Correction: Mapping and predictive variations of soil bacterial richness across France

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons

Background

The ability to compare samples or studies easily using metabarcoding so as to better interpret microbial ecology results is an upcoming challenge. A growing number of metabarcoding pipelines are available, each with its own benefits and limitations. However, very few have been developed to offer the opportunity to characterize various microbial communities (e.g., archaea, bacteria, fungi, photosynthetic microeukaryotes) with the same tool.

Results

BIOCOM-PIPE is a flexible and independent suite of tools for processing data from high-throughput sequencing technologies, Roche 454 and Illumina platforms, and focused on the diversity of archaeal, bacterial, fungal, and photosynthetic microeukaryote amplicons. Various original methods were implemented in BIOCOM-PIPE to (1) remove chimeras based on read abundance, (2) align sequences with structure-based alignments of RNA homologs using covariance models, and (3) a post-clustering tool (ReClustOR) to improve OTUs consistency based on a reference OTU database. The comparison with two other pipelines (FROGS and mothur) and Amplicon Sequence Variant definition highlighted that BIOCOM-PIPE was better at discriminating land use groups.

Conclusions

The BIOCOM-PIPE pipeline makes it possible to analyze 16S, 18S and 23S rRNA genes in the same packaged tool. The new post-clustering approach defines a biological database from previously analyzed samples and performs post-clustering of reads with this reference database by using open-reference clustering. This makes it easier to compare projects from various sequencing runs, and increased the congruence among results. For all users, the pipeline was developed to allow for adding or modifying the components, the databases and the bioinformatics tools easily, giving high modularity for each analysis.

Ganoderma lucidum cultivation affect microbial community structure of soil, wood segments and tree roots

The popular medicinal mushroom Ganoderma lucidum (Fr.) Karst. [Ling Zhi] has been widely used for the general promotion of health and longevity in Asian countries. Continuous cultivation may affect soil microbe and soil properties. However, the effect of G. lucidum cultivation on related wood segments, soil and tree roots microbial communities and soil properties is remain unknown. In our study, the microbial communities of soils, wood segments, and tree roots before and after G. lucidum cultivation were investigated by Illumina Miseq sequencing of both ITS and 16S rDNA, and taxonomic composition of eukaryotic and prokaryotic microorganisms were observed. Indices of microbial richness, diversity and evenness significantly differed between before and after G. lucidum cultivation. Each of the investigated sampling type harbored a distinctive microbial community and differed remarkably before and after G. lucidum cultivation. Ascomycota and Basidiomycota (fungi), Proteobacteria and Actinobacteria (bacteria) showed significant differences after Ling Zhi cultivation. The soil property values also changed after cultivation. The redundancy analysis (RDA) showed that both the fungal and bacterial community structure significantly correlated with soil humus, pH, nitrogen, carbon and trace elements (Fe, Zn, Mn, Cu) contents. The results indicated that G. lucidum cultivation may have significant differed the associated microbial community structures and soil properties. The study will provide useful information for G. lucidum cultivation and under-forest economic development.

Soil parameters, land use, and geographical distance drive soil bacterial communities along a European transect

To better understand the relationship between soil bacterial communities, soil physicochemical properties, land use and geographical distance, we considered for the first time ever a European transect running from Sweden down to Portugal and from France to Slovenia. We investigated 71 sites based on their range of variation in soil properties (pH, texture and organic matter), climatic conditions (Atlantic, alpine, boreal, continental, Mediterranean) and land uses (arable, forest and grassland). 16S rRNA gene amplicon pyrosequencing revealed that bacterial communities highly varied in diversity, richness, and structure according to environmental factors. At the European scale, taxa area relationship (TAR) was significant, supporting spatial structuration of bacterial communities. Spatial variations in community diversity and structure were mainly driven by soil physicochemical parameters. Within soil clusters (k-means approach) corresponding to similar edaphic and climatic properties, but to multiple land uses, land use was a major driver of the bacterial communities. Our analyses identified specific indicators of land use (arable, forest, grasslands) or soil conditions (pH, organic C, texture). These findings provide unprecedented information on soil bacterial communities at the European scale and on the drivers involved; possible applications for sustainable soil management are discussed.