Research Opportunities

Understanding the role of transposable elements as gene regulators is important, because they make up 50% of the genome, but are relatively understudied in comparison to genes which make up 2% of the genome. In our lab, we take on the exciting challenge of understanding the role of locus-specific Transposable elements as regulators of gene expression in development by studying the activity of transposable elements in single cells using our unique method CELLO-seq using long read sequencing. 

In this PhD project, you will learn a diverse set of techniques (CRISPR, embryonic stem cell cultures, third generation sequencing technologies and in-depth quantitative analysis) and work together with others in an team comprised of molecular biologists, developmental biologists, biochemists, and data scientists.  We will teach you how to perform high-quality science and design your own experiments to develop your own project and make use of the training you received. This research, carried out together with collaborators at the University of Oxford, the University of Edinburgh, and elsewhere, should lead to new discoveries and insights that inform our quantitative understanding of locus-specific transposable elements as new regulators of gene expression in development. These discoveries will advance this novel exciting field while contributing to the next generation of single cell long read methods.   

Asthma is the world’s most common chronic lung disease. It is increasing rapidly in incidence, but it is not known why. It arises from the complex interplay of genetic predispositions and the influence of environmental factors early in life, particularly early life infections with bacteria like haemophilus influenzae and with viruses like respiratory syncytial virus (RSV), and this points strongly to epigenetic changes being induced in the airway epithelium. 

It has been previously hard to decipher these mechanisms, but that is now becoming possible due to the ability to obtain direct airway samples at bronchoscopy and via nasal brushings and importantly the advent of technologies allowing analysis of epigenetics on small tissue samples and even at a single cell level.

We have generated large epigenetic and immunological datasets of airway epithelium and bronchial biopsies at single cell resolution. We also have DNA methylation data from a large paediatric cohort with early life RSV. We have developed a murine model of long-term infection with haemophilus influenzae and so, for the first time, can model the interactions and consequences of early life bacterial / viral coinfection.

We aim to exploit these datasets and models to understand in detail the specific immunoepigenetic changes in asthma and early life infection in mice and humans with a view to developing targeted epigenetic therapies. 

Infections pose a major risk to health globally. Antimicrobial resistance (AMR) threatens effective treatment of infection and healthcare associated infections (HCAIs) impact more than 10% of all hospital patients.

Advances in data availability and new artificial intelligence (AI) methods offer the chance to develop:
 

• More responsive, comprehensive, and automated HCAI/AMR surveillance generating better breadth and depth of intelligence to drive action and changes in practice to protect diverse populations at local, regional, and national levels.
• Predictive tools to improve care of individual patients and combat AMR.
• Methods, infrastructure and skills to optimally use rapidly-evolving electronic healthcare record and patient-contributed data, and emerging AI technologies. 

Several possible projects are available, including:
 

• Developing/testing automated electronic surveillance approaches for rapidly detecting changes in infections and identifying at-risk populations; and deploying these tools in hospitals and national systems
• Extending and piloting in hospitals predictions of personal AMR risk to optimise infection treatment, prevention and control, developing generalisable methods that can update over time/to new locations, and approaches for safely implementing them
• Pre-emptive surveillance, investigating which metrics of hospital processes (e.g. isolation/screening/diagnostic use/cleaning) are associated with HCAI/AMR to inform prevention

Our lab is interested in unconventional and innate-like cells (such as MAIT cells) and what their role is in immune protection and pathology.Tmic cells are one such cell - a recently described subset of T helper cells which are Microbially reactive, Innnate-like and Class II restricted. These cells are abundant in the human colon and marked out by high expression of CD161 – a feature normally associated with unconventional T cells – along with other evolving markers. However, as well as behaving like innate-like cells they bear conventional TCRs and are restricted by MHC Class II. They are also found in mice where they adopt a double negative phenotype over time in response to gut commensals. We think these cells are important in gut homeostasis (also other organs potentially) and we have shown they are involved in inflammation, but there are many questions as to their origin and overall functionality to be answered.

This project would explore the development of these cells using CBIR mice which over-express a commensal-reactive TCR, first by scRNASeq and scATACseq to define the steps along the pathway from conventional tissue memory to Tmic phenotype. Secondly using spatial transcriptomic methods to define their colocalization in the steady state and after challenge. Finally we will use a new in vivo CRISPR screen method (CHIME) to define the critical steps in development of Tmics and explore their functions in vivo. We will aim to compare mouse and human Tmic populations to define this novel conserved cell population and explore its role in health and disease.

