100 research outputs found

    The LATERAL ORGAN BOUNDARIES Domain gene family in grapevine: Genome-wide characterization and expression analyses during developmental processes and stress responses

    Get PDF
    LATERAL ORGAN BOUNDARIES (LOB) DOMAIN (LBD) constitute a family of plant-specific transcription factors with key roles in the regulation of plant organ development, pollen development, plant regeneration, pathogen response, and anthocyanin and nitrogen metabolisms. However, the role of LBDs in fruit ripening and in grapevine (Vitis vinifera L.) development and stress responses is poorly documented. By performing a model curation of LBDs in the latest genome annotation 50 genes were identified. Phylogenetic analysis showed that LBD genes can be grouped into two classes mapping on 16 out of the 19 V. vinifera chromosomes. New gene subclasses were identified that have not been characterized in other species. Segmental and tandem duplications contributed significantly to the expansion and evolution of the LBD gene family in grapevine as noticed for other species. The analysis of cis-regulatory elements and transcription factor binding sites in the VviLBD promoter regions suggests the involvement of several hormones in the regulation of LBDs expression. Expression profiling suggest the involvement of LBD transcription factors in grapevine development, berry ripening and stress responses. Altogether this study provides valuable information and robust candidate genes for future functional analysis aiming to clarify mechanisms responsible for the onset of fruit ripening and fruit defense strategies. © 2017 The Author(s)

    Learning to breathe: developmental phase transitions in oxygen status

    Get PDF
    Plants are developmentally disposed to considerable changes in oxygen availability, yet our understanding of the importance of hypoxia is almost entirely limited to stress biology. Differential patterns of the abundance of oxygen, nitric oxide (.NO) and reactive oxygen species (ROS), and redox potential occur in organs and meristems, and examples are emerging in the literature of mechanistic relationships of these to development. Here, we describe the convergence of these cues in meristematic and reproductive tissues, and discuss the evidence for regulated hypoxic niches, within which oxygen-, ROS-, .NO- and redox-dependent signalling curate developmental transitions in plants

