Identifying communities from multiplex biological networks by randomized optimization of modularity [version 2; referees: 1 approved, 3 approved with reservations]

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challenge benchmark, the results strongly depend on the selected GWAS dataset and enrichment p-value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

Approches pour explorer les réseaux biologiques multiplex et application aux maladies du vieillissement prématuré

Les gènes et les protéines n’agissent pas de manière isolée dans les cellules, mais interagissent plutôt pour faire leurs fonctions dans les processus biologiques. Ces interactions peuvent être représentées sous forme de grands réseaux dans lesquels les nœuds sont des gènes ou des protéines et les arêtes représentent leurs interactions. Diverses approches basées sur la théorie des graphes ont été développées pour extraire la connaissance fonctionnelle contenue dans ces réseaux. Néanmoins, ces méthodes ont été principalement appliquées à des réseaux individuels, en ignorant la diversité des interactions biologiques. Nous déclarons que ces différents types d’interactions peuvent être représentés sous la forme de réseaux multiplexes, c’est-à-dire des ensembles de réseaux partageant les mêmes nœuds, ce qui permet une description plus précise des systèmes biologiques. Cette thèse est focalisée sur le développement de nouveaux algorithmes étendant aux réseaux multiplexes certaines méthodes populaires de la théorie des graphes en biologie computationnelle, ainsi que sur leur application à l’étude des maladies humaines. Du côté des applications, nous nous concentrons sur les maladies liées au vieillissement prématuré, un groupe de maladies génétiques ressemblant à certains aspects du vieillissement physiologique à un âge précoce. Nous avons appliqué nos algorithmes pour détecter les modules associés à plus de 70 syndromes annotés avec un phénotype lié au vieillissement prématuré. Les résultats ont révélé le paysage des processus moléculaires perturbés dans ces maladies, qui peuvent être mis en parallèle avec les caractéristiques du vieillissement physiologique.Genes and proteins do not act isolated in cells but rather interact to perform their functions in signaling pathways, molecular complexes, or, more generally, biological processes. These interactions can be represented as large networks in which nodes are genes or proteins and edges represent their interactions. Various graph-theory based approaches have been developed to extract the functional knowledge contained in biological networks. Nevertheless, these methods have been mainly applied to individual networks, ignoring the diversity of biological interactions. We state here that these different types of interactions can be represented as multiplex networks, i.e. collections of networks sharing the same nodes, leading to a more accurate description of biological systems. This thesis focuses on the extension from individual to multiplex networks of some of the state-of-the-art guilt-by-association methods in computational biology, and on their application to the study of human diseases. On the application side, we concentrate on premature aging diseases, a group of rare genetic disorders that resemble some aspects of physiological aging at an early age. In this framework, we applied our algorithms to detect the modules associated to more than 70 disorders annotated with at least one premature aging related phenotype. The results revealed the landscape of perturbed molecular processes in premature aging diseases, which can be paralleled with the hallmarks of physiological aging to help identifying common and specific features

MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their efficiency for tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several layers containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE method with Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its efficiency. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in the task of link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering

Prediction of Disease-associated Genes by advanced Random Walk with Restart on Multiplex and Heterogeneous Biological Networks

International audienceRare monogenic diseases globally affect millions of persons, but many causative genes remain to be discovered. Several computational approaches have been developed to predict disease-associated genes. Guilt-by-association strategies on protein interaction networks, in particular, postulate that proteins lying in a close network vicinity are functionally-related and implicated in similar phenotypes. However, current network approaches are limited as they do not exploit the richness of biological networks, which are both multiplex (i.e., containing different layers of physical and functional interactions between genes and proteins), and heterogeneous (i.e., containing both interactions between genes/proteins, and interactions between diseases). In the present study, we extended the Random Walk with Restart algorithm to leverage these complex biological networks. We compared our algorithm to classical random walks thanks to a leave-one-out strategy. The Random Walk with Restart on multiplex and heterogeneous networks takes advantage of data pluralism and shows increased performances to predict known disease-associated genes. We finally applied it to predict candidate genes for the Wiedemann-Rautenstrauch Syndrome

Random walk with restart on multiplex and heterogeneous biological networks

International audienceRecent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each others in the networks. In the present study, we extended the random walk with restart algorithm to multiplex and heterogeneous networks. The walk can now explore different layers of physical and functional interactions between genes and proteins, such as protein-protein interactions and co-expression associations. In addition, the walk can also jump to a network containing different sets of edges and nodes, such as phenotype similarities between diseases. We devised a leave-one-out cross-validation strategy to evaluate the algorithms abilities to predict disease-associated genes. We demonstrate the increased performances of the multiplex-heterogeneous random walk with restart as compared to several random walks on monoplex or heterogeneous networks. Overall, our framework is able to leverage the different interaction sources to outperform current approaches. Finally, we applied the algorithm to predict genes candidate for being involved in the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome. The source code and the software are freely available at: https://github. com/alberto-valdeolivas/RWR-MH

Integrative proteomic and phosphoproteomic profiling of prostate cell lines

International audienc