University of Glasgow: Precision Medicine Projects

Below you will find an exciting and diverse range of available 20/21 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 Bayesian Modeling and rt-fMRI Neurofeedback Approach to Understanding Autism and Predictive Coding in the Insula

Supervisors:
Dr Rajeev Krishnadas
Prof Frank E Pollick

Dr Peggy Seriès

Project summary:
A Bayesian approach to disorders has become increasingly popular. In this approach, a combination of prior experiences and future expectations influence our perception of the world. The objective for the brain is to minimize prediction error (i.e., the difference between expectations and actual outcome). Looking at Autism Spectrum Disorder (ASD) from a Bayesian inference perspective reframes the traditional view of a social disorder into one of a perceptual disorder and allows for modelling of the neuronal mechanisms underlying behavioural symptoms. In the somatosensory domain, predictive coding is integrated with somatosensory input in the insula, a structure that has repeatedly been reported to be atypical in ASD. This structure is also involved in large-scale networks, such as the salience network, and has been associated with cognitive, affective, and sensorimotor processing. Thus, it encompasses a broad range of aspects that are symptomatic in ASD. The process of integration and predictive coding in the insula can be investigated using fMRI, providing a clearer picture on the formation of perceptions in ASD and a potential target for therapeutic interventions. Using these outcomes, realtime fMRI can be used to explore the possibility of altering this model on an individual level and measure corresponding perceptual changes.


Applying machine learning models to genome data to understand the evolution of drug resistance from virus to cancer evolution

Supervisors:
Prof David L Robertson
Dr Ke Yuan 

PhD Project Summary:
Treatment of cancer and chronic infectious diseases often fail due to the evolution of resistance to therapy. Underpinning this phenomena is the generation of changes (mutations) in their genetic material. Mutations generate high levels of differences in the genomes of cancer cells or intra-patient virus populations that leads to their ability to evolve in response to drugs. Recent advances in genome sequencing have revealed genomic alterations that drive cancer progression and pathogen infection. These data give insight into the diseases' underlying evolutionary dynamics which follow predictions of both Darwin’s theory of evolution and Motoo Kimura’s theory of molecular evolution. Yet, how evolutionary dynamics interact with mutational processes, and whether these processes can predict clinical outcome is largely unknown. Due to the variety and complexity of genomic alterations observed across human, cancer and virus evolution, unified mathematical equations of evolution are often intractable. We propose to leverage state of the art machine learning methods applied to large scale genome sequencing data sets to build biologically informed data-driven models of evolutionary dynamics. These models permit efficient data analysis that account for the variety and complexity of genomic alterations observed across human, cancer and virus evolution. They will infer the life histories of disease processes and predict disease progression and effects of interventions. Early prediction of resistance to therapies is essential to maximising the potency of interventions and switching treatments when necessary. The student will be trained in a combination of data science and bioinformatics, with substantial elements of computation, programming and statistics/machine learning.


Approaches to detect and block transition stages in Trypanosoma brucei

Supervisors:

Dr Thomas D. Otto 

Prof Richard McCulloch

Prof Keith Matthews

PhD Project Summary:
Trypanosomatids are a grouping of single-celled parasites belonging to the order Kinetoplastea. One of these parasites, Trypanosoma brucei, is responsible for the sleeping sickness diseases transmitted by the tsetse fly, affecting humans and livestock in Africa. Only a handful of drugs are available, many of which are not efficient and have severe side effects. Emerging data has explored the factors needed for some, but by no means all, aspects of transmission between the distinct parasites forms found as the parasite moves from mammals to vector and back. Notably, the developmental programme in the tsetse vector is poorly understood and, crucially, little work has examined the temporal order of gene expression changes during life cycle transitions. In this PhD we aim to detect genes essential in the transition from the vector stages to the mammalian stages in T. brucei in order to identify pathways that could be targeted by novel compounds. We will obtain and analyse single cell RNA-seq from these stages. Through pseudo time and branching analysis we will detect genes crucial for transition. Finally, the student will ablate these genes and attempt to block the factors, to ask how they act in the life cycle and test if they might be drug targets. This is an interdisciplinary project to train students in quantitative methods and brings together a supervisor team with a diverse background. By the end of the PhD, the student will have learned novel genomics and laboratory techniques that are crucial in the field of precision medicine.


