MRC Precision Medicine Research Projects 2021 (Glasgow registered)
Aligning chemokine receptor expression with monocyte/macrophage phenotype and function
Macrophages are a key part of the innate immune system and are central players in antimicrobial responses. To achieve this they must navigate to infected sites and this process is regulated by chemokines and their receptors with which we have worked for over 30 years. Chemokine receptors are notoriously complex and precisely defining their role in macrophage biology has previously been difficult. We have generated unique in vivo models to help us to specifically address this issue giving us an international lead in this important research area. This project will specifically study the role for individual chemokine receptors in regulating macrophage dynamics in response to bacterial and viral infections. It will involve a combination of in vivo experimentation and transcriptomics with a view to assigning specific receptors to discrete macrophage functions. The student will receive training in in vivo biology, imaging and transcriptomic analysis and will be immersed in a world quality inflammatory biology environment.
Artificial Intelligence in Modelling the Influence of Socio-Economic Factors on the Risk of Cardiovascular Events
Barriers to education, housing, and a low income are associated with a high prevalence of cardiovascular risk factors and a greater risk of developing serious cardiac problems or die prematurely. Therefore, improving socio-economic factors might lead to better health and less health inequalities.
We are a large multidisciplinary team of researchers, including experts in machine learning, cardiologists and statisticians. For this project, we will use a large set of anonymised healthcare records, such as blood tests, clinical appointments, and investigations that are routinely collected, every day in the West of Scotland. Advanced machine learning techniques will be developed to model interdependencies in longitudinal EHR and predict key adverse clinical events.
We will investigate the relationship between socio-economic factors and the incidence and outcome of severe cardiovascular disease, including strokes, heart attacks, atrial fibrillation and heart failure This information will help doctors to target interventions that might prevent the onset or improve the outcome of serious cardiovascular disease, especially for those at the greatest socio-economic disadvantage.
The successful candidate is required to have prior knowledge in Machine Learning and/or a degree in related field.
Cardiovascular risk factor identification in general population: Data Sciences approach
Heart conditions are one of the biggest health problems and they are disproportionately common in disadvantaged groups. The onset of risk factors such as high blood pressure and high cholesterol is often at an earlier age in vulnerable groups and left untreated. By applying data sciences to three population datasets, a risk prediction model will be developed and validated for unrecognized high blood pressure and high cholesterol levels. This in turn, can lead to targeted case finding, early identification and risk mitigation of cardiovascular risk, improved healthcare outcomes, and reduction in health inequalities.
Developing tailored interventions for stroke rehabilitation: improving motor function by targeting brain connectivity with fMRI neurofeedback
Stroke is a common neurological disorder and one of the leading causes of long-term adult disability. There is a fundamental need to develop adjunct rehabilitation approaches to enhance motor recovery. Neurofeedback is an emergent closed-loop technique that allows participants to endogenously modulate their brain activity or connectivity by measuring it and displaying it in real-time. This can potentially be used to alter abnormal brain activity into more beneficial patterns in stroke patients. Neurofeedback is a flexible tool that can be used to specifically target cortical and subcortical structures and directionally modulate brain signals, opening up the possibility of tailoring the intervention to the needs and impairment level of the patient.
Brain connectivity studies have shown that disconnection within the brain motor network is associated with poorer movement outcomes in stroke patients. Therefore, increasing connectivity within the brain motor network may be a useful approach in stroke rehabilitation. Similarly, ageing is associated with reduced connectivity within the motor network and declines in movement performance. Neurofeedback presents an unprecedented opportunity to non-invasively modulate brain connectivity and directly assess effects on motor performance.
This PhD project will examine whether older adults and stroke survivors are able to use fMRI neurofeedback at rest, without performing any movements, to alter motor network connectivity and improve motor outcomes. Neurofeedback allows to tailor the target brain connectivity to the individual and to progressively customize the goal for each participant and training session. The effectiveness of this approach will be assessed with behavioural tasks and clinical scores. We will additionally test which markers (behavioural, MRI and EEG) best predict neurofeedback performance and behavioural/clinical outcomes. This will inform future applications on participant stratification and tailored neurofeedback protocols.
