Taxonomic Precision of Different Hypervariable Regions of 16S rRNA Gene and Annotation Methods for Functional Bacterial Groups in Biological Wastewater Treatment

High throughput sequencing of 16S rRNA gene leads us into a deeper understanding on bacterial diversity for complex environmental samples, but introduces blurring due to the relatively low taxonomic capability of short read. For wastewater treatment plant, only those functional bacterial genera categorized as nutrient remediators, bulk/foaming species, and potential pathogens are significant to biological wastewater treatment and environmental impacts. Precise taxonomic assignment of these bacteria at least at genus level is important for microbial ecological research and routine wastewater treatment monitoring. Therefore, the focus of this study was to evaluate the taxonomic precisions of different ribosomal RNA (rRNA) gene hypervariable regions generated from a mix activated sludge sample. In addition, three commonly used classification methods including RDP Classifier, BLAST-based best-hit annotation, and the lowest common ancestor annotation by MEGAN were evaluated by comparing their consistency. Under an unsupervised way, analysis of consistency among different classification methods suggests there are no hypervariable regions with good taxonomic coverage for all genera. Taxonomic assignment based on certain regions of the 16S rRNA genes, e.g. the V1&V2 regions – provide fairly consistent taxonomic assignment for a relatively wide range of genera. Hence, it is recommended to use these regions for studying functional groups in activated sludge. Moreover, the inconsistency among methods also demonstrated that a specific method might not be suitable for identification of some bacterial genera using certain 16S rRNA gene regions. As a general rule, drawing conclusions based only on one sequencing region and one classification method should be avoided due to the potential false negative results.     

基于分子标记的宏基因组16S rRNA基因高变区选择策略

随着新一代DNA测序技术出现,人们能够同时对多个DNA样本的宏基因组进行并行分析,尤其是以16S rRNA基因高变区为分子标记的测序已经成为微生物多样性研究最为简洁有效的方法. 目前二代高通量测序的读长不能覆盖16S rRNA基因的全长,需要选择一个有效的高变区进行测序.十多年来,对于16S rRNA基因高变区的选择策略没有统一的标准.本文分析了常用的高变区选择策略,指出不同环境条件是影响高变区选择的重要因素之一.在此基础上,提出了高变区选择的参考准则,同时建议应对选择的高变区进行有效评估.     

Estimating Protistan Diversity Using High‐Throughput Sequencing

Sequencing hypervariable regions from the 18S rRNA gene is commonly employed to characterize protistan biodiversity, yet there are concerns that short reads do not provide the same taxonomic resolution as full‐length sequences. A total of 7,432 full‐length sequences were used to perform an in silico analysis of how sequences of various lengths and target regions impact downstream ecological interpretations. Sequences that were longer than 400 nucleotides and included the V4 hypervariable region generated results similar to those derived from full‐length 18S rRNA gene sequences. Present high‐throughput sequencing capabilities are approaching protistan diversity estimation comparable to whole gene sequences.     

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.     

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.     

Analysis of the Cystic Fibrosis Lung Microbiota via Serial Illumina Sequencing of Bacterial 16S rRNA Hypervariable Regions

The characterization of bacterial communities using DNA sequencing has revolutionized our ability to study microbes in nature and discover the ways in which microbial communities affect ecosystem functioning and human health. Here we describe Serial Illumina Sequencing (SI-Seq): a method for deep sequencing of the bacterial 16S rRNA gene using next-generation sequencing technology. SI-Seq serially sequences portions of the V5, V6 and V7 hypervariable regions from barcoded 16S rRNA amplicons using an Illumina short-read genome analyzer. SI-Seq obtains taxonomic resolution similar to 454 pyrosequencing for a fraction of the cost, and can produce hundreds of thousands of reads per sample even with very high multiplexing. We validated SI-Seq using single species and mock community controls, and via a comparison to cystic fibrosis lung microbiota sequenced using 454 FLX Titanium. Our control runs show that SI-Seq has a dynamic range of at least five orders of magnitude, can classify >96% of sequences to the genus level, and performs just as well as 454 and paired-end Illumina methods in estimation of standard microbial ecology diversity measurements. We illustrate the utility of SI-Seq in a pilot sample of central airway secretion samples from cystic fibrosis patients.     

