Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4–5 log10 decline in virus within 1 day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.
Abstract1. Sexually reproducing organisms require males and females to find each other.Increased difficulty of females finding mates as male density declines is the most frequently reported mechanism of Allee effects in animals. Evolving more effective mate search may alleviate Allee effects, but may depend on density regimes a population experiences. In particular, high-density populations may evolve mechanisms that induce Allee effects which become detrimental when populations are reduced and maintained at a low density.2. We develop an individual-based, eco-genetic model to study how mating systems and fitness trade-offs interact with changes in population density to drive evolution of the rate at which males or females search for mates. Finite mate search rate triggers Allee effects in our model and we explore how these Allee effects respond to such evolution.3. We allow a population to adapt to several population density regimes and examine whether high-density populations are likely to reverse adaptations attained at low densities. We find density-dependent selection in most of scenarios, leading to search rates that result in lower Allee thresholds in populations kept at lower densities. This mainly occurs when fecundity costs are imposed on mate search, and provides an explanation for why Allee effects are often observed in anthropogenically rare species.4. Optimizing selection, where the attained trait value minimizes the Allee threshold independent of population density, depended on the trade-off between search and survival, combined with monogamy when females were searching. Other scenarios led to runaway selection on the mate search rate, including evolutionary suicide.Trade-offs involved in mate search may thus be crucial to determining how density influences the evolution of Allee effects.5. Previous studies did not examine evolution of a trait related to the strength of Allee effects under density variation. We emphasize the crucial role that mating systems, fitness trade-offs and the evolving sex have in determining the density threshold for population persistence, in particular since evolution need not always take the Allee threshold to its minimum value. K E Y W O R D Sevolutionary suicide, mate competition, mating, optimizing selection, positive density dependence, quantitative genetics, runaway selection, sexual selection
Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4-5 log 10 decline in virus within one day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and further insight into the regulation of viral control.
Arthropod-borne viruses pose a major threat to global public health. Thus, innovative strategies for their control and prevention are urgently needed. Here, we exploit the natural capacity of viruses to generate defective viral genomes (DVGs) to their detriment. While DVGs have been described for most viruses, identifying which, if any, can be used as therapeutic agents remains a challenge. We present a combined experimental evolution and computational approach to triage DVG sequence space and pinpoint the fittest deletions, using Zika virus as an arbovirus model. This approach identifies fit DVGs that optimally interfere with wild-type virus infection. We show that the most fit DVGs conserve the open reading frame to maintain the translation of the remaining non-structural proteins, a characteristic that is fundamental across the flavivirus genus. Finally, we demonstrate that the high fitness DVG is antiviral in vivo both in the mammalian host and the mosquito vector, reducing transmission in the latter by up to 90%. Our approach establishes the method to interrogate the DVG fitness landscape, and enables the systematic identification of DVGs that show promise as human therapeutics and vector control strategies to mitigate arbovirus transmission and disease.
Summary Continual effort is needed to reduce the impact of exotic species in the context of increased globalization. Any innovation in this respect would be an asset. We assess the potential of combining two pest control techniques: the well‐established sterile insect technique (SIT) and a novel male‐killing technique (MKT), which comprises inoculation of a pest population with bacteria that kill the infected male embryos. Population models are developed to assess the efficiency of using the MKT for insect pest control, either alone or together with the SIT. We seek for conditions under which the MKT weakens requirements on the SIT. Regarding the SIT, we consider both non‐heritable and inherited sterility. In both cases, the MKT and SIT benefit one another. The MKT may prevent the SIT from failing when not enough sterilized males are released due to high production costs and/or uncertainty on their mating ability following a high irradiation dose. Conversely, with already established SIT, pest eradication can be achieved after introduction of male‐killing bacteria with lower vertical transmission efficiency than if the MKT was applied alone. For tephritid fruit flies with non‐heritable sterility, maximal impact of the SIT is achieved when the released males are fully sterile. Conversely, for lepidopterans with inherited sterility, maximal impact of the SIT is achieved for intermediate irradiation doses. In both cases, increasing vertical transmission efficiency of male‐killing bacteria benefits the SIT; high enough vertical transmission efficiency allows for pest eradication where the SIT is absent or induces only pest suppression when used alone. Synthesis and applications. While both techniques can suppress or eliminate the pest on their own, combined application of the male‐killing technique and the sterile insect technique substantially increases pest control efficiency. If male‐killing bacteria are already established in the pest, any assessment of the sterile insect technique needs to account for their presence; otherwise, management recommendations could be exaggerated and unnecessarily costly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.