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The Ty1 retrotransposon family is maintained in a functional but dormant state by its host, Saccharomyces cerevisiae . Several hundred RHF and RTT genes encoding co-factors and restrictors of Ty1 retromobility, respectively, have been identified. Well-characterized examples include MED3 and MED15 , encoding subunits of the Mediator transcriptional co-activator complex; control of retromobility by Med3 and Med15 requires the Ty1 promoter in the U3 region of the long terminal repeat. To characterize the U3-dependence of other Ty1 regulators, we screened a library of 188 known rhf and rtt mutants for altered retromobility of Ty1 his3AI expressed from the strong, TATA-less TEF1 promoter or the weak, TATA-containing U3 promoter. Two classes of genes, each including both RHF s and RTT s, were identified. The first class comprising 82 genes that regulated Ty1 his3AI retromobility independently of U3 is enriched for RHF genes that restrict the G1 phase of the cell cycle and those involved in transcriptional elongation and mRNA catabolism. The second class of 51 genes regulated retromobility of Ty1 his3AI driven only from the U3 promoter. Nineteen U3-dependent regulators (U3DRs) also controlled retromobility of Ty1 his3AI driven by the weak, TATA-less PSP2 promoter, suggesting reliance on the low activity of U3. Thirty-one U3DRs failed to modulate P PSP2 -Ty1 his3AI retromobility, suggesting dependence on the architecture of U3. To further investigate the U3-dependency of Ty1 regulators, we developed a novel fluorescence-based assay to monitor expression of p22-Gag, a restriction factor expressed from the internal Ty1i promoter. Many U3DRs had minimal effects on levels of Ty1 RNA, Ty1i RNA or p22-Gag. These findings uncover a role for the Ty1 promoter in integrating signals from diverse host factors to modulate Ty1 RNA biogenesis or fate. 

Virus-like particles resemble infectious virus particles in size, shape, and molecular composition; however, they fail to productively infect host cells. Historically, the presence of virus-like particles has been inferred from total particle counts by microscopy, and infectious particle counts or plaque-forming-units (PFUs) by plaque assay; the resulting ratio of particles-to-PFUs is often greater than one, easily 10 or 100, indicating that most particles are non-infectious. Despite their inability to hijack cells for their reproduction, virus-like particles and the defective genomes they carry can exhibit a broad range of behaviors: interference with normal virus growth during co-infections, cell killing, and activation or inhibition of innate immune signaling. In addition, some virus-like particles become productive as their multiplicities of infection increase, a sign of cooperation between particles. Here, we review established and emerging methods to count virus-like particles and characterize their biological functions. We take a critical look at evidence for defective interfering virus genomes in natural and clinical isolates, and we review their potential as antiviral therapeutics. In short, we highlight an urgent need to better understand how virus-like genomes and particles interact with intact functional viruses during co-infection of their hosts, and their impacts on the transmission, severity, and persistence of virus-associated diseases. 

Abstract Although viruses in their natural habitats add up to less than 10% of the biomass, they contribute more than 90% of the genome sequences [1]. These viral sequences or ‘viromes’ encode viruses that populate the Earth’s oceans [2, 3] and terrestrial environments [4, 5], where their infections impact life across diverse ecological niches and scales [6, 7], including humans [8–10]. Most viruses have yet to be isolated and cultured [11–13], and surprisingly few efforts have explored what analysis of available data might reveal about their nature. Here, we compiled and analyzed seven decades of one-step growth and other data for viruses from six major families, including their infections of archaeal, bacterial and eukaryotic hosts [14–191]. We found that the use of host cell biomass for virus production was highest for archaea at 10%, followed by bacteria at 1% and eukarya at 0.01%, highlighting the degree to which viruses of archaea and bacteria exploit their host cells. For individual host cells, the yield of virus progeny spanned a relatively narrow range (10–1000 infectious particles per cell) compared with the million-fold difference in size between the smallest and largest cells. Furthermore, healthy and infected host cells were remarkably similar in the time they needed to multiply themselves or their virus progeny. Specifically, the doubling time of healthy cells and the delay time for virus release from infected cells were not only correlated (r = 0.71, p < 10−10, n = 101); they also spanned the same range from tens of minutes to about a week. These results have implications for better understanding the growth, spread and persistence of viruses in complex natural habitats that abound with diverse hosts, including humans and their associated microbes. 

Abstract Why do some biological systems and communities persist while others fail? Robustness, a system's stability, and resilience, the ability to return to a stable state, are key concepts that span multiple disciplines within and outside the biological sciences. Discovering and applying common rules that govern the robustness and resilience of biological systems is a critical step toward creating solutions for species survival in the face of climate change, as well as the for the ever-increasing need for food, health, and energy for human populations. We propose that network theory provides a framework for universal scalable mathematical models to describe robustness and resilience and the relationship between them, and hypothesize that resilience at lower organization levels contribute to robust systems. Insightful models of biological systems can be generated by quantifying the mechanisms of redundancy, diversity, and connectivity of networks, from biochemical processes to ecosystems. These models provide pathways towards understanding how evolvability can both contribute to and result from robustness and resilience under dynamic conditions. We now have an abundance of data from model and non-model systems and the technological and computational advances for studying complex systems. Several conceptual and policy advances will allow the research community to elucidate the rules of robustness and resilience. Conceptually, a common language and data structure that can be applied across levels of biological organization needs to be developed. Policy advances such as cross-disciplinary funding mechanisms, access to affordable computational capacity, and the integration of network theory and computer science within the standard biological science curriculum will provide the needed research environments. This new understanding of biological systems will allow us to derive ever more useful forecasts of biological behaviors and revolutionize the engineering of biological systems that can survive changing environments or disease, navigate the deepest oceans, or sustain life throughout the solar system. 

Rapid growth of genome data provides opportunities for updating microbial evolutionary relationships, but this is challenged by the discordant evolution of individual genes. Here we build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on a comprehensive set of 381 markers, using multiple strategies. Our trees indicate remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that were limited to fewer “core” genes, such as the ribosomal proteins. The robustness of the results was tested with respect to several variables, including taxon and site sampling, amino acid substitution heterogeneity and saturation, non-vertical evolution, and the impact of exclusion of candidate phyla radiation (CPR) taxa. Our results provide an updated view of domain-level relationships.