Research Opportunities

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.

 Cambridge and NIH have strong pre-clinical and clinical research programs. Both teams are developing novel methods to image metabolism in vivo and from tissue samples. The tools to be used as part of this project include hyperpolarised carbon-13 MRI and deuterium metabolic imaging for non-invasive imaging, as well as bulk mass spectrometry, mass spectrometry imaging and NMR on tissue extracts. The teams will combine expertise to study how these methods can be used to probe the spatial distribution of metabolism in tumour and immune compartments using both pre-clinical and clinical models of cancer. The goal is to use more accurately phenotype cancer using metabolism, and to detect early changes in this metabolism in space and time as biomarkers of successful response to therapy. Ultimately this will be used to improve the management of patients with a wide range of cancers where metabolism is known to play a significant role.

Our lab uses a variety of molecular, cellular and behavioural techniques to determine mechanisms of osteoarthritis pathogenesis and pain, using both mouse and naked mole-rat as model organisms. We are particularly interested in studying cell-cell interactions, for example identifying signalling pathways between fibroblast-like synoviocytes and knee-innervating neurons, using a combination of electrophysiology and behavioural assays. Past work has included the use of viral based modulation of neuronal function (i.e. chemogenetics), as well as exploring how mesenchymal stem cells modulate pain in osteoarthritis.

The G1/S transition is a critical checkpoint in the cell cycle, controlling the decision of cells to either proceed into DNA replication or enter quiescence. Disruption of this checkpoint is a hallmark of cancer, often driven by hyperactivation of CDK4/6, which is known for its role in phosphorylating the retinoblastoma protein (Rb). However, recent evidence suggests that CDK4/6 targets other substrates beyond Rb that play important but less explored roles in regulating the G1/S checkpoint. In this project, we aim to identify and characterize novel CDK4/6 substrates and their phosphorylation patterns, exploring how these mechanisms contribute to cell cycle control and tumorigenesis. Through a combination of cutting-edge biochemical techniques and quantitative live-cell imaging, we will investigate how these new CDK4/6 substrates modulate the decision-making process during cell division in both normal and cancerous cells. The PhD candidate will have the opportunity to develop a multidisciplinary skill set, combining advanced molecular biology, cell biology, and state-of-the-art microscopy. The project will include extensive biochemical assays to define phosphorylation events, CRISPR/Cas9-mediated gene editing to study the functional impact of these substrates, and live-cell imaging to assess the dynamics of G1/S transition in real-time. Our ultimate goal is to uncover how dysregulation of these novel substrates drives aberrant cell proliferation in cancers, potentially opening up new therapeutic strategies targeting the CDK4/6 axis. The candidate will benefit from a collaborative environment, receiving mentorship across disciplines and contributing to a highly impactful area of cancer research.

The scholar will work on a project integrating neuroimaging and genetics across the entire lifespan with the goal of gaining a more fine-grained understanding of the biological mechanisms driving brain morphological changes across the lifespan in health and disease.

Cell-to-cell communication by extracellular vesicles (EVs) is a growing field of investigation in basic cell biology research, biomarker discovery and therapeutic drug delivery. Our lab is investigating how different cargoes are loaded into EVs and the pathways that regulate EV biogenesis, release and uptake.  There are various chaperone proteins within the cell that aid the sorting of cargoes into EVs.  We are particularly interested in the aggregate-prone proteins that are associated with different neurodegenerative diseases (e.g. alpha-synuclein, SOD1, TDP-43, tau and huntingtin) and have shown that these proteins can be loaded into EVs and secreted from cells. We have recently identified that members of the small heat shock protein (sHSP) family can interact with various aggregate-prone proteins to facilitate their loading into EVs and their intercellular spreading.  In particular, we have demonstrated that one of the sHSP family, HSPB1, can interact with the autophagy cargo receptor p62/SQSTM1 to modulate its unconventional secretion by EVs. In cells expressing mutant huntingtin (the aggregate-prone protein associated with Huntington’s disease), these HSPB1-loaded EVs are capable of inducing the spreading of mutant huntingtin to non-expressing cells. Importantly, these findings reveal a novel mechanism for the spreading and seeding of protein aggregates, which may have wider implications for and impact the pathobiological mechanisms underlying other neurodegenerative disorders. In addition, we have identified several signalling pathways and regulatory proteins that are essential for the formation of mutant huntingtin-carrying EVs. 

