Research Opportunities

Invent and implement new radioactive probes for imaging specific molecular targets in animal and human brain with positron emission tomography

The aim of this project will be to apply Next Generation Sequencing (NGS) approaches simultaneously to both host and the HIV provirus. The candidate will apply and improve methods to produce full-length viral haplotype and integration site data (already developed in the lab) from cohorts of individuals with treated early HIV infection, many of whom will receive experimental interventions and stop antiretroviral therapy.

Simultaneously, unbiased transcriptomic profiling (RNASeq) and analysis of DNA accessibility (ATAC-Seq) will be incorporated to allow a global interrogation of viral and host genomics, with potential to extend this to single cell analyses. Following method development, clinical samples from UK cohorts will be analysed to characterise the reservoir and to inform the source of rebound viraemia on treatment interruption. The work will therefore have both a cross-sectional and longitudinal component, promising significant analytical power. Working collaboratively with other group members and projects to link cell phenotype and subset with viral phylogenetics to identify the source of viraemia will be an important part of the work.

The candidate would be expected to have interests in both the laboratory wet-lab and bioinformatic components of the project, to achieve a unified problem-solving approach.

This project will explore ex vivo the characteristics of those cells which may contribute to persistent HIV infection. Undertaken in state-of-the-art facilities at the Peter Medawar Building in the University of Oxford, the project will combine flow cytometry, cell sorting and gene expression approaches (including RNAseq and single cell technologies) to characterise in detail those cells that contain latent HIV infection. Applying these techniques to individuals who stop antiretroviral therapy and are monitored longitudinally for viral rebound will allow further definition of the environment in which viral transcription is initiated.

The candidate will map the immune responses of individuals started on early antiretroviral therapy to determine how these are impacted by starting ART and which components of the immune response correlate with the reservoir and persisting viraemia. Additionally, samples from individuals under-going treatment interruption will allow further detailed  characterisation of immune correlation of rebound and remission.

The candidate will combine flow cytometry and molecular techniques to help clarify the phenotype of cells latently infected with HIV. Further objectives will explore changes in cell phenotype and gene expression profile that associate with rebound viraemia. Additionally, exploration of the antigen-specificity of latently infected cells through other approaches such as TCR sequencing and the ability to which these cells can be targeted by the immune system will be a parallel component of this project.

1. Biomarkers of the HIV reservoir and remission in primary HIV infection
2. Simultaneous host and pathogen 'omics to interrogate the HIV reservoir
3. Microfluidic and Lab-on-a-Chip approaches to characterising the HIV reservoir

*This project is available for the 2021 Oxford-NIH Pilot Programme*

Asthma is the world’s commonest chronic lung disease, affecting 350 million people worldwide. The advent of novel ‘biologic’ therapies targeting specific phenotypes of asthma is currently revolutionizing the treatment of patients with type 2 inflammation. However, there are no specific treatments available for the 50% of patients with type 2 low disease. The Levine group has identified a novel pathobiologic mechanism involving dysregulation of apoplipoproteins, which may play an important role in this phenotype by regulating the recruitment and function of innate and adaptive immune cells, which may have relevance for resistance to corticosteroids. Peptide mimetics of these molecules have potential as novel therapies for asthma, especially for patients with type 2 low neutrophilic inflammation. Dr Hinks group uses in vitro, murine and ex vivo human studies on highly phenotyped asthmatics to explore the biology of the inflamed airway mucosa, particularly innate and adaptive immune cells. Through this collaboration the student would use a range of techniques and a mix of wet lab science and human experimental medicine to understand the translational potential of apolipoprotein biology in human asthma.

The Domingos laboratory researches neuroimmune mechanisms underlying obesity. We discovered the sympathetic neuro-adipose junction, a functional synapse-like connection between the sympathetic nervous system and white adipocytes (Cell 2015). We found that this neuro-adipose junction is necessary and sufficient for fat mass reduction via norepinephrine (NE) signaling (Cell 2015, Nature Comm 2017). We then discovered Sympathetic neuron-Associated Macrophages (SAMs) that directly import and metabolize NE. Abrogation of SAM function promotes long-term amelioration of obesity independently of food intake (Nature Medicine, 2017). Given the recent discovery of SAMs, virtually nothing is insofar known about the cell biology of these cells or what other immune cells populate the SNS to cross talk with SAMs.