Reference: https://www.nature.com/articles/s41467-022-35126-3

Serum Amyloid A-1 (SAA-1), a crucial acute-phase protein, is significantly upregulated during inflammation, associating with high-density lipoprotein (HDL) and modulating immune responses and tissue repair. Elevated SAA-1, however, is implicated in chronic inflammatory diseases and neurodegenerative conditions. This project investigates the role of SAA-1 in neuroinflammation and cognitive deficits following traumatic brain injury (TBI), particularly in Alzheimer’s-like pathology. We will use targeted siRNA within liposomes to selectively inhibit hepatic SAA-1 production, isolating peripheral SAA-1 effects on neuroinflammatory markers and behavior in TBI models. Parallel experiments will use adeno-associated viral vectors to knock down brain-derived SAA-1, assessing neuroinflammation and behavior to differentiate peripheral versus central SAA-1 contributions. Additionally, we will combine both liver and brain-specific knockdowns to evaluate potential synergistic effects in mitigating neuroinflammatory damage. Complementary studies will track exogenous, radiolabelled SAA-1-HDL complexes crossing the blood-brain barrier to elucidate SAA-1’s brain entry mechanisms and impact on neuroinflammation. This project aims to clarify SAA-1’s contributions to delirium post-TBI, potentially guiding targeted interventions to mitigate neurocognitive symptoms.

Radiotherapy (RT) treatment plays a key role in the management of many solid tumours and involves the precise delivery of high energy X-rays to localised tumours. In the curative setting, treatment can be used alone, or in combination with chemotherapy or immunotherapy. Although technological improvements have enabled the ability to deliver novel, highly effective RT treatments such as stereotactic ablative body radiotherapy (SABR), there is no approach that is able to fully spare healthy tissues from receiving some radiotherapy, often leading to significant side-effects. Obtaining greater tumour control by simply increasing the delivered dose is therefore not an adequate solution. A more tractable approach is to develop treatments which selectively render tumours more sensitive to radiation without exerting an effect on normal tissues. Our group have previously conducted high-throughput compound and genetic screens to identify novel, clinically translatable targets and compounds to specifically render tumours more sensitive to radiation. This project aims to characterise these potential therapeutic targets and develop novel therapeutic approaches against these targets. Our ultimate goal is to translate our laboratory findings into clinical trials.

Large-scale electronic health record data can potentially answer a far greater number of questions about infection management than traditional epidemiological studies using questionnaires. Their volume and scale are continuously increasing as larger amounts of healthcare data are linked and de-identified for research. Examples and challenges include
 

• Can we identify a wider range of risk factors for infection to target interventions? The sheer number of factors that could be considered, many with substantial amounts of missing data, poses challenges to traditional epidemiological approaches. A recent novel statistical analysis approach called ‘doublethink’ has been proposed which could be applied to a range of microbiologically and/or syndromically defined infections to identify novel populations to target to reduce infection risks, and compared with other methods including machine learning.
• Can we work out how best to use diagnostic tests for infection, widely considered to be a key tool to improve antimicrobial stewardship, in the real-world? In what patient populations are they and should they be used, how often, and what are their ultimate effects on both antimicrobials prescribed and patient outcomes? 
   

Projects can exploit an existing large datawarehouse of de-identified individual patient data, the Infections in Oxfordshire Database. They would suit students interested in infections/antimicrobial usage and coding, who wish to gain experience in design of studies using healthcare records to answer real-world questions. There will be opportunities to learn how to manage and use large-scale electronic health record data, and apply a range of quantitative methods including novel causal epidemiological methods.

The human body is colonised by a diverse community of commensal microorganisms (bacteria, fungi, viruses) with beneficial roles to human health. However, many microbial species naturally inhabiting body sites such as the skin and gut also have the potential to cause disease. In this project, we aim to integrate  bioinformatics, microbiology, metagenomics (genetics and genomics) and immunology to advance our understanding of the role of the human microbiome in health and disease. A key focus of our research is developing and applying new methods for strain-level resolution and exploring how the microbiome influences the emergence of antimicrobial-resistant pathogens. Ultimately, this research could inform new therapeutic strategies to combat infections and promote microbiome-based interventions for improved health outcomes over a human lifespan.

A population approach to disease prevention aims to shift the distributions of risk factors such as diet, physical activity and adiposity by changing the environments — economic, digital, physical and social — that influence everyone’s behaviour. To inform the next generation of environmental and policy strategies to prevent non-communicable diseases, we need to better understand the ways in which these interventions work, or could work, and the extent to which these are transferable between populations and contexts. This implies a need to improve our causal understanding of the disease prevention pathways linking a variety of environmental exposures via more proximal behavioural or metabolic outcomes to a variety of clinical outcomes.  