    A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

    Get PDF
    Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules. © 2012 Rodrigo et al.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) grants BFU2009-06993 (S. F. E.) and BIO2006-13107 (C. L.) and by Generalitat Valenciana grant PROMETEO2010/016 (S. F. E.). G. R. is supported by a graduate fellowship from the Generalitat Valenciana (BFPI2007-160) and J.C. by a contract from MICINN grant TIN2006-12860. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Carrera Montesinos, J.; Ruiz-Ferrer, V.; Del Toro, F.; Llave, C.; Voinnet, O.; Elena Fito, SF. (2012). A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS ONE. 7(7):40526-40526. https://doi.org/10.1371/journal.pone.0040526S405264052677Peng, X., Chan, E. Y., Li, Y., Diamond, D. L., Korth, M. J., & Katze, M. G. (2009). Virus–host interactions: from systems biology to translational research. Current Opinion in Microbiology, 12(4), 432-438. doi:10.1016/j.mib.2009.06.003Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: towards an integrated view of plant–pathogen interactions. Nature Reviews Genetics, 11(8), 539-548. doi:10.1038/nrg2812Maule, A., Leh, V., & Lederer, C. (2002). The dialogue between viruses and hosts in compatible interactions. Current Opinion in Plant Biology, 5(4), 279-284. doi:10.1016/s1369-5266(02)00272-8Whitham, S. A., Quan, S., Chang, H.-S., Cooper, B., Estes, B., Zhu, T., … Hou, Y.-M. (2003). Diverse RNA viruses elicit the expression of common sets of genes in susceptibleArabidopsis thalianaplants. The Plant Journal, 33(2), 271-283. doi:10.1046/j.1365-313x.2003.01625.xBailer, S., & Haas, J. (2009). Connecting viral with cellular interactomes. Current Opinion in Microbiology, 12(4), 453-459. doi:10.1016/j.mib.2009.06.004Whitham, S. A., Yang, C., & Goodin, M. M. (2006). Global Impact: Elucidating Plant Responses to Viral Infection. Molecular Plant-Microbe Interactions, 19(11), 1207-1215. doi:10.1094/mpmi-19-1207MacPherson, J. I., Dickerson, J. E., Pinney, J. W., & Robertson, D. L. (2010). Patterns of HIV-1 Protein Interaction Identify Perturbed Host-Cellular Subsystems. PLoS Computational Biology, 6(7), e1000863. doi:10.1371/journal.pcbi.1000863Jenner, R. G., & Young, R. A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nature Reviews Microbiology, 3(4), 281-294. doi:10.1038/nrmicro1126Andeweg, A. C., Haagmans, B. L., & Osterhaus, A. D. (2008). Virogenomics: the virus–host interaction revisited. Current Opinion in Microbiology, 11(5), 461-466. doi:10.1016/j.mib.2008.09.010Elena, S. F., Carrera, J., & Rodrigo, G. (2011). A systems biology approach to the evolution of plant–virus interactions. Current Opinion in Plant Biology, 14(4), 372-377. doi:10.1016/j.pbi.2011.03.013Tan, S.-L., Ganji, G., Paeper, B., Proll, S., & Katze, M. G. (2007). Systems biology and the host response to viral infection. Nature Biotechnology, 25(12), 1383-1389. doi:10.1038/nbt1207-1383De la Fuente, A. (2010). From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases. Trends in Genetics, 26(7), 326-333. doi:10.1016/j.tig.2010.05.001Albert, R. (2005). Scale-free networks in cell biology. Journal of Cell Science, 118(21), 4947-4957. doi:10.1242/jcs.02714Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., … Vidal, M. (2008). High-Quality Binary Protein Interaction Map of the Yeast Interactome Network. Science, 322(5898), 104-110. doi:10.1126/science.1158684Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5(2), 101-113. doi:10.1038/nrg1272Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378-382. doi:10.1038/35019019Mukhtar, M. S., Carvunis, A.-R., Dreze, M., Epple, P., Steinbrenner, J., … Moore, J. (2011). Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network. Science, 333(6042), 596-601. doi:10.1126/science.1203659Calderwood, M. A., Venkatesan, K., Xing, L., Chase, M. R., Vazquez, A., Holthaus, A. M., … Johannsen, E. (2007). Epstein-Barr virus and virus human protein interaction maps. Proceedings of the National Academy of Sciences, 104(18), 7606-7611. doi:10.1073/pnas.0702332104De Chassey, B., Navratil, V., Tafforeau, L., Hiet, M. S., Aublin‐Gex, A., Agaugué, S., … Lotteau, V. (2008). Hepatitis C virus infection protein network. Molecular Systems Biology, 4(1), 230. doi:10.1038/msb.2008.66Shapira, S. D., Gat-Viks, I., Shum, B. O. V., Dricot, A., de Grace, M. M., Wu, L., … Hacohen, N. (2009). A Physical and Regulatory Map of Host-Influenza Interactions Reveals Pathways in H1N1 Infection. Cell, 139(7), 1255-1267. doi:10.1016/j.cell.2009.12.018Dyer, M. D., Murali, T. M., & Sobral, B. W. (2008). The Landscape of Human Proteins Interacting with Viruses and Other Pathogens. PLoS Pathogens, 4(2), e32. doi:10.1371/journal.ppat.0040032Golem, S., & Culver, J. N. (2003). Tobacco mosaic virusInduced Alterations in the Gene Expression Profile ofArabidopsis thaliana. Molecular Plant-Microbe Interactions, 16(8), 681-688. doi:10.1094/mpmi.2003.16.8.681Espinoza, C., Medina, C., Somerville, S., & Arce-Johnson, P. (2007). Senescence-associated genes induced during compatible viral interactions with grapevine and Arabidopsis. Journal of Experimental Botany, 58(12), 3197-3212. doi:10.1093/jxb/erm165Yang, C., Guo, R., Jie, F., Nettleton, D., Peng, J., Carr, T., … Whitham, S. A. (2007). Spatial Analysis ofArabidopsis thalianaGene Expression in Response toTurnip mosaic virusInfection. Molecular Plant-Microbe Interactions, 20(4), 358-370. doi:10.1094/mpmi-20-4-0358Agudelo-Romero, P., Carbonell, P., de la Iglesia, F., Carrera, J., Rodrigo, G., Jaramillo, A., … Elena, S. F. (2008). Changes in the gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus. Virology Journal, 5(1), 92. doi:10.1186/1743-422x-5-92Agudelo-Romero, P., Carbonell, P., Perez-Amador, M. A., & Elena, S. F. (2008). Virus Adaptation by Manipulation of Host’s Gene Expression. PLoS ONE, 3(6), e2397. doi:10.1371/journal.pone.0002397Ascencio-Ibáñez, J. T., Sozzani, R., Lee, T.