Food safety and zoonotic enteric pathogens: sources, risk factors and transmission routes of human salmonellosis and campylobacteriosis

Salmonella and Campylobacter are common causes of human gastroenteritis. Their epidemiology is complex and a multi-tiered approach to control is needed, taking into account the different reservoirs, pathways and risk factors. In this thesis, trends in human gastroenteritis and food-borne outbreak notifications in Italy were explored. Moreover, the improved sensitivity of two recently-implemented regional surveillance systems in Lombardy and Piedmont was evidenced, providing a basis for improving notification at the national level. Trends in human Salmonella serovars were explored: serovars Enteritidis and Infantis decreased, Typhimurium remained stable and 4,[5],12:i:-, Derby and Napoli increased, suggesting that sources of infection have changed over time. Attribution analysis identified pigs as the main source of human salmonellosis in Italy, accounting for 43–60% of infections, followed by Gallus gallus (18–34%). Attributions to pigs and Gallus gallus showed increasing and decreasing trends, respectively. Potential bias and sampling issues related to the use of non-local/non-recent multilocus sequence typing (MLST) data in Campylobacter jejuni/coli source attribution using the Asymmetric Island (AI) model were investigated. As MLST data become increasingly dissimilar with increasing geographical/temporal distance, attributions to sources not sampled close to human cases can be underestimated. A combined case-control and source attribution analysis was developed to investigate risk factors for human Campylobacter jejuni/coli infection of chicken, ruminant, environmental, pet and exotic origin in The Netherlands. Most infections (~87%) were attributed to chicken and cattle. Individuals infected from different reservoirs had different associated risk factors: chicken consumption increased the risk for chicken-attributed infections; animal contact, barbecuing, tripe consumption, and never/seldom chicken consumption increased that for ruminant-attributed infections; game consumption and attending swimming pools increased that for environment-attributed infections; and dog ownership increased that for environment- and pet-attributed infections. Person-to-person contacts around holiday periods were risk factors for infections with exotic strains, putatively introduced by returning travellers.

Characterization of West Nile virus strains isolated in Italy

West Nile virus (WNV) is a neurotropic flavivirus that is maintained in an enzootic cycle between mosquitoes and birds, but can also infect and cause disease in humans and other vertebrate species. Most of WNV infections in humans are asymptomatic, but approximately 20% of infected people develop clinical symptoms, although severe neurological diseases are observed in less than 1% of them. WNV is the most widely distributed arbovirus in the world and has been recently associated with outbreaks of meningo-encephalitis in Europe, including Italy, caused by different viral strains belonging to distinct lineages 1 and 2. The hypothesis is that genetic divergence among viral strains currently circulating in Italy might reflect on their pathogenic potential and that the rapid spread of WNV with increased pathogenicity within naïve population suggest that epidemic forms of the virus may encode mechanisms to evade host immunity. Infection with WNV triggers a delayed host response that includes a delay in the production of interferon-α (IFN-α). IFNs are a family of immuno-modulatory cytokines that are produced in response to virus infection and serve as integral signal initiators of host intracellular defenses. The increased number of human cases and the lack of data about virulence of European WNV isolates highlight the importance to achieve a better knowledge on this emerging viral infection. In the present study, we investigate the phenotypic and IFN-α-regulatory properties of different WNV lineage 1 and 2 strains that are circulating in Europe/Italy in two cell lines: Vero and 1321N1. We demonstrate that: Vero and 1321N1 cells are capable of supporting WNV replication where different WNV strains show similar growth kinetics; WNV lineage 2 strain replicated in Vero and 1321N1 cells as efficiently as WNV lineage 1 strains; and both lineages 1 and 2 were highly susceptible to the antiviral actions of IFN-α.

Evaluation of glycoconjugate antigens as vaccine candidates against group A Streptococcus and human immunodeficiency virus infections

