The Effects of Alignment Quality,Distance Calculation Method,Sequence Filtering,and Region on the Analysis of 16S rRNA Gene-Based Studies

Pyrosequencing of PCR-amplified fragments that target variable regions within the 16S rRNA gene has quickly become a powerful method for analyzing the membership and structure of microbial communities. This approach has revealed and introduced questions that were not fully appreciated by those carrying out traditional Sanger sequencing-based methods. These include the effects of alignment quality, the best method of calculating pairwise genetic distances for 16S rRNA genes, whether it is appropriate to filter variable regions, and how the choice of variable region relates to the genetic diversity observed in full-length sequences. I used a diverse collection of 13,501 high-quality full-length sequences to assess each of these questions. First, alignment quality had a significant impact on distance values and downstream analyses. Specifically, the greengenes alignment, which does a poor job of aligning variable regions, predicted higher genetic diversity, richness, and phylogenetic diversity than the SILVA and RDP-based alignments. Second, the effect of different gap treatments in determining pairwise genetic distances was strongly affected by the variation in sequence length for a region; however, the effect of different calculation methods was subtle when determining the sample''s richness or phylogenetic diversity for a region. Third, applying a sequence mask to remove variable positions had a profound impact on genetic distances by muting the observed richness and phylogenetic diversity. Finally, the genetic distances calculated for each of the variable regions did a poor job of correlating with the full-length gene. Thus, while it is tempting to apply traditional cutoff levels derived for full-length sequences to these shorter sequences, it is not advisable. Analysis of β-diversity metrics showed that each of these factors can have a significant impact on the comparison of community membership and structure. Taken together, these results urge caution in the design and interpretation of analyses using pyrosequencing data.     

Estimating Bacterial Diversity for Ecological Studies: Methods,Metrics, and Assumptions

Methods to estimate microbial diversity have developed rapidly in an effort to understand the distribution and diversity of microorganisms in natural environments. For bacterial communities, the 16S rRNA gene is the phylogenetic marker gene of choice, but most studies select only a specific region of the 16S rRNA to estimate bacterial diversity. Whereas biases derived from from DNA extraction, primer choice and PCR amplification are well documented, we here address how the choice of variable region can influence a wide range of standard ecological metrics, such as species richness, phylogenetic diversity, β-diversity and rank-abundance distributions. We have used Illumina paired-end sequencing to estimate the bacterial diversity of 20 natural lakes across Switzerland derived from three trimmed variable 16S rRNA regions (V3, V4, V5). Species richness, phylogenetic diversity, community composition, β-diversity, and rank-abundance distributions differed significantly between 16S rRNA regions. Overall, patterns of diversity quantified by the V3 and V5 regions were more similar to one another than those assessed by the V4 region. Similar results were obtained when analyzing the datasets with different sequence similarity thresholds used during sequences clustering and when the same analysis was used on a reference dataset of sequences from the Greengenes database. In addition we also measured species richness from the same lake samples using ARISA Fingerprinting, but did not find a strong relationship between species richness estimated by Illumina and ARISA. We conclude that the selection of 16S rRNA region significantly influences the estimation of bacterial diversity and species distributions and that caution is warranted when comparing data from different variable regions as well as when using different sequencing techniques.     

Benchmarking of 16S rRNA gene databases using known strain sequences

16S rRNA gene analysis is the most convenient and robust method for microbiome studies. Inaccurate taxonomic assignment of bacterial strains could have deleterious effects as all downstream analyses rely heavily on the accurate assessment of microbial taxonomy. The use of mock communities to check the reliability of the results has been suggested. However, often the mock communities used in most of the studies represent only a small fraction of taxa and are used mostly as validation of sequencing run to estimate sequencing artifacts. Moreover, a large number of databases and tools available for classification and taxonomic assignment of the 16S rRNA gene make it challenging to select the best-suited method for a particular dataset. In the present study, we used authentic and validly published 16S rRNA gene type strain sequences (full length, V3-V4 region) and analyzed them using a widely used QIIME pipeline along with different parameters of OTU clustering and QIIME compatible databases. Data Analysis Measures (DAM) revealed a high discrepancy in ratifying the taxonomy at different taxonomic hierarchies. Beta diversity analysis showed clear segregation of different DAMs. Limited differences were observed in reference data set analysis using partial (V3-V4) and full-length 16S rRNA gene sequences, which signify the reliability of partial 16S rRNA gene sequences in microbiome studies. Our analysis also highlights common discrepancies observed at various taxonomic levels using various methods and databases.     