Deep impact – mining the meningococcal metagenome for antimicrobial resistance to provide public health intelligence data

Supervisors:
Dr Andrew Smith
Dr Claire Cameron 
Prof Martin Maiden

PhD project summary:
This project will focus on meningococcal disease which is a severe and potentially fatal infection of the brain and blood stream with highest incidence in teenagers and young children. In particular, we will focus on understanding the movement of genes controlling antimicrobial resistance (AMR). To help understanding of meningococcal biology a whole genome sequence (WGS) database has been established with over 35,000 Worldwide isolates and >8,000 sets of WGS data (http://pubmlst.org/neisseria). We have also developed a unique collection of clinical metadata for Scottish cases. This PhD programme will build on this activity by undertaking bioinformatic investigations of AMR gene families to determine associations with phenotypic susceptibility data and clinical outcomes. This type of bioinformatics characterization is needed for accurate monitoring, surveillance and effective vaccination which will also help control AMR in meningococci. Project bioinformatics output will also include visualisation of data to communicate results to a wide audience. This project will be embedded within the Scottish Meningococcal National Reference Laboratory and the applicant will form part of a multi-skilled team (including bioinformaticians) providing intelligence data to clinicians and Health Protection Scotland. Academic mentoring will also be facilitated with links to Oxford University and the Swedish meningococcal reference laboratory.

 


Developing non-invasive transcranial neuro-stimulation into a precision tool for cognitive enhancement in Schizophrenia using multi-modal neuroimaging

Supervisors: 

Prof Gregor Thut
Prof Peter Uhlhaas

PhD Project Summary:
Impairments in cognitive processes in Schizophrenia (ScZ) constitute a fundamental aspect of the disorder and encompass both basic perceptual and higher cognitive functions. These deficits appear to be present prior to the onset of psychosis and determine the functionaloutcome of ScZ-patients better than psychopathological variables. Despite this overwhelmingevidence, the neurobiological mechanisms that give rise to neurocognitive impairments in ScZremain elusive and treatment options limited. The current PhD project will examine thehypothesis that aberrant neural oscillations constitute a neurobiological basis for cognitiveimpairments in ScZ and that these neural oscillations and cognitive deficits can be restoredusing novel non-invasive intervention approaches based on rhythmic neuro-stimulation.The research involves a combination of advanced transcranial brain stimulation and multimodalneuroimaging techniques in a new principled framework for guiding interventions. Specifically,the PhD-student will employ cutting-edge magnetoencephalography (MEG) and detailedanatomical information from MRI to guide rhythmic non-invasive (transcranial) brainstimulation (NTBS) by individually tailored parameters to optimize outcome and systematicallytest specific hypotheses in both healthy controls and patients.Combining expertise in clinical and human systems neuroscience and behaviouralneurophysiology, this project will produce new mechanistic insights into the origin of cognitivedeficits and circuit dysfunctions in ScZ, and help develop novel treatment strategies by testingnon-invasive brain stimulation and oscillatory targets as to their therapeutic promise.


