- Total grants
- Total funders
- Total recipients
- Earliest award date
- 30 Jan 2018
- Latest award date
- 30 Sep 2018
- Total GBP grants
- Total GBP awarded
- Largest GBP award
- Smallest GBP award
- Total Non-GBP grants
Delivering Care Through AI Systems 08 May 2018
For this project, I aim to examine 4 issues. First, I will consider whether introducing machine learning (ML) systems requires a revision of the ‘standard of care’ for clinicians, by understanding the moral permissibility of using second-hand information (from ‘black box’ systems) and whether practitioners’ medical expertise justifies judgments about such systems. Second, given the possibility of ML systems systematically underserving groups that are underrepresented in the training data, I will consider accounts of distributive justice to operationalize ‘equal access to care’. Third, to address the disagreements between clinicians on how to trade-off risks in clinical choices, I will catalogue the factual, rational, and moral sources of this disagreement to yield a principled method of evaluating these trade-offs. Finally, I will weigh the potential harms and gains from deploying AI systems in healthcare so that certain ethical and legal arguments don’t deprive society of the good such systems can provide. Key goals: To represent the ethical concerns in deploying AI systems over the appropriate standard of care, ensuring equal access to care, and representing reasoning about risk trade-offs. To balance these concerns against the benefits of such a deployment. To deliver practical ethical guidance to healthcare policy-makers and AI system-builders.
There is an urgent need to develop new antibiotics against multidrug resistant Gram-negative bacteria such as Pseudomonas aeruginosa and Klebsiella pneumoniae. These organisms are major causes of pneumonia and sepsis, with recent reports identifying hospital isolates of each resistant to all known antibiotics. The present research focuses on the mode of action of a family of antibiotic proteins known as nuclease bacteriocins that have not been developed as antimicrobials, but show promise in animal models of infection. Nuclease bacteriocins are species-specific toxins that are used by bacteria to compete with their neighbours. Although folded proteins these molecules are capable of penetrating the defences of Gram-negative bacteria to deliver an enzyme to the organism’s cytoplasm to degrade essential nucleic acids by an unknown mechanism. Two types of nuclease bacteriocin will be investigated, pyocin AP41 which targets Pseudomonas aeruginosa, and klebicin G which targets Klebsiella pneumoniae. Preliminary computational and experimental work on pyocin AP41 has identified potential candidate proteins involved in its import. This will be followed up with structure and function studies of AP41, a dissection of its import mechanism and new studies on klebicin G, a nuclease bacteriocin that has only recently been identified.
The team at MORU will focus on closing the gap between research and implementing interventions, building capacity and expertise to support early translation to improve the health of people in low-income countries.
A long research tradition in cognitive neuroscience has investigated the role of the prefrontal cortex (PFC) in flexible goal-directed behaviour. This work has yielded important insights, e.g., that the PFC’s coding properties flexibly adapt when task demands change to prioritise goal-relevant information. However, it relies primarily upon the use of familiar and explicitly instructed tasks that provide poor fit to human behaviour in naturalistic and open-ended environments, and likely miss essential aspects PFC function. I therefore propose to study the representations that form within PFC as humans learn to perform complex categorisation tasks with only minimal external instruction. I hypothesise that during learning the PFC will form abstract representations of the task’s latent structure, that support not only performance in the given task, but also provide provides a scaffold for learning in similar environments. Using representational similarity analyses, in combination with multiple brain recording techniques (fMRI, MEG, ECoG), I will measure how stimulus-evoked neural responses change adaptively during learning, and test if these changes generalise to novel task environments with similar structure. These findings will provide important new insights about the coding mechanisms by which PFC supports flexible behaviour in complex, naturalistic environments.
Evolutionary, pain is a protective sensation. However, it can persist beyond its usefulness and become debilitating for patients. Chronic pain affects up to one half of the population in the UK (Fayaz et al, 2016). Currently, the treatment options are limited and discovering new drug targets is of great importance. In a recent genetic study (genome-wide association study), we identified a gene (SLC8A3 encoding the protein NCX3), which was associated with higher pain sensitivity to experimental pain stimuli in healthy participants. My thesis will therefore focus on studying the function of NCX3 on a molecular, cellular and systems level. NCX3 is an important part of the machinery that moves ions in and out of cells. Its role in pain is poorly understood, but previous reports show that it is involved in regulating Ca2+ levels in pain-sensing neurones. Inhibition of NCX3 can cause increased Ca2+ in these cells leading to higher activation of the central nervous system and increasing pain sensation. To investigate the function of NCX3, I will use genetically modified mice lacking the gene as well as isolated pain-sensing neurones. Our genetic data, combined with published results, makes NCX3 an attractive target for future research and drug discovery.
