Evaluating fundamental life-history traits for Tobacco etch potyvirus

Los virus de ARN son probablemente algunos de los parásitos más extendidos que se pueden encontrar en todas las formas de vida. Estos agentes infecciosos parecen particularmente propensos a causar enfermedades emergentes en plantas, seres humanos y otros animales. Sus habilidades para escapar al sistema inmune, evadir estrategias antivirales o para infectar a nuevas especies son una consecuencia directa de su enorme capacidad para evolucionar rápidamente. Comprender los procesos básicos del cambio evolutivo puede ser unos de los principales pasos necesarios en el diseño de nuevas estrategias de intervención. Desde una perspectiva evolutiva, una de las principales características de los virus de ARN es su capacidad para mutar. El conocimiento de las tasas de mutación y el espectro molecular de mutaciones espontáneas son importantes para entender la evolución de la composición genética de las poblaciones virales. Estudios anteriores han demostrado que la tasa de mutaciones espontáneas de los virus de ARN varía 14 ampliamente entre 0.01 y 2 mutaciones por genoma y generación, ocupando los virus de plantas la parte inferior de esta escala de valores. En este estudio, se propuso analizar el espectro mutacional y la tasa de mutación del virus del grabado del tabaco (Tobacco etch virus, TEV), como modelo para los virus de ARN de cadena positiva. Nuestro experimento minimiza la acción purificadora de la selección en el espectro mutacional, dando así una imagen exacta de qué tipo de mutación ha producido la replicasa viral. Hemos calculado la tasa de mutación espontánea de este virus, hallándose en el intervalo de valores entre 10-6 - 10-5 mutaciones por sitio y generación. Nuestras estimaciones se encuentran en el mismo rango de valores que los anteriormente descritos para otros virus ARN de plantas. La recombinación de un virus de ARN es un parámetro evolutivo que contribuye significativamente a la diversidad y a la evolución de los virus. La tasa de recombinación depende de dos parámetros: de la frecuencia de intercambio genético entre genomas virales dentro de una célula infectada del huésped y de la frecuencia de las células doblemente-infectadas. A pesar 15   de la importancia del conocimiento de dichos factores, actualmente solo se ha estimado experimentalmente la tasa de recombinación del retrovirus virus del mosaico de la coliflor (Cauliflower mosaic virus, CaMV) (Froissart et al., 2005), mediante una cuantificación in vivo y directa de la tasa de recombinación de dicho virus durante la infección de un huésped. Dicha tasa se estimó en 4×10-5 eventos de recombinación por nucleótido y por ciclo de replicación. En los potyvirus, observaciones in vivo han demostrado que aislados del mismo virus se segregan espacialmente durante infecciones mixtas. Esta segregación debería reducir al mínimo la posibilidad de dos genotipos de infectar las mismas células y así limitar eventos de recombinación durante la replicación del ARN viral. Para conciliar estas observaciones, hemos evaluado la tasa de recombinación y la multiplicidad de infección (MOI) del virus TEV, in vivo. La tasa de recombinación se estimó en 1.03×10-5 eventos de recombinación por nucleótido y generación. Este valor se encuentra en el mismo orden de magnitud que la tasa de mutación del TEV, lo que sugiere que la recombinación tiene una importancia 16   comparable a la mutación puntual en la creación de variabilidad. La multiplicidad de infección celular se define como el número de genomas virales que logran infectar de manera eficaz una célula. Dos estudios recientes han mostrado estimaciones in vivo de la MOI para el virus del mosaico del tabaco (Tobacco mosaic virus, TMV) y del CaMV, gracias a métodos sofisticados que miden la distribución de dos genotipos virales en las células del huésped. Aquí presentamos un análisis detallado de la dinámica temporal y espacial de la MOI celular durante la colonización de una planta por el TEV. Observamos una baja frecuencia tanto del número de células infectadas por el virus (media ± SD: 0.100 ± 0.073), como de las infectadas por dos genotipos del TEV (media ± SD: 0.012 ± 0.023). Se usó un nuevo método basado en un modelo de selección para determinar la MOI. Los valores de MOI predichos fueron bajos, oscilando entre 1.0 (hoja tres, 3 días después de la inoculación (dpi)) a 1.6 (hoja cuatro, 7 dpi). Por último, la alta diversidad genética de las poblaciones virales da lugar a una nube de variantes, todas 17   vinculadas a través de mutación, que interactúan y contribuyen colectivamente, conocido también por el término de cuasiespecies. Las poblaciones virales pueden adaptarse rápidamente a sus entornos dinámicos, y son notablemente inestables en determinadas condiciones como hemos