Recent advances in sequencing technologies generate new predictions and hypotheses about the functional roles of environmental microorganisms. Yet, until we can test these predictions at a scale that matches our ability to generate them, most of them will remain as hypotheses. Function-based mining of metagenomic libraries can provide direct linkages between genes, metabolic traits and microbial taxa and thus bridge this gap between sequence data generation and functional predictions. Here we developed high-throughput screening assays for function-based characterization of activities involved in plant polymer decomposition from environmental metagenomic libraries. The multiplexed assays use fluorogenic and chromogenic substrates, combine automated liquid handling and use a genetically modified expression host to enable simultaneous screening of 12,160 clones for 14 activities in a total of 170,240 reactions. Using this platform we identified 374 (0.26%) cellulose, hemicellulose, chitin, starch, phosphate and protein hydrolyzing clones from fosmid libraries prepared from decomposing leaf litter. Sequencing on the Illumina MiSeq platform, followed by assembly and gene prediction of a subset of 95 fosmid clones, identified a broad range of bacterial phyla, including Actinobacteria, Bacteroidetes, multiple Proteobacteria sub-phyla in addition to some Fungi. Carbohydrate-active enzyme genes from 20 different glycoside hydrolase (GH) families were detected. Using tetranucleotide frequency (TNF) binning of fosmid sequences, multiple enzyme activities from distinct fosmids were linked, demonstrating how biochemically-confirmed functional traits in environmental metagenomes may be attributed to groups of specific organisms. Overall, our results demonstrate how functional screening of metagenomic libraries can be used to connect microbial functionality to community composition and, as a result, complement large-scale metagenomic sequencing efforts.
Keywords: carbon cycling; decomposers; enzyme activity; functional metagenomics; gene annotation; metagenomics; microbial communities; trait-based modeling.