Metagenomic and small-subunit rRNA analyses reveal the genetic diversity of bacteria, archaea, fungi, and viruses in soil

Recent studies have highlighted the surprising richness of soil bacterial communities; however, bacteria are not the only microorganisms found in soil. To our knowledge, no study has compared the diversities of the four major microbial taxa, i.e., bacteria, archaea, fungi, and viruses, from an individual soil sample. We used metagenomic and small-subunit RNA-based sequence analysis techniques to compare the estimated richness and evenness of these groups in prairie, desert, and rainforest soils. By grouping sequences at the 97% sequence similarity level (an operational taxonomic unit [OTU]), we found that the archaeal and fungal communities were consistently less even than the bacterial communities. Although total richness levels are difficult to estimate with a high degree of certainty, the estimated number of unique archaeal or fungal OTUs appears to rival or exceed the number of unique bacterial OTUs in each of the collected soils. In this first study to comprehensively survey viral communities using a metagenomic approach, we found that soil viruses are taxonomically diverse and distinct from the communities of viruses found in other environments that have been surveyed using a similar approach. Within each of the four microbial groups, we observed minimal taxonomic overlap between sites, suggesting that soil archaea, bacteria, fungi, and viruses are globally as well as locally diverse.     

Metagenomic and Small-Subunit rRNA Analyses Reveal the Genetic Diversity of Bacteria, Archaea, Fungi, and Viruses in Soil

Recent studies have highlighted the surprising richness of soil bacterial communities; however, bacteria are not the only microorganisms found in soil. To our knowledge, no study has compared the diversities of the four major microbial taxa, i.e., bacteria, archaea, fungi, and viruses, from an individual soil sample. We used metagenomic and small-subunit RNA-based sequence analysis techniques to compare the estimated richness and evenness of these groups in prairie, desert, and rainforest soils. By grouping sequences at the 97% sequence similarity level (an operational taxonomic unit [OTU]), we found that the archaeal and fungal communities were consistently less even than the bacterial communities. Although total richness levels are difficult to estimate with a high degree of certainty, the estimated number of unique archaeal or fungal OTUs appears to rival or exceed the number of unique bacterial OTUs in each of the collected soils. In this first study to comprehensively survey viral communities using a metagenomic approach, we found that soil viruses are taxonomically diverse and distinct from the communities of viruses found in other environments that have been surveyed using a similar approach. Within each of the four microbial groups, we observed minimal taxonomic overlap between sites, suggesting that soil archaea, bacteria, fungi, and viruses are globally as well as locally diverse.     

VITCOMIC2: visualization tool for the phylogenetic composition of microbial communities based on 16S rRNA gene amplicons and metagenomic shotgun sequencing

Background

The 16S rRNA gene-based amplicon sequencing analysis is widely used to determine the taxonomic composition of microbial communities. Once the taxonomic composition of each community is obtained, evolutionary relationships among taxa are inferred by a phylogenetic tree. Thus, the combined representation of taxonomic composition and phylogenetic relationships among taxa is a powerful method for understanding microbial community structure; however, applying phylogenetic tree-based representation with information on the abundance of thousands or more taxa in each community is a difficult task. For this purpose, we previously developed the tool VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community), which is based on the genome-sequenced microbes’ phylogenetic information. Here, we introduce VITCOMIC2, which incorporates substantive improvements over VITCOMIC that were necessary to address several issues associated with 16S rRNA gene-based analysis of microbial communities.

Results

We developed VITCOMIC2 to provide (i) sequence identity searches against broad reference taxa including uncultured taxa; (ii) normalization of 16S rRNA gene copy number differences among taxa; (iii) rapid sequence identity searches by applying the graphics processing unit-based sequence identity search tool CLAST; (iv) accurate taxonomic composition inference and nearly full-length 16S rRNA gene sequence reconstructions for metagenomic shotgun sequencing; and (v) an interactive user interface for simultaneous representation of the taxonomic composition of microbial communities and phylogenetic relationships among taxa. We validated the accuracy of processes (ii) and (iv) by using metagenomic shotgun sequencing data from a mock microbial community.

