Development of the method presented here has been carried out with various sources of funding, including Genome Canada and Genome Quebec, Environment Canada STAGE program and internal NRC funds.

1. 16S rRNA Gene Amplicon Library Preparation by the Fusion Method
<ol>
	<li>Thaw the primers (Forward, F343-IonA-MIDXX: 5&#8217;-CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG XXX XXX XXX XTA CGG RAG GCA GCA G-3&#8217;; reverse, R533-IonP1: 5&#8217;-CCT CTC TAT GGG CAG TCG GTG ATA TTA CCG CGG CTG CTG GC-3&#8217;; bold: Ion Torrent specific adapters; italics: sequencing key for calibrating signal intensity at the beginning of the sequencing run; regular font: template-specific primers; X&#8230;X: 10 bp barcode, see Table 1 for some barcode sequences), the 2x HotStartTaq Plus master mix and the samples. Mix thoroughly before use, except for the master mix that should be mixed gently.</li>
	<li>Prepare a PCR mix (20 &#181;l reactions) with 0.5 &#181;M of the reverse primer, 0.4 mg/ml of Bovine Serum Albumine (BSA) and 1x HotStartTaq Plus Master mix. Add 18.5&#160;&#181;l of the master mix to each PCR tube.</li>
	<li>Add a forward primer (0.5 &#181;l at 20 mM) with a different barcode for each of the samples that will be sequenced together. Add 1&#160;&#181;l of sample (1-10 ng/&#181;l) to the PCR tube.</li>
	<li>Place the tubes in a PCR machine and run the following program: initial denaturation at 95 &#176;C for 5 min; 25 cycles of 95 &#176;C for 30 sec, 55 &#176;C for 30 sec and 72 &#176;C for 45 sec; final elongation at 72&#176;C for 10 min. PCR products can be kept at 4 &#176;C O/N or frozen until used.</li>
</ol>
2. Amplicon Purification, Quantification and Pooling
<ol>
	<li>Prepare a 2% agarose gel using the largest combs available. Add 5&#160;&#181;l of 6x gel loading dye to the reaction and load on the gel, separating each sample by an empty well. Run the gel at 70 V for 50 min.</li>
	<li>Take a picture of the gel and cut the bands (expected size around 250 bp: 190 bp product plus 63 bp adapters and barcodes) using a clean scalpel.</li>
	<li>Weigh the excised bands and proceed with the gel extraction and purification of the PCR products (with e.g., Qiaquick Gel Extraction kit).</li>
	<li>Quantify each PCR product with PicoGreen and make dilutions to obtain a stock solution at 5 x 109 molecules/&#181;l (approximately 1 ng/&#181;l). If some samples show lower amplification, adjust the dilutions to a lower concentration, keeping in mind that the lowest concentration suitable for subsequent procedures is 1.57 x 107 molecules/&#181;l (26 pM).</li>
	<li>Pool 10&#160;&#181;l of each diluted PCR product to obtain an equimolar pool of samples. Prepare one separate pool for each of the planned sequencing run. The pooled PCR products can be kept in the freezer until used.</li>
</ol>
3. Emulsion PCR and Sequencing
<ol>
	<li>Perform emulsion PCR:
	<ol>
		<li>Dilute the pooled PCR products to 26 pM in a volume of 25&#160;&#181;l. Thaw reagents from an Ion PGM template kit.</li>
		<li>Prepare the emulsion PCR mix (reagents provided in the kit) in a PCR hood and add the pooled PCR products and the sphere particles (provided) on the bench. Mix thoroughly and insert the mixture in a filter cartridge and carefully top with emulsion oil (provided).</li>
		<li>Slowly reverse the filter cartridge and load on the automated emulsion PCR apparatus (e.g., Ion One Touch 2 instrument). Select the appropriate program and start the procedure.</li>
	</ol>
	</li>
	<li>After the automated emulsion PCR has completed, remove the supernatant from the collection tubes and retrieve the sphere particles at the bottom of the tubes. Resuspend the spheres in 100 &#181;l of wash solution (provided in the kit) and take a 2.0 &#181;l aliquot for library quantification.</li>
	<li>Perform the library quantification.
	<ol>
		<li>Mix 100 &#181;l of SSPE buffer (150 mM NaCl, 10mM NaPO4, 1mM Na2-EDTA) with 2 &#181;l of Adapter B&#8217;-Fam (5&#8217;-FAM-CTG AGA CTG CCA AGG CAC ACA GGG GAT AGG-3&#8217;) probe and 2 &#181;l of Adapter A-Cy5 (5&#8217;-Cy5-CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG-3&#39;) probe. Add 52 &#181;l of this mixture to the 2.0 &#181;l aliquot taken in step 3.2 and add 2 &#181;l of wash solution (from the template kit) to the remaining 52 &#181;l as a blank for quantitation.</li>
		<li>Incubate at 95 &#176;C for 2 min, then at 37 &#176;C for 2 min. Transfer the mixtures in 1.5 ml tubes.</li>
		<li>Add 1.0 ml of TEX buffer (10 mM Tris, 1 mM EDTA, 0.01% Triton X-100, pH 8.0), mix and centrifuge at 15,500 x g for 3 min at RT. Remove the supernatant and leave 20 &#181;l in each tube. Repeat this washing procedure two times.</li>
		<li>Add 180 &#181;l of TEX buffer, resuspend the pelleted particles and transfer into 500 &#181;l Qubit tubes.</li>
		<li>Measure fluorescence for FAM and Cy5 using a Qubit apparatus and calculate the ratio of positive spheres using the calculator available on the Ion Community website.</li>
	</ol>
	</li>
	<li>Enrich the remainder of the sphere particles for positive spheres (containing amplified DNA) using an automated enrichment system (e.g., Ion One Touch Enrichment System).
	<ol>
		<li>Pipet reagents in the appropriate wells of the provided 8-well strip: Well 1: sphere particles from step 3.2; Well 2: pre-washed MyOne Streptavidin C1 beads (13 &#181;l); Well 3, 4 and 5: wash solution (provided in the template kit); Well 6: empty; Well 7: melt-off solution (125 mM NaOH, 0.1% Tween 20); Well 8: empty.</li>
		<li>Install a tip on the pipetting arm and a 0.2 ml tube for sample collection. Start the instrument. At the end of the enrichment, add 10 &#181;l of neutralizing solution (provided). The enriched beads can be kept a 4 &#176;C for up to 15 days.</li>
	</ol>
	</li>
	<li>Initialize the sequencing instrument:
	<ol>
		<li>Connect to the sequencing instrument and prepare a sequencing run plan, specifying the details of the sequencing run.</li>
		<li>Install the wash solution #2 (provided in the Ion PGM sequencing kit), the wash solution #1 (350 &#181;l 100 mM NaOH) and the auto-pH buffer solution (provided).</li>
		<li>Start the initialization procedure and when prompted add the dATP, dGTP dCTP and dTTP nucleotides (provided) in their respective 50 ml tubes. Screw the tubes onto the sequencing machine and continue the initialization procedure. When the initialization is completed, start the run procedure immediately.</li>
	</ol>
	</li>
	<li>Prepare samples for sequencing and load the chip:
	<ol>
		<li>Collect the enriched spheres from step 3.4 at the bottom of the tube by centrifuging at 15,500 x g for 1.5 min. Remove the supernatant leaving exactly 3 &#181;l.</li>
		<li>Add 3 &#181;l of sequencing primer (provided in the sequencing kit), mix thoroughly by pipetting up and down to resuspend the particles. Incubate at 95 &#176;C for 2 min, then at 37 &#176;C for 2 min.</li>
		<li>Add 1 &#181;l of polymerase (provided), mix well and incubate at RT for 5 min. Proceed to load the enzyme-sphere mixture on the sequencing chip within 30 min.</li>
		<li>During incubation, load the sequencing chip on the sequencer and perform a chip quality check. Remove the liquid from the chip by centrifugation and load the sample in the chip by slowly pipeting down the enzyme-sphere mix (7 &#181;l) and avoiding the introduction of bubbles in the chip.</li>
		<li>Centrifuge the chip three times for 1 min in a microcentrifuge. Change the orientation of the chip at each centrifugation and mix by pipetting up and down in between each run.</li>
		<li>Load the chip in the instrument and start the run.</li>
	</ol>
	</li>
</ol>
4. Basic Sequence Data Analysis
<ol>
	<li>Connect to the sequencing data server and download the fastq file. Create a tab-delimited &#8220;oligos&#8221; file containing the primer and barcode information (see example in Table 1). Download the Greengenes reference files from the Mothur website (<a href="http://www.mothur.org/wiki/Taxonomy_outline">http://www.mothur.org/wiki/Taxonomy_outline</a>).</li>
	<li>Launch Mothur and perform analyses:
	<ol>
		<li>Convert the fastq file to a fasta and a quality file using the following command: fastq.info(fastq=test.fastq)</li>
		<li>Determine the group membership of each sequence and trim the sequences using the following command: trim.seqs(fasta=test.fasta, oligos=barcode.txt, qfile=test.qual, qwindowaverage=20, qwindowsize=50, minlength=150, keepforward=T)</li>
		<li>The various options can be modified according to the stringency desired</li>
		<li>Classify the sequences in the greengenes taxonomy using the following command: classify.seqs(fasta=test.trim.fasta, method=wang, group=test.groups, template=gg_99_otus.fasta, taxonomy=gg_99_otus.tax, cutoff=50)</li>
	</ol>
	</li>
</ol>

