Despite the recent developments in breast cancer treatments, the high variability of cancer cells and their related drug resistance still pose a huge obstacle to improving clinical outcomes. It is now well-known that cancer cells must reprogram their cellular metabolism (chemical processes that occur within the cell to maintain life) to support rapid proliferation and promote acquired drug resistance. However, the underlying mechanisms regulating such biological changes are neither fully understood nor sufficiently treated. Only recently, with the advent of single cell analysis (a novel technique that allows the analysis of individual cancer cells), it has been possible to analyse changes at the cellular level that have helped in identifying four main breast cancer subtypes (i.e., Luminal A, Luminal B, TNB, and HER2 positive) and developing different treatment routes. However, patient survival remains low - especially for the most aggressive breast cancer subtypes - since cellular changes cannot be easily connected to an alteration in the metabolic state that promotes drug resistance. Moreover, the lack of specific tools to analyse a vast quantity of single cell metabolic profiles makes single-cell analysis at the metabolic level still impractical. This proposal aims at initiating an international collaboration between Teesside University (UK) and Cornell University (US) to characterise the metabolic profile of 32 different breast cancer cell types (i.e., cell lines) from the four main breast cancer subtypes to identify metabolic dysregulations and allow informed treatment decisions. Advanced computation techniques (i.e., artificial intelligence) will be applied to identify the metabolic reactions and changes responsible for cancer progression in each cancer subtype. The final objective of the proposed collaboration will be to elucidate the main differences among breast cancer subtypes at the metabolic level to inform the development of targeted drugs and support clinical decisions. First, mathematical techniques will be applied to develop metabolic models (through a set of mathematical equations) of 32 different breast cancer cell lines. These 32 models will mathematically describe the metabolic reactions taking place inside the different cancer cells. This will be achieved by integrating the expertise in metabolic modelling of the PI (Dr Occhipinti) with the knowledge of single cell analysis of the International Partner (Dr Betel). Second, advanced computational techniques will be applied to identify the key features affecting the proliferation of each of the 32 cancer cell types. Such features will include a set of biological elements (i.e., information related to cancer metabolism) specific to each cell type, which can be used to predict cell-specific drug resistance or inform clinical decisions. Finally, the selected key features (e.g. the metabolic reactions that are contributing the most to the growth of each cancer cell type) will be validated through computational and lab experiments and shared with breast cancer clinicians and experts through regular meetings and discussions that will be arranged during the project. Specifically, the academic team will coordinate a wide range of activities, including regular meetings with breast cancer experts from the NHS, designed to provide feedback on the developed computational model through knowledge and skills exchange while promoting connectivity across different sectors both in the medical and computational areas. The proposed project brings together academics from two centres of excellence in the healthcare sector (i.e., Weill Cornell Medicine at Cornell University and the National Horizon Centre at Teesside University), who have a strong track record in working with cell analysis, metabolic modelling, and artificial intelligence to better understand the metabolic mechanisms of cancer development and improve cancer outcomes.