Evaluation of XRN1 as a therapeutic target to develop personalised medicine approaches to treat different viral strains and species
In the last years, the 5’-3’ exonuclease XRN1 has emerged as a master regulator of the viral lifecycle, although whether it promotes or restricts infection remains controversial. We showed that XRN1 is functionally activated upon Sindbis virus (SINV) and SARS-Cov-2 infection, leading to a pervasive degradation of cellular mRNA. Ablation of XRN1 makes cells refractory to infection, suggesting that it is critically important for viruses. Given the central role of this protein in virus infection, we envision it can represent a potential broad-spectrum antiviral target. The student will employ cutting-edge transcriptomic and computational approaches to determine which mRNAs are degraded by XRN1 in cells infected with SINV or SARS-Cov-2. He/She will employ computational approaches to discover which molecular signatures determine if a cellular mRNA is susceptible or refractory to XRN1-dependent degradation after infection. Moreover, we will also test if the XRN1 is also critical for related and unrelated pathogenic RNA viruses. This project combines virology, novel cutting-edge transcriptomic methods and computational approaches to shed light on the mechanisms by which XRN1 regulates infection and will define its potential as therapeutic target.
Genetics, lifestyle and brain ageing in large-scale cohorts
Apolipoprotein e (APOE) e4 genotype, found in around 25% of the population, has been known as a genetic risk factor for Alzheimer’s disease (AD) since 1993, but we still do not understand the underlying mechanisms in terms of brain structure and function, nor the conditions (lifestyle; environmental; genetic) in which its effect is most pronounced. Most studies conducted to date are small, do not consider potential confounds (e.g. smoking or deprivation), and employed widely varying methods in brain structure/function analyses. To demonstrate interactions between APOE and other risk factors reliably in high-quality existing datasets would have significant potential implications for worldwide public health and clinical practice. This project will make use of UK Biobank and Lothian Birth Cohort 1936, two cohorts with dovetailing and complimentary characteristics, to understand what factors modify the effects of APOE on brain health (cognitive abilities; longitudinal decline across time; clinical dementia), and via what aspects of brain structure and function (assessed by brain magnetic resonance imaging).
Identifying therapeutic targets that could transform Rheumatoid Arthritis from disease remission into cure
Remission from symptoms is achievable for arthritis, but there is always the risk of flares. Remission is not the same as cure, and our goal is to discover how to get patients from remission into cure. First, we had to understand What is the biological barrier between remission and cure? We found that in remission, joint inflammation can be temporarily kept at bay by protective macrophages. However, the presence of T and B-cells that mistakenly recognize the tissues of the joint as foreign object persist(autoimmunity). They can overcome the functions of protective macrophages and trigger joint inflammation (flare) at any time. T and B-cell activity is controlled by dendritic cells (DCs). In health, DCs delete the autoreactive T and B-cells. We have found a unique type of DCs in arthritis joints in remission. They are similar, but not identical to the DCs in healthy joints. In this project we will investigate these subtle differences because they might tell us why autoimmunity is not completely eradicated in remission RA. Our aim is to find out how to reprogram remission DCs into healthy normal DCs. We hope that this new understanding will provide new treatment strategies to bring RA patients from remission to cure and can be applicable to other autoimmune diseases.
Improving machine learning interpretability of images by optimising tissue handling and staining for digital pathology
This project applies machine learning to pathology analysis. We will improve the techniques used to stain samples and detect specific cell types, by iteratively testing different staining conditions and using samples to train a deep learning network and assess its predictive accuracy. In particular, we will test different general stains and use the resulting images to infer the presence or absence of tumour cells; and we will different classes of white blood cells, for example T cells and natural killer cells, from tissues stained with general antibodies against immune cells.
The student, who will have a degree in computing, statistics or mathematics and strong coding expertise, will be trained in generation and interpretation of digital pathology images, and will thus gain multidisciplinary expertise in an exceptionally sought-after field. The project will be based in Glasgow’s Institute for Cancer Sciences (ICS); supervisors include experts in mathematical biology, digital pathology, and open-access imaging software.