demostrado en este trabajo durante la evolución del TEV en el caso de redundancia genética y funcional. Después de varios pasos a tiempos largos de infección en plantas transgénicas que expresan la replicasa viral NIb, se observaron grandes deleciones y múltiples partículas defectuosas. Este resultado demuestra la gran plasticidad del genoma de los virus ARN y su capacidad para eliminar cualquier carga genética innecesaria.RNA viruses are probably some of the most pervasive parasites that can be found in all life forms. These infectious agents seem particularly prone to causing emerging diseases in plants, humans and other animals. Their abilities to escape the immune system, evade antiviral strategies or to jump to new species are a direct consequence of their enormous capacity to evolve quickly. Understanding basic processes of evolutionary change may be a necessary primary step in designing new intervention strategies. One of the key characteristics of RNA viruses, especially from an evolutionary perspective, is their capacity for mutation. Knowing mutation rates and the molecular spectrum of spontaneous mutations is important to understanding how the genetic composition of viral populations evolves. Previous studies have shown that the rate of spontaneous mutations for RNA viruses widely varies between 0.01 and 2 mutations per genome and generation, with plant RNA viruses always occupying the lower side of this range. Here we analyse the spontaneous mutational spectrum 20 and the mutation rate of Tobacco etch potyvirus (TEV), a model system of positive sense RNA viruses. Our experimental set up minimizes the action of purifying selection on the mutational spectrum, thus giving a picture of what types of mutations are produced by the viral replicase. We have estimated that the spontaneous mutation rate for this virus was in the range 10-6 - 10-5 mutations per site and generation. Our estimates are in the same biological ballpark as previous values reported for plant RNA viruses. Recombination is a virus characteristic that also contributes significantly to the diversity and evolution of viruses. The recombination rate depends on two parameters: the frequency of genetic exchange between viral genomes within an infected host cell and the frequency of co-infected cells. Despite this importance, only one direct quantification of the in vivo recombination rate for an RNA virus during host infection has been reported: the in vivo recombination rate for Cauliflower mosaic virus (CaMV) (Froissart et al., 2005) was reported to be 4×10-5 events per nucleotide site and per replication cycle. In plant potyviruses, in vivo observations have 21   shown that strains of the same virus segregate spatially during mixed infections. This segregation shall minimize the chances of two genotypes to co-infect the same cells and henceforth, precludes template switching and recombination events during genomic RNA replication. To reconcile these confronting observations, we have evaluated the in vivo TEV recombination rate and multiplicity of infection (MOI). TEV recombination rate was estimated to 1.03×10−5 recombination events per nucleotide site and generation. This value is in the same order of magnitude than TEV mutation rate, suggesting that recombination should be at least as important as point mutation in creating variability. The multiplicity of cellular infection is defined as the number of viral genome infecting effectively a cell. Two recent studies have reported in vivo MOI estimates for Tobacco mosaic virus (TMV) and CaMV, using sophisticated approaches to measure the distribution of two virus genotypes over host cells. Here, we present a detailed analysis of spatial and temporal dynamics of the cellular MOI during colonization of a host plant by TEV. We observe a low frequency of virus-infected 22   cells (mean ± SD: 0.100 ± 0.073), and cells infected by both virus variants (mean ± SD: 0.012 ± 0.023). A new, model-selection- based method was used to determine the MOI, and the predicted MOIs values were low, ranging from 1.0 (leaf three, 3 days post inoculation (dpi)) to 1.6 (leaf four, 7 dpi). Finally, high genetic diversity in viral population gives rise to a cloud of variants; linked through mutation, interacting and contributing collectively; also called quasi-species. Viral populations can rapidly adapt to dynamic environments, remaining remarkably unstable under certain conditions as observed during evolution of TEV in case of genetic and functional redundancy. Large deletions and multiple defective particles were observed after various passages of long time TEV infection in transgenics plants expressing the viral replicase NIb. This result demonstrates the great genome plasticity of RNA virus and their capacity to eliminate any useless genetic load

