Cellulase-secreting yeast can substantially reduce commercial enzyme requirements in lignocellulosic ethanol production

Background Cellulase enzyme secretion by cells of 2G S. cerevisiae yeast is considered a promising strategy to reduce the prohibitive cost of commercial cellulase cocktails in lignocellulosic bioethanol production. Here, we have developed Cellusec ® strains by engineering enzyme secretion capacity into an industrial lignocellulosic bioethanol strain and demonstrated its potential in simultaneous saccharification and fermentation (SSF) experiments of real-world feedstocks. Results A single copy each of CBHI, CBHII and EG encoding genes, and three copies of a BGL encoding gene were inserted into the strain, followed by overexpression of HAC1i . Importantly, all intermediate strains were evaluated throughout the engineering process, allowing identification of performance trade-offs associated with successive genetic modifications. We show that cell-free supernatant from Cellusec® strains reduced exogenous cellulase cocktail requirements by up to 53% during crystalline cellulose saccharification under low-temperature, enzyme-limited conditions. Under similar conditions, ethanol titers in SSF of cellulosic material obtained from furfural production (furfural residues, FR) were improved by up to 14% in Cellusec ® strains, compared to the non-secreting parent. In SSF of pretreated softwood from forestry waste (FW), Cellusec ® strains achieved ethanol titers of up to 5.15% (v/v), corresponding to a 25% improvement compared to the non-secreting parent strain. Distinct substrate-dependent synergies were observed between CTec3-HS and the different secreted enzymes. Notably, the recently discovered processive endoglucanase Rf GH5_4 was expressed in S. cerevisiae for the first time and showed particularly strong synergy with CTec3-HS in FW SSF. Conclusion These results demonstrate that cellulase-secreting yeast can substantially reduce commercial enzyme requirements in lignocellulosic bioethanol production in SSF setups. They also highlight the importance of tailoring enzyme combinations to specific feedstocks and/or further optimizing cocktails of secreted enzymes.