Conclusions

The improvements incorporated into VITCOMIC2 enable users to acquire an intuitive understanding of microbial community composition based on the 16S rRNA gene sequence data obtained from both metagenomic shotgun and amplicon sequencing.

     

Winter Planktonic Microbial Communities in Highland Aquatic Habitats

Winter conditions in aquatic habitats of the temperate zone markedly differ from those present in warmer seasons, nevertheless, relatively scarce information is available on planktonic microbial composition, as sites are not easily accessible and it was supposed traditionally that microbial activity is low during this cold period. Since microorganisms could have great impact on the ecosystem even during winter, we explored various sites in the Eastern Carpathians regarding the abundance and taxonomic composition of planktonic microorganisms. Although many of the studied environments were extreme habitats, planktonic microbial communities were abundant and mostly diverse with the presence of previously unidentified taxa.     

The use of indicator taxa as representatives of communities in bioassessment

1. Sampling and processing of benthic macroinvertebrate samples is time consuming and expensive. Although a number of cost‐cutting options exist, a frequently asked question is how representative a subset of data is of the whole community, in particular in areas where habitat diversity is high (like Dutch surface water habitats). 2. Weighted averaging was used to reassign 650 samples to a typology of 40 community types, testing the representativeness of different subsets of data: (i) four different types of data (presence/absence, raw, ²log‐ and ln‐transformed abundance), (ii) three subsets of ‘indicator’ taxa (taxa with indicator weights 4–12, 7–12, and 10–12) and (iii) single taxonomic groups (n = 14) by determining the classification error. 3. ²log‐ and ln‐transformed abundances resulted in the lowest classification error, whilst the use of qualitative data resulted in a reduction of 10% of the samples assigned to their original community type compared to the use of ln‐transformed abundance data. 4. Samples from community types with a high number of unique indicator taxa had the lowest classification error, and classification error increased as similarity among community types increased. Using a subset of indicator taxa resulted in a maximum increase of the classification error of 15% when only taxa with an indicator weight 10–12 were included (error = 49.1%). 5. Use of single taxonomic groups resulted in high classification error, the lowest classification error was found using Trichoptera (68%), and was related to the frequency of the taxonomic group among samples and the indicator weights of the taxa. 6. Our findings that the use of qualitative data, subsets of indicator taxa or single taxonomic groups resulted in high classification error implies low taxonomic redundancy, and supports the use of all taxa in characterising a macroinvertebrate community, in particular in areas where habitat diversity is high.     

Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier

Current methods to identify unknown insect (class Insecta) cytochrome c oxidase (COI barcode) sequences often rely on thresholds of distances that can be difficult to define, sequence similarity cut‐offs, or monophyly. Some of the most commonly used metagenomic classification methods do not provide a measure of confidence for the taxonomic assignments they provide. The aim of this study was to use a naïve Bayesian classifier (Wang et al. Applied and Environmental Microbiology, 2007; 73: 5261) to automate taxonomic assignments for large batches of insect COI sequences such as data obtained from high‐throughput environmental sequencing. This method provides rank‐flexible taxonomic assignments with an associated bootstrap support value, and it is faster than the blast ‐based methods commonly used in environmental sequence surveys. We have developed and rigorously tested the performance of three different training sets using leave‐one‐out cross‐validation, two field data sets, and targeted testing of Lepidoptera, Diptera and Mantodea sequences obtained from the Barcode of Life Data system. We found that type I error rates, incorrect taxonomic assignments with a high bootstrap support, were already relatively low but could be lowered further by ensuring that all query taxa are actually present in the reference database. Choosing bootstrap support cut‐offs according to query length and summarizing taxonomic assignments to more inclusive ranks can also help to reduce error while retaining the maximum number of assignments. Additionally, we highlight gaps in the taxonomic and geographic representation of insects in public sequence databases that will require further work by taxonomists to improve the quality of assignments generated using any method.     