Our research programme offers the scope for a variety of PhD projects using the methods of epidemiology, natural experimental evaluation, qualitative causal process observation or evidence synthesis, singly or in combination, to investigate the mechanisms by which interventions can more effectively and equitably shift population dietary and physical activity patterns. Potential lines of inquiry include:   
•    Mechanism-focused systematic reviews of potential intervention strategies, e.g. using EBM+ or similar methods  
•    Epidemiological analyses to clarify causal pathways for interventions and novel intervention targets in the food or transport environments  
•    Investigating causal mechanisms in intervention studies in the food or transport environments.
•    Interactions between food and activity environment exposures 
•    Using routinely available georeferenced data on environmental changes and linking with well characterised cohorts to conduct statistical and spatial analyses

Supervisor(s): Jean Adams, Louise Foley, David Ogilvie, Jenna Panter, Martin White  

Programme: Population Health Interventions  

A population approach to disease prevention aims to shift the distributions of risk factors such as diet, physical activity and adiposity by changing the environments — economic, digital, physical and social — that influence everyone’s behaviour. To inform the next generation of environmental and policy strategies to prevent non-communicable diseases, we need to better understand the ways in which these interventions work, or could work, and the extent to which these are transferable between populations and contexts. This implies a need to improve our causal understanding of the disease prevention pathways linking a variety of environmental exposures via more proximal behavioural or metabolic outcomes to a variety of clinical outcomes.  

Our research programme offers the scope for a variety of PhD projects using the methods of epidemiology, natural experimental evaluation, qualitative causal process observation or evidence synthesis, singly or in combination, to investigate the mechanisms by which interventions can more effectively and equitably shift population dietary and physical activity patterns. Potential lines of inquiry include:  
•    Mechanism-focused systematic reviews of potential intervention strategies, e.g. using EBM+ or similar methods  
•    Epidemiological analyses to clarify causal pathways for interventions and novel intervention targets in the food or transport environments  
•    Investigating causal mechanisms in intervention studies in the food or transport environments.
•    Interactions between food and activity environment exposures 
•    Using routinely available georeferenced data on environmental changes and linking with well characterised cohorts to conduct statistical and spatial analyses

Supervisor(s): Jean Adams, Louise Foley, David Ogilvie, Jenna Panter, Martin White  

Programme: Population Health Interventions  

It is clear that stress responses and immunity are closely entwined, and epidemiological research shows that their complex interplay is key to the development of both psychiatric and autoimmune disorders (Teicher 2013 PMID:23982148, Dube 2009 PMID: 19188532). For example, a major component of the stress response is cortisol release, and depression is associated with hypercortisolaemia and glucocorticoid (GC) resistance. Abnormal responses to GCs likely contribute to the chronic inflammation observed in many patients with depression, as GCs can prime inflammatory responses, and GC-resistant immune cells produce increased levels of pro-inflammatory cytokines. Cytokines can act on the brain to produce the sickness-like behaviours characteristic of depression, and other aspects of GC-induced immune dysregulation (e.g. effects on neutrophils) may also play a role.  Epidemiological studies show that psychological stress interacts with genetic risk to lead to depression and psychosis (e.g. Wang 2023 PMID:36717542), but the risk variants involved and the cellular mechanisms of this effect are unknown. We hypothesize that some risk variants for psychiatric disorders act through glucocorticoid responsive regulatory elements in specific immune cell subsets to lead to symptoms. We further hypothesize that by dissecting the cell subset- and context-specific effects of glucocorticoids in health and in patients, we can develop a better biomarker of impaired neuroendocine signalling in psychiatric disorders, opening the door to biomarker development and more personalised approaches to treatment in stress-responsive autoimmune and psychiatric disorders.   

You would work with Dr Luis M. Franco and Dr Mary-Ellen Lynall to investigate these hypotheses using immunogenetic and functional genomic techniques, gaining training in cutting edge bioinformatics, statistical genetics, immunology, clinical phenotyping, and (if desired) wet-lab experimental approaches.   You would integrate emerging genetic association results in autoimmune and psychiatric disorders with (a) in-house glucocorticoid-response datasets (see https://www.niams.nih.gov/labs/franco-lab) (b) healthy and patient bulk and single cell datasets from our laboratories.   

Dr Franco's group in the Functional Immunogenomics Section at the NIAMS focuses on the immunobiology of glucocorticoid responses (e.g. Franco 2019 PMID:30674564). 

Dr Lynall's group in the Dept of Psychiatry at Cambridge focuses on immunogenetic analyses and immunophenotyping in psychiatric patients and population cohorts (e.g. Lynall 2022 PMID:36243721).

