TDP43 is an ubiquitously expressed RNA-binding protein implicated in several aspects of RNA metabolism. It can shuttle between the nucleus and the cytoplasm; however, when it is mutated in some familial Amyotrophic Lateral Sclerosis (ALS) cases, it undergoes nuclear clearance and cytoplasmic accumulation, driving neuronal degeneration. The same phenotype is present in patients bearing ALS-inducing mutations in other genes and ALS sporadic patients, defining TDP43 proteinopathy as a common feature in this pathology. Why does it cause specific motor neuron death? Our quantitative proteomics analysis of the TDP43 interactome revealed the interaction with components of the mRNA surveillance pathway, suggesting a still undiscovered function in nonsense-mediated decay. We demonstrated that TDP43 acts translation- and SMG1-dependently as a mRNA decay enhancer of specific transcripts by binding their 3’UTR. In particular, it leads to the down-regulation of transcripts with a long 3’UTR. From our sequencing data of spinal cords from TDP43Q331K transgenic mouse model and of motor neuron-like NSC-34 cells silenced for TDP43 emerged that TDP43 plays another striking role in the 3’UTR, modulating mRNA alternative polyadenylation and promoting the generation of shorter transcripts. This finding is supported by the direct interaction of TDP43 with the cleavage stimulation factor, a core component of the polyadenylation machinery. These results broaden our knowledge of the role of TDP43 in the post-transcriptional gene expression regulation. The impairment of these two biological processes by TDP43 proteinopathy could have implications in ALS pathogenesis, representing possible new targets for therapeutic approaches.

In this thesis we introduce, define and quantitatively assess the stability of the algorithms for the econstruction of networks. We will focus on theory, development and implementation of operative procedures and algorithms for the assessment of stability in complex networks for biological systems, with gene regulatory networks as the key example. A major issue affecting network inference is indeed the high variability of network reconstruction and network topology inferred after data perturbation, different parameter choices and alternative methods. Network stability will thus be used to measure reliability of inferred topology, also obtaining confidence intervals for the outcomes. The methods will be employed to introduce a new approach to reproducibility in the study of complex networks. It will also be coupled with statistical machine learning models, in order to integrate feature selection and network inference within a pathway profiling approach. The evaluation of similarity between networks will be the first and central operative procedure of the developed pipelines, the key point being the identification of distances that can compare network structures improving over classical measures based on the confusion matrix, too coarse for this task. A combination of spectral and edit distances especially tailored for biological networks will be investigated and applied to several high-throughput biological datasets of different nature and with different tasks in oncogenomics, neurogenomics and exposomics.

To date little effort has been expended on the construction of cellular mimics from a minimum number of component parts. Such systems are desirable, because the cellular mimics could serve as useful tools to more deeply delve into the systems level reactions that sustain life and as a platform from which new types of technologies could be generated. Herein the building of cellular mimics that can sense and respond to external stimuli is presented. The majority of our efforts in building cellular mimics are directed towards the sensory element. Initially, previously characterized natural and artificial RNA sensors, i.e. a riboswitches, are exploited. Subsequently, the cellular mimics are implemented as chemical translators for natural bacterial cells. To expand the capabilities of the engineered cellular mimics, we sought to develop a methodology for the selection of new RNA-based sensors capable of detecting new analytes. The tested methodologies were based on mRNA display and strand displacement reactions. The mRNA display selection did not lead to the identification of a sensor responsive to malachite green after eight cycles of selection. Conversely, via ligand induced triggering of a strand displacement reaction, new RNA sensors for thiamine pyrophosphate were selected from a small library. The sensors displayed translational control ability as is typical of certain classes of riboswitches. The strand displacement-based selection method represents a first step towards the in vitro evolution of sensing elements than can be exploited for new cellular mimics with programmable sensing capability.

For many years, computers have played an important role in helping scientists to store, manipulate, and analyze data coming from many different disciplines. In recent years, however, new technological capabilities and new ways of thinking about the usefulness of computer science is extending the reach of computers from simple analysis of collected data to hypothesis generation. The aim of this work is to provide a contribution in the Computational Systems Biology field. The main purpose of this recent discipline is to enhance the intertwined relationship connecting Biology and Computer Science, by developing tools and theoretical frameworks able to formally and quantitatively investigate the interactions among the components of biological systems. The final goal of these efforts is to assemble the different pieces into a working model of a living, responding, reproducing cell; a model that can be used for performing in-silico tests and simulations in order to understand and predict possible emergent properties. In this thesis we present the application to real biological case studies of a specific concurrent modelling language (derived by the metaphors of molecules-as-object" -introduced by Fontana- and "cells-as-computations" -introduced by Regev and Shapiro- at the end of last century) and the development and implementation of a tool for inferring knowledge from experimental data in order to link the numerical aspects of a model to real wet-lab data."

