Discovering off-target side-effects and drug repurposing candidates using expression perturbation data

We are offering an exciting opportunity to carry out a PhD that will use cutting-edge genetic and bioinformatic methods to explore side-effects of drugs and identify potential repurposing opportunities. Your research could lead to changes in the treatment or prevention of disease, and will provide you with an excellent foundation for a career in translational biomedical research. You would be based in the Data Mining programme, part of the MRC Integrative Epidemiology Unit at the University of Bristol, an internationally-leading leading centre for the development and application of causal analysis and evidence triangulation methods. You will be supported by an interdisciplinary team of academic staff who are experts in their fields, and join a diverse cohort of students working across many different areas of health sciences. For more information about the MRC Integrative Epidemiology Unit and the PhD programme, please visit the website.

Rationale

Mendelian Randomization (MR) is a genetic epidemiology method which utilises variants sourced from genome-wide association studies (GWAS) to assess causality between risk/protective factors and disease outcomes in a manner less biased to observational studies¹. Recently, applications of MR to drug target prioritization have gained a lot of interest. One approach is to use expression or protein quantitative trait loci (QTL) for a drug target gene/protein as exposures with the aim of establishing the effects of perturbing the intended target directly (“on-target”)².

However, most drugs will have broader molecular consequences, either in parallel (“off-target”) or downstream of the intended target. These will sometimes cause adverse side-effects (adverse drug reactions). In some cases, they may also cause beneficial effects which can be exploited to repurpose drugs for other diseases. One way to identify these broader molecular consequences is to mine high-throughput expression datasets of various cell lines exposed to small molecule drugs and genetic perturbations³. These could then be subjected to MR (including 2-step MR and multivariable MR) to gain an understanding of the specificity of action of a drug, and its broader effects.

Aims and objectives

Using publicly-available experimental molecular perturbation datasets the project will develop and apply novel approaches to discover:

1. off-target side effects of drugs and
2. drug repurposing opportunities

Methods

You will use data on transcriptional responses to drugs from expression perturbation databases and other resources to identify additional genes and proteins for off-target side effect prediction, using multivariable MR to identify direct effects. In addition, you will use protein-protein interaction and pathway data collated in our locally developed resource EpiGraphDB⁴ to further refine gene sets to instrument.

You will also use drug perturbation data sources to evaluate the potential to repurpose drugs with a previously-published approach⁵ that compares disease-associated transcriptomic profiles to in vitro drug transcriptomic profiles to identify profiles that may reverse the effect of disease on gene expression. You will consider tissue-specific and pathway-specific transcriptomic profiles for a variety of diseases and explore whether this identifies additional repurposing opportunities. You will validate drug repurposing and off-target side effect predictions using observational data from UK Biobank and electronic health records, in addition to MR.

Candidate requirements:

We strongly encourage applications from a range of disciplines (e.g., mathematics, statistics, computer science, life or natural sciences, psychology, social sciences or other related quantitative discipline). Applications are sought from high performing individuals who have, or are expected to obtain, a 2.1 or higher degree (or equivalent). Possession of a relevant Master's degree or research experience would be advantageous but is not expected.

How to apply

When applying, candidates must select the Population Health PhD programme and enter supervisor names as listed under the project title for which they are applying. Please state IEU funding in the funding box. Full details on what to include in your application can be found in the Admissions Statement.

Personal statement: Please also provide a personal statement that describes your training and experience so far, your motivation for doing a PhD, your motivations for applying to the University of Bristol, and why you think we should select you. We are keen to support applicants from minority and under-represented backgrounds (based on protected characteristics) and those who have experienced other challenges or disadvantages. We encourage you to use your personal statement to ensure we can take these factors into account. 

University of Bristol, Bristol Medical School

Bristol Medical School is the largest and one of the most diverse Schools in the University of Bristol, with approximately 1100 members of staff, 1350 undergraduate, 250 postgraduate taught and 240 postgraduate doctoral research students. The Head of School is Professor Matt Hickman. The Medical School has two departments: Population Health Sciences and Translational Health Sciences. The School is a leading centre for research and teaching across these areas. Research in the School is collaborative and multi-disciplinary, with staff coming from a wide range of academic disciplines and clinical specialties.

The 2021 Research Excellence Framework (REF) confirmed the University of Bristol’s position as a leading centre for health research. Bristol Medical School contributed to three Units of Assessment including UoA1 (Clinical Medicine), UoA2 (Public Health, Health Services and Primary Care) and UoA4 (Psychology, Psychiatry and Neuroscience). The UoA2 submission, comprising predominantly Medical School staff. was ranked 3rd in the UK with 94% of our submitted research outputs rated as world leading (4*) or internationally excellent (3*). Submissions to UoA1 and UoA4 were shared with varying degrees of representation with the Faculty of Life Sciences. Respectively UoA1 and UoA4 had 94% and 84% of submitted research ranked as 4* or 3*, which represented increases in each category in the proportions of 4* ranked papers as well in growth in GPA rankings above the previous REF2014.

The School is committed to delivering a positive working environment for all staff, it holds Silver Athena SWAN Awards in recognition of the ongoing commitment to promote equality, diversity and inclusion within the School.