Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota.

The plant root defines the interface between a multicellular eukaryote and soil, one of the richest microbial ecosystems on Earth. Notably, soil bacteria are able to multiply inside roots as benign endophytes and modulate plant growth and development, with implications ranging from enhanced crop productivity to phytoremediation. Endophytic colonization represents an apparent paradox of plant innate immunity because plant cells can detect an array of microbe-associated molecular patterns (also known as MAMPs) to initiate immune responses to terminate microbial multiplication. Several studies attempted to describe the structure of bacterial root endophytes; however, different sampling protocols and low-resolution profiling methods make it difficult to infer general principles. Here we describe methodology to characterize and compare soil- and root-inhabiting bacterial communities, which reveals not only a function for metabolically active plant cells but also for inert cell-wall features in the selection of soil bacteria for host colonization. We show that the roots of Arabidopsis thaliana, grown in different natural soils under controlled environmental conditions, are preferentially colonized by Proteobacteria, Bacteroidetes and Actinobacteria, and each bacterial phylum is represented by a dominating class or family. Soil type defines the composition of root-inhabiting bacterial communities and host genotype determines their ribotype profiles to a limited extent. The identification of soil-type-specific members within the root-inhabiting assemblies supports our conclusion that these represent soil-derived root endophytes. Surprisingly, plant cell-wall features of other tested plant species seem to provide a sufficient cue for the assembly of approximately 40% of the Arabidopsis bacterial root-inhabiting microbiota, with a bias for Betaproteobacteria. Thus, this root sub-community may not be Arabidopsis-specific but saprophytic bacteria that would naturally be found on any plant root or plant debris in the tested soils. By contrast, colonization of Arabidopsis roots by members of the Actinobacteria depends on other cues from metabolically active host cells.

Symbiosis insights through metagenomic analysis of a microbial consortium.

Symbioses between bacteria and eukaryotes are ubiquitous, yet our understanding of the interactions driving these associations is hampered by our inability to cultivate most host-associated microbes. Here we use a metagenomic approach to describe four co-occurring symbionts from the marine oligochaete Olavius algarvensis, a worm lacking a mouth, gut and nephridia. Shotgun sequencing and metabolic pathway reconstruction revealed that the symbionts are sulphur-oxidizing and sulphate-reducing bacteria, all of which are capable of carbon fixation, thus providing the host with multiple sources of nutrition. Molecular evidence for the uptake and recycling of worm waste products by the symbionts suggests how the worm could eliminate its excretory system, an adaptation unique among annelid worms. We propose a model that describes how the versatile metabolism within this symbiotic consortium provides the host with an optimal energy supply as it shuttles between the upper oxic and lower anoxic coastal sediments that it inhabits.

Unifying the known and unknown microbial coding sequence space.

Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71 M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.

It is estimated that scientists do not know what half of microbial genes actually do. When these genes are discovered in microorganisms grown in the lab or found in environmental samples, it is not possible to identify what their roles are. Many of these genes are excluded from further analyses for these reasons, meaning that the study of microbial genes tends to be limited to genes that have already been described. These limitations hinder research into microbiology, because information from newly discovered genes cannot be integrated to better understand how these organisms work. Experiments to understand what role these genes have in the microorganisms are labor-intensive, so new analytical strategies are needed. To do this, Vanni et al. developed a new framework to categorize genes with unknown roles, and a computational workflow to integrate them into traditional analyses. When this approach was applied to over 400 million microbial genes (both with known and unknown roles), it showed that the share of genes with unknown functions is only about 30 per cent, smaller than previously thought. The analysis also showed that these genes are very diverse, revealing a huge space for future research and potential applications. Combining their approach with experimental data, Vanni et al. were able to identify a gene with a previously unknown purpose that could be involved in antibiotic resistance. This system could be useful for other scientists studying microorganisms to get a more complete view of microbial systems. In future, it may also be used to analyze the genetics of other organisms, such as plants and animals.

RibAlign: a software tool and database for eubacterial phylogeny based on concatenated ribosomal protein subunits.

BACKGROUND: Until today, analysis of 16S ribosomal RNA (rRNA) sequences has been the de-facto gold standard for the assessment of phylogenetic relationships among prokaryotes. However, the branching order of the individual phlya is not well-resolved in 16S rRNA-based trees. In search of an improvement, new phylogenetic methods have been developed alongside with the growing availability of complete genome sequences. Unfortunately, only a few genes in prokaryotic genomes qualify as universal phylogenetic markers and almost all of them have a lower information content than the 16S rRNA gene. Therefore, emphasis has been placed on methods that are based on multiple genes or even entire genomes. The concatenation of ribosomal protein sequences is one method which has been ascribed an improved resolution. Since there is neither a comprehensive database for ribosomal protein sequences nor a tool that assists in sequence retrieval and generation of respective input files for phylogenetic reconstruction programs, RibAlign has been developed to fill this gap. RESULTS: RibAlign serves two purposes: First, it provides a fast and scalable database that has been specifically adapted to eubacterial ribosomal protein sequences and second, it provides sophisticated import and export capabilities. This includes semi-automatic extraction of ribosomal protein sequences from whole-genome GenBank and FASTA files as well as exporting aligned, concatenated and filtered sequence files that can directly be used in conjunction with the PHYLIP and MrBayes phylogenetic reconstruction programs. CONCLUSION: Up to now, phylogeny based on concatenated ribosomal protein sequences is hampered by the limited set of sequenced genomes and high computational requirements. However, hundreds of full and draft genome sequencing projects are on the way, and advances in cluster-computing and algorithms make phylogenetic reconstructions feasible even with large alignments of concatenated marker genes. RibAlign is a first step in this direction and may be particularly interesting to scientists involved in whole genome sequencing of representatives of new or sparsely studied eubacterial phyla. RibAlign is available at http://www.megx.net/ribalign.