Model-selection-based approach for calculating cellular multiplicity of infection during virus colonization of multi-cellular hosts

The cellular multiplicity of infection (MOI) is a key parameter for describing the interactions between virions and cells, predicting the dynamics of mixed-genotype infections, and understanding virus evolution. Two recent studies have reported in vivo MOI estimates for Tobacco mosaic virus (TMV) and Cauliflower mosaic virus (CaMV), using sophisticated approaches to measure the distribution of two virus variants over host cells. Although the experimental approaches were similar, the studies employed different definitions of MOI and estimation methods. Here, new model-selection-based methods for calculating MOI were developed. Seven alternative models for predicting MOI were formulated that incorporate an increasing number of parameters. For both datasets the best-supported model included spatial segregation of virus variants over time, and to a lesser extent aggregation of virus-infected cells was also implicated. Three methods for MOI estimation were then compared: the two previously reported methods and the best-supported model. For CaMV data, all three methods gave comparable results. For TMV data, the previously reported methods both predicted low MOI values (range: 1.04-1.23) over time, whereas the best-supported model predicted a wider range of MOI values (range: 1.01-2.10) and an increase in MOI over time. Model selection can therefore identify suitable alternative MOI models and suggest key mechanisms affecting the frequency of coinfected cells. For the TMV data, this leads to appreciable differences in estimated MOI values.This work was supported by grant BFU2012-30805 (SFE) and by 'Juan de la Cierva' postdoctoral contract JCI-2011-10379 (MPZ) from the Spanish Secretaria de Estado de Investigacion, Desarrollo e Innovacion. 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Shrinkage of genome size in a plant RNA virus upon transfer of an essential viral gene into the host genome

Nonretroviral integrated RNA viruses (NIRVs) are genes of nonretroviral RNA viruses found in the genomes of many eukaryotic organisms. NIRVs are thought to sometimes confer virus resistance, meaning that they could impact spread of the virus in the host population. However, a NIRV that is expressed may also impact the evolution of virus populations within host organisms. Here, we experimentally addressed the evolution of a virus in a host expressing a NIRV using Tobacco etch virus (TEV), a plant RNA virus, and transgenic tobacco plants expressing its replicase, NIb. We found that a virus missing the NIb gene, TEV-ΔNIb, which is incapable of autonomous replication in wild-type plants, had ahigher fitness than the full-length TEV in the transgenic plants. Moreover, when the full-length TEV was evolved by serial passages in transgenic plants, we observed genomic deletions within NIb - and insome cases the adjacent cistrons - starting from the first passage. When we passaged TEV and TEV-ΔNIb in transgenic plants, we found mutations in proteolytic sites, but these only occurred in TEV-ΔNIb lineages, suggesting the adaptation of polyprotein processing to altered NIb expression. These results raise the possibility that NIRV expression can indeed induce the deletion of the corresponding genes in the viral genome, resulting in the formation of viruses that are replication defective in hosts that do not express the same NIRV. Moreover, virus genome evolution was contingent upon the deletion of the viral replicase, suggesting NIRV expression could also alter patternsof virus evolution. © The Author(s) 2014.This project was supported by grant 22371 from the John Templeton Foundation to S. F. E. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. Additional support was provided by the Spanish Ministerio de Economia y Competitividad (MINECO) grant BFU2009-06993 and European Commission FP7-ICT program grant 610427-EvoEvo to S. F. E., by a predoctoral fellowship from MINECO to N.T., and by MINECO grant JCI2011-10379 and Rubicon grant from the Netherlands Organization for Scientific Research (www.nwo.nl) to M.P.Z.Peer Reviewe

