A multicellular organism is not a monolayer of cells in a flask; it is a complex, spatially structured environment, offering both challenges and opportunities for viruses to thrive. Whereas virus infection dynamics at the host and within-cell levels have been documented, the intermediate between-cell level remains poorly understood. Here, we used flow cytometry to measure the infection status of thousands of individual cells in virus-infected plants. This approach allowed us to determine accurately the number of cells infected by two virus variants in the same host, over space and time as the virus colonizes the host. We found a low overall frequency of cellular infection (<0.3), and few cells were coinfected by both virus variants (<0.1). We then estimated the cellular contagion rate (R), the number of secondary infections per infected cell per day. R ranged from 2.43 to values not significantly different from zero, and generally decreased over time. Estimates of the cellular multiplicity of infection (MOI), the number of virions infecting a cell, were low (<1.5). Variance of virus-genotype frequencies increased strongly from leaf to cell levels, in agreement with a low MOI. Finally, there were leaf-dependent differences in the ease with which a leaf could be colonized, and the number of virions effectively colonizing a leaf. The modeling of infection patterns suggests that the aggregation of virus-infected cells plays a key role in limiting spread; matching the observation that cell-to-cell movement of plant viruses can result in patches of infection. Our results show that virus expansion at the between-cell level is restricted, probably due to the host environment and virus infection itself.

Over the years, agriculture across the world has been compromised by a succession of devastating epidemics caused by new viruses that spilled over from reservoir species or by new variants of classic viruses that acquired new virulence factors or changed their epidemiological patterns. Viral emergence is usually associated with ecological change or with agronomical practices bringing together reservoirs and crop species. The complete picture is, however, much more complex, and results from an evolutionary process in which the main players are ecological factors, viruses' genetic plasticity, and host factors required for virus replication, all mixed with a good measure of stochasticity. The present review puts emergence of plant RNA viruses into the framework of evolutionary genetics, stressing that viral emergence begins with a stochastic process that involves the transmission of a preexisting viral strain into a new host species, followed by adaptation to the new host.

Microcystis is a genus of freshwater cyanobacteria, which causes harmful blooms in ecosystems worldwide. Some Microcystis strains produce harmful toxins such as microcystin, impacting drinking water quality. Microcystis colony morphology, rather than genetic similarity, is often used to classify Microcystis into morphospecies. Yet colony morphology is a plastic trait, which can change depending on environmental and laboratory culture conditions, and is thus an inadequate criterion for species delineation. Furthermore, Microcystis populations are thought to disperse globally and constitute a homogeneous gene pool. However, this assertion is based on relatively incomplete characterization of Microcystis genomic diversity. To better understand these issues, we performed a population genomic analysis of 33 newly sequenced genomes mainly from Canada and Brazil. We identified 17 Microcystis clusters of genomic similarity, five of which correspond to monophyletic clades containing at least three newly sequenced genomes. Four out of these five clades match to named morphospecies. Notably, M. aeruginosa is paraphyletic, distributed across 12 genomic clusters, suggesting it is not a coherent species. A few clades of closely related isolates are specific to a unique geographic location, suggesting biogeographic structure over relatively short evolutionary time scales. Higher homologous recombination rates within than between clades further suggest that monophyletic groups might adhere to a Biological Species-like concept, in which barriers to gene flow maintain species distinctness. However, certain genes-including some involved in microcystin and micropeptin biosynthesis-are recombined between monophyletic groups in the same geographic location, suggesting local adaptation.

Cyanobacterial blooms occur in lakes worldwide, producing toxins that pose a serious public health threat. Eutrophication caused by human activities and warmer temperatures both contribute to blooms, but it is still difficult to predict precisely when and where blooms will occur. One reason that prediction is so difficult is that blooms can be caused by different species or genera of cyanobacteria, which may interact with other bacteria and respond to a variety of environmental cues. Here we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterise the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community-including non-cyanobacteria-prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloom events are predictable. We used probabilistic assemblages of OTUs to characterise the bloom-associated community, and to classify samples into bloom or non-bloom categories, achieving up to 92% classification accuracy (86% after excluding cyanobacterial sequences). Finally, using symbolic regression, we were able to predict the start date of a bloom with 78-92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables.