Resistance to natural and synthetic gene drive systems.

Scientists are rapidly developing synthetic gene drive elements intended for release into natural populations. These are intended to control or eradicate disease vectors and pests, or to spread useful traits through wild populations for disease control or conservation purposes. However, a crucial problem for gene drives is the evolution of resistance against them, preventing their spread. Understanding the mechanisms by which populations might evolve resistance is essential for engineering effective gene drive systems. This review summarizes our current knowledge of drive resistance in both natural and synthetic gene drives. We explore how insights from naturally occurring and synthetic drive systems can be integrated to improve the design of gene drives, better predict the outcome of releases and understand genomic conflict in general.

Gene drives in plants: opportunities and challenges for weed control and engineered resilience.

Plant species, populations and communities are under threat from climate change, invasive pathogens, weeds and habitat fragmentation. Despite considerable research effort invested in genome engineering for crop improvement, the development of genetic tools for the management of wild plant populations has rarely been given detailed consideration. Gene drive systems that allow direct genetic management of plant populations via the spread of fitness-altering genetic modifications could be of great utility. However, despite the rapid development of synthetic tools and their enormous promise, little explicit consideration has been given to their application in plants and, to date, they remain untested. This article considers the potential utility of gene drives for the management of wild plant populations, and examines the factors that might influence the design, spread and efficacy of synthetic drives. To gain insight into optimal ways to design and deploy synthetic drive systems, we investigate the diversity of mechanisms underlying natural gene drives and their dynamics within plant populations and species. We also review potential approaches for engineering gene drives and discuss their potential application to plant genomes. We highlight the importance of considering the impact of plant life-history and genetic architecture on the dynamics of drive, investigate the potential for different types of resistance evolution, and touch on the ethical, regulatory and social challenges ahead.

Strategies of host resistance to pathogens in spatially structured populations: An agent-based evaluation.

There is growing theoretical evidence that spatial structure can affect the ecological and evolutionary outcomes of host-parasite interactions. Locally restricted interactions have been shown in particular to affect host resistance and tolerance. In this study we investigate the evolution of several types of host disease resistance strategies, alone or in combination, in spatially structured populations. We construct a spatially explicit, individual-based stochastic model where hosts and parasites interact with each other in a spatial lattice, and interactions are restricted to a given neighbourhood of varying size. We investigate several host resistance strategies, including constitutive (expressed in all resistant hosts), induced (expressed only upon infection), and combinations thereof. We show that a costly constitutive resistance cannot reach fixation, whereas an inducible resistance strategy may become fixed in the population if the cost remains low, particularly if it impacts host recovery. We also demonstrate that mixed strategies can be maintained in the host population, and that a higher investment in a recovery-boosting inducible resistance allows for a higher investment in a constitutive response. Our simulations reveal that the spatial structure of the population impacts the selection for resistance in a complex fashion. While single strategies of resistance are generally favoured in less structured populations, mixed strategies can sometimes prevail only in highly structured environments, e.g. when combining constitutive and transmission-blocking induced responses Overall these results shed new light on the dynamics of disease resistance in a spatially-structured host-pathogen system, and advance our theoretical understanding of the evolutionary dynamics of disease resistance, a necessary step to elaborate more efficient and sustainable strategies for disease management.

Mathematical models as aids for design and development of experiments: the case of transgenic mosquitoes.

We demonstrate the utility of models as aids in the design and development of experiments aimed at measuring the effects of proposed vector population control strategies. We describe the exploration of a stochastic, age-structured model that simulates field cage experiments that test the ability of a female-killing strain of the mosquito Aedes aegypti (L.) to suppress a wild-type population. Model output predicts that choices of release ratio and population size can impact mean extinction time and variability in extinction time among experiments. We find that unless fitness costs are >80% they will not be detectable in experiments with high release ratios. At lower release ratios, the predicted length of the experiment increases significantly for fitness costs >20%. Experiments with small populations may more accurately reflect field conditions, but extinction can occur even in the absence of a functional female-killing construct because of stochastic effects. We illustrate how the model can be used to explore experimental designs that aim to study the impact of density dependence and immigration; predictions indicate that cage population eradication may not always be obtainable in an operationally realistic time frame. We propose a method to predict the extinction time of a cage population based on the rate of population reduction with the goal of shortening the duration of the experiment. We discuss the model as a tool for exploring and assessing the utility of a wider range of scenarios than would be feasible to test experimentally because of financial and temporal restraints.

Gene drive strategies of pest control in agricultural systems: Challenges and opportunities.

Recent advances in gene-editing technologies have opened new avenues for genetic pest control strategies, in particular around the use of gene drives to suppress or modify pest populations. Significant uncertainty, however, surrounds the applicability of these strategies to novel target species, their efficacy in natural populations and their eventual safety and acceptability as control methods. In this article, we identify issues associated with the potential use of gene drives in agricultural systems, to control pests and diseases that impose a significant cost to agriculture around the world. We first review the need for innovative approaches and provide an overview of the most relevant biological and ecological traits of agricultural pests that could impact the outcome of gene drive approaches. We then describe the specific challenges associated with using gene drives in agricultural systems, as well as the opportunities that these environments may offer, focusing in particular on the advantages of high-threshold gene drives. Overall, we aim to provide a comprehensive view of the potential opportunities and the remaining uncertainties around the use of gene drives in agricultural systems.

Experimental evolution of specialization by a microsporidian parasite.

BACKGROUND: Evolutionary theory predicts that the pressure for parasites to specialize on one host or to become generalists on a wide range of hosts is driven by the diversity or temporal variability of the host's population and by genetic trade-offs in the adaptation to different hosts. We give experimental evidence for this idea by letting the parasite Brachiola algerae evolve on one of four genetically homogeneous lines of the mosquito Aedes aegypti, on a mixture of the four lines or on an alternating sequence of the four lines. The first regime was expected to lead to specialists, the other two to generalists. After 13 generations, we tested the evolved parasites on each of the four lines of the mosquito. RESULTS: The specialized parasites were most infective on their own isofemale line and least infective on other isofemale lines, while the generalist parasites had intermediate infection success on all lines. The success of a specialist on its matched mosquito line was negatively correlated with its success on other lines, suggesting an evolutionary cost to specialization. This trade-off was corroborated by the observation that the generalists had higher average mean infectivity than the specialists over all isofemale lines. CONCLUSIONS: Overall, our experiment reveals the potential for specialization of a parasite to individual genotypes of its host and provides experimental evidence of the cost associated with the evolution of specialization, an important feature for understanding the coevolutionary dynamics between hosts and parasites.

Gene technologies in weed management: a technical feasibility analysis.

With the advent of new genetic technologies such as gene silencing and gene drive, efforts to develop additional management tools for weed management is gaining significant momentum. These technologies promise novel ways to develop sustainable weed control options because gene silencing can switch-off genes mediating adaptation (e.g. growth, herbicide resistance), and gene drive can be used to spread modified traits and to engineer wild populations with reduced fitness. However, applying gene silencing and/or gene drive is expected to be inherently complex as their application is constrained by several methodological and technological difficulties. In this review we explore the challenges of these technologies, and discuss strategies and resources accessible to accelerate the development of gene-tech based tools for weed management. We also highlight how gene technologies can be integrated into existing management tactics such as classical biological control, and their possible interactions.

Density-dependent intraspecific competition in the larval stage of Aedes aegypti (Diptera: Culicidae): revisiting the current paradigm.

Density-dependent intraspecific competition has been considered an important determinant of the dynamics of larval stages of Aedes aegypti. A model was published in 1984 providing a mathematical description of this density dependence, based on field data, that has since been widely used. This description, however, is based on the strong assumption that all mortality is density-dependent. We re-examine the data without this premise and find a reduced importance of density dependence, as well as a different functional form. Based on these discrepancies, we emphasize that the characterization of density dependence in the larval stages of Ae. aegypti should be based on a more complete dataset, and we use artificially generated data to explore how such additional information could help developing a better description of this density dependence. We review other empirical studies on larval competition, discuss the need for further dedicated studies, and provide a few simple guidelines for the design of such studies.

