Biology subjects

Sanchez C., H. M.

Publications and source records attributed to Sanchez C., H. M..

3 recordsLinked to original sources

bioRxiv2018

MGDrivE: A modular simulation framework for the spread of gene drives through spatially-explicit mosquito populations

Malaria, dengue, Zika, and other mosquito-borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide-based tools and antimalarial drugs. The advent of CRISPR/Cas9-based gene editing and its demonstrated ability to streamline the development of gene drive systems has reignited interest in the application of this technology to the control of mosquitoes and the diseases they transmit. The versatility of this technology has also enabled a wide range of gene drive architectures to be realized, creating a need for their population-level and spatial dynamics to be explored. To this end, we present MGDrivE (Mosquito Gene Drive Explorer): a simulation framework designed to investigate the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. A key strength of the MGDrivE framework is its modularity: a) a genetic inheritance module accommodates the dynamics of gene drive systems displaying user-defined inheritance patterns, b) a population dynamic module accommodates the life history of a variety of mosquito disease vectors and insect agricultural pest species, and c) a landscape module accommodates the distribution of insect metapopulations connected by migration in space. Example MGDrivE simulations are presented to demonstrate the application of the framework to CRISPR/Cas9-based homing gene drive for: a) driving a disease-refractory gene into a population (i.e. population replacement), and b) disrupting a gene required for female fertility (i.e. population suppression), incorporating homing-resistant alleles in both cases. We compare MGDrivE with other genetic simulation packages, and conclude with a discussion of future directions in gene drive modeling.

genetics↗

bioRxiv2017

Effects of Spatial Heterogeneity on Transmission Potential in Vectorial-Contact Networks: A Comparison of Three Aedes aegypti Control Strategies

Dengue, chikungunya and zika are all transmitted by the Aedes aegypti mosquito. Despite the strong influence of host spatial distribution and movement patterns on the ability of mosquito vectors to transmit pathogens, there is little understanding how these complex interactions modify the spread of disease in spatially heterogeneous populations. In light of present fears of a worldwide zika epidemic, and failures to eradicate dengue and chikungunya; there is a pressing need to get a better picture of how high-resolution details such as human movement in a small landscape, modify the patterns of transmission of these diseases and how different mosquito-control interventions could be affected by these movements.\n\nIn this work we use a computational agent-based model (ABM) to simulate mosquito-human interactions in two different levels of spatial heterogeneity, with human movement, and in the presence of three mosquito-control interventions (spatial spraying, the release of Wolbachia-infected mosquitoes and release of insects with dominant lethal gene). To analyse the results from each of these experiments we examined mosquito population dynamics and host to host contact networks that emerged from the distribution of consecutive bites across humans. We then compared results across experiments to understand the differential effectiveness of different interventions in both the presence and absence of spatial heterogeneities, and analysed network measures of epidemiological relevance (degree probability distributions, mean path length, network density and small-worldness).\n\nFrom our experiments we conclude that spatial heterogeneity greatly influences how a pathogen may spread in a host population when mediated by a mosquito vector, and that these important heterogeneities also strongly affect effectiveness of interventions. Finally, we demonstrate that these host to host vectorial-contact networks can provide operationally important information to inform selection of optimal vector-control strategies.\n\nAuthor SummaryMosquito-borne diseases transmission patterns arise from the complex interactions between hosts and vector. Because these interactions are influenced by host and vector behaviour, spatial constraints, and other factors they are amongst the most difficult to understand. In this work, we use our computational agent-based model: SoNA3BS; to simulate two spatially different settings in the presence and absence of three different mosquito-control interventions: fogging, the release of Wolbachia-infected mosquitoes and the release of insects with dominant lethal gene. Throughout these simulations, we record mosquito population dynamics and mosquito bites on persons. We then compare mosquito population dynamics to the vectorial-contact networks (that emerge from subsequent mosquito bites between humans) and, after performing these comparisons, we proceeded to show that even when mosquito population sizes are almost equal in both spatial settings, the resulting vectorial-contact networks are radically different. This has profound implications in our understanding of how mosquito-borne diseases spread in human populations and is relevant to the effective use of resources allocated to stop these pathogens from causing more harm in human populations.

bioinformatics↗

bioRxiv2016

Overcoming evolved resistance to population-suppressing homing-based gene drives

The use of homing-based gene drive systems to modify or suppress wild populations of a given species has been proposed as a solution to a number of significant ecological and public health related problems, including the control of mosquito-borne diseases. The recent development of a CRISPR-Cas9-based homing system for the suppression of Anopheles gambiae, the main African malaria vector, is encouraging for this approach; however, with current designs, the slow emergence of homing-resistant alleles is expected to result in suppressed populations rapidly rebounding, as homing-resistant alleles have a significant fitness advantage over functional, population-suppressing homing alleles. To explore this concern, we develop a mathematical model to estimate tolerable rates of homing-resistant allele generation to suppress a wild population of a given size. Our results suggest that, to achieve meaningful population suppression, tolerable rates of resistance allele generation are orders of magnitude smaller than those observed for current designs for CRISPR-Cas9-based homing systems. To remedy this, we propose a homing system architecture in which guide RNAs (gRNAs) are multiplexed, increasing the effective homing rate and decreasing the effective resistant allele generation rate. Modeling results suggest that the size of the population that can be suppressed increases exponentially with the number of multiplexed gRNAs and that, with six multiplexed gRNAs, a mosquito species could potentially be suppressed on a continental scale. We also demonstrate successful multiplexing in vivo in Drosophila melanogaster using a ribozyme-gRNA-ribozyme (RGR) approach - a strategy that could readily be adapted to engineer stable, homing-based suppression drives in relevant organisms.

evolutionary biology↗