22-ICRAD Call 2 - Q-Net-Assess (Improved molecular surveillance and assessment of host adaptation and virulence of Coxiella burnetii in Europe)
Lead Research Organisation:
Moredun Research Institute
Department Name: Disease Control
Abstract
Q fever is an important zoonotic pathogen caused by the bacterium Coxiella burnetii. Clinical signs in humans range from more common flu-like symptoms to persistent and potentially fatal infections. Ruminant livestock, and sheep and goats in particular, are the primary source of human infections, although C. burnetii can infect a wide range of other animals including wildlife and ticks. In ruminants, C. burnetii can cause abortion, stillbirth and weak offspring, particularly in sheep and goats, although most infections are asymptomatic. Thus, host range and outcome of infection is highly variable. However, our understanding of how C. burnetii genotype contributes to this variation is limited.
The main genotyping methods currently used for C. burnetii generate only limited genomic information and are difficult to standardise between laboratories. Whole genome sequencing (WGS) has revolutionised molecular epidemiology and surveillance of many zoonotic pathogens as it provides comprehensive genetic information and is easily standardised. However, few C. burnetii strains are currently available for WGS, largely due to difficulties in isolating the bacteria from field samples.
We have assembled a consortium with unique expertise in C. burnetii surveillance and genomics to allow collation of C. burnetii positive samples from a wide range of hosts (livestock, wildlife and humans) with accurate clinical data. C. burnetii will be isolated from these samples using optimised isolation methods. Isolated strains, plus available archived strains, will be submitted for WGS to generate a comprehensive database of annotated C. burnetii genomes, which will include phenotypic data from the field and in-vitro cellular assays. WGS data will be analysed using novel bioinformatics approaches to identify molecular determinants of C. burnetii host range and virulence. Finally, project outputs will be synthesised into a recommended framework for future molecular surveillance of C. burnetii.
The main genotyping methods currently used for C. burnetii generate only limited genomic information and are difficult to standardise between laboratories. Whole genome sequencing (WGS) has revolutionised molecular epidemiology and surveillance of many zoonotic pathogens as it provides comprehensive genetic information and is easily standardised. However, few C. burnetii strains are currently available for WGS, largely due to difficulties in isolating the bacteria from field samples.
We have assembled a consortium with unique expertise in C. burnetii surveillance and genomics to allow collation of C. burnetii positive samples from a wide range of hosts (livestock, wildlife and humans) with accurate clinical data. C. burnetii will be isolated from these samples using optimised isolation methods. Isolated strains, plus available archived strains, will be submitted for WGS to generate a comprehensive database of annotated C. burnetii genomes, which will include phenotypic data from the field and in-vitro cellular assays. WGS data will be analysed using novel bioinformatics approaches to identify molecular determinants of C. burnetii host range and virulence. Finally, project outputs will be synthesised into a recommended framework for future molecular surveillance of C. burnetii.
Technical Summary
The Q-Net-Assess project will take a functional genomics approach to understand C. burnetii host adaptation and virulence. Combined analysis of Whole genome Sequencing data from C. burnetii strains from different hosts (domestic ruminants, humans, wildlife), of in vitro phenotypes and of geo-referenced meta-data will reveal molecular determinants that control C. burnetii host adaptation and link genetic traits to differences in zoonotic potential and clinical relevance. This information can be used to determine upcoming zoonotic threats posed by C. burnetii, and provide a framework for assessing the risk and severity of future Q fever outbreaks.
The major difficulty in C. burnetii genomics is the lack of sequenced strains and associated meta-data. We have assembled a consortium with unique expertise in C. burnetii surveillance and genomics, and with direct or indirect links to six European national Q fever Reference Laboratories. This has allowed the collation of >100 C. burnetii isolates for genome sequencing, and a biobank of >350 samples with high C. burnetii loads for bacterial isolation. This will be supplemented by prospective sampling from each participating country. In addition, this project will optimise C. burnetii isolation methodologies, and explore long-read sequencing to directly sequence from clinical samples and to assemble fully annotated genomes.
Key to genome association studies is accurate phenotypic data, which will be obtained from surveillance laboratories and through use of cutting-edge in vitro assays. Novel bioinformatic and artificial intelligence approaches will be used to identify molecular determinants of C. burnetii host range and virulence, which may be targeted using specific molecular probles to rapidly determine host/virulence potential. Finally, we will synthesise project outputs into a recommended Pan-European framework for molecular surveillance of C. burnetii.
The major difficulty in C. burnetii genomics is the lack of sequenced strains and associated meta-data. We have assembled a consortium with unique expertise in C. burnetii surveillance and genomics, and with direct or indirect links to six European national Q fever Reference Laboratories. This has allowed the collation of >100 C. burnetii isolates for genome sequencing, and a biobank of >350 samples with high C. burnetii loads for bacterial isolation. This will be supplemented by prospective sampling from each participating country. In addition, this project will optimise C. burnetii isolation methodologies, and explore long-read sequencing to directly sequence from clinical samples and to assemble fully annotated genomes.
Key to genome association studies is accurate phenotypic data, which will be obtained from surveillance laboratories and through use of cutting-edge in vitro assays. Novel bioinformatic and artificial intelligence approaches will be used to identify molecular determinants of C. burnetii host range and virulence, which may be targeted using specific molecular probles to rapidly determine host/virulence potential. Finally, we will synthesise project outputs into a recommended Pan-European framework for molecular surveillance of C. burnetii.