Development of a SMART biosensor of remote diagnosis and therapy in vascular access for cardiovascular disease

Supervisors: 

Dr John Mercer
Dr Srinjoy Mitra

PhD Project Summary:
Despite significant improvements in healthcare provision, cardiovascular disease remains the number one cause of death in the Western World. Atherosclerosis is the pathological condition that underlies two thirds of Heart attacks and Strokes that contribute to more than 4.3 million premature deaths in Europe per annum. The economic burden to the European Union for cardiovascular disease is estimated at over €196 Billion. The current clinical approaches of stenting coronary artery atherosclerotic plaques and surgical resection have significant risk and associated costs. This proposal builds on an established multidisciplinary project to develop a SMART self reporting stent that can be deployed using existing methods, yet provides advanced technological properties that are predicted to reduce patient morbidity and mortality. An implantable stent device which can interact and report on its own vessel status would have profound benefit to physicians. One that can then respond by altering the local environmental conditions would be transformative. This project will be based at the British Heart Foundation Cardiovascular Research Centre, University of Glasgow and the Scottish Microelectronics Centre, Edinburgh. This project is suitable for diligent bio/engineer/physics student who is keen to apply their theoretical knowledge to real world problems. You will be part of a supportive team of basic scientists, engineers and NHS clinicians working towards developing the next generation of implantable devices. Opportunities exist for international travel to collaborators and partners facilities.


Exploring how genetic predisposition to serious mental illness influences cardiovascular disease

Supervisors:
Dr Rona J. Strawbridge

Dr Bruna Gigante

PhD Project Summary:
People suffering from severe mental illnesses (such as depression, schizophrenia and bipolar disorder) have a substantially risk of cardiovascular disease. Reasons for this are unclear, but could be due medication, socioeconomic or lifestyle factors or shared pathological mechanisms. This project use genetic predisposition to serious mental illness to explore the extent to which they influence cardiovascular outcomes, phenotypes and biomarkers, and to clarify the mechanisms involved. Further analysis will determine whether effects are sex-specific or generic. 

The project will use statistical genetic approaches in both population-based and cardiovascular cohorts with extensive phenotyping to identify which processes leading to cardiometabolic diseases are influenced by severe mental illness. 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, metabolic and cardiovascular genetics.

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 and/or statistical genetics is an advantage.


Investigating the role of the extracellular matrix in stroke in the general population

Supervisors:

Dr Tom Van Agtmael

Prof Jesse Dawson 

Dr Graham Hamilton

PhD Project Summary:
Stroke is a major cause of death and there is an urgent need to increase our understanding of the genetic basis and molecular mechanisms of stroke to develop effective treatments. Recent evidence has indicated that the extracellular matrix plays a major role in stroke and cerebrovascular disease. For example, we have previously identified that mutations in collagen IV, an important matrix component, cause haemorrhagic stroke. However, it remains unclear in how many stroke patients the extracellular matrix plays a role and the nature of the underlying defects remain unknown.

This project will combine population genetics with molecular cell biology to provide insight into the basis of stroke in the general population and the role of the extracellular matrix therein. UK biobank data combined exome sequencing will be exploited to identify putative variants in different extracellular matrix proteins. Using CRISPR-Cag9 genome editing, the mechanism of these variants will then be investigated to increase our understanding of how they can affect the extracellular matrix and cell function. In so doing this project will greatly increase our knowledge of the genetic and molecular basis of stroke in the general population.


Omics based strategies for precision medicine in hypertension and cardiovascular disease

Supervisors: 
Prof Eleanor Davies 

Dr Scott MacKenzie 

Dr John McClure

PhD Project Summary:
This project will utilize a systems biology "-omics" approach to improve diagnosis of endocrine forms of hypertension while also investigating the role of microRNAs in the development of these conditions. Up to 15% of hypertension has an endocrine cause resulting from excessive hormone production and leading to a range of co-morbidities that include stroke, heart failure, atrial fibrillation, left ventricular hypertrophy and obesity. While endocrine hypertension is readily treatable, its accurate diagnosis is time-consuming and costly, which delays the administration of appropriate treatment. We are currently part of a large international study tasked with using circulating microRNA profiles to improve diagnosis of endocrine hypertension by analyzing a large cohort of carefully-phenotyped human subjects with endocrine hypertension (www.ensat-ht.eu). MicroRNAs are regulatory non-coding RNAs that are also released by tissues into the bloodstream where they circulate in a stable, cell-free form, and can act as relatively stable and accessible biomarkers for specific disease states. Circulating microRNAs that associate with forms of endocrine hypertension are also likely to provide insights into the underlying pathophysiological mechanisms of these conditions. The identification and investigation of these microRNAs will form the core of this highly important and timely research project.


