Targeted discovery of glycoside hydrolases from a switchgrass-adapted compost community.

TitleTargeted discovery of glycoside hydrolases from a switchgrass-adapted compost community.
Publication TypeJournal Article
Year of Publication2010
AuthorsAllgaier M, Reddy A, Park JI, Ivanova N, D'haeseleer P, Lowry S, Sapra R, Hazen TC, Simmons BA, VanderGheynst JS, Hugenholtz P
JournalPloS one
Volume5
Issue1
Paginatione8812
Date Published2010
ISSN1932-6203
KeywordsBiomass, Bioreactors, Cellulase, Glycoside Hydrolases, Molecular Sequence Data, Poaceae, RNA, Ribosomal, Soil
Abstract

Development of cellulosic biofuels from non-food crops is currently an area of intense research interest. Tailoring depolymerizing enzymes to particular feedstocks and pretreatment conditions is one promising avenue of research in this area. Here we added a green-waste compost inoculum to switchgrass (Panicum virgatum) and simulated thermophilic composting in a bioreactor to select for a switchgrass-adapted community and to facilitate targeted discovery of glycoside hydrolases. Small-subunit (SSU) rRNA-based community profiles revealed that the microbial community changed dramatically between the initial and switchgrass-adapted compost (SAC) with some bacterial populations being enriched over 20-fold. We obtained 225 Mbp of 454-titanium pyrosequence data from the SAC community and conservatively identified 800 genes encoding glycoside hydrolase domains that were biased toward depolymerizing grass cell wall components. Of these, approximately 10% were putative cellulases mostly belonging to families GH5 and GH9. We synthesized two SAC GH9 genes with codon optimization for heterologous expression in Escherichia coli and observed activity for one on carboxymethyl cellulose. The active GH9 enzyme has a temperature optimum of 50 degrees C and pH range of 5.5 to 8 consistent with the composting conditions applied. We demonstrate that microbial communities adapt to switchgrass decomposition using simulated composting condition and that full-length genes can be identified from complex metagenomic sequence data, synthesized and expressed resulting in active enzyme.

Alternate JournalPLoS ONE