Most bacterial pathogens are lysogens, namely carry DNA of active phages within their genome, referred to as prophages. While these prophages have the potential to turn under stress into infective viruses which kill their host bacterium in a matter of minutes, it is unclear how pathogens manage to survive this internal threat under the stresses imposed by their invasion into mammalian cells. In the proposed project, we will study the hypothesis that a complex bacteria-phage cooperative adaptation supports virulence during mammalian infection while preventing inadvertent killing by phages. Several years ago, we uncovered a novel pathogen-phage interaction, in which an infective prophage promotes the virulence of its host, the bacterial pathogen Listeria monocytogenes (Lm), via adaptive behaviour. More recently, we discovered that the prophage, though fully infective, is non-autonomous- completely dependent on regulatory factors derived from inactive prophage remnants that reside in the Lm chromosome. These findings lead us to propose that the intimate cross-regulatory interactions between all phage elements within the genome (infective and remnant), are crucial in promoting bacteria-phage patho-adaptive behaviours in the mammalian niche and thereby bacterial virulence. In the proposed project, we will investigate specific cross-regulatory and cooperative mechanisms of all the phage elements, study the domestication of phage remnant-derived regulatory factors, and examine the hypothesis that they collectively form an auxiliary phage-control system that tempers infective phages. Finally, we will examine the premise that the mammalian niche drives the evolution of temperate phages into patho-adaptive phages, and that phages that lack this adaptation may kill host pathogens during infection. This work is expected to provide novel insights into bacteria-phage coexistence in mammalian environments and to facilitate the development of innovative phage therapy strategies.

Bone regeneration is a critical challenge in the treatment of fractures, bone loss due to tumor resection, and alveolar bone deficiencies. Currently, approximately 2.2 million bone graft procedures are performed annually worldwide. Despite significant progress in bone tissue engineering, there is an unmet need for patient-specific long-lasting bone restoration to reproduce the unique physical, chemical, and biological properties of hierarchically structured bone in a personalized manner. While bones can often naturally self-heal, critical-size bone defects lead to a failed repair process. Expanding on the current understanding of bone regeneration, I will integrate the biomechanical and immunological triggers of the healing process into an artificial bio-mimicking scaffold to specifically target critical defects. Thus, I aim to develop a conceptually new approach of personalized layered 3D-printed supramolecular scaffolds. I intend to use a bottom-up multi-component co-assembly to produce tailored, layer-by-layer printed, extracellular-matrix-mimicking scaffolds that not only fit the defect shape, but also mimic the bone composition around the defect. For this purpose, I will significantly expand the repertoire of our proprietary peptide-based hydrogel technology by chemical modifications that allow interaction with bone minerals, slow release of growth factors, and activation of the immune system to trigger healing. I will combine computerized tomography scans and computer-assisted manufacturing to design personalized scaffolds that can be studied in an alveolar bone model, and be customized to accommodate bone type, structure, gender, age, and systemic diseases. PersonalBone aims to develop customized supramolecular scaffolds that will promote personalized therapy for bone regenerative medicine, thus significantly advancing the fields of tissue engineering and materials science while offering a novel solution to a major healthcare issue.

Campylobacter jejuni is the most common foodborne contamination in Europe, affecting millions of people, and costing billions of Euros. Current procedures to treat this contamination do not offer sufficient solutions. Here I present a unique approach to eradicate the pathogen from food by utilizing a cost-effective and safe product that does not alter the taste, texture, or appearance of the food. This innovation involves a spray composed of proprietary phage-based particles, which inject antibacterial genes into C. jejuni, thus killing the pathogen. Current phage-based technologies for decontaminating food encounter a major hurdle, because large-scale phage production in the fastidious and pathogenic C. jejuni strain is highly challenging. However, a major advantage of my product is that it can be prepared in a safe and easy-to-grow Escherichia coli host rather than in C. jejuni. Another significant advantage is that the technology producing the phages enables rapid and efficient modifications to the phage-based particles. This platform thus allows easy isolation and manufacture of cocktails of phage-based particles able to target a variety of pathogenic serotypes of C. jejuni. Furthermore, the proprietary particles all have a common scaffold, thus simplifying the regulation, safety, and route of manufacture. I propose a clear commercialization activity with a highly qualified team that I recruited, from both the scientific and commercialization fields. Developing and commercializing this product will provide a proof-of-concept to demonstrate the strength of this approach and will thus pave the way for additional innovative materials based on this technology.

Face recognition is a fascinating domain: no other domain seems to present as much value when analysing casual photos; it is one of the few domains in machine learning in which millions of classes are routinely learned; and the trade-off between subtle inter-identity variations and pronounced intra-identity variations forms a unique challenge. The advent of deep learning has brought machines to what is considered a human level of performance. However, there are many research questions that are left open. At the top most level, we ask two questions: what is unique about faces in comparison to other recognition tasks that also employ deep networks and how can we make the next leap in performance of automatic face recognition? We consider three domains of research. The first is the study of methods that promote effective transfer learning. This is crucial since all state of the art face recognition methods rely on transfer learning. The second domain is the study of the tradeoffs that govern the optimal utilization of the training data and how the properties of the training data affect the optimal network design. The third domain is the post transfer utilization of the learned deep networks, where given the representations of a pair of face images, we seek to compare them in the most accurate way. Throughout this proposal, we put an emphasis on theoretical reasoning. I aim to support the developed methods by a theoretical framework that would both justify their usage as well as provide concrete guidelines for using them. My goal of achieving a leap forward in performance through a level of theoretical analysis that is unparalleled in object recognition, makes our research agenda truly high-risk/ high-gains. I have been in the forefront of face recognition for the last 8 years and my lab's recent achievements in deep learning suggest that we will be able to carry out this research. To further support its feasibility, we present very promising initial results.