-J., Chu, T.-M., Wolfinger, R. D., Cella, R., & Hanley-Bowdoin, L. (2008). Global Analysis of Arabidopsis Gene Expression Uncovers a Complex Array of Changes Impacting Pathogen Response and Cell Cycle during Geminivirus Infection. Plant Physiology, 148(1), 436-454. doi:10.1104/pp.108.121038Babu, M., Griffiths, J. S., Huang, T.-S., & Wang, A. (2008). Altered gene expression changes in Arabidopsis leaf tissues and protoplasts in response to Plum pox virus infection. BMC Genomics, 9(1), 325. doi:10.1186/1471-2164-9-325De Vienne, D. M., Giraud, T., & Martin, O. C. (2007). A congruence index for testing topological similarity between trees. Bioinformatics, 23(23), 3119-3124. doi:10.1093/bioinformatics/btm500Wise, R. P., Moscou, M. J., Bogdanove, A. J., & Whitham, S. A. (2007). Transcript Profiling in Host–Pathogen Interactions. Annual Review of Phytopathology, 45(1), 329-369. doi:10.1146/annurev.phyto.45.011107.143944Handford, M. G., & Carr, J. P. (2007). A defect in carbohydrate metabolism ameliorates symptom severity in virus-infected Arabidopsis thaliana. Journal of General Virology, 88(1), 337-341. doi:10.1099/vir.0.82376-0Hou, B., Lim, E.-K., Higgins, G. S., & Bowles, D. J. (2004). N-Glucosylation of Cytokinins by Glycosyltransferases ofArabidopsis thaliana. Journal of Biological Chemistry, 279(46), 47822-47832. doi:10.1074/jbc.m409569200Schwender, J., Goffman, F., Ohlrogge, J. B., & Shachar-Hill, Y. (2004). Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature, 432(7018), 779-782. doi:10.1038/nature03145Pagán, I., Alonso-Blanco, C., & García-Arenal, F. (2008). Host Responses in Life-History Traits and Tolerance to Virus Infection in Arabidopsis thaliana. PLoS Pathogens, 4(8), e1000124. doi:10.1371/journal.ppat.1000124Carrera, J., Rodrigo, G., Jaramillo, A., & Elena, S. F. (2009). Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biology, 10(9), R96. doi:10.1186/gb-2009-10-9-r96Geisler-Lee, J., O’Toole, N., Ammar, R., Provart, N. J., Millar, A. H., & Geisler, M. (2007). A Predicted Interactome for Arabidopsis. Plant Physiology, 145(2), 317-329. doi:10.1104/pp.107.103465Ma, S., Gong, Q., & Bohnert, H. J. (2007). An Arabidopsis gene network based on the graphical Gaussian model. Genome Research, 17(11), 1614-1625. doi:10.1101/gr.6911207Yamada, T., & Bork, P. (2009). Evolution of biomolecular networks — lessons from metabolic and protein interactions. Nature Reviews Molecular Cell Biology, 10(11), 791-803. doi:10.1038/nrm2787Humphries, M. D., & Gurney, K. (2008). Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence. PLoS ONE, 3(4), e0002051. doi:10.1371/journal.pone.0002051Stumpf, M. P. H., & Ingram, P. J. (2005). Probability models for degree distributions of protein interaction networks. Europhysics Letters (EPL), 71(1), 152-158. doi:10.1209/epl/i2004-10531-8Khanin, R., & Wit, E. (2006). How Scale-Free Are Biological Networks. Journal of Computational Biology, 13(3), 810-818. doi:10.1089/cmb.2006.13.810Daudin, J.-J., Picard, F., & Robin, S. (2007). A mixture model for random graphs. Statistics and Computing, 18(2), 173-183. doi:10.1007/s11222-007-9046-7Uetz, P. (2006). Herpesviral Protein Networks and Their Interaction with the Human Proteome. Science, 311(5758), 239-242. doi:10.1126/science.1116804Choi, I.-R., Stenger, D. C., & French, R. (2000). Multiple Interactions among Proteins Encoded by the Mite-Transmitted Wheat Streak Mosaic Tritimovirus. Virology, 267(2), 185-198. doi:10.1006/viro.1999.0117Guo, D., Saarma, M., Rajamäki, M.-L., & Valkonen, J. P. T. (2001). Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. Journal of General Virology, 82(4), 935-939. doi:10.1099/0022-1317-82-4-935Lin, L., Shi, Y., Luo, Z., Lu, Y., Zheng, H., Yan, F., … Wu, Y. (2009). Protein–protein interactions in two potyviruses using the yeast two-hybrid system. Virus Research, 142(1-2), 36-40. doi:10.1016/j.virusres.2009.01.006Shen, W., Wang, M., Yan, P., Gao, L., & Zhou, P. (2010). Protein interaction matrix of Papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virologica, 54(1), 49-54. doi:10.4149/av_2010_01_49Redner, S. (2008). Teasing out the missing links. Nature, 453(7191), 47-48. doi:10.1038/453047aIrizarry, R. A. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249-264. doi:10.1093/biostatistics/4.2.249Smyth, G. K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1-25. doi:10.2202/1544-6115.1027Allemeersch, J., Durinck, S., Vanderhaeghen, R., Alard, P., Maes, R., Seeuws, K., … Kuiper, M. T. R. (2005). Benchmarking the CATMA Microarray. A Novel Tool forArabidopsis Transcriptome Analysis. Plant Physiology, 137(2), 588-601. doi:10.1104/pp.104.051300Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829-836. doi:10.1080/01621459.1979.10481038Tarraga, J., Medina, I., Carbonell, J., Huerta-Cepas, J., Minguez, P., Alloza, E., … Dopazo, J. (2008). GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Research, 36(Web Server), W308-W314. doi:10.1093/nar/gkn303Al-Shahrour, F., Minguez, P., Vaquerizas, J. M., Conde, L., & Dopazo, J. (2005). BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Research, 33(Web Server), W460-W464. doi:10.1093/nar/gki456Al-Shahrour, F., Minguez, P., Tárraga, J., Medina, I., Alloza, E., Montaner, D., & Dopazo, J. (2007). FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Research, 35(suppl_2), W91-W96. doi:10.1093/nar/gkm260Mueller, L. A., Zhang, P., & Rhee, S. Y. (2003). AraCyc: A Biochemical Pathway Database for Arabidopsis. Plant Physiology, 132(2), 453-460. doi:10.1104/pp.102.017236Navratil, V., de Chassey, B., Combe, C., & Lotteau, V. (2011). When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. BMC Systems Biology, 5(1), 13. doi:10.1186/1752-0509-5-13Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.

