Investigating Microbial Eukaryotic Diversity from a Global Census: Insights from a Comparison of Pyrotag and Full-Length Sequences of 18S rRNA Genes

Next-generation DNA sequencing (NGS) approaches are rapidly surpassing Sanger sequencing for characterizing the diversity of natural microbial communities. Despite this rapid transition, few comparisons exist between Sanger sequences and the generally much shorter reads of NGS. Operational taxonomic units (OTUs) derived from full-length (Sanger sequencing) and pyrotag (454 sequencing of the V9 hypervariable region) sequences of 18S rRNA genes from 10 global samples were analyzed in order to compare the resulting protistan community structures and species richness. Pyrotag OTUs called at 98% sequence similarity yielded numbers of OTUs that were similar overall to those for full-length sequences when the latter were called at 97% similarity. Singleton OTUs strongly influenced estimates of species richness but not the higher-level taxonomic composition of the community. The pyrotag and full-length sequence data sets had slightly different taxonomic compositions of rhizarians, stramenopiles, cryptophytes, and haptophytes, but the two data sets had similarly high compositions of alveolates. Pyrotag-based OTUs were often derived from sequences that mapped to multiple full-length OTUs at 100% similarity. Thus, pyrotags sequenced from a single hypervariable region might not be appropriate for establishing protistan species-level OTUs. However, nonmetric multidimensional scaling plots constructed with the two data sets yielded similar clusters, indicating that beta diversity analysis results were similar for the Sanger and NGS sequences. Short pyrotag sequences can provide holistic assessments of protistan communities, although care must be taken in interpreting the results. The longer reads (>500 bp) that are now becoming available through NGS should provide powerful tools for assessing the diversity of microbial eukaryotic assemblages.     

Impact of sequence processing and taxonomic classification approaches on eukaryotic community structure from environmental samples with emphasis on diatoms

Next‐generation sequencing is a common method for analysing microbial community diversity and composition. Configuring an appropriate sequence processing strategy within the variety of tools and methods is a nontrivial task and can considerably influence the resulting community characteristics. We analysed the V4 region of 18S rRNA gene sequences of marine samples by 454‐pyrosequencing. Along this process, we generated several data sets with QIIME, mothur, and a custom‐made pipeline based on DNAStar and the phylogenetic tree‐based PhyloAssigner. For all processing strategies, default parameter settings and punctual variations were used. Our results revealed strong differences in total number of operational taxonomic units (OTUs), indicating that sequence preprocessing and clustering had a major impact on protist diversity estimates. However, diversity estimates of the abundant biosphere (abundance of ≥1%) were reproducible for all conducted processing pipeline versions. A qualitative comparison of diatom genera emphasized strong differences between the pipelines in which phylogenetic placement of sequences came closest to light microscopy‐based diatom identification. We conclude that diversity studies using different sequence processing strategies are comparable if the focus is on higher taxonomic levels, and if abundance thresholds are used to filter out OTUs of the rare biosphere.     

A Comparison of Methods for Clustering 16S rRNA Sequences into OTUs

Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.     

StatFingerprints: a friendly graphical interface program for processing and analysis of microbial fingerprint profiles

Molecular fingerprint methods are widely used to compare microbial communities in various habitats. The free program StatFingerprints can import, process, and display fingerprint profiles and perform numerous statistical analyses on them, and also estimate diversity indexes. StatFingerprints works with the free program R, providing an environment for statistical computing and graphics. No programming knowledge is required to use StatFingerprints, thanks to its friendly graphical user interface. StatFingerprints is useful for analysing the effect of a controlled factor on the microbial community and for establishing the relationships between the microbial community and the parameters of its environment. Multivariate analyses include ordination, clustering methods and hypothesis-driven tests like 50-50 multivariate analysis of variance, analysis of similarity or similarity percentage procedure and the program offers the possibility of plotting ordinations as a three-dimensional display.     

Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel‐contaminated soil

Ecotoxicology is primarily concerned with predicting the effects of toxic substances on the biological components of the ecosystem. In remote, high latitude environments such as Antarctica, where field work is logistically difficult and expensive, and where access to adequate numbers of soil invertebrates is limited and response times of biota are slow, appropriate modeling tools using microbial community responses can be valuable as an alternative to traditional single‐species toxicity tests. In this study, we apply a Bayesian nonparametric model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. We model community change in terms of OTUs (operational taxonomic units) in response to a range of total petroleum hydrocarbon (TPH) concentrations. The Shannon diversity of the microbial community, clustering of OTUs into groups with similar behavior with respect to TPH, and effective concentration values at level x, which represent the TPH concentration that causes x% change in the community, are presented. This model is broadly applicable to other complex data sets with similar data structure and inferential requirements on the response of communities to environmental parameters and stressors.     

微生物生态研究中BIOLOG方法数据分析及R语言实现

微生物生态研究中,对微生物群落结构、群落特征以及其与环境因素的关系的揭示,一直受到广泛关注;适当的数据分析方法有助于更清晰地刻画微生物群落结构特征,明确其与环境因素的关系。结合实例,对微生物生态研究中基于BIOLOG微平板技术的数据分析方法进行梳理,分别介绍数据读取整理、特征指数计算、非限制性排序、限制性排序、聚类分析、环境向量拟合、蒙特尔检验等常用数据操作及生态分析方法;针对不同方法结论,结合研究目标和生态理论给出具有统计学意义的解释,并评价不同方法特点及适用场景;分析过程以R语言实现,并提供全部代码。结果表明,BIOLOG方法产生数据能从多个角度表征微生物群落功能特征,并结合环境指标梯度进行分析;但BIOLOG数据可能不满足正态性分布,在基于正态分布的分析前应提前进行检验;排序分析时应慎用主成分分析,可优先采用其他基于距离矩阵的排序方法;R语言能够简化BIOLOG数据读取及操作,易于完成各类统计分析。本研究能够对微生物生态研究者科学选择应用统计分析方法、提高数据处理效率提供直接参考。     

Genotypic diversity in root‐endophytic fungi reflects efficient dispersal and environmental adaptation

Studying community structure and dynamics of plant‐associated fungi is the basis for unravelling their interactions with hosts and ecosystem functions. A recent sampling revealed that only a few fungal groups, as defined by internal transcribed spacer region (ITS) sequence similarity, dominate culturable root endophytic communities of nonmycorrhizal Microthlaspi spp. plants across Europe. Strains of these fungi display a broad phenotypic and functional diversity, which suggests a genetic variability masked by ITS clustering into operational taxonomic units (OTUs). The aims of this study were to identify how genetic similarity patterns of these fungi change across environments and to evaluate their ability to disperse and adapt to ecological conditions. A first ITS‐based haplotype analysis of ten widespread OTUs mostly showed a low to moderate genotypic differentiation, with the exception of a group identified as Cadophora sp. that was highly diverse. A multilocus phylogeny based on additional genetic loci (partial translation elongation factor 1α, beta‐tubulin and actin) and amplified fragment length polymorphism profiling of 185 strains representative of the five dominant OTUs revealed a weak association of genetic differences with geography and environmental conditions, including bioclimatic and soil factors. Our findings suggest that dominant culturable root endophytic fungi have efficient dispersal capabilities, and that their distribution is little affected by environmental filtering. Other processes, such as inter‐ and intraspecific biotic interactions, may be more important for the local assembly of their communities.     

Evolutionary criteria outperform operational approaches in producing ecologically relevant fungal species inventories

Analyses of the structure and function of microbial communities are highly constrained by the diversity of organisms present within most environmental samples. A common approach is to rely almost entirely on DNA sequence data for estimates of microbial diversity, but to date there is no objective method of clustering sequences into groups that is grounded in evolutionary theory of what constitutes a biological lineage. The general mixed Yule-coalescent (GMYC) model uses a likelihood-based approach to distinguish population-level processes within lineages from processes associated with speciation and extinction, thus identifying a distinct point where extant lineages became independent. Using two independent surveys of DNA sequences associated with a group of ubiquitous plant-symbiotic fungi, we compared estimates of species richness derived using the GMYC model to those based on operational taxonomic units (OTUs) defined by fixed levels of sequence similarity. The model predicted lower species richness in these surveys than did traditional methods of sequence similarity. Here, we show for the first time that groups delineated by the GMYC model better explained variation in the distribution of fungi in relation to putative niche-based variables associated with host species identity, edaphic factors, and aspects of how the sampled ecosystems were managed. Our results suggest the coalescent-based GMYC model successfully groups environmental sequences of fungi into clusters that are ecologically more meaningful than more arbitrary approaches for estimating species richness.     

