title: Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders.
abstract: BACKGROUND
  Efficient industrial processes for converting plant lignocellulosic materials into biofuels are a key to global efforts to come up with alternative energy sources to fossil fuels. Novel cellulolytic enzymes have been discovered in microbial genomes and metagenomes of microbial communities. However, the identification of relevant genes without known homologs, and the elucidation of the lignocellulolytic pathways and protein complexes for different microorganisms remain challenging.
  
  
  RESULTS
  We describe a new computational method for the targeted discovery of functional modules of plant biomass-degrading protein families, based on their co-occurrence patterns across genomes and metagenome datasets, and the strength of association of these modules with the genomes of known degraders. From approximately 6.4 million family annotations for 2,884 microbial genomes, and 332 taxonomic bins from 18 metagenomes, we identified 5 functional modules that are distinctive for plant biomass degraders, which we term "plant biomass degradation modules" (PDMs). These modules incorporate protein families involved in the degradation of cellulose, hemicelluloses, and pectins, structural components of the cellulosome, and additional families with potential functions in plant biomass degradation. The PDMs were linked to 81 gene clusters in genomes of known lignocellulose degraders, including previously described clusters of lignocellulolytic genes. On average, 70% of the families of each PDM were found to map to gene clusters in known degraders, which served as an additional confirmation of their functional relationships. The presence of a PDM in a genome or taxonomic metagenome bin furthermore allowed us to accurately predict the ability of any particular organism to degrade plant biomass. For 15 draft genomes of a cow rumen metagenome, we used cross-referencing to confirmed cellulolytic enzymes to validate that the PDMs identified plant biomass degraders within a complex microbial community.
  
  
  CONCLUSIONS
  Functional modules of protein families that are involved in different aspects of plant cell wall degradation can be inferred from co-occurrence patterns across (meta-)genomes with a probabilistic topic model. PDMs represent a new resource of protein families and candidate genes implicated in microbial plant biomass degradation. They can also be used to predict the plant biomass degradation ability for a genome or taxonomic bin. The method is also suitable for characterizing other microbial phenotypes.
authors.affiliation: Max-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, University Campus E1 4, Saarbrücken, 66123 Germany ; Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany.
authors.forename: Sebastian Ga
authors.initials: SG
authors.lastname: Konietzny
authors.affiliation: Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Post Office Box 5003, 1432 Ås, Norway.
authors.forename: Phillip B
authors.initials: PB
authors.lastname: Pope
authors.affiliation: Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany.
authors.forename: Aaron
authors.initials: A
authors.lastname: Weimann
authors.affiliation: Max-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, University Campus E1 4, Saarbrücken, 66123 Germany ; Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany.
authors.forename: Alice C
authors.initials: AC
authors.lastname: McHardy
keywords: (Ligno)cellulose degradation; Feature ranking; Gene clusters; LDA; Latent Dirichlet allocation; Pectin degradation; Phenotype-based identification of functional modules; Plant biomass degradation; Polysaccharide utilization loci; Probabilistic topic models