Assembling networks of microbial genomes using linear programming

Interactions in the microbiome: communities of organisms and communities of genes

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'

Being Aquifex aeolicus: Untangling a hyperthermophile's checkered past

Lateral gene transfer (LGT) is an important factor contributing to the evolution of prokaryotic genomes. The Aquificae are a hyperthermophilic bacterial group whose genes show affiliations to many other lineages, including the hyperthermophilic Thermotogae, the Proteobacteria, and the Archaea. Previous phylogenomic analyses focused on Aquifex aeolicus identified Thermotogae and Aquificae either as successive early branches or sisters in a rooted bacterial phylogeny, but many phylogenies and cellular traits have suggested a stronger affiliation with the Epsilonproteobacteria. Different scenarios for the evolution of the Aquificae yield different phylogenetic predictions. Here, we outline these scenarios and consider the fit of the available data, including three sequenced Aquificae genomes, to different sets of predictions. Evidence from phylogenetic profiles and trees suggests that the Epsilonproteobacteria have the strongest affinities with the three Aquificae analyzed. However, this pattern is shown by only a minority of encoded proteins, and the Archaea, many lineages of thermophilic bacteria, and members of genus Clostridium and class Deltaproteobacteria also show strong connections to the Aquificae. The phylogenetic affiliations of different functional subsystems showed strong biases: Most but not all genes implicated in the core translational apparatus tended to group Aquificae with Thermotogae, whereas a wide range of metabolic and cellular processes strongly supported the link between Aquificae and Epsilonproteobacteria. Depending on which sets of genes are privileged, either Thermotogae or Epsilonproteobacteria is the most plausible adjacent lineage to the Aquificae. Both scenarios require massive sharing of genes to explain the history of this enigmatic group, whose history is further complicated by specific affinities of different members of Aquificae to different partner lineages.

Telling the whole story in a 10,000-genome world

BACKGROUND

Genome sequencing has revolutionized our view of the relationships among genomes, particularly in revealing the confounding effects of lateral genetic transfer (LGT). Phylogenomic techniques have been used to construct purported trees of microbial life. Although such trees are easily interpreted and allow the use of a subset of genomes as "proxies" for the full set, LGT and other phenomena impact the positioning of different groups in genome trees, confounding and potentially invalidating attempts to construct a phylogeny-based taxonomy of microorganisms. Network and graph approaches can reveal complex sets of relationships, but applying these techniques to large data sets is a significant challenge. Notwithstanding the question of what exactly it might represent, generating and interpreting a Tree or Network of All Genomes will only be feasible if current algorithms can be improved upon.

RESULTS

Complex relationships among even the most-similar genomes demonstrate that proxy-based approaches to simplifying large sets of genomes are not alone sufficient to solve the analysis problem. A phylogenomic analysis of 1173 sequenced bacterial and archaeal genomes generated phylogenetic trees for 159,905 distinct homologous gene sets. The relationships inferred from this set can be heavily dependent on the inclusion of other taxa: for example, phyla such as Spirochaetes, Proteobacteria and Firmicutes are recovered as cohesive groups or split depending on the presence of other specific lineages. Furthermore, named groups such as Acidithiobacillus, Coprothermobacter and Brachyspira show a multitude of affiliations that are more consistent with their ecology than with small subunit ribosomal DNA-based taxonomy. Network and graph representations can illustrate the multitude of conflicting affinities, but all methods impose constraints on the input data and create challenges of construction and interpretation.

CONCLUSIONS

These complex relationships highlight the need for an inclusive approach to genomic data, and current methods with minor alterations will likely scale to allow the analysis of data sets with 10,000 or more genomes. The main challenges lie in the visualization and interpretation of genomic relationships, and the redefinition of microbial taxonomy when subsets of genomic data are so evidently in conflict with one another, and with the "canonical" molecular taxonomy.