This PhD thesis discusses the rationale for design and use of synthetic oligosaccharides for the development of glycoconjugate vaccines and the role of physicochemical methods in the characterization of these vaccines. The study concerns two infectious diseases that represent a serious problem for the national healthcare programs: human immunodeficiency virus (HIV) and Group A Streptococcus (GAS) infections. Both pathogens possess distinctive carbohydrate structures that have been described as suitable targets for the vaccine design. The Group A Streptococcus cell membrane polysaccharide (GAS-PS) is an attractive vaccine antigen candidate based on its conserved, constant expression pattern and the ability to confer immunoprotection in a relevant mouse model. Analysis of the immunogenic response within at-risk populations suggests an inverse correlation between high anti-GAS-PS antibody titres and GAS infection cases. Recent studies show that a chemically synthesized core polysaccharide-based antigen may represent an antigenic structural determinant of the large polysaccharide. Based on GAS-PS structural analysis, the study evaluates the potential to exploit a synthetic design approach to GAS vaccine development and compares the efficiency of synthetic antigens with the long isolated GAS polysaccharide. Synthetic GAS-PS structural analogues were specifically designed and generated to explore the impact of antigen length and terminal residue composition. For the HIV-1 glycoantigens, the dense glycan shield on the surface of the envelope protein gp120 was chosen as a target. This shield masks conserved protein epitopes and facilitates virus spread via binding to glycan receptors on susceptible host cells. The broadly neutralizing monoclonal antibody 2G12 binds a cluster of high-mannose oligosaccharides on the gp120 subunit of HIV-1 Env protein. This oligomannose epitope has been a subject to the synthetic vaccine development. The cluster nature of the 2G12 epitope suggested that multivalent antigen presentation was important to develop a carbohydrate based vaccine candidate. I describe the development of neoglycoconjugates displaying clustered HIV-1 related oligomannose carbohydrates and their immunogenic properties.

Analysis of human body kinematics using a hybrid markerless video acquisition and processing

A main objective of the human movement analysis is the quantitative description of joint kinematics and kinetics. This information may have great possibility to address clinical problems both in orthopaedics and motor rehabilitation. Previous studies have shown that the assessment of kinematics and kinetics from stereophotogrammetric data necessitates a setup phase, special equipment and expertise to operate. Besides, this procedure may cause feeling of uneasiness on the subjects and may hinder with their walking. The general aim of this thesis is the implementation and evaluation of new 2D markerless techniques, in order to contribute to the development of an alternative technique to the traditional stereophotogrammetric techniques. At first, the focus of the study has been the estimation of the ankle-foot complex kinematics during stance phase of the gait. Two particular cases were considered: subjects barefoot and subjects wearing ankle socks. The use of socks was investigated in view of the development of the hybrid method proposed in this work. Different algorithms were analyzed, evaluated and implemented in order to have a 2D markerless solution to estimate the kinematics for both cases. The validation of the proposed technique was done with a traditional stereophotogrammetric system. The implementation of the technique leads towards an easy to configure (and more comfortable for the subject) alternative to the traditional stereophotogrammetric system. Then, the abovementioned technique has been improved so that the measurement of knee flexion/extension could be done with a 2D markerless technique. The main changes on the implementation were on occlusion handling and background segmentation. With the additional constraints, the proposed technique was applied to the estimation of knee flexion/extension and compared with a traditional stereophotogrammetric system. Results showed that the knee flexion/extension estimation from traditional stereophotogrammetric system and the proposed markerless system were highly comparable, making the latter a potential alternative for clinical use. A contribution has also been given in the estimation of lower limb kinematics of the children with cerebral palsy (CP). For this purpose, a hybrid technique, which uses high-cut underwear and ankle socks as “segmental markers” in combination with a markerless methodology, was proposed. The proposed hybrid technique is different than the abovementioned markerless technique in terms of the algorithm chosen. Results showed that the proposed hybrid technique can become a simple and low-cost alternative to the traditional stereophotogrammetric systems.

Phenotypic and genotypic characterisation of antimicrobial resistance in Escherichia coli indicator of animal, food and human origin.

This thesis presents AMR phenotypic evaluation and whole genome sequencing analysis of 288 Escherichia coli strains isolated from different sources (livestock, companion animal, wildlife, food and human) in Italy. Our data reflects general resistance trends in Europe, reporting tetracycline, ampicillin, sulfisoxazole and aminoglycosides resistance as the most common phenotypic AMR profile among livestock, pets, wildlife and humans. Identification of human and animal (livestock and companion animal) AMR profiles in niches with a rare (fishery, mollusc) or absent (vegetable, wild animal, wild boar) direct exposure to antimicrobials, suggests widespread environmental pollution with ARGs conferring resistance to these antimicrobials. Phenotypic resistance to highest priority critically important antimicrobials was mainly observed in food-producing animals and related food such as rabbit, poultry, beef and swine. Discrepancies between AMR phenotypic pattern and genetic profile were observed. In particular, phenotypic aminoglycoside, cephalosporin, meropenem, colistin resistance and ESBL profile did not have a genetic explanation in different cases. This data could suggest the diffusion of new genetic variants of ARGs, associated to these antimicrobial classes. Generally, our collection shows a virulence profile typical of extraintestinal pathogenic Escherichia coli (ExPEC) pathotype. Different pandemic and emerging ExPEC lineages were identified, in particular in poultry meat (ST10; ST23; ST69, ST117; ST131). Rabbit was suggested as a source of ST20-ST40 potential hybrid pathogens. Wildlife carried a high average number (10) of VAGs (mostly associated to ExPEC pathotype) and different predominant ExPEC lineages (ST23, ST117, ST648), suggesting its possible involvement in maintenance and diffusion of virulence determinants. In conclusion, our study provides important knowledge related to the phenotypic/genetic AMR and virulence profiles circulating in E. coli in Italy. The role of different niches in AMR dynamics has been discussed. In particular, food-producing animals are worthy of continued investigation as a source of potential zoonotic pathogens, meanwhile wildlife might contribute to VAGs spread.

