Exploring the Mosquito–Arbovirus Network: A Survey of Vector Competence Experiments

Chen, Binqi; Sweeny, Amy R.; Wu, Velen Yifei; Christofferson, Rebecca C.; Ebel, Gregory D.; Fagre, Anna C.; Gallichotte, Emily N.; Kading, Rebekah C.; Ryan, Sadie J.; Carlson, Colin J.

doi:10.4269/ajtmh.22-0511

Cited by 7 publications

(5 citation statements)

References 45 publications

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“…albopictus and the context within which these studies were carried out, but the model is intended to be extendable to other mosquito-virus systems. As suggested by Chen et al [50], establishing an open-source repository where results from vector competence experiments could be submitted when published would assist the ability for meta-analyses of existing data. When vector competence studies are carried out, we suggest the design of experiments that produce data permitting the quantification of both the dose-response of the probability of mosquito midgut infection and the time-response of the probability of dissemination.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Mechanistic modelling of within-mosquito viral dynamics: Insights into infection and dissemination patterns

Lord,

Bonsall

2023

PLoS Comput Biol

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Vector or host competence can be defined as the ability of an individual to become infected and subsequently transmit a pathogen. Assays to measure competence play a key part in the assessment of the factors affecting mosquito-borne virus transmission and of potential pathogen-blocking control tools for these viruses. For mosquitoes, competence for arboviruses can be measured experimentally and results are usually analysed using standard statistical approaches. Here we develop a mechanistic approach to studying within-mosquito virus dynamics that occur during vector competence experiments. We begin by developing a deterministic model of virus replication in the mosquito midgut and subsequent escape and replication in the hemocoel. We then extend this to a stochastic model to capture the between-individual variation observed in vector competence experiments. We show that the dose-response of the probability of mosquito midgut infection and variation in the dissemination rate can be explained by stochastic processes generated from a small founding population of virions, caused by a relatively low rate of virion infection of susceptible cells. We also show that comparing treatments or species in competence experiments by fitting mechanistic models could provide further insight into potential differences. Generally, our work adds to the growing body of literature emphasizing the importance of intrinsic stochasticity in biological systems.

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Section: Plos Computational Biologymentioning

confidence: 99%

Mechanistic modelling of within-mosquito viral dynamics: Insights into infection and dissemination patterns

Lord,

Bonsall

2023

PLoS Comput Biol

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“…Models are fundamentally encapsulating our best attempt at representing reality. Our understanding of the structure that a model purports to describe evolves with time (e.g., we can re ne mechanisms of pathogen transmission cycles to include more components as we learn how to measure them [54], or the parameterization of components takes di ferent shapes (e.g., transitioning from linear descriptions of systems to non-linear). Building on models allows them to evolve, perhaps even displacing 'older' formulations in favor of improved descriptions of mechanistic processes.…”

Section: Rule 9: Decide On Acceptable Performance Before You Startmentioning

confidence: 99%

Ten Simple Rules to build a Model Life Cycle

Poisot,

Becker,

Brookson

et al. 2024

Preprint

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Just like data, models have their own life cycle. By recognizing how one’s model fits within the life cycle of the data (or at least, ensuring that the model life cycle is understood), we can identify opportunities to foster new collaborations, encourage better practices in data analysis, and ultimately accelerate research. In this manuscript, we introduce the Model Life Cycle and develop a series of ten simple rules aimed at facilitating collaborations between data collectors, curators, users, and modellers, as well as maximizing the potential for re-use of models. We explore the idea of a Model Life Cycle, starting from the assumption that it will address machine learning (ML) models, i.e. models that can be trained and deployed iteratively, and whose focus is on prediction of quantifiable phenomena. Specifically, we are interested in clarifying the use of models in large, interdisciplinary groups, where the actual modelling exercise may involve only a subset of the group (e.g., with others collecting and standardizing data).

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“…However, by the time a mosquito has completed egg development, parasite development may or may not be complete, although transmission to the next host is dependent on the mosquito locating an oviposition site to complete its ‘gonotrophic cycle’ [1]. While several studies suggest widespread changes in mosquito behavior and physiology associated with the gonotrophic cycle [2, 3, 4, 5, 6, 7, 8], few have determined if and how changes in oviposition status influences parasite development rates and transmission potential [9]; in general, measures of parasite fitness and transmission potential are performed in mosquitoes prevented from completing their gonotrophic cycle (e.g., see [10, 11, 12, 13] and references therein). A greater understanding of how parasite fitness is shaped by oviposition status of mosquitoes is important for at least two reasons.…”

Section: Introductionmentioning

confidence: 99%

Oviposition status and sugar availability influence measures of transmission potential in malaria-infected mosquitoes

Shiau,

Garcia-Diaz,

Kyle

et al. 2024

Preprint

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Background:Like other oviparous organisms, the gonotrophic cycle of mosquitoes is not complete until they have selected a suitable habitat to oviposit. In addition to the evolutionary constraints associated with selective oviposition behavior, the physiological demands relative to an organisms oviposition status also influences their nutrient requirement from the environment. Yet, studies that measure transmission potential (vectorial capacity or competence) of mosquito-borne parasites rarely consider if the rates of parasite replication and development could be influenced by these constraints resulting from whether mosquitoes have completed their gonotrophic cycle.Methods:Anopheles stephensimosquitoes were infected withPlasmodium bergheithe rodent analog of human malaria and maintained on 1% or 10% dextrose and either provided oviposition sites (oviposited herein) to complete their gonotrophic cycle or forced to retain eggs (non-oviposited). Transmission potential in the four groups was measured up to 27 days post-infection as 1) the rates of vector survival, 2) rates of sporozoite migration to the salivary glands (extrinsic incubation period or EIP), and 3), sporozoite densities.Results:In the two groups of oviposited mosquitoes, rates of sporozoite migration and densities in the salivary glands were clearly higher in mosquitoes fed 10% dextrose. In non-oviposited mosquitoes however, rates of sporozoite migration and densities were independent of sugar concentrations, although both measures were slightly lower than oviposited mosquitoes fed 10% dextrose. Rates of vector survival were higher in non-oviposited mosquitoes.Conclusions:Taken together, these results suggest vectorial capacity for malaria parasites may be dependent on nutrient availability and oviposition/gonotrophic status and as such, argue for more careful consideration of this interaction when estimating transmission potential; costs to parasite fitness and vector survival were buffered against changes in nutritional availability from the environment in non-oviposited mosquitoes, but not oviposited mosquitoes. In general, however, these patterns suggest parasite fitness may be dependent on complex interactions between physiological (nutrition) and evolutionary (egg-retention) trade-offs in the vector, with potential implications for disease transmission and management. For instance, while reducing availability of oviposition sites and environmental sources of nutrition are key components of integrated vector management strategies, their abundance and distribution is under strong selection pressure from the patterns associated with climate change.

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Exploring the Mosquito–Arbovirus Network: A Survey of Vector Competence Experiments

Cited by 7 publications

References 45 publications

Mechanistic modelling of within-mosquito viral dynamics: Insights into infection and dissemination patterns

Mechanistic modelling of within-mosquito viral dynamics: Insights into infection and dissemination patterns

Ten Simple Rules to build a Model Life Cycle

Oviposition status and sugar availability influence measures of transmission potential in malaria-infected mosquitoes

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