Mixed viral infection constrains the genome formula of multipartite cucumber mosaic virus

Many plant viruses have a multipartite organization, with multiple genome segments packaged into separate virus particles. The genome formula describes the relative frequencies of all viral genome segments, and previous work suggests rapid changes in these frequencies facilitate virus adaptation. Many studies have reported mixed viral infections in plants, often resulting in strong virus–virus interactions. Here, we tested whether mixed infections with tripartite alfalfa mosaic virus (AMV) and monopartite potato virus Y (PVY) affected the genome formula of the tripartite cucumber mosaic virus (CMV), our experimental model. We found that the CMV titer was reduced in mixed infections with its tripartite Bromoviridae relative AMV and in triple infections with both AMV and PVY, indicating notable virus–virus interactions. The variability of the CMV genome formula was significantly lower in mixed infections (CMV and AMV, CMV and PVY, and CMV and AMV and PVY) than in single infections (CMV only). These observations led to the surprising conclusion that mixed infections with two distinct viruses constrain the CMV genome formula. It remains unclear how common these effects are for different combinations of virus species and strains and what the underlying mechanisms are. We, therefore, extended a simulation model to consider three putative scenarios in which a second virus affected the genome formula. The simulation results also suggested that shifts in the genome formula occur, but may not be widespread due to the required conditions. One scenario modeled—co-infection exclusion through niche differentiation—was congruent with the experimental data, as this scenario led to reductions in genome formula variability and titer of the multipartite virus. Whereas previous studies highlighted host–species effects, our results indicate that the genome formula is also affected by mixed infections, suggesting that there is a broader set of environmental cues that affect the genome formula

Invasive Group A Streptococcal Infection in High School Football Players, New York City, 2003

After being notified that 2 high school football teammates were hospitalized with confirmed or suspected invasive group A streptococcal infections, we conducted an investigation of possible spread among other team members. This investigation highlights a need for guidelines on management of streptococcal and other infectious disease outbreaks in team sport settings

Robust Approaches to the Quantitative Analysis of Genome Formula Variation in Multipartite and Segmented Viruses

When viruses have segmented genomes, the set of frequencies describing the abundance of segments is called the genome formula. The genome formula is often unbalanced and highly variable for both segmented and multipartite viruses. A growing number of studies are quantifying the genome formula to measure its effects on infection and to consider its ecological and evolutionary implications. Different approaches have been reported for analyzing genome formula data, including qualitative description, applying standard statistical tests such as ANOVA, and customized analyses. However, these approaches have different shortcomings, and test assumptions are often unmet, potentially leading to erroneous conclusions. Here, we address these challenges, leading to a threefold contribution. First, we propose a simple metric for analyzing genome formula variation: the genome formula distance. We describe the properties of this metric and provide a framework for understanding metric values. Second, we explain how this metric can be applied for different purposes, including testing for genome-formula differences and comparing observations to a reference genome formula value. Third, we re-analyze published data to illustrate the applications and weigh the evidence for previous conclusions. Our re-analysis of published datasets confirms many previous results but also provides evidence that the genome formula can be carried over from the inoculum to the virus population in a host. The simple procedures we propose contribute to the robust and accessible analysis of genome-formula data

Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula

Viruses show great diversity in their genome organization. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (Nicotiana tabacum, Nicotiana benthamiana, and Chenopodium quinoa) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasize the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial

Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula

Viruses show great diversity in their genome organization. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (Nicotiana tabacum, Nicotiana benthamiana, and Chenopodium quinoa) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasize the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial

Oncolog, Volume 28, Number 04, October-December 1983

Implant Relieves Chronic Pain, Delivers Drug Directly to Central Nervous System New Drug Combination Improves Survival For Patients with Metastatic Prostate Cancer Leukocyte Interferon Inhibits Growth Of Virally Related Laryngeal Tumorhttps://openworks.mdanderson.org/oncolog/1004/thumbnail.jp