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<li>Bell, T. H., Yergeau, E., Juck, D., Whyte, L. G., Greer, C. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Alteration+of+microbial+community+structure+affects+diesel+biodegradation+in+an+Arctic+soil%5BTitle%5D)+AND+%22FEMS+Microbiol.+Ecol%22%5BJournal%5D)">Alteration of microbial community structure affects diesel biodegradation in an Arctic soil.</a> FEMS Microbiol. Ecol. 85, 51-61 (2013).</li>
<li>Bell, T. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Predictable+bacterial+composition+and+hydrocarbon+degradation+in+Arctic+soils+following+diesel+and+nutrient+disturbance%5BTitle%5D)+AND+%22ISME+J%22%5BJournal%5D)">Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance.</a> ISME J. 7, 1200-1210 (2013).</li>
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<li>Yergeau, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Aerobic+biofilms+grown+from+Athabasca+watershed+sediments+are+inhibited+by+increasing+bituminous+compounds+concentrations%5BTitle%5D)+AND+%22Appl.+Environ.+Microbiol%22%5BJournal%5D)">Aerobic biofilms grown from Athabasca watershed sediments are inhibited by increasing bituminous compounds concentrations.</a> Appl. Environ. Microbiol. 79, 7398-7412 (2013).</li>
<li>Yergeau, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Microbial+expression+profiles+in+the+rhizosphere+of+willows+depend+on+soil+contamination%5BTitle%5D)+AND+%22ISME+J%22%5BJournal%5D)">Microbial expression profiles in the rhizosphere of willows depend on soil contamination.</a> ISME J. 8, 344-358 (2013).</li>
<li>Deagle, B. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantifying+sequence+proportions+in+a+DNA-based+diet+study+using+Ion+Torrent+amplicon+sequencing%3A+which+counts+count%5BTitle%5D)+AND+%22Mol.+Ecol.+Resour%22%5BJournal%5D)">Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count.</a> Mol. Ecol. Resour. 13, 620-633 (2013).</li>
<li>Milani, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Assessing+the+fecal+microbiota%3A+an+optimized+Ion+Torrent+16S+rRNA+gene-based+analysis+protocol%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">Assessing the fecal microbiota: an optimized Ion Torrent 16S rRNA gene-based analysis protocol.</a> PLoS ONE. 8, e68739(2013).</li>
<li>Jünemann, S., Prior, P., Szczepanowski, R., Harks, I., Ehmke, B., Goesmann, A., Stoye, J., Dag Harmsen, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Bacterial+community+shift+in+treated+periodontitis+patients+revealed+by+Ion+Torrent+16S+rRNA+gene+amplicon+sequencing%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">Bacterial community shift in treated periodontitis patients revealed by Ion Torrent 16S rRNA gene amplicon sequencing.</a> PLoS ONE. 7, e41606(2012).</li>
<li>Petrof, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stool+substitute+transplant+therapy+for+the+eradication+of+Clostridium+difficile+infection%3A+%27RePOOPulating%27+the+gut%5BTitle%5D)+AND+%22Microbiome%22%5BJournal%5D)">Stool substitute transplant therapy for the eradication of Clostridium difficile infection: 'RePOOPulating' the gut.</a> Microbiome. 1, 3(2013).</li>
<li>Whiteley, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Microbial+16S+rRNA+Ion+Tag+and+community+metagenome+sequencing+using+the+Ion+Torrent+%28PGM%29+Platform%5BTitle%5D)+AND+%22J.+Microbiol.+Meth%22%5BJournal%5D)">Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform.</a> J. Microbiol. Meth. 91, 80-88 (2012).</li>
<li>Caporaso, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ultra-high-throughput+microbial+community+analysis+on+the+Illumina+HiSeq+and+MiSeq+platforms%5BTitle%5D)+AND+%22ISME+J%22%5BJournal%5D)">Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.</a> ISME J. 6, 1621-1624 (2012).</li>
<li>Kozich, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Development+of+a+dual-index+sequencing+strategy+and+curation+pipeline+for+analyzing+amplicon+sequence+data+on+the+MiSeq+Illumina+sequencing+platform%5BTitle%5D)+AND+%22Appl.+Environ.+Microbiol%22%5BJournal%5D)">Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.</a> Appl. Environ. Microbiol. 79, 5112-5120 (2013).</li>
<li>Hamady, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fast+UniFrac%3A+facilitating+high-throughput+phylogenetic+analyses+of+microbial+communities+including+analysis+of+pyrosequencing+and+PhyloChip+data%5BTitle%5D)+AND+%22ISME+J%22%5BJournal%5D)">Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data.</a> ISME J. 4, 17-27 (2010).</li>
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<li>Huse, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ironing+out+the+wrinkles+in+the+rare+biosphere+through+improved+OTU+clustering%5BTitle%5D)+AND+%22Environ.+Microbiol%22%5BJournal%5D)">Ironing out the wrinkles in the rare biosphere through improved OTU clustering.</a> Environ. Microbiol. 12, 1889-1898 (2010).</li>
<li>Edwards, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterization+of+the+dynamics+of+initial+bacterial+colonization+of+nonconserved+forage+in+the+bovine+rumen%5BTitle%5D)+AND+%22FEMS+Microbiol.+Ecol%22%5BJournal%5D)">Characterization of the dynamics of initial bacterial colonization of nonconserved forage in the bovine rumen.</a> FEMS Microbiol. Ecol. 62, 323-335 (2007).</li>
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<li>Baker, G. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Review+and+re-analysis+of+domain-specific+16S+primers%5BTitle%5D)+AND+%22J.+Microbiol.+Methods%22%5BJournal%5D)">Review and re-analysis of domain-specific 16S primers.</a> J. Microbiol. Methods. 55, 541-555 (2003).</li>
<li>Schloss, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+effects+of+alignment+quality%2C+distance+calculation+method%2C+sequence+filtering%2C+and+region+on+the+analysis+of+16S+rRNA+gene-based+studies%5BTitle%5D)+AND+%22PLoS+Comput.+Biol%22%5BJournal%5D)">The effects of alignment quality, distance calculation method, sequence filtering, and region on the analysis of 16S rRNA gene-based studies.</a> PLoS Comput. Biol. 6, e1000844(2010).</li>
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</ol>

