TY - GEN AB - All polyN tracts of length 5 or more nucleotides in sequences of genes from OG1. Sequences were extracted and scanned prior to automatic correction for frameshifts implemented in the RAST pipeline. (CSV 133 kb) AU - Sigalova, Olga M. AU - Chaplin, Andrei V. AU - Bochkareva, Olga AU - Shelyakin, Pavel V. AU - Filaretov, Vsevolod A. AU - Akkuratov, Evgeny E. AU - Burskaia, Valentina AU - Gelfand, Mikhail S. ID - 9898 TI - Additional file 21 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction ER - TY - GEN AB - Clusters of Orthologous Genes (COGs) and corresponding functional categories assigned to OGs. (CSV 117 kb) AU - Sigalova, Olga M. AU - Chaplin, Andrei V. AU - Bochkareva, Olga AU - Shelyakin, Pavel V. AU - Filaretov, Vsevolod A. AU - Akkuratov, Evgeny E. AU - Burskaia, Valentina AU - Gelfand, Mikhail S. ID - 9901 TI - Additional file 9 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction ER - TY - GEN AB - Summary of orthologous groups (OGs) for 227 genomes of genus Chlamydia. (CSV 362 kb) AU - Sigalova, Olga M. AU - Chaplin, Andrei V. AU - Bochkareva, Olga AU - Shelyakin, Pavel V. AU - Filaretov, Vsevolod A. AU - Akkuratov, Evgeny E. AU - Burskaia, Valentina AU - Gelfand, Mikhail S. ID - 9899 TI - Additional file 2 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction ER - TY - GEN AB - Pan-genome statistics by species. (CSV 3 kb) AU - Sigalova, Olga M. AU - Chaplin, Andrei V. AU - Bochkareva, Olga AU - Shelyakin, Pavel V. AU - Filaretov, Vsevolod A. AU - Akkuratov, Evgeny E. AU - Burskaia, Valentina AU - Gelfand, Mikhail S. ID - 9900 TI - Additional file 5 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction ER - TY - JOUR AB - A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non‐manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent‐based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta‐diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche‐based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral‐based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them. AU - Ovaskainen, Otso AU - Rybicki, Joel AU - Abrego, Nerea ID - 6936 IS - 11 JF - Ecography SN - 0906-7590 TI - What can observational data reveal about metacommunity processes? VL - 42 ER - TY - JOUR AB - Gene Drives are regarded as future tools with a high potential for population control. Due to their inherent ability to overcome the rules of Mendelian inheritance, gene drives (GD) may spread genes rapidly through populations of sexually reproducing organisms. A release of organisms carrying a GD would constitute a paradigm shift in the handling of genetically modified organisms because gene drive organisms (GDO) are designed to drive their transgenes into wild populations and thereby increase the number of GDOs. The rapid development in this field and its focus on wild populations demand a prospective risk assessment with a focus on exposure related aspects. Presently, it is unclear how adequate risk management could be guaranteed to limit the spread of GDs in time and space, in order to avoid potential adverse effects in socio‐ecological systems. The recent workshop on the “Evaluation of Spatial and Temporal Control of Gene Drives” hosted by the Institute of Safety/Security and Risk Sciences (ISR) in Vienna aimed at gaining some insight into the potential population dynamic behavior of GDs and appropriate measures of control. Scientists from France, Germany, England, and the USA discussed both topics in this meeting on April 4–5, 2019. This article summarizes results of the workshop. AU - Giese, B AU - Friess, J L AU - Schetelig, M F AU - Barton, Nicholas H AU - Messer, Philip AU - Debarre, Florence AU - Meimberg, H AU - Windbichler, N AU - Boete, C ID - 6857 IS - 11 JF - BioEssays TI - Gene Drives: Dynamics and regulatory matters – A report from the workshop “Evaluation of spatial and temporal control of Gene Drives”, 4 – 5 April 2019, Vienna VL - 41 ER - TY - CHAP AB - Describing the protein interactions that form pleomorphic and asymmetric viruses represents a considerable challenge to most structural biology techniques, including X-ray crystallography and single particle cryo-electron microscopy. Obtaining a detailed understanding of these interactions is nevertheless important, considering the number of relevant human pathogens that do not follow strict icosahedral or helical symmetry. Cryo-electron tomography and subtomogram averaging methods provide structural insights into complex biological environments and are well suited to go beyond