University of Glasgow: Precision Medicine Projects

University of Glasgow: Precision Medicine Projects

Below you will find an exciting and diverse range of available 19/20 MRC DTP in Precision Medicine projects. The projects listed below are all based at the University of Glasgow. 

Information on 'How to Apply' is available here.  

A collaborative systems biology-based approach to dissect novel mechanisms of cardiovascular disease

Prof Christian Delles, Institute of Cardiovascular and Medical Sciences
Prof Stuart Nicklin, Institute of Cardiovascular and Medical Sciences
Dr Simon Rogers, School of Computing Science, University of Glasgow

PhD project summary:
Cardiovascular diseases (CVD) are the major cause of morbidity and mortality worldwide. Whilst some of the key mechanisms of the development of CVD are firmly established the interplay of such mechanisms and the contribution of additional, yet unknown pathophysiological principles is subject to ongoing research. We propose a project that will address this gap in knowledge by employing an interdisciplinary (cardiovascular research / computing science) and translational (preclinical / clinical) approach using systems biology methods. We have generated complex omics datasets in a range of preclinical models and clinical cohorts with cardiovascular diseases that will form the basis for in-depth analysis within this project. Whilst this proposal will train the successful candidate in systems biology and bioinformatics techniques it will be firmly embedded in our active research groups addressing mechanistic and clinical questions in CVD. As such we are reaching out to candidates with a background in statistics, mathematics or computing science who are interested in cross-disciplinary training that will include basic wet lab and clinical research techniques. The project will compare rodent and human datasets and dissect concordant and discordant molecular features that will be subject to further validation and, in the longer term, development of therapeutic approaches.

Applying "big data" techniques to clinical trials in heart failure to inform prognosis

Prof Jim Lewsey, Institute of Health and Wellbeing
Dr Pardeep Jhund, Institute of Cardiovascular and Medical Sciences
Dr David Kao, University of Colorado School of Medicine

PhD project summary:
In randomised trials, the repeated clinical, symptom-related and quality of life measurements made at study visits at different time points are often not fully modelled which is both an inefficient use of the data collected, and could lead to important prognostic information being overlooked. Over the last decade, there has been growing interest in the joint modelling of longitudinal and survival data that are collected during randomised trials as they have a number of important advantages over traditional methods. In this PhD project, the aim is to identify distinct recovery/improvement trajectories among heart failure trial patients, and determine what baseline patient characteristics are associated with this. Such information would be valuable for clinical decision-making. Further, this research will explore whether treatment influences the trajectories, and, in turn, the association with survival. Such findings identified for population subgroups would have particular importance for trials that produced overall null effects.

As well as involving exciting and important research, this PhD will train the student in key data science skills using software and version control favoured by experts. Further, the student will develop expertise in statistical modelling and in developing web-based apps to improve the translation of results to clinical audiences.


Assessing the value of high-priced novel treatments in oncology in an era of precision medicine: methodological and policy tools

Prof Andrew Briggs, Institute of Health and Wellbeing
Dr Kathleen Boyd, Institute of Health and Wellbeing
Dr Peter Bach, Center for Health Policy & Outcomes, Memorial Sloan Kettering Cancer Center

PhD project summary:
Health systems around the world are struggling to accommodate the increasing cost of new cancer treatments. In the UK a new version of the cancer drugs fund has recently been initiated and both the European Society of Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) have recently published ‘value frameworks’. This PhD will explore both methodological challenges in showing value for new oncology products (such as: use of surrogate endpoints; the need for extrapolation; biomarker/subgroup analysis; adjusting for cross-over) and policy tools designed to improve the value of these new treatments to the health systems (such as: cancer drugs fund in UK; patient access schemes; outcomes-based contracting in US; use of value frameworks, application of stratified/precision medicine). A unique feature of this PhD will be the opportunity to collaborate not only within the University of Glasgow, but also with researchers at Memorial Sloan Kettering Cancer Center in New York through international collaborative links with that organisation. Research into the value of cancer therapies is timely and this PhD represents a real opportunity to address a contemporary health care policy problem from an international perspective.


Defining Plasmodium falciparum interactions in the bone marrow

Prof Matthias Marti, Institute of Infection, Immunity and Inflammation
Prof Alexandra Rowe, Centre for Immunity, Infection and Evolution/School of Biological Sciences/University of Edinburgh
Dr Thomas Otto, Institute of Infection, Immunity and Inflammation

PhD project summary:
Malaria remains a major global health threat, despite continuous elimination efforts. The human malaria parasite, Plasmodium falciparum can infect a large proportion of red blood cells and sequester in the vasculature of various organs, resulting in various complications including severe disease and death. We have recently identified the bone marrow niche as a major reservoir for P. falciparum and other parasites including the rodent model P. berghei. This project involves supervision from experts in parasite biology, transcriptomics and clinical malaria. In this project the PhD candidate will define the parasite host interactions underlying parasite sequestration in the bone marrow niche, using a combination of state-of-the-art in vivo and in vitro studies (e.g., various microscopy techniques, single cell transcriptomics etc), including a research visit to Malawi to study material from malaria patients. The findings will shed light on an as yet uncharacterized part of the parasite cycle and provide a basis for novel intervention strategies. The student will work with an interdisciplinary team and learn both wet lab and computational methods to prepare him/her for a future in precision medicine.

DHX9 coordinated stress responses in prostate cancer – implications for diagnosis and therapy

Prof George Baillie, Institute of Cardiovascular and Medical Sciences
Dr Joanne Edwards, Institute of Cancer Sciences
Prof Ralf Hoffmann, Institute of Cardiovascular and Medical Science, Philips (Eindhoven)

PhD project summary:
Emerging evidence demonstrates that human helicases like DHX9 could act as hubs in the cellular networks to coordinate cellular stress responses for survival or to trigger cell death. Gene expression profiling of tumor cells after DHX9 suppression revealed the de-regulation of a range of pathways involved in tumor development or progression. Further, DHX9 has lately been described as a major player to support maintenance of genomic stability. Disruption of this process may lead to cancer development and progression. Understanding the role and regulation of DHX9 in prostate cancer may have important implications in the diagnosis and treatment of this disease. We have identified an interaction between PDE4D7 and the helicase DHX9. In addition, we could show that DHX9 is phosphorylated in vitro by cAMP-dependent protein kinase A (PKA). This raises the question whether the interaction with PDE4D7 affects the phosphorylation by regulating cyclic AMP in the micro-domain of DHX9. In light of this, PDE4D7 might play a crucial role in controlling directly or indirectly the function of DHX9 in prostate cancer progression.

The aims of the proposal are to:
- elucidate the role of DHX9 in the progression of prostate cancer. 
- develop a NGS RNAseq gene expression signature as a read-out for DHX9 activity.
- identify genomic interaction sites for DHX9 in prostate cancer cells using DHX9-ChIP-Seq (Chromatin Immunoprecipitation Sequencing) experiments. 
- understand the regulatory impact of PDE4D7 on DHX9.
- define whether the modulation of PDE4D7-DHX9 activity.

Genetic Epidemiology of Rheumatoid Arthritis in UK Biobank

Dr Donald M Lyall, Institute of Health and Wellbeing
Dr Stefan Siebert, Institute of Infection, Immunity and Inflammation

PhD project summary:
Rheumatoid arthritis (RA) and high circulating rheumatoid factor (RF) are associated with important comorbidities, like cardiometabolic conditions, cognitive impairment, and poorer sleep. There is however a lack of understanding over the causal relationships of these factors with RA: what comes first, and are the associations causal? It is unclear whether clustering is due to shared genetic or lifestyle factors, or rather causal influences of one factor on another.

It can be difficult to evaluate associations between risk factors and outcomes, because a limitation of cross-sectional data is that measurements are usually taken simultaneously – it is a ‘snapshot’. Links between risk factors like poor sleep and RA cannot parse out what came first, and to what extent the association is confounded by other variables like smoking.

A method called ‘Mendelian Randomization’ generates more causal estimates and directionality of associations. It uses genetic mutations known to associate with individual modifiable risk factors (like blood pressure) as proxies for lifetime exposure to that factor.

UK Biobank is a population study of whom around 5,000 have reported RA and 502,600 people have RF biomarker data. This project will apply Mendelian randomization to estimate causal associations between cardiometabolic, sleep and cognitive factors, and clinical RA/high circulating RF.

Genomic Surveillance to guide Rabies Elimination

Dr Katie Hampson, Institute of Biodiversity, Animal Health and Comparative Medicine
Dr Andrew Rambaut, Institute of Evolutionary Biology, University of Edinburgh
Dr Roman Biek, Institute of Biodiversity, Animal Health and Comparative Medicine

PhD project summary:
Rabies kills thousands of people around the world every year but a global target has now been set to eliminate human deaths from dog-mediated rabies by 2030 and large-scale mass dog vaccination programmes are being undertaken around the world working towards this aim. Extensive genomics and bioinformatics research has generated valuable insights into the diversity, emergence and spread of rabies viruses. Building on this background, this PhD will investigate how genomic data can be incorporated into surveillance to inform rabies elimination programmes. A new data-centric bioinformatics platform will be used to consolidate and curate publically available rabies sequences and new sequence data from across settings in sub-Saharan Africa and Southeast Asia through an extended collaborative network. Circulating viral diversity will be quantified and the RABV distribution characterized, to identify gaps in need of improved surveillance. Robust genomic metrics will be developed to measure progress made towards rabies elimination. The project will focus on developing tools to support the endgame including variant discrimination, revealing undetected transmission, identifying sources of incursions and visualizing and communicating insights from genomic surveillance to policymakers, practitioners and the general public to inform control.

