Blueberry red ringspot virus genomes from Florida inferred through analysis of blueberry root transcriptomes.

A growing number of metagenomics-based approaches have been used for the discovery of viruses in insects, cultivated plants, and water in agricultural production systems. In this study, sixteen blueberry root transcriptomes from eight clonally propagated blueberry plants of cultivar 'Emerald' (interspecific hybrid of Vaccinium corymbosum and V. darrowi) generated as part of a separate study on varietal tolerance to soil salinity were analyzed for plant viral sequences. The objective was to determine if the asymptomatic plants harbored the latent blueberry red ringspot virus (BRRV) in their roots. The only currently known mechanism of transmission of BRRV is through vegetative propagation; however, the virus can remain latent for years with some plants of 'Emerald' never developing red ringspot symptoms. Bioinformatic analyses of 'Emerald' transcriptomes using de novo assembly and reference-based mapping approaches yielded eight complete viral genomes of BRRV (genus Soymovirus, family Caulimoviridae). Validation in vitro by PCR confirmed the presence of BRRV in 100% of the 'Emerald' root samples. Sequence and phylogenetic analyses showed 94% to 97% nucleotide identity between BRRV genomes from Florida and sequences from Czech Republic, Japan, Poland, Slovenia, and the United States. Taken together, this study documented the first detection of a complete BRRV genome from roots of asymptomatic blueberry plants and in Florida through in silico analysis of plant transcriptomes.

Effective Altruism as an Ethical Lens on Research Priorities.

Effective altruism is an ethical framework for identifying the greatest potential benefits from investments. Here, we apply effective altruism concepts to maximize research benefits through identification of priority stakeholders, pathosystems, and research questions and technologies. Priority stakeholders for research benefits may include smallholder farmers who have not yet attained the minimal standards set out by the United Nations Sustainable Development Goals; these farmers would often have the most to gain from better crop disease management, if their management problems are tractable. In wildlands, prioritization has been based on the risk of extirpating keystone species, protecting ecosystem services, and preserving wild resources of importance to vulnerable people. Pathosystems may be prioritized based on yield and quality loss, and also factors such as whether other researchers would be unlikely to replace the research efforts if efforts were withdrawn, such as in the case of orphan crops and orphan pathosystems. Research products that help build sustainable and resilient systems can be particularly beneficial. The "value of information" from research can be evaluated in epidemic networks and landscapes, to identify priority locations for both benefits to individuals and to constrain regional epidemics. As decision-making becomes more consolidated and more networked in digital agricultural systems, the range of ethical considerations expands. Low-likelihood but high-damage scenarios such as generalist doomsday pathogens may be research priorities because of the extreme potential cost. Regional microbiomes constitute a commons, and avoiding the "tragedy of the microbiome commons" may depend on shifting research products from "common pool goods" to "public goods" or other categories. We provide suggestions for how individual researchers and funders may make altruism-driven research more effective.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology.

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.