73 research outputs found

    Live cell dynamics of production, explosive release and killing activity of phage tail-like weapons for Pseudomonas kin exclusion.

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    Interference competition among bacteria requires a highly specialized, narrow-spectrum weaponry when targeting closely-related competitors while sparing individuals from the same clonal population. Here we investigated mechanisms by which environmentally important Pseudomonas bacteria with plant-beneficial activity perform kin interference competition. We show that killing between phylogenetically closely-related strains involves contractile phage tail-like devices called R-tailocins that puncture target cell membranes. Using live-cell imaging, we evidence that R-tailocins are produced at the cell center, transported to the cell poles and ejected by explosive cell lysis. This enables their dispersal over several tens of micrometers to reach targeted cells. We visualize R-tailocin-mediated competition dynamics between closely-related Pseudomonas strains at the single-cell level, both in non-induced condition and upon artificial induction. We document the fatal impact of cellular self-sacrifice coupled to deployment of phage tail-like weaponry in the microenvironment of kin bacterial competitors, emphasizing the necessity for microscale assessment of microbial competitions

    Non-stationary covariance function modelling in 2D least-squares collocation

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    Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account for spatial dependence in the ob-served data. However, the assumption that the spatial dependence is constant through-out the region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of, e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from spatial statistics for non-stationary covariance structures, and demonstrate its advantage fordealing with non-stationarity in geodetic data. We then compared stationary and non-stationary covariance functions in 2D LSC to the empirical example of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary. The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve upon standard (stationary) LSC

    The effect of EGM2008-based normal, normal-orthometric and Helmert orthometric height systems on the Australian levelling network

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    This paper investigates the normal-orthometric correction used in the definition of the Australian Height Datum, and also computes and evaluates normal and Helmert orthometric corrections for the Australian National Levelling Network (ANLN). Testing these corrections in Australia is important to establish which height system is most appropriate for any new Australian vertical datum. An approximate approach to assigning gravity values to ANLN benchmarks (BMs) is used, where the EGM2008-modelled gravity field is used to "re-construct" observed gravity at the BMs. Network loop closures (for first- and second-order levelling) indicate reduced misclosures for all height corrections considered, particularly in the mountainous regions of south eastern Australia. Differences between Helmert orthometric and normal-orthometric heights reach 44 cm in the Australian Alps, and differences between Helmert orthometric and normal heights are about 26 cm in the same region. Normal orthometric heights differ from normal heights by up to 18 cm in mountainous regions >2,000 m. This indicates that the quasigeoid is not compatible with normal-orthometric heights in Australia

