Multi-omics network model reveals key genes associated with p-coumaric acid stress response in an industrial yeast strain

Ciamponi, F.E.*; Procópio, D. P.*; Murad, N.F.; Franco, T. T.; Basso, T. O.; Brandão, M.M.

* Both authors has equally contributed to this work

Sci Rep 12, 22466 (2022). https://doi.org/10.1038/s41598-022-26843-2

Abstract

The production of ethanol from lignocellulosic sources presents increasingly difficult issues for the global biofuel scenario, leading to increased production costs of current second-generation (2G) ethanol when compared to first-generation (1G) plants. Among the setbacks encountered in industrial processes, the presence of chemical inhibitors from pre-treatment processes severely hinders the potential of yeasts in producing ethanol at peak efficiency. However, some industrial yeast strains have, either naturally or artificially, higher tolerance levels to these compounds. Such is the case of S. cerevisiae SA-1, a Brazilian fuel ethanol industrial strain that has shown high resistance to inhibitors produced by the pre-treatment of cellulosic complexes. Our study focuses on the characterization of the transcriptomic and physiological impact of an inhibitor of this type, p-coumaric acid (pCA), on this strain under chemostat cultivation via RNAseq and quantitative physiological data. It was found that strain SA-1 tend to increase ethanol yield and production rate while decreasing biomass yield when exposed to pCA, in contrast to pCA-susceptible strains, which tend to decrease their ethanol yield and fermentation efficiency when exposed to this substance. This suggests increased metabolic activity linked to mitochondrial and peroxisomal processes. The transcriptomic analysis also revealed a plethora of differentially expressed genes located in co-expressed clusters that are associated with changes in biological pathways linked to biosynthetic and energetical processes. Furthermore, it was also identified 20 genes that act as interaction hubs for these clusters, while also having association with altered pathways and changes in metabolic outputs, potentially leading to the discovery of novel targets for metabolic engineering toward a more robust industrial yeast strain.