A metagenomic assessment of microbial eukaryotic diversity in the global ocean

Surveying microbial diversity and function is accomplished by combining complementary molecular tools. Among them, metagenomics is a PCR free approach that contains all genetic information from microbial assemblages and is today performed at a relatively large scale and reasonable cost, mostly based on very short reads. Here, we investigated the potential of metagenomics to provide taxonomic reports of marine microbial eukaryotes. We prepared a curated database with reference sequences of the V4 region of 18S rDNA clustered at 97% similarity and used this database to extract and classify metagenomic reads. More than half of them were unambiguously affiliated to a unique reference whilst the rest could be assigned to a given taxonomic group. The overall diversity reported by metagenomics was similar to that obtained by amplicon sequencing of the V4 and V9 regions of the 18S rRNA gene, although either one or both of these amplicon surveys performed poorly for groups like Excavata, Amoebozoa, Fungi and Haptophyta. We then studied the diversity of picoeukaryotes and nanoeukaryotes using 91 metagenomes from surface down to bathypelagic layers in different oceans, unveiling a clear taxonomic separation between size fractions and depth layers. Finally, we retrieved long rDNA sequences from assembled metagenomes that improved phylogenetic reconstructions of particular groups. Overall, this study shows metagenomics as an excellent resource for taxonomic exploration of marine microbial eukaryotes.     

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.     

Reconstruction of ribosomal RNA genes from metagenomic data

Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.     

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.     

Short-Read Assembly of Full-Length 16S Amplicons Reveals Bacterial Diversity in Subsurface Sediments

In microbial ecology, a fundamental question relates to how community diversity and composition change in response to perturbation. Most studies have had limited ability to deeply sample community structure (e.g. Sanger-sequenced 16S rRNA libraries), or have had limited taxonomic resolution (e.g. studies based on 16S rRNA hypervariable region sequencing). Here, we combine the higher taxonomic resolution of near-full-length 16S rRNA gene amplicons with the economics and sensitivity of short-read sequencing to assay the abundance and identity of organisms that represent as little as 0.01% of sediment bacterial communities. We used a new version of EMIRGE optimized for large data size to reconstruct near-full-length 16S rRNA genes from amplicons sheared and sequenced with Illumina technology. The approach allowed us to differentiate the community composition among samples acquired before perturbation, after acetate amendment shifted the predominant metabolism to iron reduction, and once sulfate reduction began. Results were highly reproducible across technical replicates, and identified specific taxa that responded to the perturbation. All samples contain very high alpha diversity and abundant organisms from phyla without cultivated representatives. Surprisingly, at the time points measured, there was no strong loss of evenness, despite the selective pressure of acetate amendment and change in the terminal electron accepting process. However, community membership was altered significantly. The method allows for sensitive, accurate profiling of the “long tail” of low abundance organisms that exist in many microbial communities, and can resolve population dynamics in response to environmental change.     

Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples

Objectives

There is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology.

Methods

This paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit^™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline.

Results

Results are presented at family and genus level. The Kullback-Leibler divergence (D_KL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average D_KL while the V4 gave the lowest (best) average D_KL. In addition to having a high D_KL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria.

Conclusions

The results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.     

Variable copy number, intra-genomic heterogeneities and lateral transfers of the 16S rRNA gene in Pseudomonas

Even though the 16S rRNA gene is the most commonly used taxonomic marker in microbial ecology, its poor resolution is still not fully understood at the intra-genus level. In this work, the number of rRNA gene operons, intra-genomic heterogeneities and lateral transfers were investigated at a fine-scale resolution, throughout the Pseudomonas genus. In addition to nineteen sequenced Pseudomonas strains, we determined the 16S rRNA copy number in four other Pseudomonas strains by Southern hybridization and Pulsed-Field Gel Electrophoresis, and studied the intra-genomic heterogeneities by Denaturing Gradient Gel Electrophoresis and sequencing. Although the variable copy number (from four to seven) seems to be correlated with the evolutionary distance, some close strains in the P. fluorescens lineage showed a different number of 16S rRNA genes, whereas all the strains in the P. aeruginosa lineage displayed the same number of genes (four copies). Further study of the intra-genomic heterogeneities revealed that most of the Pseudomonas strains (15 out of 19 strains) had at least two different 16S rRNA alleles. A great difference (5 or 19 nucleotides, essentially grouped near the V1 hypervariable region) was observed only in two sequenced strains. In one of our strains studied (MFY30 strain), we found a difference of 12 nucleotides (grouped in the V3 hypervariable region) between copies of the 16S rRNA gene. Finally, occurrence of partial lateral transfers of the 16S rRNA gene was further investigated in 1803 full-length sequences of Pseudomonas available in the databases. Remarkably, we found that the two most variable regions (the V1 and V3 hypervariable regions) had probably been laterally transferred from another evolutionary distant Pseudomonas strain for at least 48.3 and 41.6% of the 16S rRNA sequences, respectively. In conclusion, we strongly recommend removing these regions of the 16S rRNA gene during the intra-genus diversity studies.     

