Sviluppo di un'applicazione bioinformatica per la gestione dei dati di antibiotico sensibilità di isolati clinici

Il problema dell'antibiotico-resistenza è un problema di sanità pubblica per affrontare il quale è necessario un sistema di sorveglianza basato sulla raccolta e l'analisi dei dati epidemiologici di laboratorio. Il progetto di dottorato è consistito nello sviluppo di una applicazione web per la gestione di tali dati di antibiotico sensibilità di isolati clinici utilizzabile a livello di ospedale. Si è creata una piattaforma web associata a un database relazionale per avere un’applicazione dinamica che potesse essere aggiornata facilmente inserendo nuovi dati senza dover manualmente modificare le pagine HTML che compongono l’applicazione stessa. E’ stato utilizzato il database open-source MySQL in quanto presenta numerosi vantaggi: estremamente stabile, elevate prestazioni, supportato da una grande comunità online ed inoltre gratuito. Il contenuto dinamico dell’applicazione web deve essere generato da un linguaggio di programmazione tipo “scripting” che automatizzi operazioni di inserimento, modifica, cancellazione, visualizzazione di larghe quantità di dati. E’ stato scelto il PHP, linguaggio open-source sviluppato appositamente per la realizzazione di pagine web dinamiche, perfettamente utilizzabile con il database MySQL. E’ stata definita l’architettura del database creando le tabelle contenenti i dati e le relazioni tra di esse: le anagrafiche, i dati relativi ai campioni, microrganismi isolati e agli antibiogrammi con le categorie interpretative relative al dato antibiotico. Definite tabelle e relazioni del database è stato scritto il codice associato alle funzioni principali: inserimento manuale di antibiogrammi, importazione di antibiogrammi multipli provenienti da file esportati da strumenti automatizzati, modifica/eliminazione degli antibiogrammi precedenti inseriti nel sistema, analisi dei dati presenti nel database con tendenze e andamenti relativi alla prevalenza di specie microbiche e alla chemioresistenza degli stessi, corredate da grafici. Lo sviluppo ha incluso continui test delle funzioni via via implementate usando reali dati clinici e sono stati introdotti appositi controlli e l’introduzione di una semplice e pulita veste grafica.

Chemopreventive effects of eicosapentaenoic acid free fatty acid in a murine model of colitis-associated colorectal cancer

Inflammatory bowel diseases are associated with increased risk of developing colitis-associated colorectal cancer (CAC). Epidemiological data show that the consumption of ω-3 polyunsaturated fatty acids (ω-3 PUFAs) decreases the risk of sporadic colorectal cancer (CRC). Importantly, recent data have shown that eicosapentaenoic acid-free fatty acid (EPA-FFA) reduces polyps formation and growth in models of familial adenomatous polyposis. However, the effects of dietary EPA-FFA are unknown in CAC. We tested the effectiveness of substituting EPA-FFA, for other dietary fats, in preventing inflammation and cancer in the AOM-DSS model of CAC. The AOM-DSS protocols were designed to evaluate the effect of EPA-FFA on both initiation and promotion of carcinogenesis. We found that EPA-FFA diet strongly decreased tumor multiplicity, incidence and maximum tumor size in the promotion and initiation arms. Moreover EPA-FFA, in particular in the initiation arm, led to reduced cell proliferation and nuclear β-catenin expression, whilst it increased apoptosis. In both arms, EPA-FFA treatment led to increased membrane switch from ω-6 to ω-3 PUFAs and a concomitant reduction in PGE2 production. We observed no significant changes in intestinal inflammation between EPA-FFA treated arms and AOM-DSS controls. Importantly, we found that EPA-FFA treatment restored the loss of Notch signaling found in the AOM-DSS control, resulted in the enrichment of Lactobacillus species in the gut microbiota and led to tumor suppressor miR34-a induction. In conclusion, our data suggest that EPA-FFA is an effective chemopreventive agent in CAC.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.

