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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Results
  • Discussion
  • Disclosures
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

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.

Abstract

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’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.

Introduction

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'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 1–2 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–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 (8–12 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.

Protocol

The protocol was approved by the Institutional Animal Care and Use Committee of the University of Iowa.

1. Fecal Sample Collection and Handling

  1. Sterilize the divider boxes (see Table of Materials, Supplementary Figure 1) with 70% ethanol.
  2. Pre-label microcentrifuge tubes (one per mouse) with the sample ID and treatment group (if applicable).
  3. Place the mice in sterilized divider boxes and allow them to defecate normally for up to 1 h.
  4. 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.
  5. Place the microcentrifuge tube containing fecal pellets in a -80 °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.

2. Extraction of DNA

  1. 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 °C as needed to collect 200 mg or the required amount from stock samples.
  2. Use 200 mg of starting materials and elute DNA to a final volume of 50 µL.
  3. 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.
  4. 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.
  5. Isolate DNA from individual mouse fecal samples using a bacterial DNA isolation kit following the manufacturer’s protocol (see Table of Materials) with minor modifications. Quantify the isolated DNA by loading 1 μL 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 500–2,500 ng when starting with 200 mg of the fecal sample.
  6. Adjust the concentration of DNA to 4–20 ng/μL 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.

3. 16S rRNA Gene Amplification (PCR1)

  1. Set up 16S rRNA gene amplification (PCR1) in a 96 well PCR plate using a 25 μL reaction volume.
  2. Using a multichannel pipette, add 12.5 μL 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 μL total volume (adjust the total volume with PCR grade water): 1 μL (each) of forward and reverse primers at 1 μM concentration.
    NOTE: Sequences of the primers are as follows:
    forward primer = 5'-TCGTCGGCAGCGTCA GATGTGTATAAGAGA CAGCCTACGGGNGGCWGCAG-3' reverse primer = 5'-GTCTCGTGGGCTCGGAGATGTGTA TAAGAGACAGGACTACHVGGGTATCTAATC C-3'. Include a kit blank from step 2.3 (kit reagent control) in the PCR plate.
  3. Seal the PCR plate and centrifuge at 1,000 x g at 20 °C for 1 min in a tabletop plate centrifuge (see Table of Materials) and perform PCR in a thermal cycler programmed for: 95 °C for 3 min; 25 cycles of 1) 95 °C for 30 s, 2) 55 °C for 30 s, 3) 72 °C for 30 s; final extension at 72 °C for 5 min; and hold at 4 °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.
  4. Confirm the size of PCR1 product by loading 1 μL of the DNA on a high sensitivity electrophoresis chip. Alternatively, run 5 µL 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’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.

4. DNA Library Construction Using Indexed PCR (PCR2)

  1. Place the Index 1 and Index 2 barcoded primers in a special rack (see Table of Materials) for 96 libraries.
    1. Arrange Index 1 primer in columns 1–12 and Index 2 primer in rows A–H of the special rack.
    2. Add 2.5 μL of Index 1 in columns 1–12 and Index 2 primer in rows A–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 °C freezer.
  2. Using a multichannel pipette, add 12.5 μL of 2x high fidelity polymerase enzyme mix containing a buffer, in addition to dNTPs (see Table of Materials); 5 μL of PCR grade water and 2.5 μL 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’s instruction provided in the 16S metagenomics sequencing method (e.g., Illumina).
  3. Seal and centrifuge the Indexed PCR plate at 1,000 x g at 20 °C for 1 min and perform PCR in a thermal cycler programmed for: 95 °C for 3 min; 8 cycles of 1) 95 °C for 30 s, 2) 55 °C for 30 s, 3) 72 °C for 30 s; and final extension at 72 °C for 5 min.
  4. Confirm the 630 base size of the indexed PCR product by loading 1 μL of DNA on a high sensitivity electrophoresis chip. Alternatively, run 5 µL of indexed PCR product on a 1.5% agarose gel to confirm the size and intensity of the product.

5. Clean-up of Indexed PCR (PCR2) and Quantification

  1. Pool 5 μL 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).
  2. Transfer the pooled product from the multichannel reservoir tray into an empty, pre-labeled 1.5 mL microcentrifuge tube and vortex to mix.
  3. Purify PCR2 product using standard magnetic beads kit (see Table of Materials) as per the manufacturer’s instruction. Seal and store the remaining 20 μL of PCR2 in the same plate at -80 °C for further use, if needed.
  4. Prepare fresh 80% ethanol by adding 4 mL of 100% ethanol to 1 mL of PCR grade water.
  5. Equilibrate the magnetic bead to RT and vortex for 30 s to disperse the beads evenly.
  6. Briefly vortex and spin down the pooled PCR2 amplicon samples.
  7. Add 80 μL of magnetic beads into a pre-labeled, sterile 1.5 mL microcentrifuge tube with 80 μL of pooled PCR products, then vortex and spin down briefly to evenly resuspend the magnetic beads.
  8. Incubate the contents at RT without disturbing the tubes for 15 min.
  9. Place the tube with the DNA and magnetic beads on a magnetic stand (see Table of Materials) for 5 min.
  10. Carefully remove and discard 150 μL of the supernatant.
  11. Add 200 μL of freshly prepared 80% ethanol without disturbing the beads and incubate for 30 s.
  12. Carefully remove, and discard all the supernatant.
  13. Repeat steps 5.11–5.12. 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.
  14. Remove from the magnetic stand. Add 33 μL 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.
  15. Transfer 30 μL of the supernatant (clean PCR products) to a pre-labeled 1.5 mL microcentrifuge tube.
  16. Quantify the purified pool by loading 1 μL of the purified pool on a fluorimeter or high sensitivity electrophoresis chip, as this will be required during sequencing. Perform MiSeq as detailed below.

