Learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization

Parallel evolution between genomic segments of seasonal human influenza viruses reveals RNA-RNA relationships

The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To explore putative intersegmental RNA-RNA relationships, we quantified similarity between phylogenetic trees comprising each vRNA segment from seasonal human IAV. Intersegmental tree similarity differed between subtype and lineage. While intersegmental relationships were largely conserved over time in H3N2 viruses, they diverged in H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, suggesting that IAV evolution could also be driven by RNA-RNA interactions. Finally, we used confocal microscopy to determine that colocalization of highly coevolved vRNA segments is enriched over other assembly intermediates at the nuclear periphery during productive viral infection. This study illustrates how putative RNA interactions underlying selective assembly of IAV can be interrogated with phylogenetics.

The viruses responsible for influenza evolve rapidly during infection. Changes typically emerge in two key ways: through random mutations in the genetic sequence of the virus, or by reassortment. Reassortment can occur when two or more strains infect the same cell. Once in a cell, viral particles ‘open up’ to release their genetic material so it can make copies of itself using the cell’s machinery. The new copies of the genetic material of the virus are used to make new viral particles, which then envelop the genetic material and are released from the cell to infect other cells. If several strains of a virus infect the same cell, a new viral particle may pick up genetic segments from each of the infecting strains, creating a new strain via reassortment.

Several factors are known to affect the success of the reassortment process. For example, if the new strain acquires a genetic defect that hinders its replication cycle, it is likely to die out quickly. Other times, this trading of genetic information can create a strain that is more resistant to the human immune system, allowing it to sweep across the globe and cause a deadly pandemic. However, a key part of the reassortment process that still remains unclear is how genome segments from two different influenza strains recognize each other before merging together to create hybrid daughter viruses.

To explore this further, Jones et al. used a technique called fluorescence microscopy. They found that genome segments that evolved along similar paths were more likely to cluster in the same area inside infected cells, and therefore, more likely to be reassorted together into a new strain during assembly of daughter viruses. This suggests that assembly may guide the evolutionary path taken by individual genomic segments. Jones et al. also looked at the evolution of different genome segments collected from patients suffering from seasonal influenza, and found that these segments had a distinct evolutionary path to those in pandemic-causing strains.

This research provides new insights into the role of reassortment in the evolution of influenza viruses during infection. In particular, it suggests that how the genome segments interact with one another may have a previously unknown and important role in guiding this evolution. These insights could be used to predict future reassortment events based on evolutionary relationships between influenza virus genomic segments, and may in the future be used as part of risk assessment tools to predict the emergence of new pandemic strains.

Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

Synthetic Virology: Building Viruses to Better Understand Them

Generally comprised of less than a dozen components, RNA viruses can be viewed as well-designed genetic circuits optimized to replicate and spread within a given host. Understanding the molecular design that enables this activity not only allows one to disrupt these circuits to study their biology, but it provides a reprogramming framework to achieve novel outputs. Recent advances have enabled a "learning by building" approach to better understand virus biology and create valuable tools. Below is a summary of how modifying the preexisting genetic framework of influenza A virus has been used to track viral movement, understand virus replication, and identify host factors that engage this viral circuitry.

Selective flexible packaging pathways of the segmented genome of influenza A virus

The genome of influenza A viruses (IAV) is encoded in eight distinct viral ribonucleoproteins (vRNPs) that consist of negative sense viral RNA (vRNA) covered by the IAV nucleoprotein. Previous studies strongly support a selective packaging model by which vRNP segments are bundling to an octameric complex, which is integrated into budding virions. However, the pathway(s) generating a complete genome bundle is not known. We here use a multiplexed FISH assay to monitor all eight vRNAs in parallel in human lung epithelial cells. Analysis of 3.9 × 10⁵ spots of colocalizing vRNAs provides quantitative insights into segment composition of vRNP complexes and, thus, implications for bundling routes. The complexes rarely contain multiple copies of a specific segment. The data suggest a selective packaging mechanism with limited flexibility by which vRNPs assemble into a complete IAV genome. We surmise that this flexibility forms an essential basis for the development of reassortant viruses with pandemic potential.

