Computational investigation of the Plasmodium falciparum fatty acid biosynthetic pathway toward the discovery of novel antimalarials

The structural peculiarities of a protein are related to its biological function. In the fatty acid elongation cycle, one small carrier protein shuttles and delivers the acyl intermediates from one enzyme to the other. The carrier has to recognize several enzymatic counterparts, specifically interact with each of them, and finally transiently deliver the carried substrate to the active site. Carry out such a complex game requires the players to be flexible and efficiently adapt their structure to the interacting protein or substrate. In a drug discovery effort, the structure-function relationships of a target system should be taken into account to optimistically interfere with its biological function. In this doctoral work, the essential role of structural plasticity in key steps of fatty acid biosynthesis in Plasmodium falciparum is investigated by means of molecular simulations. The key steps considered include the delivery of acyl substrates and the structural rearrangements of catalytic pockets upon ligand binding. The ground-level bases for carrier/enzyme recognition and interaction are also put forward. The structural features of the target have driven the selection of proper drug discovery tools, which captured the dynamics of biological processes and could allow the rational design of novel inhibitors. The model may be perspectively used for the identification of novel pathway-based antimalarial compounds.

Modelling coupled physical-biogeochemical processes in ice-covered oceans

The last decades have seen a large effort of the scientific community to study and understand the physics of sea ice. We currently have a wide - even though still not exhaustive - knowledge of the sea ice dynamics and thermodynamics and of their temporal and spatial variability. Sea ice biogeochemistry is instead largely unknown. Sea ice algae production may account for up to 25% of overall primary production in ice-covered waters of the Southern Ocean. However, the influence of physical factors, such as the location of ice formation, the role of snow cover and light availability on sea ice primary production is poorly understood. There are only sparse localized observations and little knowledge of the functioning of sea ice biogeochemistry at larger scales. Modelling becomes then an auxiliary tool to help qualifying and quantifying the role of sea ice biogeochemistry in the ocean dynamics. In this thesis, a novel approach is used for the modelling and coupling of sea ice biogeochemistry - and in particular its primary production - to sea ice physics. Previous attempts were based on the coupling of rather complex sea ice physical models to empirical or relatively simple biological or biogeochemical models. The focus is moved here to a more biologically-oriented point of view. A simple, however comprehensive, physical model of the sea ice thermodynamics (ESIM) was developed and coupled to a novel sea ice implementation (BFM-SI) of the Biogeochemical Flux Model (BFM). The BFM is a comprehensive model, largely used and validated in the open ocean environment and in regional seas. The physical model has been developed having in mind the biogeochemical properties of sea ice and the physical inputs required to model sea ice biogeochemistry. The central concept of the coupling is the modelling of the Biologically-Active-Layer (BAL), which is the time-varying fraction of sea ice that is continuously connected to the ocean via brines pockets and channels and it acts as rich habitat for many microorganisms. The physical model provides the key physical properties of the BAL (e.g., brines volume, temperature and salinity), and the BFM-SI simulates the physiological and ecological response of the biological community to the physical enviroment. The new biogeochemical model is also coupled to the pelagic BFM through the exchange of organic and inorganic matter at the boundaries between the two systems . This is done by computing the entrapment of matter and gases when sea ice grows and release to the ocean when sea ice melts to ensure mass conservation. The model was tested in different ice-covered regions of the world ocean to test the generality of the parameterizations. The focus was particularly on the regions of landfast ice, where primary production is generally large. The implementation of the BFM in sea ice and the coupling structure in General Circulation Models will add a new component to the latters (and in general to Earth System Models), which will be able to provide adequate estimate of the role and importance of sea ice biogeochemistry in the global carbon cycle.