    Human Uterine Wall Tension Trajectories and the Onset of Parturition

    Get PDF
    Uterine wall tension is thought to be an important determinant of the onset of labor in pregnant women. We characterize human uterine wall tension using ultrasound from the second trimester of pregnancy until parturition and compare preterm, term and twin pregnancies. A total of 320 pregnant women were followed from first antenatal visit to delivery during the period 2000–2004 at the John Hunter Hospital, NSW, Australia. The uterine wall thickness, length, anterior-posterior diameter and transverse diameter were determined by serial ultrasounds. Subjects were divided into three groups: women with singleton pregnancies and spontaneous labor onset, either preterm or term and women with twin pregnancies. Intrauterine pressure results from the literature were combined with our data to form trajectories for uterine wall thickness, volume and tension for each woman using the prolate ellipsoid method and the groups were compared at 20, 25 and 30 weeks gestation. Uterine wall tension followed an exponential curve, with results increasing throughout pregnancy with the site of maximum tension on the anterior wall. For those delivering preterm, uterine wall thickness was increased compared with term. For twin pregnancies intrauterine volume was increased compared to singletons (), but wall thickness was not. There was no evidence for increased tension in those delivering preterm or those with twin gestations. These data are not consistent with a role for high uterine wall tension as a causal factor in preterm spontaneous labor in singleton or twin gestations. It seems likely that hormonal differences in multiple gestations are responsible for increased rates of preterm birth in this group rather than increased tension