Inflammation represents a pivotal, fine-tuned response in mammalian organisms. Not surprisingly, an uncontrolled and misregulated inflammation is major cause for important diseases, such as autoimmune disorders or cancer. Regulation of the inflammatory response occurs mainly through cytokines, which are strongly controlled at post-transcriptional levels by RNA binding proteins (RBPs). Among these, the Human Antigen R (HuR or ELAVL1), a member of the ELAVL family, embodies the multifaceted role of RBPs, participating in diverse processes including organismal development, cell growth, inflammation, and others, regulating roughly 7% of the full transcriptome. Consequently, small alterations in HuR expression or function can lead to severe consequences and numerous associated diseases. Importantly, HuR has been identified as a main regulator of the innate immune response, therefore its inhibition can have beneficial anti-inflammatory potential. The research for HuR inhibitors represents a challenging area in the drug discovery field, due to its pleiotropic functions and its structural complexity. Although the extensive efforts spent for this goal, no HuR inhibitor has succeeded so far in clinic. In 2015 our group identified through a High-throughput Screening a natural compound, DHTS as a novel HuR inhibitor. With a functional oriented approach, based on the chemical structure of DHTS-I, we synthesized a novel class of small molecules called Tanshinone Mimics (TMs), with a new molecular structure not previously described, aiming at increasing potency, specificity and solubility. In this thesis, I extensively characterized some of these molecules biochemically, in cellular models and in vivo. TMs are showing promising inhibitory activity against HuR modulating the immune response in murine and human macrophages. In human macrophages, TMs seem to activate caspase 3/7 apoptotic response, as well as to alter M1/M2 differentiation markers expression. Interestingly, TMs also affected some cancer traits in triple negative breast cancer cells. Studies on drug cell entrance suggest the TMs to have a quick metabolism within the cells. Characterization of TMs activity in vivo, appears in line with drug cell entrance studies and suggest the need for a frequent administration. However, TMs HuR specificity is in doubt due to some general toxicity observed in HuR negative cells. Finally, in a parallel approach, with a DEL-Open screen we have identified another promising HuR inhibitor, named WUXI7, which appears to be more stable over time inside cells. In a similar perspective to TMs, WUXI derivates have been synthesized and are currently under characterization.

Brachypodium distachyon è stato recentemente proposto come pianta modello per le Triticeae che includono frumento e orzo. L’obbiettivo del presente studio è stato quello di identificare regioni genomiche associate con la resistenza quantitativa alla ruggine fogliare in Brachypodium. Le malattie causate dalle ruggini fogliari causano ingenti perdite in termini di produzione delle specie cerealicole. Una popolazione di 110 individui F2 è stata sviluppata incrociando due linee inbred di Brachypodium e una mappa di linkage di marcatori AFLP è stata create. La mappa di linkage consiste di 192 loci AFLP in dieci gruppi di linkage, e copre una lunghezza pari a 1,231 Kosambi cM. Allo scopo di identificare loci coinvolti nella resistenza quantitativa sulla mappa, i 110 individui F2 sono stati valutati per la loro reazione alla ruggine fogliare allo stadio di plantula e a quello adulto. Per confermare i risultati delle piante F2, le rispettive famiglie F3 sono state studiate per la loro resistenza alla ruggine fogliare in due esperimenti indipendenti. Due loci genomici sembrano essere maggiormente coinvolti nella resistenza. / Brachypodium distachyon has been proposed as a model species for the tribe of the Triticeae, which includes wheat and barley. The objective of our study was to identify the genomic regions associated with quantitative resistance to leaf rust in Brachypodium. Leaf rust diseases cause significant reductions annually in yield of cereal crops worldwide. An F2 mapping population of 110 individuals was generated between two Brachypodium inbred lines and a AFLP-based linkage map was developed. The linkage map consists of 192 AFLP loci in ten linkage groups, and spans a total genetic length of 1,231 Kosambi cM. To locate quantitative resistance loci on the map, the 110 F2 plants were evaluated for their reaction to the leaf rust at both seedling and adult plant stages. To improve QTL identification, F2-derived F3 families were studied for resistance to leaf rust in two independent experiments. Two major genomic regions involved in resistance to leaf rust were detected.

