Biology subjects

Zhao, X.

Publications and source records attributed to Zhao, X..

583 records · Page 33Linked to original sources

bioRxiv2017

A plant receptor-like kinase promotes cell-to-cell spread of RNAi and is targeted by a virus

RNA interference (RNAi) in plants can move from cell to cell, allowing for systemic spread of an anti-viral immune response. How this cell-to-cell spread of silencing is regulated is currently unknown. Here, we describe that the C4 protein from Tomato yellow leaf curl virus has the ability to inhibit the intercellular spread of RNAi. Using this viral protein as a probe, we have identified the receptor-like kinase (RLK) BARELY ANY MERISTEM 1 (BAM1) as a positive regulator of the cell-to-cell movement of RNAi, and determined that BAM1 and its closest homologue, BAM2, play a redundant role in this process. C4 interacts with the intracellular domain of BAM1 and BAM2 at the plasma membrane and plasmodesmata, the cytoplasmic connections between plant cells, interfering with the function of these RLKs in the cell-to-cell spread of RNAi. Our results identify BAM1 as an element required for the cell-to-cell spread of RNAi and highlight that signalling components have been co-opted to play multiple functions in plants.

plant biology↗

bioRxiv2017

A quantitative chemotherapy genetic interaction map reveals new factors associated with PARP inhibitor resistance

Nearly every cancer patient is treated with chemotherapy yet our understanding of factors that dictate response and resistance to such agents remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells that charts the impact of knockdown of 625 cancer and DNA repair related genes on sensitivity to 29 drugs, covering all classes of cancer chemotherapeutics. This quantitative map is predictive of interactions maintained in cancer cell lines and can be used to identify new cancer-associated DNA repair factors, predict cancer cell line responses to therapy and prioritize drug combinations. We identify that GPBP1 loss in breast and ovarian cancer confers resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map may help navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.

cancer biology↗

bioRxiv2017

Nucleosomes and DNA methylation shape meiotic DSB frequency in Arabidopsis transposons and gene regulatory regions

Meiotic recombination initiates via DNA double strand breaks (DSBs) generated by SPO11 topoisomerase-like complexes. Recombination frequency varies extensively along eukaryotic chromosomes, with hotspots controlled by chromatin and DNA sequence. To map meiotic DSBs throughout a plant genome, we purified and sequenced Arabidopsis SPO11-1-oligonucleotides. DSB hotspots occurred in gene promoters, terminators and introns, driven by AT-sequence richness, which excludes nucleosomes and allows SPO11-1 access. A strong positive relationship was observed between SPO11-1 DSBs and final crossover levels. Euchromatic marks promote recombination in fungi and mammals, and consistently we observe H3K4me3 enrichment in proximity to DSB hotspots at gene 5-ends. Repetitive transposons are thought to be recombination-silenced during meiosis, in order to prevent non-allelic interactions and genome instability. Unexpectedly, we found strong DSB hotspots in nucleosome-depleted Helitron/Pogo/Tc1/Mariner DNA transposons, whereas retrotransposons were coldspots. Hotspot transposons are enriched within gene regulatory regions and in proximity to immunity genes, suggesting a role as recombination-enhancers. As transposon mobility in plant genomes is restricted by DNA methylation, we used the met1 DNA methyltransferase mutant to investigate the role of heterochromatin on the DSB landscape. Epigenetic activation of transposon meiotic DSBs occurred in met1 mutants, coincident with reduced nucleosome occupancy, gain of transcription and H3K4me3. Increased met1 SPO11-1 DSBs occurred most strongly within centromeres and Gypsy and CACTA/EnSpm coldspot transposons. Together, our work reveals complex interactions between chromatin and meiotic DSBs within genes and transposons, with significance for the diversity and evolution of plant genomes.

genomics↗

bioRxiv2017

Epigenetic activation of meiotic recombination in Arabidopsis centromeres via loss of H3K9me2 and non-CG DNA methylation