    The transcriptomics of an experimentally evolved plant-virus interaction

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    [EN] Models of plant-virus interaction assume that the ability of a virus to infect a host genotype depends on the matching between virulence and resistance genes. Recently, we evolved tobacco etch potyvirus (TEV) lineages on different ecotypes of Arabidopsis thaliana, and found that some ecotypes selected for specialist viruses whereas others selected for generalists. Here we sought to evaluate the transcriptomic basis of such relationships. We have characterized the transcriptomic responses of five ecotypes infected with the ancestral and evolved viruses. Genes and functional categories differentially expressed by plants infected with local TEV isolates were identified, showing heterogeneous responses among ecotypes, although significant parallelism existed among lineages evolved in the same ecotype. Although genes involved in immune responses were altered upon infection, other functional groups were also pervasively over-represented, suggesting that plant resistance genes were not the only drivers of viral adaptation. Finally, the transcriptomic consequences of infection with the generalist and specialist lineages were compared. Whilst the generalist induced very similar perturbations in the transcriptomes of the different ecotypes, the perturbations induced by the specialist were divergent. Plant defense mechanisms were activated when the infecting virus was specialist but they were down-regulated when infecting with generalist.We thank Francisca de la Iglesia and Paula Agudo for excellent technical assistance and our labmates for useful discussions and suggestions. This work was supported by grants BFU2012-30805 from the Spanish Ministry of Economy and Competitiveness (MINECO), PROMETEOII/2014/021 from Generalitat Valenciana and EvoEvo (ICT610427) from the European Commission 7th Framework Program to SFE, and grant PROMETEOII/2014/025 to JD. JMC was supported by a JAE-doc postdoctoral contract from CSIC. JH was recipient of a predoctoral contract from MINECO.Hillung, J.; GarcĂ­a-GarcĂ­a, F.; Dopazo, J.; Cuevas Torrijos, JM.; Elena Fito, SF. (2016). The transcriptomics of an experimentally evolved plant-virus interaction. Scientific Reports. 6:1-19. https://doi.org/10.1038/srep24901S1196Duffy, S., Shackelton, L. A. & Holmes, E. C. Rates of evolutionary change in viruses: patterns and determinants. Nat. Rev. Genet. 9, 267–276 (2008).Parrish, C. R. et al. Cross-species virus transmission and the emergence of new epidemic diseases. Microbiol. Mol. Biol. Rev. 72, 457–470 (2008).Holmes, E. C. The comparative genomics of viral emergence. Proc. Natl. Acad. Sci. USA 107, 1742–1746 (2010).SanjuĂĄn, R., Nebot, M. R., Chirico, N., Mansky, L. M. & Belshaw, R. Viral mutation rates. J. Virol. 84, 9733–9748 (2010).Elena, S. F. et al. The evolutionary genetics of emerging plant RNA viruses. Mol. Plant-Microbe Interact. 24, 287–293 (2011).Holmes, E. C. The evolutionary genetics of emerging viruses. Annu. Rev. Ecol. Evol. Syst. 40, 353–372 (2009).Domingo, E. Mechanisms of viral emergence. Vet. Res. 41, 38 (2010).King, K. C. & Lively, C. M. Does genetic diversity limit disease spread in natural host populations? Heredity 109, 199–203 (2012).Kearney, C. M., Thomson, M. J. & Roland, K. E. Genome evolution of Tobacco mosaic virus populations during long-term passaging in a diverse range of hosts. Arch. Virol. 144, 1513–1526 (1999).Tan, Z. et al. Mutations in Turnip mosaic virus genomes that have adapted to Raphanus sativus . J. Gen. Virol. 88, 501–510 (2005).Rico, P., Ivars, P., Elena, S. F. & HernĂĄndez, C. Insights into the selective pressures restricting Pelargonium flower break virus genome variability: evidence for host adaptation. J. Virol. 80, 8124–8132 (2006).Wallis, C. M. et al. Adaptation of Plum pox virus to a herbaceous host (Pisum sativum) following serial passages. J. Gen. Virol. 88, 2839–2845 (2007).Agudelo-Romero, P., de la Iglesia, F. & Elena, S. F. The pleiotropic cost of host-specialization in tobacco etch potyvirus. Infect. Genet. Evol. 8, 806–814 (2008).Bedhomme, S., Lafforgue, G. & Elena, S. F. Multihost experimental evolution of a plant RNA virus reveals local adaptation and host-specific mutations. Mol. Biol. Evol. 29, 1481–1492 (2012).GarcĂ­a-Arenal, F. & Fraile A. Trade-offs in host range evolution of plant viruses. Plant Pathol. 62, S2–S9. (2013).Calvo, M., Malinowski, T. & GarcĂ­a, J. A. Single amino acid changes in the 6K1-CI region can promote the alternative adaptation of Prunus- and Nicotiana- propagated Plum pox virus C isolates to either host. Mol. Plant-Microbe Interact. 