Rare but active taxa contribute to community dynamics of benthic biofilms in glacier‐fed streams

Glaciers harbour diverse microorganisms, which upon ice melt can be released downstream. In glacier‐fed streams microorganisms can attach to stones or sediments to form benthic biofilms. We used 454‐pyrosequencing to explore the bulk (16S rDNA) and putatively active (16S rRNA) microbial communities of stone and sediment biofilms across 26 glacier‐fed streams. We found differences in community composition between bulk and active communities among streams and a stronger congruence between biofilm types. Relative abundances of rRNA and rDNA were positively correlated across different taxa and taxonomic levels, but at lower taxonomic levels, the higher abundance in either the active or the bulk communities became more apparent. Here, environmental variables played a minor role in structuring active communities. However, we found a large number of rare taxa with higher relative abundances in rRNA compared with rDNA. This suggests that rare taxa contribute disproportionately to microbial community dynamics in glacier‐fed streams. Our findings propose that high community turnover, where taxa repeatedly enter and leave the ‘seed bank’, contributes to the maintenance of microbial biodiversity in harsh ecosystems with continuous environmental perturbations, such as glacier‐fed streams.     

Leaf-Associated Bacterial and Fungal Taxa Shifts in Response to Larvae of the Tree Hole Mosquito, <Emphasis Type="Italic">Ochlerotatus triseriatus</Emphasis>

Larvae of the eastern tree hole mosquito, Ochlerotatus triseriatus (Say), and related container-breeding species are known to feed upon substrate-associated microorganisms. Although the importance of these microbial resources to larval growth has been established, almost nothing is known about the taxonomic composition and dynamics of these critical microbial food sources. We examined bacterial and fungal community compositional changes on oak leaves tethered in natural tree hole habitats of O. triseriatus. We eliminated larvae experimentally in a subset of the tree holes and examined 16S rDNA gene sequences for bacteria and ergosterol concentrations and 18S rRNA gene sequences for fungi collected from leaf material subsamples. Leaf ergosterol content varied significantly with time, but not treatment. Principal component analysis (PCA) was used to compare microbial taxonomic patterns found in leaves incubated with or without larvae present, and we found that larval presence affected both bacterial and fungal groups, either from loosely attached or strongly adherent categories. Bacterial communities generally grouped more tightly when larvae were present, and class level taxa proportions changed when larvae were present, suggesting selection by larval feeding or activities for particular taxa such as members of the Bacteroidetes, Alphaproteobacteria, and Betaproteobacteria classes. Fungal taxa composite scores also separated along PC axes related to the presence of larvae and indicated larval feeding effects on several higher taxonomic groups, including Saccharomycetes, Dothideomycetes, and Chytridiomycota. These results support the hypothesis that larval mosquito feeding and activities altered microbial communities associated with substrate surfaces, potentially leading to decreased food value of the resource and affecting decomposition of particulate matter in the system.     

Biodiversity of soil microbial communities in agricultural systems

The productivity and health of agricultural systems depend greatly upon the functional processes carried out by soil microorganisms and soil microbial communities. The biodiversity of the soil microbial communities and the effect of diversity on the stability of the agricultural system, is unknown. Taxonomic approaches to estimating biodiversity of soil microbial communities are limited by difficulties in defining suitable taxonomic units and the apparent non-culturability of the majority of the microbial species present in the soil. Analysis of functional diversity may be a more meaningful approach but is also limited by the need to culture organisms. Approaches which do not rely on culturing organisms such as fatty acid analysis and 16S/18S rRNA analysis have provided an insight into the extent of genetic diversity within communities and may be useful in the analysis of community structure. Scale effects, including successional processes associated with organic matter decomposition, local effects associated with abiotic soil factors, and regional effects including the effect of agricultural management practices, on the diversity of microbial communities are considered. Their impact is important in relation to the minimum biodiversity required to maintain system function.     