The Cambridge Lactation Laboratory (https://www.cambridgelactationlab.com/) is seeking enthusiastic and motivated prospective PhD candidates to support the Cambridgeshire Multiomics of Milk (CAMB MOM) study. Join a dynamic and growing research group in the Department of Biochemistry and Pharmacology at the University of Cambridge, under the leadership of Dr Alecia-Jane Twigger. The team is passionate about women’s and infant health hosting both experimental (wet lab) and bioinformatic (dry lab) research. Here, you will have the opportunity to receive training in both disciplines.  Despite the compelling evidence supporting the benefits of breastfeeding, there are significant gaps in our knowledge about how the mammary gland matures to perform its function of milk synthesis and secretion. Within the CAMB MOM study, we conduct multiomics analyses (lipidomics, metabolomics, proteomics, and transcriptomics) on samples from a cohort of breastfeeding participants in Cambridgeshire. The insights gained from gene-gene interaction networks will be tested using in vitro mammary organoid models and integrated into computational models. You will be able to choose which aspect of the study you are most interested in and together we will develop a tailored, dynamic and exciting research programme. The overarching aim of the project is to investigate the molecular pathways of human milk production, to resolve breastfeeding challenges and promote optimal long-term health for mothers and infants.

Traditional epidemic models attempt to fit the spread of infectious disease across populations. However, populations are ultimately an abstraction. At a more fundamental level diseases spread between individuals, not populations. This reflection motivates the network approach. Network approaches in epidemiology help to sharpen our intuitions. For example, they have shown that, in principle, heterogeneity can sustain a disease that would on average go extinct, or asymmetric transmission can alter the effectiveness of interventions. Unfortunately, applying network epidemic modelling directly to data is a challenge since we don't know the relevant contact network, or even how to measure it.   This project will develop computational and mathematical methods for fitting networked epidemic models to empirical epidemiologic data in the regime of high uncertainty. Even when data are of low quality, it may be possible to extract meaningful insights. We may never know exactly who was in contact with whom, or who was infected first, but we may still be able to determine the mode of transmission. And, in the end, this latter question is the scientific question of interest. Algorithms will be derived and validated to lay foundations for data driven approaches.  This project has potential applications for disease control and optimization of interventions. We will focus on whether contact traces – noisy and incomplete at the best of times – contain sufficient signal for answering basic research questions, so that future interventions are optimized. If they do, how do we extract this signal? If not, what modifications would make contact traces more informative without harming their immediate goals?

In the time it takes you to read this sentence, your body will produce millions of new cells. It is critical that each of them replicated their DNA accurately; errors in DNA replication can lead to genome instability and cancer. Cancerous cells often show different patterns of replication compared with healthy human cells, making DNA replication an important therapeutic target.  However, studying DNA replication at scale is a challenging problem: Existing methods either measure how a population of cells replicate, which “averages out” rare but important behaviour, or they work with single-molecule resolution but have low throughput.  

The Boemo Group (https://www.boemogroup.org) is a computational biology laboratory developing artificial intelligence software that measures the movement of replication forks from Oxford Nanopore sequencing data.  This method provides a high-throughput, inexpensive, accurate, and automated way to measure replication fork movement. The student will develop novel algorithms and computational approaches to track the movement of replication forks in both human cells and infectious microorganisms.  The student will also develop cutting-edge mathematical models of DNA replication that can be used to predict targets for replication-based therapies. This project will be highly collaborative and there will be the opportunity to learn, or improve upon, software engineering in Python/C/C++, GPU computing, deep learning with TensorFlow, the processing and management of large datasets.

Generation of reactive oxygen species (ROS) by the phagocyte NADPH oxidase is a critical and highly conserved antimicrobial function of myeloid immune cells such as neutrophils and monocytes. ROS production must be tightly regulated to ensure constant readiness for immune defence, while restraining inappropriate activation. A lack of ROS from this complex results in the devastating inborn error of immunity chronic granulomatous disease (CGD), characterised by recurrent infection but also autoinflammation and autoimmunity. Common hypomorphic variation in the genes encoding components of the phagocyte NADPH oxidase also drives pre-disposition to common autoimmune diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Excess ROS production can, however, result in   Understanding how ROS is tightly regulated is important for the development of rational therapeutics immune-mediated diseases.

Despite the elucidation of the NADPH oxidase complex structure and function, upstream regulators of ROS production remain largely undiscovered due to a lack of robust biological model systems. The Thomas Lab characterised EROS |(Essential for Reactive Oxygen Species) as an indispensable regulator of ROS generation but we believe that there are many more. Recent developments in CRISPR-Cas9 technology now allows both the introduction of precise edits (homology-directed repair, HDR) and genome-wide forward genetic screening by introducing knockout (CRISPRko) libraries. This may identify therapeutic targets in inflammatory disease. We will use CRISPR-HDR methods to endogenously tag key components of the NADPH oxidase complex with fluorescent proteins to generate reporter lines for iterative selection by flow cytometry. By screening these at genome-wide scale with CRISPRko libraries and sorting cells based on component expression, followed by functional screens using fluorescent ROS probes, we will elucidate upstream regulators of the complex expression and function. The function of these novel regulators can then be investigated and validated using primary and immortalised cells, structural biology, and selective mutagenesis. Interrogation of publicly available genomic datasets will guide ‘hit’ selection and possible therapeutic relevance.