27, 136–149 (2014).Cuevas, J. M., Willemsen, A., Hillung, J., Zwart, M. P. & Elena, S. F. Temporal dynamics of intra-host molecular evolution for a plant RNA virus. Mol. Biol. Evol. 32, 1132–1147 (2015).Minicka, J., Rymelska, N., Elena, S. F., Czerwoniec, A. & HasiĂłw-Jaroszewska, B. Molecular evolution of Pepino mosaic virus during long-term passaging in different hosts and its impact on virus virulence. Ann. Appl. Biol. 166, 389–401 (2015).Agudelo-Romero, P., Carbonell, P., PĂ©rez-Amador, M. A. & Elena, S. F. Virus adaptation by manipulation of host's gene expression. PLos ONE 3, e2397 (2008).Weigel, D. Natural variation in arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 158, 2–22 (2012).Mahajan, S. K., Chisholm, S. T., Whitham, S. A. & Carrington, J. C. Identification and characterization of a locus (RTM1) that restricts long-distance movement of Tobacco etch virus in Arabidopsis thaliana . Plant J. 14, 177–186 (1998).Whitham, S. A., Yamamoto, M. L. & Carrington, J. C. Selectable viruses and altered susceptibility mutants in Arabidopsis thaliana . Proc. Natl. Acad. Sci. USA 96, 772–777 (1999).Whitham, S. A., Anderberg, R. J., Chisholm, S. T. & Carrington, J. C. Arabidopsis RTM2 gene is necessary for specific restriction of Tobacco etch virus and encodes an unusual small heat shock-like protein. Plant Cell 12, 569–582 (2000).Chisholm, S. T., Mahajan, S. K., Whitham, S. A., Yamamoto, M. L. & Carrington, J. C. Cloning of the Arabidopsis RTM1 gene, which controls restriction of long-distance movement of Tobacco etch virus . Proc. Natl. Acad. Sci. USA 97, 489–494 (2000).Chisholm, S. T., Parra, M. A., Anderberg, R. J. & Carrington, J. C. Arabidopsis RTM1 and RTM2 genes function in phloem to restrict long-distance movement of Tobacco etch virus . Plant Physiol. 127, 1667–1675 (2001).Cosson, P. et al. RTM3, which controls long-distance movement of potyviruses, is a member of a new plant gene family encoding a MEPRIN and TRAF homology domain-containing protein. Plant Physiol. 154, 222–232 (2010).Cosson, P., Sofer, L., Schurdi-Levraud, V. & Revers, F. A member of a new plant gene family encoding a MEPRIN and TRAF homology (MATH) domain-containing protein is involved in restriction of long distance movement of plant viruses. Plant Signal. Behav. 5, 1321–1323 (2010).Agudelo-Romero P. et al. Changes in gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus . Virol. J. 5, 92 (2008).Lalić, J., Agudelo-Romero, P., Carrasco, P. & Elena, S. F. Adaptation of tobacco etch potyvirus to a susceptible ecotype of Arabidopsis thaliana capacitates it for systemic infection of resistant ecotypes. Phil. Trans. R. Soc. B 65, 1997–2008 (2010).Hillung, J., Cuevas, J. M. & Elena, S. F. Transcript profiling of different Arabidopsis thaliana ecotypes in response to tobacco etch potyvirus infection. Front. Microbiol. 3, 229 (2012).Hillung, J., Cuevas, J. M. & Elena, S. F. Evaluating the within-host fitness effects of mutations fixed during virus adaptation to different ecotypes of a new host. Phil. Trans. R. Soc. B 370, 20140292 (2015).Hillung, J., Cuevas, J. M., Valverde, S. & Elena, S. F. Experimental evolution of an emerging plant virus in host genotypes that differ in their susceptibility to infection. Evolution 68, 2467–2480 (2014).Sartor, M. A., Leikauf, G. D. & Medvedovic, M. LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 25, 211–217 (2009).Montaner, D. & Dopazo, J. Multidimensional gene set analysis of genomic data. PLos ONE 5, e10348 (2010).Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLos ONE 6, e21800 (2011).Grennan, A. K. Regulation of starch metabolism in Arabidopsis leaves. Plant Physiol. 142, 1343–1345 (2006).Johnson, P. R. & Ecker, J. R. The ethylene gas signal transduction pathway: a molecular perspective. Annu. Rev. Genet. 32, 227–254 (1998).Wang, K. L., Li, H. & Ecker, J. R. Ethylene biosynthesis and signaling networks. Plant Cell 14, S131–S151 (2002).Binns, D. et al. QuickGO: a web-based tool for gene ontology searching. Bioinformatics 25, 3045–3046 (2009).Stintzi, A., Weber, H., Reymond, P., Browse, J. & Farmer, E. E. Plant defense in the absence of jasmonic acid: the role of cyclopentenones. Proc. Natl. Acad. Sci. USA 98, 12837–12842 (2001).Luna, E. et al. Callose deposition: a multifaceted plant defense response. Mol. Plant-Microbe Interact. 24, 183–193 (2011).Ghoshroy, S., Freedman, K., Lartey, R. & Citovsky, V. Inhibition of plant viral systemic infection by non-toxic concentrations of cadmium. Plant J. 13, 591–602 (1998).Hayashi, N. et al. Nef of HIV-1 interacts directly with calcium-bound calmodulin. Protein Sci. 11, 529–537 (2002).Zacharias, D. A., Violin, J. D., Newton, A. C. & Tsien, R. Y. Partitioning of lipic-modified monomeric GFPs into membrane microdomains of live cells. Science 296, 913–916 (2002).Rojas, M. R. et al. Functional analysis of proteins involved in movement of the monopartite begomovirus, Tomato yellow leaf curl virus. Virology 291, 110–125 (2001).Padmanabhan, M. S., Goregaoker, S. P., Golem, S., Shiferaw, H. & Culver, J. N. Interaction of the Tobacco mosaic virus replicase protein with the Aux/IAA protein PAP1/IAA26 is associated with disease development. J. Virol. 79, 2549–2558 (2005).Lurin, C. et al. Genome-wide analysis of Arabidopsis pentatricopeptide repeat proteins reveals their essential role in organelle biogenesis. Plant Cell 16, 2089–2103 (2004).Takenaka, M., Verbitskiy, D., Zehrmann, A. & Brennicke, A. Reverse genetic screening identifies five E-class PPR proteins involved in RNA editing in mitochondria of Arabidopsis thaliana . J. Biol. Chem. 285, 27122–27129 (2010).Gillissen, B. et al. A new family of high-affinity transporters for adenine, cytosine, and purine derivatives in Arabidopsis . Plant Cell 12, 291–300 (2000).Li, S., Fu, Q., Chen, L., Huang, W. & Yu, D. Arabidopsis thaliana WRKY25, WRKY26, and WRKY33 coordinate induction of plant thermotolerance. Planta 233, 1237–1252 (2011).Divol, F. et al. Involvement of the xyloglucan endotransglycosylase/hydrolases encoded by celery XTH1 and Arabidopsis XTH33 in the phloem response to aphids. Plant Cell. Environ. 