Analysis of Fecal Microbiota Dynamics in Lupus-Prone Mice Using a Simple, Cost-Effective DNA Isolation Method
Summary
This protocol provides a simple, cost-effective DNA isolation method for the analysis of murine gut microbiota alterations during the development of autoimmune disease.
Abstract
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.
Introduction
Humans and bacteria have coexisted for a long time. They have established a codependent relationship with mutual beneficial effects that influences host immune responses in quantitative and qualitative ways1. Recent studies suggest an association between the gut microbiota composition and the pathogenesis of autoimmune diseases that include multiple sclerosis2, rheumatoid arthritis3, type 2 diabetes4, Inflammatory bowel disease5, and systemic lupus erythematosus (SLE)6. However, whether the gut microbiota is the main cause or a secondary effect of these autoimmune diseases is still unclear7. Potentially, gut microbiota could exacerbate the disease during the effector phase of autoimmune disorders or play a role in regulating the induction of these diseases8.
Intestinal dysbiosis has been reported in female lupus-prone MRL/Mp-Faslpr (MRL/lpr) mice, and gut microbiota changes with a significant depletion of Lactobacilli were observed9. When a mixture of five Lactobacillus strains was orally administered, lupus-like symptoms were largely attenuated in these mice, suggesting an essential role of microbiota in regulating SLE pathogenesis.
The following technique of DNA extraction allows to follow microbiota fluctuations and analyze them qualitatively and quantitively during the course of murine SLE-like disease in lupus-prone mice. Whether to examine the healthy gut microbiota or define dysbiosis, it is important to critically examine how data is collected and whether it is accurate and reproducible10. Every step is critical in this process. An appropriate methodology must be used to extract microbial DNA as any possible problem introducing biases during the DNA extraction process could result in inaccurate microbial representation. While the phenol-chloroform method is described here, there are commercially available kits to extract DNA from bacteria that work well in particular cases11. However, their usability is limited by the cost and required sample quantity.
The protocol presented here is cost-effective and requires only a small amount of sample. It works fine with any kind of stool sample and is useful in studying the dynamics of the gut microbiota over time as well as comparisons among groups. DNA is isolated with a method of alcohol purification, which uses phenol, chloroform, and isoamyl alcohol. Alcohol-based extraction helps to clean and remove the sample of proteins and lipids, where DNA is precipitated in the final step. The proposed method has significantly high efficiency and quality and has been proven to be accurate in identifying bacterial populations. One critical note during the procedure is that DNA contamination can occur, and thus appropriate sample handling is required12.
The DNA is then analyzed by the Next Generation Sequencing platforms for the 16S rRNA gene, such as the Illumina MiSeq. In particular, the V4 hyper-variable region is analyzed to provide a better quantification for high-rank taxa13. The subsequent bioinformatics analysis is outsourced, followed by in-house analysis using standard statistical methods. There are numerous open-source bioinformatics software programs available for downstream sequencing, and the type of analyses performed depends heavily on the specific biological question of interest14. This protocol focuses specifically on the experimental steps prior to sequencing and provides a more versatile, cost-effective, comparable, and efficient method to obtain DNA from fecal samples.
Protocol
The Cx3cr1gfp/gfp locus of B6.129P2(Cg)-Cx3cr1tm1Litt/J mice was backcrossed to MRL/MpJ-Faslpr/J (MRL/lpr) for 10 generations to generate MRL/lpr-CX3CR1gfp/gfp mice. Genome screening using single nucleotide polymorphism (SNP) panels confirmed that the genetic background of newly generated mice was more than 97% identical to that of MRL/lpr. After that, mice were bred and maintained in a specific pathogen-free environment following the specific requirements of the Institutional Animal Care and Use Committee (IACUC) at Virginia Tech. Samples were collected when the mice were 13, 14, and 15 weeks old.
1. Collection of microbiota samples from mice
- Take each mouse individually out of its cage. Collect a fecal pellet directly from the anus and store it at -80 °C until being processed. Process the samples with sterile and clean forceps, tubes, and gloves.