This project will use a range of cell-based and in vivo assays to investigate how such signalling proteins regulate the interplay between autophagy and unconventional secretion and determine how this affects the accumulation and spreading of neurodegenerative disease-causing proteins. The first part of the project will involve over-expression and knockdown of these signalling proteins in vitro (in cell-based assays), where a range of biochemical and microscopy techniques will be deployed to look at protein interactions, localisation and spreading of these proteins. These findings will be then validated in vivo using a combination of zebrafish fluorescent reporter lines and neurodegenerative disease models. Finally, by using genetic and pharmacological activation and inhibition of signalling pathways, we will monitor EVs in vivo and characterise how perturbation of unconventional secretion can impact the disease progression. 

Our progress in understanding and engineering living systems, and developing therapies, is severely limited by inability to build predictive, data driven models of cellular processes. Much of current cellular biology research, including work with human stem cells, microbes, and cell lines, proceeds by optically labelling cellular components such as proteins and by measuring and manipulating physiological signals optically. Microscope imaging is then used to track and quantify the interactions of these signals and components in living cells, including cells that have been genetically engineered or exposed to pharmacological agents. Quantities of interest, such as where proteins aggregate, or how rapidly cells grow are then extracted from images or movies and then quantified. This is challenging, slow and error prone because the experiments are often done piecemeal, often by hand, and focus on a handful of types of molecules or cellular interactions that are inferred from a condensed snapshot of the data, such as an average protein density.  

This project leverages recent advances in AI to analyse image data gathered from microbial populations (E coli). Our goal is to build predictive models of processes such as cell division and virus infection using high throughput microscope data. We approach this using a fusion of simulated and real data, with model-based predictions tested in automated, high throughput experiments. We wish to scale this up to cover other types of cells, including human stem cells and microbiota through collaboration with suitable groups at NIH.  This project would suit trainees with strong quantitative skills, a first degree in a STEM discipline and proficiency in coding in more than one language.

A major goal in systems neuroscience is to connect the activity of populations of neurons to specific behaviors. However, large scale recordings of neural activity during the execution of learned tasks and during the experience of familiar stimuli have revealed that neural activity patterns continually change over extended periods. This so-called Representational Drift is not accompanied by obvious alterations in behavior, learning or systemic physiology, which raises profound questions about its origin and its implications for learning and memory. For example, textbook theories of learning and memory assert that stable memories require stable relationships between neural activity and learned associations. Representational drift brings these theories into question, while raising practical problems for understanding neural data, designing experiments and developing technology such as brain-machine interfaces.  

This project uses a mix of data science, computational modelling and theory, and collaboration with experimentalists to understand the causes and implications of Representational Drift. We use a variety of statistical methods as well as modelling and analysis of artificial neural networks to generate and test hypotheses. We work closely with experimentalists in Harvard Medical School and UCL, and wish to find experimental partners in the NIH to further this research.  Key skills include proficiency in numerical methods, simulation, strong coding skills and a working knowledge of advanced statistical methods, including generalized linear models and Bayesian inference.  

Key recent publications include:  
Micou, C., & O'Leary, T. (2023). Representational drift as a window into neural and behavioural plasticity. Current opinion in neurobiology, 81, 102746. https://www.sciencedirect.com/science/article/pii/S0959438823000715  Rule, M. E., & O’Leary, T. (2022). 

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations. Proceedings of the National Academy of Sciences, 119(7), e2106692119. https://www.pnas.org/doi/abs/10.1073/pnas.2106692119