Human congenital cytomegalovirus infection: characteristics and pathogenesis of fetal brain damage

Human cytomegalovirus (HCMV) causes congenital neurological lifelong disabilities. The study analyzed 10 HCMV-infected human fetuses at 21 weeks of gestation to evaluate the characteristics and pathogenesis of brain injury related to congenital human CMV (cCMV) infection. Specifically, tissues from cortical and white matter areas, subventricular zone, thalamus, hypothalamus, hippocampus, basal ganglia and cerebellum were analysed by: i) immunohistochemistry (IHC) to detect HCMV-infected cell distribution, ii) hematoxylin-eosin staining to evaluate histological damage and iii) real-time PCR to quantify tissue viral load (HCMV-DNA). Viral tropism was assessed by double IHC to detect HCMV-antigens and neural/neuronal markers: nestin (expressed in early differentiation stage), doublecortin (DCX, identifying neuronal precursor cells) and neuronal nuclei (NeuN, identifying mature neurons). HCMV-positive cells and viral DNA were found in the brain of 8/10 (80%) fetuses. For these cases, brain damage was classified in mild (n=4, 50%), moderate (n=3, 37.5%) and severe (n=1, 12.5%) based on presence of i) diffuse astrocytosis, microglial activation and vascular changes; ii) occasional (in mild) or multiple (in moderate/severe) microglial nodules and iii) necrosis (in severe). The highest median HCMV-DNA level was found in the hippocampus (212 copies/5ng of humanDNA [hDNA], range: 10-7,505) as well as the highest mean HCMV-infected cell value (2.9 cells, range: 0-23), followed by that detected in subventricular zone (1.8 cells, range: 0-19). This suggests a preferential HCMV tropism for immature neuronal cells, residing in these regions, confirmed by the detection of DCX and nestin in 94% and 63.3% of HCMV-positive cells, respectively. NeuN was not found among HCMV-positive cells and was nearly absent in the brain with severe damage, suggesting HCMV does not infect mature neurons and immature HCMV-infected neuronal cells do not differentiate into neurons. HCMV preferential tropism in immature neural/neuronal cells delays/inhibits their differentiation interfering with brain development processes that lead to structural and functional brain defects.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

The predictive role of the human gut microbiota in covid-19: implications for disease severity and post-covid syndrome

The COVID-19 pandemic, sparked by the SARS-CoV-2 virus, stirred global comparisons to historical pandemics. Initially presenting a high mortality rate, it later stabilized globally at around 0.5-3%. Patients manifest a spectrum of symptoms, necessitating efficient triaging for appropriate treatment strategies, ranging from symptomatic relief to antivirals or monoclonal antibodies. Beyond traditional approaches, emerging research suggests a potential link between COVID-19 severity and alterations in gut microbiota composition, impacting inflammatory responses. However, most studies focus on severe hospitalized cases without standardized criteria for severity. Addressing this gap, the first study in this thesis spans diverse COVID-19 severity levels, utilizing 16S rRNA amplicon sequencing on fecal samples from 315 subjects. The findings highlight significant microbiota differences correlated with severity. Machine learning classifiers, including a multi-layer convoluted neural network, demonstrated the potential of microbiota compositional data to predict patient severity, achieving an 84.2% mean balanced accuracy starting one week post-symptom onset. These preliminary results underscore the gut microbiota's potential as a biomarker in clinical decision-making for COVID-19. The second study delves into mild COVID-19 cases, exploring their implications for ‘long COVID’ or Post-Acute COVID-19 Syndrome (PACS). Employing longitudinal analysis, the study unveils dynamic shifts in microbial composition during the acute phase, akin to severe cases. Innovative techniques, including network approaches and spline-based longitudinal analysis, were deployed to assess microbiota dynamics and potential associations with PACS. The research suggests that even in mild cases, similar mechanisms to hospitalized patients are established regarding changes in intestinal microbiota during the acute phase of the infection. These findings lay the foundation for potential microbiota-targeted therapies to mitigate inflammation, potentially preventing long COVID symptoms in the broader population. In essence, these studies offer valuable insights into the intricate relationships between COVID-19 severity, gut microbiota, and the potential for innovative clinical applications.