Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein–protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and (c) experimentally validates the impact of six of the nine regulators identified on lipid accumulation, with variations in lipid content from +43.2% to −31.2% on glucose or glycerol.
System biology: gene regulatory network for lipid production Bioinformatics allow us to get new insight in yeast gene regulation for the biological production of lipid-derived industrial compounds. This work, led by Jean-Marc Nicaud and Mohamed Elati from the INRA and University of Evry, aimed to identify the gene regulatory network and new regulatory elements associated with lipid production in Yarrowia lipolytica, a yeast of industrial interest. Using context-specific gene expression data and a data mining approach, the team was able to build a model of gene regulation from which they identified the main regulators as well as the cooperative relationships between them. These predictions were then confirmed experimentally. Additionally, the team managed to determine different stages of lipid production using a new measure of regulator activity: the influence. These tools will be useful to model and design industrial yeast for various applications.