Effects of rhizoma peanut cultivars (Arachis glabrata Benth.) on the soil bacterial diversity and predicted function in nitrogen fixation

There is a growing awareness of the importance of soil microorganisms in agricultural management practices. Currently, much less is known about whether different crop cultivar has an effect on the taxonomic structure and diversity, and specific functions of soil bacterial communities. Here, we examined the changes of the diversity and composition and enzyme‐encoding nitrogenase genes in a long‐term field experiment with seven different rhizoma peanut cultivars in southeastern USA, coupling high‐throughput 16S rRNA gene sequencing and the sequence‐based function prediction with Tax4Fun. Of the 32 phyla detected (Proteobacteria class), 13 were dominant: Acidobacteria, Alphaproteobacteria, Actinobacteria, Betaproteobacteria, Bacteroidetes, Verrucomicrobia, Gammaproteobacteria, Deltaproteobacteria, Gemmatimonadetes, Firmicutes, Nitrospirae, Chloroflexi, and Planctomycetes (relative abundance >1%). We found no evidence that the diversity and composition of bacterial communities were significantly different among different cultivars, but the abundance of some dominant bacterial groups that have N‐fixation potentials (at broad or fine taxonomic level) and predicted abundances of some enzyme‐encoding nitrogenase genes showed significant across‐cultivar differences. The nitrogenase genes were notably abundant in Florigraze and Latitude soils while remarkably lower in Arbook and UF_TITO soils when compared with other cultivars, indicating different nitrogen fixation potentials among different cultivars. The findings also suggest that the abundance of certain bacterial taxa and the specific function bacteria perform in ecosystems can have an inherent association. Our study is helpful to understand how microbiological responses and feedback to different plant genotypes through the variation in structure and function of their communities in the rhizosphere.     

RiboFR-Seq: a novel approach to linking 16S rRNA amplicon profiles to metagenomes

16S rRNA amplicon analysis and shotgun metagenome sequencing are two main culture-independent strategies to explore the genetic landscape of various microbial communities. Recently, numerous studies have employed these two approaches together, but downstream data analyses were performed separately, which always generated incongruent or conflict signals on both taxonomic and functional classifications. Here we propose a novel approach, RiboFR-Seq (Ribosomal RNA gene flanking region sequencing), for capturing both ribosomal RNA variable regions and their flanking protein-coding genes simultaneously. Through extensive testing on clonal bacterial strain, salivary microbiome and bacterial epibionts of marine kelp, we demonstrated that RiboFR-Seq could detect the vast majority of bacteria not only in well-studied microbiomes but also in novel communities with limited reference genomes. Combined with classical amplicon sequencing and shotgun metagenome sequencing, RiboFR-Seq can link the annotations of 16S rRNA and metagenomic contigs to make a consensus classification. By recognizing almost all 16S rRNA copies, the RiboFR-seq approach can effectively reduce the taxonomic abundance bias resulted from 16S rRNA copy number variation. We believe that RiboFR-Seq, which provides an integrated view of 16S rRNA profiles and metagenomes, will help us better understand diverse microbial communities.     

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.

     

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.     

Can Abundance of Protists Be Inferred from Sequence Data: A Case Study of Foraminifera

Protists are key players in microbial communities, yet our understanding of their role in ecosystem functioning is seriously impeded by difficulties in identification of protistan species and their quantification. Current microscopy-based methods used for determining the abundance of protists are tedious and often show a low taxonomic resolution. Recent development of next-generation sequencing technologies offered a very powerful tool for studying the richness of protistan communities. Still, the relationship between abundance of species and number of sequences remains subjected to various technical and biological biases. Here, we test the impact of some of these biological biases on sequence abundance of SSU rRNA gene in foraminifera. First, we quantified the rDNA copy number and rRNA expression level of three species of foraminifera by qPCR. Then, we prepared five mock communities with these species, two in equal proportions and three with one species ten times more abundant. The libraries of rDNA and cDNA of the mock communities were constructed, Sanger sequenced and the sequence abundance was calculated. The initial species proportions were compared to the raw sequence proportions as well as to the sequence abundance normalized by rDNA copy number and rRNA expression level per species. Our results showed that without normalization, all sequence data differed significantly from the initial proportions. After normalization, the congruence between the number of sequences and number of specimens was much better. We conclude that without normalization, species abundance determination based on sequence data was not possible because of the effect of biological biases. Nevertheless, by taking into account the variation of rDNA copy number and rRNA expression level we were able to infer species abundance, suggesting that our approach can be successful in controlled conditions.     

