Theoretical studies of microfluidics: "walking" drops and "melting" snowflakes.

The present PhD thesis summarizes two examples of research in microfluidics. Both times water was the subject of interest, once in the liquid state (droplets adsorbed on chemically functionalized surfaces), the other time in the solid state (ice snowflakes and their fractal behaviour). The first problem deals with a slipping nano-droplet of water adsorbed on a surface with photo-switchable wettability characteristics. Main focus was on identifying the underlying driving forces and mechanical principles at the molecular level of detail. Molecular Dynamics simulation was employed as investigative tool owing to its record of successfully describing the microscopic behaviour of liquids at interfaces. To reproduce the specialized surface on which a water droplet can effectively “walk”, a new implicit surface potential was developed. Applying this new method the experimentally observed droplet slippage could be reproduced successfully. Next the movement of the droplet was analyzed at various conditions emphasizing on the behaviour of the water molecules in contact with the surface. The main objective was to identify driving forces and molecular mechanisms underlying the slippage process. The second part of this thesis is concerned with theoretical studies of snowflake melting. In the present work snowflakes are represented by filled von Koch-like fractals of mesoscopic beads. A new algorithm has been developed from scratch to simulate the thermal collapse of fractal structures based on Monte Carlo and Random Walk Simulations (MCRWS). The developed method was applied and compared to Molecular Dynamics simulations regarding the melting of ice snowflake crystals and new parameters were derived from this comparison. Bigger snow-fractals were then studied looking at the time evolution at different temperatures again making use of the developed MCRWS method. This was accompanied by an in-depth analysis of fractal properties (border length and gyration radius) in order to shed light on the dynamics of the melting process.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Aberrant c-MET activation impairs perinuclear actin cap organization with YAP1 cytosolic relocation

Growing evidence indicates that cell and nuclear deformability plays a crucial role in the determination of cancer cells tumorigenic and metastatic potential. The perinuclear actin cap, by wrapping the nucleus with a functional network of actomyosin cables, can modulate nuclear architecture and consequently cell/nuclear elasticity. The hepatocyte growth factor receptor (MET) stands out among other membrane receptors as crucial player of the actin filaments organization, but no data are available on a specific role for MET in the actin cap assembly and the overall nuclear architecture organization. In a cell system characterized by MET hyperactivation, we observed a strong rearrangement of the cellular actin caps, with a complete dismantling of apical stress fibers and a strikingly enhanced nuclear height. CRISPR/Cas9 silencing of MET completely reverted the aberrant phenotype, resulting in flattened cells with perfectly aligned perinuclear actomyosin bundles, as well as decreased MAPK and PI3K/AKT signaling, cell proliferation rate and aggressiveness. Interestingly, MET ablated cells acquired a remarkably directed and polarized migratory phenotype, contrarily to cells with MET sustained activation showing meandering random walk. A pathway enrichment analysis comparing MET-activated and MET-KO cells RNAseq data, unveiled the contribution of multiple pathways associated with cytoskeleton remodeling, regulation of cell shape and response to mechanical stimuli. In line, the co-transcriptional activator YAP1, playing a major role in cell mechanosensing and focal adhesions/actin stabilization, appeared the culprit of the genetic reassembling of KO cells. Indeed, MET silencing was shown to induce YAP1 nuclear shuttling and increased co-transcriptional activity. Finally, we were able to induce in a normal epithelial model a phenotype closer to MET activated cancer cells only by introducing a constitutive fusion protein of MET. Taken together, our results demonstrate a new mechanism of MET-mediated actin remodeling responsible for a tumor-initiating capacity and meandering random migration, which requires YAP1 inactivation.