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Results A full-length JcIPI cDNA consisting of 1355 bp was cloned. It encoded a protein of 305 amino acids. Analysis of deduced amino acid sequence predicted the presence of conserved active sites, metal binding sites and the NUDIX motif, which were consistent with other IPIs. Phylogenetic analysis indicated a significant evolutionary relatedness with Ricinus communis. Southern blot analysis showed the presence of an IPI multigene family in J. curcas. Comparative expression analysis of tissue specific JcIPI demonstrated the highest transcript level in flowers. Abiotic factors could induce the expression of JcIPI. Subcellular distribution showed that JcIPI was localized in chloroplasts. Conclusion This is the first report of cloning and characterization of IPI from J. curcas. Our study will be of significant interest to understanding the regulatory role of IPI in the biosynthesis of terpenoids, although its function still needs further confirmation.
    Preview · Article · Nov 2014 · Electronic Journal of Biotechnology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Increased flux through the hexosamine biosynthetic pathway and the corresponding increase in intracellular glycosylation of proteins via O-linked β-N-acetylglucosamine (O-GlcNAc) is sufficient to induce insulin resistance (IR) in multiple systems. Previously, our group used shotgun proteomics to identify multiple rodent adipocytokines and secreted proteins whose levels are modulated upon the induction of IR by indirectly and directly modulating O-GlcNAc levels. We have validated the relative levels of several of these factors using immunoblotting. Since adipocytokines levels are regulated primarily at the level of transcription and O-GlcNAc alters the function of many transcription factors, we hypothesized that elevated O-GlcNAc levels on key transcription factors are modulating secreted protein expression. Here, we show that upon the elevation of O-GlcNAc levels and the induction of IR in mature 3T3-F442a adipocytes, the transcript levels of multiple secreted proteins reflect the modulation observed at the protein level. We validate the transcript levels in male mouse models of diabetes. Using inguinal fat pads from the severely IR db/db mouse model and the mildly IR diet-induced mouse model, we have confirmed that the secreted proteins regulated by O-GlcNAc modulation in cell culture are likewise modulated in the whole animal upon a shift to IR. By comparing the promoters of similarly regulated genes, we determine that Sp1 is a common cis-acting element. Furthermore, we show that the LPL and SPARC promoters are enriched for Sp1 and O-GlcNAc modified proteins during insulin resistance in adipocytes. Thus, the O-GlcNAc modification of proteins bound to promoters, including Sp1, is linked to adipocytokine transcription during insulin resistance.
    Preview · Article · Nov 2014 · Frontiers in Endocrinology
  • Source
    Chi Zhang · Sha Cao · Ying Xu
    [Show abstract] [Hide abstract]
    ABSTRACT: A computational analysis of genome-scale transcriptomic data collected on ∼1,700 tissue samples of three cancer types: breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consists of (at least) two major subpopulations of cancer cells with different capabilities to handle fluctuating O2 levels. The two populations have distinct genomic and transcriptomic characteristics, one accelerating its proliferation under hypoxic conditions and the other proliferating faster with higher O2 levels, referred to as the hypoxia and the reoxygenation subpopulations, respectively. The proportions of the two subpopulations within a cancer tissue change as the average O2 level changes. They both contribute to cancer development but in a complementary manner. The hypoxia subpopulation tends to have higher proliferation rates than the reoxygenation one as well as higher apoptosis rates; and it is largely responsible for the acidic environment that enables tissue invasion and provides protection against attacks from T-cells. In comparison, the reoxygenation subpopulation generates new extracellular matrices in support of further growth of the tumor and strengthens cell-cell adhesion to provide scaffolds to keep all the cells connected. This subpopulation also serves as the major source of growth factors for tissue growth. These data and observations strongly suggest that these two major subpopulations within each tumor work together in a conjugative relationship to allow the tumor to overcome stresses associated with the constantly changing O2 level due to repeated growth and angiogenesis. The analysis results not only reveal new insights about the population dynamics within a tumor but also have implications to our understanding of possible causes of different cancer phenotypes such as diffused versus more tightly connected tumor tissues.
    Full-text · Article · Oct 2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Pancreatic cancer is the deadliest of all cancers with worst outcome and poor survival rate. Chemotherapy with gemcitabine works well for early stage cancer, but becomes ineffective for advanced-stage cancer. As such, there is a dire need for new approaches to treat this cancer. The metabolism of tumor cells is very different from that of normal cells. In particular, the differences in amino acid metabolism are gaining increasing attention in cancer biology. Selective amino acid transporters are upregulated in cancer in response to the increased demands for amino acids in tumor cells. Such tumor-selective amino acid transporters are logical druggable targets for cancer therapy. As such, pharmacologic blockade of such upregulated transporters would lead to cell death selectively in tumor cells by depriving the tumor cells of essential nutrients. With this in mind, we analyzed 8 different publically available microarray datasets in Gene Expression Omnibus for the amino acid transporters that are upregulated
    No preview · Article · Oct 2014 · Cancer Research
  • Juan Cui · Ying Xu