Investigation of alpha-synuclein-dependent, redox-controlled mitochondrial proteins as potential new targets in Parkinson’s disease
Maintenance of functional mitochondria is essential for life and an important therapeutic target in neurodegeneration. Alpha-synuclein is a major regulator of synaptic pathology in PD and mutations in the gene encoding this synaptic protein were among the first found in familial forms of the disease. Alpha-synuclein mediates synaptic stability by regulating synaptic mitochondria. We have identified a number of mitochondrial proteins that are affected by alpha-synuclein and the project will address how these interact to exert their function in mitochondria. As many of them are redox-regulated it will be critical to understand the interplay between these proteins and redox balance pathways in the mitochondria. The project will employ an interdisciplinary strategy. It will focus on specific metabolite transporters in the mitochondrial inner membranes and how the they are modulated by redox-active proteins from both sides of the membrane. The student will combine in vitro approaches working with cell lines and isolated mitochondria with whole organism pathology and synapse proteomics to clarify the role and interactions of these critical mitochondrial proteins in mitochondria function and protein homeostasis under stress. The project will then move on to investigate how these redox-active mitochondrial proteins underpin synaptic degeneration and the development of disease, eventually leading to novel, mitochondria-focused therapies.
Malaria: mechanisms of immune evasion and persistence in the hematopoietic niche
Malaria parasites have the capacity to persist in their host in the face of both immune attack as well as drug treatment. Work at WCIP has discovered that the erythropoietic niche found in tissues that support blood production (e.g. bone marrow and spleen) plays an important role in drug and immune evasion, supporting recrudescent malaria infections. Work at Centre for Regenerative Medicine (CRM) has created an in vitro model of the erythropoietic niche, containing both developing red blood cells and immune cells (macrophages), This PhD project aims to investigate the impact of malaria parasite infection on the hematopoietic niche. The project aims to apply the in vitro erythroid island (EI) technique to understand the impact of the malaria parasite, Plasmodium sp. on the phenotype of cells comprising the EI, specifically the island, or nurse macrophage. The project will then investigate the role of the EI niche in drug resistance to anti-malarial drugs. Overall, this project provides a unique opportunity to examine a novel, cutting edge research question under guidance from four highly experienced world-leading scientists. The individual will be given the opportunity to straddle the disciplines of stem cell research, molecular parasitology and immunology whilst addressing a question of global significance for world health.
Metabolomic approaches to identify pathophysiological biomarkers of stroke
Globally stroke is second only to coronary artery disease as a cause of death, killing over 6 million people each year and is the commonest cause of adult disability. Stroke is ischaemic, due to either blood vessel occlusion in the brain, of haemorrhagic, due to bleeding within the brain. Transient ischemic attacks are self-resolving episodes, typically of a few hours or less, where blood vessel occlusion occurs but is transient and symptoms rapidly improve, with or without small areas of ischaemic brain damage. Lack of oxygen is a critical causal effect, but other biochemical changes associated with stroke contribute to damage. These include changes to metabolic pathways, such as the polyamine pathway and increased inflammation, and changes in pathways such as the kynurenine pathway. Our previous work has shown changes to these pathways as well as the discovery of a novel metabolite, related to trimethylamine N-oxide that are associated with stroke. Therapeutic options to manage stroke are rare: this project aims to learn more about the nature of these changes, the contribution these metabolites make to stroke related pathology and ultimately whether their detection can improve recognition of stroke and TIA and if they may be targeted by new compounds that can be developed towards new therapies for stroke management. The project will combine laboratory skills (handling patient samples, preparing metabolites and application to mass spectrometry) alongside crucial informatics skills in data handling and analysis (metabolomics data mining, probing publicly available transcriptomic datasets and metabolite structure analysis through mass spectrometry and NMR.
Personalized management of blood testing for renal function in patients with and without diabetes
Testing of renal function using blood and urine samples is a key method of monitoring health of patients with diabetes and is recommended every year. Renal function is also tested ad hoc in people without diabetes. However, the evidence base to support either of these approaches is weak and may well result in unnecessary cost burdens to the NHS and/or missing people who may benefit additional screening.
Using Scotland’s world class health informatics, we propose a data science approach encapsulating routinely collected health data in NHS Greater Glasgow and Clyde, with 1.2M registered patients, to develop an evidence-based algorithm for personalized renal function testing. The project will provide a holistic overview of who is currently getting renal function tested in practice, and what the optimal approaches for the future would look like. We will perform parallel external validation studies in a similar healthcare extraction from the region of Stockholm, Sweden, allowing collaboration for the student with recognized International experts. This transformative approach has the potential to influence future clinical guidelines. The student will gain exposures to key quantitative skills and well as developing clinical insights and experience in data synthesis from review of clinical guidelines.
Single cell transcriptomics to understand and block infection by Trypanosoma brucei
Trypanosoma brucei one of a number of trypanosome parasites responsible for trypanosomiasis diseases that affect humans and their domesticated animals worldwide. Only a handful of drugs are available, many of which are inefficient and for which resistance is emerging. One novel route towards tackling trypanosome diseases would be by transmission blocking. T. brucei is transmitted between mammals by the tsetse fly and our understanding of the intricate developmental transitions during its life cycle is incomplete.
