The authors acknowledge funding from the NIAID/NIH (1R01AI137075-01), the Carver Trust Medical Research Initiative Grant, and the University of Iowa Environmental Health Sciences Research Center, NIEHS/NIH (P30 ES005605).

The protocol was approved by the Institutional Animal Care and Use Committee of the University of Iowa.
1. Fecal Sample Collection and Handling
<ol>
	<li>Sterilize the divider boxes (see Table of Materials, Supplementary Figure 1) with 70% ethanol.</li>
	<li>Pre-label microcentrifuge tubes (one per mouse) with the sample ID and treatment group (if applicable).</li>
	<li>Place the mice in sterilized divider boxes and allow them to defecate normally for up to 1 h.</li>
	<li>Collect the fecal pellets in an empty, pre-labeled 1.5 mL microcentrifuge tube using sterile forceps and close the tube securely. Sterilize the forceps after collecting from each mouse.</li>
	<li>Place the microcentrifuge tube containing fecal pellets in a -80 &#176;C freezer until further processing. 
	NOTE: The divider boxes are advantageous because they allow simultaneous collection of fecal samples from multiple mice (up to 12) at one time.</li>
</ol>
2. Extraction of DNA
<ol>
	<li>Remove the fecal samples (mouse or human) from the freezer and thaw at room temperature (RT). 
	NOTE: It is advisable to thaw human stool samples overnight at 4 &#176;C as needed to collect 200 mg or the required amount from stock samples.</li>
	<li>Use 200 mg of starting materials and elute DNA to a final volume of 50 &#181;L.</li>
	<li>Include a DNA isolation kit blank in which no fecal sample is added but is processed through all DNA extraction steps. 
	NOTE: A specific DNA isolation kit (see Table of Materials) was used, as it contains specific reagents to remove inhibiting materials such as biosolids, undigested plant material, and heme compounds from lysed red blood cells present in human and mouse stool samples.</li>
	<li>Place the bead tube into a homogenizer (see Table of Materials) and homogenize the samples for 45 s at RT and a speed of 4.5 m/s. 
	NOTE: Bead-beating homogenizer from any manufacturer can be used. However, it is recommended to standardize the method, specifically the speed and duration of homogenization, when using bead-beating homogenizer from another vendor.</li>
	<li>Isolate DNA from individual mouse fecal samples using a bacterial DNA isolation kit following the manufacturer&#8217;s protocol (see Table of Materials) with minor modifications. Quantify the isolated DNA by loading 1 &#956;L of the DNA on a fluorimeter or on the high sensitivity electrophoresis chip (see Table of Materials). 
	NOTE: The expected yield of DNA can range from 500&#8211;2,500 ng when starting with 200 mg of the fecal sample.</li>
	<li>Adjust the concentration of DNA to 4&#8211;20 ng/&#956;L using elution buffer. Requantify the DNA (as done in step 2.5) before proceeding to 16S rRNA gene amplification (PCR1), if PCR1 is not performed on the same day.</li>
</ol>
3. 16S rRNA Gene Amplification (PCR1)
<ol>
	<li>Set up 16S rRNA gene amplification (PCR1) in a 96 well PCR plate using a 25 &#956;L reaction volume.</li>
	<li>Using a multichannel pipette, add 12.5 &#956;L of 2x high-fidelity polymerase enzyme mix including buffer, in addition to dNTPs (see Table of Materials): 40 ng of DNA in up to 10.5 &#956;L total volume (adjust the total volume with PCR grade water): 1 &#956;L (each) of forward and reverse primers at 1 &#956;M concentration. 
	NOTE: Sequences of the primers are as follows: 
	forward primer = 5&#39;-TCGTCGGCAGCGTCA GATGTGTATAAGAGA CAGCCTACGGGNGGCWGCAG-3&#39; reverse primer = 5&#39;-GTCTCGTGGGCTCGGAGATGTGTA TAAGAGACAGGACTACHVGGGTATCTAATC C-3&#39;. Include a kit blank from step 2.3 (kit reagent control) in the PCR plate.</li>