6. MiSeq Sequencing

  1. 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).
  2. Dilute the pooled libraries from step 5.15 to 4 nM.
  3. Denature pooled libraries by combining 5 μL of the 4 nM library pool with 5 μL 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.
  4. Add 990 μL of ice-cold hybridization buffer (HB buffer) and pipette gently to mix. This will yield a 20 pM library.
  5. Combine 2 μL of the 10 nM control library with 3 μL of EBT buffer (10 mM Tris-HCl, pH = 8.5, with 0.1% Tween 20) to yield a 4 nM control library. Add 5 µL of freshly prepared 0.2 N NaOH and vortex briefly to mix. Incubate for 5 min at RT.
  6. Add 990 μL 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 °C up to for 4 weeks. After 4 weeks, cluster numbers tend to decrease.
    1. Combine 210 µL of the 20 pM library with 40 µL of the 20 pM control library (final concentration = ~18%), and add 350 µL of HB buffer. Load the library at a final concentration of 7 pM.
      NOTE: Input details should be adjusted as per run performance.
  7. Incubate samples for 2 min at 96 °C. Put on ice for 5 min. Load 600 µL 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.

7. Data Processing and Sequence Analysis

  1. Use R software (version 3.5) for DADA2 data processing and analyses. For steps 7.1–7.4, use the open-access software as outlined in the previously developed DADA2 online tutorial found at <https://benjjneb.github.io/dada2/tutorial.html>.
    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).
  2. Visualize the quality profiles of the forward and reverse reads using the plotQualityProfile command.
  3. 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.
  4. Process the raw 16S data as fastq files by the DADA2 pipeline as outlined in the online tutorial (found at <https://benjjneb.github.io/dada2/tutorial.html>) 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.
  5. 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.
  6. 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 <https://benjjneb.github.io/dada2/tutorial.html>.
  7. 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.
    1. Upload the taxonomic abundance table (CSV format) and select Samples in the column.
    2. Upload the mapping file (CSV format) and select Samples in a row.
    3. Select Options to remove variables with over 50% zeroes, and exclude unassigned and unmapped reads.
    4. Select Options to normalize rows by sum and log normalize columns.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Click the Outlier tab on top to visualize samples that are outliers.
    10. 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.

NOTE: For detailed statistical tests performed during microbiome analysis, refer to the works of Chen et al. and Hugerth et al.14,30.

Results

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...

Discussion

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...

Disclosures

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.

Acknowledgements

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).

Materials

NameCompanyCatalog NumberComments
1.5 ml Natural Microcentriguge TubeUSA, Scientific1615-5500Fecal collection
3M hand applicator squeegee PA1-G3M, MN, US7100038651Squeeger for proper sealing of PCR Plate
Agencourt AMPure XPBeckman Coulter, IN, USAA63880PCR Purification, NGS Clean-up, PCR clean-up
Agilent DNA 1000 REAGENTAgilent Technologies, CA, USA5067-1504DNA quantification and quality control
Bioanalyzer DNA 1000 chipAgilent Technologies, CA, USA5067-1504DNA quantification and quality control
Index Adopter Replacement CapsIllumina, Inc., CA, USA15026762New cap for Index 1 and 2 adopter primer
DNeasy PowerLyzer PowerSoil KitMoBio now part of QIAGEN, Valencia, CA, USA12855-100DNA isolation
KAPA HiFi HotStart ReadyMiX (2X)Kapa Biosystem, MA, USAKK2602PCR ready mix for Amplicon PCR1 and Indexed PCR2
Lewis Divider BoxesLewis Bins, WI, USND03080Fecal collection
Magnetic standNew England BioLabs, MA, USAS1509SFor PCR clean-up
MicroAmp Fast Optical 96-Well Reaction PlateApplied Biosystems, Thermo Fisher Scientific, CA, USA4346906PCR Plate
MicroAmp Optical Adhesive FilmApplied Biosystems, Thermo Fisher Scientific, CA, USA4311971PCR Plate Sealer
Microfuge 20 CentrifugeBeckman Coulter, IN, USAB30154Centrifuge used for DNA isolation
MiSeq Reagent Kit (600 cycles)v.3Illumina, Inc., CA, USAMS-102-3003For MiSeq Sequencing
Nextera XT DNA Library Preparation KitIllumina, Inc., CA, USAFC-131-100116S rRNA DNA Library Preparation
Reagent Reservoirs Multichannel TraysASI, FL,USARS71-1For Pooling of PCR2 Product
Plate CetrifugeThermo Fisher Scientific, CA, USA75004393For PCR reagent mixing and removing air bubble from Plate
PhiX ControlIllumina, Inc., CA, USAFC-110-3001For MiSeq Sequencing control
Microbiome DNA Purification KitThermo Fisher Scientific, CA, USAA29789For purification of PCR1 product
PowerLyzer 24 HomogenizerOmni International, GA, USA19-001Bead beater for DNA Isolation
Qubit dsDNA HS (High Sensitivity) assay kitThermo Fisher Scientific, CA, USAQ32854DNA quantification
TruSeq Index Plate FixtureIllumina, Inc., CA, USAFC-130-1005For Arranging of the index primers
Vertical Dividers (large)Lewis Bins, WI, USDV-2280Fecal collection
Vertical Dividers (small)Lewis Bins, WI, USDV-1780Fecal collection

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