Frustration and fidelity in influenza genome assembly

The genome of the influenza virus consists of eight distinct single-stranded RNA segments, each encoding proteins essential for the viral life cycle. When the virus infects a host cell, these segments must be replicated and packaged into new budding virions. The viral genome is assembled with remarkably high fidelity: experiments reveal that most virions contain precisely one copy of each of the eight RNA segments. Cell-biological studies suggest that genome assembly is mediated by specific reversible and irreversible interactions between the RNA segments and their associated proteins. However, the precise inter-segment interaction network remains unresolved. Here, we computationally predict that tree-like irreversible interaction networks guarantee high-fidelity genome assembly, while cyclic interaction networks lead to futile or frustrated off-pathway products. We test our prediction against multiple experimental datasets. We find that tree-like networks capture the nearest-neighbour statistics of RNA segments in packaged virions, as observed by electron tomography. Just eight tree-like networks (of a possible 262 144) optimally capture both the nearest-neighbour data and independently measured RNA–RNA binding and co-localization propensities. These eight do not include the previously proposed hub-and-spoke and linear networks. Rather, each predicted network combines hub-like and linear features, consistent with evolutionary models of interaction gain and loss.

Trans-Acting RNA-RNA Interactions in Segmented RNA Viruses

RNA viruses represent a large and important group of pathogens that infect a broad range of hosts. Segmented RNA viruses are a subclass of this group that encode their genomes in two or more molecules and package all of their RNA segments in a single virus particle. These divided genomes come in different forms, including double-stranded RNA, coding-sense single-stranded RNA, and noncoding single-stranded RNA. Genera that possess these genome types include, respectively, Orbivirus (e.g., Bluetongue virus), Dianthovirus (e.g., Red clover necrotic mosaic virus) and Alphainfluenzavirus (e.g., Influenza A virus). Despite their distinct genomic features and diverse host ranges (i.e., animals, plants, and humans, respectively) each of these viruses uses trans-acting RNA–RNA interactions (tRRIs) to facilitate co-packaging of their segmented genome. The tRRIs occur between different viral genome segments and direct the selective packaging of a complete genome complement. Here we explore the current state of understanding of tRRI-mediated co-packaging in the abovementioned viruses and examine other known and potential functions for this class of RNA–RNA interaction.

The Development and Use of Reporter Influenza B Viruses

Influenza B viruses (IBVs) are major contributors to total human influenza disease, responsible for ~1/3 of all infections. These viruses, however, are relatively less studied than the related influenza A viruses (IAVs). While it has historically been assumed that the viral biology and mechanisms of pathogenesis for all influenza viruses were highly similar, studies have shown that IBVs possess unique characteristics. Relative to IAV, IBV encodes distinct viral proteins, displays a different mutational rate, has unique patterns of tropism, and elicits different immune responses. More work is therefore required to define the mechanisms of IBV pathogenesis. One valuable approach to characterize mechanisms of microbial disease is the use of genetically modified pathogens that harbor exogenous reporter genes. Over the last few years, IBV reporter viruses have been developed and used to provide new insights into the host response to infection, viral spread, and the testing of antiviral therapeutics. In this review, we will highlight the history and study of IBVs with particular emphasis on the use of genetically modified viruses and discuss some remaining gaps in knowledge that can be addressed using reporter expressing IBVs.

Teaching an Old Virus New Tricks: A Review on New Approaches to Study Age-Old Questions in Influenza Biology

Influenza viruses have been studied for over 80 years, yet much about the basic viral lifecycle remain unknown. However, new imaging, biochemical, and sequencing techniques have revealed significant insight into many age-old questions of influenza virus biology. In this review, we will cover the role of imaging techniques to describe unique aspects of influenza virus assembly, biochemical techniques to study viral genomic organization, and next-generation sequencing to explore influenza genomic evolution. Our goal is to provide a brief overview of how emerging techniques are being used to answer basic questions about influenza viruses. This is not a comprehensive list of emerging techniques, rather ones that we feel will continue to make significant contributions to field of influenza biology.