Quantitative analysis of intrinsic antibiotic resistance in the major nosocomial pathogen Klebsiella pneumoniae

Background

Antibiotic resistance poses a global and severe threat to human, animal and planetary health. Typically, resistance arises through genetic mutations or via the acquisition of genes that allow bacteria to resist antibiotics. Transcription factors (TF) are part of the intrinsic response to antibiotic challenge and when upregulated control multiple genes. It has, for example, been shown that the last-line antibiotic, tigecycline, directly selects for enhanced expression of the global regulatory protein RamA. Importantly, increases in RamA levels are not limited to tigecycline exposure alone but extend to other antibiotics, thereby highlighting the relevance of RamA in the intrinsic resistome.

Aims

In this project, we set out to quantitatively characterise the gene regulatory mechanisms of RamA that allow the clinically relevant bacterium Klebsiella pneumoniae to adapt their gene expression machinery to antibiotic challenges. We will produce cutting-edge transcriptomics data and use machine learning and bioinformatics to identify promoter signatures that lead to differential expression of RamA-regulated genes.

Training Outcomes

Candidates from both ‘wet’ and ‘dry’ backgrounds with a keen interest in interdisciplinary research are encouraged to apply, ideally, with a first-class degree (or equivalent) in Computational Biology (or similar such as Mathematics, Physics, Computer Science) or Biology (or similar, e.g. Biomedicine, Biochemistry).  

The project will equip the student with a range of highly sought-after quantitative data and experimental skills. It will be an opportunity for truly interdisciplinary research training as part of the teams led by Dr Andrea Weisse (School of Biological Sciences and School of Informatics) and Dr Thamarai Schneiders (School of Medicine). 

Further Reading

https://www.frontiersin.org/articles/10.3389/fmicb.2020.00828/full 

https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1004627