Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders.

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.

De novo prediction of the genomic components and capabilities for microbial plant biomass degradation from (meta-)genomes.

BACKGROUND: Understanding the biological mechanisms used by microorganisms for plant biomass degradation is of considerable biotechnological interest. Despite of the growing number of sequenced (meta)genomes of plant biomass-degrading microbes, there is currently no technique for the systematic determination of the genomic components of this process from these data. RESULTS: We describe a computational method for the discovery of the protein domains and CAZy families involved in microbial plant biomass degradation. Our method furthermore accurately predicts the capability to degrade plant biomass for microbial species from their genome sequences. Application to a large, manually curated data set of microbial degraders and non-degraders identified gene families of enzymes known by physiological and biochemical tests to be implicated in cellulose degradation, such as GH5 and GH6. Additionally, genes of enzymes that degrade other plant polysaccharides, such as hemicellulose, pectins and oligosaccharides, were found, as well as gene families which have not previously been related to the process. For draft genomes reconstructed from a cow rumen metagenome our method predicted Bacteroidetes-affiliated species and a relative to a known plant biomass degrader to be plant biomass degraders. This was supported by the presence of genes encoding enzymatically active glycoside hydrolases in these genomes. CONCLUSIONS: Our results show the potential of the method for generating novel insights into microbial plant biomass degradation from (meta-)genome data, where there is an increasing production of genome assemblages for uncultured microbes.

Inferring functional modules of protein families with probabilistic topic models.

BACKGROUND: Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context. RESULTS: We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules. CONCLUSIONS: We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.

Whole-genome comparison of disease and carriage strains provides insights into virulence evolution in Neisseria meningitidis.

Neisseria meningitidis is a leading cause of infectious childhood mortality worldwide. Most research efforts have hitherto focused on disease isolates belonging to only a few hypervirulent clonal lineages. However, up to 10% of the healthy human population is temporarily colonized by genetically diverse strains mostly with little or no pathogenic potential. Currently, little is known about the biology of carriage strains and their evolutionary relationship with disease isolates. The expression of a polysaccharide capsule is the only trait that has been convincingly linked to the pathogenic potential of N. meningitidis. To gain insight into the evolution of virulence traits in this species, whole-genome sequences of three meningococcal carriage isolates were obtained. Gene content comparisons with the available genome sequences from three disease isolates indicate that there is no core pathogenome in N. meningitidis. A comparison of the chromosome structure suggests that a filamentous prophage has mediated large chromosomal rearrangements and the translocation of some candidate virulence genes. Interspecific comparison of the available Neisseria genome sequences and dot blot hybridizations further indicate that the insertion sequence IS1655 is restricted only to N. meningitidis; its low sequence diversity is an indicator of an evolutionarily recent population bottleneck. A genome-based phylogenetic reconstruction provides evidence that N. meningitidis has emerged as an unencapsulated human commensal from a common ancestor with Neisseria gonorrhoeae and Neisseria lactamica and consecutively acquired the genes responsible for capsule synthesis via horizontal gene transfer.

Genomic sequence of chorioallantois vaccinia virus Ankara, the ancestor of modified vaccinia virus Ankara.

Chorioallantois vaccinia virus Ankara (CVA) is the parental virus of modified vaccinia virus Ankara (MVA), which was derived from CVA by more than 570 passages in chicken embryo fibroblasts (CEF). MVA became severely host-cell-restricted to avian cells and has strongly diminished virulence in mammalian hosts, while maintaining good immunogenicity. We determined the complete coding sequence of the parental CVA and mapped the exact positions of the six major deletions that emerged in the MVA genome. All six major deletions occurred in regions of the CVA genome where one or more truncated or fragmented open reading frames (ORFs) pre-existed. The CVA genome contains 229 ORFs of which 51 are fragments of full-length orthopoxvirus (OPV) genes, including fragmented orthologues of C9L and M1L (encoding two well-conserved ankyrin-like proteins), A39R (encoding a semaphorin-like protein) and A55R (encoding a kelch-like protein). Phylogenetic analysis demonstrated that MVA was most closely related to CVA, followed by the vaccinia virus (VACV) strain DUKE, a patient-derived isolate of the Dryvax vaccine virus. Loss or mutation of genes outside the six major deletions are assumed to contribute to the restricted host range phenotype of MVA. In support of this notion, deletions, insertions and non-synonymous mutations were found in 122 of the 195 ORFs remaining in MVA when compared with their CVA counterparts. Thus, detailed knowledge of the CVA genomic sequence is a prerequisite to further dissect the genetic basis of the MVA host range phenotype as well as the particular immunological properties of MVA.

Complete genome sequence of the myxobacterium Sorangium cellulosum.

The genus Sorangium synthesizes approximately half of the secondary metabolites isolated from myxobacteria, including the anti-cancer metabolite epothilone. We report the complete genome sequence of the model Sorangium strain S. cellulosum So ce56, which produces several natural products and has morphological and physiological properties typical of the genus. The circular genome, comprising 13,033,779 base pairs, is the largest bacterial genome sequenced to date. No global synteny with the genome of Myxococcus xanthus is apparent, revealing an unanticipated level of divergence between these myxobacteria. A large percentage of the genome is devoted to regulation, particularly post-translational phosphorylation, which probably supports the strain's complex, social lifestyle. This regulatory network includes the highest number of eukaryotic protein kinase-like kinases discovered in any organism. Seventeen secondary metabolite loci are encoded in the genome, as well as many enzymes with potential utility in industry.