The role of the Arabidopsis FUSCA3 transcription factor during inhibition of seed germination at high temperature

Abstract Background Imbibed seeds integrate environmental and endogenous signals to break dormancy and initiate growth under optimal conditions. Seed maturation plays an important role in determining the survival of germinating seeds, for example one of the roles of dormancy is to stagger germination to prevent mass growth under suboptimal conditions. The B3-domain transcription factor FUSCA3 (FUS3) is a master regulator of seed development and an important node in hormonal interaction networks in Arabidopsis thaliana. Its function has been mainly characterized during embryonic development, where FUS3 is highly expressed to promote seed maturation and dormancy by regulating ABA/GA levels. Results In this study, we present evidence for a role of FUS3 in delaying seed germination at supraoptimal temperatures that would be lethal for the developing seedlings. During seed imbibition at supraoptimal temperature, the FUS3 promoter is reactivated and induces de novo synthesis of FUS3 mRNA, followed by FUS3 protein accumulation. Genetic analysis shows that FUS3 contributes to the delay of seed germination at high temperature. Unlike WT, seeds overexpressing FUS3 (ML1:FUS3-GFP) during imbibition are hypersensitive to high temperature and do not germinate, however, they can fully germinate after recovery at control temperature reaching 90% seedling survival. ML1:FUS3-GFP hypersensitivity to high temperature can be partly recovered in the presence of fluridone, an inhibitor of ABA biosynthesis, suggesting this hypersensitivity is due in part to higher ABA level in this mutant. Transcriptomic analysis shows that WT seeds imbibed at supraoptimal temperature activate seed-specific genes and ABA biosynthetic and signaling genes, while inhibiting genes that promote germination and growth, such as GA biosynthetic and signaling genes. Conclusion In this study, we have uncovered a novel function for the master regulator of seed maturation, FUS3, in delaying germination at supraoptimal temperature. Physiologically, this is important since delaying germination has a protective role at high temperature. Transcriptomic analysis of seeds imbibed at supraoptimal temperature reveal that a complex program is in place, which involves not only the regulation of heat and dehydration response genes to adjust cellular functions, but also the activation of seed-specific programs and the inhibition of germination-promoting programs to delay germination

Quantitative Interactor Screening with next-generation Sequencing (QIS-Seq) identifies Arabidopsis thaliana MLO2 as a target of the Pseudomonas syringae type III effector HopZ2

<p>Abstract</p> <p>Background</p> <p>Identification of protein-protein interactions is a fundamental aspect of understanding protein function. A commonly used method for identifying protein interactions is the yeast two-hybrid system.</p> <p>Results</p> <p>Here we describe the application of next-generation sequencing to yeast two-hybrid interaction screens and develop Quantitative Interactor Screen Sequencing (QIS-Seq). QIS-Seq provides a quantitative measurement of enrichment for each interactor relative to its frequency in the library as well as its general stickiness (non-specific binding). The QIS-Seq approach is scalable and can be used with any yeast two-hybrid screen and with any next-generation sequencing platform. The quantitative nature of QIS-Seq data make it amenable to statistical evaluation, and importantly, facilitates the standardization of experimental design, data collection, and data analysis. We applied QIS-Seq to identify the <it>Arabidopsis thaliana </it>MLO2 protein as a target of the <it>Pseudomonas syringae </it>type III secreted effector protein HopZ2. We validate the interaction between HopZ2 and MLO2 <it>in planta </it>and show that the interaction is required for HopZ2-associated virulence.</p> <p>Conclusions</p> <p>We demonstrate that QIS-Seq is a high-throughput quantitative interactor screen and validate MLO2 as an interactor and novel virulence target of the <it>P. syringae </it>type III secreted effector HopZ2.</p

An “Electronic Fluorescent Pictograph” Browser for Exploring and Analyzing Large-Scale Biological Data Sets

Background. The exploration of microarray data and data from other high-throughput projects for hypothesis generation has become a vital aspect of post-genomic research. For the non-bioinformatics specialist, however, many of the currently available tools provide overwhelming amounts of data that are presented in a non-intuitive way. Methodology/Principal Findings. In order to facilitate the interpretation and analysis of microarray data and data from other large-scale data sets, we have developed a tool, which we have dubbed the electronic Fluorescent Pictograph – or eFP – Browser, available a

ePlant and the 3D Data Display Initiative: Integrative Systems Biology on the World Wide Web

Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed “ePlant” (http://bar.utoronto.ca/eplant) – a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the “3D Data Display Initiative” (http://3ddi.org)

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Abstract: In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here,as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Expansion and Diversification of the Populus R2R3-MYB Family of Transcription Factors1[W][OA]

The R2R3-MYB proteins comprise one of the largest families of transcription factors in plants. R2R3-MYB family members regulate plant-specific processes, such as the elaboration of specialized cell types, including xylem, guard cells, trichomes, and root hairs, and the biosynthesis of specialized branches of metabolism, including phenylpropanoid biosynthesis. As such, R2R3-MYB family members are hypothesized to contribute to the emergence of evolutionary innovations that have arisen in specific plant lineages. As a first step in determining the role played by R2R3-MYB family members in the emergence of lineage-specific innovations in the genus Populus, the entire Populus trichocarpa R2R3-MYB family was characterized. The Populus R2R3-MYB complement is much larger than that found in other angiosperms with fully sequenced genomes. Phylogenetic analyses, together with chromosome placement, showed that the expansion of the Populus R2R3-MYB family was not only attributable to whole genome duplication but also involved selective expansion of specific R2R3-MYB clades. Expansion of the Populus R2R3-MYB family prominently involved members with expression patterns that suggested a role in specific components of Populus life history, including wood formation and reproductive development. An expandable compendium of microarray-based expression data (PopGenExpress) and associated Web-based tools were developed to better enable within- and between-species comparisons of Populus R2R3-MYB gene expression. This resource, which includes intuitive graphic visualization of gene expression data across multiple tissues, organs, and treatments, is freely available to, and expandable by, scientists wishing to better understand the genome biology of Populus, an ecologically dominant and economically important forest tree genus