Proteome-level diversity in RNA viruses

Supervisors: 
Dr Edward Hutchison 

Dr Richard Burchmore

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 viral protein mutation and modification, using viral mass spectra collected by the Hutchinson group. Maps of protein-level diversity will be compared to next-generation sequencing of the viral genome and transcriptome, as well as to tens of thousands of published viral genomes, mapping sites in viral proteins which can tolerate variation, 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.


Target Autophagy and Aberrant Mitochondrial Folate Metabolism in Leukaemic Stem Cells

Supervisors:

Dr Vignir Helgason 

Dr Zuzana Brabcova

Summary of PhD Project:
Chronic myeloid leukaemia (CML) develops following a specific mutation in a single blood stem cell. Therefore, these cells serve as crucial target for therapeutic intervention. We have shown that patient-derived CML stem cells are not eradicated with currently available drugs. Therefore, drug combination therapy is required to cure CML patients. Our previous work has illustrated that autophagy (“self-eating”), a recycling process that maintains cell integrity, is an attractive target for CML stem cell eradication. We have also shown that CML stem cells rely on mitochondria metabolism for survival. This is an area of great importance in CML, and other stem cell driven cancer types where inhibition of autophagy/metabolism might also improve therapy. The overall aim of this project is to further understand how leukaemic stem cells use autophagy/mitochondrial metabolism to escape drug treatment and to test a newly developed pre-clinical inhibitors.


The effect of prior exposure on influenza vaccine effectiveness

Supervisors:

Dr Richard Reeve 

Prof Olwyn Byron 

Dr Pablo Murcia

Prof David Bhella

PhD Project Summary:
Influenza viruses are notorious for the speed at which they evolve, with new mutants that differ in antigenic phenotype emerging constantly. This process, known as antigenic drift, drives regular updates to seasonal flu vaccines – an area which can benefit directly from modelling of the evolutionary process. In a process known as ‘original antigenic sin’ the human immune system preferentially mounts antibody responses that cross-react to previously circulating strains, as opposed to optimally targeting new viral strains. As a result, the immune system of different individuals may react differently to new influenza mutations according to their history of exposure. In particular their reaction is anticipated to vary according to the first influenza strains to which they were exposed.

Immunodominance refers to the hierarchy in importance of regions of the haemagglutinin protein at eliciting antibodies. Understanding the pattern of change in immunodominance through time can help us to predict how an individual may react to mutations in current epitopes based on knowledge of the strains to which they were likely exposed in childhood, and is critical to the design and optimal delivery of vaccines that provide immunity in all individuals, regardless of age and exposure history. The aims of this project are to 1) develop models of change in epitope immunodominance; 2) correlate immunodominance of childhood strains to prevalence of antigenic variants through time; 3) provide information on how differently aged individuals might be expected to react to mutations in emerging antigenic variants.

Environment
The studentship will be based in the award-winning Boyd Orr Centre for Population and Ecosystem Health – http://www.gla.ac.uk/boydorr – and the Institute of Biodiversity, Animal Heath and Comparative Medicine – http://www.gla.ac.uk/bahcm , working closely with supervisors in the MRC-University of Glasgow Centre for Virus Research – https://www.gla.ac.uk/cvr (the home of the Scottish Centre for Macromolecular Imaging) and the School of Life Sciences – https://www.gla.ac.uk/lifesciences . Please get in touch with Richard Reeve – http://www.gla.ac.uk/people/richardreeve – if you have any questions about the project.