Eukaryotic centromeres contain the kinetochore, which connects chromosomes to the spindle allowing segregation. During meiosis centromeres are suppressed for crossovers, as recombination in these regions can cause chromosome mis-segregation. Plant centromeres are surrounded by repetitive, transposon-dense heterochromatin that is epigenetically silenced by histone 3 lysine 9 dimethylation (H3K9me2), and DNA methylation in CG and non-CG sequence contexts. Here we show that disruption of Arabidopsis H3K9me2 and non-CG DNA methylation pathways increases meiotic DNA double strand breaks (DSBs) within centromeres, whereas crossovers increase within pericentromeric heterochromatin. Increased pericentromeric crossovers in H3K9me2/non-CG mutants occurs in both inbred and hybrid backgrounds, and involves the interfering crossover repair pathway. Epigenetic activation of recombination may also account for the curious tendency of maize transposon Ds to disrupt CHROMOMETHYLASE3 when launched from proximal loci. Thus H3K9me2 and non-CG DNA methylation exert differential control of meiotic DSB and crossover formation in centromeric and pericentromeric heterochromatin.

genomics↗

bioRxiv2017

Isolation And Characterization Of Key Genes That Promote Flavonoid Accumulation In Purple-Leaf Tea (Camellia sinensis L.)

There were several high concentrations of flavonoid components in tea leaves that present health benefits. A novel purple-leaf tea variety, Mooma1, was obtained from the natural hybrid population of Longjing 43 variety. The buds and young leaves of Mooma1 were displayed in bright red. HPLC and LC-MS analysis showed that anthocyanins and O-Glycosylated flavonols were remarkably accumulated in the leaves of Mooma1, while the total amount of catechins in purple-leaf leaves was slightly decreased compared with the control. A R2R3-MYB transcription factor (CsMYB6A) and a novel UGT gene (CsUGT72AM1), that were highly expressed in purple leaf were isolated and identified by transcriptome sequencing. The over-expression of transgenic tobacco confirmed that CsMYB6A can activate the expression of flavonoid-related structural genes, especially CHS and 3GT, controlling the accumulation of anthocyanins in the leaf of transgenic tobacco. Enzymatic assays in vitro confirmed that CsUGT72AM1 has catalytic activity as a flavonol 3-O-glucosyltransferase, and displayed broad substrate specificity. The results were useful for further elucidating the molecular mechanisms of the flavonoid metabolic fluxes in the tea plant.

plant biology↗

bioRxiv2017

VaPoR: a high-speed validation approach for structural variation using long-read sequencing technology.

AbstractSummaryAlthough there are numerous algorithms that have been developed to identify structural variation (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as manual inspection of each region. Here, we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long read sequencing data. We assess of the performance of VaPoR on both simulated and real SVs with regards to various features including accuracy and sensitivity of breakpoint evaluation and report a high fidelity rate.\n\nAvailabilityhttps://github.com/mills-lab/VaPoR\n\nContactremills@umich.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. Systematic integration of these heterogeneous data not only serves as a promising tool for identifying new drug-target interactions (DTIs), which is an important step in drug development, but also provides a more complete understanding of the molecular mechanisms of drug action. In this work, we integrate diverse drug-related information, including drugs, proteins, diseases and side-effects, together with their interactions, associations or similarities, to construct a heterogeneous network with 12,015 nodes and 1,895,445 edges. We then develop a new computational pipeline, called DTINet, to predict novel drug-target interactions from the constructed heterogeneous network. Specifically, DTINet focuses on learning a low-dimensional vector representation of features for each node, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for DTI prediction. Moreover, we have experimentally validated the novel interactions between three drugs and the cyclooxygenase (COX) protein family predicted by DTINet, and demonstrated the new potential applications of these identified COX inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs. The source code of DTINet and the input heterogeneous network data can be downloaded from http://github.com/luoyunan/DTINet.

bioinformatics↗