This PhD project aims to uncover  biological mechanisms of SAM biology. Using single cell sequencing methods, the student will unravel the heterogeneity of the sympathetic neuro-immune cross talk involving SAMs. By identifying novel immune cell mediators, we will have a better understanding of how SAMs are regulated, and pave the way to the identification of cellular and molecular targets that would then be amenable to drug delivery. We will be guided by singe cell sequencing dataset for formulating hypothesis that model fundamental aspects regarding the biology of these cells. The interactions between SNS axons and SAMs, or other resident cells identified by single cell sequencing, will be resolved by super resolution microscopy and 3D reconstruction, for a better understanding of the intricate topology of SAMs’ morphology in relation to SNS axons (Nature Medicine 2017). The PhD candidate can also use optogenetics to probe neuro-immune interactions (as in Nature Medicine 2017), as well as 3DISCO imaging for mapping the distribution of the aforementioned cells in adipose tissues. This project will give a candidate a tremendous opportunity to apply cutting-edge methods in the growing field of neuroimmune biology.

Alzheimer disease (AD), dementia with Lewy bodies (DLB) and Parkinson disease (PD) are the most common neurodegenerative diseases that create an enormous public health burden with a rapidly aging population. There is currently no definitive test that allows doctors to determine if someone has or will get one of these disorders. At present, the diagnosis is purely based on the symptoms, but by the time this is made the disease process is already too advanced for any therapies to have full impact. There is a clear and urgent need for reliable diagnostic tests that can identify early signs of dementia and movement disorders, and methods that can distinguish between the different types of neurodegeneration e.g. AD / DLB / PD so that drug treatment can be prescribed in a patient specific manner. Early, sensitive and reliable diagnostics will inevitably open an invaluable window for not only to early treatment but also towards finding a cure.

The intention of this DPhil project is to develop such a diagnostic method where samples taken from patients can be  interrogated using a highly sensitive and specific clinic-ready technique/ “kit” called Real-Time Quaking-Induced Conversion (RT-QuIC). An RT-QuIC method, which detects multiple sticky proteins in cerebrospinal fluid (CSF) as a surrogate marker of brain pathology has already been established by the Parkkinen lab to identify signs of early onset Parkinson’s disease (http://www.bbc.co.uk/news/health-37196619). Our aim here is to develop complementary RT-QuIC methods for AD and DLB patients. We anticipate that this will serve as a powerful predictive tool for patients at high risk of developing dementia i.e. patients with mild cognitive impairment, and enable a personalised test that can identify specific variants of the disease. In addition, we are using the RT-QuIC method to understand the disease pathogenesis, particularly the role of different conformational variants, or “strains” of proteins that may contribute to the tremendous heterogeneity of neurodegenerative diseases by showing different morphology, seeding and/or cross-seeding propensities, strain-specific neuropathology and different levels of neurotoxicity.

The proposed project will require vast biochemical, biophysical, molecular neuroscience and pathological skills and knowledge provided by the unique translational training environment formed by Dr. Parkkinen and her local and international collaborators who are all experts on the field. Dr. Parkkinen’s Molecular Neuropathology research group is based at state-of-the-art facilities at the Academic Unit of Neuropathology (AUN) and Oxford Brain Bank which are part of the Nuffield Department of Clinical Neurosciences (NDCN) in the University of Oxford. Research into neurodegenerative diseases has a high priority and profile within the University of Oxford and especially NDCN. Dr. Parkkinen is also an integral part of the Oxford Parkinson’s Disease Centre (https://www.dpag.ox.ac.uk/opdc) which is a multidisciplinary group of internationally recognised scientists with strengths in genomics, imaging, neuropathology, biomarkers and cell and animal models. Regular meetings are arranged for all post-graduate students within AUN/NDCN, and feedback and guidance is provided to ensure completion of the degree within the allocated time. All the necessary funding is provided by Dr. Parkkinen’s successful Weston Brain Institute and Parkinson’ UK grants.

At the National Cancer Institute, we have demonstrated that higher levels of moderate to vigorous intensity physical activity are associated with a lower risk of cancer, including cancer in the breast, colon, endometrium, bladder, kidney, and stomach1. However, due to a reliance on self-reported measures of physical activity, a number of key questions remain unanswered on what overall volume of physical activity, and what types of physical activity, are associated with lower cancer risk. In addition, previous studies are observational by nature and are therefore unable to determine causality due to unmeasured or residual confounding.