30, 187–201 (2007).Vissenberg, K., Fry, S. C., Pauly, M., Höfte, H. & Verbelen, J. P. XTH acts at the microfibril-matrix interface during cell elongation. J. Exp. Bot. 56, 673–683 (2005).Ham, B. K., Li, G., Kang, B. H., Zeng, F. & Lucas, W. J. Overexpression of Arabidopsis plasmodesmata germin-like proteins disrupts root growth and development. Plant Cell 24, 3630–3648 (2012).Bae, M. S., Cho, E. J., Choi, E. Y. & Park, O. K. Analysis of the Arabidopsis nuclear proteome and its response to cold stress. Plant J. 36, 652–663 (2003).Zargar, S. M. et al. Correlation analysis of proteins responsive to Zn, Mn, or Fe deficiency in Arabidopsis roots based on iTRAQ analysis. Plant Cell Rep. 34, 157–166 (2015).Kleffmann, T. et al. The Arabidopsis thaliana chloroplast proteome reveals pathway abundance and novel protein functions. Curr. Biol. 14, 354–362 (2004).Zybailov, B. et al. Sorting signals, N-terminal modifications and abundance of the chloroplast proteome. PLos ONE 3, e1994 (2008).Wu, P. et al. Phosphate starvation triggers distinct alterations of gene expression in Arabidopsis roots and leaves. Plant Physiol. 132, 1260–1271 (2003).Oh, S. A., Lee, S. Y., Chung, I. K., Lee, C. H. & Nam H. G. A senescence-associated gene of Arabidopsis thaliana is distinctively regulated during natural and artificially induced leaf senescence. Plant Mol. Biol. 30, 739–754 (1996).Schenk, P. M., Kazan, K., Rusu, A. G., Manners, J. M. & Maclean, D. J. The SEN1 gene of Arabidopsis is regulated by signals that link plant defence responses and senescence. Plant Physiol. Biochem. 43, 997–1005 (2005).FernĂĄndez-Calvino, L. et al. Activation of senescence-associated dark-inducible (DIN) genes during infection contributes to enhanced susceptibility to plant viruses. Mol. Plant Pathol. 17, 3–15 (2016).Vierstra, R. D. Proteolysis in plants: mechanisms and functions. Plant Mol. Biol. 32, 275–302 (1996).Bögre, L., OkrĂ©sz, L., Henriques, R. & Anthony, R. G. Growth signalling pathways in Arabidopsis and the AGC protein kinases. Trends Plant Sci. 8, 424–431 (2003).An, L. et al. A zinc finger protein gene ZFP5 integrates phytohormone signalling to control root hair development in Arabidopsis . Plant J. 72, 474–490 (2012).Zhou, Z., An, L., Sun, L. & Gan, Y. ZFP5 encodes a functionally equivalent GIS protein to control trichome initiation. Plant Signal. Behav. 7, 28–30 (2012).Zhou, Z. et al. Zinc finger protein 5 is required for the control of trichome initiation by acting upstream of zinc finger protein 8 in Arabidopsis . Plant Physiol. 157, 673–682 (2011).Lee, D. J. et al. Genome-wide expression profiling of ARABIDOPSIS RESPONSE REGULATOR 7 (ARR7) overexpression in cytokinin response. Mol. Genet. Genomics 277, 115–137 (2007).Theologis, A. et al. Sequence and analysis of chromosome 1 of the plant Arabidopsis thaliana . Nature 408, 816–820 (2000).Heyndrickx, K. S. & Vandepoele, K. Systematic identification of functional plant modules through the integration of complementary data sources. Plant Physiol. 159, 884–901 (2012).Martinoia, E. et al. Multifunctionality of plant ABC transporter - more than just detoxifiers. Planta 214, 345–355 (2002).Kaneda, M. et al. ABC transporters coordinately expressed during lignification of Arabidopsis stems include a set of ABCBs associated with auxin transport. J. Exp. Bot. 62, 2063–2077 (2011).Alejandro, S. et al. AtABCG29 is a monolignol transporter involved in lignin biosynthesis. Curr. Biol. 22, 1207–1212 (2012).Riechmann, J. L. et al. Arabidopsis transcription factors: genome-wide comparative analysis among eukaryotes. Science 290, 2105–2110 (2000).Ohashi-Ito, K. & Bergmann, D. C. Regulation of the Arabidopsis root vascular initial population by LONESOME HIGHWAY . Development 134, 2959–2968 (2007).Averyanov, A. Oxidative burst and plant disease resistance. Front. Biosci. 1, 142–152 (2009).Flury, P., Klauser, D., Schulze, B., Boller, T. & Bartels, S. The anticipation of danger: microbe-associated molecular pattern perception enhances AtPep-triggered oxidative burst. Plant Physiol. 161, 2023–2035 (2013).Tanaka, K., Nguyen, C. T., Liang, Y., Cao, Y. & Stacey, G. Role of LysM receptors in chitin-triggered plant innate immunity. Plant Signal. Behav. 8, e22598 (2013).Nakamura, K. & Matsuoka, K. Protein targeting to the vacuole in plant cells. Plant Physiol. 101, 1–5 (1993).Elena, S. F., Agudelo-Romero, P. & Lalić, J. The evolution of viruses in multi-host fitness landscapes. Open Virol. J. 3, 1–6 (2009).Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).Benjamini,Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29, 1165–1188 (2001).Sneath, P. & Sokal, R. Numerical Taxonomy. ( W.H. Freeman, 1973).D'Haeseler, P. How does gene expression clustering work? Nat. Biotech. 23, 1499–1501 (2005).Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006)

