Research Opportunities

This project will investigate how chromosomal instability (CIN) shapes systemic and tumour-associated myeloid responses in oesophagogastric cancer (OGC), with a particular focus on neutrophil-driven immunosuppression as a therapeutic target. Building on the hypothesis that CIN promotes a targetable, immunosuppressive myeloid infiltrate, the work will combine longitudinal patient sampling with high-dimensional immune profiling to characterise circulating and tumour-resident myeloid populations at baseline and during chemo-immunotherapy. Using matched tumour and blood samples, CIN status will be quantified via cGAS-positive micronuclei, and correlated with dynamic changes in circulating immune cells and chemokine profiles to identify biomarkers of treatment response and resistance. Parallel analyses in novel CIN-high and CIN-low mouse models will enable mechanistic dissection and testing of strategies to therapeutically target myeloid-mediated immunosuppression. Together, this work aims to define clinically actionable biomarkers and vulnerabilities in CIN-high cancers, enabling earlier patient stratification and more effective, personalised treatment approaches.

Despite the improvement in cancer treatment achieved through immune checkpoint blockade (ICB), durable curative responses are realised in only a minority of patients with certain cancers. This is best evidenced in colorectal cancer (CRC), where only the small subset of patients with deficient mismatch repair disease receive real benefit from ICB. A fundamental challenge for most CRC patients is the high prevalence of liver metastases. This is characteristic of late-stage disease and is strongly associated with poor response to treatment and survival. The overarching aim of this project is to better understand key suppressive mechanisms in primary and metastatic tumours, insight that is vital to support the design of better treatments combinations tailored for different CRC patients.

To interrogate the immunosuppressive mechanisms that form in CRC, we have developed sophisticated orthotopic models utilising tumour organoid implantation directly into the colon wall via colonoscopy. Slow growing primary tumours form and then naturally metastasise to the liver. Exploiting dynamic labelling approaches pioneered with the lab, mechanistic studies focus on understanding how different tumour microenvironments shape immune cell fate and function in real time. These studies are then further supported by conditional knockout mouse models and other interventions (e.g. blocking antibodies) to test effects on anti-tumour responses.

This project will provide a wealth of training opportunities and is ideal for students wishing to develop expertise in studying immune responses to cancer utilising the most advanced in vivo models available. The lab is medium-sized (~10 members), with dedicated technical support for in vivo research and a collaborative and supportive ethos.

The shape, spatial organisation, and communication pathways of subcellular organelles are central to neuronal function and are profoundly disrupted in neurodegenerative diseases such as ALS, Alzheimer’s, and Parkinson’s disease. This PhD project will combine advanced live-cell and super-resolution microscopy, multiomic profiling, and CRISPR-based gene editing in human iPSC-derived neurons to define how organelle form supports neuronal function, and how these relationships break down in neurodegeneration. The student will develop and apply quantitative imaging approaches to map organelle architecture and interactions, integrate these data with proteomic and lipidomic analyses, and functionally perturb candidate pathways to establish causal links between organelle organisation and neuronal dysfunction.

Advances in single-cell transcriptomic technologies are enabling the discovery of many novel cell phenotypes. However, this emerging knowledge remains fragmented across the scientific literature. Natural language processing (NLP) using large language models (LLMs) and other artificial intelligence methods offers a promising approach to extract and organize this information at scale, but the inconsistent nomenclature used to describe cell phenotypes in the literature limits the effectiveness of straightforward NLP approaches, and more advanced NLP methods requiring well-annotated corpora for development and evaluation are needed. We recently developed the NLM CellLink corpus, a corpus of excerpts from full-text articles that contain information about cell phenotypes. This corpus was manually annotated with mentions of human and mouse cell types and linked to Cell Ontology (CL) identifiers. This PhD project will use this corpus to support the development and evaluation of AI machine learning models for automatically identifying cell types in the scientific literature, including novel cell types, and their relationships with other key biological entities (e.g., marker genes, anatomical structures, perturbation responses, disease states) for translation into standardized semantically structured assertions and their incorporation into the NLM Cell Knowledge Network (NLM-CKN).