	<li>Seal the PCR plate and centrifuge at 1,000 x g at 20 &#176;C for 1 min in a tabletop plate centrifuge (see Table of Materials) and perform PCR in a thermal cycler programmed for: 95 &#176;C for 3 min; 25 cycles of 1) 95 &#176;C for 30 s, 2) 55 &#176;C for 30 s, 3) 72 &#176;C for 30 s; final extension at 72 &#176;C for 5 min; and hold at 4 &#176;C. 
	NOTE: Although this 16S rRNA gene amplification method should work with different types of biospecimens, it is advised to standardize the number of amplification cycles when starting a new project.</li>
	<li>Confirm the size of PCR1 product by loading 1 &#956;L of the DNA on a high sensitivity electrophoresis chip. Alternatively, run 5 &#181;L of 16S rRNA-amplified product on a 1.5% agarose gel to confirm 550 bp of the PCR1 product. 
	NOTE: Clean-up of 16S rRNA amplified product is optional and depends on the DNA isolation kit/method being used. If using an in-house DNA isolation method, PCR1 product can be cleaned utilizing a microbiome DNA purification kit as per the manufacturer&#8217;s protocol (see Table of Materials). The DNA isolation kit used here yields an ultrapure DNA and does not require clean-up of the PCR1 product.</li>
</ol>
4. DNA Library Construction Using Indexed PCR (PCR2)
<ol>
	<li>Place the Index 1 and Index 2 barcoded primers in a special rack (see Table of Materials) for 96 libraries.
	<ol>
		<li>Arrange Index 1 primer in columns 1&#8211;12 and Index 2 primer in rows A&#8211;H of the special rack.</li>
		<li>Add 2.5 &#956;L of Index 1 in columns 1&#8211;12 and Index 2 primer in rows A&#8211;H using multichannel pipettes. Place the new cap (see Table of Materials) on Index 1 and Index 2 adopter primers and store it in a -20 &#176;C freezer.</li>
	</ol>
	</li>
	<li>Using a multichannel pipette, add 12.5 &#956;L of 2x high fidelity polymerase enzyme mix containing a buffer, in addition to dNTPs (see Table of Materials); 5 &#956;L of PCR grade water and 2.5 &#956;L of 16S rRNA-amplified product. 
	NOTE: Add unique indices to each sample for multiplexing of more than 96 libraries in a single run, as described in Kiernan et al.27. The present protocol uses adapters from a commercial kit (e.g., Nextera XT Index Kit) as per the manufacturer&#8217;s instruction provided in the 16S metagenomics sequencing method (e.g., Illumina).</li>
	<li>Seal and centrifuge the Indexed PCR plate at 1,000 x g at 20 &#176;C for 1 min and perform PCR in a thermal cycler programmed for: 95 &#176;C for 3 min; 8 cycles of 1) 95 &#176;C for 30 s, 2) 55 &#176;C for 30 s, 3) 72 &#176;C for 30 s; and final extension at 72 &#176;C for 5 min.</li>
	<li>Confirm the 630 base size of the indexed PCR product by loading 1 &#956;L of DNA on a high sensitivity electrophoresis chip. Alternatively, run 5 &#181;L of indexed PCR product on a 1.5% agarose gel to confirm the size and intensity of the product.</li>
</ol>
5. Clean-up of Indexed PCR (PCR2) and Quantification
<ol>
	<li>Pool 5 &#956;L of PCR2 amplicon using multichannel pipettes from each well into a multichannel reservoir tray free of detectable DNase, RNase, human DNA, and Pyrogenic bacteria (see Table of Materials).</li>
	<li>Transfer the pooled product from the multichannel reservoir tray into an empty, pre-labeled 1.5 mL microcentrifuge tube and vortex to mix.</li>
	<li>Purify PCR2 product using standard magnetic beads kit (see Table of Materials) as per the manufacturer&#8217;s instruction. Seal and store the remaining 20 &#956;L of PCR2 in the same plate at -80 &#176;C for further use, if needed.</li>
	<li>Prepare fresh 80% ethanol by adding 4 mL of 100% ethanol to 1 mL of PCR grade water.</li>
