Pascoe LabUniversity of Oxford · Ineos Oxford Institute

Research

Four questions about bacterial populations.

One Health field sampling in a food-system setting
Sampling people, animals, food systems and environments together.

Transmission

How do bacteria spread within and between environments, animals and humans?

We combine coordinated sampling, epidemiology and source attribution to reconstruct movement across food systems, households, livestock, wildlife and environmental reservoirs.

The central challenge is to distinguish simple co-occurrence from plausible transmission while accounting for uneven sampling, recombination and local population structure.

Enteric disease in AfricaThailand One Health
Illustrated bacterium representing adaptation and genetic exchange
Gene flow, population structure and adaptation across ecological boundaries.

Evolution

How do bacteria evolve and adapt to new hosts?

Host shifts are shaped by mutation, recombination, mobile genetic elements and repeated ecological contact. We study how those processes generate host specialism, generalism and emerging pathogenic lineages.

Campylobacter provides a powerful system because extensive gene flow can be studied alongside strong ecological and host-associated structure.

Peru programmeEvolution publications
Portable sequencing equipment used for accessible genomic surveillance
Portable sequencing, reproducible workflows and stable population nomenclature.

Open tools

Can we build open and accessible tools to study bacterial genomes?

Genomic surveillance becomes useful when methods, nomenclature and analytical workflows can be understood and reused outside a single project.

We develop open protocols, source-attribution methods, population annotation and version-controlled workflows that connect raw sequence data to interpretable bacterial populations.

Software and protocols
Figure showing genomic source-attribution models and prediction probabilities
Interpretable models linking genomic variation to source, resistance and disease.

Prediction

Can genomics and AI help predict disease outcomes?

Machine-learning and association approaches can identify genomic signals linked to source, resistance, host adaptation and clinical phenotype.

Models are evaluated against population structure, uncertainty and biological plausibility so that genomic signals can support surveillance and intervention.

Prediction case study