A killer-rescue system for self-limiting gene drive of anti-pathogen constructs.

A number of genetic mechanisms have been suggested for driving anti-pathogen genes into natural populations. Each of these mechanisms requires complex genetic engineering, and most are theoretically expected to permanently spread throughout the target species' geographical range. In the near term, risk issues and technical limits of molecular methods could delay the development and use of these mechanisms. We propose a gene-drive mechanism that can be self-limiting over time and space, and is simpler to build. This mechanism involves one gene that codes for toxicity (killer) and a second that confers immunity to the toxic effects (rescue). We use population-genetic models to explore cases with one or two independent insertions of the killer gene and one insertion of the rescue gene. We vary the dominance and penetrance of gene action, as well as the magnitude of fitness costs. Even with the fitness costs of 10 per cent for each gene, the proportion of mosquitoes expected to transmit the pathogen decreases below 5 per cent for over 40 generations after one 2:1 release (engineered:wild) or after four 1:2 releases. Both the killer and rescue genes will be lost from the population over time, if the rescue construct has any associated fitness cost. Molecular approaches for constructing strains are discussed.

A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs.

The spread of drug resistance represents a significant challenge to many disease control efforts. The evolution of resistance is a complex process influenced by transmission dynamics between hosts as well as infection dynamics within these hosts. This study aims to investigate how these two processes combine to impact the evolution of resistance in malaria parasites. We introduce a stochastic modelling framework combining an epidemiological model of Plasmodium transmission and an explicit within-human infection model for two competing strains. Immunity, treatment and resistance costs are included in the within-host model. We show that the spread of resistance is generally less likely in areas of intense transmission, and therefore of increased competition between strains, an effect exacerbated when costs of resistance are higher. We also illustrate how treatment influences the spread of resistance, with a trade-off between slowing resistance and curbing disease incidence. We show that treatment coverage has a stronger impact on disease prevalence, whereas treatment efficacy primarily affects resistance spread, suggesting that coverage should constitute the primary focus of control efforts. Finally, we illustrate the importance of feedbacks between modelling scales. Overall, our results underline the importance of concomitantly modelling the evolution of resistance within and between hosts.

Comparison of Two Detailed Models of Aedes aegypti Population Dynamics.

The success of control programs for mosquito borne diseases can be enhanced by crucial information provided by models of the mosquito populations. Models, however, can differ in their structure, complexity and biological assumptions, and these differences impact their predictions. Unfortunately, it is typically difficult to determine why two complex models make different predictions because we lack structured side-by-side comparisons of models using comparable parameterization. Here we present a detailed comparison of two complex, spatially-explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue, yellow fever, chikungunya and Zika viruses. Both models describe the mosquito's biological and ecological characteristics, but differ in complexity and specific assumptions. We compare the predictions of these models in two selected climatic settings, a tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbed conditions in both settings, by adjusting parameters regulating densities in each model (number of larval development sites and amount of nutritional resources). We show that the models differ in their sensitivity to environmental conditions (temperature and rainfall), and trace differences to specific model assumptions. Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more markedly under strongly seasonal Buenos Aires conditions. We use both models to simulate killing of larvae and/or adults with insecticides in selected areas. We show that predictions of population recovery by the models differ substantially, an effect likely related to model assumptions regarding larval development and (direct or delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model assumptions on population dynamics predictions in unperturbed and perturbed conditions.

A cMUT probe for ultrasound-guided focused ultrasound targeted therapy.