The authors have nothing to disclose.

The method presented here is straightforward and inexpensive, and should allow many laboratories to access the power of metagenomic sequencing. Although it varies depending on the sequencing platform used, once the libraries are constructed very little hands-on time is required, with most of the process being automatized. For the sequencing platform used here (Ion Torrent PGM), the complete procedure can be performed within two days of work. At the moment of writing (September 2013), the reagent costs related to the example detailed above were as follows: PCR amplification of 36 samples: $25, gel purification and PicoGreen DNA quantitation of 36 samples: $125, emulsion PCR for one pooled amplicon sample: $150 and sequencing reagents: $250, for a total of $550 or $15 per sample or $0.0015 per quality-filtered read. This price does not include instrument service contract, instrument depreciation, technician salary and laboratory space usage.
One of the most important steps is to pool all the products in an equimolar ratio, in order to retrieve similar number of reads for each of the samples. PicoGreen quantification was used here, but other methods might be suitable, though less accurate (e.g., UV quantification, gel-based quantification). Even by doing the most accurate quantification and pooling, there is some variability in the number of reads per sample, and in the typical run detailed in Table 2, it ranges from 4,380 to 32,750 reads, with an average of 10,338 reads. If processing large number of samples (more than 40-50), single column gel purification can be replaced by gel purification in plate or purification using beads with a stringent size cutoff (e.g., AMPure beads).
To date, the most used next-generation sequencing technology for the 16S rRNA gene is 454. The Ion Torrent sequencing technology used in this protocol is conceptually very similar to 454 and both technologies are prone to the same type of sequencing errors. Not surprisingly, it was shown that Ion Torrent sequencing resulted in sequencing results very similar to 454 sequencing10. Recently, many researchers have explored the use of Illumina technology for 16S rRNA gene amplicon sequencing18,19. In any case, it would be easy to adapt the current protocol for other benchtop sequencers like the Illumina MiSeq or the 454 GS Junior by changing the fusion primer sequences to match the adapters and barcodes needed for these sequencing technologies, like in the method recently described for the Illumina MiSeq19. Alternatively, researchers could follow steps 1 and 2 of the protocol detailed here and send the pooled amplicons to a sequencing center where the emulsion PCR and sequencing would be performed.
The 16S rRNA gene reads were trimmed and classified using Mothur, but many other analyses can be performed on 16S rRNA gene amplicons. For instance, beta diversity can be evaluated by calculating the Unifrac distances between each sample pair using the procedure outlined at <a href="http://unifrac.colorado.edu/">http://unifrac.colorado.edu/</a>20. Alpha diversity indices and number of operational taxonomical units of each sample can be calculated using tools within QIIME like AmpliconNoise 21 or using the procedure outlined by Huse et al.22 and available within Mothur.
The primers used here amplified the variable regions 3 and 4 from the 16S rRNA gene, but many other regions could be targeted. In present study, 16S rRNA genes were amplified from plant material and the choice of primer was made to avoid amplification of chloroplast 16S rRNA gene23,24. There are a wide variety of other primers available that vary in term of the product length, taxonomic power and usefulness25,26. However, in all cases 200-400 bp reads of the 16S rRNA gene cannot be reliably classified at the species level, and analyses are limited to the genus and higher taxonomical levels. Other genes could be more appropriate if species level information is needed, like the cpn60 and rpoB genes27,28. Future drastic drops in the cost of sequencing and increases in the power of analytical tools might make it feasible to replace 16S rRNA gene sequencing by shotgun metagenomics, but until then 16S rRNA gene sequencing remains the gold standard of environmental microbiology.