Signal transduction of the GPCR Smoothened: a key protein in Hedgehog-regulated morphogenesis and oncogenesis 30 Sep 2018
In complex multicellular organisms, cell-to-cell communication is often managed by morphogen gradients. The secreted Hedgehog ligands fall within this class, as they act in this manner during embryonic development. The Hedgehog signalling pathway (stimulated by these morphogens) tightly regulates crucial developmental processes including body patterning and symmetry. Serious developmental disorders result from inactivation of this pathway during embryogenesis, including holoprosencephaly and cyclopia. Hedgehog signalling is also active through stem cell programs throughout adult life, and aberrant Hedgehog activation, either ligand dependent or mutations in pathway components, can lead to cancer including medulloblastoma and basal cell carcinoma. The G-protein coupled receptor (GPCR) Smoothened is a key protein of this pathway, as it initiates the intracellular cascade, and is already targeted by anti-cancer drugs including Vismodegib and Sonidegib. However, the mechanism of signal transduction has only been poorly characterised. This project aims to explore this using both structural and biophysical approaches. We will study the mechanism and interplay of the two identified ligand-binding sites and the dynamics of agonist association with Smoothened. The ultimate goal is to determine the structure of active-state Smoothened and hence describe the mechanism by which its signal is transmitted across the plasma membrane.
Large-scale data integration to advance mechanistic inference and precision medicine in type 2 diabetes 17 Jul 2018
Advances in understanding the genetic and genomic basis of complex diseases have had limited impact on the delivery of translational goals, including those concerning personalised management. Recently, we have shown that, by integrating information on quantitative trait associations and tissue-specific regulatory annotation, genetic variants influencing type 2 diabetes (T2D) predisposition can be characterised in terms of the pathophysiological processes through which they operate. The central hypothesis of this proposal is that this allows a deconstruction of T2D pathophysiology that addresses phenotypic and clinical heterogeneity, promotes mechanistic insights, and reveals novel translational opportunities. The approach begins with generation of "process-based" genetic risk scores that better capture patterns of individual T2D-predisposition and phenotype. I will refine these risk scores, more precisely characterise the cellular, molecular and physiological events they reflect, and describe their relationships to clinical outcomes. For multifactorial diseases, there are limits to the clinical prediction achievable through genetics alone: I will combine genetic risk scores with measures of individual external and internal environment, and with clinical and biomarker data, to generate "integrated risk profiles".This approach aims to advance understanding of the pathophysiological basis of T2D and deliver precise, personalised information for key clinical outcomes including complication risk and therapeutic response.
Vivax malaria remains a major global health problem. Because of the existence of a hypnozoite stage and the clinical relapses this causes, elimination strategies are more difficult to design and implement successfully than for falciparum malaria. Vaccines and new well-tolerated anti-relapse drugs are badly needed. To accelerate vaccine development, we will develop and assess the feasibility of conducting Plasmodium vivax volunteer infection studies in Thailand, recruiting semi-immune volunteers from endemic areas representative of target populations for vaccine deployment. We will draw on the participating institutions' expertise in clinical malaria, immunology, entomology, parasitology, volunteer infection studies, and vaccine development. We plan to develop vivax controlled human vivax malaria infection models able to test protective efficacy of the pre-erythocytic and blood stage vivax malaria vaccines currently in development. During this programme we plan to conduct six volunteer infection studies, determine immunological correlates of protection, and test four vaccine candidates. The programme will lay the groundwork for developing models to test future transmission blocking vaccines and new anti-relapse drugs for radical cure. The volunteer infection studies will be accompanied by a programme of social science and empirical ethics research to assess their acceptability and the understanding of volunteers, patients, researchers and policy-makers.