NOTE: A container (e.g., a beaker) can be used to place the mouse while waiting for the fecal sample. Clean the container with ethanol before and after each mouse. - Add a frozen pellet to a pre-weighed 2 mL screw-cap tube. Record the fecal weight in grams, which should be between 0.02-0.05 g per fecal pellet.
- Add to the pellet a thin layer of 0.1 mm glass beads, 500 µL of lysis buffer (50 mM NaCl, 5 mM Tris, and 50 mM EDTA), and 200 µL of 20% SDS.
- Bead-beat the tube for 4 min in a homogenizer (at maximum power), followed by 3-5 min of vortexing.
- Spin for a short time to eliminate the bubbles and foam (press the button on the centrifuge until it reaches 1,000-1,200 x g, and then release).
2. DNA extraction
- Transfer 350 µL of supernatant obtained in step 1.5 (ensuring not to carry any debris) to a new 1.5 mL snap cap tube. Add 500 µL of phenol-chloroform-isoamyl alcohol (PCI; 25:24:1, v/v) mixture and vortex for 1 min.
NOTE: From now on, samples need to be handled inside a chemical hood. PCI is toxic. - Spin at 6,000 x g for 3 min at 4 °C. Transfer 180 µL of the aqueous phase (top layer) into a new 1.5 mL snap cap tube. Add 180 µL (1:1) of chloroform, invert, and mix.
NOTE: Minimize contamination from the bottom layer when transferring the top layer. - Spin at 18,400 x g for 3 min at 4 °C. Transfer 180 µL of the aqueous phase into a new snap cap tube.
NOTE: After discarding PCI and chloroform, the following steps can be performed in a biological safety cabinet. - Add 180 µL of cold isopropanol and 36 µL of 5M NH4Ac. Invert a few times to mix, and place the tube on ice for 20 min.
- Spin at 18,400 x g for 20 min at 4 °C. Pour to discard the supernatant. Wash the pellet several times with 500 µL of cold 70% ethanol while inverting.
NOTE: Ethanol should be diluted with double deionized sterile water. - Spin at 18,400 x g for 3 min at 4 °C. Discard the supernatant to remove residual ethanol.
- Air-dry the tube upside down on tissue paper for 20 min, until the pellet becomes clear. Suspend the pellet in 50 µL of molecular-grade water. Heat the liquid at 37 °C for 10 min to fully dissolve large DNA pellets.
- Spin tubes after heating to recover condensation droplets on the lid (3,400 x g for 1 min).
- Measure the concentration and obtain the 260/280 ratio using a spectrophotometer. Use molecular-grade water as a blank. Good quality DNA should have 260/280 ratios ranging between 1.8 and 2.
NOTE: Expected DNA yield is at least 0.03 g per fecal pellet.
3. Sample preparation for 16S rRNA gene sequencing
- Send the DNA samples to Argonne National Laboratory, where they undergo library preparation and are sequenced on the Illumina Miseq.
- Send samples in fully-skirted 96-well plates, with samples arranged in rows (A1-A12, B1-B12, etc.), and sealed with foil plate seals.
- Send a final volume of 20 µL per sample per well, ranging in concentration from 1-50 ng/µL.
NOTE: Dilutions should be done with molecular-grade water. - After preparing the samples, spin at 600 x g for 1 min at room temperature before freezing the samples at -80 °C for 24 h.
- Send overnight on dry ice.
Representative Results
The results from Argonne National Laboratory are analyzed by a qualified bioinformatician, followed by an in-house analysis of the data using standard statistical methods. Typical microbiome analyses involve clustering of similar sequences into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) as a proxy for different microorganisms in a sample. Counts of OTUs or ASVs across samples can then be used to test for changes in within-sample (alpha) diversity and between-sample (beta) diversity. They can also be used to test for taxa that are differentially abundant across metadata categories of interest.
As shown in Figure 1, the mouse strain lacking the receptor CX3CR1 has a different gut microbiota composition. Compared to wildtype MRL/lpr, MRL/lpr-CX3CR1gfp/gfp mice (expressing the green fluorescent protein instead of CX3CR1) show significant differences in certain genera relevant to potentially pathogenic bacteria such as those in the phylum Proteobacteria. In addition, some differences were noted for "healthy" commensal bacteria such as Bifidobacterium, but not Lactobacillus.