Metagenomic sequencing is a very powerful technology as it targets the entirety of the genetic information contained in an environmental sample. There are different flavors of metagenomic sequencing, including shotgun sequencing, large-insert libraries and amplicon sequencing. Amplicon sequencing offers the advantage of being relatively inexpensive, fast and able to produce reads from a single genomic region that can be generally aligned. In addition, the data analysis workflow for amplicon sequencing is mostly standardized. However, since it is based on PCR, it has all the biases related to incomplete specificity, incomplete coverage and primer biases1,2, which makes this approach semi-quantitative at best. Several genomic regions can be targeted for amplicon sequencing including functional genes, but the most popular options are to use marker genes such as the 16S rRNA gene to generate a community profile. Traditionally, 16S rRNA gene amplicon sequencing was carried out using labor intensive techniques that included cloning in E. coli, colony picking and plasmid extraction followed by Sanger sequencing on the isolated plasmids, and, consequently, most studies analyzed fewer than 100 clones per sample. Next-generation sequencing brought two major advances: massive parallelization of the sequencing reactions and, most importantly, clonal separation of templates without the need to insert gene fragments in a host. This has simplified tremendously the sequencing of 16S rRNA gene amplicons, which is now back as a routine feature of many environmental microbiology studies, resulting in a &#8220;renaissance&#8221; for 16S rRNA gene amplicon sequencing 3.
Since the advent of Roche 454 sequencing in 20054, several other next-generation sequencing technologies have appeared on the market (e.g., Illumina, Solid, PacBio). More recently, the introduction of bench-top sequencers brought to small labs the sequencing capacity once exclusive to large sequencing centers. Five benchtop machines are currently available: the 454 GS Junior, the Ion Torrent Personal Genome Machine (PGM) and Proton, and the Illumina MiSeq and NextSeq 500. While all these sequencers offer less reads per run and fewer bases per dollar than most full-scale sequencers, they are more flexible, rapid, and their low acquisition and run costs makes them affordable for small academic laboratories. Benchtop sequencers are particularly well suited for amplicon, small genome and low-complexity metagenome sequencing in environmental microbiology studies, because this type of studies generally does not require an extreme depth of sequencing. For example, it is generally agreed that for 16S rRNA gene sequencing studies the number of reads per sample is not paramount, as ~1,000 reads can generate the same patterns as multi-million reads datasets5. Having said that, benchtop next-generation sequencers still generate large amounts of sequence data, with maximal yields of ~35 Mbp (454 GS Junior), ~2 Gbp (Ion Torrent PGM), ~10-15 Gbp (Ion Torrent Proton), ~10 Gbp (Illumina MiSeq) and ~100 Gbp (Illumina Next Seq 500), which is more than enough for most environmental microbiology studies.
Next-generation sequencing of 16S rRNA amplicons using benchtop sequencers has been recently applied to a wide variety of environments. For example, the Ion Torrent PGM has been used for community analyses of uranium mine tailings that had particularly high pH and low permeability6, of recirculating aquaculture systems7, of hydrocarbon-contaminated Arctic soils8,9, of oil sands mining affected sediments and biofilms from the Athabasca River10,11, of the rhizosphere of willows planted in contaminated soils12, of the human and animal bodies13-16 and of anaerobic digesters17.
In this contribution we detail our approach to sequence 16S rRNA gene amplicons in-house using a benchtop next-generation sequencer (the Ion Torrent PGM). After DNA extraction, 16S rRNA genes are amplified using domain-level bacterial primers that contain sequencing adapters and unique, sample-specific sequences (barcodes). The amplicons are purified, quantified and pooled at an equimolar ratio. The pooled samples are then clonally amplified in an emulsion PCR and sequenced. Resulting sequences are analyzed using publicly available bioinformatics tools (e.g., Mothur).

<table><tbody><tr><td>Ion 314 Chip Kit v2</td><td>Life Technologies</td><td>4482261</td><td></td></tr><tr><td>Ion PGM Sequencing 200 Kit v2</td><td>Life Technologies</td><td>4482006</td><td></td></tr><tr><td>Ion PGM Template OT2 200 Kit</td><td>Life Technologies</td><td>4480974</td><td></td></tr><tr><td>HotStarTaq Plus Master Mix Kit</td><td>Qiagen</td><td>203646</td><td></td></tr><tr><td>Primers and probes</td><td>IDT</td><td>NA</td><td></td></tr><tr><td>Qiaquick Gel Extraction Kit</td><td>Qiagen</td><td>28704</td><td></td></tr><tr><td>BSA 20 mg/ml</td><td>Roche</td><td>10,711,454,001</td><td></td></tr><tr><td>Dynabeads MyOne Streptavidin C1</td><td>Life Technologies</td><td>65001</td><td></td></tr></tbody></table>

next-generation sequencing of 16s ribosomal rna gene amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

