7 research outputs found
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 rĂ©gulent l'expression gĂ©nĂ©tique par des sĂ©ries de gĂšnes et de protĂ©ines qui interagissent entre eux. Elles sont dĂ©terminĂ©es en commençant par marquer les gĂšnes dĂ©rĂ©gulĂ©s, identifiĂ©s par l'analyse de l'expression diffĂ©rentielle, sur des rĂ©seaux biologiques. Ensuite, lâextraction de sous-rĂ©seaux identifie des rĂ©gions enrichis en gĂšnes dĂ©rĂ©gulĂ©s. Cependant, la plupart des mĂ©thodes construisent ou extraient des rĂ©seaux statiques et donc, la dynamique est perdue.Les rĂ©seaux multicouches peuvent combiner plusieurs types de donnĂ©es et facteurs. Dans ce projet de thĂšse, j'ai dĂ©veloppĂ© une mĂ©thode qui combine des donnĂ©es temporelles dâexpression gĂ©nĂ©tique Ă des rĂ©seaux multicouches, crĂ©ant ainsi ce que l'on appelle des rĂ©seaux multicouches temporels (tMLNs). Chaque couche est un rĂ©seau biologique avec des gĂšnes dĂ©rĂ©gulĂ©s Ă un point temporel. Les couches sont reliĂ©es entre elles en suivant l'axe temporel. Pour prĂ©dire les voies cellulaires, j'ai adaptĂ© l'extraction classique de sous-rĂ©seaux aux tMLNs. J'ai implĂ©mentĂ© cette approche dans l'application Cytoscape TimeNexus. Je l'ai testĂ©e sur des donnĂ©es de levure pour Ă©valuer son efficacitĂ© Ă extraire les principaux rĂ©gulateurs du cycle cellulaire, ainsi que sur des donnĂ©es de souris pour identifier les sous-rĂ©seaux impliquĂ©s dans l'inflammation des neurones sensoriels. Dans un projet parallĂšle, j'ai explorĂ© le mĂ©tabolisme des lipides de la microalgue Chlorella sp. HS2. L'analyse de l'expression diffĂ©rentielle a montrĂ© que le surplus de cofacteurs mĂ©taboliques induirait une production de lipides dans l'eau salĂ©e.TimeNexus est la premiĂšre mĂ©thode d'extraction de sous-rĂ©seaux Ă 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 proteinâprotein 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 acetylâCoA 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 oxygenâevolving thylakoids as observed in a direct measure of photosystem activities