Ragonnet-Cronin, M. | Search bioRxiv

bioRxiv2022

Genome-first detection of emerging resistance to novel therapeutic agents for SARS-CoV-2

Some COVID-19 patients are unable to clear their infection or are at risk of severe disease, requiring treatment with neutralising monoclonal antibodies (nmAb) and/or antivirals. The rapid roll-out of novel therapeutics means there is limited understanding of the likely genetic barrier to drug resistance. Unprecedented genomic surveillance of SARS-CoV-2 in the UK has enabled a genome-first approach to the detection of emerging drug resistance. Here we report the accrual of mutations in Delta and Omicron cases treated with casirivimab+imdevimab and sotrovimab respectively. Mutations occur within the epitopes of the respective nmAbs. For casirivimab+imdevimab these are present on contiguous raw reads, simultaneously affecting both components. Using surface plasmon resonance and pseudoviral neutralisation assays we demonstrate these mutations reduce or completely abrogate antibody affinity and neutralising activity, suggesting they are driven by immune evasion. In addition, we show that some mutations also reduce the neutralising activity of vaccine-induced serum.

genetics↗

bioRxiv2018

FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

MotivationThe ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like HIV may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain \"truth\" sets on which to test them and properly measure their performance.\n\nResultsWe introduce FAVITES, a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees, and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters.\n\nAvailability and implementationFAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).

bioinformatics↗