The student will join an interdisciplinary team at the National Library of Medicine that is applying computational biology and data science techniques to characterize cellular phenotypes and their roles in health and disease at scale.  Training will be provided in advanced computational and statistical analysis of multi-omics data, natural language processing using artificial intelligence methods, and the development and use of ontologies and other sematic web technology for biomedical knowledge representation.

Cells are the fundamental units of life. Single cell genomic technologies are revolutionizing our understanding of cellular phenotypes. Large single cell data consortia, including the NIH BRAIN Initiative and the Human BioMolecular Atlas Program (HuBMAP), have generated single cell atlas data from millions of cells/nuclei spanning multiple organs and biological systems. At the National Library of Medicine (NLM), we are building the NLM Cell Knowledge Network (http://cell-kn-mvp.org), a knowledgebase that focuses on representing the cell phenotypes and associated characteristics derived from single cell genomics data. It integrates data-driven information with knowledge from trustworthy reference ontologies, NCBI resources, and text mining efforts, resulting in a large-scale semantic knowledge network for innovative data mining and knowledge discovery.

This project consists of two main research components: 
i) developing novel computational methods for single cell and spatial transcriptomics analysis using machine learning and advanced statistics techniques, and 
ii) developing network analysis strategies for knowledge mining using cutting-edge artificial intelligence technologies. 

Students interested in one or both research components are encouraged to apply. The project team has interdisciplinary background, ranging from molecular biology, genetics, statistics, and computer science, providing a strong supporting system for students’ academic growth. Dr. Yun (Renee) Zhang is a tenure-track investigator at NLM and an alumnus of the University of Oxford
 

Sympathetic neurons have a wide range of physiological functions and their hypoactivity contributes to obesity and diabetes, among other syndromes. Sympathomimemic drugs rescue this deficiency but this drug class, mostly composed of brain-penetrant amphetamines and adrenergic agonists, is both cardiotoxic and highly controlled. Our recent publication puts forward new class of drugs  named Sympathofacilitators that do not enter the brain and have an anti-obesity and cardio-neutral effect in vivo. The first-in-class was published in Mahu I et al Domingos, Cell Metabolism 2020; Fig. 3C of this paper demonstrated a neuro-facilitatory effect, rather than neuro-excitatory one. 

This new class is in needed of novel chemical entities which can be screened in vitro on cultured iPSC-derived sympathetic neurones. The screen would be based on fluorescent readouts of calcium activity reporter, screening for a facilitation of responses to acetylcholine (similar to Fig. 3C of Mahu I et al). 

The prospect of identifying natural compounds that have a Sympathofacilitatory effect is tangible when performed in collaboration with the laboratory of Barry O’Keefe. The student will learn lab how to grow and scale-up iPSC-derived sympathetic neurones in Domingos lab, and optimize an in vitro assay based on Fig. 3C. The student will then transfer this knowledge to the lab of Barry O’Keefe where the screen will be performed using a fluorescent plate reader, robotic liquid handling, and a library of natural compounds.

While X-ray crystal structures of proteins are terrifically informative, both in basic research (6 Nobel prizes in 25 years) and drug design, the major stumbling block remains the need to obtain crystals of the biological sample (protein, protein complex, virus) suitable for X-ray analysis.  Even for a biochemically well-behaved sample, the likelihood of it crystallizing is always low, and there are no rules or reliable protocols to improve this.  Structure-based drug discovery similarly requires diverse crystal forms to be available for the protein; this too cannot currently be easily induced.