Introducing DOTUR, a Computer Program for Defining Operational Taxonomic Units and Estimating Species Richness

Although copious qualitative information describes the members of the diverse microbial communities on Earth, statistical approaches for quantifying and comparing the numbers and compositions of lineages in communities are lacking. We present a method that addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences. We developed a computer program, DOTUR, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level. DOTUR uses the frequency at which each OTU is observed to construct rarefaction and collector's curves for various measures of richness and diversity. We analyzed 16S rRNA gene libraries derived from Scottish and Amazonian soils and the Sargasso Sea with DOTUR, which assigned sequences to OTUs rapidly and reliably based on the genetic distances between sequences and identified previous inconsistencies and errors in assigning sequences to OTUs. An analysis of the two 16S rRNA gene libraries from soil demonstrated that they do not contain enough sequences to support a claim that they contain different numbers of bacterial lineages with statistical confidence (P > 0.05), nor do they contain enough sequences to provide a robust estimate of species richness when an OTU is defined as containing sequences that are no more than 3% different from each other. In contrast, the richness of OTUs at the 3% level in the Sargasso Sea collection began to plateau after the sampling of 690 sequences. We anticipate that an equivalent extent of sampling for soil would require sampling more than 10,000 sequences, almost 100 times the size of typical sequence collections obtained from soil.     

Comparison of Species Richness Estimates Obtained Using Nearly Complete Fragments and Simulated Pyrosequencing-Generated Fragments in 16S rRNA Gene-Based Environmental Surveys