Chloroflexi bacteria are more diverse, abundant, and similar in high than in low microbial abundance sponges

Some marine sponges harbor dense and phylogenetically complex microbial communities [high microbial abundance (HMA) sponges] whereas others contain only few and less diverse microorganisms [low microbial abundance (LMA) sponges]. We focused on the phylum Chloroflexi that frequently occurs in sponges to investigate the different associations with three HMA and three LMA sponges from New Zealand. By applying a range of microscopical and molecular techniques a clear dichotomy between HMA and LMA sponges was observed: Chloroflexi bacteria were more abundant and diverse in HMA than in LMA sponges. Moreover, different HMA sponges contain similar Chloroflexi communities whereas LMA sponges harbor different and more variable communities which partly resemble Chloroflexi seawater communities. A comprehensive phylogenetic analysis of our own and publicly available sponge-derived Chloroflexi 16S rRNA gene sequences (>?780 sequences) revealed the enormous diversity of this phylum within sponges including 29 sponge-specific and sponge-coral clusters (SSC/SCC) as well as a 'supercluster' consisting of >?250 sponge-derived and a single nonsponge-derived 16S rRNA gene sequence. Interestingly, the majority of sequences obtained from HMA sponges, but only a few from LMA sponges, fell into SSC/SCC clusters. This indicates a much more specific association of Chloroflexi bacteria with HMA sponges and suggests an ecologically important role for these prominent bacteria.     

Targeted recovery of novel phylogenetic diversity from next-generation sequence data

Next-generation sequencing technologies have led to recognition of a so-called ‘rare biosphere''. These microbial operational taxonomic units (OTUs) are defined by low relative abundance and may be specifically adapted to maintaining low population sizes. We hypothesized that mining of low-abundance next-generation 16S ribosomal RNA (rRNA) gene data would lead to the discovery of novel phylogenetic diversity, reflecting microorganisms not yet discovered by previous sampling efforts. Here, we test this hypothesis by combining molecular and bioinformatic approaches for targeted retrieval of phylogenetic novelty within rare biosphere OTUs. We combined BLASTN network analysis, phylogenetics and targeted primer design to amplify 16S rRNA gene sequences from unique potential bacterial lineages, comprising part of the rare biosphere from a multi-million sequence data set from an Arctic tundra soil sample. Demonstrating the feasibility of the protocol developed here, three of seven recovered phylogenetic lineages represented extremely divergent taxonomic entities. These divergent target sequences correspond to (a) a previously unknown lineage within the BRC1 candidate phylum, (b) a sister group to the early diverging and currently recognized monospecific Cyanobacteria Gloeobacter, a genus containing multiple plesiomorphic traits and (c) a highly divergent lineage phylogenetically resolved within mitochondria. A comparison to twelve next-generation data sets from additional soils suggested persistent low-abundance distributions of these novel 16S rRNA genes. The results demonstrate this sequence analysis and retrieval pipeline as applicable for exploring underrepresented phylogenetic novelty and recovering taxa that may represent significant steps in bacterial evolution.     

Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs

Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ∼8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.     

Lineage-dependent ecological coherence in bacteria

Bacteria comprise an essential element of all ecosystems, including those present on and within the human body. Understanding bacterial diversity therefore offers enormous scientific and medical benefit, but significant questions remain regarding how best to characterize that diversity and organize it into biologically meaningful units. Bacterial communities are routinely characterized based on the relative abundances of taxa at the genus or even the phylum level, but the ecological coherence of these high-level taxonomic units is uncertain. Using human microbiota from the skin and gut as our model systems, we tested the ecological coherence of bacteria by investigating the habitat associations of bacteria at all levels of the taxonomic hierarchy. We observed four distinct taxonomic patterns of habitat association, reflecting different levels of ecological coherence among taxa. Our results support the hypothesis that deep-branch bacterial clades could be ecologically coherent and suggest that the phylogenetic depth of ecological coherence varies among the bacterial lineages and is an important factor to consider in studies of human microbiome associations.     

Life-history strategies of soil microbial communities in an arid ecosystem

The overwhelming taxonomic diversity and metabolic complexity of microorganisms can be simplified by a life-history classification; copiotrophs grow faster and rely on resource availability, whereas oligotrophs efficiently exploit resource at the expense of growth rate. Here, we hypothesize that community-level traits inferred from metagenomic data can distinguish copiotrophic and oligotrophic microbial communities. Moreover, we hypothesize that oligotrophic microbial communities harbor more unannotated genes. To test these hypotheses, we conducted metagenomic analyses of soil samples collected from copiotrophic vegetated areas and from oligotrophic bare ground devoid of vegetation in an arid-hyperarid region of the Sonoran Desert, Arizona, USA. Results supported our hypotheses, as we found that multiple ecologically informed life-history traits including average 16S ribosomal RNA gene copy number, codon usage bias in ribosomal genes and predicted maximum growth rate were higher for microbial communities in vegetated than bare soils, and that oligotrophic microbial communities in bare soils harbored a higher proportion of genes that are unavailable in public reference databases. Collectively, our work demonstrates that life-history traits can distill complex microbial communities into ecologically coherent units and highlights that oligotrophic microbial communities serve as a rich source of novel functions.Subject terms: Microbial ecology, Community ecology     

