Research Opportunities

 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.

Asthma is a common, complex, and chronic disease that is characterized by inflammation of the airways, airway hyperresponsiveness, and bronchospasms. It has major health disparities, and unfortunately populations that bear the greatest burden of disease are minimally represented in genomics research. The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) seeks to discover genes and mechanisms conferring risk to asthma in populations of African ancestry, utilizing multi-omic data. A multi-omics approach in nasal epithelium using RNASeq and DNA methylation in CAAPA led to the confirmation of well-known T2 mechanisms in asthma risk, but also identified novel wound healing and medication response signatures, providing new information about the biological mechanisms underlying asthma in the underrepresented African ancestry populations. We have a greatly expanded opportunity including serum proteomics, RNASeq on PBMCs, and additional DNA methylation to test if an expanded systems biology / integrative omics approach can further refine axes of dysregulation in CAAPA and develop models to predict asthma endotypes that are derived off ‘local’ and ‘systemic’ signatures of asthma pertaining to the nasal epithelium and serum/PBMCs, respectively. 

Asthma is a common, complex, and chronic disease that is characterized by inflammation of the airways, airway hyperresponsiveness, and bronchospasms. It has major health disparities, and unfortunately populations that bear the greatest burden of disease are minimally represented in genomics research. The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) seeks to discover genes and mechanisms conferring risk to asthma in populations of African ancestry, utilizing multi-omic data. A multi-omics approach in nasal epithelium using RNASeq and DNA methylation in CAAPA led to the confirmation of well-known T2 mechanisms in asthma risk, but also identified novel wound healing and medication response signatures, providing new information about the biological mechanisms underlying asthma in the underrepresented African ancestry populations. We have a greatly expanded opportunity including serum proteomics, RNASeq on PBMCs, and additional DNA methylation to test if an expanded systems biology / integrative omics approach can further refine axes of dysregulation in CAAPA and develop models to predict asthma endotypes that are derived off ‘local’ and ‘systemic’ signatures of asthma pertaining to the nasal epithelium and serum/PBMCs, respectively. 

Asthma is a common, complex, and chronic disease that is characterized by inflammation of the airways, airway hyperresponsiveness, and bronchospasms. It has major health disparities, and unfortunately populations that bear the greatest burden of disease are minimally represented in genomics research. The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) seeks to discover genes and mechanisms conferring risk to asthma in populations of African ancestry, utilizing multi-omic data. A multi-omics approach in nasal epithelium using RNASeq and DNA methylation in CAAPA led to the confirmation of well-known T2 mechanisms in asthma risk, but also identified novel wound healing and medication response signatures, providing new information about the biological mechanisms underlying asthma in the underrepresented African ancestry populations. We have a greatly expanded opportunity including serum proteomics, RNASeq on PBMCs, and additional DNA methylation to test if an expanded systems biology / integrative omics approach can further refine axes of dysregulation in CAAPA and develop models to predict asthma endotypes that are derived off ‘local’ and ‘systemic’ signatures of asthma pertaining to the nasal epithelium and serum/PBMCs, respectively. 

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

The transmission of viruses between species faces significant barriers due to differences in host immune systems. A virus adapted to an animal host might not be well-equipped to evade the human immune system. However, mutations and other viral adaptations can occasionally overcome these barriers, leading to zoonotic infections. This concept is exemplified by the ongoing avian influenza pandemic which is now spread from birds to mammals, including livestock cow herds. Understanding and strengthening antiviral immunity is therefore crucial in preventing and controlling zoonotic diseases and for improving human and livestock health by, for example, driving next-generation vaccine development.   The molecular and cellular mechanisms by which human cells sense and respond to infection are well characterised and known to be essential for host defence against viruses. Despite their importance as sources of food, their economic importance, and as sources of zoonotic pathogens, for the majority of livestock species these innate immune systems are relatively poorly defined.  

In this project we the student will define the functions of PRRs that sense viral nucleic acids across multiple species, including sheep, cows, chickens, and ducks and compare them to humans. The project will employ loss of function assays, using CRISPR/Cas9, signalling and targeted perturb-seq experiments to understand the functions of these receptors. The project will also include a range of virus infection models, for example influenza viruses and poxviruses, to define how PRRs from these key livestock species impacts antiviral responses in the context of zoonotic infections.

