Biology subjects

Champer, S. E.

Publications and source records attributed to Champer, S. E..

9 recordsLinked to original sources

bioRxiv2025

Modeling control of invasive fire ants by gene drive

Solenopsis invicta, commonly known as the fire ant, is characterized by aggressive behavior and exceptional invasive capabilities, rendering conventional control methods largely ineffective. Here, we explore the implementation of homing suppression gene drive in fire ants by developing a spatially explicit model that incorporates both monogyne and polygyne colony structures, enabling comprehensive evaluation of genetic control strategies. Ants may present unique challenges for gene drive due to their colony structure and haplodiploidy. Our results reveal that after an extended period of time, gene drive effectively eliminates polygyne colonies, but monogyne populations often persist at low level. Though standard suppression drives in haplodiploids have reduced power, new dominant-sterile resistance or two-target strategies, as well as drives that affect the colony structure, can restore high suppressive capability. Interspecific competition can also exert a positive effect on gene drive-mediated population suppression dynamics. In particular, a gene drive release during the invasion phase significantly enhances population suppression, enabling native ants to successfully recolonize their original habitats. We also identify several conserved female fertility genes in fire ants, together with gRNA targets that may support efficient, low-resistance suppression drive designs. Overall, we conclude that while gene drive in fire ants may take place over extended time scales, its long-term results, even with imperfect efficiency, are quite promising.

ecology↗

bioRxiv2024

Population genetics meets ecology: a guide to individual-based simulations in continuous landscapes

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection - in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

evolutionary biology↗

bioRxiv2024

Resource-explicit interactions in spatial population models

Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.

ecology↗

bioRxiv2023

A framework for identifying fertility gene targets for mammalian pest control

O_LIFertility-targeted gene drives have been proposed as an ethical genetic approach for managing wild populations of vertebrate pests for public health and conservation benefit. C_LIO_LIThis manuscript introduces a framework to identify and evaluate target gene suitability based on biological gene function, gene expression, and results from mouse knockout models. C_LIO_LIThis framework identified 16 genes essential for male fertility and 12 genes important for female fertility that may be feasible targets for mammalian gene drives and other non-drive genetic pest control technology. Further, a comparative genomics analysis demonstrates the conservation of the identified genes across several globally significant invasive mammals. C_LIO_LIIn addition to providing important considerations for identifying candidate genes, our framework and the genes identified in this study may have utility in developing additional pest control tools such as wildlife contraceptives. C_LI

genetics↗

bioRxiv2022

Finding the strongest gene drive: Simulations reveal unexpected performance differences between Anopheles homing suppression drive candidates

Recent experiments have produced several Anopheles gambiae homing gene drives that disrupt female fertility genes, thereby eventually inducing population collapse. Such drives may be highly effective tools to combat malaria. One such homing drive, based on the zpg promoter driving CRISPR/Cas9, was able to eliminate a cage population of mosquitoes. A second version, purportedly improved upon the first by incorporating an X-shredder element (which biases inheritance towards male offspring), was similarly successful. Here, we re-analyze the data of each of these gene drives and suggest an alternative interpretation of their performance. We assess each suppression drive within an individual-based simulation framework that models mosquito population dynamics in continuous space. We find that the combined homing/X-shredder drive is actually less effective at population suppression within the context of our mosquito population model. In particular, the combined drive often fails to completely suppress the population, instead resulting in an unstable equilibrium between drive and wild-type alleles. By contrast, otherwise similar drives based on the nos promoter may prove to be more promising candidates for future development due to potentially superior performance.

genetics↗

bioRxiv2020

Modeling CRISPR gene drives for suppression of invasive rodents

Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these new invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse. Such systems might be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit and allows for overlapping generations and a fluctuating population size. Our model includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, but only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning for identifying the key parameters and processes that determine the population dynamics of a complex evolutionary system.

