Dataset Information

Species identification and profiling of complex microbial communities using shotgun Illumina sequencing of 16S rRNA amplicon sequences.

ABSTRACT: The high throughput and cost-effectiveness afforded by short-read sequencing technologies, in principle, enable researchers to perform 16S rRNA profiling of complex microbial communities at unprecedented depth and resolution. Existing Illumina sequencing protocols are, however, limited by the fraction of the 16S rRNA gene that is interrogated and therefore limit the resolution and quality of the profiling. To address this, we present the design of a novel protocol for shotgun Illumina sequencing of the bacterial 16S rRNA gene, optimized to amplify more than 90% of sequences in the Greengenes database and with the ability to distinguish nearly twice as many species-level OTUs compared to existing protocols. Using several in silico and experimental datasets, we demonstrate that despite the presence of multiple variable and conserved regions, the resulting shotgun sequences can be used to accurately quantify the constituents of complex microbial communities. The reconstruction of a significant fraction of the 16S rRNA gene also enabled high precision (>90%) in species-level identification thereby opening up potential application of this approach for clinical microbial characterization.

SUBMITTER: Ong SH

PROVIDER: S-EPMC3620293 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Species identification and profiling of complex microbial communities using shotgun Illumina sequencing of 16S rRNA amplicon sequences.

Ong Swee Hoe SH Kukkillaya Vinutha Uppoor VU Wilm Andreas A Lay Christophe C Ho Eliza Xin Pei EX Low Louie L Hibberd Martin Lloyd ML Nagarajan Niranjan N

PloS one 20130408 4

The high throughput and cost-effectiveness afforded by short-read sequencing technologies, in principle, enable researchers to perform 16S rRNA profiling of complex microbial communities at unprecedented depth and resolution. Existing Illumina sequencing protocols are, however, limited by the fraction of the 16S rRNA gene that is interrogated and therefore limit the resolution and quality of the profiling. To address this, we present the design of a novel protocol for shotgun Illumina sequencing ...[more]

PMID: 23579286

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Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Project description:Metagenomic sequencing of bacterial samples has become the gold standard for profiling microbial populations, but 16S rRNA profiling remains widely used due to advantages in sample throughput, cost, and sensitivity even though the approach is hampered by primer bias and lack of specificity. We hypothesized that a hybrid approach, that combined targeted PCR amplification with high-throughput sequencing of multiple regions of the genome, would capture many of the advantages of both approaches. We developed a method that identifies and quantifies members of bacterial communities through simultaneous analysis of multiple variable regions of the bacterial 16S rRNA gene. The method combines high-throughput microfluidics for PCR amplification, short read DNA sequencing, and a custom algorithm named MVRSION (Multiple 16S Variable Region Species-Level IdentificatiON) for optimizing taxonomic assignment. MVRSION performance was compared to single variable region analyses (V3 or V4) of five synthetic mixtures of human gut bacterial strains using existing software (QIIME), and the results of community profiling by shotgun sequencing (COPRO-Seq) of fecal DNA samples collected from gnotobiotic mice colonized with a defined, phylogenetically diverse consortium of human gut bacterial strains. Positive predictive values for MVRSION ranged from 65%-91% versus 44%-61% for single region QIIME analyses (p < .01, p < .001), while the abundance estimate r2 for MVRSION compared to COPRO-Seq was 0.77 vs. 0.46 and 0.45 for V3-QIIME and V4-QIIME, respectively. MVRSION represents a generally applicable tool for taxonomic classification that is superior to single-region 16S rRNA methods, resource efficient, highly scalable for assessing the microbial composition of up to thousands of samples concurrently, with multiple applications ranging from whole community profiling to targeted tracking of organisms of interest in diverse habitats as a function of specified variables/perturbations.

Project description:BackgroundSequential membrane filtration as a pre-processing step for capturing sediment-associated microorganisms could provide good quality and integrity DNA that can be preserved and kept at ambient temperatures before community profiling through culture-independent molecular techniques. However, the effects of sample pre-processing via filtration on DNA-based profiling of sediment-associated microbial community diversity and composition are poorly understood. Specifically, the influences of pre-processing on the quality and quantity of extracted DNA, high-throughput DNA sequencing reads, and detected microbial taxa need further evaluation.ResultsWe assessed the impact of pre-processing freshwater sediment samples by sequential membrane filtration (from 10, 5 to 0.22 μm pore size) for 16S rRNA-based community profiling of sediment-associated microorganisms. Specifically, we examined if there would be method-driven differences between non- and pre-processed sediment samples regarding the quality and quantity of extracted DNA, PCR amplicon, resulting high-throughput sequencing reads, microbial diversity, and community composition. We found no significant difference in the qualities and quantities of extracted DNA and PCR amplicons, and the read abundance after bioinformatics processing (i.e., denoising and chimeric-read filtering steps) between the two methods. Although the non- and pre-processed sediment samples had more unique than shared amplicon sequence variants (ASVs), we report that their shared ASVs accounted for 74% of both methods' absolute read abundance. More so, at the genus level, the final collection filter identified most of the genera (95% of the reads) captured from the non-processed samples, with a total of 51 false-negative (2%) and 59 false-positive genera (3%). We demonstrate that while there were differences in shared and unique taxa, both methods revealed comparable microbial diversity and community composition.ConclusionsOur observations highlight the feasibility of pre-processing sediment samples for community analysis and the need to further assess sampling strategies to help conceptualize appropriate study designs for sediment-associated microbial community profiling.

Project description:BACKGROUND:The 16S rRNA gene-based amplicon sequencing analysis is widely used to determine the taxonomic composition of microbial communities. Once the taxonomic composition of each community is obtained, evolutionary relationships among taxa are inferred by a phylogenetic tree. Thus, the combined representation of taxonomic composition and phylogenetic relationships among taxa is a powerful method for understanding microbial community structure; however, applying phylogenetic tree-based representation with information on the abundance of thousands or more taxa in each community is a difficult task. For this purpose, we previously developed the tool VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community), which is based on the genome-sequenced microbes' phylogenetic information. Here, we introduce VITCOMIC2, which incorporates substantive improvements over VITCOMIC that were necessary to address several issues associated with 16S rRNA gene-based analysis of microbial communities. RESULTS:We developed VITCOMIC2 to provide (i) sequence identity searches against broad reference taxa including uncultured taxa; (ii) normalization of 16S rRNA gene copy number differences among taxa; (iii) rapid sequence identity searches by applying the graphics processing unit-based sequence identity search tool CLAST; (iv) accurate taxonomic composition inference and nearly full-length 16S rRNA gene sequence reconstructions for metagenomic shotgun sequencing; and (v) an interactive user interface for simultaneous representation of the taxonomic composition of microbial communities and phylogenetic relationships among taxa. We validated the accuracy of processes (ii) and (iv) by using metagenomic shotgun sequencing data from a mock microbial community. CONCLUSIONS:The improvements incorporated into VITCOMIC2 enable users to acquire an intuitive understanding of microbial community composition based on the 16S rRNA gene sequence data obtained from both metagenomic shotgun and amplicon sequencing.

Dataset Information

Species identification and profiling of complex microbial communities using shotgun Illumina sequencing of 16S rRNA amplicon sequences.

Publications

Species identification and profiling of complex microbial communities using shotgun Illumina sequencing of 16S rRNA amplicon sequences.

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