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Partitioning primary and secondary changes of gene expression caused by gene mutations


Project Description

Control of gene expression is a complex cell process, which involves balancing both the synthesis and the degradation of RNA and protein so as to establish optimal levels of each protein. Hence mutations that inactivate or over-activate a gene involved in one of these processes will affect gene expression. Effects can come as direct consequences for its primary target RNAs or proteins, as well as secondary consequences caused by the imbalance to the expression of those targets. How the balance of primary versus secondary impacts of gene-activation mutations combine to cause changes in phenotype or manifest as a disease state is not well explored. This project will fill this gap by studying how defects in a well-characterised simple eukaryotic protein (Puf3) have widespread consequences for cell functions. The approaches developed here could then be applied to other systems including those involved in human disease.

Puf3 is an RNA-binding protein that is implicated in regulating mRNA stability and protein synthesis. It contributes to cell homeostasis and gene expression control on a global scale via regulating expression of nuclear-encoded mitochondrial proteins. In previous papers we have demonstrated that Puf3 may play a broader role and be involved in many more biological processes. Hence Puf3 provides a great example to explore the network of interactions that exist to control the overall proteome.

The project will enhance our understanding much further by developing a systemic model of the role of Puf3 in the overall programme of yeast gene expression. The student will undertake a mix of lab and computational experiments including generating and analysing multi-omics data and building a quantitative-informed model of regulation. The project builds on the existing knowledge and complementary expertise of the different laboratories involved. It represents an ideal opportunity for an individual willing to learn a wide range of current experimental methodologies including next-generation sequencing, proteomics as well as systems-biology and mathematical-modelling approaches.

The project will use a mix of computational and wet-lab experimentation.
The wet lab work will involve molecular biology, microbiology, PCR and high-throughput-based techniques such as RNA-Seq and shotgun proteomics. Moreover, biochemistry will be used to fractionate ribosomes in order to analyse the translatome.
The computational work will comprise a significant proportion of the work, >60%. The student will create and run their own pipeline for the analysis of RNA-Seq data. This will involve using standard bioinformatics tools as well as writing their own scripts. Subsequently, the student will gain expertise in other areas of functional genomics analyses including networks and pathways analyses. Finally, the student will build his or her own quantitative model of regulation of gene expression using ordinary differential equation based modelling and learn mathematical techniques such as parameter estimation and sampling.

Entry Requirements:
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in Bioinformatics/Computational Biology or with an interest in Mathematical Modelling are encouraged to apply.

Funding Notes

Applications are invited from self-funded students. This project has a Band 2 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Rowe W, Kershaw CJ, Castelli LM, Costello JL, Ashe MP, Grant CM, Sims PFG, Pavitt GD, Hubbard SJ. Puf3p induces translational repression of genes linked to oxidative stress. Nucleic Acids Res. 2014. 42(2):1026-1041.

Kershaw CJ, Costello JL, Talavera D, Rowe W, Castelli LM, Sims PFG, Grant CM, Ashe MP, Hubbard SJ, Pavitt GD. Integrated multi-omics analyses reveals pleiotropic nature of control of gene expression by Puf3p. Sci Rep. 2015. 5: 15518.

Talavera D, Kershaw CJ, Costello JL, Castelli LM, Rowe W, Sims PFG, Ashe MP, Grant CM, Pavitt GD, Hubbard SJ. Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions. Sci Rep. 2018. 8:7949.

Crawford RA, Pavitt GD. Translational regulation in response to stress in Saccharomyces cerevisiae. Yeast. 2019. 36:5-21. doi: 10.1002/yea.3349.

Schwartz JM, Otokuni H, Akutsu T, Nacher JC. Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues. Nature Communications. 2019. 10: 2725.

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