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

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

Between life and death: exploring the sociocultural context of antenatal mental distress in rural Ethiopia

The high prevalence of antenatal common mental disorders in sub-Saharan Africa compared to high-income countries is poorly understood. This qualitative study explored the sociocultural context of antenatal mental distress in a rural Ethiopian community. Five focus group discussions and 25 in-depth interviews were conducted with purposively sampled community stakeholders. Inductive analysis was used to develop final themes. Worry about forthcoming delivery and fears for the woman’s survival were prominent concerns of all participants, but only rarely perceived to be pathological in intensity. Sociocultural practices such as continuing physical labour, dietary restriction, prayer and rituals to protect against supernatural attack were geared towards safe delivery and managing vulnerability. Despite strong cultural norms to celebrate pregnancy, participants emphasised that many pregnancies were unwanted and an additional burden on top of pre-existing economic and marital difficulties. Short birth interval and pregnancy out of wedlock were both seen as shameful and potent sources of mental distress. The notion that pregnancy in traditional societies is uniformly a time of joy and happiness is misplaced. Although antenatal mental distress may be self-limiting for many women, in those with enduring life difficulties, including poverty and abusive relationships, poor maternal mental health may persist

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

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.

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

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

Relationship between Symptoms and Gene Expression Induced by the Infection of Three Strains of Rice dwarf virus

BACKGROUND: Rice dwarf virus (RDV) is the causal agent of rice dwarf disease, which often results in severe yield losses of rice in East Asian countries. The disease symptoms are stunted growth, chlorotic specks on leaves, and delayed and incomplete panicle exsertion. Three RDV strains, O, D84, and S, were reported. RDV-S causes the most severe symptoms, whereas RDV-O causes the mildest. Twenty amino acid substitutions were found in 10 of 12 virus proteins among three RDV strains. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed the gene expression of rice in response to infection with the three RDV strains using a 60-mer oligonucleotide microarray to examine the relationship between symptom severity and gene responses. The number of differentially expressed genes (DEGs) upon the infection of RDV-O, -D84, and -S was 1985, 3782, and 6726, respectively, showing a correlation between the number of DEGs and symptom severity. Many DEGs were related to defense, stress response, and development and morphogenesis processes. For defense and stress response processes, gene silencing-related genes were activated by RDV infection and the degree of activation was similar among plants infected with the three RDV strains. Genes for hormone-regulated defense systems were also activated by RDV infection, and the degree of activation seemed to be correlated with the concentration of RDV in plants. Some development and morphogenesis processes were suppressed by RDV infection, but the degree of suppression was not correlated well with the RDV concentration. CONCLUSIONS/SIGNIFICANCE: Gene responses to RDV infection were regulated differently depending on the gene groups regulated and the strains infecting. It seems that symptom severity is associated with the degree of gene response in defense-related and development- and morphogenesis-related processes. The titer levels of RDV in plants and the amino acid substitutions in RDV proteins could be involved in regulating such gene responses

The transcriptomics of an experimentally evolved plant-virus interaction

[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)

The gut microbiota of Colombians differs from that of Americans, Europeans and Asians

ABSTRACT: The composition of the gut microbiota has recently been associated with health and disease, particularly with obesity. Some studies suggested a higher proportion of Firmicutes and a lower proportion of Bacteroidetes in obese compared to lean people; others found discordant patterns. Most studies, however, focused on Americans or Europeans, giving a limited picture of the gut microbiome. To determine the generality of previous observations and expand our knowledge of the human gut microbiota, it is important to replicate studies in overlooked populations. Thus, we describe here, for the first time, the gut microbiota of Colombian adults via the pyrosequencing of the 16S ribosomal DNA (rDNA), comparing it with results obtained in Americans, Europeans, Japanese and South Koreans, and testing the generality of previous observations concerning changes in Firmicutes and Bacteroidetes with increasing body mass index (BMI). Results: We found that the composition of the gut microbiota of Colombians was significantly different from that of Americans, Europeans and Asians. The geographic origin of the population explained more variance in the composition of this bacterial community than BMI or gender. Concerning changes in Firmicutes and Bacteroidetes with obesity, in Colombians we found a tendency in Firmicutes to diminish with increasing BMI, whereas no change was observed in Bacteroidetes. A similar result was found in Americans. A more detailed inspection of the Colombian dataset revealed that five fiber-degrading bacteria, including Akkermansia, Dialister, Oscillospira, Ruminococcaceae and Clostridiales, became less abundant in obese subjects. Conclusion: We contributed data from unstudied Colombians that showed that the geographic origin of the studied population had a greater impact on the composition of the gut microbiota than BMI or gender. Any strategy aiming to modulate or control obesity via manipulation of this bacterial community should consider this effect

Antagonism of cannabinoid receptor 2 pathway suppresses IL-6-induced immunoglobulin IgM secretion

Background: Cannabinoid receptor 2 (CB2) is expressed predominantly in the immune system, particularly in plasma cells, raising the possibility that targeting the CB2 pathway could yield an immunomodulatory effect. Although the role of CB2 in mediating immunoglobulin class switching has been reported, the effects of targeting the CB2 pathway on immunoglobulin secretion per se remain unclear. Methods: Human B cell line SKW 6.4, which is capable of differentiating into IgM-secreting cells once treated with human IL-6, was employed as the cell model. SKW 6.4 cells were incubated for 4 days with CB2 ligands plus IL-6 (100 U/ml). The amount of secreted IgM was determined by an ELISA. Cell proliferation was determined by the 3H-Thymidine incorporation assay. Signal molecules involved in the modulation of IgM secretion were examined by real-time RT-PCR and Western blot analyses or by using their specific inhibitors. Results: We demonstrated that CB2 inverse agonists SR144528 and AM630, but not CB2 agonist HU308 or CB1 antagonist SR141716, effectively inhibited IL-6-induced secretion of soluble IgM without affecting cell proliferation as measured by thymidine uptake. SR144528 alone had no effects on the basal levels of IgM in the resting cells. These effects were receptor mediated, as pretreatment with CB2 agonist abrogated SR144528-mediated inhibition of IL-6 stimulated IgM secretion. Transcription factors relevant to B cell differentiation, Bcl-6 and PAX5, as well as the protein kinase STAT3 pathway were involved in the inhibition of IL-6-induced IgM by SR144528. Conclusions: These results uncover a novel function of CB2 antagonists and suggest that CB2 ligands may be potential modulators of immunoglobulin secretion