NEXTflex™ PCR Master Mix
NEXTflex™ 16S V1-V3 PCR I Primer Mix
NEXTflex™ PCR II Barcoded Primer Mix
Required Materials Not Provided
1 ng - 50 ng high-quality genomic DNA in up to 36 µL nuclease-free water for each library
96 well PCR Plate Non-skirted (Phenix Research, Cat # MPS-499) or similar
Adhesive PCR Plate Seal (Bio-Rad®, Cat # MSB1001)
Agencourt® AMPure® XP 5 mL (Beckman Coulter® Genomics, Cat # A63880)
Magnetic Stand - 96 (Thermo Fisher Scientific®, Cat # AM10027) or similar
2, 10, 20, 200 and 1000 µL pipettes / multichannel pipettes
Nuclease-free barrier pipette tips
80% Ethanol, freshly prepared (room temperature)
16S rRNA Amplicon Sequencing Offers Enhanced Metagenomic Detection
The NEXTflex™ 16S V1-V3 Amplicon-Seq Kit and Illumina® MiSeq® 2x300 read chemistry allow for genus-level identification
Before the development of high-throughput methods to identify and characterize microbial populations, our understanding of the role microbes play in environmental, agricultural, and health-related settings was limited. The application of next generation sequencing (NGS) has provided an unprecedented ability to identify and categorize microbial taxonomy. Determining the complexity of species present in a sample can be achieved by sequencing a genomic region, conserved in all species, that contains evolutionarily divergent sequences that allow identification of unique taxa. A commonly used phylogenetic marker in metagenomics is the 16S ribosomal RNA (rRNA) gene. This ubiquitous locus is comprised of highly conserved regions flanking nine hyper-variable regions, referred to as V1-V9 (Figure 1). Here we demonstrate the utility of the NEXTflex™ 16S V1-V3 Amplicon-Seq Kit combined with the longer read chemistry of Illumina MiSeq (2x300) for enabling accurate identification of genera present in highly complex microbial communities across a vast number of samples.
Figure 1. Schematic representing conserved and hyper-variable regions of the 16S rRNA gene.
DNA Isolation and Microbiome Enrichment
DNA was isolated from human saliva using the QIAGEN® DNeasy® Blood & Tissue kit with minor modifications (1). Quality and quantity of DNA was assessed by spectrophotometry. DNA extracted from saliva was enriched for microbial DNA, and DNA quantity was determined by fluorometer.
16S V1-V3 Library Preparation
20 ng of microbial enriched DNA was used as starting material for a NEXTflex 16S V1-V3 Amplicon-Seq library prep. Targeted PCR amplification of the 16S V1-V3 region was performed using the universal primers contained in the kit, which contain library-specific overhangs and are complementary to the conserved domains flanking the hyper-variable regions of interest. After AMPure® XP bead cleanup, a subsequent PCR was performed with an indexing set of primers containing Illumina flow cell binding sites, sequencing primer complementary sequences compatible with paired-end sequencing, and indexing barcodes for high-throughput multiplexing of up to 384 unique libraries (Figure 2).
Figure 2. NEXTflex 16S V1-V3 Amplicon-Seq Kit workflow.
Sequencing and Data Analysis
Normalized libraries were clustered on-board, and paired-end sequencing was performed on the MiSeq. FASTQ files for each library were submitted to the online metagenomics analysis server, MG-RAST (2). Sequences were quality controlled and filtered before a nucleic acid similarity search against several databases of known 16S rRNA sequences was performed. Organisms detected in the total oral microbiome are shown as percent of reads mapping to genus-specific 16S rRNA references out of the total number of reads passing filter for each oral microbiome library (Figure 3).
RESULTS AND CONCLUSIONS
We explored the microbial community composition in human saliva using the NEXTflex 16S V1-V3 Amplicon-Seq Kit. High proportions of the genera Veillonella and Streptococcus were identified (Figure 3). Veillonella requires the presence of Streptococcus to adhere to the oral biofilm (plaque) and prefers lactate, the byproduct of metabolic process of Streptococcus, as its substrate of metabolism (3, 4). The top six genera present in this analysis: Prevotella, Veillonella, Streptococcus, Actinomyces, Fusobacterium and Leptotrichia represent abundant genera present in normal human oral microbiomes (5). Furthermore, the detection of low abundance microbes enables studies examining not only populations, but also active microbial evolution.