Learning the Signatures of Cancer 30 Sep 2018
Cancer is a genetic disease that is the second leading cause of death worldwide. Developing effective personalised therapies requires characterisation of the genetic factors driving malignancy. This is challenging as cancer is highly complex, heterogeneous, and dependent on cellular context. Cancer stratification aims to group cancers that share similar features, and are therefore likely to respond similarly to treatment, however, current stratification methods ignore many important genetic and epigenetic markers that likely influence cancer pathology, which would result in sub-optimal treatment. We propose to use whole genome-and-epigenome profiling and machine learning to extract clinically meaningful features of the host and cancer genomes that can be used to improve patient stratification and reveal novel cancer subtypes. As a proof of principle, we will apply these methods to predict the site of origin in patients with metastatic cancer but unknown primary (CUP), which could help improve diagnosis and prognosis for patients with this complex disease. We envision the robust stratification of cancer patients using genome profiling could lead to direct prediction of optimal treatment decision for all cancer patients.
In the nucleus of every cell DNA is present as pairs of parentally-inherited chromosomes, from which genes are expressed to perform biological functions. In most mammals, including humans and mice, females tend to have two X chromosomes whereas males have one X and a Y chromosome, which lacks most of the genes present on the X. Thus in order to ensure that the dosage of gene expression from essential X-linked genes is similar between both sexes, almost all genes on one female X chromosome are silenced during development. X inactivation is mediated by a long non-coding RNA, Xist, which spreads to coat the chromosome and coordinates silencing through the recruitment of relatively few factors implicated in specific chromatin remodelling pathways. Beyond its intrinsic biological significance in mammalian development, it is a tractable model system for investigating general molecular mechanisms by which chromosomes are silenced. My reseach will focus on the question of how transcription factors that normally bind enhancers and promoters to activate genes are prevented from performing their functions as the X chromosome is silenced. I will investigate this question in cellular and in vivo models of X inactivation, including in mutant cell lines defective for chromosome silencing.
Monocarboxylate Transporter 4: A Potential Therapeutic Target in Refractory Rheumatoid Arthritis 31 May 2018
Rheumatoid Arthritis (RA) occurs as a complex set of interactions between adaptive and innate immune cells and stromal fibroblasts. Using mouse models of RA we have previously shown that tissue fibroblasts can both promote inflammation and tissue repair. Using a combination of immunohistochemistry, multi-parameter cytometry and single cell RNAseq of both primary human biopsy material and mouse RA models we have identified three different populations of fibroblasts, inflammation lining fibroblasts, resolving sub-lining fibroblasts and pericytes. From analysis of gene expression signatures on these different tissue fibroblast populations we have identified difference in metabolic function. One of key proteins we have identified that is up regulated in inflammatory joint fibroblasts is the monocarboxylate transporter 4 (mct4) which has a key role in the export of lactate resulting from glycolysis. This receptor has been shown to a have a key role in cancer metabolism and disease progression which has lead to the development and clinical trial of high affinity inhibitors. The aim of this project will be to further characterise mct4 expression and function in rheumatoid arthritis fibroblasts using gene expression, immunohistochemistry and functional assays to determine if a mct4 inhibitor could potentially make a novel treatment for treatment refractory rheumatoid arthritis.
The neural network mechanisms of inferential reasoning The ability to make inferences, as defined by conclusions drawn from given evidence (Peirce, 1868), is a hallmark of higher cognitive function (Vasconcelos, 2008) that relies on internal models of past knowledge, including that of experienced environments (Markovits and Vachon, 1990; Piaget, 1987). The neural representation of such mnemonic models is thought to be shaped by life experience (Barlett, 1929, Lee, 2009) but the neural circuit-level mechanisms supporting the neural representation-to-behaviour translation of inferences remain to be identified. The goals of my project are: To investigate the neural circuit mechanisms underlying the ability to make an inference based on prior knowledge with large-scale neural recording techniques in the mouse brain. To test whether dopamine promotes neural mechanisms underlying inferential reasoning using state-of-the art neural manipulation methods.
