Many bacteria that are a concern for public health vary in their clinical presentations (the associated symptoms of disease). Such variations can include asymptomatic carriage, minor infection or invasive disease, where the bacteria are found in parts of the body that are typically germ-free, such as blood (e.g., bacteraemia).
Research is trying to pinpoint likely candidates in the bacterial genomes that are responsible for these different clinical outcomes. These variations or regions of the genome are referred to as virulence genes. While many of these have been identified for different pathogens (an organism that causes disease), they often do not fully explain the differences in symptoms a certain bacterial strain in a patient.
Streptococcus pyogenes (Group A Streptococcus, GAS) is a pathogen of major health concern, which can cause very different clinical outcomes in patients. These include minor infections such as pharyngitis, or skin infections, or in the worst-case scenario severe invasive disease. Repeated GAS infections can lead to acute rheumatic fever, rheumatic heart disease or kidney disease in severe cases, and factors like social deprivation and systemic biases place some New Zealanders at a higher risk of severe disease outcomes.
New Zealand experiences rates of invasive disease up to four times higher than other developed countries, with Māori and Pasifika communities disproportionally affected, and the very young and old being particularly vulnerable.
This project aims to understand the genetic drivers of invasive GAS bacterial strains. We are using a machine learning algorithm to identify genetic variants that are associated with more invasive bacteria (i.e., are more likely to cause severe disease), which requires a large and diverse dataset to train the machine. The dataset will comprise of a collection of GAS genomes for bacteria isolated from samples collected in those communities that are most affected by this disease, as well as genomes from GAS isolates where the bacteria invaded the body.
The ultimate goal is to develop a method to predict bacterial characteristics for GAS infections. This will help us understand if there are particular strains of the bacteria that need priority treatment and improved surveillance. Ultimately, we want to make the method species agnostic, meaning it can be applied to different bacterial species. So, we will test the same approach on a dataset of invasive and non-invasive Campylobacter jejuni isolates, another common pathogen that can cause foodborne illnesses.
A detailed population genomics study of invasive and non-invasive GAS in New Zealand will provide information on diversity and virulence factors, potentially helping to treat GAS-based diseases, and aid vaccine development efforts.
This work is a collaboration between ESR, the University of Otago and Massey University.
Outcomes
- Machine learning model to predict phenotypes from genetic variation for GAS
- Adaption of developed prediction model for other pathogens of public health concern (e.g., Campylobacter jejuni, Mycobacterium tuberculosis, Salmonella sp.)
- Provide training for New Zealand researchers on the model for other pathogens
- Detailed population genomics study of Group A Streptococcus pyogenes in New Zealand
- Create national connections.
Team
- Paul Gardner, University of Otago, co-leader
- Joep de Ligt, ESR, co-leader
- Christina Straub, ESR
- Julie Bennett, University of Otago
- Nigel French, Massey University
Community engagement and consultation
- Anneka Andersen, University of Auckland, National Hauora Coalition
- Rachel Brown, National Hauora Coalition
Project advisory team
- Una Ren, ESR
- Patrick Biggs, Massey University