Discovery of novel cellulases from “genomic dark matter” via gene neighborhood analysis
Thursday, May 1, 2014: 1:00 PM
Grand Ballroom D-E, lobby level (Hilton Clearwater Beach)
Hailan Piao1, Jeff Froula2, Chongbin Du2, Tae-Wan Kim3, Erik Hawley2, Stefan Bauer3, Zhong Wang2, Nathalia Ivanova2, Douglas S. Clark4, Hans-Peter Klenk5 and Matthias Hess1, (1)Washington State University, Richland, WA, (2)DOE Joint Genome Institute, Walnut Creek, (3)Energy Biosciences Institute, Berkeley, CA, (4)Chemical and Biomolecular Engineering, University of California - Berkeley, Berkeley, CA, (5)Leibniz Institute-DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
Recent advances in DNA sequencing technologies and sequence similarity-based genome annotation resulted in the identification of thousands of biomass-degrading enzymes from microorganisms. A major drawback of similarity-based annotation is the fact that it relies on a certain degree of sequence similarity between two nucleotide sequences. Approximately 1/3 of the genes within microbial genomes lacks currently any functional assignment and represents “genomic dark matter”. New strategies to access and extract biological information from “genomic dark matter” might hold a key to increase the number and diversity of cellulases; enzymes which are required for improved biomass conversion processes. In the study presented here, we utilized a gene neighborhood-based approach that analyzes the spatial context of a gene to predict its function. Screening of more than 5,500 microbial genomes resulted in the identification of 17 putative cellulases that had too little sequence similarity to be classified as such by traditional sequence-based gene prediction and annotation algorithms. We cloned and expressed these putative cellulases and tested the recombinant proteins for their activity against a suite of different substrates, including Miscanthus. Eleven of the tested enzymes showed activity against at least one of the employed substrates, suggesting that our approach was used successfully to assign functional information to genome fragments that would otherwise be annotated as “genomic dark matter”. The verified cellulases can be used to populate the sequence space of known cellulases, simultaneously adding diversity to known cellulases and facilitating the discovery of new functional cellulases using conventional sequence similarity-based approaches.