	<li>Equilibrate the magnetic bead to RT and vortex for 30 s to disperse the beads evenly.</li>
	<li>Briefly vortex and spin down the pooled PCR2 amplicon samples.</li>
	<li>Add 80 &#956;L of magnetic beads into a pre-labeled, sterile 1.5 mL microcentrifuge tube with 80 &#956;L of pooled PCR products, then vortex and spin down briefly to evenly resuspend the magnetic beads.</li>
	<li>Incubate the contents at RT without disturbing the tubes for 15 min.</li>
	<li>Place the tube with the DNA and magnetic beads on a magnetic stand (see Table of Materials) for 5 min.</li>
	<li>Carefully remove and discard 150 &#956;L of the supernatant.</li>
	<li>Add 200 &#956;L of freshly prepared 80% ethanol without disturbing the beads and incubate for 30 s.</li>
	<li>Carefully remove, and discard all the supernatant.</li>
	<li>Repeat steps 5.11&#8211;5.12. Remove the remaining volume with a P10 pipette. Allow the beads to air-dry for 15 min, with the index PCR tube remaining on the magnetic stand.</li>
	<li>Remove from the magnetic stand. Add 33 &#956;L of elution buffer (elution buffer of DNA kit is acceptable). Vortex well, and perform a quick spin to remove any remaining liquid on the side. Incubate for 2 min and place on the magnetic stand for 5 min.</li>
	<li>Transfer 30 &#956;L of the supernatant (clean PCR products) to a pre-labeled 1.5 mL microcentrifuge tube.</li>
	<li>Quantify the purified pool by loading 1 &#956;L of the purified pool on a fluorimeter or high sensitivity electrophoresis chip, as this will be required during sequencing. Perform MiSeq as detailed below.</li>
</ol>
6. MiSeq Sequencing
<ol>
	<li>Create a sample sheet containing sample-specific barcode information for metagenomics workflow and demultiplexing on the MiSeq instrument (see Table of Materials). Upload this sample sheet to the software (e.g., Illumina Experiment Manager).</li>
	<li>Dilute the pooled libraries from step 5.15 to 4 nM.</li>
	<li>Denature pooled libraries by combining 5 &#956;L of the 4 nM library pool with 5 &#956;L of freshly prepared 0.2 M NaOH in a 1.5 mL microcentrifuge tube. Vortex briefly to mix, centrifuge briefly and incubate for 5 min at ambient RT.</li>
	<li>Add 990 &#956;L of ice-cold hybridization buffer (HB buffer) and pipette gently to mix. This will yield a 20 pM library.</li>
	<li>Combine 2 &#956;L of the 10 nM control library with 3 &#956;L of EBT buffer (10 mM Tris-HCl, pH = 8.5, with 0.1% Tween 20) to yield a 4 nM control library. Add 5 &#181;L of freshly prepared 0.2 N NaOH and vortex briefly to mix. Incubate for 5 min at RT.</li>
	<li>Add 990 &#956;L ice-cold hybridization buffer (HB buffer) and pipette gently to mix. This will yield 20 pM control libraries. 
	NOTE: Denatured 20 pM control libraries can be stored at -20 &#176;C up to for 4 weeks. After 4 weeks, cluster numbers tend to decrease.
	<ol>
		<li>Combine 210 &#181;L of the 20 pM library with 40 &#181;L of the 20 pM control library (final concentration = ~18%), and add 350 &#181;L of HB buffer. Load the library at a final concentration of 7 pM. 
		NOTE: Input details should be adjusted as per run performance.</li>
	</ol>
	</li>
	<li>Incubate samples for 2 min at 96 &#176;C. Put on ice for 5 min. Load 600 &#181;L of the final pool into the appropriate well of the MiSeq cartridges. 
	NOTE: Section 6 above can be performed at a genomic/DNA core facility.</li>
</ol>
7. Data Processing and Sequence Analysis
<ol>
	<li>Use R software (version 3.5) for DADA2 data processing and analyses. For steps 7.1&#8211;7.4, use the open-access software as outlined in the previously developed DADA2 online tutorial found at &#60;https://benjjneb.github.io/dada2/tutorial.html&#62;. 
