Accounting for cellular-level variation in lysis: implications for virus-host dynamics.

Viral impacts on microbial populations depend on interaction phenotypes-including viral traits spanning the adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of the latent period via one-step growth curves is systematically biased-generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events, which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations.IMPORTANCEQuantifying viral traits-including the adsorption rate, burst size, and latent period-is critical to characterize viral infection dynamics and develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation-the one-step growth curve-to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically underestimate the mean latent period and, in turn, overestimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series, we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

Functional genomics of a Spiroplasma associated with the carmine cochineals Dactylopius coccus and Dactylopius opuntiae.

BACKGROUND: Spiroplasma is a widely distributed endosymbiont of insects, arthropods, and plants. In insects, Spiroplasma colonizes the gut, hemolymph, and reproductive organs of the host. Previous metagenomic surveys of the domesticated carmine cochineal Dactylopius coccus and the wild cochineal D. opuntiae reported sequences of Spiroplasma associated with these insects. However, there is no analysis of the genomic capabilities and the interaction of this Spiroplasma with Dactylopius. RESULTS: Here we present three Spiroplasma genomes independently recovered from metagenomes of adult males and females of D. coccus, from two different populations, as well as from adult females of D. opuntiae. Single-copy gene analysis showed that these genomes were > 92% complete. Phylogenomic analyses classified these genomes as new members of Spiroplasma ixodetis. Comparative genome analysis indicated that they exhibit fewer genes involved in amino acid and carbon catabolism compared to other spiroplasmas. Moreover, virulence factor-encoding genes (i.e., glpO, spaid and rip2) were found incomplete in these S. ixodetis genomes. We also detected an enrichment of genes encoding the type IV secretion system (T4SS) in S. ixodetis genomes of Dactylopius. A metratranscriptomic analysis of D. coccus showed that some of these T4SS genes (i.e., traG, virB4 and virD4) in addition to the superoxide dismutase sodA of S. ixodetis were overexpressed in the ovaries. CONCLUSION: The symbiont S. ixodetis is a new member of the bacterial community of D. coccus and D. opuntiae. The recovery of incomplete virulence factor-encoding genes in S. ixodetis of Dactylopius suggests that this bacterium is a non-pathogenic symbiont. A high number of genes encoding the T4SS, in the S. ixodetis genomes and the overexpression of these genes in the ovary and hemolymph of the host suggest that S. ixodetis use the T4SS to interact with the Dactylopius cells. Moreover, the transcriptional differences of S. ixodetis among the gut, hemolymph and ovary tissues of D. coccus indicate that this bacterium can respond and adapt to the different conditions (e.g., oxidative stress) present within the host. All this evidence proposes that there is a strong interaction and molecular signaling in the symbiosis between S. ixodetis and the carmine cochineal Dactylopius.

Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model.

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multi-week periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities.

Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 690,000 confirmed cases and more than 33,000 confirmed deaths globally as of March 30, 2020 [1-4]. At present two central public health control strategies have emerged: mitigation and suppression (e.g, [5]). Both strategies focus on reducing new infections by reducing interactions (and both raise questions of sustainability and long-term tactics). Complementary to those approaches, here we develop and analyze an epidemiological intervention model that leverages serological tests [6, 7] to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, i.e., to develop what we term 'shield immunity' at the population scale. Recovered individuals, in the present context, represent those who have developed protective, antibodies to SARS-CoV-2 and are no longer shedding virus [8]. The objective of a shield immunity strategy is to help sustain the interactions necessary for the functioning of essential goods and services (including but not limited to tending to the elderly [9], hospital care, schools, and food supply) while decreasing the probability of transmission during such essential interactions. We show that a shield immunity approach may significantly reduce the length and reduce the overall burden of an outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well recognized roles in estimating prevalence [10, 11] and in the potential development of plasma-based therapies [12-15].

Modeling shield immunity to reduce COVID-19 epidemic spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.

Functional and Comparative Genomic Analysis of Integrated Prophage-Like Sequences in "Candidatus Liberibacter asiaticus".

Huanglongbing disease (HLB; yellow shoot disease) is a severe worldwide infectious disease for citrus family plants. The pathogen "Candidatus Liberibacter asiaticus" is an alphaproteobacterium of the Rhizobiaceae family that has been identified as the causative agent of HLB. The virulence of "Ca. Liberibacter asiaticus" has been attributed, in part, to prophage-carried genes. Prophage and prophage-like elements have been identified in 12 of the 15 available "Ca. Liberibacter asiaticus" genomes and are classified into three prophage types. Here, we reexamined all 15 "Ca. Liberibacter asiaticus" genomes using a de novo prediction approach and expanded the number of prophage-like elements from 16 to 33. Further, we found that all of the "Ca. Liberibacter asiaticus" genomes contained at least one prophage-like sequence. Comparative analysis revealed a prevalent, albeit previously unknown, prophage-like sequence type that is a remnant of an integrated prophage. Notably, this remnant prophage is found in the Ishi-1 "Ca. Liberibacter asiaticus" strain that had previously been reported as lacking prophages. Our findings provide both a resource for data and new insights into the evolutionary relationship between phage and "Ca. Liberibacter asiaticus" pathogenicity.IMPORTANCE Huanglongbing (HLB) disease is threatening citrus production worldwide. The causative agent is "Candidatus Liberibacter asiaticus." Prior work using mapping-based approaches identified prophage-like sequences in some "Ca. Liberibacter asiaticus" genomes but not all. Here, we utilized a de novo approach that expands the number of prophage-like elements found in "Ca. Liberibacter asiaticus" from 16 to 33 and identified at least one prophage-like sequence in all "Ca. Liberibacter asiaticus" strains. Furthermore, we identified a prophage-like sequence type that is a remnant of an integrated prophage-expanding the number of prophage types in "Ca. Liberibacter asiaticus" from 3 to 4. Overall, the findings will help researchers investigate the role of prophage in the ecology, evolution, and pathogenicity of "Ca. Liberibacter asiaticus."