After purification on gel, with 25 cycles of PCR amplification, the amplification products are usually at a concentration of 0.2-10.0 ng in 50 &#181;l of water. This may vary widely depending on the starting DNA concentration, the type of sample and the purification kit used. It is recommended to keep the number of PCR cycles to the lowest possible to avoid chimera formation and decrease amplification biases, keeping in mind that all samples should be amplified using the same number of cycles. To minimize the number of polyclonal reads and empty spheres and maximize the number of reads, the Qubit ratio should be between 0.1 and 0.3 and the FAM fluorescence should be above 200. Using a 314 chip on an Ion Torrent PGM, the average output is around 0.3-0.5 M good quality reads after filtering of the results in Mothur. Table 2 shows a typical breakdown of the number of reads after each step of the procedure for a run containing 36 multiplexed environmental samples amplified with primers targeting the V3-4 region of the 16S and analyzed using Mothur. In Mothur, the trim.seqs procedure generate a &#8220;*.trim.fasta&#8221; file containing the sequences that passed the quality filters and a &#8220;*.scrap.fasta&#8221; that contains the sequences that did not pass the quality filters along with the reason for rejection in the sequence header. When supplied with barcodes in the &#8220;oligos&#8221; file, this command will also generate a &#8220;*.groups&#8221; file that contains the group membership of every sequence based on the barcode sequence. The classify.seqs procedure generates a &#8220;.tax.summary&#8221; that can be opened in Excel. This file contains the summary of the taxonomic affiliation (in lines) for each of the samples (in columns). This file can be used for downstream statistical analyses and to visualize community composition at various taxonomic levels. The &#8220;.taxonomy&#8221; file contains the detailed taxonomic affiliation for each sequence. The average community composition at the phylum/class level across all 36 samples is shown in Figure 1.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51709/51709fig1highres.jpg" width="500" /> 
Figure 1. Average community composition at the phylum/class level across all samples.
<table border="0" cellpadding="0" cellspacing="0" width="248">
	<colgroup>
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:64px;">forward</td>
			<td style="width:139px;">TACGGRAGGCAGCAG</td>
			<td style="width:47px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CTAAGGTAAC</td>
			<td>Sample01</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TAAGGAGAAC</td>
			<td>Sample02</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>AAGAGGATTC</td>
			<td>Sample03</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TACCAAGATC</td>
			<td>Sample04</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CAGAAGGAAC</td>
			<td>Sample05</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CTGCAAGTTC</td>
			<td>Sample06</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TTCGTGATTC</td>
			<td>Sample07</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TTCCGATAAC</td>
			<td>Sample08</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TGAGCGGAAC</td>
			<td>Sample09</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CTGACCGAAC</td>
			<td>Sample10</td>
		</tr>
	</tbody>
</table>
Table 1. Example &#8220;oligos&#8221; file for use in Mothur.
<table border="0" cellpadding="0" cellspacing="0" width="660">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;"></td>
			<td style="width:67px;"># of reads</td>
			<td style="width:123px;">% of previous step</td>
			<td style="width:107px;">Avg. per sample</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Number of wells</td>
			<td align="center">1,262,519</td>
			<td>-</td>
			<td align="center">35,070</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Wells with beads</td>
			<td align="center">1,114,108</td>
			<td align="center">88.20%</td>
			<td align="center">30,947</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Beads with templates</td>
			<td align="center">1,112,746</td>
			<td align="center">99.90%</td>
			<td align="center">30,910</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Monoclonal beads</td>
			<td align="center">826,805</td>
			<td align="center">74.30%</td>
			<td align="center">22,967</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Good quality reads (Output from the sequencer)</td>
			<td align="center">782,204</td>
			<td align="center">94.60%</td>
			<td align="center">21,728</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Pass Mothur filters (min. avg. quality score of 20 over a 50bp window, min. length of 150bp)</td>
			<td align="center">372,168</td>
			<td align="center">47.60%</td>
			<td align="center">10,338</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Classified at the phylum level in GreenGenes (50% confidence threshold)</td>
			<td align="center">342,171</td>
			<td align="center">91.90%</td>
			<td align="center">9,505</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Classified at the family level in GreenGenes (50% confidence threshold)</td>
			<td align="center">316,512</td>
			<td align="center">92.50%</td>
			<td align="center">8,792</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Classified at the genus level in GreenGenes (50% confidence threshold)</td>
			<td align="center">289,899</td>
			<td align="center">91.60%</td>
			<td align="center">8,053</td>
		</tr>
	</tbody>
</table>
Table 2. Number of reads produced from a typical run for 36 environmental samples multiplexed on one Ion Torrent 314 chip.

Watch this Scientific Journal Video about Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons at JoVE.com

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

next-generation-sequencing-of-16s-ribosomal-rna-gene-amplicons

Research

JoVE Journal

Biology

83.3K Views.  National Research Council Canada. Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer. 