Integrating circadian, neuroimaging and genetic data to investigate major depression and bipolar disorder

Prof Daniel Smith, Institute of Health and Wellbeing
Dr Heather Whalley, Centre for Clinical Brain Sciences, University of Edinburgh

PhD project summary:
Disruption to daily circadian rhythms is associated with greater risk of mood disorder and impaired cognitive ability. However, so far most studies have used subjective measures of rhythmicity, small or cross-sectional samples, and haven’t examined potential mediators and moderators of effects.

This interdisciplinary project will use chronobiological, neuroimaging and genetic data from the landmark UK Biobank cohort. The cohort includes lifestyle, sociodemographic and genetic data for over 500,000 individuals and brain MRI for 25,000; and our group has already derived objective accelerometry-based measures of circadian rest-activity rhythms for 100,000 individuals. Using this wealth of data, machine learning and regression methods can help determine which objective rhythmicity variables best predict mood disorder and related outcomes; and whether the influence of circadian disruption on mood and cognition is partly due to effects on brain structure, or is moderated by genetic factors. Using data from linked health records of UK Biobank participants, models will be developed to assess whether mood disorder episodes can be predicted from a combination of circadian rhythmicity, neuroimaging, genetic, sociodemographic and lifestyle factors. This project will suit students with backgrounds in statistics, epidemiology, psychology, genetics or imaging, and the student will have the opportunity to develop high-level skills in each of these areas under the supervision of a diverse multidisciplinary team.


Integrating transcriptomic and metabolomic data from people with rheumatoid arthritis to predict clinical response to drug treatment

Prof Michael Barrett, Institute of Infection, Immunity and Inflammation
Prof Iain McInnes, Institute of Infection, Immunity and Inflammation
Prof Mick Watson, Personal Chair of Bioinformatics and Computational Biology
Dr Simon Rogers, School of Computing Science

PhD project summary:
Modern biology has implemented technologies which enable the generation of massive datasets, dissecting the molecular composition of living things. Comparative analyses between healthy and diseased individuals are possible. Moreover, different individuals carrying what is apparently the same disease may respond differently to therapeutics used to treat those diseases. However, the very complexity of humans makes it difficult to find single, obvious markers of disease. What is more, since we can now sequence genomes, assess which genes are expressed at any given time, and also determine the abundance of the small molecule metabolites that assemble to build a human, we have the potential to seek the ways by which genes and their expression influence those metabolic building blocks and in turn how these influence disease. However, sophisticated understanding of bioinformatics, covering a range of different datatypes is required to enable this route to enhanced inference. In this project students will take existing transcriptomic and metabolomics datasets from RA patients with clearly characterized responses to drug treatment. Individual data types and integrated data will be mined and probed in novel ways to seek new biomarkers that can predict how patients will respond to drugs.

Machine- and Deep-Learning Methods for Analysing Complex Health Simulations

Dr Eric Silverman, Institute of Health and Wellbeing
Dr Claudio Angione, Department of Computer Science and Information Systems (Teesside University)

PhD project summary:
Population health research has made significant strides in recent decades, but some health challenges facing society remain very difficult to study. Issues like the ageing population, increasing obesity, and multi-morbidity are driven not by simple cause-and-effect relationships, but are influenced by behavioural, environmental and social factors. Increasingly, we are turning toward interdisciplinary computational modelling techniques such as agent-based modelling (ABM) to unravel the complex interplay of factors that drive these urgent problems in population health. ABMs are computer simulations that model the behaviours of individual people in complex virtual environments, and consequently help us better understand how the interaction of the individual, the environment, and the social realm lead to poor health.

This project will tackle this exciting area of research head-on by applying ABM to key problems in population health, and using cutting-edge AI techniques to better understand the behavior of these complex models. Machine- and deep-learning methods can improve the theoretical understanding of the ABM, help calibrate the model, and facilitate interpretation of the results relevant to end users. The PhD candidate will develop novel frameworks for the analysis of large simulation models using machine learning and deep neural networks. These innovations will facilitate the development of new techniques, protocols and software for the analysis and dissemination of complex simulation studies in population health, opening up new avenues of research on major health challenges.