This project addresses the pressing need for rapid and simple protein engineering techniques to make samples crystallize routinely.  This will entail, depending on the student's background, skills and preferences:

• Develop streamlined protocols for parallel generation of large numbers of crystallization chaperones, e.g. using in vivo selection directly coupled to over-expression, with DNA libraries of binding scaffolds;
• Evolve binders that favour crystallization, by adapting existing motifs through iterative design supported by high-throughput crystallization, e.g. Gluebodies [Ye et al, 2024]; 
• Adapt protein design algorithms to find surface mutations that better allow the protein to pack into crystals, e.g. by machine learning of crystal contacts in the PDB;
• Streamline protocols for rapid large-volume parallel expression, purification and crystallization of large numbers of diverse protein variants; 
• Develop generic scaffolds that crystallize robustly, and ways of tethering target proteins to the scaffold.

Cardiometabolic disease (CMD) remains the leading cause of global mortality, contributing to a heavy burden on healthcare systems. It is well accepted that lifestyle factors, such as poor diet, physical inactivity, smoking, and excessive alcohol consumption, are important contributors to CMD incidence and mortality but underlying biological mechanisms linking lifestyle factors with cardiometabolic health are largely unknown. Research on such mechanisms may uncover novel biomarkers that more accurately predict disease onset, progression, and response to lifestyle interventions. 

The China Kadoorie Biobank study (CKB) is a large prospective cohort study with >0.5 million Chinese adults from 10 different locations in China (www.ckbiobank.org). Over the past 20 years, CKB has accumulated multi-dimensional data on lifestyle and other exposures, physical and other measurements (e.g. adiposity, blood pressure, liver steatosis and fibrosis, ECG, carotid artery intima media thickness and plaque, bone mineral density, and retinal images), incidence and mortality of major diseases including CMD, as well as multi-omics data including genomics (GWAS genotyping as well as WGS), proteomics (>10,000 proteins from Olink and Somalogic), metabolomics (>220 NMR and >5400 Metabolon metabolites, which cover 8 super biological pathways, e.g. amino acid metabolism, nucleotide metabolism, and microbiome metabolism, and 70 major pathways) and gut/oral metagenomics (shotgun sequencing). This large and rich resource will enable us to investigate the potential relevance of different lifestyle factors, as well as their interplay, for a range of cardiometabolic health conditions. Findings from CKB could be compared with those from the UK Biobank, which is an open resource for global researchers.

Epigenetic regulation plays a crucial role in maintaining homeostasis by fine-tuning gene expression. As we age, epigenetic patterns become deregulated, contributing to age-related conditions such as senescence, metabolic disorders (e.g. diabetes, atherosclerosis), immune dysfunction, and cancer, which collectively decrease life expectancy and quality of life in the elderly. Histone deacetylases (HDACs), enzymes that remove acetyl groups from lysine residues thus promoting chromatin condensation, are key regulators of gene expression and their activity contributes to age-related deficiencies in homeostasis. HDAC inhibitors offer potential to reverse aging by restoring health-promoting and anti-senescence acetylation patterns, improving both cellular and systemic functions and alleviating the aging process. Our previous research demonstrated that HDAC inhibition has strong immunomodulatory effects in aging mice where it is able to revitalise the immune response, and potentially overcome immune related senescence. Specifically, enhanced MHC I and II expression, increased activity of immune cells and infiltration into the tumour microenvironment. This project will investigate the effects of clinical HDAC inhibitors on immune aging, with a focus on restoring healthy immune responses and protection against cancer and age-related diseases, both in vitro and in vivo. The DPhil student will be trained in cutting-edge molecular, biochemical, genomic, bioinformatic and immunological techniques including but not limited to ChIP-seq, single cell RNA seq, CRISPR/Cas9 gene editing, flow cytometry, and ELIspot.

Liu G, Barczak W, Lee LN, Shrestha A, Provine NM, Albayrak G, Zhu H, Hutchings C, Klenerman P, La Thangue NB. The HDAC inhibitor zabadinostat is a systemic regulator of adaptive immunity. Commun Biol. 2023 Jan 26;6(1):102.