We have established an approach to accelerate vaccine development, through our Plug-and-Protect platform. A limiting factor in vaccine generation is the difficulty of turning a promising target protein into the kind of assembly that would give long-lasting disease protection. We demonstrated potent immunization towards the global health challenge of malaria. This approach is now being used by many groups against cancer and various infectious diseases, e.g. HIV, influenza, coronaviruses and other outbreak pathogens. This project will involve creating new protein antigen and nanoparticle designs to achieve the most effective and broadly protective immune responses. By inducing potent mucosal immunity, the project will contribute to developing a new generation of vaccine systems, towards protection against the most challenging diseases.

The aberrant modification status of proteins is universally recognized as a crucial component of disease. In order to develop therapeutic agents to combat disease, we need to understand the role that posttranslational modifications (PTMs) play within pathological systems. Focusing on infectious diseases using mutant cell lines, mouse models and patient data, we will study the link between PTM status and subcellular location which has been so far poorly captured in the majority of experimental workflows. The knowledge of the PTM affecting relocalization of the protein and, in turn, its function, will be pivotal to the correct drug design. This project combines development of state of the art quantitative proteomics methodologies, computational workflows and whole cell modelling which will be used to decipher the mechanism of immunity to infection and propose new ways of treatment. 

Genetics only explain a small proportion of phenotypic variance, with common diseases typically having 10%-30% heritability (Loh et al. 2017 Nature Genetics). This project aims to explain the remaining 70%-90% of variance using molecular data. Past efforts have attributed genetic variance to expression data (Yao et al. 2020 Nature Genetics) and different tissues (Amariuta et al. 2023 Nature Genetics); yet limited attention is paid to the non-genetic variance.  We aim to develop methods to provide an unbiased estimate of the environment variance in complex traits that are mediated through molecular traits. Specifically, we are interested in the proportion of non-genetic variance that are mediated by gene expression, protein level, and metabolomics. We will utilize large-scale proteomic and metabolomic data that are linked to electronic health records to validate the model and provide the molecular explanation for common complexity traits.

Mitochondrial diseases are caused by defects in genes required for energy production and oxidative phosphorylation (OxPhos). We find it intriguing that some patients with mitochondrial disease present late in life, with very tissue-specific phenotypes. It seems that not all cells and tissues are equally susceptible to mitochondrial disease.  We mainly study how mitochondrial dysfunction and mutations in the mitochondrial genome affect neural stem cell behaviour in Drosophila and mouse. 

The questions we address are:  
(1)    how mitochondrial dysfunction affects normal and pathological cell fate decisions in the developing brain. We previously showed that neural stem cells in the brain rely heavily on mitochondrial energy production and now study how they interact with the glial cells that make up their stem cell niche. 
(2)    how transcription of the nuclear genome is regulated when a cell is confronted with mitochondrial dysfunction. We employ and develop innovative DamID-seq based in vivo chromatin profiling technology to study metabolism of chromatin modification.  
(3)    how mutations in the mitochondrial genome evolve over time, during brain development and aging. We use single-cell forward genetic CRISPR screening to identify novel regulators of mitochondrial genome maintenance.    

In order to study these questions in an in vivo context, in (stem) cells surrounded by their appropriate tissue environment, our primary model system is the fruit fly, Drosophila melanogaster. In addition, we actively translate our findings and the technology we develop into mammalian model systems, in particular the mouse embryonic cortex.   

Key References: 
Viscomi C, van den Ameele J, Meyer KC, Chinnery PF. Opportunities for mitochondrial disease gene therapy. Nat Rev Drug Discov. 2023 Jun;22(6):429-430. 

van den Ameele J, Krautz R, Cheetham SW, et al., Reduced chromatin accessibility correlates with resistance to Notch activation. Nat Commun. 2022;13(1):2210. 

van den Ameele J, Li AYZ, Ma H, Chinnery PF. Mitochondrial heteroplasmy beyond the oocyte bottleneck. Semin Cell Dev Biol. 2020 Jan. 97:156-66.  

van den Ameele J, Brand AH. Neural stem cell temporal patterning and brain tumour growth rely on oxidative phosphorylation. eLife. 2019;8:e47887.