Four decades after its identification, the human immunodeficiency virus (HIV) continues to infect almost 40 million people worldwide, causing hundreds of thousands of deaths each year. Infection by HIV mainly targets white blood cells and, if left untreated, can lead to a severe, potentially fatal, acquired immunodeficiency syndrome (AIDS). Antiviral drugs have been developed and have drastically improved the life expectancy and quality of people living with HIV. However, these drugs require lifelong administration, and do not lead to a cure. Importantly, HIV persists in a dormant (latent) form in a subset of white blood cells (called CD4 T-lymphocytes) for the entire lifespan of infected individuals. This latency allows the virus to elude antiviral drugs and the immune system. Several studies have been conducted with the aim to identify features of persistently infected cells that could allow us to specifically target these cells for elimination and, thus, potentially cure the infection. Although multiple features have been proposed, few have the specificity required for safe therapeutic application, and most of the studies have failed to decrease the frequency of persistently infected cells. No current treatment to target persistently HIV-infected cells is approved. One of the therapeutic approaches undergoing clinical testing is based on our research showing that persistently infected cells have specific alterations in energy metabolism. Our studies, as well as those of other groups, however, focussed on whole cells. In reality, most cellular energy production is located within highly specialised subcellular compartments. To allow us to identify functional, structural, and/or spatial features of metabolic regulation that distinguish cells harbouring the virus from those that do not, the proposed project will produce an in-depth characterization (identikit) of persistently HIV-infected cells. We will approach this work both using CD4 T-lymphocytes infected with HIV in vitro and using CD4 T-lymphocytes isolated from the blood of individuals living with HIV. We will first separate cells infected in vitro based on the presence and stage of infection, and then individually study the main cellular sites of energy production and storage (i.e., nucleus/cytoplasm and mitochondria). The data obtained will comprehensively capture the content of proteins and metabolites, as well as the genetic regulation underpinning their production. These data will be computationally combined and complemented by experimental assessments of the functionality of each major cellular metabolic step in order to identify distinguishing features of persistently infected cells that can be explored for their potential to serve as therapeutic targets. We will then use computer models to predict effective drug candidates and put these predictions in practice by testing drugs in the laboratory for their target affinity and ability to selectively eliminate persistently infected cells. Overall, this study will aim to provide a unified profile of metabolic determinants of HIV persistence and furnish pre-clinical evidence for novel therapeutic strategies to eliminate infected cells resistant to currently available antiviral drugs. Ultimately, this work could lead to a therapeutic approach to remove the virus from its cellular hideout in infected individuals.

The human microbiome is a term used to describe the bacteria and other microorganisms that live on, and in, the human body. These microorganisms coexist with us. They are with us from birth and they play an important role in shaping the development of our immune system. Much of the interaction between the microbiome and the immune system occurs at specialised barrier surfaces, such as those found in the gut and lung. These surfaces are adapted to protect the body from invasion. They also enable human immune cells to interact with both good and potentially harmful microbes and the substances (metabolites) they produce. Genetic diseases (diseases caused by errors in human DNA sequence) may sometimes result in disruption of normal function at barrier surfaces. This is true of diseases such as cystic fibrosis (CF) and inflammatory bowel disease (IBD), as well as a number of rare genetic conditions. Under these circumstances, the breakdown of normal interactions between the body (in particular the immune system) and its microbiome may contribute significantly to disease development. Understanding the contribution of the human microbiome to genetic diseases involving disruption to barrier surfaces is therefore important. It may lead to a better understanding of how these diseases develop and opportunities for new drug development. It may also lead to opportunities to manipulate the microbiome itself as a novel form of treatment. A major challenge that limits our ability to understand the role of the microbiome in disease development is its complexity. Trillions of microbes inhabit a single human body and microbiomes can vary greatly between individuals. Mouse models are therefore an essential tool in microbiome research because they allow for the microbiome to be tightly controlled or even removed entirely (so called germ-free mice) so that its impact on disease can be studied and understood. As part of the MRC Mouse Genetics Network, we will bring together a range of clinical, immunological, and microbiome expertise from across the UK to form a cluster that addresses the role of the microbiome in genetic diseases involving barrier surface malfunction. Our 'Microbiome and Barrier Function' cluster will achieve two complementary goals: First, it will develop an experimental pipeline for creating and studying mouse models of human genetic diseases involving barrier surfaces, with a focus on understanding the impact of the microbiome in these diseases. Second, it will establish a national infrastructure for cutting-edge mouse microbiome research that will be accessible to all UK researchers. Key deliverables for Aim 1 include studying three different mouse models of human genetic diseases involving barrier surface disruption in the gut and lung. We will apply state-of-the-art microbiome research techniques (such as generating germ-free mice and generating synthetic microbiome communities) to each model along with in-depth immunological analysis. In combination, these approaches will help us to identify precisely how the microbiome contributes to disease development and identify new treatment opportunities. To better understand the relevance of these results to human disease, we will simultaneously apply computational approaches to better characterize the mouse microbiome and compare its functional potential to human microbiomes in relevant disease groups. Key deliverables for Aim 2 include working with the Mary Lyon Centre to establish new standards and best practices in mouse microbiome research. In addition, we will provide training to other UK researchers in the computational and experimental techniques developed by our cluster. Finally, we will expand our experimental pipeline to other related genetic disease models involving barrier surface malfunction, as well as other models of diseases where the microbiome is thought to play a key role (e.g. colorectal cancer).