    Effect of Host Species on the Distribution of Mutational Fitness Effects for an RNA Virus

    Get PDF
    Knowledge about the distribution of mutational fitness effects (DMFE) is essential for many evolutionary models. In recent years, the properties of the DMFE have been carefully described for some microorganisms. In most cases, however, this information has been obtained only for a single environment, and very few studies have explored the effect that environmental variation may have on the DMFE. Environmental effects are particularly relevant for the evolution of multi-host parasites and thus for the emergence of new pathogens. Here we characterize the DMFE for a collection of twenty single-nucleotide substitution mutants of Tobacco etch potyvirus (TEV) across a set of eight host environments. Five of these host species were naturally infected by TEV, all belonging to family Solanaceae, whereas the other three were partially susceptible hosts belonging to three other plant families. First, we found a significant virus genotype-by-host species interaction, which was sustained by differences in genetic variance for fitness and the pleiotropic effect of mutations among hosts. Second, we found that the DMFEs were markedly different between Solanaceae and non-Solanaceae hosts. Exposure of TEV genotypes to non-Solanaceae hosts led to a large reduction of mean viral fitness, while the variance remained constant and skewness increased towards the right tail. Within the Solanaceae hosts, the distribution contained an excess of deleterious mutations, whereas for the non-Solanaceae the fraction of beneficial mutations was significantly larger. All together, this result suggests that TEV may easily broaden its host range and improve fitness in new hosts, and that knowledge about the DMFE in the natural host does not allow for making predictions about its properties in an alternative host

    Causes of Adverse Pregnancy Outcomes and the Role of Maternal Periodontal Status – A Review of the Literature

    Get PDF
    Preterm (PT) and Low birth weight (LBW) are considered to be the most relevant biological determinants of newborn infants survival, both in developed and in developing countries. Numerous risk factors for PT and LBW have been defined in the literature. Infections of the genitourinary tract infections along with various biological and genetic factors are considered to be the most common etiological factors for PT/LBW deliveries. However, evidence suggests that sub-clinical infection sites that are also distant from the genitor-urinary tract may be an important cause for PT/LBW deliveries. Maternal periodontal status has also been reported by many authors as a possible risk factor for PT and LBW, though not all of the actual data support such hypothesis. The aim of this paper is to review the evidence from various published literature on the association between the maternal periodontal status and adverse pregnancy outcomes. Although this review found a consistent association between periodontitis and PT/LBW, this finding should be treated with great caution until the sources of heterogeneity can be explained

    The transcriptomics of an experimentally evolved plant-virus interaction

    Full text link
    [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.; García-García, 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).Sanjuán, 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. & Hernández, 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).García-Arenal, F. & Fraile A. Trade-offs in host range evolution of plant viruses. Plant Pathol. 62, S2–S9. (2013).Calvo, M., Malinowski, T. & García, 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. & Hasiów-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., Pérez-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).Fernández-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., Okrész, 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)