At Oxford, our group has shown that wearable sensors such as wrist-worn accelerometers can be used to noninvasively measure physical activity status in large-scale biomedical studies. For example, we have measured physical activity status in 103,712 UK Biobank participants who agreed to wear a wrist-worn accelerometer for seven days2. These measurements are now actively used by health researchers worldwide to demonstrate that simple measures of overall activity are cross-sectionally associated with cancer outcomes3. However, no large study of device measured physical activity has yet taken place to assess associations with incident cancer outcomes with sufficient longitudinal follow-up. Furthermore, activity trackers often capture ~180 million data points/participant/week and therefore have the potential to identify other powerful behavioural signals to detect future cancer risk.

Machine learning methods can help maximise the utility of data from wearable sensors. These methods attempt to automatically detect patterns in data and then use those uncovered patterns to predict future data. Our group has demonstrated the utility of supervised machine learning to identify sleep and functional physical activity behaviours from raw accelerometer data4. However, there is a broad concern around the lack of reproducibility of machine learning models in health data science5. It is therefore important to carefully consider how to promote robust machine learning findings and reject irreproducible ones, to ensure credibility and trustworthiness.

This DPhil project therefore proposes to use the world’s largest available datasets to investigate what types of physical activity are associated with a lower incidence of cancer. Working with colleagues at the University of Oxford and the National Cancer Institute, you will have the opportunity to address the following important questions:

1. What behavioural measurements of physical activity status can be reliably ascertained from accelerometer datasets?
You will have the opportunity to develop reproducible machine learning skills to develop methods to identify physical activity behaviours from raw accelerometer datasets. Specifically, you will develop semi-supervised machine learning methods which seek to combine supervised methods (good quality labels, small datasets) with unsupervised methods (no labels but large datasets which are less prone to sampling bias). This will involve use of the largest available accelerometer datasets with reference measurements for physical activity behaviours in free-living environments (using wearable cameras)6.

2. What physical activity behaviours are associated with incident cancer events?
Here, you will have the opportunity to develop new skills in epidemiological data analysis. You will have the opportunity to use the UK Biobank dataset which has collected wrist worn accelerometer data from 103,712 participants2. This dataset includes information on participants’ first hospital admission or death from cancer, identified from linkages to the national death index, Hospital Episode Statistics, and cancer registries.

3. Are physical activity behaviours potentially causally associated with cancer?
You will have the opportunity to develop genetic epidemiology skills by implementing two-sample Mendelian Randomization7 to assess potential causal effects of accelerometer measured physical activity and cancer. For cancer outcomes, summary genetic association data will be obtained from existing collaborators from International cancer consortia.

Candidates should have a BSc, or ideally MSc, in a discipline with a substantive epidemiological, computational, or quantitative component. We very much welcome prospective candidates to directly contact us to further develop this proposal.

The mortality rate for COVID-19 pandemic has been two-fold higher in men than women. Similar observation extends to susceptibility and outcome of most other infectious diseases. For instance, after initial Hepatitis C Virus infection women are 2-3 times more likely to spontaneously clear the virus without any interventions and in HIV infection females are 5 times more likely to achieve elite control (complete suppression virus without therapy) than men. However, a consequence of the more vigorous immune response observed in females is more immunopathology and auto-immune diseases (such as lupus) in women than men. For the same reasons, females make stronger immune responses to vaccines but suffer more adverse events. Despite large evidence for sex differences in autoimmune diseases and susceptibility and outcome of infectious diseases, data addressing the biological mechanism are remarkably scarce.

In this project you will use computational and experimental methods to probe differences in immune system that lead to sex differences in infectious diseases. We will investigate this question across many infections including HCV, HBV, HIV and COVID-19. You will start with analysing the available RNA-seq and genomic data from our cohorts and other public databases to understand the role of heterogeneity in X chromosome inactivation in female immune cells and the transcriptional consequence and its contribution to better outcome in infectious diseases. In the next stage you will stimulate male and female immune cells with different immunogens and perform single cell RNA-sequencing to evaluate differential responses across distinct cell types and their association with sex. The project will also use samples and data from vaccine clinical trials. The baseline samples will be compared to the post-vaccination samples and differences in immune systems between sexes will be investigated.

