Identifying the genetic determinants shaping the Neisseria gonorrhoeae population structure

background

The bacterium, Neisseria gonorrhoeae (Ng), is a leading cause of the sexually transmitted infection gonorrhoea with over 82 million new cases diagnosed every year that are exacerbated with increasing resistance to antibiotic treatment. 

On the basis of core genome allelic diversity, Ng can be organised into discrete lineages that persist over time, some of which associated with antimicrobial resistance. The maintenance of distinct lineages in spite of high levels of genetic exchange indicates that non-overlapping allelic combinations are positively selected for, likely as a result of co-adaptation of particular genetic loci. For example, Ng auxotypes, that have differing metabolic preferences, may segregate into distinct lineages that are better adapted for the exploitation of separate environmental niches such as the oropharynx, the urogenital tract or the female reproductive tract. 

Establishing the contribution of allelic co-adaptation in lineage maintenance will be important in understanding the impact interventions such as antimicrobials and/or vaccination may have on the Ng population. Identifying the determinants driving lineage structure that are consequently essential for Ng fitness, will also be important in developing therapeutic interventions that limit infection and/or transmission.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This project aims to identify and characterise the genetic determinants driving Ng population structure, and, in so doing, inform on the treatment and prevention of this important human pathogen. It will make use of the extensive collection of whole genome sequence data for which at the time of writing, there were >20,000 records. All data are publicly available and can be accessed using the web-based platform, PubMLST, which facilitates the gene-by-gene annotation of Ng. 

The project’s objectives are:

1. To elucidate the drivers of Ng lineage structure through the application of machine learning techniques
2. To examine associations between co-adapting genetic determinants and understand their role in Ng fitness
3. To characterise identified determinants using protein modelling studies and AI technology.

The student will gain experience in microbial genomics and the analysis of big data. They will develop skills in analytical techniques, research planning, data analysis and presentation skills. They will be supported to publish in peer-reviewed papers and encouraged to present at major international conferences. 

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

This project does not require any fieldwork. Training in bioinformatics, the use of python, R and genomics platforms will be provided. 

PROSPECTIVE  STUDENT

The ideal candidate will have a Master's degree in biology/microbiology/biomedicine/public health. This post is particularly suited to someone with an interest in bioinformatics, microbial genomics and infectious diseases.