Combating mosquito-borne diseases using genetic control technologies

Mosquito-borne diseases, such as dengue and malaria, pose significant global health burdens. Unfortunately, current control methods based on insecticides and environmental maintenance have fallen short of eliminating the disease burden. Scalable, deployable, genetic-based solutions are sought to reduce the transmission risk of these diseases. Pathogen-blocking Wolbachia bacteria, or genome engineering-based mosquito control strategies including gene drives have been developed to address these problems, both requiring the release of modified mosquitoes into the environment. Here, we review the latest developments, notable similarities, and critical distinctions between these promising technologies and discuss their future applications for mosquito-borne disease control.

MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics

Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project’s CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.

Malaria, dengue and other mosquito-borne diseases continue to pose a major global health burden through much of the world. Currently available tools, such as insecticides and antimalarial drugs, are not expected to be sufficient to eliminate these diseases from highly-endemic areas, hence there is interest in novel strategies including genetics-based approaches. In recent years, the advent of CRISPR-based gene-editing has greatly expanded the range of genetic control tools available, and MGDrivE 1 (Mosquito Gene Drive Explorer 1) was proposed to simulate the dynamics of these systems through spatially-structured mosquito populations. As the technology has advanced and potential field trials are being discussed, models are now needed that incorporate additional details, such as life history parameters that respond to daily and seasonal environmental fluctuations, and transmission of pathogens between mosquito and vertebrate hosts. Here, we present MGDrivE 2, a gene drive simulation framework that significantly improves upon MGDrivE 1 by addressing these modeling needs. MGDrivE 2 has also been reformulated as a stochastic Petri net, enabling model specification to be decoupled from simulation, making it easier to adapt the model for application to other insect and mammalian species.

Probability of consolidation constrains novel serotype emergence in dengue fever virus

Since their first sequencing 40 years ago, Dengue virus (DENV) genotypes have shown extreme coherence regarding the serotype class they encode. Considering that DENV is a ribonucleic acid (RNA) virus with a high mutation rate, this behavior is intriguing. Here, we explore the effect of various parameters on likelihood of new serotype emergence. In order to determine the time scales of such an event, we used a Timed Markov Transmission Model to explore the influences of sylvatic versus peri-urban transmission, viral mutation rate, and vertical transmission on the probabilities of novel serotype emergence. We found that around 1 000 years are required for a new serotype to emerge, consistent with phylogenetic analysis of extant dengue serotypes. Furthermore, we show that likelihood of establishing chains of mosquito-human-mosquito infection, known as consolidation, is the primary factor which constrains novel serotype emergence. Our work illustrates the restrictions on and provides a mechanistic explanation for the low probability of novel dengue virus serotype emergence and the low number of observed DENV serotypes.