Biology subjects

Tichit, L.

Publications and source records attributed to Tichit, L..

5 recordsLinked to original sources

bioRxiv2022

Modular analysis of a logical regulatory network reveals how Fe-S cluster biogenesis is controlled in the face of stress

Iron-sulfur (Fe-S) clusters are important cofactors conserved in all domains of life, yet their synthesis and stability is compromised in stressful conditions such as iron deprivation and oxidative stress. Two conserved machineries, Isc and Suf, assemble and transfer Fe-S clusters to client proteins. The model bacterium Escherichia coli, where Fe-S biogenesis has been extensively studied, possesses both Isc and Suf machineries and their utilisation is under the control of a complex regulatory network. To better understand the underlying regulatory mechanisms and the dynamics behind Fe-S biogenesis in function of environmental conditions, we built a logical model describing Fe-S biogenesis in E. coli. This logical model is centered on three modules : 1) the Fe-S biogenesis module containing the Fe-S cluster assembly machineries Isc and Suf and the transcription factor IscR, the main regulator of Fe-S homeostasis; 2) the iron homeostasis module containing the free intracellular iron regulated by the iron sensing regulator Fur and the non-coding regulatory RNA RyhB involved in iron sparing; 3) the oxidative stress module representing intracellular H2O2 accumulation, which activates OxyR, the regulator of catalases and peroxidases that decompose H2O2 and limit Fenton reaction. Inputs of the model represent extracellular iron and oxygen environmental conditions, whereas ErpA, NfuA (Fe-S carrier proteins), and Suf are output nodes of the model. Analysis of this comprehensive model reveals 5 different types of system behaviours depending on environmental conditions, and provides a better understanding on how oxidative stress and iron homeostasis combine and control Fe-S biogenesis. Using the model, we were able to predict that an iscR mutant would present growth defects in iron starvation, due to partial inability to build Fe-S clusters, and we validated this prediction experimentally. Author summaryIron sulfur (Fe-S) clusters appeared at the origins of life, when oxygen tension was low and iron plentiful, and have been used since as important cofactors for a wide variety of proteins involved in a plethora of reactions. However, synthesis and stability of Fe-S clusters is compromised in conditions where iron is low or in presence of reactive oxygen species. Living organisms have developed complex regulatory network to allow biogenesis of Fe-S clusters in function of environmental conditions. Thus, understanding this regulation is of primary importance as changes in Fe-S cluster biogenesis impact the physiology of the cell and is for instance involved in resistance of bacteria to certain antibiotics. To do this, we used a modeling approach to gain a global systemic understanding of the process. We thus developed a mathematical logical model which extensively describes the regulatory network that controls biogenesis of Fe-S clusters in the model bacterium Escherichia coli. Analysis of the model reveals how Fe-S biogenesis is organized in function of environmental conditions and reveals how oxidative stress and iron homeostasis combine and control Fe-S biogenesis.

systems biology↗

bioRxiv2021

An integrated analysis tool reveals intrinsic biases in gene set enrichment

Generating meaningful interpretations of gene lists remains a challenge for all large-scale studies. Many approaches exist, often based on evaluating gene enrichment among pre-determined gene classes. Here, we conceived and implemented yet another analysis tool (YAAT), specifically for data from the widely-used model organism C. elegans. YAAT extends standard enrichment analyses, using a combination of co-expression data and profiles of phylogenetic conservation, to identify groups of functionally-related genes. It additionally allows class clustering, providing inference of functional links between groups of genes. We give examples of the utility of YAAT for uncovering unsuspected links between genes and show how the approach can be used to prioritise genes for in-depth study. Our analyses revealed several limitations to the meaningful interpretation of gene lists, specifically related to data sources and the "universe" of gene lists used. We hope that YAAT will represent a model for integrated analysis that could be useful for large-scale exploration of biological function in other species.

bioinformatics↗

bioRxiv2021

Inferring cell cycle phases from a partially temporal network of protein interactions

The temporal organisation of biological systems into phases and subphases is often crucial to their functioning. Identifying this multiscale organisation can yield insight into the underlying biological mechanisms at play. To date, however, this identification requires a priori biological knowledge of the system under study. Here, we recover the temporal organisation of the cell cycle of budding yeast into phases and subphases, in an automated way. To do so, we model the cell cycle as a partially temporal network of protein-protein interactions (PPIs) by combining a traditional static PPI network with protein concentration or RNA expression time series data. Then, we cluster the snapshots of this temporal network to infer phases, which show good agreement with our biological knowledge of the cell cycle. We systematically test the robustness of the approach and investigate the effect of having only partial temporal information. Our results show for the first time that a temporal network with only partial temporal information, i.e. for some of the PPIs, is sufficient to infer the temporal organization of a system. The generality of the method makes it suitable for application to other, less well-known biological systems for which the temporal organisation of processes plays an important role.

systems biology↗

bioRxiv2020

A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks

The identification of subnetworks of interest - or active modules - by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in multiplex biological networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. AvailabilityMOGAMUN is available at https://github.com/elvanov/MOGAMUN. Contactelva.novoa@inserm.fr, anais.baudot@univ-amu.fr

systems biology↗

bioRxiv2017

Random Walk With Restart On Multiplex And Heterogeneous Biological Networks

Recent 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.\n\nIn 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.\n\nWe 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.\n\nFinally, 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.\n\nThe source code and the software are freely available at: https://github.com/alberto-valdeolivas/RWR-MH.

systems biology↗