Johnson and Arnvig Lab | The role of noisy 5' leaders in Mycobacterium tuberculosis heterogeneity

Johnson and Arnvig Lab | The role of noisy 5' leaders in Mycobacterium tuberculosis heterogeneity

Full-Time On-site
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Johnson and Arnvig Lab | The role of noisy 5' leaders in Mycobacterium tuberculosis heterogeneity

Key information

Research topics

A 2027 Crick-University College London Joint PhD project with Eachan Johnson (Crick) and Kristine Arnvig (UCL).

Please note: to apply to this project, candidates must be eligible for home tuition fee status.

Project background and description

The human pathogen Mycobacterium tuberculosis (Mtb) is renowned for its pronounced cell-to-cell variability within populations of genetically identical cells[1]. This “phenotypic heterogeneity” is based on stochastic gene expression, and the result is a bacterial population with a selective advantage, increasing the likelihood that individual members of the population survive when exposed to a wide range of stress conditions (bet hedging). This phenomenon is a prerequisite for the generation of phenotypically drug-tolerant cells which may be responsible for the requisite six-month duration of tuberculosis chemotherapy[2]. Therefore, understanding the molecular mechanisms behind cell-to-cell variability may reveal new intervention points and strategies.
While previous studies have focussed on the role of promoter-driven noise and transcriptional fluctuations[3] the contributions of post-transcriptional noise from 5' leaders remain poorly understood. Premature termination, riboswitches, and idiosyncratic short, translated leaders (uORFs) are emerging as important but previously underappreciated features of Mtb post-transcriptional regulation.
The Arnvig lab has a strong track-record in functional characterisation in Mtb of riboswitches, metabolite-sensing RNA leaders that control downstream gene expression often through ligand-dependent changes in premature transcription termination[4]. Her lab has also identified hundreds of uncharacterised, regulatory 5' leaders using termseq[5]. These leaders, some potentially new riboswitches, provide a rich resource for further studies on post-transcriptional noise.
Beyond the transcriptional stochasticity (noise) in Mtb characterised by the Johnson lab, this project will investigate the extent of stochasticity associated with selected 5' leaders and the role they play in population survival of lethal antibiotic doses in Mycobacterium smegmatis and Mtb. We will use dual fluorescence reporter system established in the Johnson lab coupled with flow cytometry and next-generation sequencing (FACS-seq) to establish distributions of expression levels driven by multiplexed genomic sequences, thus yielding the bacterium-to-bacterium variability of expression influenced by many different 5' leaders in parallel.
Noisy leaders will be further characterised by our newly devised strategy involving overexpressing of the leader regions of interest in Mycobacterium smegmatis and Mtb. This will promote ligand sequestration and trigger changes in regulatory and metabolic pathways. We will subsequently apply multi-omics (RNA-seq, proteomics and metabolomics) and integrate their outputs using systems biology approaches to identify dysregulated metabolic pathways and potential ligands, which will be validated in vitro using inline probing and SHAPE-seq. The identification and characterisation of noisy leaders and how they respond to their cognate ligands represent new points of potential intervention in the fight against tuberculosis and wider antimicrobial resistance.

Candidate background

This project is aimed at candidates with a background in biochemistry, molecular biology and computational biology or biological engineering. The student will have an interest in fundamental mechanisms of pathogen biology including transcriptional and post-transcriptional regulation of gene expression, broader RNA biology and host-pathogen interactions. Computational experience is required.

Lab-specific question

Please provide a citation and PubMed ID of a journal article (not from your previous labs or our labs) that has strongly influenced your scientific interests to date. Which figure do you find most compelling and why? What would you have done differently?

References

1. Dhar, N., McKinney, J. and Manina, G. (2016)

Phenotypic Heterogeneity in Mycobacterium tuberculosis.

Microbiology Spectrum 4. PubMed abstract

2. Rego, E.H., Audette, R.E. and Rubin, E.J. (2017)

Deletion of a mycobacterial divisome factor collapses single-cell phenotypic heterogeneity.

Nature 546: 153–157. PubMed abstract

3. Engl, C., Jovanovic, G., Brackston, R.D., Kotta-Loizou, I. and Buck, M. (2020)

The route to transcription initiation determines the mode of transcriptional bursting in E. coli.

Nature Communications 11: 2422. PubMed abstract

4. Millman, A., Dar, D., Shamir, M. and Sorek, R. (2017)

Computational prediction of regulatory, premature transcription termination in bacteria.

Nucleic Acids Research 45: 886–893. PubMed abstract

5. D'Halluin, A., Polgar, P., Kipkorir, T., Patel, Z., Cortes, T. and Arnvig, K.B. (2023)

Premature termination of transcription is shaped by Rho and translated uORFS in Mycobacterium tuberculosis.

iScience 26: 106465. PubMed abstract

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Johnson and Arnvig Lab | The role of noisy 5' leaders in Mycobacterium tuberculosis heterogeneity employer: The Francis Crick Institute Limited

The Francis Crick Institute is an exceptional employer, offering a vibrant and collaborative work culture that fosters innovation and scientific discovery. With state-of-the-art facilities and a commitment to employee well-being, including generous leave and comprehensive support services, the Crick provides ample opportunities for professional growth and development in a diverse and inclusive environment. Located in the heart of London, this prestigious biomedical research institute empowers its staff to pursue groundbreaking research that has a meaningful impact on human health.

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Contact Details:

The Francis Crick Institute Limited Recruitment Team