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Precision Medicine DTP - Remodelling of cellular RNA metabolism during infection and disease


School of Biological Sciences

Prof D Tollervey , Dr D Oyarzun Thursday, January 07, 2021 Competition Funded PhD Project (Students Worldwide)

About the Project

Background:
Precision medicine aims to deliver the right treatment to the right person at the right time. Recent ‘omics technologies can generate vast amounts of detailed molecular data on individuals. These offer potential opportunities to characterize diseases and stratify patients across stages of disease progression.
However, the key interactions in such large, complex data sets may not be readily discerned by traditional reductionist approaches. A promising alternative is the use of machine learning algorithms for initial data analysis, followed up by hypothesis-driven analyses of specific components highlighted by the algorithms. In this project the student will use this quantitative approach to study the role of RNA-protein interactions in disease models employing cutting-edge experimental techniques.
The Tollervey group developed techniques for functional characterisation of RNA-protein interactions: CRAC allows precise mapping of binding sites for proteins on RNAs (for example see (1)), whereas TRAPP identifies and quantifies changes in the RNA-bound proteome (2). They are applying these techniques to human disease models alongside proteomics, RNA sequencing (RNAseq) and ribosome profiling (RIBOseq).
The student will work together with the Oyarzún group, which has a track record in developing computational methods for the analysis of molecular networks (3,4). Their expertise in machine learning and network analyses will allow the development of integrative models capable of generating biological insights from experimental datasets.

Aims:
All cells and organisms must deal with the challenges imposed by diverse stresses, and in all systems analysed this involves rapid alterations in RNA-protein interactions to remodel gene expression, particularly translation. In this project we will study two systems.

1: Hijacking of cellular systems after infection by SARS-CoV-2
Following infection, SARS-CoV-2 is predicted to massively reorganize host cell RNA metabolism, especially protein synthesis, and this is likely to be an important feature of viral replication. We are determining the effects of SARS-CoV-2 infection on human RNA metabolism. In parallel, we will test the effects of expression of individual SARS-CoV-2 proteins on host RNA metabolism.
2: Neuronal development and differentiation in a model for human disease

We are characterizing changes during differentiation in a model system for neuronal development and developing disease models, starting with the loss of SNORD115 and SNORD116. These are disease-linked, imprinted,”orphan” members of the human box C/D class of small nucleolar RNAs (snoRNAs) that show brain-specific expression. Loss of both genes causes Prader-Wili syndrome, wherease loss of only SNORD116 leads to hyperphagy.
We are characterizing the defects in differentiation and gene expression in cells deleted for the expressed allele of SNORD115 or SNORD116. Changes in RNA and protein metabolism over time courses, employing time-series clustering to detect relations between system components.
For training, complex, multidimensional datasets are already available for analysis. The student will initially participate more in data collection, to ensure a complete understanding of the strengths and limitations of the experimental approaches. In parallel, training in the computational methods will allow the student to adapt the experimental analyses to optimize them to generate the data most useful for the bioinformatics.

The project will expose the student to the challenges of noisy, high-dimensional biological datasets and offer a broad range of skills that will enhance their future employment opportunities in both industry and academia:
1) Strain construction using CRISPR techniques in yeast and human cells.
2) Generation and analysis of high-throughput data: RNAseq, RIBOseq, Proteomics, Phosphoproteomics, Crosslinking and analysis of cDNAs (CRAC), Total RNA-assocaited proteome purification (TRAPP), Protein-protein crosslinking, Mass-spectrometry
3) Statistical and Machine Learning approaches for regression, classification, and time-series clustering.
4) Data processing, scientific computing, algorithm testing and deployment.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2021

Qualifications criteria: Applicants applying for an MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,285 (UKRI rate 2020/21).

Full eligibility details are available: View Website

Enquiries regarding programme:

References

1. Delan-Forino, C., Spanos, C., Rappsilber, J. and Tollervey, D. (2020) Substrate specificity of the TRAMP nuclear surveillance complexes. Nat Commun, 11, 3122.
2. Shchepachev, V., Bresson, S., Spanos, C., Petfalski, P., Fischer, L., Rappsilber, J. and Tollervey, D. (2019) Defining the RNA Interactome by Total RNA-Associated Protein Purification. Mol. Sys. Biol., 15, e8689.
3. Tonn, M.K., Thomas, P., Barahona, M. and Oyarzún, D.A. (2019) Stochastic modelling reveals mechanisms of metabolic heterogeneity. Commun Biol, 2, 108.
4. Hartline, C. J., Mannan, A. A., Liu, D., Zhang, F., & Oyarzún, D. A. (2020). Metabolite sequestration enables rapid recovery from fatty acid depletion in Escherichia coli. MBio, 11(2),

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