Ultrasound-mediated targeted therapy represents a promising strategy in the arsenal of modern therapy. Capacitive micromachined ultrasonic transducer (cMUT) technology could overcome some difficulties encountered by traditional piezoelectric transducers. In this study, we report on the design, fabrication, and characterization of an ultrasound-guided focused ultrasound (USgFUS) cMUT probe dedicated to preclinical evaluation of targeted therapy (hyperthermia, thermosensitive liposomes activation, and sonoporation) at low frequency (1 MHz) with simultaneous ultrasonic imaging and guidance (15 to 20 MHz). The probe embeds two types of cMUT arrays to perform the modalities of targeted therapy and imaging respectively. The wafer-bonding process flow employed for the manufacturing of the cMUTs is reported. One of its main features is the possibility of implementing two different gap heights on the same wafer. All the design and characterization steps of the devices are described and discussed, starting from the array design up to the first in vitro measurements: optical (microscopy) and electrical (impedance) measurements, arrays' electroacoustic responses, focused pressure field mapping (maximum peak-to-peak pressure = 2.5 MPa), and the first B-scan image of a wire-target phantom.

Evaluation of bias voltage modulation sequence for nonlinear contrast agent imaging using a capacitive micromachined ultrasonic transducer array.

Many clinical diagnoses have now been improved thanks to the development of new techniques dedicated to contrast agent nonlinear imaging. Over the past few years, Capacitive Micromachined Ultrasonic Transducers (cMUTs) have emerged as a promising alternative to traditional piezoelectric transducers. One notable advantage of cMUTs is their wide frequency bandwidth. However, their use in nonlinear imaging approaches such as those used to detect contrast agents have been challenging due their intrinsic nonlinear character. We propose a new contrast imaging sequence, called bias voltage modulation (BVM), specifically developed for cMUTs to suppress their inherent nonlinear behavior. Theoretical and experimental results show that a complete cancellation of the nonlinear signal from the source can be reached when the BVM sequence is implemented. In-vitro validation of the sequence is performed using a cMUT probe connected to an open scanner and a flow phantom setup containing SonoVue microbubbles. Compared to the standard amplitude modulation imaging mode, a 6 dB increase of contrast-to-tissue ratio was achieved when the BVM sequence is applied. These results reveal that the problem of cMUT nonlinearity can be addressed, thus expanding the potential of this new transducer technology for nonlinear contrast agent detection and imaging.

Quantitative evaluation of atrial radio frequency ablation using intracardiac shear-wave elastography.

PURPOSE: Radio frequency catheter ablation (RFCA) is a well-established clinical procedure for the treatment of atrial fibrillation (AF) but suffers from a low single-procedure success rate. Recurrence of AF is most likely attributable to discontinuous or nontransmural ablation lesions. Yet, despite this urgent clinical need, there is no clinically available imaging modality that can reliably map the lesion transmural extent in real time. In this study, the authors demonstrated the feasibility of shear-wave elastography (SWE) to map quantitatively the stiffness of RFCA-induced thermal lesions in cardiac tissues in vitro and in vivo using an intracardiac transducer array. METHODS: SWE was first validated in ex vivo porcine ventricular samples (N = 5). Both B-mode imaging and SWE were performed on normal cardiac tissue before and after RFCA. Areas of the lesions were determined by tissue color change with gross pathology and compared against the SWE stiffness maps. SWE was then performed in vivo in three sheep (N = 3). First, the stiffness of normal atrial tissues was assessed quantitatively as well as its variation during the cardiac cycle. SWE was then performed in atrial tissue after RFCA. RESULTS: A large increase in stiffness was observed in ablated ex vivo regions (average shear modulus across samples in normal tissue: 22 ± 5 kPa, average shear-wave speed (ct): 4.5 ± 0.4 m s(-1) and in determined ablated zones: 99 ± 17 kPa, average ct: 9.0 ± 0.5 m s(-1) for a mean shear modulus increase ratio of 4.5 ± 0.9). In vivo, a threefold increase of the shear modulus was measured in the ablated regions, and the lesion extension was clearly visible on the stiffness maps. CONCLUSIONS: By its quantitative and real-time capabilities, Intracardiac SWE is a promising intraoperative imaging technique for the evaluation of thermal ablation during RFCA.

Modeling the dynamics of a non-limited and a self-limited gene drive system in structured Aedes aegypti populations.

