Monitoring Needs for Gene Drive Mosquito Projects: Lessons From Vector Control Field Trials and Invasive Species

MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations

Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Gene drives for the extinction of wild metapopulations

Population-suppressing gene drives may be capable of extinguishing wild populations, with proposed applications in conservation, agriculture, and public health. However, unintended and potentially disastrous consequences of release of drive-engineered individuals are extremely difficult to predict. We propose a model for the dynamics of a sex ratio-biasing drive, and using simulations, we show that failure of the suppression drive is often a natural outcome due to stochastic and spatial effects. We further demonstrate rock-paper-scissors dynamics among wild-type, drive-infected, and extinct populations that can persist for arbitrarily long times. Gene drive-mediated extinction of wild populations entails critical complications that lurk far beyond the reach of laboratory-based studies. Our findings help in addressing these challenges.

Manipulating the Destiny of Wild Populations Using CRISPR

Genetic biocontrol aims to suppress or modify populations of species to protect public health, agriculture, and biodiversity. Advancements in genome engineering technologies have fueled a surge in research in this field, with one gene editing technology, CRISPR, leading the charge. This review focuses on the current state of CRISPR technologies for genetic biocontrol of pests and highlights the progress and ongoing challenges of using these approaches.

MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community.

Incorporating ecology into gene drive modelling

Gene drive technology, in which fast-spreading engineered drive alleles are introduced into wild populations, represents a promising new tool in the fight against vector-borne diseases, agricultural pests and invasive species. Due to the risks involved, gene drives have so far only been tested in laboratory settings while their population-level behaviour is mainly studied using mathematical and computational models. The spread of a gene drive is a rapid evolutionary process that occurs over timescales similar to many ecological processes. This can potentially generate strong eco-evolutionary feedback that could profoundly affect the dynamics and outcome of a gene drive release. We, therefore, argue for the importance of incorporating ecological features into gene drive models. We describe the key ecological features that could affect gene drive behaviour, such as population structure, life-history, environmental variation and mode of selection. We review previous gene drive modelling efforts and identify areas where further research is needed. As gene drive technology approaches the level of field experimentation, it is crucial to evaluate gene drive dynamics, potential outcomes, and risks realistically by including ecological processes.

Fine-scale spatial distribution of deltamethrin resistance and population structure of Anopheles funestus and Anopheles arabiensis populations in Southern Mozambique

BACKGROUND

Insecticide resistance in malaria vectors can be spatially highly heterogeneous, yet population structure analyses frequently find relatively high levels of gene flow among mosquito populations. Few studies have contemporaneously assessed phenotypic, genotypic and population structure analysis on mosquito populations and none at fine geographical scales. In this study, genetic diversity, population structure, and insecticide resistance profiles of Anopheles funestus and Anopheles arabiensis were examined across mosquito populations from and within neighbouring villages.

METHODS

Mosquitoes were collected from 11 towns in southern Mozambique, as well as from different neighbourhoods within the town of Palmeira, during the peak malaria transmission season in 2016. CDC bottle bioassay and PCR assays were performed with Anopheles mosquitoes at each site to determine phenotypic and molecular insecticide resistance profiles, respectively. Microsatellite analysis was conducted on a subsample of mosquitoes to estimate genetic diversity and population structure.

RESULTS

Phenotypic insecticide resistance to deltamethrin was observed in An. funestus sensu stricto (s.s.) throughout the area, though a high level of mortality variation was seen. However, 98% of An. funestus s.s. were CYP6P9a homozygous resistant. An. arabiensis was phenotypically susceptible to deltamethrin and 99% were kdr homozygous susceptible. Both Anopheles species exhibited high allelic richness and heterozygosity. Significant deviations from Hardy-Weinberg equilibrium were observed, and high linkage disequilibrium was seen for An. funestus s.s., supporting population subdivision. However, the FST values were low for both anophelines (- 0.00457 to 0.04213), Nm values were high (9.4-71.8 migrants per generation), AMOVA results showed almost 100% genetic variation among and within individuals, and Structure analysis showed no clustering of An. funestus s.s. and An. arabiensis populations. These results suggest high gene flow among mosquito populations.

CONCLUSION

Despite a relatively high level of phenotypic variation in the An. funestus population, molecular analysis shows the population is admixed. These data indicate that CYP6P9a resistance markers do not capture all phenotypic variation in the area, but also that resistance genes of high impact are likely to easily spread in the area. Conversely, other strategies, such as transgenic mosquito release programmes will likely not face challenges in this locality.

A perspective on the expansion of the genetic technologies to support the control of neglected vector-borne diseases and conservation

Genetic-based technologies are emerging as promising tools to support vector population control. Vectors of human malaria and dengue have been the main focus of these development efforts, but in recent years these technologies have become more flexible and adaptable and may therefore have more wide-ranging applications. Culex quinquefasciatus, for example, is the primary vector of avian malaria in Hawaii and other tropical islands. Avian malaria has led to the extinction of numerous native bird species and many native bird species continue to be threatened as climate change is expanding the range of this mosquito. Genetic-based technologies would be ideal to support avian malaria control as they would offer alternatives to interventions that are difficult to implement in natural areas, such as larval source reduction, and limit the need for chemical insecticides, which can harm beneficial species in these natural areas. This mosquito is also an important vector of human diseases, such as West Nile and Saint Louis encephalitis viruses, so genetic-based control efforts for this species could also have a direct impact on human health. This commentary will discuss the current state of development and future needs for genetic-based technologies in lesser studied, but important disease vectors, such as C. quinquefasciatus, and make comparisons to technologies available in more studied vectors. While most current genetic control focuses on human disease, we will address the impact that these technologies could have on both disease and conservation focused vector control efforts and what is needed to prepare these technologies for evaluation in the field. The versatility of genetic-based technologies may result in the development of many important tools to control a variety of vectors that impact human, animal, and ecosystem health.

Target Product Profiles for Mosquito Gene Drives: Incorporating Insights From Mathematical Models

Mosquito-borne diseases such as malaria continue to pose a major global health burden, and the impact of currently-available interventions is stagnating. Consequently, there is interest in novel tools to control these diseases, including gene drive-modified mosquitoes. As these tools continue to be refined, decisions on whether to implement them in the field depend on their alignment with target product profiles (TPPs) that define product characteristics required to achieve desired entomological and epidemiological outcomes. TPPs are increasingly being used for malaria and vector control interventions, such as attractive targeted sugar baits and long-acting injectable drugs, as they progress through the development pipeline. For mosquito gene drive products, reliable predictions from mathematical models are an essential part of these analyses, as field releases could potentially be irreversible. Here, we review the prior use of mathematical models in developing TPPs for malaria and vector control tools and discuss lessons from these analyses that may apply to mosquito gene drives. We recommend that, as gene drive technology gets closer to field release, discussions regarding target outcomes engage a wide range of stakeholders and account for settings of interest and vector species present. Given the relatively large number of parameters that describe gene drive products, machine learning approaches may be useful to explore parameter space, and an emphasis on conservative fitness estimates is advisable, given the difficulty of accurately measuring these parameters prior to field studies. Modeling may also help to inform the risk, remediation and cost dimensions of mosquito gene drive TPPs.