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

Abstract

Background: 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. Results: 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. The amount of the gathered data prompted us to present them in the form of a searchable catalog – the YaliFunTome database (https://sparrow.up.poznan.pl/tsdatabase/)—to facilitate the withdrawal of biological sense from numerical data. We succeeded in the identification of TFs that act as omni-boosters of protein synthesis, enhance resistance to limited oxygen availability, and improve protein synthesis capacity under inorganic nitrogen provision. Conclusions: All potential users are invited to browse YaliFunTome in the search for homologous TFs and the TF-driven phenotypes of interest.