	NOTE: A readily usable R script has been attached as a Supplemental File 2, and users must change the name and source of sequencing files (e.g., SAMPLENAME_R1_001.fastq and SAMPLENAME_R2_001.fastq).</li>
	<li>Visualize the quality profiles of the forward and reverse reads using the plotQualityProfile command.</li>
	<li>Trim nucleotides from forward and reverse reads based on the quality plot. These parameters are specified by the truncLen parameter in DADA2. 
	NOTE: Here, 280 is used as the length threshold for which the forward reads would be discarded, and 260 is used as the length threshold for which the reverse reads would be discarded.</li>
	<li>Process the raw 16S data as fastq files by the DADA2 pipeline as outlined in the online tutorial (found at &#60;https://benjjneb.github.io/dada2/tutorial.html&#62;) to merge R1 and R2 reads and form amplicon sequence variants (ASVs), which are then used to assign taxa with the Silva reference database23. 
	NOTE: A sample amplicon sequence variant table generated from the DADA2 pipeline is included as Supplemental File 3. Either a Greengene or Silva reference database can be used, as no differences in bacterial classification were found using either of these databases.</li>
	<li>Generate a user-defined mapping file that contains the metadata (i.e., genotype, gender, treatment, etc.) for each sample. A sample metadata file has been included as Supplemental File 4.</li>
	<li>Calculate alpha diversity (Shannon index) and beta diversity using principal coordinates analysis (PCA) based on the rarefied OTU counts using Phyloseq28 as outlined in the online tutorial, found at &#60;https://benjjneb.github.io/dada2/tutorial.html&#62;.</li>
	<li>Perform the following analysis in METAGENassist29. 
	NOTE: Perform the differential abundance analysis using the Wilcoxon rank-sum test at the genus level. Heat maps and differentially abundant taxa are highlighted using METAGENassist29, a publicly available and web-based analysis pipeline.
	<ol>
		<li>Upload the taxonomic abundance table (CSV format) and select Samples in the column.</li>
		<li>Upload the mapping file (CSV format) and select Samples in a row.</li>
		<li>Select Options to remove variables with over 50% zeroes, and exclude unassigned and unmapped reads.</li>
		<li>Select Options to normalize rows by sum and log normalize columns.</li>
		<li>Make a Volcano, PCA, or PLSDA plot by clicking the same in the left-hand column and click Remove samples name to make the graph.</li>
		<li>Perform a t-test (if only two groups) or ANOVA (if greater than two groups) to visualize the features (bacteria) that differ among groups. Click Selected features to visualize specific bacteria that differ between groups.</li>
		<li>Click Dendrogram or Heat map to create respective plots. Additional analysis, such as sample visualization by groups or t-test/ANOVA-based top 25 features, can be performed.</li>
		<li>Click RandomForest to create graphs showing features that can be used for classification. Click the Variance tab on top to create graphs for the top features that differ between/among groups. Click Feature details to see a list of bacteria that differ among groups and click each bacterium to create a graphical summary of the same.</li>
		<li>Click the Outlier tab on top to visualize samples that are outliers.</li>
		<li>Finally, click Download and select either 1) a zip file containing all the analysis performed or 2) the desired features to download. This file should be saved as a unique name and will need to be unzipped before use.</li>
	</ol>
	</li>
</ol>
NOTE: For detailed statistical tests performed during microbiome analysis, refer to the works of Chen et al. and Hugerth et al.14,30.

