Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Caratterizzazione dei sintomi neurovegetativi e neuropsicologici nella malattia di Parkinson associata a mutazioni del gene glucocerebrosidasi

Pochi studi hanno indagato il profilo dei sintomi non-motori nella malattia di Parkinson associata al gene glucocerebrosidasi (GBA). Questo studio è mirato alla caratterizzazione dei sintomi non-motori, con particolare attenzione alla valutazione delle funzioni neurovegetativa, cognitiva e comportamentale, nel parkinsonismo associato a mutazione del gene GBA con la finalità di verificare se tali sintomi non-motori siano parte dello spettro clinico di questi pazienti. E’ stato condotto su una coorte di pazienti affetti da malattia di Parkinson che erano stati tutti sottoposti ad una analisi genetica per la ricerca di mutazioni in uno dei geni finora associati alla malattia di Parkinson. All’interno di questa coorte omogenea sono stati identificati due gruppi diversi in relazione al genotipo (pazienti portatori della mutazione GBA e pazienti non portatori di nessuna mutazione) e le caratteristiche non-motorie sono state confrontate nei due gruppi. Sono state pertanto indagati il sistema nervoso autonomo, mediante studio dei riflessi cardiovascolari e analisi dei sintomi disautonomici, e le funzioni cognitivo-comportamentali in pazienti affetti da malattia di Parkinson associata a mutazione del gene GBA. I risultati sono stati messi a confronto con il gruppo di controllo. Lo studio ha mostrato che i pazienti affetti da malattia di Parkinson associata a mutazione del gene GBA presentavano maggiore frequenza di disfunzioni ortosimpatiche, depressione, ansia, apatia, impulsività, oltre che di disturbi del controllo degli impulsi rispetto ai pazienti non portatori. In conclusione, i pazienti GBA positivi possono esprimere una sintomatologia non-motoria multidominio con sintomi autonomici, cognitivi e comportamentali in primo piano. Pertanto l’impostazione terapeutica in questi pazienti dovrebbe includere una accurata valutazione dei sintomi non-motori e un loro monitoraggio nel follow up clinico, allo scopo di ottimizzare i risultati e ridurre i rischi di complicazioni.

Diet Effects on Activation and Maturation of Feed Control over the Gastrointestinal Defence Barrier in Piglets

Weaning is an important and complex step involving many stresses that interfere deeply with feed intake, gastro-intestinal tract (GIT) development and adaptation to the weaning diet in young pigs. The health of the pig at weaning, its nutrition in the immediate post-weaning period, and the physical, microbiological and psychological environment are all factors that interact to determine food intake and subsequent growth. GIT disorders, infections and diarrhoea increase at the time of weaning, in fact pathogens such as enterotoxigenic Escherichia coli (ETEC) are major causes of mucosal damage in post-weaning disease contributing to diarrhoea in suckling and post-weaned pigs. The European ban in 2006 put on antibiotic growth promoters (AGP) has stimulated research on the mechanisms of GIT disorders and on nutritional approaches for preventing or reducing such disturbances avoiding AGPs. Concerning these aspects here are presented five studies based on the interplay among nutrition, genomic, immunity and physiology with the aim to clarify some of these problematic issues around weaning period in piglets. The first three evaluate the effects of diets threonine or tryptophan enriched on gut defence and health as possible alternatives to AGP in the gut. The fourth is focused on the possible immunological function related with the development of the stomach. The fifth is a pilot study on the gastric sensing and orexygenic signal given by fasting or re-feeding conditions. Although some results are controversial, it appears that both tryptophan and threonine supplementation in weaning diets have a preventive role in E.coli PWD and favorable effects in the gut especially in relation to ETEC susceptible genotype. While the stomach is believed as almost aseptic organ, it shows an immune activity related with the mucosal maturation. Moreover it shows an orexygenic role of both oxyntic mucosa and pyloric mucosa, and its possible relation with nutrient sensing stimuli.

The biological basis of autism spectrum disorders: evaluation of oxidative stress and erytrocyte membrane alterations

This case-control study involved a total of 29 autistic children (Au) aged 6 to 12 years, and 28 gender and age-matched typically developing children (TD). We evaluated a high number of peripheral oxidative stress parameters, erythrocyte and lymphocyte membrane functional features and membrane lipid composition of erythrocyte. Erythrocyte TBARS, Peroxiredoxin II, Protein Carbonyl Groups and urinary HEL and isoprostane levels were elevated in AU (confirming an imbalance of the redox status of Au); other oxidative stress markers or associated parameters (urinary 8-oxo-dG, plasma Total antioxidant capacity and plasma carbonyl groups, erythrocyte SOD and catalase activities) were unchanged, whilst peroxiredoxin I showed a trend of elevated levels in red blood cells of Au children. A very significant reduction of both erythrocyte and lymphocyte Na+, K+-ATPase activity (NKA), a reduction of erythrocyte membrane fluidity, a reduction of phospatydyl serine exposition on erythrocyte membranes, an alteration in erythrocyte fatty acid membrane profile (increase in MUFA and in ω6/ω3 ratio due to decrease in EPA and DHA) and a reduction of cholesterol content of erythrocyte membrane were found in Au compared to TD, without change in erythrocyte membrane sialic acid content and in lymphocyte membrane fluidity. Some Au clinical features appear to be correlated with these findings; in particular, hyperactivity score appears to be related with some parameters of the lipidomic profile and membrane fluidity, and ADOS and CARS score are inversely related to peroxiredoxin II levels. Oxidative stress and erythrocyte structural and functional alterations may play a role in the pathogenesis of Autism Spectrum Disorders and could be potentially utilized as peripheral biomarkers.