Pyrosequencing-based 16S rRNA gene surveys are increasingly utilized to study highly diverse bacterial communities, with special emphasis on utilizing the large number of sequences obtained (tens to hundreds of thousands) for species richness estimation. However, it is not yet clear how the number of operational taxonomic units (OTUs) and, hence, species richness estimates determined using shorter fragments at different taxonomic cutoffs correlates with the number of OTUs assigned using longer, nearly complete 16S rRNA gene fragments. We constructed a 16S rRNA clone library from an undisturbed tallgrass prairie soil (1,132 clones) and used it to compare species richness estimates obtained using eight pyrosequencing candidate fragments (99 to 361 bp in length) and the nearly full-length fragment. Fragments encompassing the V1 and V2 (V1+V2) region and the V6 region (generated using primer pairs 8F-338R and 967F-1046R) overestimated species richness; fragments encompassing the V3, V7, and V7+V8 hypervariable regions (generated using primer pairs 338F-530R, 1046F-1220R, and 1046F-1392R) underestimated species richness; and fragments encompassing the V4, V5+V6, and V6+V7 regions (generated using primer pairs 530F-805R, 805F-1046R, and 967F-1220R) provided estimates comparable to those obtained with the nearly full-length fragment. These patterns were observed regardless of the alignment method utilized or the parameter used to gauge comparative levels of species richness (number of OTUs observed, slope of scatter plots of pairwise distance values for short and nearly complete fragments, and nonparametric and parametric species richness estimates). Similar results were obtained when analyzing three other datasets derived from soil, adult Zebrafish gut, and basaltic formations in the East Pacific Rise. Regression analysis indicated that these observed discrepancies in species richness estimates within various regions could readily be explained by the proportions of hypervariable, variable, and conserved base pairs within an examined fragment.Culture-independent 16S rRNA gene surveys are now routinely utilized to examine the microbial diversity in various environmental habitats. However, in surveys of highly diverse ecosystems, the size of clone libraries typically constructed (100 to 500 clones) allows for the identification only of members of the community that are present in high abundance (2, 13, 14, 17, 24, 51). In addition to the failure to detect the rare members of the ecosystem, these relatively small datasets provide inaccurate estimates when used for computing species richness within an ecosystem. Regardless of the approach utilized to estimate species richness, the estimates obtained are highly dependent on sample size, and smaller datasets typically result in the underestimation of species richness (14, 44, 47, 55).The use of a pyrosequencing-based approach (40) in 16S gene-based diversity surveys promises to overcome both of the above-mentioned problems associated with inadequate sampling. The large number of 16S rRNA gene sequences produced (hundreds of thousands) allows access to rare members of the community (25; J. M. Tiedje, presented at the 108th General Meeting of the American Society for Microbiology, Boston, MA, 2008), as well as a relatively more accurate estimation of species richness. However, with the introduction of this new technology, it is necessary to correlate the results obtained from newer pyrosequencing-based surveys to the extensive collection of longer, capillary sequence-generated 16S rRNA gene sequences that has been deposited in public databases during the last 2 decades. Several recent studies have examined the utility of pyrosequencing fragments in providing an accurate survey of overall community structure (36) and investigated the ability of various fragments spanning the 16S rRNA gene to accurately predict the phylogenetic affiliation of pyrosequencing-generated fragments at various taxonomic cutoffs (35, 54). As such, these admirable efforts gave useful insights into the advantages and limitations of the pyrosequencing approach in 16S-based community surveys, pinpointed specific regions that provide better phylogenetic resolution than other pyrosequencing-generated regions, and provided a quantitative assessment of binning accuracy at various empirical cutoffs.However, while issues regarding correlating phylogenies of shorter and longer fragments are actively being addressed, efforts to calibrate species richness data obtained from various pyrosequencing fragments at various taxonomic cutoffs to estimates obtained using longer 16S rRNA gene fragments are still lacking. It is unclear how pairwise distances and, hence, operational taxonomic unit (OTU) assignments and species richness estimates computed using various shorter fragments spanning various regions of the 16S rRNA gene will correlate to pairwise distances computed using the nearly complete 16S rRNA gene. Elucidating such differences between shorter and nearly complete fragments, as well as between shorter fragments representing different regions in the 16S rRNA gene, is absolutely necessary for accurate meta-analysis of species richness in previously published and future datasets constructed using various sequencing approaches.Here, we constructed, sequenced, and analyzed a 16S rRNA library of 1,132 clones generated from an undisturbed tallgrass prairie soil in central Oklahoma and compared the numbers of OTUs and species richness values obtained using the full-length data sets (with and without the application of the Lane mask filter that excludes hypervariable regions from the phylogenetic analysis) (32) and fragments simulating pyrosequencing output generated by clipping where known conserved bacterial primers are encountered in the 16S rRNA gene. The lengths of the chosen simulated-pyrosequencing fragments represent amplicons that have been generated using the original GS20 pyrosequencing platform (≈100 bp) (25, 44, 48), similar to those currently being generated using the GS FLX pyrosequencing platform (≈250 bp) (1, 20, 35) or amplicons produced using the anticipated increase in the new GS XLR pyrosequencing platform (>250 bp). We show that the choice of the pyrosequenced fragment could indeed impact the number of OTUs calculated at different taxonomic cutoffs, with some fragments underestimating and others overestimating such parameters compared to the results with longer, nearly complete 16S rRNA gene fragments. We also show that even more marked differences could be encountered when comparing two pyrosequencing fragments within the same molecule. Further, we established a regression analysis that explains the nature of the observed discrepancies using the proportions of the hypervariable, variable, and conserved bases within fragments.     

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.     

Toward a census of bacteria in soil

For more than a century, microbiologists have sought to determine the species richness of bacteria in soil, but the extreme complexity and unknown structure of soil microbial communities have obscured the answer. We developed a statistical model that makes the problem of estimating richness statistically accessible by evaluating the characteristics of samples drawn from simulated communities with parametric community distributions. We identified simulated communities with rank-abundance distributions that followed a truncated lognormal distribution whose samples resembled the structure of 16S rRNA gene sequence collections made using Alaskan and Minnesotan soils. The simulated communities constructed based on the distribution of 16S rRNA gene sequences sampled from the Alaskan and Minnesotan soils had a richness of 5,000 and 2,000 operational taxonomic units (OTUs), respectively, where an OTU represents a collection of sequences not more than 3% distant from each other. To sample each of these OTUs in the Alaskan 16S rRNA gene library at least twice, 480,000 sequences would be required; however, to estimate the richness of the simulated communities using nonparametric richness estimators would require only 18,000 sequences. Quantifying the richness of complex environments such as soil is an important step in building an ecological framework. We have shown that generating sufficient sequence data to do so requires less sequencing effort than completely sequencing a bacterial genome.     