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.     

Clustering of fungal community internal transcribed spacer sequence data obscures taxonomic diversity

Next‐generation DNA sequencing has enabled a rapid expansion in the size of molecular fungal ecology studies employing the nuclear internal transcribed spacer (ITS) region. Many sequence‐processing pipelines and protocols require sequence clustering to generate operational taxonomic units (OTUs) based on sequence similarity as a step to reduce total data quantity and complexity prior to taxonomic assignment. However, the consequences of ITS sequence clustering in regard to sample taxonomic coverage have not been carefully examined. Here we demonstrate that typically used clustering thresholds for fungal ITS sequences result in statistically significant losses in taxonomic coverage. Analyses using environmentally derived fungal sequences indicated an average of 3.1% of species went undetected (P < 0.05) if the sequences were denoised and clustered at a 97% threshold prior to taxonomic assignment. Additionally, an in silico analysis using a reference fungal ITS database suggested that approximately 25% of species went undetected if the sequences were clustered prior to taxonomic assignment. Finally, analysis of sequences derived from pure‐cultured fungal isolates of known identity indicated sequence denoising and clustering were not critical in improving identification accuracy.     

高通量测序分析环保肥料增效剂对马铃薯根际土壤真菌多样性变化影响

【目的】高通量测序技术对研究环境样品中微生物群落组成具有很大的应用价值。土壤微生物群落结构和多样性及其变化在一定程度上反映了土壤的质量。旨在从微生物群落结构的角度阐述环保肥料增效剂对马铃薯根际土壤主要真菌类群结构的影响。【方法】通过高通量测序结果对比分析应用增效剂前后马铃薯根际真菌宏基因组ITS1区,并依据RDP中设置的分类阈值对序列进行物种分类。【结果】测序结果经过质量控制,共获得有效条带437 375条,依据97%的序列相似性做聚类分析,获得全部样品的可分类操作单元(OTUs)共633个。子囊菌的总体数量在所有样品中最多(相对丰度在56.95%-97.23%之间),且处理后呈增加趋势(HY除外),而担子菌门数量在处理后呈下降的趋势。基于真菌ITS1高通量测序结果获得的α指数显示,样品内部处理和对照之间真菌物种多样性有差别。基于真菌ITS1高通量测序获得的β指数显示,处理组与对照组的真菌多样性之间没有差别,这表明真菌多样性之间的差异更多地取决于样品采集地点。【结论】土壤特性是影响真菌种群的重要因素之一,环保肥料增效剂显著改善了土壤真菌的种群结构。     

Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution

The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We present a clustering-free approach to multi-sample Illumina data sets that can identify independent bacterial subpopulations regardless of the similarity of their 16S tag sequences. Using published data from a longitudinal time-series study of human tongue microbiota, we are able to resolve within standard 97% similarity OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S tags differing by as little as one nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such subpopulations are shared between the two communities at 100% sequence identity, and that dynamical similarity between subpopulations in one host is strongly predictive of dynamical similarity between the same subpopulations in the other host. Our method can also be applied to samples collected in cross-sectional studies and can be used with the 454 sequencing platform. We discuss how the sub-OTU resolution of our approach can provide new insight into factors shaping community assembly.     

A spatial genomic approach identifies time lags and historical barriers to gene flow in a rapidly fragmenting Appalachian landscape