A. M. received royalties from Mayo Clinic (paid by Evelo Biosciences) as one of the inventors of a technology claiming the use of Prevotella histicola for the treatment of autoimmune diseases.

The described protocol is simple, with easy-to-follow steps to perform microbiome profiling using 16S rRNA metagenomic sequencing from a large number of biospecimens of interest. Next-generation sequencing has transformed microbial ecology studies, especially in human and pre-clinical disease models31,32. The main advantage of this technique is its ability to successfully analyze complex microbial compositions (culturable and non-culturable microbes) in a given b...

Microbiota refers to a collection of microorganisms (bacteria, viruses, archaea, bacteriophages, and fungi) living in a particular environment, and the microbiome refers to the collective genome of resident microorganisms. As bacteria are one of the most abundant microbes in humans and mice, this study is focused only on bacterial profiling. The human gut is colonized by trillions of bacteria and hundreds of bacterial strains1. The normal gut microbiota plays a vital role in maintaining a healthy state in the host by regulating functions (i.e., maintenance of an intact intestinal barrier, food metabolism, energy homeostasis, inhibition of colonization by pathogenic organisms, and regulation of immune responses)2,3,4,5. Compositional perturbations of the gut microbiota (gut dysbiosis) have been linked to a number of human diseases, including gastrointestinal disorders6, obesity7,8, stroke9, cancer10, diabetes8,11, rheumatoid arthritis12, allergies13, and central nervous system-related diseases such as multiple sclerosis (MS)14,15 and Alzheimer&#39;s disease (AD)8,16. Therefore, in recent years, there has been growing interest in tools for identifying bacterial composition at different body sites. A reliable method should have characteristics such as being high-throughput and easy-to-use, having the ability to classify bacterial microbiota with high resolution, and being low-cost.
Culture-based microbiological techniques are not sensitive enough to identify and characterize the complex gut microbiome due to the failure of several gut bacteria to grow in culture. The advent of the sequencing-based technology, especially 16S rRNA-based metagenomic sequencing, has overcome some of these challenges and transformed microbiome research17. Advanced 16S rRNA-based sequencing technology has helped in establishing a critical role for the gut microbiome in human health. The Human Microbiome Project, a National Institutes of Health initiative18, and the MetaHIT project (a European initiative)19 have both helped in establishing a basic framework for microbiome analysis. These initiatives helped kick-start multiple studies to determine the role of the gut microbiome in human health and disease.
A number of groups have shown gut dysbiosis in patients with inflammatory diseases12,14,15,20,21,22. Despite being widely used for taxonomic profiling due to the ability to multiplex and low costs, there are no uniform protocols for 16S rRNA-based taxonomic profiling. Another limitation is the low resolution of taxonomic assignment owing to smaller sequencing reads (150 bp or 250 bp) and use of only forward sequencing read (R1) due to low quality reverse sequencing reads (R2). However, advances in sequencing technology have helped to overcome some of these challenges, such as the ability to sequence longer reads using paired-end reads (e.g., Illumina MiSeq 2x300bp).
The present sequencing technology can sequence 600 bp good quality reads, which allows merging of R1 and R2 reads. These merged longer R1 and R2 reads allow better taxonomic assignments, especially with open-access R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) platform. DADA2 utilizes amplicon sequence variant (ASV)-based assignments instead of operational taxonomic unit (OTU) assignments based on 97% similarity utilized by QIIME23. ASV matches result in an exact sequence match in the database within 1&#8211;2 nucleotides, which leads to assignment at genus and species levels. Thus, the combination of longer, good quality paired-end reads and better taxonomic assignment tools (such as DADA2) have transformed microbiome studies.
Provided here is a step-by-step guide for performing bacterial profiling using two-step amplification of the V3&#8211;V4 region of 16S rRNA and data analysis using DADA2, Phyloseq, and METAGENassist pipelines. For this study, human leukocyte antigen (HLA) class II transgenic mice are used, as certain HLA class II alleles are linked with a predisposition to autoimmune diseases such as MS20,24,25. However, the importance of HLA class II genes in regulating the composition of gut microbiota is unknown. It is hypothesized that the HLA class II molecule will influence gut microbial community by selecting for specific bacteria. Major histocompatibility complex (MHC) class II knockout mice (AE.KO) or mice expressing human HLA-DQ8 molecules (HLA-DQ8)24,25,26 were used in order to understand the importance of HLA class II molecules in shaping the gut microbial community. It is believed that this complete and simplified workflow with R-based data analysis will serve as an excellent tool for researchers interested in performing microbiome profiling studies.
The generation of mice lacking endogenous murine MHC class II genes (AE.KO) and AE-/-.HLA-DQA1*0103, DQB1*0302 (HLA-DQ8) transgenic mice with a C57BL/6J background has been described previously26. Fecal samples are collected from mice of both sexes (8&#8211;12 weeks of age). Mice were previously bred and maintained in the University of Iowa animal facility as per the NIH and institutional guidelines. Contamination control strategies such as weaning of the mice inside a laminar flow cabinet, changing of gloves between different strains of mice, and proper maintenance of mice are critical steps for profiling of gut microbiome.
Proper personal protective equipment (PPE) are highly recommended during the entire procedure. Appropriate negative controls should be included when performing DNA isolation, PCR1 and PCR2 amplification, and sequencing steps. Use of sterile, DNase-free, RNase-free, and pyrogen-free supplies is recommended. Designated pipettor for microbiome work and filtered pipette tips should be used throughout the protocol. Microbiota analysis consists of seven steps: 1) fecal sample collection and processing; 2) extraction of DNA; 3) 16S rRNA gene amplification; 4) DNA library construction using indexed PCR; 5) clean-up and quantification of indexed PCR (library); 6) MiSeq sequencing; and 7) data processing and sequence analysis. A schematic diagram of all protocol steps is shown in Figure 1.