Fungi ahoy! Diversity on marine wooden substrata in the high North

Marine fungi are severely understudied in the polar regions. We used molecularly identified cultures to study fungi inhabiting 50 intertidal and sea-floor logs along the North Norwegian coast. The aim was to explore the taxonomic and ecological diversity and to examine factors shaping the marine wood-inhabiting fungal communities. The 577 pure cultures analyzed clustered into 147 operational taxonomic units (OTUs) based on 97 % ITS sequence similarity. Ascomycota dominated, but OTUs belonging to Basidiomycota, Mucoromycotina and Chytridiomycota were also isolated. Nine OTUs could not be assigned to any fungal phylum. Almost half of the OTUs were considered non-marine. The western and eastern part of the Norwegian Barents Sea coast hosted different communities. Geography, substratum and site level variables contributed to shaping these communities. We characterized a previously overlooked fungal community in a poorly studied area, discovered high diversity and report many taxa for the first time from the marine environment.     

Elevational patterns and hierarchical determinants of biodiversity across microbial taxonomic scales

Microbial biogeography is gaining increasing attention due to recent molecular methodological advance. However, the diversity patterns and their environmental determinants across taxonomic scales are still poorly studied. By sampling along an extensive elevational gradient in subarctic ponds of Finland and Norway, we examined the diversity patterns of aquatic bacteria and fungi from whole community to individual taxa across taxonomic coverage and taxonomic resolutions. We further quantified cross‐phylum congruence in multiple biodiversity metrics and evaluated the relative importance of climate, catchment and local pond variables as the hierarchical drivers of biodiversity across taxonomic scales. Bacterial community showed significantly decreasing elevational patterns in species richness and evenness, and U‐shaped patterns in local contribution to beta diversity (LCBD). Conversely, no significant species richness and evenness patterns were found for fungal community. Elevational patterns in species richness and LCBD, but not in evenness, were congruent across bacterial phyla. When narrowing down the taxonomic scope towards higher resolutions, bacterial diversity showed weaker and more complex elevational patterns. Taxonomic downscaling also indicated a notable change in the relative importance of biodiversity determinants with stronger local environmental filtering, but decreased importance of climatic variables. This suggested that niche conservatism of temperature preference was phylogenetically deeper than that of water chemistry variables. Our results provide novel perspectives for microbial biogeography and highlight the importance of taxonomic scale dependency and hierarchical drivers when modelling biodiversity and species distribution responses to future climatic scenarios.     

Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa

Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies.     

A network approach to elucidate and prioritize microbial dark matter in microbial communities

Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.Subject terms: Microbial ecology, Metagenomics     

Accurate phylogenetic classification of variable-length DNA fragments

Metagenome studies have retrieved vast amounts of sequence data from a variety of environments leading to new discoveries and insights into the uncultured microbial world. Except for very simple communities, the encountered diversity has made fragment assembly and the subsequent analysis a challenging problem. A taxonomic characterization of metagenomic fragments is required for a deeper understanding of shotgun-sequenced microbial communities, but success has mostly been limited to sequences containing phylogenetic marker genes. Here we present PhyloPythia, a composition-based classifier that combines higher-level generic clades from a set of 340 completed genomes with sample-derived population models. Extensive analyses on synthetic and real metagenome data sets showed that PhyloPythia allows the accurate classification of most sequence fragments across all considered taxonomic ranks, even for unknown organisms. The method requires no more than 100 kb of training sequence for the creation of accurate models of sample-specific populations and can assign fragments >or=1 kb with high specificity.