Epidemiological and functional characterization of Streptococcus pneumoniae pili

Streptococcus pneumoniae is an important life threatening human pathogen causing agent of invasive diseases such as otitis media, pneumonia, sepsis and meningitis, but is also a common inhabitant of the respiratory tract of children and healthy adults. Likewise most streptococci, S. pneumoniae decorates its surface with adhesive pili, composed of covalently linked subunits and involved in the attachment to epithelial cells and virulence. The pneumococcal pili are encoded by two genomic regions, pilus islet 1 (PI-1), and pilus islet-2 (PI-2), which are present in about 30% and 16% of the pneumococcal strains, respectively. PI-1 exists in three clonally related variants, whereas PI-2 is highly conserved. The presence of the islets does not correlate with the serotype of the strains, but with the genotype (as determined by Multi Locus Sequence Typing). The prevalence of PI-1 and PI-2 positive strains is similar in isolates from invasive disease and carriage. To better dissect a possible association between PIs presence and disease we evaluated the distribution of the two PIs in a panel of 113 acute otitis media (AOM) clinical isolates from Israel. PI-1 was present in 30.1% (N=34) of the isolates tested, and PI-2 in 7% (N=8). We found that 50% of the PI-1 positive isolates belonged to the international clones Spain9V-3 (ST156) and Taiwan19F-14 (ST236), and that PI-2 was not present in the absence of Pl-1. In conclusion, there was no correlation between PIs presence and AOM, and, in general, the observed differences in PIs prevalence are strictly dependent upon regional differences in the distribution of the clones. Finally, in the AOM collection the prevalence of PI-1 was higher among antibiotic resistant isolates, confirming previous indications obtained by the in silico analysis of the MLST database collection. Since the pilus-1 subunits were shown to confer protection in mouse models of infection both in active and passive immunization studies, and were regarded as potential candidates for a new generation of protein-based vaccines, the functional characterization was mainly focused on S. pneumoniae pilus -1 components. The pneumococcal pilus-1 is composed of three subunits, RrgA, RrgB and RrgC, each stabilized by intra-molecular isopeptide bonds and covalently polymerized by means of inter-molecular isopeptide bonds to form an extended fibre. The pilus shaft is a multimeric structure mainly composed by the RrgB backbone subunit. The minor ancillary proteins are located at the tip and at the base of the pilus, where they have been proposed to act as the major adhesin (RrgA) and as the pilus anchor (RrgC), respectively. RrgA is protective in in vivo mouse models, and exists in two variants (clades I and II). Mapping of the sequence variability onto the RrgA structure predicted from X-ray data showed that the diversity was restricted to the “head” of the protein, which contains the putative binding domains, whereas the elongated “stalk” was mostly conserved. To investigate whether this variability could influence the adhesive capacity of RrgA and to map the regions important for binding, two full-length protein variants and three recombinant RrgA portions were tested for adhesion to lung epithelial cells and to purified extracellular matrix (ECM) components. The two RrgA variants displayed similar binding abilities, whereas none of the recombinant fragments adhered at levels comparable to those of the full-length protein, suggesting that proper folding and structural arrangement are crucial to retain protein functionality. Furthermore, the two RrgA variants were shown to be cross-reactive in vitro and cross-protective in vivo in a murine model of passive immunization. Taken together, these data indicate that the region implicated in adhesion and the functional epitopes responsible for the protective ability of RrgA may be conserved and that the considerable level of variation found within the “head” domain of RrgA may have been generated by immunologic pressure without impairing the functional integrity of the pilus.

Antiepileptic drugs and pregnancy. Population based pharmaco-epidemiological study on prescription patterns, pregnancy outcome and foetal health

Aims of the study: To assess the prevalence of Antiepileptic Drug (AED) exposure in pregnant women with or without epilepsy and the comparative risk of terminations of pregnancy (TOPs), spontaneous abortions, stillbirth, major congenital malformations (MCMs) and foetal growth retardation (FGR) following intrauterine AED exposure in the Emilia Romagna region (RER), Northern Italy (4 million inhabitants). Methods: Data were obtained from official regional registries: Certificate of Delivery Assistance, Hospital Discharge Card, reimbursed prescription databases and Registry of Congenital Malformations. We identified all the deliveries, hospitalized abortions and MCMs occurred between January 2009 and December 2011. Results: We identified 145,243 pregnancies: 111,284 deliveries (112,845 live births and 279 stillbirths), 16408 spontaneous abortions and 17551 TOPs. Six hundred and eleven pregnancies (0.42% 95% Cl: 0.39-0.46) were exposed to AEDs. Twenty-one per cent of pregnancies ended in TOP in the AED group vs 12% in the non-exposed (OR:2.24; CI 1.41-3.56). The rate of spontaneous abortions and stillbirth was comparable in the two groups. Three hundred fifty-three babies (0.31%, 95% CI: 0.28-0.35) were exposed to AEDs during the first trimester. The rate of MCMs was 2.3% in the AED group (2.2% in babies exposed to monotherapy and 3.1% in babies exposed to polytherapy) vs 2.0% in the non-exposed. The risk of FGR was 12.7 % in the exposed group compared to 10% in the non-exposed. Discussion and Conclusion: The prevalence of AED exposure in pregnancy in the RER was 0.42%. The rate of MCMs in children exposed to AEDs in utero was almost superimposable to the one of the non-exposed, however polytherapy carried a slightly increased risk . The rate of TOPs was significantly higher in the exposed women. Further studies are needed to clarify whether this high rate reflects a higher rate of MCMs detected prenatally or other more elusive reasons.

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.