Aureobasidium pullulans as biological control agent: modes of action

The postharvest phase has been considered an environment very suitable for successful application of biological control agents (BCAs). However, the tri-interaction between fungal pathogen, host (fruit) and antagonist is influenced by several parameters such as temperature, oxidative stresses, oxygen composition, water activity, etc. that could be determining for the success of biocontrol. Knowledge of the modes of action of BCAs is essential in order to enhance their viability and increase their potentialities in disease control. The thesis focused on the possibility to explain the modes of action of a biological control agent (BCA): Aureobasidium pullulans, in particular the strains L1 and L8, control effective against fruit postharvest fungal pathogen. In particular in this work were studied the different modes of action of BCA, such as: i) the ability to produce volatile organic compounds (VOCs), identified by SPME- gas chromatography-mass spectrometry (GC-MS) and tested by in vitro and in vivo assays against Penicillium spp., Botrytis cinerea, Colletotrichum acutatum; ii) the ability to produce lytic enzymes (exo and endo chitinase and β-1,3-glucanase) tested against Monilinia laxa, causal agent of brown rot of stone fruits. L1 and L8 lytic enzymes were also evaluated through their relative genes by molecular tools; iii) the competition for space and nutrients, such as sugars (sucrose, glucose and fructose) and iron; the latter induced the production of siderophores, molecules with high affinity for iron chelation. A molecular investigation was carried out to better understand the gene regulation strictly correlated to the production of these chelating molucules. The competition for space against M. laxa was verified by electron microscopy techniques; iv) a depth bibliographical analysis on BCAs mechanisms of action and their possible combination with physical and chemical treatments was conducted.

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.

RNA interference technology as a potential control method for fruit and horticultural crops pathogens Botrytis cinerea and Plasmopara viticola

Chapter 1, a general introduction on Botrytis cinerea and its threat to crop production is presented. What Botrytis looks like, its life cycle, why it is a threat to agricultural production, its worldwide pest status, and its current state of management is further elaborated on. Chapter 2, a general introduction on Plasmopara viticola, its threat to grape production and management strategies presented. Chapter 3, titled " RNA Interference Strategies for Future Management of Plant Pathogenic Fungi: Prospects and Challenges ", presents the rapid improvement and extensive implementation of RNA interference (RNAi) technology for the management of fungal pathogens. In this chapter, we describe the application of exogenous RNAi involved in plant pathogenic fungi and discuss dsRNA production, formulation, and RNAi delivery methods. Chapter 4, titled " Exogenous dsRNAs against chitin synthase and glucan synthase genes suppress the growth of the pathogenic fungus Botrytis cinerea " addresses two important questions: Is RNAi technology functional for B. cinerea control ? And which target genes can be exploited for RNAi-based B.cinerea disease control ? Upon target genes selections, an exogenous RNAi protocol was set up and we could effectively deliver a known dose of bacterially produced double stranded RNA (dsRNA) to induce RNAi in B. cinerea. Chapter 5, titled " Double-Stranded RNA Targeting Dicer-Like Genes Compromises the Pathogenicity of Plasmopara viticola on Grapevine “, which deals mainly on RNAi induction against Plasmopara viticola. This chapter addresses two main questions: Is RNAi technology functional in contrasting Plasmopara viticola? And which target genes can be exploited for RNAi-based disease control in Plasmopara viticola?. In the last Chapter (Chapter 6) titled “General discussions and perspectives for future research”, the major research findings from this thesis are discussed together with perspectives for future research.

Exploring the biofilm of Streptococcus agalactiae to identify virulence factors

Streptococcus agalactiae, also known as Group B Streptococcus (GBS) is the primary colonizer of the anogenital mucosa of up to 40% of healthy women and an important cause of invasive neonatal infections worldwide. Among the 10 known capsular serotypes, GBS type III accounts for 30-76% of the cases of neonatal meningitis. Biofilms are dense aggregates of surface-adherent microorganisms embedded in an exopolysaccharide matrix. Centers for Disease Control and Prevention estimate that 65% of human bacterial infections involve biofilms (Post et al., 2004). In recent years, the ability of GBS to form biofilm attracted attention for its possible role in fitness and/or virulence. Here, a new in vitro biofilm formation protocol was developed to guarantee more stringent conditions, to better discriminate between strong-, low- and non- biofilm forming strains and reduce ambiguous data interpretation. This protocol was applied to screen the in vitro biofilm formation ability of more than 350 GBS clinical isolates from pregnant women and neonatal infections belonging to different serotype, in relation to media composition and pH. The results showed the enhancement of GBS biofilm formation in acidic condition and identified a subset of isolates belonging to serotypes III and V that forms strong biofilms in these conditions. Interestingly, the best biofilm formers belonged to the serotype III hypervirulent clone ST-17.It was also found that pH 5.0 induces down-regulation of the capsule but that this reduction is not enough by itself to ensure biofilm formation. Moreover, the ability of proteinase K to strongly inhibit biofilm formation and to disaggregate mature biofilms suggested that proteins play an essential role in promoting GBS biofilm formation and contribute to the biofilm structural stability. Finally, a set of proteins potentially expressed during the GBS in vitro biofilm formation were identified by mass spectrometry.