Model-based inference from multiple dose, time course data reveals Wolbachia effects on infection profiles of type 1 dengue virus in Aedes aegypti

Abstract

Infection is a complex and dynamic process involving a population of invading microbes, the host and its responses, aimed at controlling the situation. Depending on the purpose and level of organization, infection at the organism level can be described by a process as simple as a coin toss, or as complex as a multi-factorial dynamic model; the former, for instance, may be adequate as a component of a population model, while the latter is necessary for a thorough description of the process beginning with a challenge with an infectious inoculum up to establishment or elimination of the pathogen. Experimental readouts in the laboratory are often static, snapshots of the process, assayed under some convenient experimental condition, and therefore cannot comprehensively describe the system. Different from the discrete treatment of infection in population models, or the descriptive summarized accounts of typical lab experiments, in this manuscript, infection is treated as a dynamic process dependent on the initial conditions of the infectious challenge, viral growth, and the host response along time. Here, experimental data is generated for multiple doses of type 1 dengue virus, and pathogen levels are recorded at different points in time for two populations of mosquitoes: either carrying endosymbiont bacteria Wolbachia or not. A dynamic microbe/host-response mathematical model is used to describe pathogen growth in the face of a host response like the immune system, and to infer model parameters for the two populations of insects, revealing a slight—but potentially important—protection conferred by the symbiont.

Reply to: “Enhancement of Aedes aegypti susceptibility to dengue by Wolbachia is not supported”

“…Meanwhile, despite being based on low numbers and small effect sizes, the consistency of higher infected proportions among Wolbachia carriers at low challenge doses is noticeable. Ant et al 4 finalise their inspection into the Souto-Maior et al 1 dataset by noting a slight discrepancy between the raw data and the proportions infected shown in Fig. 1a of King et al 3 .…”

“…They highlight that a fraction of the mosquitoes assigned as infected by dengue may be false positives, without which the increased mean susceptibility of the Wolbachia-carrying group inferred from the dose-response model might not be found, making our reported conclusions appear as an artefact of the analysis. They criticise Souto-Maior et al 1 based on the observation that a small number of negative control mosquitoes were classified as positive, a shortcoming of the original experimental study which neither affects the type of analyses conducted therein nor does it influence the conclusions of King et al 3 , as per modified analysis suggested by Ant et al 4 (Fig. 1).…”

“…To demonstrate how an alternative threshold for false positives applied to Souto-Maior et al 1 impacts the results of King et al 3 , we test sensitivity by fitting the dose-response model to the highlighted dataset with the data pre-processed, as suggested by Ant et al 4 . The results are shown in Fig.…”

“…2a). Each approach applies only in very special circumstances, which led us to propose a general framework 11,12 that interpolates between (1) and (2) (Fig. 2b).…”

Recent advances in threshold-dependent gene drives for mosquitoes

“…w MelPop demonstrated almost complete blocking of transmission of dengue viruses but also high fitness costs and failed to establish in target field populations [ 52 ]. w Mel shows partial blocking of dengue viruses in the laboratory (but see [ 54 , 55 ]) with lower fitness costs and in trials in Australia established and slowly spread from two of three release sites [ 53 , 56 ] ( Table 1 .)…”