Lassa fever has been identified as a priority disease by the WHO R & D Blueprint for Action to Prevent Epidemics. There is a need for improved diagnostics, therapeutics and vaccines for Lassa fever. In response to the Lassa fever outbreak in Nigeria, the Africa coalition for Epidemic Research, Response and Training (ALERRT) has activated its research response mode. ALERRT Partner Organisations are already operational and working on the Lassa fever outbreak with Nigerian colleagues and plan to establish a prospective clincail cohort with teh objectives of: Standardising the characterisation of the clinical presentation, natural history, management and outcomes of patients hospitalised with Lassa virus infection in Nigeria; Identifying suitable clinical or surrogate endpoints for clinical trials of Lassa fever therapeutics; Generating biological data and resources to support the development of new Lassa fever diagnostics, therapeutics and vaccines e.g. viral load and antibody kinetics; Developing healthcare sites for potential future clinical trials in Lassa fever patients, through training in clinical research skills, mentoring, and equipment.
Biomedical sciences increasingly recognise the importance of mechanobiology in health and disease. While most mechanisms of the immune response are adequately explained by cell-biology, biochemistry, and genetics, many of its features profoundly depend on biomechanical aspects. One such scenario involves the ability of immune cells to differently respond to antigens with similar binding affinities, highlighting additional parameters needed to fully explain antigen discrimination. Emerging evidence indicates that immune cells dynamically adjust their biomechanics to facilitate this process. The principle goal of this project is to uncover how biomechanical feedback modifies the mechanobiology of activating T-lymphocytes by altering the dynamic assembly and organisation of actin structures, hence adjusting the sensitivity of antigen recognition. With the advent of immune checkpoint blockade and T-cell re-direction there has never been more interest in controlling lymphocyte responses, and biomechanical signal integration has received relatively little attention despite the consistent failure of biochemical parameters to account for T-cell discrimination of different antigens. To address this research project, I will lead a team to apply new state-of-the-art force probing technology coupled with high-speed super-resolution microscopies, overcoming the limitations of previous approaches to generate a breakthrough understanding of mechanobiology in immune cell activation.
Complete and error-free replication of the genome is necessary for the normal development and health of all organisms. However, the sheer scale of the genome necessitates precise regulation of replication: in a human cell, 3 billion bases are replicated in about 8 hours, with replication initiating from between 12,000 and 250,000 origins spread across 23 chromosome pairs (1). Interestingly, not all of these origins fire in any given S phase and of those that do fire, not all fire at the same time. In fact, the replication timing profiles of a range of model organisms have been shown to follow precise temporal orders specific to cell type and differentiation status (2). Deviations from this timing regimen can manifest in a range of consequences including chromosomal aberrations and increased mutational load (3). While a range of factors correlating to the observed replication timing profiles have been found, the composite set of known factors still does not fully recapitulate the observed nuances of replication timing. I hope to study how spatial organization of the genome impacts replication timing, with the goal of better understanding how replication proceeds to be better able to understand how it goes awry in disease such as cancer.
Ebola East DRC August 2018 30 Sep 2018
The goal of this submission is to provide immediate support to the Institut Nationale De Recherche Biomedicale (INRB) for the implementation of Monitored Emergency Use of MAb114 during the ongoing Ebola Virus Disease (EVD) outbreak in Eastern DRC, and to strengthen capacity to implement a formal clinical trial of candidate therapeutics, as identified as a priority in the national EVD research plan. Support is requested for INRB administration, logistics, consumables and personal costs, and for training and capacity development activities.
A recent large-scale study of 39 different types of cancers has shown that the expression of the gene KLRB1, encoding the protein CD161, was the top predictor of favourable prognosis and overall survival. CD161 is expressed on various immune cells, including newly discovered populations such as MAIT cells and innate lymphoid cells. To identify the intratumoural cells contributing to improved prognosis, I will develop a tool for the bioinformatic enumeration of pure cell types from complex populations. As chromatin accessibility and enhancer landscape reflects cell identify better than mRNA levels, differentially accessible loci will be a unique signature for each cell type. Using state-of-the-art technologies, my key goals are to: Generate a comprehensive epigenetic signature matrix encompassing novel immune cell types Use this to deconvolute the epigenome of colorectal carcinoma tumours Directly identify the infiltrating CD161-expressing population correlated with improved survival in colorectal cancer By improving our understanding of how multiple immune populations collectively infiltrate tumours, including novel CD161-expressing cells, this work will enable future cancer immunotherapies to target protective immune subsets. Furthermore, the proposed work will provide a resource that can be easily applied to epigenetic data from other malignancies.