Diversity and Community Composition of Methanogenic Archaea in the Rumen of Scottish Upland Sheep Assessed by Different Methods

Ruminal archaeomes of two mature sheep grazing in the Scottish uplands were analysed by different sequencing and analysis methods in order to compare the apparent archaeal communities. All methods revealed that the majority of methanogens belonged to the Methanobacteriales order containing the Methanobrevibacter, Methanosphaera and Methanobacteria genera. Sanger sequenced 1.3 kb 16S rRNA gene amplicons identified the main species of Methanobrevibacter present to be a SGMT Clade member Mbb. millerae (≥91% of OTUs); Methanosphaera comprised the remainder of the OTUs. The primers did not amplify ruminal Thermoplasmatales-related 16S rRNA genes. Illumina sequenced V6–V8 16S rRNA gene amplicons identified similar Methanobrevibacter spp. and Methanosphaera clades and also identified the Thermoplasmatales-related order as 13% of total archaea. Unusually, both methods concluded that Mbb. ruminantium and relatives from the same clade (RO) were almost absent. Sequences mapping to rumen 16S rRNA and mcrA gene references were extracted from Illumina metagenome data. Mapping of the metagenome data to16S rRNA gene references produced taxonomic identification to Order level including 2–3% Thermoplasmatales, but was unable to discriminate to species level. Mapping of the metagenome data to mcrA gene references resolved 69% to unclassified Methanobacteriales. Only 30% of sequences were assigned to species level clades: of the sequences assigned to Methanobrevibacter, most mapped to SGMT (16%) and RO (10%) clades. The Sanger 16S amplicon and Illumina metagenome mcrA analyses showed similar species richness (Chao1 Index 19–35), while Illumina metagenome and amplicon 16S rRNA analysis gave lower richness estimates (10–18). The values of the Shannon Index were low in all methods, indicating low richness and uneven species distribution. Thus, although much information may be extracted from the other methods, Illumina amplicon sequencing of the V6–V8 16S rRNA gene would be the method of choice for studying rumen archaeal communities.     

Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness

Although copious qualitative information describes the members of the diverse microbial communities on Earth, statistical approaches for quantifying and comparing the numbers and compositions of lineages in communities are lacking. We present a method that addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences. We developed a computer program, DOTUR, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level. DOTUR uses the frequency at which each OTU is observed to construct rarefaction and collector's curves for various measures of richness and diversity. We analyzed 16S rRNA gene libraries derived from Scottish and Amazonian soils and the Sargasso Sea with DOTUR, which assigned sequences to OTUs rapidly and reliably based on the genetic distances between sequences and identified previous inconsistencies and errors in assigning sequences to OTUs. An analysis of the two 16S rRNA gene libraries from soil demonstrated that they do not contain enough sequences to support a claim that they contain different numbers of bacterial lineages with statistical confidence (P > 0.05), nor do they contain enough sequences to provide a robust estimate of species richness when an OTU is defined as containing sequences that are no more than 3% different from each other. In contrast, the richness of OTUs at the 3% level in the Sargasso Sea collection began to plateau after the sampling of 690 sequences. We anticipate that an equivalent extent of sampling for soil would require sampling more than 10,000 sequences, almost 100 times the size of typical sequence collections obtained from soil.     

Soil Bacterial Diversity Screening Using Single 16S rRNA Gene V Regions Coupled with Multi-Million Read Generating Sequencing Technologies