Notably, the developmental programme as the parasite moves from the salivary glands of the tsetse into the mammal’s bloodstream during fly feeding is barely characterised. This PhD will deploy the revolutionary strategy of single cell transcriptomics to reveal the temporal order of gene expression change during this life cycle transition, asking how the cell cycle is controlled and how the expression of a critical surface protein needed for survival in the mammal is determined. In doing so, we aim to detect genes and pathways essential for mammal infection by T. brucei, as well as determining if the pathways that direct this life cycle transition are stage-specific or are found throughout the parasite’s life cycle. The student will ablate candidate genes and attempt to block the encoded activities, thereby testing if and how these factors act in during life cycle transition. In doing so, we will reveal factors that may be targeted by existing or new compounds to impede or prevent trypanosome infection of mammals. This is an interdisciplinary project to train students in quantitative methods and learn cutting-edge strategies for genetic manipulation. 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.
Stratifying Intestinal Epithelial Cell Responses in Inflammmatory Bowel Disease Patients
Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the intestine with no current cure and individual patient responses to current treatments vary widely. A better understanding of which patients are likely to respond to specific treatments will improve patient outcomes and save money. One poorly understood aspect of IBD is how the intestinal epithelial cells (IEC) that line the gut change during inflammation and how this may be targeted to promote healing. New techniques have made it possible to isolate and grow IEC from patient biopsies. The aim of this project is to assess how patient IEC respond to inflammatory mediators released by local immune cells. We will use a technique called RNA-sequencing to assess how gene expression by IEC changes during inflammation and healing and look for shared signatures that reflect particular biological pathways. We will also use a technique called metabolomics to measure how IEC physiology changes and perform genome sequencing to look for mutations that are known to affect the risk of developing IBD. Using bioinformatics, the multi-omics data will be integrated to look for associations between particular genetic mutations and the responses of IEC to inflammation. This will allow us to stratify patients that may respond to drugs that target specific pathways in a personalized medicine strategy.
Target Autophagy and Aberrant Mitochondrial Folate Metabolism in Leukaemic Stem Cells
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 influence of infection history in precision medicine: Understanding long-term changes in lung immunity after respiratory viral infection
Our history of preceeding infections shapes our immune response to new infections, and understanding the molecular mechanisms involved is a critical component of individualised medicine. Respiratory viral infections are known to cause long-term changes in lung function and immune status. Those changes in immune status can lead to significant susceptibility to subsequent disease, as seen when influenza virus infection increases susceptibility to bacterial pneumonia. The aim of this project is to understand what changes about the immune cells in the lung, during influenza, that might explain the altered susceptibility to future disease.
We are focusing on macrophages, key cells mediating rapid immune defenses in the airways. Our hypothesis is that the transcriptional changes that we see in pulmonary macrophages after influenza virus infection are imposed by both environmental signals in infected regions of lung tissue, and by the developmental history of the macrophage cell itself. The data we generate will provide new insight into the immune defenses in the lung, and in mapping the long-term consequences of acute lung challenge. We will use a combination of infection and co-infection models, single cell and spatial transcriptomics, functional assays and disease severity scores, and we plan for an exciting and varied PhD experience with network and skills development in science, management, project design, teaching and communication.
The role of noncoding regulatory elements in the development and progression of autoimmune disease
It is estimated that over 10 million people in the UK suffer from Rheumatoid Arthritis (RA) or a similar autoimmune condition, and this costs the economy £8 billion per year. RA is associated with considerable loss of quality-of-life and premature death. Over 100 genetic loci (single nucleotide polymorphisms (SNP)) have already been associated with the risk of rheumatoid arthritis. However, there is substantial heterogeneity within the patient population which means that it is not yet possible to accurately determine how the disease will affect the individual. Recent studies have suggested that there are specific molecular signatures that associate with particular disease trajectories. Unfortunately, these results have been difficult to interpret and so far have not provided insight into how they can be used inform disease progression. This studentship will use a range of novel computational and experimental tools to investigate the relationship between SNPs within the noncoding genome (primarily at promoter regions) and disease trajectories to identify how we can use this information to fundamentally inform clinical decision making at diagnosis, and potentially aid in the development of future therapeutics.