Analysis of the immunological and functional features of the Neisserial Heparin Binding Antigen (NHBA)

Neisserial Heparin Binding Antigen (NHBA) is a surface-exposed lipoprotein ubiquitously expressed by genetically diverse Neisseria meningitidis strains and is an antigen of the multicomponent protein-based 4CMenB vaccine, able to induce bactericidal antibodies in humans and to bind heparin-like molecules. The aim of this study is to characterize the immunological and functional properties of NHBA. To evaluate immunogenicity and the contribution of aminoacid sequence variability to vaccine coverage, we constructed recombinant isogenic strains that are susceptible to bactericidal killing only by anti-NHBA antibodies and engineered them to express equal levels of selected NHBA peptides. In these recombinant strains, we observed different titres associated with the different peptide variants. These recombinant strains were then further engineered to express NHBA chimeric proteins to investigate the regions important for immunogenicity. In natural strains, anti-NHBA antibodies were found to be cross-protective against strains expressing different peptides. To investigate the functional properties of this antigen, the recombinant purified NHBA protein was tested in in vitro binding studies and was found to be able to bind epithelial cells. The binding was abolished when cells were treated specifically with heparinase III, suggesting that the interaction with the cells is mediated by heparan sulfate proteoglycans (HSPG). Mutation of the Arg-rich tract of NHBA abrogated the binding, confirming the importance of this region in mediating the binding to heparin-like molecules. In a panel of N. meningitidis strains, the deletion of nhba resulted in a reduction of adhesion with respect to each isogenic wild type strain. Furthermore, the adhesion of the wild-type strain was prevented by using anti-NHBA polyclonal sera, demonstrating the specificity of the interaction. These results suggest that NHBA could be a novel meningococcal adhesin contributing to host-cell interaction. Moreover, we analysed NHBA NalP-mediated cleavage in different NHBA peptides and showed that not all NHBA peptides are cleaved.

Harmful algae and their potential impacts on the coastal ecosystem: growth and toxin production dynamics