Blaszczak W, Liu G, Zhu H, Barczak W, Shrestha A, Albayrak G, Zheng S, Kerr D, Samsonova A, La Thangue NB. Immune modulation underpins the anti-cancer activity of HDAC inhibitors. Mol Oncol. 2021 Dec;15(12):3280-3298.

This collaboration between the Altan-Bonnet (NCI), Buckley and Coles (Oxford) labs addresses how organs generate distinct inflammatory responses despite sharing common components like immune cells, fibroblasts, and the extracellular matrix. In collaboration with additional teams from the Netherlands, and Canada, we aim to uncover the molecular, cellular, and tissue-level rules governing organ-specific inflammation.

We hypothesize that (1) organ context and cellular experience shape the perception of inflammatory signals, and (2) organ-specific hierarchies integrate responses into coordinated outcomes. Using a data-driven approach, we will combine ex vivo and in silico models of mouse and human tissues to explore these mechanisms. High-throughput robotics will generate diverse tissue models with varying sensitivities to infection or immunopathology. Multimodal datasets from these models will be analyzed using machine learning to build computational models, to guide iterative cycles of discovery.

This project will revolutionize tissue biology by creating a unified framework for understanding tissue-specific inflammation, paving the way for new treatments. We are seeking researchers with expertise in bioengineering, computer science, or immunology to join this interdisciplinary effort.

Project keywords: immunology, systems biology, biomedical engineering. 

This collaboration between the Altan-Bonnet (NCI), Buckley and Coles (Oxford) labs addresses how organs generate distinct inflammatory responses despite sharing common components like immune cells, fibroblasts, and the extracellular matrix. In collaboration with additional teams from the Netherlands, and Canada, we aim to uncover the molecular, cellular, and tissue-level rules governing organ-specific inflammation.

We hypothesize that (1) organ context and cellular experience shape the perception of inflammatory signals, and (2) organ-specific hierarchies integrate responses into coordinated outcomes. Using a data-driven approach, we will combine ex vivo and in silico models of mouse and human tissues to explore these mechanisms. High-throughput robotics will generate diverse tissue models with varying sensitivities to infection or immunopathology. Multimodal datasets from these models will be analyzed using machine learning to build computational models, to guide iterative cycles of discovery.

This project will revolutionize tissue biology by creating a unified framework for understanding tissue-specific inflammation, paving the way for new treatments. We are seeking researchers with expertise in bioengineering, computer science, or immunology to join this interdisciplinary effort.

Project keywords: immunology, systems biology, biomedical engineering. 

This collaboration between the Altan-Bonnet (NCI), Buckley and Coles (Oxford) labs addresses how organs generate distinct inflammatory responses despite sharing common components like immune cells, fibroblasts, and the extracellular matrix. In collaboration with additional teams from the Netherlands, and Canada, we aim to uncover the molecular, cellular, and tissue-level rules governing organ-specific inflammation.

We hypothesize that (1) organ context and cellular experience shape the perception of inflammatory signals, and (2) organ-specific hierarchies integrate responses into coordinated outcomes. Using a data-driven approach, we will combine ex vivo and in silico models of mouse and human tissues to explore these mechanisms. High-throughput robotics will generate diverse tissue models with varying sensitivities to infection or immunopathology. Multimodal datasets from these models will be analyzed using machine learning to build computational models, to guide iterative cycles of discovery.

This project will revolutionize tissue biology by creating a unified framework for understanding tissue-specific inflammation, paving the way for new treatments. We are seeking researchers with expertise in bioengineering, computer science, or immunology to join this interdisciplinary effort.

Project keywords: immunology, systems biology, biomedical engineering. 

Population-based cohorts have identified major modifiable risk factors for cardio-metabolic diseases, such as adiposity and physical activity, but the patterns and relevance of these factors varies greatly across populations, and previous evidence is predominantly from high-income countries. There is a high burden of cardio-metabolic diseases in South and Southeast Asian populations. However, the underlying mechanisms are yet to be fully elucidated, with previous evidence suggesting ethnically divergent body fat and muscle mass distribution to be a determining factor. Furthermore, physical activity has a complex relationship with body composition, and different patterns of physical activity between high- and low-/middle-income countries and between urban and rural areas might be an independent or explanatory factor in associations with cardio-metabolic diseases.

