Bioactive Fungal Screening to find Efficient Biomass‐Degrading Enzymes

Abstract

Traditional fuels such as coal and gasoline are limited and can produce harmful effects to the environment. Bioethanol from biomass (i.e., plant‐based materials) degradation is a sustainable and renewable form of fuel that aims to reduce these effects. The fungal secretome, known as a pool of biomass degrading enzymes, can be utilized to discover industrial enzyme candidates useful for biofuel production. The overall purpose of our research is to pre‐screen a pool of fungi collected from the Great Lakes, select candidates that can break down biomass (i.e., cellulose) efficiently, and characterize the biomass degrading enzymes. In this study, we first selected seven different fungal species, and grew them onto carboxymethyl cellulose (CMC) plates. A specific dye called Congo red was used to monitor the cellulose activity after either a five‐day or twelve‐day growth period. A clear zone present around the central fungal growth indicated the degradation of the cellulose in the culture medium. Fungal activity was measured using a ratio of enzyme activity (outer ring) and the fungal growth (central circle). The enzyme activities of these strains were compared to Aspergillus niger, a known industrial fungal strain. Overall, there were four different species that demonstrated higher cellulase activity in comparison to A.niger in the CMC assay. Additionally, the secretome and cell lysate of A. niger was prepared to determine the source of the biomass‐degrading enzymes through an enzymatic activity assay. Preparation of the secretome and lysate were obtained through ultra filtration and bead beating, respectively. Both methods resulted in acquiring a highly concentrated solution of each protein samples. It was determined that the biomass‐degrading enzymes were secreted from the fungal cells rather than contained within the cell. Future directions include secretome preparation of the four identified fungal strains to verify their biomass hydrolysis capabilities.Support or Funding InformationThis work was supported NSF award 1359457 to Dr. Si Wu.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.