    Temporal Dynamics of Intrahost Molecular Evolution for a Plant RNA Virus

    Full text link
    [EN] Populations of plant RNA viruses are highly polymorphic in infected plants, which may allow rapid within-host evolution. To understand tobacco etch potyvirus (TEV) evolution, longitudinal samples from experimentally evolved populations in the natural host tobacco and from the alternative host pepper were phenotypically characterized and genetically analyzed. Temporal and compartmental variabilities of TEV populations were quantified using high throughput Illumina sequencing and population genetic approaches. Of the two viral phenotypic traits measured, virulence increased in the novel host but decreased in the original one, and viral load decreased in both hosts, though to a lesser extent in the novel one. Dynamics of population genetic diversity were also markedly different among hosts. Population heterozygosity increased in the ancestral host, with a dominance of synonymous mutations fixed, whereas it did not change or even decreased in the new host, with an excess of nonsynonymous mutations. All together, these observations suggest that directional selection is the dominant evolutionary force in TEV populations evolving in a novel host whereas either diversifying selection or random genetic drift may play a fundamental role in the natural host. To better understand these evolutionary dynamics, we developed a computer simulation model that incorporates the effects of mutation, selection, and drift. Upon parameterization with empirical data from previous studies, model predictions matched the observed patterns, thus reinforcing our idea that the empirical patterns of mutation accumulation represent adaptive evolution.The authors thank Francisca de la Iglesia and Paula Agudo for excellent technical assistance, our labmates for useful discussions and suggestions, and Dr Jose A. Daros for gifting us the pMTEV infectious clone. This work was supported by grants BFU2009-06993 and BFU2012-30805 from the Spanish Ministry of Economy and Competitiveness (MINECO), grant PROMETEOII/2014/021 from Generalitat Valenciana, and by the European Commission 7th Framework Programme (FP7-ICT-611640 FET Proactive: Evolving Living Technologies) EvoEvo project to S.F.E. J.M.C. was supported by a JAE-doc postdoctoral contract from CSIC. A.W. was supported by the EvoEvo project. J.H. was recipient of a predoctoral contract from MINECO. M.P.Z. was supported by a Juan de la Cierva postdoctoral contract from MINECO.Cuevas, JM.; Willemsen, A.; Hillung, J.; Zwart, MP.; Elena Fito, SF. (2015). Temporal Dynamics of Intrahost Molecular Evolution for a Plant RNA Virus. Molecular Biology and Evolution. 32(5):1132-1147. https://doi.org/10.1093/molbev/msv028S1132114732

    Virus Adaptation by Manipulation of Host's Gene Expression

    Get PDF
    Viruses adapt to their hosts by evading defense mechanisms and taking over cellular metabolism for their own benefit. Alterations in cell metabolism as well as side-effects of antiviral responses contribute to symptoms development and virulence. Sometimes, a virus may spill over from its usual host species into a novel one, where usually will fail to successfully infect and further transmit to new host. However, in some cases, the virus transmits and persists after fixing beneficial mutations that allow for a better exploitation of the new host. This situation would represent a case for a new emerging virus. Here we report results from an evolution experiment in which a plant virus was allowed to infect and evolve on a naïve host. After 17 serial passages, the viral genome has accumulated only five changes, three of which were non-synonymous. An amino acid substitution in the viral VPg protein was responsible for the appearance of symptoms, whereas one substitution in the viral P3 protein the epistatically contributed to exacerbate severity. DNA microarray analyses show that the evolved and ancestral viruses affect the global patterns of host gene expression in radically different ways. A major difference is that genes involved in stress and pathogen response are not activated upon infection with the evolved virus, suggesting that selection has favored viral strategies to escape from host defenses

    Experimental Evolution of an Oncolytic Vesicular Stomatitis Virus with Increased Selectivity for p53-Deficient Cells

    Get PDF
    Experimental evolution has been used for various biotechnological applications including protein and microbial cell engineering, but less commonly in the field of oncolytic virotherapy. Here, we sought to adapt a rapidly evolving RNA virus to cells deficient for the tumor suppressor gene p53, a hallmark of cancer cells. To achieve this goal, we established four independent evolution lines of the vesicular stomatitis virus (VSV) in p53-knockout mouse embryonic fibroblasts (p53−/− MEFs) under conditions favoring the action of natural selection. We found that some evolved viruses showed increased fitness and cytotoxicity in p53−/− cells but not in isogenic p53+/+ cells, indicating gene-specific adaptation. However, full-length sequencing revealed no obvious or previously described genetic changes associated with oncolytic activity. Half-maximal effective dose (EC50) assays in mouse p53-positive colon cancer (CT26) and p53-deficient breast cancer (4T1) cells indicated that the evolved viruses were more effective against 4T1 cells than the parental virus or a reference oncolytic VSV (MΔ51), but showed no increased efficacy against CT26 cells. In vivo assays using 4T1 syngeneic tumor models showed that one of the evolved lines significantly delayed tumor growth compared to mice treated with the parental virus or untreated controls, and was able to induce transient tumor suppression. Our results show that RNA viruses can be specifically adapted typical cancer features such as p53 inactivation, and illustrate the usefulness of experimental evolution for oncolytic virotherapy
    corecore