    No preview · Article · Oct 2014 · Cancer Research
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges.
    Full-text · Article · Sep 2014 · PLoS ONE
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The availability of a large number of sequenced bacterial genomes facilitates in-depth studies about why genes (operons) in a bacterial genome are globally organized the way they are. We have previously discovered that (the relative) transcription- activation frequencies among different biological pathways encoded in a genome have a dominating role in the global arrangement of operons. One complicating factor in such a study is that some operons may be involved in multiple pathways with different activation frequencies. A quantitative model has been developed that captures this information, which tends to be minimized by the current global arrangement of operons in a bacterial (and archaeal) genome compared to possible alternative arrangements. A study is carried out here using this model on a collection of 52 closely related E. coli genomes, which revealed interesting new insights about how bacterial genomes evolve to optimally adapt to their environments through adjusting the (relative) genomic locations of the encoding operons of biological pathways once their utilization and hence transcription activation frequencies change, to maintain the above energy-efficiency property. More specifically we observed that it is the frequencies of the transcription activation of pathways relative to those of the other encoded pathways in an organism as well as the variation in the activation frequencies of a specific pathway across the related genomes that play a key role in the observed commonalities and differences in the genomic organizations of genes (and operons) encoding specific pathways across different genomes.
    Full-text · Article · Sep 2014 · Science China. Life sciences

  • No preview · Article · Sep 2014 · Nature

Publication Stats

11k Citations
1,471.29 Total Impact Points

Institutions

  • 2012-2015
    • Sichuan University
      • • Sichuan Key Laboratory of Resource Biology and Biopharmaceutical Engineering
      • • College of Life Sciences
      Hua-yang, Sichuan, China
    • Nankai University
      • College of Information Technical Science
      T’ien-ching-shih, Tianjin Shi, China
  • 2009-2015
    • Jilin University
      • College of Computer Science & Technology
      Yung-chi, Jilin Sheng, China
  • 2002-2015
    • University of California, San Francisco
      • Department of Microbiology and Immunology
      San Francisco, California, United States
    • Sandia National Laboratories
      Albuquerque, New Mexico, United States
    • University of Alberta
      • Department of Computing Science
      Edmonton, Alberta, Canada
  • 2004-2014
    • University of Georgia
      • • Department of Biochemistry and Molecular Biology
      • • Department of Psychology
      Атина, Georgia, United States
    • University of California, Riverside
      • Department of Computer Science and Engineering
      Riverside, CA, United States
  • 1995-2013
    • Oak Ridge National Laboratory
      • • Computer Science and Mathematics Division
      • • Life Sciences Division
      Oak Ridge, Florida, United States
  • 2003-2012
    • The University of Tennessee Medical Center at Knoxville
      Knoxville, Tennessee, United States
    • University of Missouri
      • Department of Computer Science and IT
      Columbia, Missouri, United States
  • 2004-2011
    • Howard Hughes Medical Institute
      Ashburn, Virginia, United States
  • 2010
    • Nanjing University of Aeronautics & Astronautics
      • Department of Biomedical Engineering
      Nan-ching, Jiangsu Sheng, China
  • 2006-2009
    • Shandong University
      • • Department of Pure Mathematics
      • • Department of Applied Mathematics
      Chi-nan-shih, Shandong Sheng, China
    • The American Society for Biochemistry and Molecular Biology
      Атина, Georgia, United States
  • 2002-2005
    • Tokyo Denki University
      • Division of Mathematical Sciences
      Edo, Tōkyō, Japan
  • 1989-1996
    • University of Colorado at Boulder
      • Department of Computer Science (CS)
      Boulder, Colorado, United States