The novel multi-million read generating sequencing technologies are very promising for resolving the immense soil 16S rRNA gene bacterial diversity. Yet they have a limited maximum sequence length screening ability, restricting studies in screening DNA stretches of single 16S rRNA gene hypervariable (V) regions. The aim of the present study was to assess the effects of properties of four consecutive V regions (V3-6) on commonly applied analytical methodologies in bacterial ecology studies. Using an in silico approach, the performance of each V region was compared with the complete 16S rRNA gene stretch. We assessed related properties of the soil derived bacterial sequence collection of the Ribosomal Database Project (RDP) database and concomitantly performed simulations based on published datasets. Results indicate that overall the most prominent V region for soil bacterial diversity studies was V3, even though it was outperformed in some of the tests. Despite its high performance during most tests, V4 was less conserved along flanking sites, thus reducing its ability for bacterial diversity coverage. V5 performed well in the non-redundant RDP database based analysis. However V5 did not resemble the full-length 16S rRNA gene sequence results as well as V3 and V4 did when the natural sequence frequency and occurrence approximation was considered in the virtual experiment. Although, the highly conserved flanking sequence regions of V6 provide the ability to amplify partial 16S rRNA gene sequences from very diverse owners, it was demonstrated that V6 was the least informative compared to the rest examined V regions. Our results indicate that environment specific database exploration and theoretical assessment of the experimental approach are strongly suggested in 16S rRNA gene based bacterial diversity studies.     

POLYPHASIC STUDY OF ANTARCTIC CYANOBACTERIAL STRAINS1

We isolated 59 strains of cyanobacteria from the benthic microbial mats of 23 Antarctic lakes, from five locations in two regions, in order to characterize their morphological and genotypic diversity. On the basis of their morphology, the cyanobacteria were assigned to 12 species that included four Antarctic endemic taxa. Sequences of the ribosomal RNA gene were determined for 56 strains. In general, the strains closely related at the 16S rRNA gene level belonged to the same morphospecies. Nevertheless, divergences were observed concerning the diversity in terms of species richness, novelty, and geographical distribution. For the 56 strains, 21 operational taxonomic units (OTUs, defined as groups of partial 16S rRNA gene sequences with more than 97.5% similarity) were found, including nine novel and three exclusively Antarctic OTUs. Sequences of Petalonema cf. involvens and Chondrocystis sp. were determined for the first time. The internally transcribed spacer (ITS) between the 16S and the 23S rRNA genes was sequenced for 33 strains, and similar groupings were observed with the 16S rRNA gene and the ITS, even when the strains were derived from different lakes and regions. In addition, 48 strains were screened for antimicrobial and cytotoxic activities, and 17 strains were bioactive against the gram‐positive Staphylococcus aureus, or the fungi Aspergillus fumigatus and Cryptococcus neoformans. The bioactivities were not in coincidence with the phylogenetic relationships, but rather were specific to certain strains.     

OTU Analysis Using Metagenomic Shotgun Sequencing Data

Because of technological limitations, the primer and amplification biases in targeted sequencing of 16S rRNA genes have veiled the true microbial diversity underlying environmental samples. However, the protocol of metagenomic shotgun sequencing provides 16S rRNA gene fragment data with natural immunity against the biases raised during priming and thus the potential of uncovering the true structure of microbial community by giving more accurate predictions of operational taxonomic units (OTUs). Nonetheless, the lack of statistically rigorous comparison between 16S rRNA gene fragments and other data types makes it difficult to interpret previously reported results using 16S rRNA gene fragments. Therefore, in the present work, we established a standard analysis pipeline that would help confirm if the differences in the data are true or are just due to potential technical bias. This pipeline is built by using simulated data to find optimal mapping and OTU prediction methods. The comparison between simulated datasets revealed a relationship between 16S rRNA gene fragments and full-length 16S rRNA sequences that a 16S rRNA gene fragment having a length >150 bp provides the same accuracy as a full-length 16S rRNA sequence using our proposed pipeline, which could serve as a good starting point for experimental design and making the comparison between 16S rRNA gene fragment-based and targeted 16S rRNA sequencing-based surveys possible.     

Intragenomic Heterogeneity of 16S rRNA Genes Causes Overestimation of Prokaryotic Diversity

