Zhang Xinyue, Sun Weitang, Kim Isabel K, Messer Philipp W, Champer Jackson
Center for Bioinformatics, School of Life Sciences, Center for Life Sciences, Peking University, Beijing, China 100871.
Department of Computational Biology, Cornell University, Ithaca, NY 14853.
bioRxiv. 2024 Aug 15:2024.08.14.607913. doi: 10.1101/2024.08.14.607913.
Due to their super-Mendelian inheritance, gene drive systems have the potential to provide revolutionary solutions to critical public health and environmental problems. For suppression drives, however, spatial structure can cause "chasing" population dynamics that may postpone target population elimination or even cause the drive to fail. In chasing, wild-type individuals elude the drive and recolonize previously suppressed areas. The drive can re-enter these recolonized areas, but often is not able to catch up to wild-type and finally eliminate it. Previous methods for chasing detection are only suitable to limited parameter ranges. In this study with expanded parameter ranges, we found that the shift from chasing dynamics to static equilibrium outcomes is continuous as drive performance is reduced. To quantify this, we defined a Weighted Average Nearest Neighbor statistic to assess the clustering degree during chasing, while also characterizing chasing by the per-generation chance of population elimination and drive loss. To detect chasing dynamics in local areas and to detect the start of chasing, we implemented Density-Based Spatial Clustering of Applications with Noise. Using these techniques, we determined the effect of arena size, resistance allele formation rate in both the germline and in the early embryo from maternally deposited Cas9, life history and reproduction strategies, and density-dependent growth curve shape on chasing outcomes. We found that larger real-world areas will be much more vulnerable to chasing and that species with overlapping generations, fecundity-based density dependence, and concave density-dependent growth curves have smaller and more clustered local chasing with a greater chance of eventual population elimination. We also found that embryo resistance and germline resistance hinder drive performance in different ways. These considerations will be important for determining the necessary drive performance parameters needed for success in different species, and whether future drives could potentially be considered as release candidates.
由于其超孟德尔遗传特性,基因驱动系统有潜力为关键的公共卫生和环境问题提供革命性的解决方案。然而,对于抑制性驱动而言,空间结构可能导致“追逐”种群动态,这可能会推迟目标种群的消除,甚至导致驱动失败。在“追逐”过程中,野生型个体避开驱动,并重新定殖先前被抑制的区域。驱动可以重新进入这些重新定殖的区域,但往往无法追上野生型并最终将其消灭。以前用于检测“追逐”的方法仅适用于有限的参数范围。在这项参数范围扩大的研究中,我们发现随着驱动性能的降低,从“追逐”动态到静态平衡结果的转变是连续的。为了量化这一点,我们定义了一个加权平均最近邻统计量来评估“追逐”过程中的聚类程度,同时还用每代种群消除和驱动丧失的概率来表征“追逐”。为了检测局部区域的“追逐”动态并检测“追逐”的开始,我们实施了带噪声的基于密度的空间聚类应用。利用这些技术,我们确定了实验区域大小、生殖系和早期胚胎中由母本沉积的Cas9产生的抗性等位基因形成率、生活史和繁殖策略以及密度依赖生长曲线形状对“追逐”结果的影响。我们发现,更大的现实世界区域将更容易受到“追逐”的影响,并且具有重叠世代、基于繁殖力的密度依赖性和凹形密度依赖生长曲线的物种具有更小且更聚集的局部“追逐”,最终种群消除的可能性更大。我们还发现胚胎抗性和生殖系抗性以不同方式阻碍驱动性能。这些考虑对于确定不同物种成功所需的必要驱动性能参数,以及未来的驱动是否有可能被视为释放候选物将是重要的。