Discovery and Pharmacological Characterization of a Novel Small Molecule Antagonist of Integrin α4β1: Focus on Antiallergic Activity

Allergy is a common hypersensitivity disorder that affects 15% to 20% of the population and its prevalence is increasing worldwide. Its severity correlates with the degree of eosinophil infiltration into the conjunctiva, which is mediated by chemokines that stimulate the production of adhesion molecules like intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) on the endothelial cell surface. The α4β1 and α4β7 integrins are expressed in eosinophils and contribute to their activation and infiltration in AC through the binding to VCAM-1 or fibronectin, expressed on vascular endothelial cells. Blockade of α4 integrins might be a therapeutical achievement in allergic eye diseases. DS 70, that show an IC50 in the nanomolar range against α4β1 integrin in Jurkat cells and in the eosinophilic cell line EOL-1. This compound was able to prevent cell adhesion to VCAM-1 and FN in vitro. In a scintillation proximity assay DS70 displaced 125I-FN binding to human α4β1 integrin and, in flow cytometry analysis, it antagonized the binding of a primary antibody to α4β1 integrin expressed on the Jurkat cells surface as well. Furthermore, we analysed also its effects on integrin α4β1 signalling. In an vivo model of allergic conjunctivitis, topical DS70 reduced the clinical aspects of EPR (early phase reaction) and LPR (late phase reaction), by reducing clinical score, eosinophil accumulation, mRNA levels of cytochines and chemochines pro-inflammatory and the conjunctival expression of α4 integrin. In conclusion, DS70 seems a novel antiallergic ocular agent that has significant effects on both early and late phases of ocular allergy.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.

Development of new molecular methods for the diagnosis and the study of viral diseases of fish

The increase in aquaculture operations worldwide has provided new opportunities for the transmission of aquatic viruses. The occurrence of viral diseases remains a significant limiting factor in aquaculture production and for the sustainability. The ability to identify quickly the presence/absence of a pathogenic organism in fish would have significant advantages for the aquaculture systems. Several molecular methods have found successful application in fish pathology both for confirmatory diagnosis of overt diseases and for detection of asymptomatic infections. However, a lot of different variants occur among fish host species and virus strains and consequently specific methods need to be developed and optimized for each pathogen and often also for each host species. The first chapter of this PhD thesis presents a complete description of the major viruses that infect fish and provides a relevant information regarding the most common methods and emerging technologies for the molecular diagnosis of viral diseases of fish. The development and application of a real time PCR assay for the detection and quantification of lymphocystivirus was described in the second chapter. It showed to be highly sensitive, specific, reproducible and versatile for the detection and quantitation of lymphocystivirus. The use of this technique can find multiple application such as asymptomatic carrier detection or pathogenesis studies of different LCDV strains. The third chapter, a multiplex RT-PCR (mRT-PCR) assay was developed for the simultaneous detection of viral haemorrhagic septicaemia (VHS), infectious haematopoietic necrosis (IHN), infectious pancreatic necrosis (IPN) and sleeping disease (SD) in a single assay. This method was able to efficiently detect the viral RNA in tissue samples, showing the presence of single infections and co-infections in rainbow trout samples. The mRT-PCR method was revealed to be an accurate and fast method to support traditional diagnostic techniques in the diagnosis of major viral diseases of rainbow trout.