<table><tbody><tr><td>1.5 mL Natural Microcentriguge Tube</td><td>USA, Scientific</td><td>1615-5500</td><td>Fecal collection</td></tr><tr><td>3M hand applicator squeegee PA1-G</td><td>3M, MN, US</td><td>7100038651</td><td>Squeeger for proper sealing of PCR Plate</td></tr><tr><td>Agencourt AMPure XP</td><td>Beckman Coulter, IN, USA</td><td>A63880</td><td>PCR Purification, NGS Clean-up, PCR clean-up</td></tr><tr><td>Agilent DNA 1000 REAGENT</td><td>Agilent Technologies, CA, USA</td><td>5067-1504</td><td>DNA quantification and quality control</td></tr><tr><td>Bioanalyzer DNA 1000 chip</td><td>Agilent Technologies, CA, USA</td><td>5067-1504</td><td>DNA quantification and quality control</td></tr><tr><td>Index Adopter Replacement Caps</td><td>Illumina, Inc., CA, USA</td><td>15026762</td><td>New cap for Index 1 and 2 adopter primer</td></tr><tr><td>DNeasy PowerLyzer PowerSoil Kit</td><td>MoBio now part of QIAGEN, Valencia, CA, USA</td><td>12855-100</td><td>DNA isolation</td></tr><tr><td>KAPA HiFi HotStart ReadyMiX (2x)</td><td>Kapa Biosystem, MA, USA</td><td>KK2602</td><td>PCR ready mix for Amplicon PCR1 and Indexed PCR2</td></tr><tr><td>Lewis Divider Boxes</td><td>Lewis Bins, WI, US</td><td>ND03080</td><td>Fecal collection</td></tr><tr><td>Magnetic stand</td><td>New England BioLabs, MA, USA</td><td>S1509S</td><td>For PCR clean-up</td></tr><tr><td>MicroAmp Fast Optical 96-Well Reaction Plate</td><td>Applied Biosystems, Thermo Fisher Scientific, CA, USA</td><td>4346906</td><td>PCR Plate</td></tr><tr><td>MicroAmp Optical Adhesive Film</td><td>Applied Biosystems, Thermo Fisher Scientific, CA, USA</td><td>4311971</td><td>PCR Plate Sealer</td></tr><tr><td>Microfuge 20 Centrifuge</td><td>Beckman Coulter, IN, USA</td><td>B30154</td><td>Centrifuge used for DNA isolation</td></tr><tr><td>MiSeq Reagent Kit (600 cycles)v.3</td><td>Illumina, Inc., CA, USA</td><td>MS-102-3003</td><td>For MiSeq Sequencing</td></tr><tr><td>Nextera XT DNA Library Preparation Kit</td><td>Illumina, Inc., CA, USA</td><td>FC-131-1001</td><td>16S rRNA DNA Library Preparation</td></tr><tr><td>Reagent Reservoirs Multichannel Trays</td><td>ASI, FL,USA</td><td>RS71-1</td><td>For Pooling of PCR2 Product</td></tr><tr><td>Plate Cetrifuge</td><td>Thermo Fisher Scientific, CA, USA</td><td>75004393</td><td>For PCR reagent mixing and removing air bubble from Plate</td></tr><tr><td>PhiX Control</td><td>Illumina, Inc., CA, USA</td><td>FC-110-3001</td><td>For MiSeq Sequencing control</td></tr><tr><td>Microbiome DNA Purification Kit</td><td>Thermo Fisher Scientific, CA, USA</td><td>A29789</td><td>For purification of PCR1 product</td></tr><tr><td>PowerLyzer 24 Homogenizer</td><td>Omni International, GA, USA</td><td>19-001</td><td>Bead beater for DNA Isolation</td></tr><tr><td>Qubit dsDNA HS (High Sensitivity) assay kit</td><td>Thermo Fisher Scientific, CA, USA</td><td>Q32854</td><td>DNA quantification</td></tr><tr><td>TruSeq Index Plate Fixture</td><td>Illumina, Inc., CA, USA</td><td>FC-130-1005</td><td>For Arranging of the index primers</td></tr><tr><td>Vertical Dividers (large)</td><td>Lewis Bins, WI, US</td><td>DV-2280</td><td>Fecal collection</td></tr><tr><td>Vertical Dividers (small)</td><td>Lewis Bins, WI, US</td><td>DV-1780</td><td>Fecal collection</td></tr></tbody></table>