Video: Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Metagenomic Analysis of Silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

Genetics

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of taxonomic and functional groups in environmental samples. Used in combination with a reverse transcriptase reaction (RT-q-PCR), it can also be employed to quantify gene transcripts. q-PCR makes use of highly sensitive fluorescent detection chemistries that allow quantification of PCR amplicons during the exponential phase of the reaction. Therefore, the biases associated with &#39;end-point&#39; PCR detected in the plateau phase of the PCR reaction are avoided. A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. First, a method for the co-extraction of DNA and RNA from coastal sediments, including the additional steps required for the preparation of DNA-free RNA, is outlined. Second, a step-by-step guide for the quantification of 16S rRNA genes and transcripts from the extracted nucleic acids via q-PCR and RT-q-PCR is outlined. This includes details for the construction of DNA and RNA standard curves. Key considerations for the use of RT-q-PCR assays in microbial ecology are included.

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of ...

Environment

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Complete genome sequences provide valuable data for the understanding of genetic diversity and unique colonization factors of urinary microbes. These data may include mobile genetic elements, such as plasmids and extrachromosomal phage, that contribute to the dissemination of antimicrobial resistance and further complicate treatment of urinary tract infection (UTI). In addition to providing fine resolution of genome structure, complete, closed genomes allow for the detailed comparative genomics and evolutionary analyses. The generation of complete genomes de novo has long been a challenging task due to limitations of available sequencing technology. Paired-end Next Generation Sequencing (NGS) produces high quality short reads often resulting in accurate but fragmented genome assemblies. On the contrary, Nanopore sequencing provides long reads of lower quality normally leading to error-prone complete assemblies. Such errors may hamper genome-wide association studies or provide misleading variant analysis results. Therefore, hybrid approaches combining both short and long reads have emerged as reliable methods to achieve highly accurate closed bacterial genomes. Reported herein is a comprehensive method for the culture of diverse urinary bacteria, species identification by 16S rRNA gene sequencing, extraction of genomic DNA (gDNA), and generation of short and long reads by NGS and Nanopore platforms, respectively. Additionally, this method describes a bioinformatic pipeline of quality control, assembly, and gene prediction algorithms for the generation of annotated complete genome sequences. Combination of bioinformatic tools enables the selection of high quality read data for hybrid genome assembly and downstream analysis. The streamlined approach for the hybrid de novo genome assembly described in this protocol may be adapted for the use in any culturable bacteria.

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

This protocol details a comprehensive approach for the culturing, sequencing, and de novo hybrid genome assembly of urinary bacteria. It provides a reproducible procedure for the generation of complete, circular genome sequences useful in studying both chromosomal and extrachromosomal genetic elements contributing to urinary colonization, pathogenesis, and antimicrobial resistance dissemination.

Complete genome sequences provide valuable data for the understanding of genetic diversity and unique colonization factors of urinary microbes. These data ...

RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of this method, in comparison to other approaches, is its ability to monitor translation by precisely mapping the position and number of ribosomes on a mRNA transcript.
In this article, we describe the consecutive stages of sample collection and preparation for RIBO-seq method in bacteria, highlighting the details relevant to the planning and execution of the experiment.
Since the RIBO-seq relies on intact ribosomes and related mRNAs, the key step is rapid inhibition of translation and adequate disintegration of cells. Thus, we suggest filtration and flash-freezing in liquid nitrogen for cell harvesting with an optional pretreatment with chloramphenicol to arrest translation in bacteria. For the disintegration, we propose grinding frozen cells with mortar and pestle in the presence of aluminum oxide to mechanically disrupt the cell wall. In this protocol, sucrose cushion or a sucrose gradient ultracentrifugation for monosome purification is not required. Instead, mRNA separation using polyacrylamide gel electrophoresis (PAGE) followed by the ribosomal footprint excision (28-30 nt band) is applied and provides satisfactory results. This largely simplifies the method as well as reduces the time and equipment requirements for the procedure. For library preparation, we recommend using the commercially available small RNA kit for Illumina sequencing from New England Biolabs, following manufacturer&#39;s guidelines with some degree of optimization.
The resulting cDNA libraries present appropriate quantity and quality required for next generation sequencing (NGS). Sequencing of the libraries prepared according to the described protocol results in 2 to 10 mln uniquely mapped reads per sample providing sufficient data for comprehensive bioinformatic analysis. The protocol we present is quick and relatively easy and can be performed with standard laboratory equipment.

Here we describe the stages of sample collection and preparation for RIBO-seq in bacteria. Sequencing of the libraries prepared according to these guidelines results in sufficient data for comprehensive bioinformatic analysis. The protocol we present is simple, uses standard laboratory equipment and takes seven days from lysis to obtaining libraries.

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of ...

Biochemistry

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in immunocompromised patients. In most cases only few data, mostly published as case reports, are available which investigate the role of such pathogens as an infectious agent. Therefore, in order to clarify the pathogenic character of such microorganisms, it is necessary to conduct epidemiologic studies which include large numbers of these bacteria. The methods used in such a surveillance study have to meet the following criteria: the identification of the strains has to be accurate according to the valid nomenclature, they should be easy to handle (robustness), economical in routine diagnostics and they have to generate comparable results among different laboratories. Generally, there are three strategies for identifying bacterial strains in a routine setting: 1) phenotypic identification characterizing the biochemical and metabolic properties of the bacteria, 2) molecular techniques such as 16S rRNA gene sequencing and 3) mass spectrometry as a novel proteome based approach. Since mass spectrometry and molecular approaches are the most promising tools for identifying a large variety of bacterial species, these two methods are described. Advances, limitations and potential problems when using these techniques are discussed.

