Extraire des voies cellulaires dynamiques avec des séries temporelles d'expression génétique : de l'analyse d'expression différentielle aux réseaux multicouches temporels

Dynamic pathways regulate gene expression by series of genes and proteins interacting with each other. They can be identified by first mapping dysregulated genes from differential expression analysis on biological networks. Second, subnetwork extraction identifies regions of the network enriched in dysregulated genes. However, most methods build or extract static networks and thus, dynamics of pathways are lost.Multilayer networks have been introduced to combine multiple data types and factors. In this thesis project, I developed a method that combines time-course gene expression datasets and multilayer networks, creating so-called temporal multilayer networks (tMLNs). Each layer represents one time-point as a biological network with dysregulated genes. Layers are linked to each other following the time axis. To predict dynamic pathways, I adapted classic subnetwork extraction to tMLNs. I implemented this approach in the Cytoscape app TimeNexus. I tested it on a yeast dataset to evaluate its efficiency to extract key cell-cycle regulators, as well as on a mouse dataset to identify subnetworks involved in the inflammation of sensory neurons. In a side project, I explored the lipid metabolism of the microalga Chlorella sp. HS2. Differential expression analysis showed that the overflow of metabolic co-factors is likely to induce a production of lipids under salt water.TimeNexus is the first method to extract subnetworks from tMLNs.Les voies cellulaires dynamiques régulent l'expression génétique par des séries de gènes et de protéines qui interagissent entre eux. Elles sont déterminées en commençant par marquer les gènes dérégulés, identifiés par l'analyse de l'expression différentielle, sur des réseaux biologiques. Ensuite, l’extraction de sous-réseaux identifie des régions enrichis en gènes dérégulés. Cependant, la plupart des méthodes construisent ou extraient des réseaux statiques et donc, la dynamique est perdue.Les réseaux multicouches peuvent combiner plusieurs types de données et facteurs. Dans ce projet de thèse, j'ai développé une méthode qui combine des données temporelles d’expression génétique à des réseaux multicouches, créant ainsi ce que l'on appelle des réseaux multicouches temporels (tMLNs). Chaque couche est un réseau biologique avec des gènes dérégulés à un point temporel. Les couches sont reliées entre elles en suivant l'axe temporel. Pour prédire les voies cellulaires, j'ai adapté l'extraction classique de sous-réseaux aux tMLNs. J'ai implémenté cette approche dans l'application Cytoscape TimeNexus. Je l'ai testée sur des données de levure pour évaluer son efficacité à extraire les principaux régulateurs du cycle cellulaire, ainsi que sur des données de souris pour identifier les sous-réseaux impliqués dans l'inflammation des neurones sensoriels. Dans un projet parallèle, j'ai exploré le métabolisme des lipides de la microalgue Chlorella sp. HS2. L'analyse de l'expression différentielle a montré que le surplus de cofacteurs métaboliques induirait une production de lipides dans l'eau salée.TimeNexus est la première méthode d'extraction de sous-réseaux à partir de tMLNs

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

International audienceAbstract Integrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus

Transcriptomic analysis of Chlorella sp. HS2 suggests the overflow of acetyl‐CoA and NADPH cofactor induces high lipid accumulation and halotolerance

International audiencePreviously, we isolated Chlorella sp. HS2 (referred hereupon as HS2) from a local tidal rock pool and demonstrated its halotolerance and high biomass productivity under different salinity conditions. To further understand acclimation responses of this alga under high salinity stress, we performed transcriptome analysis of triplicated culture samples grown in freshwater and marine conditions at both exponential and stationary growth phases. The results indicated that the transcripts involved in photosynthesis, TCA, and Calvin cycles were downregulated, whereas the upregulation of DNA repair mechanisms and an ABCB subfamily of eukaryotic type ABC transporter was observed at high salinity condition. In addition, while key enzymes associated with glycolysis pathway and triacylglycerol (TAG) synthesis were determined to be upregulated from early growth phase, salinity stress seemed to reduce the carbohydrate content of harvested biomass from 45.6 dw% to 14.7 dw% and nearly triple the total lipid content from 26.0 dw% to 62.0 dw%. These results suggest that the reallocation of storage carbon toward lipids played a significant role in conferring the viability of this alga under high salinity stress by remediating high level of cellular stress partially resulted from ROS generated in oxygen‐evolving thylakoids as observed in a direct measure of photosystem activities