Epidemiological and molecular prediction of severe infection response

Precision Medicine Project - Epidemiological and molecular prediction of severe infection response

Supervisor(s): Dr Rob Young, Dr Ting Shi & Prof Kenneth Baillie
Centre/Institute: Centre for Global Health, Usher Institute

Background

Individuals are exposed and infected by a range of pathogenic agents, including viruses such as influenza and SARS-CoV-2. How different individuals respond to infection varies dramatically from no clinical signs through to severe illness and death. For example, over six million died during the COVID-19 pandemic but it was also estimated that up to 30% of infections may have been asymptomatic1. There is therefore an urgent need to understand this differential response to infection to inform the monitoring of those patients most at risk of a severe outcome and to direct the development of future therapeutics within high-risk patient cohorts. 

In this project, you will use an epidemiological approach to identify those features that are predicted to determine infection outcome and then use bioinformatics techniques to explore their association with regulatory, molecular signals.

Aims

This project will investigate the molecular phenotypes and mechanisms associated with severe infection as predicted by observational epidemiology. We will identify risk factors within infected individuals and then use a functional genomics approach to identify molecular signatures which distinguish the transcriptomes of infected individuals with and without these risk factors. Finally, we will explore the genomic annotation and distribution of these biomarkers revealing the molecular pathways and drug targets they regulate, which may represent future biomarkers and/or therapeutic candidates.

The project has three aims:

  1. Identification of risk factors that predict severe response to pathogenic infection

We will first make use of population-wide datasets containing health events available from electronic health records, e.g. DataLoch which covers approx. 900,000 individuals in the Lothian region. We have existing ethics approval to access these (through ‘Risk Stratification on Adults with Viral Infection’) and can apply for new access if required. These datasets will be used to interrogate socio-economic, clinical, laboratory and prescription information across patients to identify those features which are most predictive of a severe response to infection, e.g. hospital admission.

  1. Transcriptomic characterisation of infected individuals 

Individuals with the most predictive risk factors who experience critical illness upon infection, alongside matched controls, will be identified within our GenOMICC study cohort2. Previously-collected blood samples across twelve such individuals (three patients and three controls for each of two risk factors) will be subjected to the functional genomics technology Cap Analysis of Gene Expression (CAGE) by long-standing collaborators at the Human Technopole, Milan. This technology can simultaneously identify and quantify the regulatory output from gene promoters and actively transcribed regulatory elements3. These data will be used to identify differentially expressed molecular targets at noncoding regulatory loci which are otherwise difficult to functionally characterise.

  1. Bioinformatics characterisation of gene regulatory targets 

We will use standard bioinformatics techniques to explore the minimum number of biomarkers required to discriminate high-risk from low-risk infections, as previously demonstrated in inflammatory bowel disease4. These analyses will also reveal whether particular gene functions, molecular pathways or druggable gene targets are associated with infection response.

Training outcomes

This project will involve quantitative analyses in the areas of epidemiology, genomics and bioinformatics. The student will become proficient in the integration of population-wide and big ‘omics’ datasets using a range of computational and statistical tools. They will also learn bioinformatics techniques to process the molecular datasets generated here.

An ideal candidate will have prior experience in computational and statistical biology, such as the use of R or working in a Linux environment but further training will be provided by the supervisory team and members of their research groups. At the end of this studentship the student will have the necessary quantitative skills to seamlessly transition between clinical, epidemiological and computational elements of biomedical science. 

References 

  1. Shang W, Kang L, Cao G, Wang Y, et al. Percentage of Asymptomatic Infections among SARS-CoV-2 Omicron Variant-Positive Individuals A Systematic Review and Meta-Analysis. Vaccines 10 (2022). https://doi.org/10.3390/vaccines10071049
  2. Pairo-Castineira E, Rawlik K, Bretherick AD, Qi T, et al. GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19. Nature 617, (2023) https://doi:10.1038/s41586-023-06034-3
  3. Young RS, Kumar Y, Bickmore WA, Taylor MS. Bidirectional transcription initiation marks accessible chromatin and is not specific to enhancers. Genome Biology 18, 242 (2017). https://doi.org/10.1186/s13059-017-1379-8
  4. Boyd M, Thodberg M, Vitezic M, Bornholdt J, et al. Characterization of the enhancer and promoter landscape of inflammatory bowel disease from human colon biopsies. Nature Communications 9 (2018) https://doi.org/10.1038/s41467-018-03766-z 

Apply Now

Click here to Apply Now

  • The deadline for 26/27 applications is Monday 12th January 2026
  • Applicants must apply to a specific project. Please ensure you include details of the project on the Recruitment Form below, which you must submit to the research proposal section of your EUCLID application.
  • Please ensure you upload as many of the requested documents as possible, including a CV, at the time of submitting your EUCLID application.  

Q&A Sessions

Supervisor(s) of each project will be holding a 30 minute Q&A session in the first two weeks of December. 

If you have any questions regarding this project, you are invited to attend the session on TBC via Microsoft Teams. Click here to join the session.

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