| ID | Title | Overview |
|---|---|---|
| BC1 | Characterising mycobacterial genomic diversity using sequencing, bioinformatics, and AI. (London) | Mycobacterium tuberculosis complex bacteria (Mtb), the cause of TB, and non-tuberculous mycobacteria (NTM) are public health concerns, responsible for global burdens of (extra)pulmonary infection. Drug resistance (DR) further complicates treatment and control. Whole-genome sequencing (WGS) of Mtb can identify mutations associated with resistance to 17 anti-TB drugs, define sub-lineages linked to virulence, and support phylogenetic analyses of transmission. In contrast, the biology, DR mechanisms, and transmission dynamics of NTM are poorly understood. This project will use WGS, including Oxford Nanopore long-read sequencing, to characterise the genomic diversity of Mtb and NTM species, including M. avium and M. abscessus complexes. Integrating genomic data across species, we will identify genetic determinants of DR, develop AI models for genotypic DR prediction, and investigate transmission using strain-relatedness to support improved diagnostics, surveillance, and clinical management. |
| BC2 | Developing a genomic framework for Acinetobacter baumannii to support outbreak investigations. (London) | Acinetobacter baumannii is a common cause of serious drug-resistant healthcare-associated infections, and outbreaks of infection can occur amongst the most vulnerable hospital patients. Pathogen sequencing is increasingly used to investigate and characterise such outbreaks to support the work of hospital infection control teams, but standardised frameworks for analysis and reporting are lacking for A. baumannii. You will analyse genomic data using bioinformatic, statistical and phylogenetic analysis to develop a novel nomenclature system for A. baumannii, based on the life identification number (LIN) coding approach. This will involve defining thresholds that are taxonomically and epidemiologically informative, and testing utility of the system with a defined set of novel genomes including known related/outbreak strains from NHS hospital partners. If successful, the scheme will be implemented within PubMLST and clinical reporting frameworks, for use by the wider community. |
| BC3 | Using tprK recombination to distinguish long-term from repeat syphilis infections. (London) | Genomic sequencing is a key tool for investigation of the life course of pathogen infections. The difference between two pathogen genomes can distinguish whether an individual testing positive repeatedly has a long-term infection or was reinfected. Treponema pallidum, the agent of syphilis, has a slow substitution rate, which limits the ability to answer this question by just counting the number of genomic positions at which the sequence differs. An alternative signal of divergence lies in recombination involving the tprK gene. 50 genomic “cassettes”, located outside tprK itself, can be copied into variable regions of the gene. Strains with a similar cassette arrangements may be more closely related to each other than those where they differ, but procedures to compare arrangements do not exist. The student will develop and apply bioinformatic methods to identify and compare tprK variation, and use it to distinguish long-term and repeat infections in T. pallidum sequences from the UK. |
| BC4 | AI for molecular prediction and therapeutic design in infectious diseases. (London) | Despite advances in sequencing and structural biology, predicting how genetic variation affects molecular function remains a major challenge. Understanding which mutations alter protein function, drive antimicrobial resistance or enable immune escape is important for disease surveillance, drug discovery and protein engineering. This project will investigate whether artificial intelligence can predict the effects of sequence variation by learning relationships between protein sequence, structure and function. Depending on student interests, the project may focus on norovirus evolution, antimicrobial resistance, or nanobody optimisation. Using protein language models, machine learning, structural bioinformatics and evolutionary analysis, students will develop predictive models, gain insight into molecular mechanisms, and contribute to AI-driven tools for biomedical research. |
| BC5 | Genetic variation & genome-scale metabolic modelling of globally successful enteric fever pathogens. (London) | Enteric fever, caused by the bacteria Salmonella Typhi and Paratyphi A, drives >14 million illnesses annually, of which >100,000 are associated with mortality. Typhi genotype 4.3.1 (formerly known as lineage H58) has been highly successful causing the majority of enteric fever illnesses in East Africa and Asia following its emergence in the 1980s and several waves of intercontinental dissemination. However, the biological basis for the success of genotype 4.3.1 has not been fully elucidated. Using Oxford Nanopore Technologies pathogen whole genome sequencing data generated through routine public health surveillance, this study will resolve the genomes of typhoidal Salmonella isolates and explore variation in gene content, mobile genetic elements, and metabolic capacity across key pathogen genotypes using bioinformatic techniques, shedding light on neglected aspects of pathogen biology related to success. Prior experience in genomics or bioinformatics is desirable, but not essential. |