Ricerc@Sapienza

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is a rare disease, characterised by the aplasia of vagina and uterus in women with a 46,XX karyotype. Most cases are sporadic, but familial recurrence has also been described. Herein, we investigated an Italian cohort of 36 unrelated MRKH patients to explore the presence of pathogenic copy number variations (CNVs) by array-CGH and MLPA assays. On the whole, aberrations were found in 9/36 (25%) patients.

The emerging Network Medicine domain is causing a shift between diagnosis based on the conventional reductionist approach, arguing that biological factors work in a simple linear way, and the analysis of perturbations within the comprehensive network map of molecular components and their interactions, i.e., the "Interactome". As a consequence, clinicians are investigating more than 140,000 interactions between more than 13,000 genes and their connections with drugs and diseases, along a sequence of "networks".

Lung adenocarcinoma is the tumor with the highest number of switch genes (298) compared to its normal tissue, followed by thyroid (227) and colorectal (183) cancers. Switch genes codifying for kinases were 14,7 and 3 respectively.We looked for three homology sequences identified across vemurafenib targets and we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch genes kinase (5-6); on the contrary, colorectal cancer has just one,with minor
homology sequence.

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm.

Purpose: Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAF V600E mutant tumours and the BRAF inhibitor vemurafenib.

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).

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators.

Systems biology focuses on the integration of experimental, mathematical and computational techniques to develop systemic views and predictive models of biological systems. In this perspective, the concept of network has been a powerful tool for the representation and the analysis of complex systems: during the last two decades, the so-called network biology approach has been fruitfully applied in many different biological areas, from gene regulation, to protein-protein interactions, to neural signals.