La neoangiogenesi nel reimpianto autologo di tessuto ovarico in pazienti a rischio di fallimento ovarico precoce: ottimizzazione dei metodi di crioconservazione di corticle ovarica

Introduzione L’efficacia dei chemio/radioterapici ha aumentato notevolmente l’aspettativa di vita delle pazienti oncologiche, tuttavia, questi trattamenti possono compromettere la funzionalità ovarica. La crioconservazione di tessuto ovarico, con il successivo reimpianto, ha lo scopo di preservare la fertilità delle pazienti a rischio di fallimento ovarico precoce. Scopo dello studio Definire la migliore procedura di crioconservazione e reimpianto in grado di ottenere la neovascolarizzazione del tessuto reimpiantato nel minor tempo possibile al fine di diminuire la perdita follicolare causata dall’ischemia durante la procedura. Materiali e metodi Per ciascuna paziente (3) le biopsie ovariche, sono state prelevate laparoscopicamente e crioconservate secondo il protocollo di congelamento lento/scongelamento rapido. Campioni di corticale ovarica sono stati processati per l’analisi istologica, ultrastrutturale, immuistochimica e confocale per valutare la preservazione morfologiaca del tessuto. Le fettine di corticale ovarica sono state scongelate e reimpiantate ortotopicamente (2), nelle ovaia e in due tasche peritoneali, o eterotopicamente (1), in due tasche create nel sottocute sovrapubico. Risultati Le analisi di microscopia hanno mostrato il mantenimento di una discreta morfologia dello stroma, e dei vasi criopreservati e un lieve ma non significativo danneggiamento dei follicoli scongelati. Tutte le pazienti hanno mostrato la ripresa della funzionalità endocrina rispettivamente dopo 2/4 mesi dal reimpianto. Il color-doppler, inoltre ha rivelato un significativo aumento della vascolarizzazione ovarica rispetto alla quasi totale assenza di vascolarizzazione prima del reimpianto, quando le pazienti mostravano una conclamata menopausa. Conclusioni Lo studio ha confermato la ripresa della vascolarizzazione dell’ovaio in seguito a reimpianto avascolare di fettine di corticale, senza l’impiego di fattori esogeni o meccanici aggiuntivi, in tempi concordanti con i dati della letteratura. I risultati sono incoraggianti e l’avanzare degli studi e della ricerca potranno contribuire allo sviluppo di nuove metodologie di reimpianto che possano avere un successo clinico ed una sicurezza superiori a quelle finora ottenute.

Small molecules exploiting emerging therapeutic opportunities for breast cancer treatment

Among the different types of breast cancer (BC), the estrogen receptor positive (ER+) subtype, which requires estrogens for its growth and proliferation, is the most common, while triple negative BC, characterized by the absence of ER, progesterone receptor and human epidermal growth factor receptor 2, often leads to poor prognosis. First-line therapies for the treatment of ER+ BC act either by suppressing estrogen production, through the inhibition of aromatase (AR) enzyme, or by blocking estrogen prooncogenic activity, via the modulation/degradation of ERs. The serious side effects and the intrinsic or acquired resistance phenomena that arise with prolonged use of these drugs limit their therapeutic application, stimulating the search for new strategies to face this disease. In this context, the development of dual acting aromatase inhibitors, able to target both the orthosteric and the recently identified allosteric pockets of AR could be an opportunity to fight ER+ BC. Another promising strategy could be the development of multitarget compounds, targeting both AR and ERs. In this scenario, here we designed and synthesized two series of new xanthones or more flexible benzophenones as potential dual acting aromatase inhibitors. Moreover, inspired from tamoxifen metabolites and a literature compound endowed with activity on both AR and ER, different structurally related series of potential multitarget compounds were developed. The biological results showed that some of the new molecules were promising candidates for further development. It was recently observed that the lately discovered histamine H4 receptor is expressed in human breast tissue, displaying a key role in biological processes mediated by histamine such as cell proliferation, senescence, and apoptosis in malignant cells, representing a potential target in triple negative BC. Thus, a broad series of methyl quinazoline sulfonamides, carrying different functional groups on the sulfonamide moiety, were designed and synthesized as potential H4 receptor ligands.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.