‘Mother(Nature) knows best’ – hijacking nature-designed transcriptional programs for enhancing stress resistance and protein production in Yarrowia lipolytica; presentation of YaliFunTome database

• Published in: Microbial cell factories Vol. 23; no. 1; pp. 1 - 20
• Main Authors: Gorczyca, Maria, Białas, Wojciech, Nicaud, Jean-Marc, Celińska, Ewelina
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 18.01.2024; BioMed Central; BMC
• Subjects: Analysis; Carbon; Design of experiments; DNA binding proteins; Engineering; Experimental design; Genomes; Genotype & phenotype; Global metabolic engineering; Glycerol; Health aspects; Identification and classification; Life Sciences; Mathematical models; Medicinal plants; Metabolism; Metabolites; Nitrogen; Oxidative stress; Phenotypes; Protein biosynthesis; Protein production; Protein synthesis; Proteins; Reporters; RNA polymerase; Strains (organisms); Stress resistance; Transcription factors; Yarrow; Yarrowia cultivation protocol; Yarrowia lipolytica; Yeast; France; Poland; Transcription factors; Yeast; Protein production; Stress resistance; Global metabolic engineering; Yarrowia cultivation protocol
• ISSN: 1475-2859; 1475-2859
• DOI: 10.1186/s12934-023-02285-x

In the era of rationally designed synthetic biology, heterologous metabolites production, and other counter-nature engineering of cellular metabolism, we took a step back and recalled that 'Mother(-Nature) knows best'. While still aiming at synthetic, non-natural outcomes of generating an 'over-production phenotype' we dug into the pre-designed transcriptional programs evolved in our host organism--Yarrowia lipolytica, hoping that some of these fine-tuned orchestrated programs could be hijacked and used. Having an interest in the practical outcomes of the research, we targeted industrially-relevant functionalities--stress resistance and enhanced synthesis of proteins, and gauged them over extensive experimental design's completion. Technically, the problem was addressed by screening a broad library of over 120 Y. lipolytica strains under 72 combinations of variables through a carefully pre-optimized high-throughput cultivation protocol, which enabled actual phenotype development. The abundance of the transcription program elicitors--transcription factors (TFs), was secured by their overexpression, while challenging the strains with the multitude of conditions was inflicted to impact their activation stratus. The data were subjected to mathematical modeling to increase their informativeness. All potential users are invited to browse YaliFunTome in the search for homologous TFs and the TF-driven phenotypes of interest.