Phylogeny of Intestinal Ciliates,Including Charonina ventriculi,and Comparison of Microscopy and 18S rRNA Gene Pyrosequencing for Rumen Ciliate Community Structure Analysis

The development of high-throughput methods, such as the construction of 18S rRNA gene clone or pyrosequencing libraries, has allowed evaluation of ciliate community composition in hundreds of samples from the rumen and other intestinal habitats. However, several genera of mammalian intestinal ciliates have been described based only on morphological features and, to date, have not been identified using molecular methods. Here, we isolated single cells of one of the smallest but widely distributed intestinal ciliates, Charonina ventriculi, and sequenced its 18S rRNA gene. We verified the sequence in a full-cycle rRNA approach using fluorescence in situ hybridization and thereby assigned an 18S rRNA gene sequence to this species previously known only by its morphology. Based on its full-length 18S rRNA gene sequence, Charonina ventriculi was positioned within the phylogeny of intestinal ciliates in the subclass Trichostomatia. The taxonomic framework derived from this phylogeny was used for taxonomic assignment of trichostome ciliate 18S rRNA gene sequence data stemming from high-throughput amplicon pyrosequencing of rumen-derived DNA samples. The 18S rRNA gene-based ciliate community structure was compared to that obtained from microscopic counts using the same samples. Both methods allowed identification of dominant members of the ciliate communities and classification of the rumen ciliate community into one of the types first described by Eadie in 1962. Notably, each method is associated with advantages and disadvantages. Microscopy is a highly accurate method for evaluation of total numbers or relative abundances of different ciliate genera in a sample, while 18S rRNA gene pyrosequencing represents a valuable alternative for comparison of ciliate community structure in a large number of samples from different animals or treatment groups.     

Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities

Next‐generation sequencing has dramatically changed the landscape of microbial ecology, large‐scale and in‐depth diversity studies being now widely accessible. However, determining the accuracy of taxonomic and quantitative inferences and comparing results obtained with different approaches are complicated by incongruence of experimental and computational data types and also by lack of knowledge of the true ecological diversity. Here we used highly diverse bacterial and archaeal synthetic communities assembled from pure genomic DNAs to compare inferences from metagenomic and SSU rRNA amplicon sequencing. Both Illumina and 454 metagenomic data outperformed amplicon sequencing in quantifying the community composition, but the outcome was dependent on analysis parameters and platform. New approaches in processing and classifying amplicons can reconstruct the taxonomic composition of the community with high reproducibility within primer sets, but all tested primers sets lead to significant taxon‐specific biases. Controlled synthetic communities assembled to broadly mimic the phylogenetic richness in target environments can provide important validation for fine‐tuning experimental and computational parameters used to characterize natural communities.     

Reduced tillage,but not organic matter input,increased nematode diversity and food web stability in European long‐term field experiments

Soil nematode communities and food web indices can inform about the complexity, nutrient flows and decomposition pathways of soil food webs, reflecting soil quality. Relative abundance of nematode feeding and life‐history groups are used for calculating food web indices, i.e., maturity index (MI), enrichment index (EI), structure index (SI) and channel index (CI). Molecular methods to study nematode communities potentially offer advantages compared to traditional methods in terms of resolution, throughput, cost and time. In spite of such advantages, molecular data have not often been adopted so far to assess the effects of soil management on nematode communities and to calculate these food web indices. Here, we used high‐throughput amplicon sequencing to investigate the effects of tillage (conventional vs. reduced) and organic matter addition (low vs. high) on nematode communities and food web indices in 10 European long‐term field experiments and we assessed the relationship between nematode communities and soil parameters. We found that nematode communities were more strongly affected by tillage than by organic matter addition. Compared to conventional tillage, reduced tillage increased nematode diversity (23% higher Shannon diversity index), nematode community stability (12% higher MI), structure (24% higher SI), and the fungal decomposition channel (59% higher CI), and also the number of herbivorous nematodes (70% higher). Total and labile organic carbon, available K and microbial parameters explained nematode community structure. Our findings show that nematode communities are sensitive indicators of soil quality and that molecular profiling of nematode communities has the potential to reveal the effects of soil management on soil quality.     

