Modeling suggests gene editing combined with vaccination could eliminate a persistent disease in livestock

Abstract

Recent breakthroughs in gene-editing technologies that can render individual animals fully resistant to infections may offer unprecedented opportunities for controlling future epidemics in farm animals. Yet, their potential for reducing disease spread is poorly understood as the necessary theoretical framework for estimating epidemiological effects arising from gene-editing applications is currently lacking. Here, we develop semistochastic modeling approaches to investigate how the adoption of gene editing may affect infectious disease prevalence in farmed animal populations and the prospects and time scale for disease elimination. We apply our models to the porcine reproductive and respiratory syndrome (PRRS), one of the most persistent global livestock diseases to date. Whereas extensive control efforts have shown limited success, recent production of gene-edited pigs that are fully resistant to the PRRS virus have raised expectations for eliminating this deadly disease. Our models predict that disease elimination on a national scale would be difficult to achieve if gene editing was used as the only disease control. However, from a purely epidemiological perspective, disease elimination may be achievable within 3 to 6 y, if gene editing were complemented with widespread and sufficiently effective vaccination. Besides strategic distribution of genetically resistant animals, several other key determinants underpinning the epidemiological impact of gene editing were identified.

Keywords: CRISPR/Cas9; PRRS; gene editing; infectious disease; mathematical model.

Fig. 1.

Predicted reduction in PRRS prevalence achieved by using genetically PRRSV-resistant pigs, depending on the average baseline PRRSV transmission potential R₀, the available proportion of resistant individuals, and their distribution across herds. PRRS prevalence is defined as the proportion of herds with effective disease transmission potential R above 1. The four graphs show four different distribution scenarios of resistant animals into herds (see Table 1 and main text for details). (A) Optimum distribution, (B) comprehensive distribution, (C) concentrated distribution, (D) unregulated distribution. Shaded areas correspond to confidence intervals comprising 95% of the predicted values from 100 simulated replicates (note that in A–C these are too narrow to be visible). Note that in the unregulated distribution scenario (D) the actual proportion of genetically resistant animals across all herds may be lower than the available proportion (presented on the x axis), explaining why elimination is not possible even if there is unlimited supply of genetically resistant pigs.

Fig. 2.

Minimum required proportion of genetically resistant animals (solid bars) and corresponding herds adopting gene editing (transparent bars) for achieving disease elimination through gene editing alone or with vaccination combined, depending on how edited animals are distributed across the herds. Results are shown for average R₀ value of 1.5 and exposure probability of either 100% (A–C) and 50% (D–F) and vaccine effectiveness of 70%. Different colors refer to different distribution scenarios (see Table 1) with blue = optimum, black = comprehensive, green = concentrated and yellow = unregulated. The proportion of edited animals in the concentrated scenarios is chosen at the smallest possible proportion for elimination under each scenario, resulting in a P_e of 0.75 for scenarios A, B, D, and E (green bars), a P_e of 0.5 for scenario C (green bars, purple fill), and a P_e of 0.1 for scenario F (green bars, red fill). For further explanation of editing and vaccination strategies and the different distribution of edited individuals across herds see the main text.

Fig. 3.

Minimum required proportion of genetically resistant animals for achieving disease elimination through gene editing and vaccination combined, depending on vaccine effectiveness ε_V and exposure probability. Dark bars: ε_V = 0.7, medium bars: ε_V = 0.5; light bars: ε_V = 0.3. Different colors refer to different distribution scenarios (see Table 1) with blue = optimum, black = comprehensive, green = concentrated and yellow = unregulated. The proportion of edited animals in the concentrated scenarios is chosen at the smallest possible proportion for elimination under each scenario, resulting in a P_e of 0.75 for scenarios A and C (green bars), a P_e of 0.5 for scenario B (green bars, purple fill), and a P_e of 0.1 for scenario D (green bars, red fill). An average transmission potential of R₀ = 1.5 was assumed.

Fig. 4.

Schematic diagram of a typical five-tier pig production structure implemented into the gene-flow model. Two maternal breeds, A (black, e.g., Yorkshire) and B (gray, e.g., Landrace), are crossed to create hybrid females. Hybrid sows are mated to males from a terminal breed T (white, e.g., Duroc) to produce commercial animals. The color composition in individual animals represents the relative breed contribution. Numbers next to the arrows denote selection proportions transferred into subsequent tiers. Gene editing is performed in all three breeds but limited to tier I only; genotyping of selection candidates is carried out in tiers I and II (see Methods: The Epidemiological Simulation Model for more details).

Fig. 5.

Time to reach proportions of resistant pigs in the population needed for PRRS elimination under different gene-editing scenarios. The indicated threshold levels refer to required numbers of genetically resistant pigs for achieving elimination under different distribution scenarios of pigs in the commercial tier (average R₀ = 1.5 and exposure probability = 100%). For visibility, not all scenarios are depicted.