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in ...

Immunology and Infection

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing technologies have allowed for the rapid and efficient characterization of bacterial communities using 16S rRNA gene sequencing from a variety of sources. Although readily available tools for 16S rRNA sequence analysis have standardized computational workflows, sample processing for DNA extraction remains a continued source of variability across studies. Here we describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs. We also delineate downstream methods for 16S rRNA gene sequencing, including generation of sequencing libraries, data quality control, and sequence analysis. The workflow can accommodate multiple samples types, including stool and swabs collected from a variety of anatomical locations and host species. Additionally, recovered DNA and RNA can be separated and used for other applications, including whole genome sequencing or RNA-seq. The method described allows for a common processing approach for multiple sample types and accommodates downstream analysis of genomic, metagenomic and transcriptional information.

We describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs for characterization of bacterial communities using 16S rRNA gene amplicon sequencing. The method allows for a common processing approach for multiple sample types and accommodates a number of downstream analytic processes.

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing ...

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. Deviations from what is considered a healthy microbiota composition (dysbiosis) may impair vital functions leading to pathologic conditions. Recent and ongoing research efforts have been directed toward the characterization of associations between microbial composition and human health and disease.
Advances in high-throughput sequencing technologies enable characterization of the gut microbial composition. These methods include 16S rRNA-amplicon sequencing and shotgun sequencing. 16S rRNA-amplicon sequencing is used to profile taxonomical composition, while shotgun sequencing provides additional information about gene predictions and functional annotation. An advantage in using a targeted sequencing method of the 16S rRNA gene variable region is its substantially lower cost compared to shotgun sequencing. Sequence differences in the 16S rRNA gene are used as a microbial fingerprint to identify and quantify different taxa within an individual sample.
Major international efforts have enlisted standards for 16S rRNA-amplicon sequencing. However, several studies report a common source of variation caused by batch effect. To minimize this effect, uniformed protocols for sample collection, processing, and sequencing must be implemented. This protocol proposes the integration of broadly used protocols starting from fecal sample collection to data analyses. This protocol includes a column-free, direct-PCR approach that enables simultaneous handling and DNA extraction of large numbers of fecal samples, along with PCR amplification of the V4 region. In addition, the protocol describes the analysis pipeline and provides a script using the latest version of QIIME (QIIME 2 version 2017.7.0 and DADA2). This step-by-step protocol is aimed to guide those interested in initiating the use of 16S rRNA-amplicon sequencing in a robust, reproductive, easy to use, detailed way.

This manuscript describes a detailed standardized protocol of high-throughput 16S rRNA-amplicon sequencing. The protocol introduces an integrated, uniformed, feasible, and inexpensive protocol starting from fecal sample collection through data analyses. This protocol enables analysis of large numbers of samples with rigorous standards and several controls.

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. ...

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective and widely available vaccines are lacking for a majority of these diseases, necessitating the development of novel disease mitigation strategies. To this end, a promising avenue of disease control involves targeting the vector microbiome, the community of microbes inhabiting the vector. The vector microbiome plays a pivotal role in pathogen dynamics, and manipulations of the microbiome have led to reduced vector abundance or pathogen transmission for a handful of vector-borne diseases. However, translating these findings into disease control applications requires a thorough understanding of vector microbial ecology, historically limited by insufficient technology in this field. The advent of next-generation sequencing approaches has enabled rapid, highly parallel sequencing of diverse microbial communities. Targeting the highly-conserved 16S rRNA gene has facilitated characterizations of microbes present within vectors under varying ecological and experimental conditions. This technique involves amplification of the 16S rRNA gene, sample barcoding via PCR, loading samples onto a flow cell for sequencing, and bioinformatics approaches to match sequence data with phylogenetic information. Species or genus-level identification for a high number of replicates can typically be achieved through this approach, thus circumventing challenges of low detection, resolution, and output from traditional culturing, microscopy, or histological staining techniques. Therefore, this method is well-suited for characterizing vector microbes under diverse conditions but cannot currently provide information on microbial function, location within the vector, or response to antibiotic treatment. Overall, 16S next-generation sequencing is a powerful technique for better understanding the identity and role of vector microbes in disease dynamics.

Here we present a next-generation sequencing protocol for 16S rRNA sequencing which enables identification and characterization of microbial communities within vectors. This method involves DNA extraction, amplification and barcoding of samples through PCR, sequencing on a flow-cell, and bioinformatics to match sequence data to phylogenetic information.

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

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