An intelligent management of integrated biomedical data for digital health via Network Medicine and its application to different human diseases

Personalized medicine aims to tailor the health care to each person’s unique signature leading to better distinguish an individual patient from the others with similar clinical manifestation. Many different biomedical data types contribute to define this patient’s unique signature, such as omics data produced trough next generation sequencing technologies. The integration of single-omics data, in a sequential or simultaneous manner, could help to understand the interplay of the different molecules thus helping to bridge the gap between genotype and phenotype. To this end, Network Medicine offers a promising formalism for multi-omics data integration by providing a holistic approach that look at the whole system at once rather than focusing on the single entities. This thesis regards the integration of various omics data following two different procedures within the framework of Network Medicine: A procedural multi-omics data integration, where a single omics was first selected to perform the main analysis, and then the other omics were used in cascade to molecularly characterize the results obtained in the main analysis. A parallel multi-omics data integration, where the result was given by the intersection of the results of each single-omics. The procedural multi-omics data integration was leveraged to study Colorectal and Breast Cancer. In the Colorectal Cancer case study, we defined the molecular signatures of a new subgroup of Colorectal Cancer possibly eligible for immune-checkpoint inhibitors therapy. Moreover, in the Breast Cancer case study we defined 11 prognostic biomarkers specific for the Basal-like subtype of Breast Cancer. Instead, the parallel multi-omics data integration was exploited to study COVID-19 and Chronic Obstructive Pulmonary Disease. In the COVID-19 case study, we defined a pool of drugs potentially repurposable for COVID-19. Whereas, in the Chronic Obstructive Pulmonary Disease case study, we discovered a group of differentially expressed and methylated genes that have a considerable biological specificity and could be related to the inflammatory pathological mechanism of Chronic Obstructive Pulmonary Disease

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

Non-Coding RNAs Improve the Predictive Power of Network Medicine

Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with protein-protein interactions, constructing the first comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA, expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases, lacked a statistically significant disease module in the protein-based interactome, but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including non-coding interactions improves both the breath and the predictive accuracy of network medicine.Comment: Paper and S

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases

A module based approach for identifying driver genes and expanding pathways from integrated biological networks

Each gene or protein has its own function which, when combined with others, allows the group to perform more complex behaviors, e.g. carry out a particular cellular task (functional module) or affect a particular disease phenotype (disease module). One of the major challenges in systems biology is to reveal the roles of genes or proteins in functional modules or disease modules. In the first part of the dissertation, I present a data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and specific types of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their targets, I focus on coherence of regulatees of a regulator, e.g. downstream targets of a transcription factor. Using simulated datasets I show that my method can reach high true positive rate and true negative rate (>80%) even the regulatory relationships is weak (only 20% of regulatees are co-expressed). Using three separate real biological datasets I was able to recover well-known and as- yet undescribed, active regulators for each disease population. In the second part of the dissertation, I develop and apply a new computational algorithm for detecting modules of functionally related genes that are likely to drive malignant transformation. The algorithm takes as input the identity and locations of a small number of known oncogenes (a seed set) on a human genome functional linkage network (FLN). It then searches for a boundary surrounding a gene set encompassing the seed, such that the magnitude of the difference in linkage weights between interior-interior gene pairs, and interior-exterior gene pairs is maximized. Starting with small seed sets for breast and ovarian cancer, I successfully identify known and novel drivers in both cancer types. In the third part of the dissertation, I propose a module based approach for expanding manually curated functional modules. I use the KEGG pathway database as an example and the results show that my approach can successfully suggest both validated pathway members (genes that are assigned to a particular pathway by other manually curated pathway databases) and novel candidate pathway genes

Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor

<p>Abstract</p> <p>Background</p> <p>Elucidating the sequence of molecular events underlying breast cancer formation is of enormous value for understanding this disease and for design of an effective treatment. Gene expression measurements have enabled the study of transcriptome-wide changes involved in tumorigenesis. This usually occurs through identification of differentially expressed genes or pathways.</p> <p>Results</p> <p>We propose a novel approach that is able to delineate new cancer-related cellular processes and the nature of their involvement in tumorigenesis. First, we define modules as densely interconnected and functionally enriched areas of a Protein Interaction Network. Second, 'differential expression' and 'differential co-expression' analyses are applied to the genes in these network modules, allowing for identification of processes that are up- or down-regulated, as well as processes disrupted (low co-expression) or invoked (high co-expression) in different tumor stages. Finally, we propose a strategy to identify regulatory miRNAs potentially responsible for the observed changes in module activities. We demonstrate the potential of this analysis on expression data from a mouse model of mammary gland tumor, monitored over three stages of tumorigenesis. Network modules enriched in adhesion and metabolic processes were found to be inactivated in tumor cells through the combination of dysregulation and down-regulation, whereas the activation of the integrin complex and immune system response modules is achieved through increased co-regulation and up-regulation. Additionally, we confirmed a known miRNA involved in mammary gland tumorigenesis, and present several new candidates for this function.</p> <p>Conclusions</p> <p>Understanding complex diseases requires studying them by integrative approaches that combine data sources and different analysis methods. The integration of methods and data sources proposed here yields a sensitive tool, able to pinpoint new processes with a role in cancer, dissect modulation of their activity and detect the varying assignments of genes to functional modules over the course of a disease.</p