Ever since Carl Woese introduced the use of 16S rRNA genes for determining the phylogenetic relationships of prokaryotes, this method has been regarded as the “gold standard” in both microbial phylogeny and ecology studies. However, intragenomic heterogeneity within 16S rRNA genes has been reported in many investigations and is believed to bias the estimation of prokaryotic diversity. In the current study, 2,013 completely sequenced genomes of bacteria and archaea were analyzed and intragenomic heterogeneity was found in 952 genomes (585 species), with 87.5% of the divergence detected being below the 1% level. In particular, some extremophiles (thermophiles and halophiles) were found to harbor highly divergent 16S rRNA genes. Overestimation caused by 16S rRNA gene intragenomic heterogeneity was evaluated at different levels using the full-length and partial 16S rRNA genes usually chosen as targets for pyrosequencing. The result indicates that, at the unique level, full-length 16S rRNA genes can produce an overestimation of as much as 123.7%, while at the 3% level, an overestimation of 12.9% for the V6 region may be introduced. Further analysis showed that intragenomic heterogeneity tends to concentrate in specific positions, with the V1 and V6 regions suffering the most intragenomic heterogeneity and the V4 and V5 regions suffering the least intragenomic heterogeneity in bacteria. This is the most up-to-date overview of the diversity of 16S rRNA genes within prokaryotic genomes. It not only provides general guidance on how much overestimation can be introduced when applying 16S rRNA gene-based methods, due to its intragenomic heterogeneity, but also recommends that, for bacteria, this overestimation be minimized using primers targeting the V4 and V5 regions.     

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.     

A diverse bacterial community in an anoxic quinoline-degrading bioreactor determined by using pyrosequencing and clone library analysis

There is a concern of whether the structure and diversity of a microbial community can be effectively revealed by short-length pyrosequencing reads. In this study, we performed a microbial community analysis on a sample from a high-efficiency denitrifying quinoline-degrading bioreactor and compared the results generated by pyrosequencing with those generated by clone library technology. By both technologies, 16S rRNA gene analysis indicated that the bacteria in the sample were closely related to, for example, Proteobacteria, Actinobacteria, and Bacteroidetes. The sequences belonging to Rhodococcus were the most predominant, and Pseudomonas, Sphingomonas, Acidovorax, and Zoogloea were also abundant. Both methods revealed a similar overall bacterial community structure. However, the 622 pyrosequencing reads of the hypervariable V3 region of the 16S rRNA gene revealed much higher bacterial diversity than the 130 sequences from the full-length 16S rRNA gene clone library. The 92 operational taxonomic unit (OTUs) detected using pyrosequencing belonged to 45 families, whereas the 37 OTUs found in the clone library belonged to 25 families. Most sequences obtained from the clone library had equivalents in the pyrosequencing reads. However, 64 OTUs detected by pyrosequencing were not represented in the clone library. Our results demonstrate that pyrosequencing of the V3 region of the 16S rRNA gene is not only a powerful tool for discovering low-abundance bacterial populations but is also reliable for dissecting the bacterial community structure in a wastewater environment.     

Effect of Primers Hybridizing to Different Evolutionarily Conserved Regions of the Small-Subunit rRNA Gene in PCR-Based Microbial Community Analyses and Genetic Profiling

Genetic profiling techniques of microbial communities based on PCR-amplified signature genes, such as denaturing gradient gel electrophoresis or single-strand-conformation polymorphism (SSCP) analysis, are normally done with PCR products of less than 500-bp. The most common target for diversity analysis, the small-subunit rRNA genes, however, are larger, and thus, only partial sequences can be analyzed. Here, we compared the results obtained by PCR targeting different variable (V) regions (V2 and V3, V4 and V5, and V6 to V8) of the bacterial 16S rRNA gene with primers hybridizing to evolutionarily conserved flanking regions. SSCP analysis of single-stranded PCR products generated from 13 different bacterial species showed fewer bands with products containing V4-V5 (average, 1.7 bands per organism) than with V2-V3 (2.2 bands) and V6-V8 (2.3 bands). We found that the additional bands (>1 per organism) were caused by intraspecies operon heterogeneities or by more than one conformation of the same sequence. Community profiles, generated by PCR-SSCP from bacterial-cell consortia extracted from rhizospheres of field-grown maize (Zea mays), were analyzed by cloning and sequencing of the dominant bands. A total of 48 sequences could be attributed to 34 different strains from 10 taxonomical groups. Independent of the primer pairs, we found proteobacteria (α, β, and γ subgroups) and members of the genus Paenibacillus (low G+C gram-positive) to be the dominant organisms. Other groups, however, were only detected with single primer pairs. This study gives an example of how much the selection of different variable regions combined with different specificities of the flanking “universal” primers can affect a PCR-based microbial community analysis.     