ecology↗

bioRxiv2019

Design and analysis of CRISPR-based underdominance toxin-antidote gene drives

CRISPR gene drive systems offer a mechanism for transmitting a desirable transgene throughout a population for purposes ranging from vector-borne disease control to invasive species suppression. In this simulation study, we assess the performance of several CRISPR-based underdominance gene drive constructs employing toxin-antidote principles. These drives disrupt the wild-type version of an essential gene using a CRISPR nuclease (the toxin) while simultaneously carrying a recoded version of the gene (the antidote). Drives of this nature allow for releases that could be potentially confined to a desired geographic location. This is because such drives have a nonzero invasion threshold frequency, referring to the critical frequency required for the drive to spread through the population. We model drives which target essential genes that are either haplosufficient or haplolethal, using nuclease promoters with expression restricted to the germline, promoters that additionally result in cleavage activity in the early embryo from maternal deposition, and promoters that have ubiquitous somatic expression. We also study several possible drive architectures, considering both "same-site" and "distant-site" systems, as well as several reciprocally targeting drives. Together, these drive variants provide a wide range of invasion threshold frequencies and options for both population modification and suppression. Our results suggest that CRISPR toxin-antidote underdominance drive systems could allow for the design of highly flexible and potentially confinable gene drive strategies.

genetics↗

bioRxiv2019

Computational and experimental performance of CRISPR homing gene drive strategies with multiplexed gRNAs

CRISPR homing gene drives potentially have the capacity for large-scale population modification or suppression. However, resistance alleles formed by the drives can prevent them from successfully spreading. Such alleles have been found to form at high rates in most studies, including those in both insects and mammals. One possible solution to this issue is the use of multiple guide RNAs (gRNAs), thus allowing cleavage by the drive even if resistance sequences are present at some of the gRNA target sequences. Here, we develop a high-fidelity model incorporating several factors affecting the performance of drives with multiple gRNAs, including timing of cleavage, reduction in homology-directed repair efficiency due to imperfect homology around the cleavage site, Cas9 activity saturation, variance in the activity level of individual gRNAs, and formation of resistance alleles due to incomplete homology-directed repair. We parameterize the model using data from homing drive experiments designed to investigate these factors and then use it to analyze several types of homing gene drives. We find that each type of drive has an optimal number of gRNAs, usually between two and eight, dependent on drive type and performance parameters. Our model indicates that utilization of multiple gRNAs is insufficient for construction of successful gene drives, but that it provides a critical boost to drive efficiency when combined with other strategies for population modification or suppression.

genetics↗

bioRxiv2019

Performance analysis of novel toxin-antidote CRISPR gene drive systems

Gene drives can potentially fixate in a population by biasing inheritance in their favor, opening up a variety of potential applications in areas such as disease-vector control and conservation. CRISPR homing gene drives have shown much promise for providing an effective drive mechanism, but they typically suffer from the rapid formation of resistance alleles. Even if the problem of resistance can be overcome, the utility of such drives would still be limited by their tendency to spread into all areas of a population. To provide additional options for gene drive applications that are substantially less prone to the formation of resistance alleles and could potentially remain confined to a target area, we developed several designs for CRISPR-based gene drives utilizing toxin-antidote (TA) principles. These drives target and disrupt an essential gene with the drive providing rescue. Here, we assess the performance of several types of TA gene drive systems using modeling and individual-based simulations. We show that Toxin-Antidote Recessive Embryo (TARE) drive should allow for the design of robust, regionally confined, population modification strategies with high flexibility in choosing drive promoters and recessive lethal targets. Toxin-Antidote Dominant Embryo (TADE) drive requires a haplolethal target gene and a germline-restricted promoter but should enable the design of both faster regional population modification drives and even regionally-confined population suppression drives. Toxin-antidote dominant sperm (TADS) drive can be used for population modification or suppression. It spreads nearly as quickly as a homing drive and can flexibly use a variety of promoters, but unlike the other TA systems, it is not regionally confined and requires highly specific target genes. Overall, our results suggest that CRISPR-based TA gene drives provide promising candidates for further development in a variety of organisms and may allow for flexible ecological engineering strategies.

genetics↗