    Research Article Comparing covariance matrices: random skewers method compared to the common principal components model

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    Comparisons of covariance patterns are becoming more common as interest in the evolution of relationships between traits and in the evolutionary phenotypic diversification of clades have grown. We present parallel analyses of covariance matrix similarity for cranial traits in 14 New World Monkey genera using the Random Skewers (RS), T-statistics, and Common Principal Components (CPC) approaches. We find that the CPC approach is very powerful in that with adequate sample sizes, it can be used to detect significant differences in matrix structure, even between matrices that are virtually identical in their evolutionary properties, as indicated by the RS results. We suggest that in many instances the assumption that population covariance matrices are identical be rejected out of hand. The more interesting and relevant question is, How similar are two covariance matrices with respect to their predicted evolutionary responses? This issue is addressed by the random skewers method described here

    Quantitative trait loci × environment interactions for plant morphology vary over ontogeny in Brassica rapa

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    Growth in plants occurs via the addition of repeating modules, suggesting that the genetic architecture of similar subunits may vary between earlier‐ and later‐developing modules. These complex environment × ontogeny interactions are not well elucidated, as studies examining quantitative trait loci (QTLs) expression over ontogeny have not included multiple environments. Here, we characterized the genetic architecture of vegetative traits and onset of reproduction over ontogeny in recombinant inbred lines of Brassica rapa in the field and glasshouse. The magnitude of genetic variation in plasticity of seedling internodes was greater than in those produced later in ontogeny. We correspondingly detected that QTLs for seedling internode length were environment‐specific, whereas later in ontogeny the majority of QTLs affected internode lengths in all treatments. The relationship between internode traits and onset of reproduction varied with environment and ontogenetic stage. This relationship was observed only in the glasshouse environment and was largely attributable to one environment‐specific QTL. Our results provide the first evidence of a QTL × environment × ontogeny interaction, and provide QTL resolution for differences between early‐ and later‐stage plasticity for stem elongation. These results also suggest potential constraints on morphological evolution in early vs later modules as a result of associations with reproductive timing
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