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.     

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.     

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.     

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.     

16S rRNA二级结构可变区图形分析在放线菌分类中的应用

采用16S rRNA可变区二级结构图形分析,比较了姜氏菌属及几个相关属种可变区二级结构的变化。结果表明,在9个可变区二级结构中茎的长度、环的数目和类型、茎的碱基对、以及环内部碱基均有不同。尤其在V5和V6两个区,这种差别尤为明显。这为姜氏菌属的建立提供了又一个证据,并认为16S rRNA可变区二级结构分析,可以应用于属以上原核生物的分类。     

16SrRNA二级结构可变区图形分析在放线菌分类中的应用*

采用16SrRNA可变区二级结构图形分析,比较了姜氏菌属及几个相关属种可变区二级结构的变化。结果表明,在9个可变区二级结构中茎的长度、环的数目和类型、茎的碱基对、以及环内部碱基均有不同。尤其在V5和V6两个区,这种差别尤为明显。这为姜氏菌属的建立提供了又一个证据,并认为16SrRNA可变区二级结构分析,可以应用于属以上原核生物的分类。     

Vikodak - A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

Background

The overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak—a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.

Results

Validation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.

Conclusions

With novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.

Availability and Implementation

A web implementation of Vikodak can be publicly accessed at: http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users (academic as well as commercial).     

ClaMS: A Classifier for Metagenomic Sequences

ClaMS - "Classifier for Metagenomic Sequences" - is a Java application for binning assembled contigs in metagenomes using user-specified training sets and initial parameters. Since ClaMS trains on sequence composition-based genomic signatures, it is much faster than binning tools that rely on alignments to homologs; ClaMS can bin ~20,000 sequences in 3 minutes on a laptop with a 2.4 GH× Intel Core 2 Duo processor and 2 GB RAM. ClaMS is meant to be a desktop application for biologists and can be run on any machine under any Operating System on which the Java Runtime Environment can be installed.     

Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies

Bacterial 16S ribosomal DNA (rDNA) amplicons have been widely used in the classification of uncultured bacteria inhabiting environmental niches. Primers targeting conservative regions of the rDNAs are used to generate amplicons of variant regions that are informative in taxonomic assignment. One problem is that the percentage coverage and application scope of the primers used in previous studies are largely unknown. In this study, conservative fragments of available rDNA sequences were first mined and then used to search for candidate primers within the fragments by measuring the coverage rate defined as the percentage of bacterial sequences containing the target. Thirty predicted primers with a high coverage rate (>90%) were identified, which were basically located in the same conservative regions as known primers in previous reports, whereas 30% of the known primers were associated with a coverage rate of <90%. The application scope of the primers was also examined by calculating the percentages of failed detections in bacterial phyla. Primers A519–539, E969–983, E1063–1081, U515 and E517, are highly recommended because of their high coverage in almost all phyla. As expected, the three predominant phyla, Firmicutes, Gemmatimonadetes and Proteobacteria, are best covered by the predicted primers. The primers recommended in this report shall facilitate a comprehensive and reliable survey of bacterial diversity in metagenomic 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.     

Evaluations of Different Hypervariable Regions of Archaeal 16S rRNA Genes in Profiling of Methanogens by Archaea-Specific PCR and Denaturing Gradient Gel Electrophoresis

Different hypervariable (V) regions of the archaeal 16S rRNA gene (rrs) were compared systematically to establish a preferred V region(s) for use in Archaea-specific PCR-denaturing gradient gel electrophoresis (DGGE). The PCR products of the V3 region produced the most informative DGGE profiles and permitted identification of common methanogens from rumen samples from sheep. This study also showed that different methanogens might be detected when different V regions are targeted by PCR-DGGE. Dietary fat appeared to transiently stimulate Methanosphaera stadtmanae but inhibit Methanobrevibacter sp. strain AbM4 in rumen samples.     

Life in Hot Spring Microbial Mats Located in the Trans-Mexican Volcanic Belt: A 16S/18S rRNA Gene and Metagenomic Analysis

The geothermal system of the Araró region, located in the Trans-Mexican Volcanic Belt of México, hosts various hot springs with unique physicochemical characteristics, including temperatures ranging from 45°C to 78°C. The microbial diversity in these hot springs has been explored only by culture-dependent surveys. In this study, we performed metagenomic Illumina MiSeq, and 16S and 18S rRNA pyrosequencing analysis of the microbial life are residing in the microbial mats of the springs called “Tina–Bonita”. Our results show the presence of 186 operational taxonomic units, 99.7% of which belong to bacteria, 0.27% to eukaryotes, and 0.03% to archaea. The most abundant bacterial divisions are the Proteobacteria, Chloroflexi, and Cyanobacteria, which include 105 genera. The ecological indexes indicate that the microbial mats have moderate microbial diversity. An abundant group of genes that participate in photosynthesis, including photosynthetic electron transport, as well as photosystems I and II, were detected. Another cluster of genes was found that participates in sulfur, nitrogen, and methane metabolism. Finally, this phylogenetic and metagenomic analysis revealed an unexpected taxonomic and genetic diversity, expanding our knowledge of microbial life under specific extreme conditions.     

