Sophia Tsouka and Vassily Hatzimanikatis are the Winners of the 2020 Best Article Award From FEMS Yeast Research
28-01-21
Dr Sophia Tsouka and Prof Vassily Hatzimanikatis (@Vassily_13, @realLCSB) from the École Polytechnique Fédérale de Lausanne (Switzerland) are the winners of the 2020 best article award from FEMS Yeast Research. Their winning paper is titled redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast.
We interviewed Sophia and Vassily to find out more about the paper and their answers can be found bellow.
Could you provide a brief, simple overview of the topic your paper covers?
Our paper presents the most exhaustive network model of yeast lipid metabolism to date. redLips is a high-quality mechanistic model, curated with respect to the stoichiometry of individual reactions using a semi-automatic workflow. The model’s reactions are exactly balanced with respect to elements, cofactors and small molecules, and we additionally provide an estimate of the thermodynamic properties for each compound and reaction of the network, in terms of the Gibbs free energy of formation and the change in Gibbs free energy, respectively. redLips stands out from traditional context-specific models due to the fact that it has been expressively curated to ensure the production of each biomass building block from available extracellular resources, while satisfying various metabolic tasks, such as the biosynthesis of growth-essential lipids and gene essentiality. Finally, we show that redLips performs equally or even better than well-established metabolic models of yeast concerning the metabolism of lipids, thus providing a comprehensive platform that can facilitate studies in multiple contexts.”
What research areas can your model help us understand?
redLips can certainly be used as a scaffold for the integration of lipidomics and other omics data (such as metabolomics and fluxomics). Due to its detailed network, the majority of known lipid species have been included and the presented workflow can accommodate seamlessly the addition of additional species and reactions in the future. The model and workflow can be used for the lipidomics curation of other organisms such as human, as the lipid networks in S. cerevisiae include most of the lipid classes currently identified and exhibit a high degree of homology with the human genome. Also, multiple regulatory mechanisms are preserved between the two species, thus yeast could perform brilliantly as a platform to study lipid dysregulation in human cells.”
What encouraged you to review research this area of microbiology?
The metabolism of lipids and its regulation has been closely associated with numerous physiopathologies, and although lipid pathways are in principle well characterized, the intricacies of their networks are yet to be completely understood. While traditionally established work on cellular lipid metabolism has been limited to the analysis of individual classes of lipids or specific lipid species, progress in MS-based methodologies has allowed the analysis of the entirety of the lipids in a cell. Lipidome characterization is accompanied by the need of appropriate tools for the analysis of the data and statistical quantification thereof. For that reason, we decided to develop a comprehensive metabolic model that focuses on the lipid network, and which can serve as a scaffold that can accommodate multiple datasets and studies.”
What do you see as the next steps in this area of research?
We believe that in the future yeast lipid models such as redLips could be systematically modified and used as a concise platform for studying lipid dysregulation across different species, and a valuable tool for health or industry related research. Additionally, such models provide a coherent base in order to link cell signaling routes to the lipid pathways, since signaling cascades have been proven to play a big part in the cell’s regulatory mechanisms.”
Read the 2020 award winning paper: redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast