This proposal aims to provide insight into persister resuscitation. Persisters are multidrug-tolerant cells that are transiently non-growing and able to generate viable offspring by resuming growth when antibiotic pressure is removed. Despite their implication in relapses of many infectious diseases, and progress in understanding how persisters form through the action of toxin-antitoxin modules, the mechanisms underlying resuscitation of these persisters are still unknown. The interaction between Salmonella and host macrophages has proven to be a powerful and relevant model to study persister biology since the bacteria specifically respond to engulfment by the host defence cells by forming high proportions of persisters. Upon encounter with the host, Salmonella activates 14 toxins to arrest growth and persist in this environment. With the recent discovery of a detoxifying enzyme (Pth) counteracting the action of a persister-inducing toxin (TacT), thus allowing growth resumption of Salmonella persisters, we can now begin to address the pending question of persister regrowth. The consolidation of my research group around the experimental plan proposed here, will enable us to dissect the balance between intoxication vs. detoxification or entry vs. exit from persistence induced by Tact and Pth respectively. This is the first couple of toxin/detoxifying enzymes to be identified. I intend to extend this knowledge to the whole repertoire of toxins involved in Salmonella persister formation through systematic identification and characterization of the detoxifying mechanisms allowing resuscitation. Additionally I will investigate the lag phase leading to regrowth of persisters; and target the toxins involved in persister formation to force persisters out of growth arrest. This work will unravel persister resuscitation and could ultimately provide ways to force persisters out of that state so they become re-sensitized to antibiotics.

Gene regulation is fundamental to biological diversity, driving differences between cell types within an organism and contributing to variation between species. DNA regions known as enhancers play a critical role in this process. Enhancers can be located millions of base pairs away from the genes they regulate, interacting with the promoters of these genes through 3D contacts within the nucleus. How enhancers find their target genes remains largely elusive. Evolution offers a unique “natural perturbation experiment” to study the principles of enhancer-promoter communication. Enhancers, and especially the sequences spanning enhancer-promoter contacts, evolve faster than the gene expression patterns they control. This raises important questions about the genetic and epigenetic factors that sustain and drive the evolution of long-range gene regulation. To address these questions, my computational project will leverage an unpublished, high-resolution dataset on enhancer-promoter contacts from five mammalian species and three tissues, along with corresponding genomic sequences, enhancer annotations, and gene expression data. My analyses will: (1) map how enhancer-promoter contacts co-evolve with genomic sequences, chromatin states, and gene expression, (2) establish how large-scale genomic rearrangements disrupt or constrain these contacts, and (3) develop and train deep learning models to predict DNA sequences underpinning the evolution of enhancer-promoter contacts. By integrating functional genomics, evolutionary biology, and machine learning, this project will uncover the mechanisms behind the evolution of long-range gene regulation, with broad implications for understanding non-coding genetic variation in evolution, disease, and synthetic biology. The fellowship will enhance my expertise in comparative genomics, 3D chromatin biology, and deep learning, while fostering new collaborations and bringing evolutionary insights to the host lab.

When bionic devices such as cochlear implants, bionic eyes and brain-machine interfaces are implanted into the body they induce an inflammatory response that is difficult to control. Metals used historically for these types of devices are both stiff and inorganic, which makes them recognisable as foreign to the soft and organic human nervous system. Consequently, these implants are tolerated by the body rather than integrated and the device is walled off in a scar tissue capsule. As a result high powered and unsafe currents are required to activate tissues and produce a therapeutic response. I have brought together concepts from tissue engineering for regenerative medicine and bionic device technologies to pioneer living bioelectronics – creating a functional neural cell component as part of the device to avert scar formation. My laboratory has established a range of novel conductive polymeric biomaterials which can be used to coat existing devices or fabricate new devices from conductive polymers, hydrogels, proteins and cells. Living Bionics is based on a world-wide unique combination of technologies and proposes to combine electronic devices with cell laden polymers to generate devices that can bridge the implant interface and improve tissue integration. Pioneering and ground breaking research within Living Bionics includes: • An engineered hydrogel that can support differentiation of stem cells into neural cell networks on devices • 3D patterning of living polymer electrode arrays that contain cells • Understanding of the combined effects of environmental, biological and electrical cues to guide cell fate and create connections to nerve tissues • In vivo proof of principle in the murine model Living Bionics will be a ground breaking step towards safer neural cell stimulation, which is more compatible with tissue survival and regeneration. This research will create a paradigm shift in biomedical electrode design with tremendous impact on healthcare worldwide.

The RAMBEA project will develop a novel computational strategy for accurate and efficient simulations of historical masonry bridges subject to extreme environmental actions, including loadings induced by earthquakes and flooding. The aim is to provide a comprehensive tool for realistic assessment with the potential of transforming current practice related to strengthening of critical assets, contributing to an increased resilience of the built environment and the preservation of important elements of the architectural heritage, thus responding to the safety and socio-economic needs highlighted in Horizon 2020. Old masonry bridges still play a critical role within the European transportation system. Moreover, they belong to the architectural heritage representing a valuable expression of past construction technology. Many of these structures are located in seismic regions and in areas subject to floods and hydrogeological instability which have been aggravated by climate change. Thus they can be exposed to extreme environmental actions which may potentially lead to bridge failure causing significant economic damage and the loss of structures with cultural and historical value. Currently, the response of masonry bridges under extreme loading is evaluated using simplified models due to the lack of efficient detailed models. However, these approaches do not allow for the complex 3D behaviour potentially leading to unrealistic and unsafe predictions. The main challenge of this project is the development of a more advanced strategy, based on a novel numerical description allowing for the 3D interaction between the different bridge components under extreme loading. More specifically, I will develop an efficient 3D finite element representation with macro-elements for the masonry parts of the bridge, an accurate description for the physical interface between masonry and backfill and an effective model calibration strategy utilising the results of non-destructive tests.

Machines capable of analysing and interpreting medical scans with super-human performance would transform healthcare as much as medical imaging itself did over the last century. With an increasing complexity and volume of data the interpretation of images and extraction of clinically useful information push human abilities to the limit. There is high risk that critical patterns of disease go undetected. We require powerful and trustworthy computational tools based on machine intelligence to support experts and go beyond human performance to tackle the major challenges in clinical practice. Two key ingredients are currently missing: 1) interpretable statistical representations that capture important information while reducing complexity; 2) intelligent algorithms that leverage knowledge across multiple tasks to solve the most challenging problems such as early detection of pathology. This project is devoted to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning. Key to the project is its unique access to some of the largest and most comprehensive imaging databases combined with world-leading expertise in machine learning and medical imaging. An overarching objective is to harvest information from population data to construct what will be the most advanced statistical models of anatomy. In contrast to previous attempts that focus primarily on specific organs or pathology, here shared representations are learned from highly complex data by jointly solving multiple tasks. Linking the representations with demographics, lifestyle, genetics and disease allows probing of genetic and environmental determinants related to specific anatomical and pathological phenotypes across organs. This will provide insights into complex diseases, and enables a novel approach to abnormality detection that aims to automatically find subtle signs of pathology in new medical scans.