bioRxiv Science⌕ Search

Biology subjects

Kerioui, M.

Publications and source records attributed to Kerioui, M..

2 recordsLinked to original sources

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

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.

microbiology↗

Galton-Watson Process And Bayesian Inference: A Turnkey Method For The Viability Study Of Small Populations

O_LISharp prediction of extinction times is needed in biodiversity monitoring and conservation management. C_LIO_LIThe Galton-Watson process is a classical stochastic model for describing population dynamics. Its evolution is like the matrix population model where offspring numbers are random. Extinction probability, extinction time, abundance are well known and given by explicit formulas. In contrast with the deterministic model, it can be applied to small populations. C_LIO_LIParameters of this model can be estimated through the Bayesian inference framework. This enables to consider nonarbitrary scenarios. C_LIO_LIWe show how coupling Bayesian inference with the Galton-Watson model provides several features: i) a flexible modelling approach with easily understandable parameters ii) compatibility with the classical matrix population model (Leslie type model) iii) A non-computational approach which then leads to more information with less computing iv) a non-arbitrary choice for scenarios, parameters... It can be seen to go one step further than the classical matrix population model for the viability problem. C_LIO_LITo illustrate these features, we provide analysis details for two examples whose one of which is a real life example. C_LI

ecology↗