structures of perfectly symmetric viruses. This chapter discusses recent developments showing that cryo-ET and subtomogram averaging can provide high-resolution insights into hitherto unknown structural features of pleomorphic and asymmetric virus particles. It also describes how these methods have significantly added to our understanding of retrovirus capsid assemblies in immature and mature viruses. Additional examples of irregular viruses and their associated proteins, whose structures have been studied via cryo-ET and subtomogram averaging, further support the versatility of these methods. AU - Obr, Martin AU - Schur, Florian KM ED - Rey, Félix A. ID - 6890 SN - 0065-3527 T2 - Complementary Strategies to Study Virus Structure and Function TI - Structural analysis of pleomorphic and asymmetric viruses using cryo-electron tomography and subtomogram averaging VL - 105 ER - TY - JOUR AB - We study the effect of a linear tunneling coupling between two-dimensional systems, each separately exhibiting the topological Berezinskii-Kosterlitz-Thouless (BKT) transition. In the uncoupled limit, there are two phases: one where the one-body correlation functions are algebraically decaying and the other with exponential decay. When the linear coupling is turned on, a third BKT-paired phase emerges, in which one-body correlations are exponentially decaying, while two-body correlation functions exhibit power-law decay. We perform numerical simulations in the paradigmatic case of two coupled XY models at finite temperature, finding evidences that for any finite value of the interlayer coupling, the BKT-paired phase is present. We provide a picture of the phase diagram using a renormalization group approach. AU - Bighin, Giacomo AU - Defenu, Nicolò AU - Nándori, István AU - Salasnich, Luca AU - Trombettoni, Andrea ID - 6940 IS - 10 JF - Physical Review Letters SN - 0031-9007 TI - Berezinskii-Kosterlitz-Thouless paired phase in coupled XY models VL - 123 ER - TY - JOUR AU - Qi, Chao AU - Minin, Giulio Di AU - Vercellino, Irene AU - Wutz, Anton AU - Korkhov, Volodymyr M. ID - 6919 IS - 9 JF - Science Advances TI - Structural basis of sterol recognition by human hedgehog receptor PTCH1 VL - 5 ER - TY - JOUR AB - Malaria, a disease caused by parasites of the Plasmodium genus, begins when Plasmodium-infected mosquitoes inject malaria sporozoites while searching for blood. Sporozoites migrate from the skin via blood to the liver, infect hepatocytes, and form liver stages which in mice 48 h later escape into blood and cause clinical malaria. Vaccine-induced activated or memory CD8 T cells are capable of locating and eliminating all liver stages in 48 h, thus preventing the blood-stage disease. However, the rules of how CD8 T cells are able to locate all liver stages within a relatively short time period remains poorly understood. We recently reported formation of clusters consisting of variable numbers of activated CD8 T cells around Plasmodium yoelii (Py)-infected hepatocytes. Using a combination of experimental data and mathematical models we now provide additional insights into mechanisms of formation of these clusters. First, we show that a model in which cluster formation is driven exclusively by T-cell-extrinsic factors, such as variability in “attractiveness” of different liver stages, cannot explain distribution of cluster sizes in different experimental conditions. In contrast, the model in which cluster formation is driven by the positive feedback loop (i.e., larger clusters attract more CD8 T cells) can accurately explain the available data. Second, while both Py-specific CD8 T cells and T cells of irrelevant specificity (non-specific CD8 T cells) are attracted to the clusters, we found no evidence that non-specific CD8 T cells play a role in cluster formation. Third and finally, mathematical modeling suggested that formation of clusters occurs rapidly, within few hours after adoptive transfer of CD8 T cells, thus illustrating high efficiency of CD8 T cells in locating their targets in complex peripheral organs, such as the liver. Taken together, our analysis provides novel insights into and attempts to discriminate between alternative mechanisms driving the formation of clusters of antigen-specific CD8 T cells in the liver. AU - Kelemen, Réka K AU - Rajakaruna, H AU - Cockburn, IA AU - Ganusov, VV ID - 6983 JF - Frontiers in Immunology SN - 1664-3224 TI - Clustering of activated CD8 T cells around Malaria-infected hepatocytes is rapid and is driven by antigen-specific cells VL - 10 ER -