The Rate and Spectrum of Spontaneous Mutations in a Plant RNA Virus

Knowing mutation rates and the molecular spectrum of spontaneous mutations is important to understanding how the genetic composition of viral populations evolves. Previous studies have shown that the rate of spontaneous mutations for RNA viruses widely varies between 0.01 and 2 mutations per genome and generation, with plant RNA viruses always occupying the lower side of this range. However, this peculiarity of plant RNA viruses is based on a very limited number of studies. Here we analyze the spontaneous mutational spectrum and the mutation rate of Tobacco etch potyvirus, a model system of positive sense RNA viruses. Our experimental setup minimizes the action of purifying selection on the mutational spectrum, thus giving a picture of what types of mutations are produced by the viral replicase. As expected for a neutral target, we found that transitions and nonsynonymous (including a few stop codons and small deletions) mutations were the most abundant type. This spectrum was notably different from the one previously described for another plant virus. We have estimated that the spontaneous mutation rate for this virus was in the range 10−6−10−5 mutations per site and generation. Our estimates are in the same biological ballpark that previous values reported for plant RNA viruses. This finding gives further support to the idea that plant RNA viruses may have lower mutation rates than their animal counterparts

Two cases of type-a Haemophilus influenzae meningitis within the same week in the same hospital are phylogenetically unrelated but recently exchanged capsule genes.

Haemophilus influenzae causes common and sometimes severe adult and pediatric disease including chronic obstructive respiratory disease, otitis media and infections of the central nervous system. Serotype b strains, with a b-type capsule, have been the historical cause of invasive disease, and the introduction of a serotype b-specific vaccine has led to their decline. However, unencapsulated or non-b-type H. influenzae infections are not prevented by the vaccine and appear to be increasing in frequency. Here we report two pediatric cases of severe central nervous system H. influenzae infection presenting to the same hospital in San Diego, California during the same week in January 2016. Due to good vaccine coverage in this part of the world, H. influenzae cases are normally rare and seeing two cases in the same week was unexpected. We thus suspected a recent transmission chain, and possible local outbreak. To test this hypothesis, we isolated and sequenced whole genomes from each patient and placed them in a phylogenetic tree spanning the known diversity of H. influenzae. Surprisingly, we found that the two isolates (SD2016_1 and SD2016_2) belonged to distantly related lineages, suggesting two independent transmission events and ruling out a local outbreak. Despite being distantly related, the two isolates belong to two different lineages that have exchanged capsule loci in the recent past. Therefore, as in other bacterial pathogens, capsule switching by horizontal gene transfer may be an important evolutionary mechanism of vaccine evasion in H. influenzae

Environment and host species shape the skin microbiome of captive neotropical bats

Background A wide range of microorganisms inhabit animal skin. This microbial community (microbiome) plays an important role in host defense against pathogens and disease. Bats (Chiroptera: Mammalia) are an ecologically and evolutionarily diversified group with a relatively unexplored skin microbiome. The bat skin microbiome could play a role in disease resistance, for example, to white nose syndrome (WNS), an infection which has been devastating North American bat populations. However, fundamental knowledge of the bat skin microbiome is needed before understanding its role in health and disease resistance. Captive neotropical frugivorous bats Artibeus jamaicensis and Carollia perspicillataprovide a simple controlled system in which to characterize the factors shaping the bat microbiome. Here, we aimed to determine the relative importance of habitat and host species on the bat skin microbiome. Methods We performed high-throughput 16S rRNA gene sequencing of the skin microbiome of two different bat species living in captivity in two different habitats. In the first habitat, A. jamaicensis and C. perspicillata lived together, while the second habitat contained only A. jamaicensis. Results We found that both habitat and host species shape the composition and diversity of the skin microbiome, with habitat having the strongest influence. Cohabitating A. jamaicensis and C. perspicillata shared more similar skin microbiomes than members of the same species (A. jamaicensis) across two habitats. Discussion These results suggest that in captivity, the skin microbial community is homogenised by the shared environments and individual proximities of bats living together in the same habitat, at the expense of the innate host species factors. The predominant influence of habitat suggests that environmental microorganisms or pathogens might colonize bat skin. We also propose that bat populations could differ in pathogen susceptibility depending on their immediate environment and habitat