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.

Role of nuclear redox control, intra-population heterogeneity and oxygen tension in human amniotic fluid stem cells aging

Amniotic fluid stem cells (hAFSC) are emerging as a potential therapeutic approach for various disorders. The low number of available hAFSC requires their ex vivo expansion prior to clinical use, however, during their in vitro culture, hAFSC quickly reach replicative senescence. The principal aim of this study was to investigate the aging process occurring during in vitro expansion of hAFSC, focusing on the redox control that has been reported to be affected in premature and physiological aging. My results show that a strong heterogeneity is present among samples that reflects their different behaviour in culture. I identified three proteins, namely Nox4, prelamin A and PML, which expression increases during hAFSC aging process and could be used as new biomarkers to screen the samples. Furthermore, I found that Nox4 degradation is regulated by sumoylation via proteasome and involves interactions with PML bodies and prelamin A. Since various studies revealed that donor-dependent differences could be explained by cell-to-cell variation within each patient, I studied in deep this phenomenon. I showed that the heterogeneity among samples is also accompanied by a strong intra-population heterogeneity. Separation of hAFSC subpopulations from the same donor, using Celector® technology, showed that an enrichment in the last eluted fraction could improve hAFSC application in regenerative medicine. One of the other problems is that nowadays hAFSC are expanded under atmospheric O2 concentration, which is higher than the O2 tension in their natural niches. This higher O2 concentration might cause environmental stress to the in vitro cultured hAFSCs and accelerate their aging process. Here, I showed that prolonged low oxygen tension exposure preserves different hAFSC stemness properties. In conclusion, my study pointed different approaches to improve in vitro hAFSC expansion and manipulation with the purpose to land at stem cell therapy.

Preventing worker’s musculoskeletal disorders with wearable inertial sensors: ergonomics and modelling of physical human-robot interactions

The most widespread work-related diseases are musculoskeletal disorders (MSD) caused by awkward postures and excessive effort to upper limb muscles during work operations. The use of wearable IMU sensors could monitor the workers constantly to prevent hazardous actions, thus diminishing work injuries. In this thesis, procedures are developed and tested for ergonomic analyses in a working environment, based on a commercial motion capture system (MoCap) made of 17 Inertial Measurement Units (IMUs). An IMU is usually made of a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer that, through sensor fusion algorithms, estimates its attitude. Effective strategies for preventing MSD rely on various aspects: firstly, the accuracy of the IMU, depending on the chosen sensor and its calibration; secondly, the correct identification of the pose of each sensor on the worker’s body; thirdly, the chosen multibody model, which must consider both the accuracy and the computational burden, to provide results in real-time; finally, the model scaling law, which defines the possibility of a fast and accurate personalization of the multibody model geometry. Moreover, the MSD can be diminished using collaborative robots (cobots) as assisted devices for complex or heavy operations to relieve the worker's effort during repetitive tasks. All these aspects are considered to test and show the efficiency and usability of inertial MoCap systems for assessing ergonomics evaluation in real-time and implementing safety control strategies in collaborative robotics. Validation is performed with several experimental tests, both to test the proposed procedures and to compare the results of real-time multibody models developed in this thesis with the results from commercial software. As an additional result, the positive effects of using cobots as assisted devices for reducing human effort in repetitive industrial tasks are also shown, to demonstrate the potential of wearable electronics in on-field ergonomics analyses for industrial applications.

Integrating structural variant calling, annotation and prioritization into whole genome analysis workflows: a practical application in the molecular diagnosis of neurodevelopmental disorders

Background: WGS is increasingly used as a first-line diagnostic test for patients with rare genetic diseases such as neurodevelopmental disorders (NDD). Clinical applications require a robust infrastructure to support processing, storage and analysis of WGS data. The identification and interpretation of SVs from WGS data also needs to be improved. Finally, there is a need for a prioritization system that enables downstream clinical analysis and facilitates data interpretation. Here, we present the results of a clinical application of WGS in a cohort of patients with NDD. Methods: We developed highly portable workflows for processing WGS data, including alignment, quality control, and variant calling of SNVs and SVs. A benchmark analysis of state-of-the-art SV detection tools was performed to select the most accurate combination for SV calling. A gene-based prioritization system was also implemented to support variant interpretation. Results: Using a benchmark analysis, we selected the most accurate combination of tools to improve SV detection from WGS data and build a dedicated pipeline. Our workflows were used to process WGS data from 77 NDD patient-parent families. The prioritization system supported downstream analysis and enabled molecular diagnosis in 32% of patients, 25% of which were SVs and suggested a potential diagnosis in 20% of patients, requiring further investigation to achieve diagnostic certainty. Conclusion: Our data suggest that the integration of SNVs and SVs is a main factor that increases diagnostic yield by WGS and show that the adoption of a dedicated pipeline improves the process of variant detection and interpretation.