Mapping the transcriptomic, genomic and breakomic landscape of radiation resistance in pancreatic carcinoma and glioblastoma

Dr David Chang, Institute of Cancer Sciences
Dr Ross Carruthers, Institute of Cancer Sciences
Prof Anthony Chalmers, Institute of Cancer Sciences
Prof Martin Taylor, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh

PhD project summary:
The determinants of response to radiotherapy in common tumour sites remain largely enigmatic, with no clinically useful biomarkers of radiation sensitivity being available. Both pancreatic ductal adenocarcinoma (PDAC) and glioblastoma (GBM) are highly aggressive, treatment refractory tumours with poor survival, nonetheless radiation provides useful palliation or tumour response in a significant subset of patients. A precision medicine strategy to identify radiosensitive subtypes of tumours and radioresistant tumours which are amenable to radiation sensitization through inhibition of DNA repair is urgently needed to improve patient outcomes. This project will utilize genomic and transcriptomic data in combination with conventional laboratory based radiobiological assays as well as cutting-edge genomic mapping techniques to unravel the complex biological landscape of radiation resistance, allowing development of biomarkers to facilitate rational use of radiotherapy and DNA damage response inhibition in future early phase clinical trials in these tumour types.

Proteome-level diversity in RNA viruses

Dr Edward Hutchinson, Institute of Infection, Immunity and Inflammation
Dr Richard Burchmore, Institute of Infection, Immunity and Inflammation

PhD project summary:
RNA viruses are major threats to human and animal health and account for the majority of emerging infectious diseases. This is in large part due to the highly error-prone replication of RNA virus genomes, which results in rapid evolution. These high mutation rates should also affect viral gene transcription. However, the diversity of protein products that result, and how these could affect infection and immunity, is unclear.

This project will consider influenza viruses, a genus which includes both endemic and emerging threats to human health. Novel data analysis tools will be developed to identify signatures of mutation and variant post-translational modification in viral proteins, using mass spectra collected from purified viruses by the Hutchinson group. Maps of protein-level diversity will be compared to next-generation sequencing of the viral genome and transcriptome, in order to map of sites in viral proteins which can tolerate variation and sites which select against it.

The project will provide the first indication of how the high levels of mutation in an RNA virus genome actually relate to protein polymorphisms. By mapping sites of protein conservation and plasticity it will expand our understanding of targets for the immune response, including by ‘universal’ vaccines, and provide a rational basis for viral protein engineering.


Reverse migration in neutrophils – using mathematics to understand medicine

Prof Robert Insall, CRUK Beatson Institute, University of Glasgow
Dr Sonja Vermeren, Centre for Inflammation Research, University of Edinburgh

PhD project summary:
This interdisciplinary project seeks to understand how cells of the immune system migrate away from sites of infection after their jobs are done. It is currently not understood how this process works, but it is believed that when it fails it causes autoimmune disease such as arthritis and chronic problems such as COPD. We will start using computational modelling to find and expand ways in which metabolism of chemokines and other attractive molecules can turn them from attractants into repellents. These will be generated in the Insall lab. We will then test and refine the models using microscopy of living neutrophils, which will be isolated and cultured in the Vermeren lab and examined in both Edinburgh and Glasgow.

The aims of the project are twofold. First, to understand a feature of inflammation that is extremely important to health; Second, to provide a fully crossdisciplinary training to equip the student for future developments in quantitative biology, by training scientists with degrees in quantitative disciplines in wet biology labs.

Sexually dimorphic development of the human fetal brain: critical role in the programming of neurodevelopmental disorders

Dr Michelle Bellingham, Institute of Biodiversity, Animal Health and Comparative Medicine
Dr Amanda Drake, Queen’s Medical Research Institute, University of Edinburgh
Prof Paul A. Fowler, Institute of Medical Sciences, University of Aberdeen

PhD project summary:
The fetal environment plays a key role in programming normal brain development in the womb. Maternal lifestyle factors such as malnutrition, obesity, smoking and alcohol consumption are associated with altered brain development and behaviour, including attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and anxiety disorders in the offspring in later life. There are known sex differences in predisposition to many neurodevelopmental disorders. However, surprisingly little is known about the mechanisms responsible. Development of the male and female brain is sexually dimorphic (different between the sexes) and influenced by steroid hormones during critical windows of development. Alterations to these normal patterns of hormone secretion during sexually dimorphic brain development by adverse maternal factors during pregnancy, may be critical in programming neurodevelopmental disorders in later life. This unique project will characterize the sexually dimorphic patterns of brain development in the human fetus across gestation and determine how these are altered by maternal factors such as smoking and obesity. The proposed study will shed light on the fundamental processes underlying sex differences in human brain development. Ultimately this will help us understand how such changes in fetal brain development may be involved in programming long-term risk for diseases of the brain.