Figure 1: Comparisons of gut microbiota at the genus level for MRL/lpr and MRL/lpr-CX3CR1 mice. The abundance of different genera at 13, 14, and 15 weeks of age (n = 5 female mice per group). Two-way ANOVA was performed. No significance ('ns'), *P < 0.05, **P < 0.01. Please click here to view a larger version of this figure.
Discussion
Balanced gut microbiota can protect the human body from diseases. Once this balance is disrupted by external or internal triggers, consequences can be devastating. This method presents a way to analyze the dynamics of gut microbiota in murine models. The method is suitable not only for comparisons among groups but also for tracking the gut microbiota over time to better identify time-dependent factors disrupting the gut microbiota.
All the mice in the experiment must be handled in the same environment. One essential matter to consider while collecting fecal samples is to be consistent with the time, day, and place. Mice are coprophagic. This means that they eat their feces or the ones around them to obtain essential nutrients. Therefore, feces cannot be collected from the cage. Samples need to be collected fresh and directly from the anus. The best method is to massage the mouse while holding it or place the mouse in a container. In addition, it is recommended that samples are processed at the same time and frozen at -80 °C while waiting for all the samples to be ready.
DNA extraction is extremely sensitive to pipetting and handling, so all the samples should be processed at the same time. Another consideration is to avoid environmental contamination; therefore, every single step not necessarily performed in the chemical hood due to the toxic reagents should be done in a biological safety cabinet. Even though the chances of environmental contamination are low and there is no step to allow bacteria to grow, this method is extremely sensitive, and avoiding any possible contamination is absolutely recommended. Additionally, weighing the pellets will help to increase consistency among samples. As an advantage over commercially available kits, this method is particularly good for achieving high concentrations of DNA. The DNA yield obtained with this protocol ranges between 0.03 g and 0.07 g depending on the weight of the fecal pellet. In comparison, commercial kits can yield between 0.005 g to 0.05 g, but with a higher chance of saturation as limited by the column retention step.
The bead-beating step is important in breaking the fecal pellet into small pieces so the lysis agent can reach all the bacteria. If this step is not well performed, pellets can be partially disintegrated, and fewer representative bacteria can be reached. In addition, when transferring supernatants, correct pipetting is important, since carrying part of the residual buffer from the previous step would alter the yield and quality of DNA at the end.
When diluting the samples for the 16S ribosomal RNA gene sequencing, it is important to act quickly before the samples are put into the freezer. It is also important to mix samples well prior to performing dilutions. Avoid leaving samples at room temperature for long periods of time. Since the volumes are small, freezing the samples in the plate before shipping them will help to minimize loss.
The Argonne National Laboratory analyses the V4 region of the 16S rRNA gene. The V4 region is better conserved than the other regions and has a better estimation for high-rank taxa13. While the other regions are better for the analysis of quickly evolving species and can help identify and better discriminate genus or species, those regions accumulate mutations faster than V4.
Once results are back and analyzed by a qualified bioinformatician, statistical software can be used to plot the results and compare groups. It is important to understand the limitations of this technique and the results after bioinformatics analysis. The lower the taxonomic level, the lesser the accuracy or greater the variations. From phylum to family, and even the genus level can be analyzed with reliability. However, annotations at the species level are not accurate with this method. Shotgun sequencing, on the other hand, can reach the species level; however, while it can provide more insight into the biological functions of a community, 16S rRNA gene sequencing is still an accurate and cost-efficient method to quantify taxonomical distributions of a microbial community15.
In summary, a cost-efficient protocol has been described to analyze the gut microbiota and study its dynamics with the maximal accuracy that can help to reveal longitudinal changes over time as well as differences among groups.
Declarações
The authors have nothing to disclose.
Acknowledgements
We appreciate the help from the Argonne National Laboratory and our collaborating bioinformaticians. This work is supported by various NIH and internal grants.
