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

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Towards a 21st-century roadmap for biomedical research and drug discovery:consensus report and recommendations

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies, and the corporate and nongovernmental organisation (NGO) sectors, in this consensus report, we analyse, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathway-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this

Computational biology helps understand how polyploid giant cancer cells drive tumor success

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) “What is the current knowledge about polyploidy in tumors?”; (ii) “What are the applications of computational studies for the understanding of cancer polyploidy?”; and (iii) “How do PGCCs contribute to tumorigenesis?

NEW PHARMACOLOGICAL TARGETS FOR CYSTIC FIBROSIS TREATMENT FROM OMICS PROFILING OF F508del-CFTR EXPRESSING CELLS.

Cystic fibrosis (CF) is a genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, encoding an anion channel expressed on the epithelial cells of a variety of tissues. The deletion of phenylalanine in position 508 (F508del) is the most frequent CF-causing mutation, causing impaired trafficking and activity of the mutant channel. Today, two classes of drugs are available to treat CF: potentiators (molecules that increase the mutant CFTR function on the cell surface) and correctors (molecules that improve the processing and the delivery of mutant CFTR to the cell surface). It has been demonstrated that the combination of molecules exploiting different mechanisms of action is needed to achieve a therapeutically relevant rescue of CFTR. The aim of this project is to apply mass spectrometry (MS)-based omics techniques to understand what is associated to CFTR rescue and thus finding new potential targets for CF pharmacological treatment. Several strategies were applied for the functional rescue of CFTR at the plasma membrane (both pharmacological and genetic rescue). Proteomic and lipidomic profilings of F508del-CFTR expressing cells were performed after the application of these rescue strategies. The proteomic experiments were performed following the SWATH label-free quantification workflow, after the optimization of the panhuman ion library for CF research. Together with these experiments, Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) workflow was also applied to detect proteins that change their subcellular localization after the treatment with the most used corrector available for CF therapy (VX-809). Several candidate targets were found to be involved in F508del-CFTR rescue. Compounds aiming at the inhibition of these putative targets were tested on both immortalized and primary cells. Further validation studies are needed to confirm these results

Functional genomics for breast cancer drug target discovery

Breast cancer is a heterogeneous disease that develops through a multistep process via the accumulation of genetic/epigenetic alterations in various cancer-related genes. Current treatment options for breast cancer patients include surgery, radiotherapy, and chemotherapy including conventional cytotoxic and molecular-targeted anticancer drugs for each intrinsic subtype, such as endocrine therapy and antihuman epidermal growth factor receptor 2 (HER2) therapy. However, these therapies often fail to prevent recurrence and metastasis due to resistance. Overall, understanding the molecular mechanisms of breast carcinogenesis and progression will help to establish therapeutic modalities to improve treatment. The recent development of comprehensive omics technologies has led to the discovery of driver genes, including oncogenes and tumor-suppressor genes, contributing to the development of molecular-targeted anticancer drugs. Here, we review the development of anticancer drugs targeting cancer-specific functional therapeutic targets, namely, MELK (maternal embryonic leucine zipper kinase), TOPK (T-lymphokine-activated killer cell-originated protein kinase), and BIG3 (brefeldin A-inhibited guanine nucleotide-exchange protein 3), as identified through comprehensive breast cancer transcriptomics

Effects of abiotic stress on plants: a systems biology perspective

The natural environment for plants is composed of a complex set of abiotic stresses and biotic stresses. Plant responses to these stresses are equally complex. Systems biology approaches facilitate a multi-targeted approach by allowing one to identify regulatory hubs in complex networks. Systems biology takes the molecular parts (transcripts, proteins and metabolites) of an organism and attempts to fit them into functional networks or models designed to describe and predict the dynamic activities of that organism in different environments. In this review, research progress in plant responses to abiotic stresses is summarized from the physiological level to the molecular level. New insights obtained from the integration of omics datasets are highlighted. Gaps in our knowledge are identified, providing additional focus areas for crop improvement research in the future