Open questions in the social lives of viruses

Phage against the Machine: The SIE-ence of Superinfection Exclusion

Prophages can alter their bacterial hosts to prevent other phages from infecting the same cell, a mechanism known as superinfection exclusion (SIE). Such alterations are facilitated by phage interactions with critical bacterial components involved in motility, adhesion, biofilm production, conjugation, antimicrobial resistance, and immune evasion. Therefore, the impact of SIE extends beyond the immediate defense against superinfection, influencing the overall fitness and virulence of the bacteria. Evaluating the interactions between phages and their bacterial targets is critical for leading phage therapy candidates like Pseudomonas aeruginosa, a Gram-negative bacterium responsible for persistent and antibiotic-resistant opportunistic infections. However, comprehensive literature on the mechanisms underlying SIE remains scarce. Here, we provide a compilation of well-characterized and potential mechanisms employed by Pseudomonas phages to establish SIE. We hypothesize that the fitness costs imposed by SIE affect bacterial virulence, highlighting the potential role of this mechanism in the management of bacterial infections.

Influenza A defective viral genome production is altered by metabolites, metabolic signaling molecules, and cyanobacteria extracts

RNA virus infections are composed of a diverse mix of viral genomes that arise from low fidelity in replication within cells. The interactions between "defective" and full-length viral genomes have been shown to shape pathogenesis, leading to intense research into employing these to develop novel antivirals. In particular, Influenza A defective viral genomes (DVGs) have been associated with milder clinical outcomes. Yet, the full potential of DVGs as broad-spectrum antivirals remains untapped due to the unknown mechanisms of their de novo production. Much of the research into the factors affecting defective viral genome production has focused on the virus, while the role of the host has been neglected. We recently showed that altering host cell metabolism away from pro-growth pathways using alpelisib increased the production of Influenza A defective viral genomes. To uncover other drugs that could induce infections to create more DVGs, we subjected active influenza infections of the two circulating human subtypes (A/H1N1 & A/H3N2) to a screen of metabolites, metabolic signaling molecules, and cyanobacteria-derived biologics, after which we quantified the defective viral genomes (specifically deletion-containing viral genomes, DelVGs) and total viral genomes using third generation long-read sequencing. Here we show that metabolites and signaling molecules of host cell central carbon metabolism can significantly alter DelVG production early in Influenza A infection. Adenosine, emerged as a potent inducer of defective viral genomes, significantly amplifying DelVG production across both subtypes. Insulin had similar effects, albeit subtype-specific, predominantly enhancing polymerase segment DVGs in TX12 infections. Tricarboxylic Acid (TCA) cycle inhibitors 4-octyl itaconate and UK5099, along with the purine analog favipiravir, increased total viral genome production across subtypes. Cyanobacterial extracts primarily affected DVG and total viral genome production in TX12, with a specific, almost complete shutdown of influenza antigenic segments. These results underscore the influence of host metabolic pathways on DVG production and suggest new avenues for antiviral intervention, including PI3K-AKT and Ras-MAPK signaling pathways, TCA cycle metabolism, purine-pyrimidine metabolism, polymerase inhibition, and cyanotherapeutic approaches. More broadly, our findings suggest that the social interactions observed between defective and full-length viral genomes, depend not only on the viral actors, but can be altered by the stage provided by the host. Our study advances our fundamental understanding of DVG production mechanisms and highlights the potential of targeting host metabolism to develop broad-spectrum influenza therapeutics.

Influenza A defective viral genomes and non-infectious particles are increased by host PI3K inhibition via anti-cancer drug alpelisib

RNA viruses produce abundant defective viral genomes during replication, setting the stage for interactions between viral genomes that alter the course of pathogenesis. Harnessing these interactions to develop antivirals has become a recent goal of intense research focus. Despite decades of research, the mechanisms that regulate the production and interactions of Influenza A defective viral genomes are still unclear. The role of the host is essentially unexplored; specifically, it remains unknown whether host metabolism can influence the formation of defective viral genomes and the particles that house them. To address this question, we manipulated host cell anabolic signaling activity and monitored the production of defective viral genomes and particles by A/H1N1 and A/H3N2 strains, using a combination of single-cell immunofluorescence quantification, third-generation long-read sequencing, and the cluster-forming assay, a method we developed to titer defective and fully-infectious particles simultaneously. Here we show that alpelisib (Piqray), a highly selective inhibitor of mammalian Class 1a phosphoinositide-3 kinase (PI3K) receptors, significantly changed the proportion of defective particles and viral genomes (specifically deletion-containing viral genomes) in a strain-specific manner, under conditions that minimize multiple cycles of replication. Alpelisib pre-treatment of cells led to an increase in defective particles in the A/H3N2 strain, while the A/H1N1 strain showed a decrease in total viral particles. In the same infections, we found that defective viral genomes of polymerase and antigenic segments increased in the A/H1N1 strain, while the total particles decreased suggesting defective interference. We also found that the average deletion size in polymerase complex viral genomes increased in both the A/H3N2 and A/H1N1 strains. The A/H1N1 strain, additionally showed a dose-dependent increase in total number of defective viral genomes. In sum, we provide evidence that host cell metabolism can increase the production of defective viral genomes and particles at an early stage of infection, shifting the makeup of the infection and potential interactions among virions. Given that Influenza A defective viral genomes can inhibit pathogenesis, our study presents a new line of investigation into metabolic states associated with less severe flu infection and the potential induction of these states with metabolic drugs.

Fractal networks: Topology, dimension, and complexity

Over the past two decades, the study of self-similarity and fractality in discrete structures, particularly complex networks, has gained momentum. This surge of interest is fueled by the theoretical developments within the theory of complex networks and the practical demands of real-world applications. Nonetheless, translating the principles of fractal geometry from the domain of general topology, dealing with continuous or infinite objects, to finite structures in a mathematically rigorous way poses a formidable challenge. In this paper, we overview such a theory that allows to identify and analyze fractal networks through the innate methodologies of graph theory and combinatorics. It establishes the direct graph-theoretical analogs of topological (Lebesgue) and fractal (Hausdorff) dimensions in a way that naturally links them to combinatorial parameters that have been studied within the realm of graph theory for decades. This allows to demonstrate that the self-similarity in networks is defined by the patterns of intersection among densely connected network communities. Moreover, the theory bridges discrete and continuous definitions by demonstrating how the combinatorial characterization of Lebesgue dimension via graph representation by its subsets (subgraphs/communities) extends to general topological spaces. Using this framework, we rigorously define fractal networks and connect their properties with established combinatorial concepts, such as graph colorings and descriptive complexity. The theoretical framework surveyed here sets a foundation for applications to real-life networks and future studies of fractal characteristics of complex networks using combinatorial methods and algorithms.

The social lives of viruses and other mobile genetic elements: a commentary on Leeks et al. 2023

Illustration of life-histories of phages and plasmids through horizontal and vertical transmission (see Figure 1 for more information).