The main goal of the present thesis was to study some harmful algal species which cause blooms in Italian coastal waters, leading to consequences for human health, coastal ecosystem, fishery and tourism. In particular, in the first part of this thesis the toxicity of Adriatic strains of the raphidophyte Fibrocapsa japonica was investigated. Despite several hypotheses have been proposed for the toxic mechanism of the raphidophytes, especially for the species Chattonella antiqua and C. marina, which have been studied more extensively, just a few studies on the toxic effects of these species for different organisms were reported. Moreover, a careful reading of the literature evidenced as any ichthyotoxic events reported worldwide can be linked to F. japonica blooms. Although recently several studies were performed on F. japonica strains from the USA, Japan, Australia, New Zealand, the Netherlands, Germany, and France in order to characterize their growth and toxicity features, the work reported in this thesis results one of the first investigation on the toxic effects of F. japonica for different organisms, such as bacteria, crustaceans and fish. Mortality effects, together with haemolysis of fish erythrocytes, probably due to the relatively high amount of PUFAs produced by this species, were observed. Mortality for fish, however, was reported only at a high cell density and after a long exposition period (9-10 days); moreover a significant increase of H2O2 obtained in the tanks where sea basses were exposed to F. japonica was also relevant. This result may justify the absence of ichthyotoxic events in the Italian coasts, despite F. japonica blooms detected in these areas were characterized by high cell densities. This work reports also a first complete characterization of the fatty acids produced and extracellularly released by the Adriatic F. japonica, and results were also compared with the fatty acid profile of other strains. The absence of known brevetoxins in F. japonica algal extracts was also highlighted, leading to the hypothesis that the toxicity of F. japonica may be due to a synergic effect of PUFAs and ROS. Another microalgae that was studied in this thesis is the benthic dinoflagellate Ostreopsis cf. ovata. This species was investigated with the aim to investigate the effect of environmental parameters on its growth and toxicity. O. cf. ovata, in fact, shows different blooming periods along the Italian coasts and even the reported toxic effects are variable. The results of this work confirmed the high variability in the growth dynamic and toxin content of several Italian strains which were isolated in recent years along the Adriatic and Tyrrhenian Seas. Moreover, the effects of temperature and salinity on the behaviour of the different isolates are in good agreement with the results obtained from field surveys, which evidence as the environmental parameters are important factors modulating O. cf. ovata proliferation. Another relevant result that was highlighted is the anomaly in the production of palytoxin-like compounds reported by one of the studied isolate, in particular the one isolated in 2008 in Ancona (Adriatic Sea). Only this strain reported the absence of two (ovatoxin-b and –c) of the five ovatoxins so far known in the toxin profile and a different relative abundance of the other toxins. The last aspect that was studied in this thesis regards the toxin biosythesis. In fact, toxins produced (palytoxin-like compounds) or supposed to be produced (brevetoxin-like compounds) by O. cf. ovata and F. japonica, respectively, are polyketides, which are highly oxygenated compounds synthesized by complex enzymes known as polyketide synthase (PKS) enzymes. These enzymes are multi-domain complexes that structurally and functionally resemble the fatty acid synthases (FASs). This work reports the first study of PKS proteins in the dinoflagellates O. cf. ovata, C. monotis and in the raphidophyte F. japonica. For the first time some PKSs were identified in these species, confirming the presence of PKS proteins predicted by the in silico translation of the transcripts found in K. brevis also in other species. The identification of O. cf. ovata PKSs and the localization of the palytoxin-like compounds produced by this dinoflagellate in a similar location (chloroplast) as that observed for other dinoflagellate and cyanobacterial toxins provides some indication that these proteins may be involved in polyketide biosynthesis. However, their potential function as fatty acid synthases cannot be ruled out, as plant fatty acid synthesis also occurs within chloroplasts. This last hypothesis is also supported by the fact that in all the investigated species, and in particular in F. japonica, PKS proteins were present. Therefore, these results provide an important contribution to the study of the polyketides and of the involvement of PKS proteins in the toxin biosynthesis.

Study of medicinal and food plants as a source of biologically active compounds.

In the last few decades, scientific evidence has pointed out the health-beneficial effects of phenolic compounds in foods, including a decrease in risk of developing degenerative and chronic diseases, known to be caused by oxidative stress. In this frame can be inserted research carried out during my PhD thesis, which concerns the phytochemical investigation of phenolic composition in sweet cherries (Prunus avium L.), apple fruits (Malus domestica L.) and quinoa seeds (Chenopodium quinoa Willd.). The first project was focused on the investigation of phytochemical profile and nutraceutical value of fruits of new sweet cherry cultivars. Their phenolic profile and antioxidant activity were investigated and compared with those of commonly commercialized cultivars. Their nutraceutical value was evaluated in terms of antioxidant/neuroprotective capacity in neuron-like SH-SY5Y cells, in order to investigate their ability to counteract the oxidative stress and/or neurodegeneration process The second project was focused on phytochemical analysis of phenolic compounds in apples of ancient cultivars with the aim of selecting the most diverse cultivars, that will then be assayed for their anti-carcinogenic and anti-proliferative activities against the hepato-biliary and pancreatic tumours. The third project was focused on the analysis of polyphenolic pattern of seeds of two quinoa varieties grown at different latitudes. Analysis of phenolic profile and in vitro antioxidant activity of seed extracts both in their free and soluble-conjugated forms, showed that the accumulation of some classes of flavonoids is strictly regulated by environmental factors, even though the overall antioxidant capacity does not differ in quinoa Regalona grown in Chile and Italy. During the internship period carried out at the Department of Organic Chemistry at Universidad Autónoma de Madrid (UAM), it was achieved the isolation of two pentacyclic triterpenoids, from an endemic Peruvian plant, Jatropha macrantha Müll. Arg., with bio-guided fractionation technique.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.