The honey bee (Apis mellifera) is commonly infected by multiple viruses. We developed an experimental system for the study of such mixed viral infections in newly emerged honey bees and in the cell line AmE-711, derived from honey bee embryos. When inoculating a mixture of iflavirids [sacbrood bee virus (SBV), deformed wing virus (DWV)] and dicistrovirids [Israeli acute paralysis virus (IAPV), black queen cell virus (BQCV)] in both live bee and cell culture assays, IAPV replicated to higher levels than other viruses despite the fact that SBV was the major component of the inoculum mixture. When a different virus mix composed mainly of the dicistrovirid Kashmir bee virus (KBV) was tested in cell culture, the outcome was a rapid increase in KBV but not IAPV. We also sequenced the complete genome of an isolate of DWV that covertly infects the AmE-711 cell line and found that this virus does not prevent IAPV and KBV from accumulating to high levels and causing cytopathic effects. These results indicate that different mechanisms of virus-host interaction affect virus dynamics, including complex virus-virus interactions, superinfections, specific virus saturation limits in cells and virus specialization for different cell types.

Pepper golden mosaic virus (PepGMV) is an important begomovirus infecting solanaceous crops in Mexico and Central America. Under controlled conditions for growth and inoculation with a low-pressure biolistic device, PepGMV-infected pepper plants consistently showed symptom remission or host recovery 12 to 15 days postinoculation (dpi). Inoculated plants initially developed the characteristic PepGMV symptoms; however, newer leaves presented a significant decrease or disappearance of symptoms. Younger asymptomatic, recovered leaves accumulated lower quantities of viral DNA and transcripts than the ones found in the symptomatic tissue. Nonetheless, viral DNA did not disappear during the evaluation period (up to 35 dpi), suggesting that a population of viral molecules escape from plant defensive mechanisms to maintain a subliminal, symptomless infection. Recovery was correlated with a specific resistance to PepGMV but not to Pepper huasteco yellow vein virus, a different gemi-nivirus commonly found in mixed infections with PepGMV. Virus-related small interfering RNAs were detected in practically all tissues (from symptomatic to recovered leaves) but it was not possible to establish a correlation between concentration and symptom severity. The participation of a posttranscriptional gene silencing mechanism in the recovery process and specific resistance is discussed.

Grapevine red blotch virus (GRBV) (Al Rwahnih et al., 2013) is a member of the recently recognized genus Grablovirus in the family Geminiviridae (Varsani et al., 2017). GRBV is the causal agent of grapevine red blotch disease (Yepes et al., 2018) affecting viticulture in North America and some other regions around the world (reviewed in (Sudarshana, Perry and Fuchs, 2015)). In this study, we sampled grapevine plants displaying red blotch symptoms in the major wine producing region of Mexico: Ensenada, Baja California, in 2016 and 2017. A total of 46 samples from cultivars Vitis vinifera L. ‘Pinot Noir’ and ‘Merlot’ were tested in 2016 by end-point PCR using primers GRBV-V2F (5’-ATGGGTTAGGGGATGAGGCT-3’) and GRBV-V1R (5’-CGGCAATGACTCCTGCGGCT-3’). Results showed amplification of the expected 798 bp product from 31 samples. One of these amplicons was cloned, sequenced and compared to reported sequences through a BLASTn search. The fragment had high nucleotide sequence identity (98%) with GRBV isolate BCRB5 fr...

Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users. In this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package “bigPint” includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The “bigPint” website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code. We emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.