Impact of variants of concern on SARS-CoV-2 viral dynamics in non-human primates

Abstract

The impact of variants of concern (VoC) on SARS-CoV-2 viral dynamics remains poorly understood and essentially relies on observational studies subject to various sorts of biases. In contrast, experimental models of infection constitute a powerful model to perform controlled comparisons of the viral dynamics observed with VoC and better quantify how VoC escape from the immune response. Here we used molecular and infectious viral load of 78 cynomolgus macaques to characterize in detail the effects of VoC on viral dynamics. We first developed a mathematical model that recapitulate the observed dynamics, and we found that the best model describing the data assumed a rapid antigen-dependent stimulation of the immune response leading to a rapid reduction of viral infectivity. When compared with the historical variant, all VoC except beta were associated with an escape from this immune response, and this effect was particularly sensitive for delta and omicron variant (p<10-6 for both). Interestingly, delta variant was associated with a 1.8-fold increased viral production rate (p = 0.046), while conversely omicron variant was associated with a 14-fold reduction in viral production rate (p<10-6). During a natural infection, our models predict that delta variant is associated with a higher peak viral RNA than omicron variant (7.6 log10 copies/mL 95% CI 6.8-8 for delta; 5.6 log10 copies/mL 95% CI 4.8-6.3 for omicron) while having similar peak infectious titers (3.7 log10 PFU/mL 95% CI 2.4-4.6 for delta; 2.8 log10 PFU/mL 95% CI 1.9-3.8 for omicron). These results provide a detailed picture of the effects of VoC on total and infectious viral load and may help understand some differences observed in the patterns of viral transmission of these viruses.

Fig 1. Longitudinal measurements of genomic RNA, subgenomic RNA and infectious titers in 78 infected cynomolgus macaques.

Both limit of quantification and detection are depicted as empty dots, the latter being lower. Upper limit of detection is depicted as filled squares.

Fig 2. Schematic model of SARS-CoV-2 infection and action of the immune system.

The basic model is a target cell limited model without any immune response. The parameters are: β the infectivity rate, k the transfer rate between non-productive and productive infected cells, δ the loss rate of productive infected cells, p the viral production rate, μ the ratio of infectious virus, g the transfer rate between the compartments of the immune response and c the loss rate of both infectious and non-infectious virus.

Fig 3. Estimated population parameters for each variant.

We represent the mean value and 95% confidence interval of populations parameters for each variant. We represent only parameters having at least one variant-specific effect. Full table for population parameters is in S2 Table. The dashed black line represents the historical value.

Fig 4. Simulation of variant of concern impact on viral load.

Using simulations, we sampled parameters considering both the uncertainty in the estimation and the inter-individual variability (see methods). We represent the mean viral load of all variants and its 95% confidence interval. Dotted lines are the limits of detections.

Fig 5. Impact of VoC on viral load metrics in the context of an infection with a low inoculum.

We represent the mean and 95% confidence interval for each variant. The dashed black line represents the historical mean value.