Understanding the interface between obesity and severe mental illness: a genomics approach

Dr Rona Strawbridge, Institute for Health and Wellbeing
Prof Ingrid Dahlman, Lipid laboratory, Department of Medicine Huddinge, Karolinska Institutet, Sweden
Prof Daniel Smith, Institute for Health and Wellbeing

PhD project summary:
People suffering from severe mental illnesses (such as depression, schizophrenia and bipolar disorder) have a substantially increased risk of obesity. Reasons for this are unclear, but could be due lifestyle factors or shared pathological mechanisms. This project will assess the impact of established genetic risk markers for severe mental illness on measures of obesity, and will identify functions of adipose tissue which are altered by genetic risk factors for serious mental illness, as well as which genes and mechanisms connect severe mental illness with obesity.

The project will use statistical genetic approaches to identify which functions of adipose tissue are influenced by severe mental illness. Unique adipose tissue expression datasets will enable prioritization of candidate genes. In vitro functional analyses of adipocytes will explore the mechanisms by which candidate genes impact on adipose tissue function. This project provides the opportunity to conduct research of global relevance with state of the art resources and in collaboration with leading experts in psychiatric and metabolic genetics and adipose tissue biology.

The student will be trained in the required methods within a data science framework. They are expected to have a BSc or MSc in a relevant field, with an understanding of genetics and statistics. Experience in data science, statistical genetics or in vitro methods is an advantage.

Uromodulin in hypertension and renal dysfunction - causal inference, mechanistic and clinical translation studies for precision medicine

Prof Sandosh Padmanabhan, Institute of Cardiovascular and Medical Sciences
Dr Tom Van Agtmael, Institute of Cardiovascular and Medical Sciences

PhD project summary:
Genome wide association identified a novel pathway for hypertension and renal function involving uromodulin. Uromodulin is exclusively expressed in the Thick Ascending limb of Henle (TAL) and Distal Convoluted tubule (DCT1) in the kidney. The main sodium transporter in TAL is NKCC2, and the potential UMOD-NKCC2 interaction on salt-sensitivity is now the basis of a BHF funded clinical trial ( NCT03354897). There are currently large gaps in our understanding of the underpinning mechanisms by which uromodulin affects sodium balance, glomerular filtration and blood pressure. This collaborative translational PhD project will bring together our extensive experience in hypertension genomics, clinical trials and molecular genetics with world leading experts in uromodulin biology and in-vivo renal studies. We will assess causal relationships between uromodulin and blood pressure and renal function. We will study the incremental value of serum/urine uromodulin and serum hepsin levels through studies of a UMOD genotype directed clinical trial. Finally, through a combination of molecular and in-vivo experiments we will dissect the role of salt-exposure and ER stress on uromodulin function. This project will provide crucial information in accelerating the next phase of clinical translation through new drug discovery and interventions for both hypertension and CKD.

Use of Routinely Collected Health Data to Predict Sudden Death and Other Catastrophic Events

Dr Pierpaolo Pellicori, Robertson Centre for Biostatistics / Institute of Health and Wellbeing
Dr Jeff Dalton, School of Computing Science, University of Glasgow
Prof Roderick Murray-Smith, School of Computing Science, University of Glasgow
Prof John Cleland, Robertson Centre for Biostatistics / Institute of Health and Wellbeing

PhD project summary:
Sudden, often unheralded, death is one of the most common modes of death. Quite apart from the personal tragedy, this often has devastating consequences for families, emotionally and economically. Many diseases of the heart and circulatory system can cause sudden death, either as their first presentation or as a subsequent complication. This analysis will describe the rate of sudden death in people with or without known heart problems in the West of Scotland and develop AI strategies to predict those at increased risk. This information will help inform doctors, policy-makers and future research on strategies to prevent events.

Supervisors will provide medical education about relevant cardiovascular diseases to help put the data and objectives into context. The successful candidate will receive training in data-linkage, analysis, and cleaning and receive hands-on training in modern machine learning techniques (e.g. deep networks, text modelling, etc.). As such, this PhD addresses two of the skills priorities highlighted by the MRC (quantitative and interdisciplinary skills).