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.

As MHC class II molecules (HLA in humans) are central players in the adaptive immune response and show strong associations with a predisposition to MS24,25,26, it was hypothesized that the HLA class II molecule would influence gut microbial composition. Therefore, mice lacking the MHC class II gene (AE.KO) or expressing human HLA-DQ8 gene (HLA-DQ8) were utilized to understand the importance of HLA class II molecules in shaping t...

Watch this Scientific Journal Video about Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing at JoVE.com

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

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 ...

microbiota-analysis-using-two-step-pcr-next-generation-16s-rrna-gene

Research

JoVE Journal

Biology

27.7K Views.  University of Iowa. Profilering van microbioom is de eerste stap in de richting van het definiëren van de rol van microbioom in gezondheid en ziekte. Hier beschrijven we een eenvoudige procedure voor het isoleren van hoogwaardig bacterieel DNA uit fecale monstervoorbereiding bibliotheek, het uitvoeren van 16SR RNA en een meta genomic sequencing en microbioom data-analyse. Dit protocol zal onderzoekers helpen nieuw op het gebied van microbioom, evenals onderzoekers die werkzaam zijn in microbioom veld bij de opr...

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

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 ...

Genetics

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in the shikimate pathway, which is virtually universal in plants. The inhibition of the enzyme stops the production of three essential amino acids: phenylalanine, tyrosine, and tryptophan. EPSPS is also present in fungi and prokaryotes, such as archaea and bacteria; thus, the use of GBP may have an impact on the microbiome composition of soils, plants, herbivores, and secondary consumers. This article aims to present general guidelines to assess the effect of GBP on microbiomes from field experiments to bioinformatics analyses and provide a few testable hypotheses. Two field experiments are presented to test the GBP on non-target organisms. First, plant-associated microbes from 10 replicated control and GBP treatment plots simulating no-till cropping are sampled and analyzed. In the second experiment, samples from experimental plots fertilized by either poultry manure containing glyphosate residues or non-treated control manure were obtained. Bioinformatics analysis of EPSPS protein sequences is utilized to determine the potential sensitivity of microbes to glyphosate. The first step in estimating the effect of GBP on microbiomes is to determine their potential sensitivity to the target enzyme (EPSPS). Microbial sequences can be obtained either from public repositories or by means of PCR amplification. However, in the majority of field studies, microbiome composition has been determined based on universal DNA markers such as the 16S rRNA and the internal transcribed spacer (ITS). In these cases, sensitivity to glyphosate can only be estimated through a probabilistic analysis of EPSPS sequences using closely related species. The quantification of the potential sensitivity of organisms to glyphosate, based on the EPSPS enzyme, provides a robust approach for further experiments to study target and non-target resistant mechanisms.

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. In this article, we introduce general guidelines to quantify the effect of GBP on microbiomes, from field experiments to bioinformatics analyses.

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme ...

Environment

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

A Method for Targeted 16S Sequencing of Human Milk Samples

Studies of microbial communities have become widespread with the development of relatively inexpensive, rapid, and high throughput sequencing. However, as with all these technologies, reproducible results depend on a laboratory workflow that incorporates appropriate precautions and controls. This is particularly important with low-biomass samples where contaminating bacterial DNA can generate misleading results. This article details a semi-automated workflow to identify microbes from human breast milk samples using targeted sequencing of the 16S ribosomal RNA (rRNA) V4 region on a low- to mid-throughput scale. The protocol describes sample preparation from whole milk including: sample lysis, nucleic acid extraction, amplification of the V4 region of the 16S rRNA gene, and library preparation with quality control measures. Importantly, the protocol and discussion consider issues that are salient to the preparation and analysis of low-biomass samples including appropriate positive and negative controls, PCR inhibitor removal, sample contamination by environmental, reagent, or experimental sources, and experimental best practices designed to ensure reproducibility. While the protocol as described is specific to human milk samples, it is adaptable to numerous low- and high-biomass sample types, including samples collected on swabs, frozen neat, or stabilized in a preservation buffer.

A semi-automated workflow is presented for targeted sequencing of 16S rRNA from human milk and other low-biomass sample types.

Studies of microbial communities have become widespread with the development of relatively inexpensive, rapid, and high throughput sequencing. However, as ...

Quantitative Polymerase Chain Reaction-based Analyses of Murine Intestinal Microbiota After Oral Antibiotic Treatment

The gut microbiota has a central influence on human health. Microbial dysbiosis is associated with many common immunopathologies such as inflammatory bowel disease, asthma and arthritis. Thus, understanding the mechanisms underlying microbiota-immune system crosstalk is of crucial importance. Antibiotic administration, while aiding pathogen clearance, also induces drastic changes in the size and composition of intestinal bacterial communities which can have an impact on human health. Antibiotic treatment in mice recapitulates the impact and long-term changes in human microbiota from antibiotic treated patients, and enables investigation of the mechanistic links between changes in microbial communities and immune cell function. While several methods for antibiotic treatment of mice have been described, some of them induce severe dehydration and weight-loss complicating the interpretation of the data. Here, we provide two protocols for oral antibiotic administration which can be used for long-term treatment of mice without inducing major weight-loss. These protocols make use of a combination of antibiotics that target both Gram-positive and Gram-negative bacteria and can be provided either ad libitum in the drinking water or by oral gavage. Moreover, we describe a method for the quantification of microbial density in fecal samples by qPCR which can be used to validate the efficacy of the antibiotic treatment. The combination of these approaches provides a reliable methodology for the manipulation of the intestinal microbiota and the study of the effects of antibiotic treatment in mice.

Here we provide detailed protocols for the oral administration of antibiotics to mice, collection of fecal samples, DNA extraction and quantification of fecal bacteria by qPCR.

The gut microbiota has a central influence on human health. Microbial dysbiosis is associated with many common immunopathologies such as inflammatory ...

Analysis of Fecal Microbiota Dynamics in Lupus-Prone Mice Using a Simple, Cost-Effective DNA Isolation Method

Gut microbiota has an important role in educating the immune system. This relationship is extremely important for understanding autoimmune diseases that are not only driven by genetic factors, but also environmental factors that can trigger the onset and/or worsen the disease course. A previously published study on the dynamics of the gut microbiota in lupus-prone MRL/lpr female mice showed how changes of the gut microbiota can alter disease progression. Here, a protocol is described for extracting representative samples from the gut microbiota for studies of autoimmunity. Microbiota samples are collected from the anus and processed, from which the DNA is extracted using a phenol-chloroform method and purified by alcohol precipitation. After PCR is performed, purified amplicons are sequenced using a Next Generation Sequencing platform at Argonne National Laboratory. Finally, the 16S ribosomal RNA gene sequencing data is analyzed. As an example, data obtained from gut microbiota comparisons of MRL/lpr mice with or without CX3CR1 are shown. Results showed significant differences in genera containing pathogenic bacteria such as those in the phylum Proteobacteria, as well as the genus Bifidobacterium, which is considered part of the healthy commensal microbiota. In summary, this simple, cost-effective DNA isolation method is reliable and can help the investigation of gut microbiota changes associated with autoimmune diseases.

This protocol provides a simple, cost-effective DNA isolation method for the analysis of murine gut microbiota alterations during the development of autoimmune disease.

Gut microbiota has an important role in educating the immune system. This relationship is extremely important for understanding autoimmune diseases that ...

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.

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 ...

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

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video