Materials
| 0.1 mm glass beads | BioSpec Products | 11079101 | |
| 2 mL screw cap tubes | Thermo Fisher Scientific | 3488 | |
| 20% SDS | FisherScientific | BP1311-1 | SDS 20% |
| 96% Ethanol, Molecular Biology Grade | Thermo Fisher Scientific | T032021000CS | |
| Ammonium Acetate (5 M) | Thermo Fisher Scientific | AM9071 | NH4AC 5M |
| B6.129P2(Cg)-Cx3cr1tm1Litt/J | Jackson Laboratory | 005582 | |
| Bullet Blender storm 24 | Next Advance | 4116-BBY24M | Homogenizer |
| Chloroform | FisherScientific | C298-500 | |
| DEPC-Treated Water | Thermo Fisher Scientific | AM9916 | |
| Ethylenediamine Tetraacetic Acid | FisherScientific | BP118-500 | EDTA |
| Foil plate seal | FisherScientific | NC0302491 | |
| Kimwipes-Kimtech 34256 | FisherScientific | 06-666C | |
| MRL/MpJ-Faslpr/J (MRL/lpr) mice | Jackson Laboratory | 000485 | |
| Nanodrop 2000 spectrophotomer | Thermo Fisher Scientific | ND2000CLAPTOP | |
| Phenol: chloroform: isoamylalchohol (25:24:1) | FisherScientific | BP1752I-400 | PCI |
| Scale with 4 decimals | Mettler Toledo | MS205DU | |
| Skirted 96-well plates | Thermo Fisher Scientific | AB-0800 | |
| Sodium chloride | FisherScientific | 15528154 | NaCl |
| Tris Hydrochloride | FisherScientific | BP1757-100 | |
| Vortex | Scientific Industries | SI-0236 |
Referências
- Lee, Y. K., Mazmanian, S. K. Has the microbiota played a critical role in the evolution of the adaptive immune system. Science. 330 (6012), 1768-1773 (2010).
- Lee, Y. K., Menezes, J. S., Umesaki, Y., Mazmanian, S. K. Proinflammatory T-cell responses to gut microbiota promote experimental autoimmune encephalomyelitis. Proceedings of the National Academy of Sciences of the United States of America. 108, 4615-4622 (2011).
- Yeoh, N., Burton, J. P., Suppiah, P., Reid, G., Stebbings, S. The role of the microbiome in rheumatic diseases. Current Rheumatology Reports. 15 (3), 314 (2013).
- Larsen, N., et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One. 5 (2), 9085 (2010).
- Alam, M. T., et al. Microbial imbalance in inflammatory bowel disease patients at different taxonomic levels. Gut Pathogens. 12 (1), (2020).
- Vieira, S. M., Pagovich, O. E., Kriegel, M. A. Diet, microbiota and autoimmune diseases. Lupus. 23 (6), 518-526 (2014).
- Mu, Q., Zhang, H., Luo, X. M. SLE: another autoimmune disorder influenced by microbes and diet. Frontiers of Immunology. 6, 608 (2015).
- Tektonidou, M. G., Wang, Z., Dasgupta, A., Ward, M. M. Burden of serious infections in adults with systemic lupus erythematosus: a national population-based study. Arthritis Care & Research. 67 (8), 1078-1085 (2015).
- Mu, Q., et al. Control of lupus nephritis by changes of gut microbiota. Microbiome. 5 (1), 73 (2017).
- Panek, M., et al. Methodology challenges in studying human gut microbiota – effects of collection, storage, DNA extraction and next generation sequencing technologies. Scientific Reports. 8 (1), 5143 (2018).
- Fiedorová, K., et al. The impact of dna extraction methods on stool bacterial and fungal microbiota community recovery. Frontiers in Microbiology. 10, 821 (2019).
- Gerasimidis, K., et al. The effect of DNA extraction methodology on gut microbiota research applications. BMC Research Notes. 9, 365 (2016).
- Bukin, Y. S., et al. The effect of 16S rRNA region choice on bacterial community metabarcoding results. Scientific Data. 6 (1), 190007 (2019).
- Galloway-Peña, J., Hanson, B. Tools for analysis of the microbiome. Digestive Diseases and Sciences. 65 (3), 674-685 (2020).
- Sharpton, T. J. An introduction to the analysis of shotgun metagenomic data. Frontiers in Plant Science. 5, 209 (2014).
