Title:
Identifying zoonotic risks through mutational signatures
Summary:
We currently have a poor understanding of the natural host species for many important RNA viruses, which compromises our ability to prevent ‘spillovers’ of animal pathogens into humans and thereby endangers public health.
Over the past few years, we have shown that host species and transmission routes can leave detectable genetic marks on pathogens through distinct mutational signatures. This enables us to identify where pathogens live and how they transmit through the mutational patterns contained in their genome sequences. We have applied this to reveal host species driving influenza virus outbreaks, identify novel transmission routes for emerging Mycobacteria (Ruis et al 2021 Nature Micro; Ruis et al 2023 Nature Comms)1,2, rapidly determine virulence of emerging virus lineages (Ruis et al 2023 Microbial Genomics)3, and demonstrate the widespread impact of a hypermutator drug on SARS-CoV-2 (Sanderson…Ruis 2023 Nature)4.
In this project, we aim to apply mutational signatures to identify the natural host species and the zoonotic potential of arboviruses, a diverse group of RNA viruses that are transmitted by arthropod (usually mosquito) vectors. We will apply computational analyses to calculate mutational signatures of a range of arboviruses with known host species, extract signatures that are host-associated, and then apply these to identify natural hosts where this is unknown. This will identify arboviruses of higher zoonotic potential (which may circulate in animals with regular human contact) and provide key insights to direct resources to disrupt transmission in natural hosts and therefore prevent spillover into humans.
References:
1Ruis et al (2021), Nature Microbiology, PMID: 34545208
2Ruis et al (2023), Nature Communications, PMID: 37925514
3Ruis et al (2023), Microbial Genomics, PMID: 37185044
4Sanderson et al (2023), Nature, PMID: 37748513
Required knowledge:
- No essential skills, computational training will be provided but existing knowledge of transmission and/or programming would be advantageous.
Supervisors:
Day-to-day supervisor: Chris Ruis (cr628@cam.ac.uk)
Co-supervisor: Andres Floto (arf27@medschl.cam.ac.uk)
Department of Medicine