Recently there have been significant advances in research on genetic strategies to control populations of disease-vectoring insects. Some of these strategies use the gene drive properties of selfish genetic elements to spread physically linked anti-pathogen genes into local vector populations. Because of the potential of these selfish elements to spread through populations, control approaches based on these strategies must be carefully evaluated to ensure a balance between the desirable spread of the refractoriness-conferring genetic cargo and the avoidance of potentially unwanted outcomes such as spread to non-target populations. There is also a need to develop better estimates of the economics of such releases. We present here an evaluation of two such strategies using a biologically realistic mathematical model that simulates the resident Aedes aegypti mosquito population of Iquitos, Peru. One strategy uses the selfish element Medea, a non-limited element that could permanently spread over a large geographic area; the other strategy relies on Killer-Rescue genetic constructs, and has been predicted to have limited spatial and temporal spread. We simulate various operational approaches for deploying these genetic strategies, and quantify the optimal number of released transgenic mosquitoes needed to achieve definitive spread of Medea-linked genes and/or high frequencies of Killer-Rescue-associated elements. We show that for both strategies the most efficient approach for achieving spread of anti-pathogen genes within three years is generally to release adults of both sexes in multiple releases over time. Even though females in these releases should not transmit disease, there could be public concern over such releases, making the less efficient male-only release more practical. This study provides guidelines for operational approaches to population replacement genetic strategies, as well as illustrates the use of detailed spatial models to assist in safe and efficient implementation of such novel genetic strategies.

An eco-physiological model of the impact of temperature on Aedes aegypti life history traits.

Physiological processes mediate the impact of ecological conditions on the life histories of insect vectors. For the dengue/chikungunya mosquito, Aedes aegypti, three life history traits that are critical to urban population dynamics and control are: size, development rate and starvation mortality. In this paper we make use of prior laboratory experiments on each of these traits at 2°C intervals between 20 and 30°C, in conjunction with eco-evolutionary theory and studies on A.aegypti physiology, in order to develop a conceptual and mathematical framework that can predict their thermal sensitivity. Our model of reserve dependent growth (RDG), which considers a potential tradeoff between the accumulation of reserves and structural biomass, was able to robustly predict laboratory observations, providing a qualitative improvement over the approach most commonly used in other A.aegypti models. RDG predictions of reduced size at higher temperatures, but increased reserves relative to size, are supported by the available evidence in Aedes spp. We offer the potentially general hypothesis that temperature-size patterns in mosquitoes are driven by a net benefit of finishing the growing stage with proportionally greater reserves relative to structure at warmer temperatures. By relating basic energy flows to three fundamental life history traits, we provide a mechanistic framework for A.aegypti development to which ecological complexity can be added. Ultimately, this could provide a framework for developing and field testing hypotheses on how processes such as climate variation, density dependent regulation, human behavior or control strategies may influence A.aegypti population dynamics and disease risk.

Assessing the feasibility of controlling Aedes aegypti with transgenic methods: a model-based evaluation.

Suppression of dengue and malaria through releases of genetically engineered mosquitoes might soon become feasible. Aedes aegypti mosquitoes carrying a conditionally lethal transgene have recently been used to suppress local vector populations in small-scale field releases. Prior to releases of transgenic insects on a wider scale, however, most regulatory authorities will require additional evidence that suppression will be effective in natural heterogeneous habitats. We use a spatially explicit stochastic model of an Ae. aegypti population in Iquitos, Peru, along with an uncertainty analysis of its predictions, to quantitatively assess the outcome of varied operational approaches for releases of transgenic strains with conditional death of females. We show that population elimination might be an unrealistic objective in heterogeneous populations. We demonstrate that substantial suppression can nonetheless be achieved if releases are deployed in a uniform spatial pattern using strains combining multiple lethal elements, illustrating the importance of detailed spatial models for guiding genetic mosquito control strategies.

Gene-drive into insect populations with age and spatial structure: a theoretical assessment.