Mock microbial community meta-analysis using different trimming of amplicon read lengths

Trimming of sequencing reads is a pre-processing step that aims to discard sequence segments such as primers, adapters and low quality nucleotides that will interfere with clustering and classification steps. We evaluated the impact of trimming length of paired-end 16S and 18S rRNA amplicon reads on the ability to reconstruct the taxonomic composition and relative abundances of communities with a known composition in both even and uneven proportions. We found that maximizing read retention maximizes recall but reduces precision by increasing false positives. The presence of expected taxa was accurately predicted across broad trim length ranges but recovering original relative proportions remains a difficult challenge. We show that parameters that maximize taxonomic recovery do not simultaneously maximize relative abundance accuracy. Trim length represents one of several experimental parameters that have non-uniform impact across microbial clades, making it a difficult parameter to optimize. This study offers insights, guidelines, and helps researchers assess the significance of their decisions when trimming raw reads in a microbiome analysis based on overlapping or non-overlapping paired-end amplicons.     

Molecular approach for assessing responses of microbial-feeding nematodes to burning and chronic nitrogen enrichment in a native grassland

A substantial proportion of the primary productivity in grassland ecosystems is allocated belowground, sustaining an abundant and diverse community of microbes and soil invertebrates. These belowground communities drive many important ecosystem functions and are responsive to a variety of environmental changes. Nematodes, an abundant and diverse component of grassland soil communities, are particularly responsive to altered environmental conditions, such as those associated with reduced fire frequency and nitrogen enrichment, with the most consistent responses displayed by microbial-feeding nematodes. However, much of the available research characterizing nematode responses to environmental change has been carried out at the taxonomic level of family or by broad trophic categories (e.g. fungivores, bacterivores). The extent to which differential responses to environmental change occurs at the genus level or below is unclear. Therefore, the objective of this study was to use molecular methods to quantify the response of microbial-feeding nematodes, at the lowest levels of taxonomic resolution, to nitrogen enrichment and changes in fire frequency. Using sequencing and quantitative polymerase chain reaction (PCR) probes for the 18S ribosomal RNA gene and the ITS1 region, we identified 19 microbial-feeding nematode taxa across four families. When nematodes were sampled across treatments, we found that some nematode taxa within a family responded similarly to nitrogen and burning treatments, while other taxa within the same family respond quite differently. Additionally, although nematodes from different families on average responded differently to nitrogen enrichment and burning, similar responses were seen in nematode taxa that span three taxonomic families. Thus, if nematodes are to be used as indicators of environmental change, care should be taken to assess the response at the lowest taxonomic level possible.     

Exploring the accuracy of amplicon‐based internal transcribed spacer markers for a fungal community

With the continual improvement in high‐throughput sequencing technology and constant updates to fungal reference databases, the use of amplicon‐based DNA markers as a tool to reveal fungal diversity and composition in various ecosystems has become feasible. However, both primer selection and the experimental procedure require meticulous verification. Here, we computationally and experimentally evaluated the accuracy and specificity of three widely used or newly designed internal transcribed spacer (ITS) primer sets (ITS1F/ITS2, gITS7/ITS4 and 5.8S‐Fun/ITS4‐Fun). In silico evaluation revealed that primer coverage varied at different taxonomic levels due to differences in degeneracy and the location of primer sets. Using even and staggered mock community standards, we identified different proportions of chimeric and mismatch reads generated by different primer sets, as well as great variation in species abundances, suggesting that primer selection would affect the results of amplicon‐based metabarcoding studies. Choosing proofreading and high‐fidelity polymerase (KAPA HiFi) could significantly reduce the percentage of chimeric and mismatch sequences, further reducing inflation of operational taxonomic units. Moreover, for two types of environmental fungal communities, plant endophytic and soil fungi, it was demonstrated that the three primer sets could not reach a consensus on fungal community composition or diversity, and that primer selection, not experimental treatment, determines observed soil fungal community diversity and composition. Future DNA marker surveys should pay greater attention to potential primer effects and improve the experimental scheme to increase credibility and accuracy.     