The resolution offered by genomic data sets coupled with recently developed spatially informed analyses are allowing researchers to quantify population structure at increasingly fine temporal and spatial scales. However, both empirical research and conservation measures have been limited by questions regarding the impacts of data set size, data quality thresholds and the timescale at which barriers to gene flow become detectable. Here, we used restriction site associated DNA sequencing to generate a 2,140 single nucleotide polymorphism (SNP) data set for the copperhead snake (Agkistrodon contortrix) and address the population genomic impacts of recent and widespread landscape modification across an ~1,000‐km² region of eastern Kentucky, USA. Nonspatial population‐based assignment and clustering methods supported little to no population structure. However, using individual‐based spatial autocorrelation approaches we found evidence for genetic structuring which closely follows the path of a historically important highway which experienced high traffic volumes from c. 1920 to 1970 before losing most traffic to a newly constructed alternative route. We found no similar spatial genomic signatures associated with more recently constructed highways or surface mining activity, although a time lag effect may be responsible for the lack of any emergent spatial genetic patterns. Subsampling of our SNP data set suggested that similar results could be obtained with as few as 250 SNPs, and a range of thresholds for missing data exhibited limited impacts on the spatial patterns we detected. While we were not able to estimate relative effects of land uses or precise time lags, our findings highlight the importance of temporal factors in landscape genetics approaches, and suggest the potential advantages of genomic data sets and fine‐scale, spatially informed approaches for quantifying subtle genetic patterns in temporally complex landscapes.     

Comparative analysis of yellow microbial communities growing on the walls of geographically distinct caves indicates a common core of microorganisms involved in their formation

Morphologically similar microbial communities that often form on the walls of geographically distinct limestone caves have not yet been comparatively studied. Here, we analysed phylotype distribution in yellow microbial community samples obtained from the walls of distinct caves located in Spain, Czech Republic and Slovenia. To infer the level of similarity in microbial community membership, we analysed inserts of 474 16S rRNA gene clones and compared those using statistical tools. The results show that the microbial communities under investigation are composed solely of Bacteria. The obtained phylotypes formed three distinct groups of operational taxonomic units (OTUs). About 60% of obtained sequences formed three core OTUs common to all three sampling sites. These were affiliated with actinobacterial Pseudonocardinae (30-50% of sequences in individual sampling site libraries), but also with gammaproteobacterial Chromatiales (6-25%) and Xanthomonadales (0.5-2.0%). Another 7% of sequences were common to two sampling sites and formed eight OTUs, while the remaining 35% were site specific and corresponded mostly to OTUs containing single sequences. The same pattern was observed when these data were compared with sequence data available from similar studies. This comparison showed that distinct limestone caves support microbial communities composed mostly of phylotypes common to all sampling sites.     

Patterns of Symbiodinium (Dinophyceae) diversity and assemblages among diverse hosts and the coral reef environment of Lizard Island,Australia

Large‐scale environmental disturbances may impact both partners in coral host–Symbiodinium systems. Elucidation of the assembly patterns in such complex and interdependent communities may enable better prediction of environmental impacts across coral reef ecosystems. In this study, we investigated how the community composition and diversity of dinoflagellate symbionts in the genus Symbiodinium were distributed among 12 host species from six taxonomic orders (Actinaria, Alcyonacea, Miliolida, Porifera, Rhizostoma, Scleractinia) and in the reef water and sediments at Lizard Island, Great Barrier Reef before the 3rd Global Coral Bleaching Event. 454 pyrosequencing of the ITS2 region of Symbiodinium yielded 83 operational taxonomic units (OTUs) at a 97% similarity cut‐off. Approximately half of the Symbiodinium OTUs from reef water or sediments were also present in symbio. OTUs belonged to six clades (A‐D, F‐G), but community structure was uneven. The two most abundant OTUs (100% matches to types C1 and A3) comprised 91% of reads and OTU C1 was shared by all species. However, sequence‐based analysis of these dominant OTUs revealed host species specificity, suggesting that genetic similarity cut‐offs of Symbiodinium ITS2 data sets need careful evaluation. Of the less abundant OTUs, roughly half occurred at only one site or in one species and the background Symbiodinium communities were distinct between individual samples. We conclude that sampling multiple host taxa with differing life history traits will be critical to fully understand the symbiont diversity of a given system and to predict coral ecosystem responses to environmental change and disturbance considering the differential stress response of the taxa within.     

Beta diversity as the variance of community data: dissimilarity coefficients and partitioning

Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a dissimilarity matrix obtained using any dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova . Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within‐ and among‐group components by manova , into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.     

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis

Background

Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities.

Methods

We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third “mediator” variable. These “mediator” variables appear to modulate the associations between pairs of bacteria.

Results

Using this analysis, we were able to assess the OTUs’ ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors.

Conclusions

By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download (http://bitbucket.org/charade/elsa).