The potential benefits and risks of genetically engineered gene-drive systems for replacing wild pest strains with more benign strains must be assessed prior to any field releases. We develop a computer simulation model to assess the feasibility of using engineered underdominance constructs to drive transgenes into age- and spatially structured mosquito populations. Our practical criterion for success is the achievement of a transgene frequency of at least 0.80 within 3 years of release. The impacts of a number of parameters that may affect the success of gene-drive, such as the release area, release age, density-dependent larval survival, fitness cost of the engineered genes, and migration probability of adults, are examined. Results show that patchy release generally requires the release of fewer engineered insects to achieve success than central release. When the fitness cost is very low, central release covering 25% of the total area can be more effective than a completely uniform release over the whole area. This study demonstrates that to determine the best method of spatial release, and the total number of engineered insects that must be released, it is important to take into account the age and sex of the released insects and spatial structure of the population.

Evaluation of location-specific predictions by a detailed simulation model of Aedes aegypti populations.

BACKGROUND: Skeeter Buster is a stochastic, spatially explicit simulation model of Aedes aegypti populations, designed to predict the outcome of vector population control methods. In this study, we apply the model to two specific locations, the cities of Iquitos, Peru, and Buenos Aires, Argentina. These two sites differ in the amount of field data that is available for location-specific customization. By comparing output from Skeeter Buster to field observations in these two cases we evaluate population dynamics predictions by Skeeter Buster with varying degrees of customization. METHODOLOGY/PRINCIPAL FINDINGS: Skeeter Buster was customized to the Iquitos location by simulating the layout of houses and the associated distribution of water-holding containers, based on extensive surveys of Ae. aegypti populations and larval habitats that have been conducted in Iquitos for over 10 years. The model is calibrated by adjusting the food input into various types of containers to match their observed pupal productivity in the field. We contrast the output of this customized model to the data collected from the natural population, comparing pupal numbers and spatial distribution of pupae in the population. Our results show that Skeeter Buster replicates specific population dynamics and spatial structure of Ae. aegypti in Iquitos. We then show how Skeeter Buster can be customized for Buenos Aires, where we only had Ae. aegypti abundance data that was averaged across all locations. In the Argentina case Skeeter Buster provides a satisfactory simulation of temporal population dynamics across seasons. CONCLUSIONS: This model can provide a faithful description of Ae. aegypti populations, through a process of location-specific customization that is contingent on the amount of data available from field collections. We discuss limitations presented by some specific components of the model such as the description of food dynamics and challenges that these limitations bring to model evaluation.

Understanding uncertainties in model-based predictions of Aedes aegypti population dynamics.

BACKGROUND: Aedes aegypti is one of the most important mosquito vectors of human disease. The development of spatial models for Ae. aegypti provides a promising start toward model-guided vector control and risk assessment, but this will only be possible if models make reliable predictions. The reliability of model predictions is affected by specific sources of uncertainty in the model. METHODOLOGY/PRINCIPAL FINDINGS: This study quantifies uncertainties in the predicted mosquito population dynamics at the community level (a cluster of 612 houses) and the individual-house level based on Skeeter Buster, a spatial model of Ae. aegypti, for the city of Iquitos, Peru. The study considers two types of uncertainty: 1) uncertainty in the estimates of 67 parameters that describe mosquito biology and life history, and 2) uncertainty due to environmental and demographic stochasticity. Our results show that for pupal density and for female adult density at the community level, respectively, the 95% prediction confidence interval ranges from 1000 to 3000 and from 700 to 5,000 individuals. The two parameters contributing most to the uncertainties in predicted population densities at both individual-house and community levels are the female adult survival rate and a coefficient determining weight loss due to energy used in metabolism at the larval stage (i.e. metabolic weight loss). Compared to parametric uncertainty, stochastic uncertainty is relatively low for population density predictions at the community level (less than 5% of the overall uncertainty) but is substantially higher for predictions at the individual-house level (larger than 40% of the overall uncertainty). Uncertainty in mosquito spatial dispersal has little effect on population density predictions at the community level but is important for the prediction of spatial clustering at the individual-house level. CONCLUSION/SIGNIFICANCE: This is the first systematic uncertainty analysis of a detailed Ae. aegypti population dynamics model and provides an approach for identifying those parameters for which more accurate estimates would improve model predictions.