Time series are critical to understand microbial plankton diversity and ecology

How diverse are marine planktonic protist communities? How much seasonality do they exhibit? For a very long time, these two old and challenging questions in the field of plankton ecology could be addressed only for large‐size protist species, based on cell counting under the microscope. The recent application of molecular techniques, notably massive marker‐gene amplicon sequencing approaches (metabarcoding), has allowed investigating with unprecedented level of resolution the small‐sized (<20 µm) planktonic eukaryotes too. An amazing diversity of these tiny organisms has been unveiled but details about their temporal dynamics remain much more elusive. In a From the Cover article in this issue of Molecular Ecology, Giner et al. (2019) introduce a new Recurrence Index (RI) to specifically look for seasonality in time‐series metabarcoding data. They inspected the temporal dynamics of all operational taxonomic units (OTUs) in a rich sequence data set of pico‐ and nanoplanktonic eukaryotes in samples collected monthly during 10 years. Although most OTUs did not show seasonality, some abundant ones did, which explains why some averaging methods can find seasonality at the less detailed level of whole planktonic communities. Not surprisingly, the very complex small‐sized eukaryotic plankton communities are composed of organisms with miscellaneous temporal dynamics.     

Ironing out the wrinkles in the rare biosphere through improved OTU clustering

Deep sequencing of PCR amplicon libraries facilitates the detection of low‐abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low‐frequency, error‐prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating operational taxonomic units (OTUs) by multiple sequence alignment and complete‐linkage clustering significantly increases the number of predicted OTUs and inflates richness estimates. We show that a 2% single‐linkage preclustering methodology followed by an average‐linkage clustering based on pairwise alignments more accurately predicts expected OTUs in both single and pooled template preparations of known taxonomic composition. This new clustering method can reduce the OTU richness in environmental samples by as much as 30–60% but does not reduce the fraction of OTUs in long‐tailed rank abundance curves that defines the rare biosphere.     

MicFunPred: A conserved approach to predict functional profiles from 16S rRNA gene sequence data

The 16S rRNA gene amplicon sequencing is a popular technique that provides accurate characterization of microbial taxonomic abundances but does not provide any functional information. Several tools are available to predict functional profiles based on 16S rRNA gene sequence data that use different genome databases and approaches. As variable regions of partially-sequenced 16S rRNA gene cannot resolve taxonomy accurately beyond the genus level, these tools may give inflated results. Here, we developed ‘MicFunPred’, which uses a novel approach to derive imputed metagenomes based on a set of core genes only, thereby minimizing false-positive predictions. On simulated datasets, MicFunPred showed the lowest False Positive Rate (FPR) with mean Spearman's correlation of 0.89 (SD = 0.03), while on seven real datasets the mean correlation was 0.75 (SD = 0.08). MicFunPred was found to be faster with low computational requirements and performed better or comparable when compared with other tools.     