云南江城和黑井盐矿沉积物未培养放线菌多样性比较

类群特异性引物的应用使得研究者可以对感兴趣的微生物类群进行针对性研究.围绕云南江城和黑井两个地区的3个盐矿样点沉积物中放线菌的多样性和群落组成,我们通过放线菌特异性引物对总DNA进行16S rRNA基因扩增,经过克隆文库构建,利用酶切并选择其中不同带型的133个克隆的16S rRNA基因插入片段进行测序.系统发育分析和统计学结果表明,两地放线菌16S rRNA基因克隆广泛分布于整个放线菌门,同时发现部分序列可能属于放线菌的新类群.分析结果还预示,江城和黑井两地盐矿虽处云南不同地域含盐区,但两地未培养放线菌物种多样性和系统发育关系均较为相似.     

Next Generation Sequencing to Define Prokaryotic and Fungal Diversity in the Bovine Rumen

A combination of Sanger and 454 sequences of small subunit rRNA loci were used to interrogate microbial diversity in the bovine rumen of 12 cows consuming a forage diet. Observed bacterial species richness, based on the V1–V3 region of the 16S rRNA gene, was between 1,903 to 2,432 species-level operational taxonomic units (OTUs) when 5,520 reads were sampled per animal. Eighty percent of species-level OTUs were dominated by members of the order Clostridiales, Bacteroidales, Erysipelotrichales and unclassified TM7. Abundance of Prevotella species varied widely among the 12 animals. Archaeal species richness, also based on 16S rRNA, was between 8 and 13 OTUs, representing 5 genera. The majority of archaeal OTUs (84%) found in this study were previously observed in public databases with only two new OTUs discovered. Observed rumen fungal species richness, based on the 18S rRNA gene, was between 21 and 40 OTUs with 98.4–99.9% of OTUs represented by more than one read, using Good’s coverage. Examination of the fungal community identified numerous novel groups. Prevotella and Tannerella were overrepresented in the liquid fraction of the rumen while Butyrivibrio and Blautia were significantly overrepresented in the solid fraction of the rumen. No statistical difference was observed between the liquid and solid fractions in biodiversity of archaea and fungi. The survey of microbial communities and analysis of cross-domain correlations suggested there is a far greater extent of microbial diversity in the bovine rumen than previously appreciated, and that next generation sequencing technologies promise to reveal novel species, interactions and pathways that can be studied further in order to better understand how rumen microbial community structure and function affects ruminant feed efficiency, biofuel production, and environmental impact.     

PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets

As 16S rRNA gene targeted massively parallel sequencing has become a common tool for microbial diversity investigations, numerous advances have been made to minimize the influence of sequencing and chimeric PCR artifacts through rigorous quality control measures. However, there has been little effort towards understanding the effect of multi-template PCR biases on microbial community structure. In this study, we used three bacterial and three archaeal mock communities consisting of, respectively, 33 bacterial and 24 archaeal 16S rRNA gene sequences combined in different proportions to compare the influences of (1) sequencing depth, (2) sequencing artifacts (sequencing errors and chimeric PCR artifacts), and (3) biases in multi-template PCR, towards the interpretation of community structure in pyrosequencing datasets. We also assessed the influence of each of these three variables on α- and β-diversity metrics that rely on the number of OTUs alone (richness) and those that include both membership and the relative abundance of detected OTUs (diversity). As part of this study, we redesigned bacterial and archaeal primer sets that target the V3-V5 region of the 16S rRNA gene, along with multiplexing barcodes, to permit simultaneous sequencing of PCR products from the two domains. We conclude that the benefits of deeper sequencing efforts extend beyond greater OTU detection and result in higher precision in β-diversity analyses by reducing the variability between replicate libraries, despite the presence of more sequencing artifacts. Additionally, spurious OTUs resulting from sequencing errors have a significant impact on richness or shared-richness based α- and β-diversity metrics, whereas metrics that utilize community structure (including both richness and relative abundance of OTUs) are minimally affected by spurious OTUs. However, the greatest obstacle towards accurately evaluating community structure are the errors in estimated mean relative abundance of each detected OTU due to biases associated with multi-template PCR reactions.