How does genetic variation shape antiviral immunity and disease susceptibility via RNA Splicing?

Background

Susceptibility to viral infection is shaped not only by which genes an individual carries, but by how those genes are then regulated. Genome-wide association studies (GWAS) have identified many disease-associated variants, yet translating these associations into molecular mechanisms remains a major challenge. One important but relatively undefined mechanism is alternative RNA splicing. Genetic variants that alter splice-site patterns can change protein sequence, abundance, localization, and interaction partners [1]. As such splicing associated quantitative trait loci (QTLs) make an important contribution to human disease risk. This is particularly relevant to antiviral immunity, where a single gene can produce isoforms with markedly different functions [2].

Equally, genome-scale CRISPR screens and other functional studies have now identified hundreds of human genes that either restrict virus replication or are exploited by viruses as host dependency factors. Integrating such datasets can distinguish reproducible host factors from screen-specific observations [3]. We will integrate these approaches and determine how disease-associated genetic variation changes antiviral immunity through altered RNA splicing. The project will link individual genetic variants to RNA isoforms, molecular phenotypes and, ultimately, disease outcome. As genomic datasets remain weighted towards European-ancestry populations, we will also examine the transferability of identified relationships between Scottish/UK and East Asian populations where suitable datasets are available, addressing a major source of inequality in genomic medicine [4].

Aims

1. Identify candidate genes which influence susceptibility to infection or immune-mediated disease. The student will integrate GWAS signals for infectious and inflammatory phenotypes with ranked evidence from antiviral and proviral genetic screens from a range of viruses and associated transcriptomic data. Interferon-responsive gene sets and other functional datasets will be used to refine a prioritised set of immune genes.

2. Determine how genetic variation influences isoform usage in these genes. Using existing population-scale genomic and transcriptomic datasets, the student will map disease-associated variants onto QTLs and transcript isoform usage. Analyses will test whether the same variant splicing relationships are observed across tissues and, where data permit, across populations of different ancestry.

3. Establish the functional consequences of selected splice variants. High-priority isoforms will be experimentally expressed or manipulated in relevant cell models and compared using screening assays for innate immune signalling and viral infection. These studies will determine whether naturally occurring changes in isoform usage can alter antiviral or proviral activity, providing a mechanistic link between human genetic variation and manifest cellular phenotype.

Training Outcomes

The student will receive interdisciplinary training spanning computational genetics, molecular virology and functional genomics. Quantitative training will include GWAS interpretation, QTL analysis, transcriptomics, splicing analysis, dataset integration and rank aggregation, statistical genetics, and data visualisation. Experimental training will include mammalian cell culture, RNA sequencing, molecular cloning and, viral or pseudotyped-virus infection models, quantitative PCR, immunoblotting, flow cytometry and/or imaging as appropriate to the selected candidates.

Through integration of human genetics with molecular biology, the student will learn to move from large-scale human datasets to experimentally testable hypotheses and back again. They will develop wider expertise in use of human genomic data, reproducible research, scientific communication and consideration of ancestry and representation in precision medicine. The project therefore provides a route to becoming an independent researcher able to work across the interface between data science, infection biology and personalised medicine.

References: 

1. Li YI et al. Science. 2016;352:600–604. 
2. Davies et al. EMBO J. 2026. 5164–5191 
3. Li B et al. Nature Communications. 2020;11:164. 
4. Fatumo S et al. Nature Medicine. 2022;28:243–250.