As a community composition study, many different 16S rRNA genes were amplified and sequenced, each with highly variable base composition, complexity and GC content (Figure 4). While the 16S rRNA region is not highly GC rich, the robust NEXTflex DNA polymerase used in the 16S V1-V3 Amplicon-Seq kit is able to amplify 45% - 65% GC content across all 16S V1-V3 regions sequenced in this experiment. Finally, the ability to detect a variety of taxa is improved by sequencing the V1-V3 regions in comparison to the V4 region alone. Using the NEXTflex 16S V1-V3 kit alone or in concert with the NEXTflex™ 16S V4 kit provides users with a time-efficient and robust method to study metagenomics, using any sample from which DNA can be obtained.
Figure 3. Genus level classification of oral microbiome from saliva sample that was enriched for microbial DNA.
Figure 4. GC content of 16S V1-V3 PCR amplicons sequenced. Y-axis represents number of reads uploaded to MG-RAST before quality control and filtering. X-axis represents percent GC content. Plotted points represent the number of reads within a GC percentage range.
1. Lazarevic et al., Analysis of the salivary microbiome using culture independent techniques. Journal of Clinical Bioinformatics. 2012, 2:4.
2. Meyer et al., The Metagenomics RAST server - A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008, 9:386.
3. Kreth et al., Bacterial and Host Interactions of Oral Streptococci. DNA and Cell Biology. 2009, 28:8.
4. Distler, W., and Kroncke, A. The lactate metabolism of the oral bacterium Veillonella from human saliva. Arch Oral Biology. 1981, 26.
5. Dewhirst et al., The Human Oral Microbiome. Journal of Bacteriology. 2010, 192:19.
Outside of novel sequencing technologies that emerge every few years, the ability to multiplex samples is the most critical and revolutionary aspect of next-generation sequencing. Multiplexing allows for acute control of throughput, amplifying the value of obtaining just enough data per sample.
To make multiplexing possible, small arbitrary sequences are incorporated into the sequencing adapters attached to all fragments of a particular sample. These sequences, known as barcodes, allow for post-sequencing processing to bin each fragment by its originating sample.
However, even high-fidelity polymerases used during sequencing reads are invariably prone to introducing errors. These errors are especially costly when landing during the barcode read, preventing proper binning and wasting associated sequencing reads. To alleviate this, the knowledge of bitwise error correction was extended to the base-wise language of sequencing.
The overall ability to correct barcode read errors stems from the differentiability between the entire set of barcodes. Differentiability can be called distance, or the number of single position changes that are required for one barcode sequence to become another. For example, the top sequence in the below figure has only one position change from the middle, while the middle has one position change from the bottom. Overall, the top to bottom sequence requires two position changes. This concept, known as the Hamming distance, is what powers barcode error correction and casual codebreaking games like Mastermind.
The greater the minimum distance separation across an entire barcode set, the stronger the differentiability. This in turn governs how many errors can be error-corrected across a barcode subset. Maximum error correction is governed by the following formula:
where d is the minimum distance across the entire set.
How does minimum distance affect generating barcode sets? By increasing the minimum distance across a subset, the overall maximum subset size decreases. One must set requirements so that sufficient barcodes are within a set of desired error correction.
We have expanded previously available barcode sequence sets in both set size and index lengths to accommodate higher levels of error correction. Also, other factors such as colorspace on Illumina instruments have been considered, leaving customers with a minimal amount of effort in selecting the best subsets for low-diversity sequencing runs.
Our new 12 nt barcode set, available with the NEXTflex™ 16S V1 – V3 Amplicon-Seq Kit, allows for up to two error corrections and has multiple low-diversity pooling options. We will continue to develop new technologies to remain the leader in quality multiplexing options.