Comparison of morphological,DNA barcoding,and metabarcoding characterizations of freshwater nematode communities

Biomonitoring approaches and investigations of many ecological questions require assessments of the biodiversity of a given habitat. Small organisms, ranging from protozoans to metazoans, are of great ecological importance and comprise a major share of the planet's biodiversity but they are extremely difficult to identify, due to their minute body sizes and indistinct structures. Thus, most biodiversity studies that include small organisms draw on several methods for species delimitation, ranging from traditional microscopy to molecular techniques. In this study, we compared the efficiency of these methods by analyzing a community of nematodes. Specifically, we evaluated the performances of traditional morphological identification, single‐specimen barcoding (Sanger sequencing), and metabarcoding in the identification of 1500 nematodes from sediment samples. The molecular approaches were based on the analysis of the 28S ribosomal large and 18S small subunits (LSU and SSU). The morphological analysis resulted in the determination of 22 nematode species. Barcoding identified a comparable number of operational taxonomic units (OTUs) based on 28S rDNA (n = 20) and fewer OTUs based on 18S rDNA (n = 12). Metabarcoding identified a higher OTU number but fewer amplicon sequence variants (AVSs) (n = 48 OTUs, n = 17 ASVs for 28S rDNA, and n = 31 OTUs, n = 6 ASVs for 18S rDNA). Between the three approaches (morphology, barcoding, and metabarcoding), only three species (13.6%) were shared. This lack of taxonomic resolution hinders reliable community identifications to the species level. Further database curation will ensure the effective use of molecular species identification.     

Comparative study of the validity of three regions of the 18S‐rRNA gene for massively parallel sequencing‐based monitoring of the planktonic eukaryote community

The nuclear 18S‐rRNA gene has been used as a metabarcoding marker in massively parallel sequencing (MPS)‐based environmental surveys for plankton biodiversity research. However, different hypervariable regions have been used in different studies, and their utility has been debated among researchers. In this study, detailed investigations into 18S‐rRNA were carried out; we investigated the effective number of sequences deposited in international nucleotide sequence databases (INSDs), the amplification bias, and the amplicon sequence variability among the three variable regions, V1–3, V4–5 and V7–9, using in silico polymerase chain reaction (PCR) amplification based on INSDs. We also examined the primer universality and the taxonomic identification power, using MPS‐based environmental surveys in the Sea of Okhotsk, to determine which region is more useful for MPS‐based monitoring. The primer universality was not significantly different among the three regions, but the number of sequences deposited in INSDs was markedly larger for the V4–5 region than for the other two regions. The sequence variability was significantly different, with the highest variability in the V1–3 region, followed by the V7–9 region, and the lowest variability in the V4–5 region. The results of the MPS‐based environmental surveys showed significantly higher identification power in the V1–3 and V7–9 regions than in the V4–5 region, but no significant difference was detected between the V1–3 and V7–9 regions. We therefore conclude that the V1–3 region will be the most suitable for future MPS‐based monitoring of natural eukaryote communities, as the number of sequences deposited in INSDs increases.     

Spatiotemporal dynamics of the bacterial microbiota on lacustrine Cladophora glomerata (Chlorophyta)

The branched periphytic green alga Cladophora glomerata, often abundant in nearshore waters of lakes and rivers worldwide, plays important ecosystem roles, some mediated by epibiotic microbiota that benefit from host‐provided surface, organic C, and O₂. Previous microscopy and high‐throughput sequencing studies have indicated surprising epibiont taxonomic and functional diversity, but have not included adequate consideration of sample replication or the potential for spatial and temporal variation. Here, we report the results of 16S rRNA amplicon‐based phylum‐to‐genus taxonomic analysis of Cladophora‐associated bacterial epibiota sampled in replicate from three microsites and at six times during the open‐water season of 2014, from the same lake locale (Picnic Point, Lake Mendota, Dane Co., WI, USA) explored by high‐throughput sequencing studies in two previous years. Statistical methods were used to test null hypotheses that the bacterial community: (i) is homogeneous across microsites tested, and (ii) does not change over the course of a growth season or among successive years. Results indicated a dynamic microbial community that is more strongly influenced by sampling day during the growth season than by microsite variation. A surprising diversity of bacterial genera known to be associated with the key function of methane‐oxidation (methanotrophy), including relatively high‐abundance of Crenothrix, Methylomonas, Methylovulum, and Methylocaldum–showed intraseasonal and interannual variability possibly related to temperature differences, and microsite preferences possibly related to variation in methane abundance. By contrast, a core assemblage of bacterial genera seems to persist over a growth season and from year to year, possibly transmitted by a persistent attached host resting stage.