Lo sviluppo delle tecnologie di sequenziamento ha portato alla nascita di strumenti in grado di produrre gigabasi di dati di sequenziamento in una singola corsa. Queste tecnologie, comunemente indicate come Next Generation Sequencing o NGS, producono grandi e complessi dataset la cui analisi comporta diversi problemi a livello bioinformatico. L'analisi di questo tipo di dati richiede la messa a punto di pipelines computazionali il cui sviluppo richiede un lavoro di scripting necessario per concatenare i softwares già esistenti. Questa tesi tratta l'aspetto metodologico dell'analisi di dati NGS ottenuti con tecnologia Illumina. In particolare in essa sono state sviluppate tre pipelines bioinformatiche applicate ai seguenti casi studio: 1) uno studio di espressione genica mediante RNA-seq in "Olea europaea" finalizzato all’indagine dei meccanismi molecolari alla base dell’acclimatazione al freddo in questa specie; 2) uno studio mediante RNA-seq finalizzato all’identificazione dei polimorfismi di sequenza nel trascrittoma di due razze bovine mirato a produrre un ampio catalogo di marcatori di tipo SNPs; 3) il sequenziamento, l’assemblaggio e l’annotazione del genoma di un ceppo di Lactobacillus plantarum che mostrava potenziali proprietà probiotiche. / The advance in sequencing technologies has led to the birth of sequencing platforms able to produce gigabases of sequencing data in a single run. These technologies commonly referred to as Next Generation Sequencing or NGS produce millions of short sequences called “reads” generating large and complex datasets that pose several challenges for Bioinformatics. The analysis of large omics dataset require the development of bioinformatics pipelines that are the organization of the bioinformatics tools in computational chains in which the output of one analysis is the input of the subsequent analysis. A work of scripting is needed to chain together a group of existing software tools.This thesis deals with the methodological aspect of the data analysis in NGS sequencing performed with the Illumina technology. In this thesis three bioinformatics pipelines were developed.to the following cases of study: 1) a global transcriptome profiling of “Oleaeuropeae” during cold acclimation, aimed to unravel the molecular mechanisms of cold acclimation in this species; 2) a SNPs profiling in the transcriptome of two cattle breeds aimed to produce an extensive catalogue of SNPs; 3) the genome sequencing, the assembly and annotation of the genome of a Lactobacillus plantarum strain showing probiotic properties.

In the last years, Genome-Wide Associations Studies (GWAS) found many variants associated with complex diseases. However, the biological and molecular links between these variants and phenotypes are still mostly unknown. Also, even if sample sizes are constantly increasing, the associated variants do not explain all the heritability estimated for many traits. Many hypotheses have been proposed to explain the problem: from variant-variant interactions, the effect of rare and ultra-rare coding variants and also technical biases related to sequencing or statistic on sexual chromosomes. In this thesis, we mainly explore the hypothesis of variant-variant interaction and, briefly, the rare coding variants hypothesis while also considering possible molecular effects like allele-specific expression and the effects of variants on protein interfaces. Some parts of the thesis are also devoted to explore the implementation of efficient computational tools to explore these effects and to perform scalable genotyping of germline single nucleotide polymorphisms (SNPs) in huge datasets. The main part of the thesis regards the development of a new resource to identify putative variant-variant interactions. In particular, we integrated ChIP-seq data from ENCODE, transcription factor binding motifs from several resources and genotype and transcript level data from GTeX and TCGA. This new dataset allows us to formalize new models, to make hypothesis and to find putative novel associations and interactions between (mainly non-coding) germline variants and phenotypes, like cancer-specific phenotypes. In particular, we focused on the characterization of breast cancer and Alzheimer’s Disease GWAS risk variants, looking for putative variants’ interactions. Recently, the study of rare variants has become feasible thanks to the biobanks that made available genotypes and clinical data of thousands of patients. We characterize and explore the possible effects of rare coding inherited polymorphisms on protein interfaces in the UKBioBank trying to understand if the change in structure of protein can be one of the causes of complex diseases. Another part of the thesis explores variants as causal molecular effect for allele-specific expression. In particular, we describe UTRs variants that can alter the post-transcriptional regulation in mRNA leading to a phenomenon where an allele is more expressed than the other. Finally, we show those variants can have prognostic significance in breast cancer. This thesis work introduces results and computational tools that can be useful to a broad community of researcher studying human polymorphisms effects.

Epigenetic alterations are observed in virtually all cancer types, yet there is limited understanding of their role in tumorigenesis and evolution. The role of DNA methylation has been particularly elusive in this context. While this epigenetic mark has been extensively profiled in healthy and cancerous samples, our ability to understand its relationship with underlying biological processes is still limited. Moreover, recent advancements in the profiling of cell-free DNA in circulation have sparked renowned attention toward tissue-specific and cancer-specific DNA methylation patterns. In this thesis, I present results to improve and refine the computational characterization of DNA methylation in cancer, focusing on metastatic castration-resistant prostate cancer. The first contribution is the development and performance assessment of Rockermeth, a computational methodology to leverage large-scale DNA methylation profiling data to nominate robust differentially methylated regions (DMRs). Rocker-meth can retrieve biologically relevant DNA methylation changes, as demonstrated by extensive integrative analyses with gene expression, chromatin states, and genomic annotations. The second contribution is the generation of a map of DNA methylation changes across prostate cancer progression. The application of Rockermeth and other tailored methodologies can be used to trace the critical evolutionary steps of this disease, from the healthy tissue to the most lethal metastatic AR-independent counterpart. The main result is the evidence of the ability of DNA methylation to capture a snapshot of the active transcription factors in each state of the disease, offering orthogonal information compared to standard genomic sequencing. The third contribution is the design and development of NEMO, a tailored liquid biopsy sequencing panel approach to allow non-invasive neuroendocrine castration-resistant prostate cancer detection in patients with metastatic disease. Based on previous results and the comprehensive analysis of multiple datasets, I designed a set of informative genomic regions to estimate disease burden and evidence of neuroendocrine transdifferentiation. The actual implementation of the NEMO panel produced a scalable and cost-effective strategy, which has been extensively benchmarked using both in silico and in vitro approaches. The application of NEMO to patient-derived cfDNA samples demonstrated accurate tumor content estimation and robust detection of neuroendocrine disease, making it a promising instance of liquid biopsy for CRPC.

Liquid biopsy and next-generation sequencing of cell-free DNA (cfDNA) in cancer patients’ plasma offer a minimally-invasive solution to detect tumor cell genomic information to aid real-time clinical decision-making. Reliability and sensitivity in the detection of genomic alterations is crucial for patient management and it is particularly relevant in the context of targeted therapies. However, biological and technical factors, such as low cfDNA tumor fraction and sequencing errors, affect the correct interpretation of genomic data limiting the utility of non-invasive cfDNA-based tumor profiling. To address these issues, we designed a prostate cancer bespoke assay, PCF_SELECT, that includes an innovative sequencing panel covering ∼25 000 high minor allele frequency SNPs and tailored analytical solutions to enable allele-informed evaluation of patients’ tumor. The framework also implements ABEMUS, an ad-hoc computational procedure we specifically designed for cfDNA samples to accurately detect somatic point mutations that could emerge under treatment pressure and as drug resistance mechanism. When applied on serial plasma samples from three patients receiving PARP inhibition harboring DNA repair gene aberrations, PCF_SELECT demonstrated high sensitivity in detecting BRCA2 allelic imbalance with decreasing tumor fractions resultant from treatment and identified complex ATM genomic states that may be incongruent with protein losses. As a step towards a more sensitive detection of tumor features in circulation of cancer patients, we next hypothesized that recent WGS-based approaches exploiting cfDNA fragments characteristics could be extrapolated for targeted sequencing data and that gene-region specific measures could improve detection metrics. Preliminary results suggest an increased sensitivity compared to copy-number-based methods supporting the integration at no extra cost in our targeted assay. Overall, this work is relevant to the context of precision oncology. It provides an innovative platform for the management of cancer patients, delivering detailed patient-specific molecular information which could be applied to guide treatment and improve clinical outcomes.