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.     

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.     

Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end illumina reads

Microbial communities host unparalleled taxonomic diversity. Adequate characterization of environmental and host-associated samples remains a challenge for microbiologists, despite the advent of 16S rRNA gene sequencing. In order to increase the depth of sampling for diverse bacterial communities, we developed a method for sequencing and assembling millions of paired-end reads from the 16S rRNA gene (spanning the V3 region; ～200 nucleotides) by using an Illumina genome analyzer. To confirm reproducibility and to identify a suitable computational pipeline for data analysis, sequence libraries were prepared in duplicate for both a defined mixture of DNAs from known cultured bacterial isolates (>1 million postassembly sequences) and an Arctic tundra soil sample (>6 million postassembly sequences). The Illumina 16S rRNA gene libraries represent a substantial increase in number of sequences over all extant next-generation sequencing approaches (e.g., 454 pyrosequencing), while the assembly of paired-end 125-base reads offers a methodological advantage by incorporating an initial quality control step for each 16S rRNA gene sequence. This method incorporates indexed primers to enable the characterization of multiple microbial communities in a single flow cell lane, may be modified readily to target other variable regions or genes, and demonstrates unprecedented and economical access to DNAs from organisms that exist at low relative abundances.     

Characterization of the fecal bacteria communities of forage-fed horses by pyrosequencing of 16S rRNA V4 gene amplicons

The diversity of the equine fecal bacterial community was evaluated using pyrosequencing of 16S rRNA gene amplicons. Fecal samples were obtained from horses fed cool-season grass hay. Fecal bacteria were characterized by amplifying the V4 region of bacterial 16S rRNA gene. Of 5898 mean unique sequences, a mean of 1510 operational taxonomic units were identified in the four fecal samples. Equine fecal bacterial richness was higher than that reported in humans, but lower than that reported in either cattle feces or soil. Bacterial classified sequences were assigned to 16 phyla, of which 10 were present in all samples. The largest number of reads belonged to Firmicutes (43.7% of total bacterial sequences), Verrucomicrobia (4.1%), Proteobacteria (3.8%), and Bacteroidetes (3.7%). The less abundant Actinobacteria, Cyanobacteria, and TM7 phyla presented here have not been previously described in the gut contents or feces of horses. Unclassified sequences represented 38.1% of total bacterial sequences; therefore, the equine fecal microbiome diversity is likely greater than that described. This is the first study to characterize the fecal bacterial community in horses by the use of 16S rRNA gene amplicon pyrosequencing, expanding our knowledge of the fecal microbiota of forage-fed horses.     

Bacterial Community Dynamics During Nursery Rearing of Pacific White Shrimp ( Litopenaeus vannamei ) Revealed via High-Throughput Sequencing

A 20-day trial was conducted to reveal bacterial community dynamics in a commercial nursery of larval Litopenaeus vannamei larvae. The bacterial communities in the ambient water were profiled by high-throughput sequencing of the V4–V5 hypervariable region of the 16S rRNA gene. The results indicated that the dominant bacterial phyla between the metamorphosis stage and postlarval stage were Bacteroidetes, Proteobacteria, Cyanobacteria, and Firmicutes, representing more than 80.09% of the bacterial operational taxonomic units. The relative abundance among bacterial phyla notably differed between the two stages. The relative abundance of Cyanobacteria was higher in the metamorphosis stage, while that of Bacteroidetes was higher and more stable in the postlarval stage. At the class level, the relative abundance of Sphingobacteriia and Alphaproteobacteria increased markedly in the postlarval stage, while that of Flavobacteriia decreased. Redundancy analysis showed that bacterial composition in the metamorphosis stage was positively correlated with salinity, alkalinity, and pH, while in the postlarval stage, it was positively correlated with ammonium nitrogen and nitrite nitrogen. Thus, microbial community diversity in the nursery phase varies per rearing stage.