Gut Microbiome of the Canadian Arctic Inuit

Diet is a major determinant of community composition in the human gut microbiome, and “traditional” diets have been associated with distinct and highly diverse communities, compared to Western diets. However, most traditional diets studied have been those of agrarians and hunter-gatherers consuming fiber-rich diets. In contrast, the Inuit of the Canadian Arctic have been consuming a traditional diet low in carbohydrates and rich in animal fats and protein for thousands of years. We hypothesized that the Inuit diet and lifestyle would be associated with a distinct microbiome. We used deep sequencing of the 16S rRNA gene to compare the gut microbiomes of Montrealers with a Western diet to those of the Inuit consuming a range of traditional and Western diets. At the overall microbial community level, the gut microbiomes of Montrealers and Inuit were indistinguishable and contained similar levels of microbial diversity. However, we observed significant differences in the relative abundances of certain microbial taxa down to the subgenus level using oligotyping. For example, Prevotella spp., which have been previously associated with high-fiber diets, were enriched in Montrealers and among the Inuit consuming a Western diet. The gut microbiomes of Inuit consuming a traditional diet also had significantly less genetic diversity within the Prevotella genus, suggesting that a low-fiber diet might not only select against Prevotella but also reduce its diversity. Other microbes, such as Akkermansia, were associated with geography as well as diet, suggesting limited dispersal to the Arctic. Our report provides a snapshot of the Inuit microbiome as Western-like in overall community structure but distinct in the relative abundances and diversity of certain genera and strains. Non-Western populations have been shown to have distinct gut microbial communities shaped by traditional diets. The hitherto-uncharacterized microbiome of the Inuit may help us to better understand health risks specific to this population such as diabetes and obesity, which increase in prevalence as many Inuit transition to a Western diet. Here we show that even Inuit consuming a mostly traditional diet have a broadly Western-like microbiome. This suggests that similarities between the Inuit diet and the Western diet (low fiber, high fat) may lead to a convergence of community structures and diversity. However, certain species and strains of microbes have significantly different levels of abundance and diversity in the Inuit, possibly driven by differences in diet. Furthermore, the Inuit diet provides an exception to the correlation between traditional diets and high microbial diversity, potentially due to their transitioning diet. Knowledge of the Inuit microbiome may provide future resources for interventions and conservation of Inuit heritage

A comparison of <i>m_I</i> estimates from Models 1, 2 and 6.

<p>The estimated MOI (<i>m_i</i>) is given for the inoculated leaf in Study 1 (Panels A, D and G), for the systemic leaf in Study 1 (Panel B, E and H), and for different systemic leaves collected at different times points in Study 2 (Panel C, F and I) using Model 1 (Panels A–C), Model 2 (Panels D–F), Model 3 (Panels G and H, blue lines and diamonds) and Model 6 (Panel I, red lines and squares). Model 3 is the best-supported model for the Study 1 data, whereas Model 6 is the best-supported model for the Study 2 data. The days post-inoculation (dpi) are given on the abscissae, whereas <i>m_I</i> is the ordinates. Error bars represent the 95% CI, and are marked with an asterisk when they extend to infinity (Panel I at 21 dpi). For the data of Study 1 (Panels A, B, D, E, G, and H), Models 1 and 2 both predict that MOI remains low throughout infection. On the other hand, Model 3 predicts that MOI increases over time, as this model incorporates the effects of spatial segregation of variants (Panels G and H). Note that Model 6 predictions are nearly identical to Model 3 predictions for Study 1. For the data of Study 2 (Panels C, F and I), model predictions are roughly similar and the dynamic pattern is the same. However, the differences in MOI over time are less pronounced for Model 6, in particular the decrease of MOI towards the end of infection. This difference is again due to predicted segregation of variants incorporated in Model 6, although the predicted effects thereof are much weaker for the data in Study 2 than in Study 1 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064657#pone-0064657-t003" target="_blank">Tables 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064657#pone-0064657-t004" target="_blank">4</a>).</p

A comparison of <i>m_T</i> and <i>m_I.</i>

<p>The relationship between <i>m_T</i> (abscissae) and <i>m_I</i> (ordinate) is plotted as the continuous line. The dotted line is a 1∶1 relationship, given for comparative purposes. <i>m_I</i>><i>m_T</i>, although for higher values (>4) the difference becomes very small. Note that <i>m_T</i> and has a range [0,∞) whilst <i>m_I</i> has a range [1,∞).</p