Looking for a moving needle in a haystack: identifying environmental niches of Salmonella Typhi using shotgun metagenomics
Salmonella enterica serovar Typhi is a pathogen that only causes disease in humans, and humans are the only known reservoir. It is however transmitted via the faecal-oral route and not directly human to human, but little is known about its “behaviour” in the environment, partly due to the fact that it is extremely difficult to isolate by culture or PCR from environmental samples. There is a variety of potential explanations for these challenges: it seems likely Typhoid bacteria are only transiently present in the environment and at much lower concentration than other members of the family Enterobacteriales (i.e. Escherichia coli), and could thus be outcompeted by faster growing bacteria in culture media. It is also possible they exist as a viable but non cultivable state, or they reside in an as yet unknown niche like an intracellular compartment (for example in single-cell eukaryotes such as amoebae), or on complex biofilms. This project is embedded within a large Gates-funded study on environmental reservoirs of S. Typhi (ERST, value $1.5 million, PI Feasey), using genomic analysis to evaluate strain diversity and ecological distribution with respect to public health.
There are several reasons why it is highly relevant to identify the transient ecological niche or indeed a novel environmental reservoir of S. Typhi. This would permit surveillance in the absence of blood culture facilities, which might act as a trigger for the deployment of conjugate vaccine and water sanitation and hygiene based public health measures. It will also permit global genomic surveillance of Typhoid fever and this approach might form the basis of a novel approach to environmental surveillance for multiple enteric pathogens in low income settings.
The approach will therefore take environmental samples and analyse them using deep sequencing, as well as culture-based single-strain genomics, to track the epidemiology and movement of S. Typhi and associated antimicrobial resistance cassettes. Nesting the power of metagenomic analysis in the context of spatial modelling will enable us to identify hotspots for typhoid in the environment. Further, preliminary short-read sequence data has indicated secondary loss of resistance elements from a resistance cassette in a Malawi S. Typhi sublineage, and the first project component will be to resolve these using long-read sequencing (Oxford Nanopore MinION). The candidate will thus get an exceptional grounding in bioinformatic analysis in a well-funded, translationally relevant study.
Where does the project lie on the Translational Pathway?
T1 – Basic Research & T2 – Human/Clinical Research & T3 – Evidence into Practice
The project is targets two core areas of LSTM: antimicrobial resistance and public health.
- The candidate will develop a strong skill set (output = PhD) and develop a network of collaborators and gain exposure to funders (Bill and Melinda Gates Foundation).
- The project will produce high quality REF returnable 3*/4* publications.
- The data will be reported directly to the Ministry of Health in Malawi through the EVIDENT network in Malawi and through the Gates Foundation Typhoid Environmental Surveillance network to the WHO
- Presentation at international conferences including the large global Coalition Against Typhoid meeting and major international infectious diseases conferences, as well as internally in Malawi at the College of Medicine Research Dissemination conference
Training will be provided in:
- bioinformatics (molecular evolution, comparative genomics, population genomics);
- novel approaches to use genomes for fine-scaled epidemiology.
The student will furthermore work with collaborators at the Institute for Disease Modelling (IDM, Bellevue, USA), Lancaster University, the MLW, and be embedded in the already closely collaborating network between the supervisory team and further collaborators.
Training as a “Bridge” scientist through interaction with an interdisciplinary consortium
The student should be comfortable working in computational biology, and have an interest in infectious disease epidemiology and phylogenetics.