Laboratório de Biologia Integrativa e Sistêmica

Multi-omics network model reveals key genes associated to p-Coumaric acid resistance in an industrial yeast strain

Escrito por The chief

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

Abstract

The production of ethanol from lignocellulosic sources presents increasingly difficult issues for the global biofuel scenario. Among the setbacks encountered in industrial processes, the presence of chemical inhibitors from pre-treatment processes severely hinder 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 SA-1, a brazilian 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 HPLC data. We show that, when exposed to pCA, SA-1 yeasts tend to increase ethanol production while reducing overall biomass yield, as opposed to pCA-susceptible strains that tend to reduce their fermentation efficiency when exposed to this compound, suggesting increased metabolic activity . Transcriptomic analysis also revealed a plethora of differentially expressed genes located in co-expressed communities that are associated with changes in biological pathways linked to biosynthetic and energetical processes. Furthermore, we also identified 20 genes that act as interaction hubs for these communities, while also having association with altered pathways and changes in metabolic outputs, potentially leading to the discovery of novel targets for genetic engineering towards a more robust industrial yeast strain.

 

Supplementary material

DOI: https://doi.org/10.25824/redu/2MRGL4

 Raw sequencing data

BioProject ID: PRJNA764240