Influence of DNA Extraction Method, 16S rRNA Targeted Hypervariable Regions,and Sample Origin on Microbial Diversity Detected by 454 Pyrosequencing in Marine Chemosynthetic Ecosystems

Next-generation sequencing (NGS) opens up exciting possibilities for improving our knowledge of environmental microbial diversity, allowing rapid and cost-effective identification of both cultivated and uncultivated microorganisms. However, library preparation, sequencing, and analysis of the results can provide inaccurate representations of the studied community compositions. Therefore, all these steps need to be taken into account carefully. Here we evaluated the effects of DNA extraction methods, targeted 16S rRNA hypervariable regions, and sample origins on the diverse microbes detected by 454 pyrosequencing in marine cold seep and hydrothermal vent sediments. To assign the reads with enough taxonomic precision, we built a database with about 2,500 sequences from Archaea and Bacteria from deep-sea marine sediments, affiliated according to reference publications in the field. Thanks to statistical and diversity analyses as well as inference of operational taxonomic unit (OTU) networks, we show that (i) while DNA extraction methods do not seem to affect the results for some samples, they can lead to dramatic changes for others; and (ii) the choice of amplification and sequencing primers also considerably affects the microbial community detected in the samples. Thereby, very different proportions of pyrosequencing reads were obtained for some microbial lineages, such as the archaeal ANME-1, ANME-2c, and MBG-D and deltaproteobacterial subgroups. This work clearly indicates that the results from sequencing-based analyses, such as pyrosequencing, should be interpreted very carefully. Therefore, the combination of NGS with complementary approaches, such as fluorescence in situ hybridization (FISH)/catalyzed reporter deposition (CARD)-FISH or quantitative PCR (Q-PCR), would be desirable to gain a more comprehensive picture of environmental microbial communities.     

16S rRNA二级结构的研究进展及其在系统分类中的应用

16S rRNA作为研究系统进化的生物大分子极受重视,是系统分类中必不可少的一个指标。然而,在进行系统发育分析时通常的序列联配方法有明显的局限性,即无法摆脱一级结构的束缚。20世纪80年代以来,一些研究者把目光投向了16S rRNA二级结构的分析研究,并且已经取得了相当不错的进展。研究表明,16S rRNA二级结构可能作为一个新的分类指标,成为系统分类的一个重要补充。     

Evolution of Hypervariable Regions, V4 and V7, of Insect 18S rRNA and Their Phylogenetic Implications

We compared primary and secondary structures of V4 (helices E23-2 to E23-5) and V7 (helix 43) regions of 18S rRNAs in insects and the other three major arthropod groups (crustaceans, myriapods, and chelicerates) known so far. We found that the lengths of primary sequences and the shapes of secondary structures of these two hypervariable regions of insect 18S rRNA even at infraclass levels are phylogenetically informative and reflect major steps in insect evolution. The long sequence insertion and bifurcated shape of helices E23-2 to E23-5 in the V4 region are unique synapomorphic characters for winged insects (Pterygota). The long sequence insertion and expanded stem length of helix 43 in the V7 region are synapomorphic characters for holometabolous insects which conduct complete metamorphosis. The strongly conserved secondary structures suggest the possibility that these hypervariable regions may be related with certain important cellular functions unknown thus far. The comparison with insect fossil records revealed that the pterygote synapomorphy (V4) and the holometabolous synapomorphy (V7) were established prior to the acquisition of insect wings (flight system) and prior to the development of complete metamorphosis, respectively. These synapomorphies have been also relatively stable over at least 300 Myr and 280 Myr, respectively as well. It implies that the expansion events of the V4 and V7 regions have not occurred simultaneously but independently at different periods during the insect evolution. Then this suggests that V4 and V7 regions are not functionally correlated as recently suggested by Crease and Coulbourn.     

阎氏菌科的16S rRNA可变区二级结构分析

采用16SrRNA可变区二级结构图形分析,比较了阎氏菌科阎氏菌属典型种与微球菌亚目中几个相关科属典型种可变区二级结构的变化。结果表明,V3、V4存在明显的不同。将16SrRNA二级结构划分成不同的结构单元,提出在9个可变区中,至少要有2个不同的结构单元才可以定为新科,存在1个不同的结构单元可以定位新属;并认为16SrRNA可变区二级结构分析,可以作为一种辅助手段,应用于原核生物属以上水平的分类。     

A renaissance for the pioneering 16S rRNA gene

Culture-independent molecular surveys using the 16S rRNA gene have become a mainstay for characterizing microbial community structure over the past quarter century. More recently this approach has been overshadowed by metagenomics, which provides a global overview of a community's functional potential rather than just an inventory of its inhabitants. However, the pioneering 16S rRNA gene is making a comeback in its own right thanks to a number of methodological advancements including higher resolution (more sequences), analysis of multiple related samples (e.g. spatial and temporal series) and improved metadata, and use of metadata. The standard conclusion that microbial ecosystems are remarkably complex and diverse is now being replaced by detailed insights into microbial ecology and evolution based only on this one historically important marker gene.     

Taxonomic re-evaluation of Aphanizomenon flos-aquae NH-5 based on morphology and 16S rRNA gene sequences

The taxonomy of Aphanizomenon flos-aquae strain NH-5, a producer of cyanotoxins, was re-evaluated by comparison with six other Aphanizomenon strains using morphological characteristics and 16S rRNA gene sequences. Strain NH-5 was concluded to be improperly identified as Aph. flos-aquae based upon (1) lack of bundle formation in the trichomes, (2) location of akinetes next to heterocytes, (3) lower similarities (less than 97.5%) in the 16S rRNA gene sequences relative to Aph. flos-aquae strains, and (4) comparison within a phylogenetic tree constructed from 16S rRNA gene sequences. The Aphanizomenon strains investigated in this study are classified to four morphological groups as described by the classical taxonomy of Komárek & Kovácik (1989). This classification was supported from the phylogenetic results of 16S rRNA gene sequences. This study also discusses the generic boundaries between Aphanizomenon and Anabaena.