Genome-Wide Identification of HrpL-Regulated Genes in the Necrotrophic Phytopathogen Dickeya dadantii 3937

BACKGROUND: Dickeya dadantii is a necrotrophic pathogen causing disease in many plants. Previous studies have demonstrated that the type III secretion system (T3SS) of D. dadantii is required for full virulence. HrpL is an alternative sigma factor that binds to the hrp box promoter sequence of T3SS genes to up-regulate their expression. METHODOLOGY/PRINCIPAL FINDINGS: To explore the inventory of HrpL-regulated genes of D. dadantii 3937 (3937), transcriptome profiles of wild-type 3937 and a hrpL mutant grown in a T3SS-inducing medium were examined. Using a cut-off value of 1.5, significant differential expression was observed in sixty-three genes, which are involved in various cellular functions such as type III secretion, chemotaxis, metabolism, regulation, and stress response. A hidden Markov model (HMM) was used to predict candidate hrp box binding sites in the intergenic regions of 3937, including the promoter regions of HrpL-regulated genes identified in the microarray assay. In contrast to biotrophic phytopathgens such as Pseudomonas syringae, among the HrpL up-regulated genes in 3937 only those within the T3SS were found to contain a hrp box sequence. Moreover, direct binding of purified HrpL protein to the hrp box was demonstrated for hrp box-containing DNA fragments of hrpA and hrpN using the electrophoretic mobility shift assay (EMSA). In this study, a putative T3SS effector DspA/E was also identified as a HrpL-upregulated gene, and shown to be translocated into plant cells in a T3SS-dependent manner. CONCLUSION/SIGNIFICANCES: We provide the genome-wide study of HrpL-regulated genes in a necrotrophic phytopathogen (D. dadantii 3937) through a combination of transcriptomics and bioinformatics, which led to identification of several effectors. Our study indicates the extent of differences for T3SS effector protein inventory requirements between necrotrophic and biotrophic pathogens, and may allow the development of different strategies for disease control for these different groups of pathogens

The effector T cell response to influenza infection

Influenza virus infection induces a potent initial innate immune response, which serves to limit the extent of viral replication and virus spread. However, efficient (and eventual) viral clearance within the respiratory tract requires the subsequent activation, rapid proliferation, recruitment, and expression of effector activities by the adaptive immune system, consisting of antibody producing B cells and influenza-specific T lymphocytes with diverse functions. The ensuing effector activities of these T lymphocytes ultimately determine (along with antibodies) the capacity of the host to eliminate the viruses and the extent of tissue damage. In this review, we describe this effector T cell response to influenza virus infection. Based on information largely obtained in experimental settings (i.e., murine models), we will illustrate the factors regulating the induction of adaptive immune T cell responses to influenza, the effector activities displayed by these activated T cells, the mechanisms underlying the expression of these effector mechanisms, and the control of the activation/differentiation of these T cells, in situ, in the infected lungs

Thousands of Rab GTPases for the Cell Biologist

Rab proteins are small GTPases that act as essential regulators of vesicular trafficking. 44 subfamilies are known in humans, performing specific sets of functions at distinct subcellular localisations and tissues. Rab function is conserved even amongst distant orthologs. Hence, the annotation of Rabs yields functional predictions about the cell biology of trafficking. So far, annotating Rabs has been a laborious manual task not feasible for current and future genomic output of deep sequencing technologies. We developed, validated and benchmarked the Rabifier, an automated bioinformatic pipeline for the identification and classification of Rabs, which achieves up to 90% classification accuracy. We cataloged roughly 8.000 Rabs from 247 genomes covering the entire eukaryotic tree. The full Rab database and a web tool implementing the pipeline are publicly available at www.RabDB.org. For the first time, we describe and analyse the evolution of Rabs in a dataset covering the whole eukaryotic phylogeny. We found a highly dynamic family undergoing frequent taxon-specific expansions and losses. We dated the origin of human subfamilies using phylogenetic profiling, which enlarged the Rab repertoire of the Last Eukaryotic Common Ancestor with Rab14, 32 and RabL4. Furthermore, a detailed analysis of the Choanoflagellate Monosiga brevicollis Rab family pinpointed the changes that accompanied the emergence of Metazoan multicellularity, mainly an important expansion and specialisation of the secretory pathway. Lastly, we experimentally establish tissue specificity in expression of mouse Rabs and show that neo-functionalisation best explains the emergence of new human Rab subfamilies. With the Rabifier and RabDB, we provide tools that easily allows non-bioinformaticians to integrate thousands of Rabs in their analyses. RabDB is designed to enable the cell biology community to keep pace with the increasing number of fully-sequenced genomes and change the scale at which we perform comparative analysis in cell biology

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits