Science, medicine, and the future. Prospecting for gold in the human genome

Doctors struggling with the daily problems of clinical medicine usually have little time for molecular and cell biology. But genetic research is producing an explosion of knowledge which doctors will need to understand in order to join in the ethical and financial debates that will inevitably follow the new treatments discovered. There may, indeed, be therapeutic gold hidden in our genes, but the price for it could be more than we can afford. This is the first of three articles introducing a series which aims to convey the excitement and potential power of biomedical science by speculating how current research will impinge on clinical management of common conditions

INSIdE NANO : a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.Peer reviewe

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.DDF, YG, AP, CWD, BBM, DH, JR, and VC have been funded by Enveda Biosciences. This work has been funded by Enveda Biosciences (https://www.envedabio.com/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SM and DRB received no specific funding for this work.Peer ReviewedPostprint (author's final draft

Microenvironment and tumor cells: two targets for new molecular therapies of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), is one of the most frequent human cancer and is characterized by a high mortality rate. The aggressiveness appears strictly related to the liver pathological background on which cancer develops. Inflammation and the consequent fibro/cirrhosis, derived from chronic injuries of several origins (viral, toxic and metabolic) and observable in almost all oncological patients, represents the most powerful risk factor for HCC and, at the same time, an important obstacle to the efficacy of systemic therapy. Multiple microenvironmental cues, indeed, play a pivotal role in the pathogenesis, evolution and recurrence of HCC as well as in the resistance to standard therapies observed in most of patients. The identification of altered pathways in cancer cells and of microenvironmental changes, strictly connected in pathogenic feedback loop, may permit to plan new therapeutic approaches targeting tumor cells and their permissive microenvironment, simultaneousl

Integration of multi-scale protein interactions for biomedical data analysis

With the advancement of modern technologies, we observe an increasing accumulation of biomedical data about diseases. There is a need for computational methods to sift through and extract knowledge from the diverse data available in order to improve our mechanistic understanding of diseases and improve patient care. Biomedical data come in various forms as exemplified by the various omics data. Existing studies have shown that each form of omics data gives only partial information on cells state and motivated jointly mining multi-omics, multi-modal data to extract integrated system knowledge. The interactome is of particular importance as it enables the modelling of dependencies arising from molecular interactions. This Thesis takes a special interest in the multi-scale protein interactome and its integration with computational models to extract relevant information from biomedical data. We define multi-scale interactions at different omics scale that involve proteins: pairwise protein-protein interactions, multi-protein complexes, and biological pathways. Using hypergraph representations, we motivate considering higher-order protein interactions, highlighting the complementary biological information contained in the multi-scale interactome. Based on those results, we further investigate how those multi-scale protein interactions can be used as either prior knowledge, or auxiliary data to develop machine learning algorithms. First, we design a neural network using the multi-scale organization of proteins in a cell into biological pathways as prior knowledge and train it to predict a patient's diagnosis based on transcriptomics data. From the trained models, we develop a strategy to extract biomedical knowledge pertaining to the diseases investigated. Second, we propose a general framework based on Non-negative Matrix Factorization to integrate the multi-scale protein interactome with multi-omics data. We show that our approach outperforms the existing methods, provide biomedical insights and relevant hypotheses for specific cancer types

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Redox control of multidrug resistance and Its possible modulation by antioxidants

Clinical efficacy of anticancer chemotherapies is dramatically hampered by multidrug resistance (MDR) dependent on inherited traits, acquired defence against toxins, and adaptive mechanisms mounting in tumours. There is overwhelming evidence that molecular events leading to MDR are regulated by redox mechanisms. For example, chemotherapeutics which overrun the first obstacle of redox-regulated cellular uptake channels (MDR1, MDR2, and MDR3) induce a concerted action of phase I/II metabolic enzymes with a temporal redox-regulated axis. This results in rapid metabolic transformation and elimination of a toxin. This metabolic axis is tightly interconnected with the inducible Nrf2-linked pathway, a key switch-on mechanism for upregulation of endogenous antioxidant enzymes and detoxifying systems. As a result, chemotherapeutics and cytotoxic by-products of their metabolism (ROS, hydroperoxides, and aldehydes) are inactivated and MDR occurs. On the other hand, tumour cells are capable of mounting an adaptive antioxidant response against ROS produced by chemotherapeutics and host immune cells. The multiple redox-dependent mechanisms involved in MDR prompted suggesting redox-active drugs (antioxidants and prooxidants) or inhibitors of inducible antioxidant defence as a novel approach to diminish MDR. Pitfalls and progress in this direction are discussed