Engineering resilient gene drives for sustainable malaria control by predicting, testing and overcoming target site resistance in Anopheles gambiae

Abstract

CRISPR-based gene drives are selfish genetic elements with the potential to spread through entire insect populations for sustainable vector control. Gene drives designed to disrupt the reproductive capacity of females can suppress laboratory populations of the malaria mosquito, Anopheles gambiae. However, any suppressive intervention will inevitably exert an evolutionary pressure for resistance, and the likelihood of resistance emerging at natural population scales remains poorly defined. Here, we present a pipeline to quantify the evolutionary space for resistance, enabling accelerated discovery, engineering, and testing of both natural and drive-induced variants that could reverse gene drive spread. We applied our approach to stress-test a best-in-class suppression gene drive that has evaded resistance in all laboratory-contained releases to date, known as Ag(QFS)1. We showed that previously undetected resistant alleles can arise at low frequency, including a novel type of partially resistant alleles that can perturb drive-invasion dynamics. Integrating experimentally derived resistance rates with population genetic modeling shows that single-target suppression drives are unlikely to be robust at natural mosquito population sizes, even at highly constrained loci. Here, we engineer and validate multiplexed gene drives in Anopheles gambiae, that target multiple conserved sites, actively removing resistant alleles. Our models predict that such gene drives could supress large natural mosquito populations in the field.