Background
The emergence of SARS-CoV-2 and COVID-19 has illustrated our limited ability to treat the damaging host response associated with severe respiratory viral infection. Traditional approaches to drug discovery have relied on sequential translation through pre-clinical models, often with an incomplete understanding of the underlying biology and with questions about the generalisability of model systems to human disease.
During the outbreak, we deployed our existing Genetics of Mortality in Critical Care (GenOMICC) study to genotype patients with COVID-19 admitted to 224 intensive care units in the UK. We have now recruited in excess of 15,000 patients, and uncovered 25 independent genetic detirminants of susceptibility to severe COVID-19. Insights from the study have resulted in the adapation of therapeutics into clinical trials (1).
As we move beyond this pandemic, these techniques may be used to produce similar insights for other pathogens which cause severe disease, such as influenza.
A challenge to fully realising the potential of these systems remains the generalisability of animal models to the human host response (2). In this project, we will combine computational and laboratory techniques to develop a methodology for resolving this issue. This will involve leveraging meta-analysis by information content (MAIC), an algorithm we developed, to integrate gene-level signals in the host response to influenza A between humans and swine (3). We will compare the the transcriptomic and proteomic consequences of targeted genome edits in human and porcine precision-cut lung slice models to compare molecular mechanisms of the host response between species (4).
Aims
1. Design and develop methodological improvements to meta-analysis by information content (MAIC) to fascilitate inter-species comparisons.
2. Evaluate the similarities between the human and porcine host response to Influenza A infection.
3. Establish and optimise human and porcine precision cut lung slice (PCLS) models of H1N1 Influenza infection.
4. For selected prioritised genes, which are highly correlated in the human and porcine reponse to Influenza, assess the transcriptomic and proteomic consequences of CRISPR/Cas9-mediated genome editing.
Training outcomes
1. Develop a detailed grounding in common bioinformatic approaches to the handling of genomic data.
2. Obtain expert knowledge and skills in systematic review design and meta-analysis. Gain expertise in the mathematical underpinning of meta-analysis by information content and extend this knowledge to adapt and improve the algorithm.
3. Receive training in basic laboratory techniques and develop competence in the management of precision cut lung slice models. Gain experience in the use of in vitro gene editing techniques.
About the Programme
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
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