The objectives of this DPhil project may be to explore associations between different measures of body composition with objective measures of physical activity between populations and their individual and joint associations with cardio-metabolic diseases across different ethnicities, using data from different large-scale prospective studies. 

This project will use data from three large prospective studies: the Indian Study of Healthy Ageing (ISHA), the Malaysian Cohort, and the South and Southeast Asian participants of the UK Biobank. It will provide unique opportunity for novel insights into disease risks and aetiology to inform global non-communicable disease control and prevention efforts, and the student will have the chance to work collaboratively across the Global Populations Studies Group led by Prof Sarah Lewington, the Oxford Wearables group led by Prof Aiden Doherty, and the Oxford Centre for Diabetes, Endocrinology and Metabolism led by Prof Fredrik Karpe.

Our research group aims to understand unique patterns of hypertensive end organ disease progression of women and their children following a hypertensive pregnancy. This information is used to develop personalised clinical tools to identify, track, and slow end organ disease to prevent early onset cardiac, cerebral and vascular disease in these families.

This project will make use of information from computational modelling to study disease progression related to a hypertensive pregnancy across multiple modalities and organs. The insights into key structural and functional changes at the organ-level that describe stages of disease will be used to help identify potential targeted intervention.

The project will make use of a collection of unique imaging and clinical datasets from clinical trials and observational studies in families with a hypertensive pregnancy history. Furthermore, our clinical trials help us understand how interventions modify the underlying disease development.

Potential areas for a PhD project include: 

• Artificial intelligence - Application of artificial intelligence and machine learning to large research imaging datasets to improve the clinical tools already available to identify those at risk, and to identify next generation imaging and management approaches.
   
• Novel markers of early disease - Using imaging and laboratory studies to identify early cardiac and vascular changes in young people at risk of cardiovascular disease.
   
• Young adult cardiovascular prevention trials - Running trials to understand how novel approaches to lifestyle and clinical management may be able to modify these early risk cardiovascular phenotypes to prevent the development of later disease. 

We are interested in understanding how cellular identity is propagated as cells divide. Using advanced flow cytometry combined with mass spectrometry1,2, we have been able to isolate and purify individual mitotic chromosomes from different species (human, mouse and fly) and comprehensively profile the proteins and RNAs that remain chromosome-associated during mitosis. We have also prospectively isolated the active and inactive X metaphase chromosomes from female cells and identified novel factors likely to be important for maintaining their distinctive states3. Although many proteins are evicted from condensing chromosomes, our studies have shown that approximately 10% remain chromosome-associated throughout mitosis1,4. These include specific DNA-binding factors, chromatin repressor complexes, DNA methylation machinery and SMC family proteins. To assess their importance for chromosome structure and epigenetic inheritance, we are systematically degrading or cleaving individual factors in metaphase and examining the biophysical, structural and molecular consequences of this using optical tweezers and Cryo-ET approaches as well as analysing nascent RNA expression in postmitotic cells. These studies bring together expertise in several fields to decipher, at a mechanistic level, how epigenetic memory is propagated. In addition, as the approaches we are developing enable native (unfixed) human chromosomes to be individually isolated and studied ex vivo, we will investigate how disease-associated chromosomal translocations impact mitotic bookmarking and genome stability.

References

1. Djeghloul et al., 2020 Nat Commun. 11, 4118. 
2. Djeghloul et al., 2023 Nat Struct Mol Biol 30, 489.
3. Djeghloul et al.,Research Square https://doi.org/10.21203/rs.3.rs-4687808/v1
4. Dimond et al.,bioRxiv https://doi.org/10.1101/2024.04.23.590758