Network-based approaches to explore complex biological systems towards network medicine

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. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

Fibronectin Contributes To Notochord Intercalation In The Invertebrate Chordate, Ciona Intestinalis

Background: Genomic analysis has upended chordate phylogeny, placing the tunicates as the sister group to the vertebrates. This taxonomic rearrangement raises questions about the emergence of a tunicate/vertebrate ancestor. Results: Characterization of developmental genes uniquely shared by tunicates and vertebrates is one promising approach for deciphering developmental shifts underlying acquisition of novel, ancestral traits. The matrix glycoprotein Fibronectin (FN) has long been considered a vertebrate-specific gene, playing a major instructive role in vertebrate embryonic development. However, the recent computational prediction of an orthologous “vertebrate-like” Fn gene in the genome of a tunicate, Ciona savignyi, challenges this viewpoint suggesting that Fn may have arisen in the shared tunicate/vertebrate ancestor. Here we verify the presence of a tunicate Fn ortholog. Transgenic reporter analysis was used to characterize a Ciona Fn enhancer driving expression in the notochord. Targeted knockdown in the notochord lineage indicates that FN is required for proper convergent extension. Conclusions: These findings suggest that acquisition of Fn was associated with altered notochord morphogenesis in the vertebrate/tunicate ancestor

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Ara-bidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species

Systematic prediction of feedback regulatory network motifs

Comprendre le câblage complexe de la régulation cellulaire reste un défi des plus redoutables.Les connaissances fondamentales sur le câblage et le fonctionnement du réseau d’homéostasiedes protéines aideront à mieux comprendre comment l’homéostasie des protéines échouedans les maladies et comment les modèles de régulation du réseau d’homéostasie desprotéines peuvent être ciblés pour une intervention thérapeutique. L’étude vise à développeret à appliquer une nouvelle méthodologie de calcul pour l’identification systématique etla caractérisation des systèmes de rétroaction en homéostasie des protéines. La rechercheproposée combine des idées et des approches issues de la science des protéines, de la biologiedes systèmes de levure, de la biologie computationnelle et de la biologie des réseaux.La difficulté dans la tâche d’incorporer des données multi-plateformes multi-omiques estamplifiée par le vaste réseau de gènes, protéines et métabolites interconnectés qui seréunissent pour remplir une fonction spécifique. Pour ma thèse de maîtrise, j’ai développéun algorithme PBPF (Path-Based Pattern Finding), qui recherche et énumère les motifsde réseau de la topologie requise. Il s’agit d’un algorithme basé sur la théorie des graphesqui utilise la combinaison d’une méthode transversale de profondeur et d’une méthodede recherche par largeur ensuite pour identifier les topologies de sous-graphes de réseaurequises. En outre, le fonctionnement de l’algorithme a été démontré dans les domainesde l’homéostasie des protéines chezSaccharomyces cerevisiae. Une approche systématiqued’intégration des données de la biologie des systèmes a été orchestrée, qui montre l’iden-tification systématique de motifs de rétroaction régulatrice connus dans l’homéostasie desprotéines. Il revendique fortement la capacité d’identifier de nouveaux motifs de rétroactionréglementaire envahissants. L’application de l’algorithme peut être étendue à d’autressystèmes biologiques, par exemple, pour identifier des motifs de rétroaction spécifiques àl’état cellulaire dans le cas de cellules souches.Understanding the intricate wiring of cellular regulation remains a most formidable chal-lenge. The fundamental insights into the wiring and functioning of the protein homeostasisnetwork will help to better understand how protein homeostasis fails in diseases and howthe regulatory patterns of protein homeostasis network can be targeted for therapeuticintervention. The study aims at developing and applying novel computational methodologyfor the systematic identification and characterization of feedback systems in proteinhomeostasis. The proposed research combines ideas and approaches from protein science,yeast systems biology, computational biology, as well as network biology. The difficultyin the task of incorporating multi-platform multi-omics data is amplified by the largenetwork of inter-connected genes, proteins and metabolites that come together to perform aspecific function. For my master’s thesis, I developed a path-based pattern finding (PBPF)algorithm, which searches and enumerates network motifs of required topology. It is a graphtheory based algorithm which utilizes the combination of depth-first transverse method andbreadth-first search method to identify the required network sub-graph topologies. Further,the functioning of the algorithm has been demonstrated in the realms of protein homeostasisinSaccharomyces cerevisiae. A systematic approach of integration of systems biologydata has been orchestrated, which shows the systematic identification of known regulatoryfeedback motifs in protein homeostasis. It claims the unique ability to identify novelpervasive regulatory feedback motifs. The application of the algorithm can be extended toother biological systems, for example, to identify cell-state specific feedback motifs in caseof stem-cells