Genome-wide association studies (GWAS) aim to identify the genetic basis of phenotypic traits using the variation that exists within natural populations. Uniquely for infectious diseases, the inter-individual heterogeneity in disease phenotype is linked to both host and pathogen genetic variation. Traditionally, genetic studies of infectious diseases have sought to explain between-individual variation in disease phenotypes by assessing genetic factors separately in humans or pathogens, under the assumption that these factors are independent. Although reasonable for some variants, there is strong theoretical and empirical evidence that genetic interactions between host and viruses play a major role in viral disease aetiology.

In this project you will integrate host and viral genomic data from the same patients to better understand viral pathogenesis and between-individual heterogeneity in disease outcomes. By analysis of paired host-virus genomic data from well-characterised cohorts you will gain novel insights on (a) host polymorphisms linked with viral sequence variation, (b) virus sites under strong host genetic selective pressures, (c) host and virus genetic factors independently contributing to disease phenotypes and (d) host-virus genetic interactions contributing to disease phenotypes. The findings have the potential to: (I) revolutionize our understanding of host-virus interactions and human biology; (II) aid in development of more effective vaccines, drug targets and immunotherapies; and (III) permit better use of therapies through patient stratification. In the age of “Big Data” and “Personalised Medicine”, analysis of paired host-pathogen genomic data will become increasingly important to uncover the mechanisms driving pathogen adaptations and heterogeneity of infection outcomes.

The Higgins laboratory are expert in the structural studies of host-parasite interactions in parasitic diseases such as malaria. They study how interactions at the heart of processes such as erythrocyte invasion are mediated. They explore how parasite surface proteins manipulate the human immune system. They examine antibodies produced in response to human vaccination and determine how these function, and they use this information to design improved vaccine components.

To discuss possible projects, contact: matthew.higgins@bioch.ox.ac.uk

Our lab is interested in membrane-bound organelles- how they form and acquire their distinctive proteome essential to carry their specialized functions. In particular, we focus on how organelle function is maintained through quality control processes. Our lab has been particular interested in a quality control process termed ERAD, which targets misfolded membrane proteins in the endoplasmic reticulum (ER). While some of the components of this process have been identified, the mechanisms by which diverse range of misfolded proteins are selected, ubiquitinated, extracted from the ER membrane and targeted for degradation by the proteasome remain elusive. To gain insight on the mechanisms of protein quality control our lab is taking multidisciplinary approaches. We are using CRISPR-based genome-wide genetic screens to delineate the molecular pathways involved in the degradation of disease-relevant misfolded proteins. In parallel, we use biochemical, proteomics and structural approaches to dissect mechanistically the multiple steps of ERAD. These studies will reveal the molecular basis of quality control processes by which misfolded and aggregation-prone proteins are handled by the cell both under normal and pathological situations. We are also interested in inter-organelle communications- which and how molecules are exchanged between organelles, which signals regulate those exchanges, etc.  Although we do mostly basic research, we are interested how these processes are disrupted in human disease.

Development of vaccines against existing complex diseases (such as malaria), or emerging pathogens, requires innovative approaches and the use of immune stimulants known as adjuvants. During an immune response to a vaccine, orchestrated cellular activation and leukocyte migration promote rapid changes throughout the body, from the site of injection to secondary lymphoid organs, such as the tissue draining lymph nodes and the spleen, to peripheral blood and the liver.

This project will focus on the characterisation of the adjuvant- and vaccine- induced innate and adaptive immune activation across these sites, with the analysis of the vaccine localisation (using intravital imaging and/or multiparameter flow cytometry) and the resulting cellular phenotypes within the innate and adaptive compartments. 

Studies will be based on a mouse model of malaria, using the malaria vaccine developed at the Jenner Institute, R21 - a virus-like particle (VLP) analogous to the currently most advanced malaria vaccine, GSK’s RTS,S in AS01 adjuvant. The R21 vaccine will be combined with new clinically relevant liposome- and emulsion-based adjuvants. The profiles of the immune response to different formulations will be correlated with protection against malaria.

Training will be provided in animal work procedures: immunisation, tissue sampling, producing and isolating Plasmodium parasites for malaria challenge, as well as a variety of immuno-profiling techniques, such as ELISA, ELISpot, multiparameter flowcytometry, bead-based cytokine arrays, CyTOF, confocal and intravital microscopy and other relevant approaches.